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INDIA Health System Performance Assessment

Aalok Ranjan

MLC Foundation ‘Shyam’ Institute


MLC Foundation 82, Aradhana Nagar Bhopal, MP-462003, India 91-755-4222756

‘Shyam’ Institute Mudian Ka Kuan, Datia, MP-475661, India 91-752-2234522 www.shyaminstitute.in

India: Health System Performance Assessment © 2013 MLC Foundation All rights reserved. No part of the publication can be reproduced or transmitted in any form or by any means including photocopying, recording or any information storage and retrieval system without permission in writing from MLC Foundation.

ISBN: 978-93-82411-06-2 Rs 900


INDIA HEALTH SYSTEM PERFORMANCE ASSESSMENT


Contents

1

Prologue

1

2

Frameworks for Health System Performance Assessment

13

3

Levels of Coverage

27

4

Coverage Inequality

49

5

Composite Coverage Index

73

6

Health System Performance Index

95

7

Epilogue

121

8

References

127

Appendix Table 3.1:

Coverage rate of 14 health interventions in the combined population

131

Appendix Table 3.2:

Coverage rate of 14 health interventions in the rural population

157

Appendix Table 4.1:

Within district inequality in the coverage rates of different health interventions

183

Appendix Table 4.2:

Inequality adjusted average coverage rate of 14 health interventions

209

Appendix Table 5.1:

Composite coverage index in the districts

237

Appendix Table 6.1:

District health system performance index

265


1 Introduction

Health status of the people has been an abiding concern to India’s development planning process right since the independence way back in 1947. Addressing the health needs of the people of the country, especially women and children, has been the priority development agenda in all Five-year Development Plans of the country. Despite this priority focus, health of the people and its impact on the social and economic production system of the country remains a major development challenging phasing country even today. In recent years, India has recorded some significant achievements in terms of economic growth and development but the gains on the economic front do not appear to have translated in an acceleration in health transition and improvements in the health status of the people. The unacceptably slow pace of health transition in India can be judged from the fact that, at the current pace of transition, India is going to miss the Millennium Development Goals in terms of reduction in child mortality and improvements in maternal health according to a recent assessment (Government of India, 2012). There has been improvement in the health and mortality situation in the country since independence but the pace of transition in both health and mortality has been substantially slower than what has been planned or expected in the context of development. The estimates prepared by the United Nations Population Division suggest that India’s record in terms of mortality and health transition can, at best, be termed as unsatisfactory. The slow pace of mortality and health transition in India has implications for global mortality and health transition as India is the second most populous country of the world. 1


Health System Performance Assessment Figure 1.1 Expectation of life at birth in India

Source: United Nations (2011)

United Nations Population Division has developed global mortality improvement schedules on the basis of empirical evidence on the increase in the expectation of life at birth during the period 1950-2005 in countries where the life expectancy ranged between 50-85 years. These schedules represent average experience of improvement in mortality and are grouped according to the 90th percentile (very fast increase), 75th percentile (fast increase), the arithmetic mean (medium increase), 25th percentile (slow increase) and 10th percentile (very slow increase) (United Nations, 2004). An application of the United Nations mortality transition model to mortality transition in India during the period 197175 through 2001-05 has revealed that improvement in the male expectation of life at birth followed the medium trajectory of model mortality schedules till 1986-1990 but slowed 2


Introduction down subsequently so that, during the period 2001-05, the increase in the expectation of life at birth followed the slow trajectory of model schedules. By comparison, the female expectation of life at birth followed the very fast and fast trajectory till the period 1991-95, but the pace of transition slowed down subsequently to follow the medium trajectory of model schedules (Chaurasia, 2009). According to United Nations estimates, India accounts for more than 17 per cent of the average annual number of deaths in the world during the period 2005-10. This proportion is highest, even higher than China, the most populous country of the world. India also accounts for the highest proportion of global child deaths and global maternal deaths. It has been argued that the pace of global mortality and health transition will depend largely on the pace of mortality and health transition in India. Among many reasons behind the slower than expected mortality and health transition in India, an important one is attributed to the unsatisfactory performance of the health care delivery system in meeting the health needs of the people. Although, health care is not the only determinant of the health status of the people, yet it is well known that delivery of health care services in the form of lifesaving and life-enhancing interventions plays a key role in preventing majority of the premature deaths, especially deaths among young children and women who constitute the most vulnerable group of population as well as in meeting the health needs of the people. India was a signatory of the Alma Ata Declaration which aimed at achieving Health for All by 2000 through primary health care. However, it is well known that the country failed to achieve the goals and objectives set for the Health for All initiative. Similarly, India is a signatory of the United Nations Millennium Declaration which has focussed attention on the attainment of eight Millennium Development Goals by the year 2015. However, an analysis of the current progress suggests that India will be missing most of the health related goals. All evidence available at present indicates that universal access to quality health care at an affordable cost still remains a distant dream to majority of the Indian population largely because the health care delivery system in India has been neither effective in terms of reaching all those who are in need of one or the other type of health care nor efficient in terms of delivering full spectrum of health care services to those who are within the reach of the system. As a result, the realised efficiency of the health care delivery system in terms of meeting the health care needs of the people remains low and affects the health status of the people. It is argued that, other things being equal, improvements in the realised efficiency of the health care delivery system by increasing the needs effectiveness of the health system in terms of reaching those who need health care and by improving 3


Health System Performance Assessment the capacity efficiency of the system in terms of delivery full spectrum of quality services can be a major contributor towards universal access to health care at an affordable cost and will lead to hastening the pace of health transition and achieving health attainment goals such as those that constitute the part of the United Nations Millennium Declaration (United Nations, 2000) or the goals laid in the Five-year Development Plan of the country as well as in the National Population Policy (Government of India, 2000) or the National Health Policy (Government of India, 2002). However, there has been very little effort to measure and monitor the realised efficiency of the health care delivery system in India in terms of meeting the health care needs of the people through the universalisation of health care. The impact of the health care delivery system in India has generally been analysed in terms of health attainment indicators that reflect the health status of the people such as expectation of life at birth, infant mortality rate, maternal mortality ratio, life time risk of a maternal death, etc. Transition in health attainment indicators, however, is not influenced by the efficiency of the health care delivery system in meeting the health care needs of the people only. There are other, beyond the health system, factors also. Moreover, measurement of the performance of the health care delivery system on the basis of health attainment indicators does not take into account the effectiveness of the system in reaching the people (needs effectiveness) and the capacity of the system in meeting the health care needs of those people to whom the system is able to reach (capacity efficiency). The health delivery system in India is characterised by a mixed ownership pattern of different systems of health care. In its simplest terms, India’s heath care delivery system can be characterised across three dimensions: 1) different systems of health care; 2) formal and informal organisation of health care delivery services; and 3) public and private ownership of health care delivery institutions. The three dimensions of the health care delivery system have their own evolution and contribution towards meeting the health needs of the people. In the context of analysing health system performance, an understanding of the three dimensions of India’s health care delivery system is necessary. The dimension of the diversity in terms of different systems of health care is revealing and reflects the complexity of the prevailing scenario in India. All major systems of health care - the ancient Indian systems of health care - Siddha and Ayurveda - as well as western systems of health care - Allopathy, Homeopathy and Unani - have strong presence in India along with numerous other approaches to meet the health needs of the people. The ancient Indian systems of health care - Siddha and Ayurveda - are the oldest medical systems known to the mankind. It is generally believed that these systems of health care 4


Introduction evolved around 10 thousand years ago. The Unani and Tibba systems of medicine, on the other hand, were introduced in India by Muslims during the 13th Century AD whereas allopathy was brought to India by the Portugese during the 16th Century AD. Homeopathy, on the other hand, gained a foothold in the country during the period 1819 through 1839. Besides these widely known systems of health care, many other approaches of health care are practised in different parts of the country. For example, the tribal people - the aboriginals - have their own system of health care which, although appears unscientific, is widely practised in tribal dominated areas of the country. There is no way to assess the relative coverage of different systems of health care as people often opt for multiple systems of health care to meet their health care needs. Similarly, health care providers of different systems of health care quite often venture into other systems in delivering health care services. The Indian systems of health care Siddha and Ayurveda - are rooted deep into the social and cultural traditions of India because the conceptual foundations of these systems are rooted in the philosophy that diet and life style play a major role in not only maintaining good health but also curing diseases and ailments. The approach to health care followed by these systems is comprehensive in coverage and holistic in scope. The focus of these systems is on promoting good health and preventing diseases and ailments rather than addressing poor health and treating or curing diseases and ailments. This wisdom is nearest to the definition of health propounded by the World Health Organization - “health is a state of complete social, mental and physical well-being and not merely the absence of disease or infirmity� - than any other system of health care that is known to the mankind. The basic approach to health care in these systems is home-based not institution-based. Because of this approach, the cost of health care in these systems is low so that health care can be afforded even by the poorest of the poor. The low cost along with the household-based approach of health care is a major factor behind almost universal access and acceptability of these systems of health care among the Indian masses. Compared to Siddha and Ayurveda, the Unani system of health care and Homeopathy have a small base in India, mostly confined to urban areas. By comparison, allopathy has a larger, expanding base, largely because of the support it received from the government during the colonial period and continues to receive even today. Unlike the Indian systems of health care, allopathy is very heavily dependent upon the institution-based care requiring properly trained and adequately skilled health care delivery personnel and extensive institutional structure. As such, nearly all the institutions of the delivery of allopathic health care services are located in the urban areas, especially metropolitan 5


Health System Performance Assessment towns and cosmopolitan cities. Being institution-based, the allopathic health care is costly and puts a severe economic burden on Indian masses. An estimate suggests that only about 25 per cent of the Indian population has access to allopathic health care. Rest primarily depend upon the ancient Indian systems - Ayurveda and Siddha. The second diversity of health care delivery system in India is rooted in health care delivery institutions. India has a very large informal, unorganised, home-based health care system. The genesis of home-based health care in India lies in the famous dictum that ‘health begins at home’. The knowledge of health care in this informal system has, since times immemorial, been used to be passed down in families and households through oral family-based learning traditions. In a sense, there is a health care provider in every family and household of the country who has some knowledge of addressing routine health problems. This knowledge, the individual has gained from the elders of the family and is still widely used at the family level in regulating dietary and life style patterns of family members so that they remain healthy and free from diseases to the extent possible. Even in case of minor ailments, the first response is generally based on the traditional family level knowledge of health care. At present, very little is known about the evolution of home-based system of health care in India. Similarly, there is no way to assess the size and structure of this unorganised, informal health care delivery system which has a presence in almost every household of the country. It can however be said with certain definiteness that the formal health care delivery system in India is just a small proportion of the huge and very diverse informal health care delivery system. Although, there has been a very substantial expansion of the formal health care delivery system since independence, yet, it is well known that the majority of Indian masses, especially poor and marginalised living in the rural and remote area, depend upon the informal, home-based system of health care in meeting most of the health care needs. The scientific foundations of this informal, homebased health care system may be found in Ayurveda and Siddha. The formal health care delivery system in India, on the other hand, includes both public and private health care delivery and is dominated by the allopathic system of health care. Ancient Indian systems of health care - Siddha and Ayurveda - as well as Unani and Homeopathy constitute only a small proportion of the formal health care delivery system in India. The health care delivery institutions in the public sector are almost entirely funded by the government out of its budgetary resources. This system comprises of primary, secondary and tertiary level health care institutions but the emphasis, definitely, is on primary health care services directed towards diseases 6


Introduction prevention through health promotion and specific protection. The public health care delivery system has a very strong presence in the rural areas of the country in the form of nested primary health care delivery system comprising of sub-health centres, primary health centres and community health centres, in addition to a range of other institutions. The primary health care institutions under the public health care delivery system were established following population-based norms established way back during the 1960s. Since then, there has been no change in these norms and the system has not been able to keep pace in creating new institutions with the increase in the population. As a result, the population served by a primary health care delivery institution has increased substantially over time and is now well above the prefixed norm. By contrast, the system is largely unorganised in the urban areas, although, almost all secondary and tertiary level public health care institutions are located in the urban areas. There is no nested primary health care delivery system in the urban areas of the country as it exists in the rural areas. Primary health care services in the urban areas are provided mainly through secondary and tertiary public health care delivery institutions. The private health care delivery system in India, on the other hand, has grown tremendously in recent years. It ranges from single doctor clinics and dispensaries and small nursing homes to large super speciality hospitals. These institutions provide mainly curative care either through clinic-based dispensing services or through institution-based services. Primary health care services, especially services related to health promotion and specific protection constitute an insignificant proportion of the services delivered through private health care delivery institutions. Services available through the private health care delivery system are not free and the cost of services provided by these institutions is often beyond the reach of average Indian. Although, dominated by the western, allopathic system of health care, this system also include institutions and practitioners of Indian systems of health care - Ayurveda and Siddha - as well as Homeopathy and Unani albeit a small proportion. Because of the fragmented nature of the health care delivery system in India in terms of the three dimensions of health care described above, a comprehensive performance assessment of the health care delivery system in India is not possible and has never been attempted. Perhaps the most challenging obstacle to this endeavour is the non-availability of the information necessary to assess the performance in terms of meeting the health needs of the people. Little is known about the functioning as well as the performance of the informal, home-based system of health care. Similarly, little is known about the services provided by the private health care providers. The public health care delivery 7


Health System Performance Assessment system, however, provides information about the services delivered through the system. This information, popularly known as the programme service statistics is notoriously poor in terms of quality and coverage as it is associated with errors of duplication of services, over time and place. Moreover, programme service statistics are insufficient to estimate the coverage rate of different public health interventions and, therefore, are of little use in assessing the performance of the health system. In the absence of the necessary information, performance of the health care delivery system in India has generally been assessed in the context of population health indicators - expectation of life at birth, infant mortality rate, maternal mortality ratio, etc. It may however be emphasised that health care services constitute one of the many factors that influence health of the people. Although, universal access to health care is an important element that determines health of the people, yet it is not the only one. It is therefore difficult to establish whether the prevailing health status of the people is the result of the prevailing performance of the health care delivery system in terms of delivering services to meet the health needs of the people. This is especially the case when there exists a large, informal, home-based health care system. Ideally, performance of the health care delivery system should be measured in terms of meeting the health needs of the people. This means that performance of the health care delivery system, essentially, has two dimensions. The first dimension is related to identifying the health needs of the people while the second dimension is related to the extent up to which identified health needs are met by the health care delivery system. One component of the second dimension is that the health care delivery system must be able to reach those who are in need of one or the other type of health care. The second component, on the other hand, is related to the capacity of the system to deliver full spectrum of services to those who are within the reach of the system. If the health care delivery system is able to reach only a small proportion of those who are in need of health care, then its performance may be termed as poor in terms of needs effectiveness. On the other hand, if the system is not able to deliver the full spectrum of services to all those who are within the reach of the system, then its performance may be termed as poor in terms of capacity efficiency. The overall performance of the health care delivery system is then the product of needs effectiveness and capacity efficiency. This means that improvements in both needs effectiveness and capacity efficiency are essential to improve health system performance. An advantage of the above approach of health system performance assessment is that improving the performance of the health care delivery system is an integral component 8


Introduction of the process of health care planning and programming which is directed towards universal access to health care. Another advantage is that it is proactive in scope. It puts the onus on the health care delivery system itself to reach those who are in need of health care and to deliver services to them to address their health needs. This is particularly relevant to community-based primary health care services. Needless to emphasise, it is only through the community-based primary health care services that the goal of universal access to basic health care services can be achieved. The Government of India has recently launched the National Rural Health Mission which aims at architectural corrections in the health care delivery system of the country in an attempt to accelerate reduction in maternal, infant and child mortality, achieve XI Five-year Development Plan objectives and Millennium Development Goals (Government of India, 2005). The Mission has adopted a decentralised approach of strengthening the health care delivery system, especially, in the rural areas of the country through: 1) increase in public spending on health and family welfare; 2)decentralisation of the public health and family welfare services delivery system; and 3)empowering the community to take care of its own health. The Mission covers the entire country but the primary focus of the Mission is on 18 priority States where the prevailing health situation continues to be a cause of concern to policy makers and programme managers. The Mission emphasises decentralised district-based approach to planning and programming for health care services delivery, especially, planning and programming for primary health care services. In order to measure and monitor up to what extent the Mission has been able to improve the performance of the health care delivery system in terms of meeting the health needs of the people, there is a need to both benchmark the performance of the health care delivery system and measure and monitor improvements in the performance at the district level. Otherwise also, policymakers and programme planners need to quantify the health system performance, identify factors influencing performance and ultimately articulate policies and programmes that will achieve better results in a variety of social, cultural and development settings. Meaningful and comparable information on the performance of the health care delivery system and key factors that influence it can strengthen scientific foundations of planning and programming for health care services delivery. The framework for the implementation of the Mission, however, does not outline any mechanism either for bench marking the performance of the health care delivery system or for measuring and monitoring improvement in the performance of the health system. It emphasises monitoring progress against preset standards, yet the process of setting up standards has not been elaborated. The framework emphasises that 9


Health System Performance Assessment benchmarks are to be agreed between the community and the health care delivery institution on the basis of independently organised surveys but the actual process of setting up benchmarks in terms of the performance remains unspecified. Despite its long felt need, there has been very little research on the performance of the health care delivery system in India. There is no system to assess the performance of the system on a regular basis either at the country or state or below state levels. Moreover, little attention appears to have been paid to health system performance assessment in health planning and programming. This is so when it is well known that regular measuring and monitoring of the performance of the health care delivery system can go a long way in improving its performance in terms of the needs effectiveness - reaching the people - and in terms of its capacity efficiency - delivering full spectrum of quality health care services to those who are within the reach of the system. According to the World Health Report 2000, India ranks 112 in terms of the performance of the health care delivery system among the 191 countries of the world (WHO, 2000). Obviously, there is substantial scope for improvement in the performance of the health care delivery system in the country. This monograph presents detailed analysis of health system performance in India through the primary health care perspective. The primary purpose is to analyse the performance of the health system at national, State and district levels in terms of its efficiency in delivering selected health care interventions, especially, interventions related to women and children and to rank States and districts in terms of the performance of their health system. A health system performance index has been developed for the purpose which is based on inter-district variation in selected output indicators of primary health care services. Outputs of primary health care services are measured in terms of the coverage rate of selected primary health care interventions. Obviously, the higher is the coverage rate, the more efficient is the health system in reaching those who are in need of services and in delivering full spectrum of health care to them. The monograph is divided into eight chapters including this introduction and the customary epilogue. The next chapter of the monograph presents a brief description of the composition of the health care delivery system in India and its evolution, especially, after independence. The health care delivery system in India is very complex. It includes both formal and a large informal health care system. The complexity of the health care delivery system in the country is a major challenge towards measuring and monitoring the performance of the system. Because of a very large informal health care system that has prevailed in India through times immemorial, measurement of the performance of 10


Introduction the health care delivery system in terms of health attainment indicators is inappropriate while a comprehensive performance assessment covering both formal and informal health care system is not possible. Similarly, it is also not possible to measure the performance of the entire formal health care delivery system as discussed in the next chapter. The third chapter of the monograph presents an overview of the frameworks developed for assessing the performance of the health care delivery system. Several frameworks have been developed for measuring the performance of the health care delivery system which are testimony to the importance given to improving the performance of the health system in meeting the health needs of the people and in achieving health attainment goals. Most of these frameworks have however been developed in developed countries settings which are essentially different from the settings that prevail in developing countries like India. This chapter also describes, in detail, the health system performance assessment framework developed by the World Health Organisation and which constitutes the basis for the present analysis. The fourth chapter of the monograph presents a detailed analysis of the inter-district and inter-State inequality in the coverage rate of selected primary health care interventions which have been used for health system performance assessment. The analysis shows that the performance of the health care delivery system varies widely horizontally within the administrative unit as well as vertically across administrative units. Because of large variation in the performance of the health care delivery system in different primary health care interventions, no single intervention can be used for measuring and monitoring the performance of the health care delivery system. Rather, a multidimensional approach is needed to capture the inherent variability that persists within the health care delivery system. The fifth chapter of the monograph develops composite coverage index that reflects the combined performance of a set of health care interventions on the basis of the coverage rate of these interventions. The index takes into account within-district and between-district variability in the coverage rates of different primary health care interventions. The composite coverage index, however, does not reflect the true performance of the health care delivery system as it is influenced by a host of factors exogenous to the health system. The composite coverage index needs to be adjusted for the exogenous factors influencing the performance of the health care delivery system. The sixth chapter of the monograph estimates the health system performance index at the national, State and district levels after adjusting the composite coverage index for the effect of two critical exogenous factors affecting the coverage rate of different health 11


Health System Performance Assessment interventions - education and standard of living. Estimates of health system performance index reveal a gloomy picture of the performance of the health system - public as well as private - in India and there are very substantial inter-State and inter-district variations. An important findings of the analysis is that the performance is relatively better in rural than in combined population which may be due to an organised primary health care delivery system in the rural areas. The epilogue of the monograph summarises the major findings of the analysis and stresses the need for analysing the stewardship function, resources creation function and financing function of the health system in the context of the health system performance index. The framework for health system performance assessment proposed by the World Health Organisation argues that delivery of health care services is contingent upon the three functions of the health system as described above. At present, however, very little is known about the variations across States/Union Territories and districts of the country in terms of health system performance in the context of stewardship, resources creation and financing of health services.

12


2 Frameworks for Health System Performance Assessment

In recent years, there has been a renewed emphasis on improving the performance of the health care delivery system to meet the health care needs of the people and health system performance assessment is increasingly being recognised as one of the tools to gather information about health systems to inform policymaking, to monitor progress towards health and to identify best practices. Health system performance assessment may also be seen as a process that permits holistic assessment of the health system and links health outcomes to strategies and functions of the health system. It uses statistical indicators of the performance of the system to monitor how the health system is functioning and to link health outcomes to strategies and functions of the health system through the outputs of the system. In this way, it can help in the optimisation of the organisation of the health system to improve health of the people. Health system performance assessment is also viewed as a step to promote transparency in the health system through measuring and demonstrating results of health system performance assessment. There is a vast and growing literature on different aspects of the health system performance assessment processes which offers varying views on what does actually constitute the health system; what are different functions of the health system and how can they be measured in quantitative terms; what is the meaning of health system performance and how to assess performance of the health system, etc. In any case, health system performance assessment has now emerged as one of the priority agenda of health systems research. 13


Health System Performance Assessment The genesis of recent and growing interest in health system performance assessment essentially lies in the World Health Report 2000 which stressed the need of improving the performance of the health care delivery system to meet three overall goals: good health, responsiveness to the expectations of the population, and fairness of financial contribution (WHO, 2000). At the same time, there is increased realisation among policy makers and development experts that strong health systems are essential to achieving and sustaining health gains (United Nations, 2005; World Bank, 2004; Travis et al, 2004). It has also been felt that vertical health programmes, community-based small scale projects and donor directed thematic health investments, etc. have not been effective enough in meeting the health needs of the people and a more holistic, system-based approach is required. Increased attention to improving the performance of the health care delivery system has also been stimulated by the Millennium Declaration that calls for achieving several health related targets - reducing child mortality, improving maternal health, arresting diseases, etc. - simultaneously by the year 2015. There is now a growing consensus that achieving and sustaining health related Millennium Development Goals is difficult without improving the performance of the health care delivery system as a whole. Assessment of the performance of the health care delivery system is obviously a logical corollary of any effort directed towards improving the performance of the health care delivery system. Concern for measuring performance of the health care delivery system is however not new. Way back in 1860s, Florence Nightingale pioneered systematic collection, analysis and dissemination of hospital data to understand and improve hospital performance. Subsequently, Codman emphasised the need for scrupulous collection and public release of surgical outcomes (Speigelhalter, 1999). However, it was only after 1990, that the vision of using large scale data sources to help improve the performance of the health care delivery system as a whole became a reality. With the growing realisation of the need of improving the performance of the health care delivery system to meet the health care needs of the people and improve the health status, several frameworks for assessing the health systems performance have been proposed (OECD, 1998; AHCPR, 1999; Aday, et al, 1998; Knowels, Leighton and Stinson, 1997; OECD, 1999; PAHO, 1999; Hoffimeyer and McCarthy 1994; Hsiao, 1995). This is the testimony to the importance given to this enterprise. Taken together, these frameworks provide rich sources of ideas and approaches for health system performance assessment, although, a review of many of these frameworks by Murray and Frenk (2000) has suggested that there is room for improvement. 14


Frameworks for Performance Assessment There are basically two aspects of any performance assessment exercise. One is theoretical related to the formulation of a conceptual framework that constitutes the foundation of the performance assessment exercise. There are many issues in developing the conceptual framework for health system performance assessment. The first and perhaps the foremost are defining the health system itself. The health system may be defined in a very narrow sense as well as in a broad sense, although the goals of the health system remains the same - improvements in the health status. In its narrowest sense, the health system may be defined as those activities which are directly under the control of the Ministry or the Department of Health. This definition may be classified further to include all preventive, promotive and curative health services or to confine to only curative services alone. Another classification may be in terms of primary health care services or secondary care services or tertiary care services or combination of all the three types of health care services. A third classification may distinguish between public health services and private health services. The second, relatively broader, definition of the health system may include individual care and community health services but excludes inter-sectoral actions directed towards health improvement. Finally, the third definition may include all actors, institutions and resources that influence, one way or the other, the health of the people. The World Health Organisation follows this definition of the health system. An advantage of this definition of the health system is that it encourages the government to bear the burden of stewardship of the health system to define and focus on a set of actions whose primary intent is to improve health. Obviously, those actions which are not directly primarily towards health improvement do not constitute the part of the health system according to this definition. The next challenge in any exercise directed towards assessing the performance of the health system is defining functions of the health system. There are as many definitions of health system functions as there are authors. Murray and Evans (2003) have grouped functions of the health system into four categories: 1) financing function; 2) resource generation function; 3) stewardship function; and 4) service provision function. Frenk and Londono (1997), on the other hand, have identified health system functions as: 1) financing; 2) services delivery; 3) modulation; and 4) articulation. Finally, according to Mills and others (2006), health system functions should include: 1) stewardship and regulation; 2) organisation; 3) financing; and 4) management. The financing function and the stewardship or the governance function are common in all descriptions of the functions of the health system. 15


Health System Performance Assessment The performance of the health system can be assessed in terms of heath system outputs as well as health system outcomes. Outputs of the health system are production or delivery of health care services. Outcomes of the health system, on the other hand, are the impact of the services on the health of the people. It is relatively easy and straightforward to define outputs of the health care delivery system. It is also possible to establish a link between the outputs, processes and inputs of the system. By comparison, defining outcomes of the health care delivery system is more complex as there are many beyond the health system factors that influence health of the people. The World Health Organisation defines health as “a state of complete physical, social and mental well-being and not merely the absence of disease and infirmity”. Although, this definition is simple and logical, yet it is very difficult to measure. Traditionally, health of the people has been measured in terms of mortality indicators and the expectation of life at birth has been and continues to be the universally preferred indicator. However, relationship of the expectation of life and birth with outputs of the health care delivery system is not direct. Moreover, with the global decline in mortality, the relevance of mortality measures as indicators of the health is waning. The World Health Organisation, for example, advocates the use of disability adjusted expectation of life at birth (DALE) to measure health. Estimation of DALE is however very data intensive. Another approach to measure health outcomes focusses on adverse outcomes of health interventions (Zee and Or, 1999). Examples of commonly-used indicators of adverse outcomes of health interventions are: •

Rates of avoidable mortality and morbidity especially in situations where there is clear evidence that timely and appropriate health care interventions would either prevent the condition or treat the disease at an early stage.

Rates of effective health care interventions which have an undisputed and important role in health gain, such as immunisation, management of diarrhoea, promotion of breastfeeding, etc.

Survival rates at a given point in time after an intervention or treatment.

Rates of adverse health events which can only be a result of health care interventions, such as hospital-acquired infections or complications in routine surgery. In using health outcomes as indicators of health system performance, it is however

important to ensure that outcome indicators are relevant to health concerns and there is a clear understanding of the relationship between health care interventions and health status. It is also important that indicators clearly relate to areas involving substantial resources or burden of disease and they are sensitive to quality of care differences. 16


Frameworks for Performance Assessment In order to address these and many other challenges in evolving the conceptual foundations of health system performance assessment, the World health organisation has adopted a framework that was first proposed by Murray and Frenk (2000) in the World Health Report 2000. This framework divides the entire health system performance process in to parts. The first part of the framework focusses on the functions that are important for the delivery of health care services by the health system while the second part of the framework focusses on the objectives of the health system. In other words, the first part of the framework concentrates on inputs, processes and outputs of the health system necessary for health services delivery whereas the second part is related to the outcomes of the health system. The framework also establishes the link between functions that the health system performs and the objectives of the health system in terms of the health of the people as well as in terms of the responsiveness of the system to people’s health needs. In this way, the framework helps in understanding the proximate determinants of health system performance in addition to contemplating major policy challenges. The framework proposed by Murray and Frenk focusses on two larger questions. The first question is related to attainment goals of the health system - measurement of the outcome of interest. The framework emphasises that attainments of the health care delivery system should be measured in terms of good health, responsiveness and fair financial contribution. The second larger question is related to the performance - how to compare attainments of the health care delivery system with what the system should be able to accomplish - the perceived goals and objectives of the health care delivery system. Performance involves measuring achievements and analysing how health systems carry out certain functions to achieve their perceived goals. The functions that the health system should carry out have been grouped into four broad categories by Murray and Frenk: 1) stewardship functions; 2) functions related to the creation of resources necessary for delivery of health care services; and 3) functions related to the financing of the health care delivery system. It is argued that these three categories of functions of the health care delivery system determine the performance of the system in terms of the delivery of health care services. The stewardship function also determines the responsiveness of the system to people’s non medical expectations while the financial function influences the fair financial contribution which is assumed to be necessary prerequisite for improving the health status of the people. Finally, the framework suggests that the responsiveness of the health system to people’s non medical expectations, delivery of personal and nonpersonal health care services and fair financial contribution, in combination, determine the health of the people. 17


Health System Performance Assessment Figure 2.1 WHO health system performance assessment framework Functions the system performs

Objectives of the system

Stewardship (Oversight)

Creating resources (investment and training)

Responsiveness (to people’s nonmedical expectations)

Service delivery

Health

Fair (financial) contribution Financing (Collection, pooling and purchasing)

The health system performance assessment framework suggested by Murray and Frenk and adopted by the World Health Organisation emphasises that three functions of the health care delivery system are critical to the delivery or provision of health care services - the oversight function, the finances mobilising function and the resources creating function. The framework also suggests that the resources creation function of the health system is also influenced by the oversight function and the finances mobilising function. This is logical as availability of appropriate physical infrastructure and knowledge skills and competencies of services providers are key elements to efficient services delivery. World Health Organisation has developed a framework that defines the building blocks of the health system (WHO, 2006). This framework defines both long-term and intermediate goals of the health system. The long term goals are health, equity, responsiveness, financial fairness, and efficiency while the intermediate goals are: access, 18


Frameworks for Performance Assessment coverage, quality, and safety. The framework argues that to achieve long-term and intermediate goals, the health system must comprise of six core, well-functioning components or the building blocks - health workforce, health information system, equitable access, health financing, leadership and governance, and services delivery. This framework, essentially, presents a prescription for strengthening the health care delivery system in contemporary settings. It provides a check list of what the health system should have to ensure that it achieves the above listed long-term and intermediate goals. The World Bank, on the other hand, has developed a health system analysis framework which focusses on dissecting different parts of the health system for separate analysis (Bitran, et al, 2010). This framework divides the health system into four components - financing, payments to services providers, organisation and services delivery, and regulation - and measures the performance of the system in terms of effectiveness and efficiency, equity, quality of care and financial protection. Another approach to assess the health system performance has been suggested by Roberts and others (Roberts et al, ). This approach offers a systematic approach to performance improvement through so called policy control knobs. This methodology is also known as the Flagship Programme approach, jointly developed by the World Bank Institute (WBI) and the faculty from the Harvard University School of Public Health. WBI has adopted it as its core training methodology for its Flagship Training Programme on Health Sector Reform and Sustainable Financing. The approach identifies five key control knobs - financing, payment, organisation, regulation, and persuasion - that policymakers should modify, alone or in combination, to achieve a desired performance. The intermediate performance measures identified in the framework are efficiency, quality and access while the longer-term performance measures include health status, customer satisfaction, and risk protection. This framework emphasises: 1) health policy and its consequences on health system performance; 2) use of ethical theory to judge health sector performance; 3) use of political economy theory to help formulate politically feasible policy changes; and 4) the use of a systematic assessment for diagnosing problems in performance. This framework is very similar to the health system building blocks framework developed by the World Health Organisation. The theoretical models developed for health system performance assessment and for strengthening the health system and improving its performance can be used in different ways in assessing the performance of the health system. For example, using the WHO framework, one can assess the health care delivery system in terms of health attainment goals only. Alternatively, one can also analyse the relative role of health care services 19


Health System Performance Assessment delivery, responsiveness of the system to non medical expectations of the people and fair financial contribution on health attainment. It is also possible to analyse effects of the oversight function on health attainment directly as well as indirectly through finance function, resources creation, delivery of health care services, responsiveness and fair financial contribution. In the same manner the direct and indirect effects of the finance function of the system on health attainment can also be analysed. Similarly, one can also assess the health system in terms of delivery of health care services and analyse the role of the oversight mechanism in place, availability of financial resources and creation of resources in the delivery of health care services. It is also possible to analyse effects of oversight function and finance function on the delivery of health care services directly as well as indirectly through the resources creation function. At the same time, direct and indirect effects of the individual functions of the health care delivery system on health outcomes may also be analysed. For example, one may be interested in analysing the role of stewardship in the responsiveness of the health system to non medical expectations of the people. Operationalising different health system performance assessment frameworks requires a business model to measure different components of the conceptual framework and link them in a logical manner. The most straightforward approach of developing the business model of health systems performance assessment is the indicator-based approach. An indicator is a quantitative or a qualitative measure derived from a series of observed facts that can reveal relative positions. When evaluated at regular intervals, an indicator can point out the direction of change across different units and through time. In the context of policy analysis indicators are useful in identifying trends and drawing attention to particular issues. They can also be helpful in setting policy priorities and in benchmarking or monitoring performance. A major issue in the identification of indicators to measure different components of conceptual models of health systems performance assessment is that all the components of these frameworks are essentially multidimensional in nature and cannot be fully captured through a single indicator. As such, more than one indicator is generally required to measure not only different functions of the health system abut also its different objectives and goals. This, however, raises the challenge of combining different indicators related to one function or one objective of the health system into a single, composite indicator for policy decisions and for establishing linkages between functions of the health system and objectives and goals of the health system. A composite indicator combines more than one individual indicator on the basis of an underlying model. 20


Frameworks for Performance Assessment Composite indicators capture the multidimensional concepts of health system and health outcomes which cannot be captured by a single indicator. For example, there are different dimensions of the delivery of health care services. Broadly, health care services can be grouped into preventive health care services, services directed towards health promotion and curative health care services. A single indicator of the delivery of health care services requires combining indicators of preventive health care, indicators of health promotion and indicators of curative health care into one composite indicator. Similarly, there is no single indicator of preventive health care services so that there is a need of combining many indicators of preventive health care services into one composite indicator. There are advantages and disadvantages of using composite indicators (Saisana and Tarantola, 2002). A composite indicator can summarise complex, multidimensional realities with a view to supporting decision makers and is easier to interpret than a battery of many separate indicators. A composite indicator can assess progress over time or compare progress across regions, reduce the visible size of a set of indicators without dropping the underlying information base. This makes it possible to include more information within the existing size limit. Composite indicators also help in placing issues of systems performance and progress at the centre of the policy arena, facilitate communication with people and their representatives and promote accountability within the system. The use of the composite indicator enables policy makers and programme managers in comparing complex dimensions effectively. At the same time, it may send misleading policy messages if poorly constructed or misinterpreted and may lead to simplistic policy conclusions. Composite indicators may be misused, especially when the process of construction of the composite indicator is not transparent and/or lacks sound statistical or conceptual principles. Finally, the selection of indicators and weights could to construct the composite indicator may be subjected to debate and dispute. A major concern with the use of composite indicators is that they may disguise serious failings in some dimensions and increase the difficulty of identifying proper remedial action, if the construction process is not transparent. Moreover, they may lead to inappropriate policies and programme priorities if key dimensions of performance, especially those that are difficult to measure, are ignored. There is no universal rule or methodology to construct a composite indicator to measure performance. Composite indicators are much like development of mathematical or statistical models. It is the conceptual clarity and analytical ability of the analyst which determines the appropriateness of the composite indicator in terms of its fitness for intended purpose and peer acceptance (Rosen, 1991). It is however obvious that whatever 21


Health System Performance Assessment methodology or approach is adopted, there is definitely some loss of information for the sake of interpreting the findings of assessment. The appropriateness of the composite indicator actually lies in minimising this loss of information. It is also important to recognise that the quality of a composite indicator and the soundness of the interpretations based on the composite indicator depends not only on the methodology ingenuity used in its construction but primarily on the quality of the theoretical framework and the data used. A composite based on a weak theoretical background or on inappropriate or inaccurate data can lead to disputable interpretations despite the use of state-of-the-art methodology in its construction. Key considerations in constructing a composite indicator include: •

Development of a theoretical framework to provide the basis for the selection and combination of a set of indicators into a meaningful composite indicator under a fitness-for-purpose principle.

Selection of the set of indicators on the basis of their analytical soundness, measurability, coverage, relevance to the phenomenon being measured and relationship to each other. The use of proxy variables may be considered when data are scarce.

Thorough examination of the data to be used about its quality and relevance. An exploratory data analysis may be carried out to identify outliers and extreme values as they can become unintended benchmarks. At the same time, attention should be paid to address the problem of missing data.

Investigation of the overall structure of the indicators using appropriate statistical techniques to provide the basis for combining indicators.

Normalisation of the data, especially when the unit of measurement of different indicators is different.

The process of aggregation and weighing after taking into consideration correlation and compensability issues among different indicators.

Analysis of the robustness and sensitivity of the composite indicator. Composite indicators should be transparent and fit to be decomposed into their underlying constituent indicators. Different approaches have been used for constructing composite indicators to measure

and monitor the performance of the health system. The approach used by the National Health Service of United Kingdom is directed towards rating health institutions/hospitals and involves such factors as economic conditions, service delivery, incentives, human resource, quality of services, etc. A composite indicator is constructed on the basis of the 22


Frameworks for Performance Assessment impact of different choices and by specifying appropriate set of weights for each component of the indicator. The composite indicator so developed constitutes the basis for star rating institutions and hospitals on a four-point scale (Jacobs et al, 2004). The World Bank, on the other hand, has used the policy score card for rating country policies (Court, 2006). The classification procedure involved grouping 16 selection criteria into four clusters. Peter (2006) has used the score cards to summarise information about different domains of the health care delivery system for assessing the performance. This approach has been applied in Afghanistan. The score card used in Afghanistan comprised of 29 indicators and indexes grouped into six domains of health care delivery. In Mexico, effective coverage and co-coverage of selected health care interventions have been used to assess the performance of the health care delivery system (Lozano and Boerma, 2006; Victora et al, 2005; Cecilia Vidal et al, 2006). The performance assessment exercise was based on 13 health care interventions. Several aggregation and rating options were explored and differential as well as fixed weights were assigned interventions included in the performance assessment exercise. At the same time, uniform scores were assigned to all the interventions used in the assessment exercise to develop composite performance score. The co-coverage score for different preventive interventions received by an individual (Measles, DPT3, BCG) and (ANC, PNC, TT) was also used to develop the composite coverage index. A major issue in the measurement and analysis of the performance of the health system is the availability of appropriate data. This is especially the case in the developing countries where the health information system is in poor shape and information related to the functions of the health system and its objectives is either not available or is available in a form which helps little in performance assessment. In view of serious data limitations in the developing countries, WHO has recommended use of coverage data derived from population-based surveys to assess the performance of the health care delivery system with emphasis on effective coverage and co-coverage of different health care interventions (WHO, 2001; HSM, 2006). Effective coverage directly reflects coordination and linkages among different components of health services management and indirectly gives fairly good idea of performance of other management support systems like logistics, human resources and capacity of health system in terms of its performance. Effective coverage combines three aspects of health care service delivery into a single measure - the need for health services, the availability of and access to health services and the quality of the services delivered by the system. Crude coverage, by contrast, represents simply the ratio of the population using a health intervention to the population in need 23


Health System Performance Assessment of that intervention. Effective coverage, essentially, adjusts the crude coverage for the quality or efficacy of the health intervention. Co-coverage, on the other hand, is obtained by combining the number of health interventions received by the same individual. Victora and others (2005) have developed a co-coverage score for preventive health interventions for children aged 1-4 years and applied it to nine demographic health surveys. The co-coverage scores have been effectively used for assessing the performance of maternal and child health related interventions such as immunisation, tetanus toxoid to the mother, antenatal care visits and deliveries, etc. Effective coverage and co-coverage have been used to assess the performance of the health system in Mexico (Lozano and Boerma, 2006; Cecilia Vidal et al, 2006). Amjad (2008) has used the coverage data available through from Pakistan Social and Living Standards Measurement Surveys and Multiple Indicators Cluster Survey to measure performance of the health care delivery system at district level in Pakistan. The performance of each indicator/intervention used in the analysis was weighted through awarding score based on absolute achievement to avoid bias of uniform weight to each intervention which means that none of the intervention was considered more important than others and the scoring criterion was based on actual performance in terms of output delivered. Amjad actually assigned 1 point or score for each 1-10 point achievement on the decimal scale in a given health intervention. These scores were used to generate performance indexes for different domains of the health system which were then summed up to obtain combined performance index for the health system as a whole. One problem in the approach used by Amjad is that efforts required to improve the coverage of health interventions vary with the level of coverage and hence allocation of the score irrespective of the level of coverage may not be the right approach. When the coverage of any health intervention is low, even small efforts may lead to substantial improvement in the coverage rate. However, when high to very high level of coverage is achieved, efforts required to further increase the coverage rate may be quite daunting. The approach followed by Amjad, however, does not take into consideration the interdistrict variation in the coverage rate of different health care interventions. Similarly, the correlation between the coverage rate of different health care interventions has also not been taken into consideration. There may be a possibility that there exists multicollinearity between the coverage rate of two health care interventions so that a high score in one health intervention automatically results in a high score for other intervention. Amjad has also grouped the variables included in the analysis purposefully. 24


Frameworks for Performance Assessment A more rational approach would have been to group different variables on the basis of inter-district variation in the coverage rate of different health care interventions using appropriate statistical techniques. The World Health Organisation has carried out performance assessment of the health system of 191 countries (WHO, 2000). The methodology adopted by WHO involves five outcome variables - health, health inequality, responsiveness-level, responsivenessdistribution and fair financial contribution - and four components of the health system stewardship, resource creation, financing and services provision. The outcome indicators were given fixed weights to arrive at the composite indicator of the performance of the health system. This composite indicator was then used to rank the countries according to the performance of their health care delivery system. The approach adopted by the World Health Organisation has however been questioned on many grounds, although it provides, for the first time, a comparative perspective of health system performance across different countries of the world. Despite many of the conceptual and methodological challenges in health systems performance assessment, many countries have now institutionalised health system performance assessment as an integral component of their health care delivery system, albeit using different conceptual and methodological foundations. Australia, has evolved a health determinants model which attempts to answer the question ‘how well is the health care delivery system performing in delivering services to improve the health of the people’. The health systems performance assessment framework of Canada, on the other hand, is a part of the Canadian health information roadmap initiative indicators framework (Canadian Institute of Health Information, 2000). The National Health Service of United Kingdom follows a balanced card approach which provides a balanced picture of the performance of health institutions and hospitals reflecting main aspects including outcomes and user perspective (Chang, 2000). The United States of America has introduced National Public Health Performance Standards Programme which is directed towards analysing the activities and capacities of the public health system and assessing how well the system is providing essential public health services (United States, no date). Health system performance assessment has also emerged a regular activity in many countries of Europe - Armenia, Belgium, England, Estonia, Georgia, Kyrgyzstan, Portugal, Turkey - to cite a few. Compared to the developed countries and countries of Europe, studies related to health system performance assessment in the developing countries are few. Besides studies in Afghanistan, Mexico and Pakistan referred above, performance of the health 25


Health System Performance Assessment system has been analysed at the district level in Indonesia in the context of decentralisation (Heywood and Choi, 2010). Indonesia has also conducted a comprehensive within-country assessment using WHO’s health systems performance assessment framework, as part of its Healthy Indonesia 2010 policy (Government of Indonesia, 2005) In South Africa, the Health Systems Trust has now published three editions of its district health barometer, which monitors about 20 indicators (Barron, Day, Monticelli, 2007). In addition to comparisons across districts, metropolitan areas and parts of the country considered to be severely disadvantaged, were included. In Afghanistan, the rapid expansion of health services was monitored using a balanced scorecard that focussed on service delivery through a comprehensive facility survey (Hansen et al, 2008). In Mexico, a report card for all states was used to assess the effects of the health system reforms during 2001–2006, including a summary measure based on 11 indicators derived from a variety of clinical and population-based data sources (Gakidou et al, 2006). In India Shankar and Kathuria (2004) have used an econometric approach which is similar to the approach used the World Health Organisation to analyse the performance of the health care delivery system in 16 States of the country. They have concluded that the States vary in terms of the performance of their health care delivery system. Recently, Purohit (2008, 2009, 2010) has carried out sub-State level analysis of the performance of the health care delivery system in selected States of India and has observed that the performance of the health care delivery system at the State level can be improved by improving the performance of the health care delivery system of the constituent districts of the State.

26


3 Level of Coverage

Perhaps the most severe bottleneck to health system performance assessment in the developing countries is the availability of the information necessary for measuring and monitoring performance. The routine health information system in most of the developing countries is too weak to provide the information necessary for assessing the performance of the health system. This is particularly the case in India which is very vast and diverse and which has a fragmented health care delivery system. Information about health care services delivered through the private health care delivery system in meeting the health needs of the people is generally not available in India. Similarly, no information is available about the services delivered through informal, home-based care. Some information about the services delivered through the public health care system is however available as programme service statistics. These statistics are however of little use in the performance assessment exercise as they provide little idea about the health needs of the people. Moreover, these statistics are widely known to be associated with substantive errors of duplication over time, place and services delivered. They do not cover services delivered through the private health care delivery system. Moreover, these statistics are largely confined to primary health care services only. They provide little information about secondary and tertiary health care services. Because of these limitations of the programme services statistics available through the routine health information system, they are of little use in assessing the performance of even the public health care delivery system. 27


Health System Performance Assessment In view of the non availability of the information necessary for a comprehensive health system performance assessment, the World Health Organization has recommended the use of the coverage rate of health interventions estimated on the basis of household survey for health system performance assessment. Unlike the programme service statistics, survey based estimates of the coverage rate of different health interventions capture health care services delivered through both public and private health care delivery system. Similarly, household survey based information covers both community-based primary health care services as well as institution-based health care. The present analysis follows the recommendations put forward by the World Health Organization to assess the health system performance in India at the national, State/Union Territory and district levels. The present analysis is based on the estimates of the coverage rate of different public health interventions available through the District Level Household and Facility Survey that was carried out throughout the country during 2007-08 (DLHS 2007-08). The District Level Household Survey Programme was launched by the Government of India as part of its Reproductive and Child Health Programme to provide estimates of key outcome indicators related to maternal and child health, family planning and other reproductive health interventions at the district level. The first DLHS was carried out in 1998-99 while the second one was organised in 2002-04. DLHS 2007-08 was the third in the series and the only source of information which facilitates assessment of the performance up to the district level, albeit in a restricted sense. In any case, even this limited information permits fairly satisfactory assessment of the performance of the health care delivery system which may be of immense help in evidence-based planning and programming for health care services delivery at the district level in the context of meeting the health needs of the people. Structured questionnaires were used for the collection of information during DLHS 2007-08. They included questionnaires for the household, ever married woman, unmarried woman and village questionnaires. In addition information from the public health institutions were also collected on the basis of pre-designed information schedules. All household level questionnaires used in the survey were bilingual, with questions in both regional language and English. The household questionnaire lists all usual residents in each sample household including visitors who had stayed in the household the night before the day of interview. Information on age, sex and marital status, relationship to the head of the household and education of each household member was collected. Marriages and deaths in each household were also recorded. Information related to the socio28


Level of Coverage demographic epidemiology of the households was also covered in the household questionnaire. The questionnaire for ever-married women in the age group 15-49 years, on the other hand, included information about age, place of birth, age at marriage, educational attainment, number and sex of biological children ever born and surviving. In addition, information about antenatal care, experience of pregnancy related complications, place of delivery, delivery attendant and post-partum care, together with history of contraceptive use, etc. as well as opinion about sex preference of children and fertility intentions were gathered recorded from the ever married women aged 15-49 years covered during the survey. Information about the immunization status of the youngest child was also collected from ever married women on the basis of either vaccination card or knowledge of the mother. DLHS 2007-08 covered 601 districts from 34 States/Union Territories of the country (IIPS, 2010). The survey was not carried out in Nagaland. Total number of households covered in DLHS 2007-08 was 720,320. In these households, 643,944 ever married women in the age group 15-49 years and 166,620 unmarried women of age 15-24 years were surveyed. A multi stage stratified systematic sampling design was adopted for DLHS 200708. In each district, 50 primary sampling units were selected at the first stage by the systematic probability proportional to size sampling procedure. The primary sampling units were census villages in the rural areas and municipal wards in the urban areas in each district. The list of villages and municipal wards in the district at the 2001 population census constituted the sampling frame for selecting the primary sampling units. All villages and municipal wards in a district were stratified into three strata on the basis of the number of households in the village/municipal ward - villages/municipal wards having less than 50 households, villages/municipal wards having 50-300 households, and villages/municipal wards having more than 300 households constituted strata III; on the basis of the proportion of Scheduled Castes/Scheduled Tribes population - below 20 per cent and above 20 per cent; and implicitly into three strata by arranging first in the ascending order, then in the descending and then again in the ascending order of female literacy. The number of primary sampling units representing a district in a State/Union Territory was worked out on the basis of immunisation, antenatal care and institutional delivery coverage at the DLHS 2002-04. The sample size in each district was either 1,000; 1,200 or 1,500 households depending upon levels of immunisation, coverage of antenatal care services and the proportion of institutional deliveries. In addition, 10 per cent over sampling of households was done to cushion for non response. The primary sampling units were allocated to rural and urban areas in each district on the basis of the actual 29


Health System Performance Assessment rural-urban population ratio. Within rural and urban domains, primary sampling units were further distributed proportionately to the different substrata formed on the basis of household size, proportion of Scheduled Castes/Scheduled Tribes population and level of female literacy. The second stage of sampling in the rural areas comprised of the selection of households from the selected primary sampling units (villages) after house listing. In the urban areas, on the other hand, the second stage sampling comprised of the selection of Census Enumeration Blocks (CEBs). Households were selected at the third stage of sampling from the second stage sampling units after house listing. Circular systematic sampling procedure was adopted for the selection of households in both rural and urban areas. All ever married women aged 15-49 years in the sampled households were covered during the survey. DLHS 2007-08 covered both rural and urban population in every district. However, at the district level, results of the survey are presented for the rural population and for the combined (rural and urban) population only. Estimates for the urban population are not presented at the district level, although the rural-urban distribution of the population varies widely across the districts of the country. In eight districts of the country, there was no rural population at the time of the survey. On the other hand, there are many districts in the country where the urban population is more than 50 per cent of the total population of the district. Based on the information available through DLHS 2007-08, the coverage rate of selected reproductive and child health interventions has been calculated for 601 districts in 28 States and 6 Union Territories of the country for the combined population as well as separately for the rural population along with other district level indicators (IIPS, 2011). Estimates of coverage rates are not available for all the districts in Nagaland as DLHS 2007-08 was not carried out in the State. Moreover, coverage rates for the rural areas are not available in eight districts of the country as the entire population of these districts was classified as urban. The coverage rates obtained through DLHS 2007-08 constitute the basic data set for the present analysis. Since the coverage rates are available for the combined (rural and urban) population of the district as well as for the rural population of the district, assessment of the health system performance has also been carried out for the combined population as well as for the rural population of each district. The present analysis uses the district level coverage rate of the following 14 public health interventions in the combined and rural populations to measure the performance of the district health system: 30


Level of Coverage 1.

Registration during the first trimester of pregnancy

2.

At least 3 antenatal care visits during pregnancy

3.

Tetanus Toxoid injection during pregnancy

4.

Institutional deliveries

5.

Post-natal care within 48 hours of delivery

6.

BCG vaccination

7.

Oral Polio vaccination

8.

DPT vaccination

9.

Measles vaccination

10.

Vitamin ‘A’ supplementation

11.

Check up of the new born within 24 hours of birth

12.

Use of ORS during diarrhoea

13.

Time of initiation of breastfeeding to the new born

14.

Exclusive breastfeeding

Description of the indicators used to measure the coverage of the aforesaid health interventions is given in table 3.1. Selection of the public health interventions for the present analysis was essentially based on three criteria. First, the selected health intervention should have a public health relevance. Second, district level estimates of the coverage rate of the selected health interventions should be available through DLHS 200708. Third, selected health interventions should be relevant to national health goals as articulated in the National Rural Health Mission as well as in the National Health Policy, National Population Policy and in the Five-year Development Plan of the country. This actually means that the selected health interventions must have a public health relevance. DLHS 2007-08 was carried out at a time when the National Rural Health Mission was in its early phase of implementation. As such, the assessment of the performance of the district health system based on the information available through DLHS 2007-08 serves as the benchmark for the National Rural Health Mission against which the progress and the performance of the Mission in terms of achieving its goals and objectives can be measured and monitored at the district level in terms of universal coverage of key health interventions which are regarded as crucial in terms of national health priorities. In fact, universal coverage of the health interventions selected for the present analysis are crucial in the context of achieving national health goals in terms of infant, child and maternal mortality. The analysis also allows ranking of the districts of the country in terms of the performance of the health system in delivering key reproductive and child health services to the people. 31


Health System Performance Assessment Table 3.1 Indicators of public health interventions used in health system performance assessment analysis V1

Proportion of women registered in the first trimester when they were pregnant with last live/still birth (%)

V2

Proportion of women who had at least 3 Ante-Natal care visits during their last pregnancy (%)

V3

Proportion of women who received at least one Tetanus Toxoid injection when they were pregnant with their last live/still birth (%)

V4

Proportion of births delivered at an institution out of total births reported during the survey (%)

V5

Proportion of women who received postnatal care within 48 hours after the delivery (%)

V6

Proportion of children aged 12-23 months who have received BCG vaccination (%)

V7

Proportion of children aged 12-23 months who have received 3 doses of Polio Vaccine (%)

V8

Proportion of children aged 12-23 months who have received 3 doses of DPT Vaccine (%)

V9

Proportion of children aged 12-23 months who have received Measles Vaccine (%)

V10

Proportion of children aged 9-35 months who have received at least one dose of Vitamin A (%)

V11

Proportion of children with diarrhoea in the last two weeks who were given ORS (%)

V12

Proportion of children who had a checkup within 24 hours after delivery (based on last live birth) (%)

V13

Proportion of children who were initiated breastfeeding within one hour of the birth (%)

V14

Proportion of children aged six months above who were exclusively breastfed during the first six months of life (%)

Coverage rates of the 14 health interventions as obtained from DLHS 2007-08 are presented in figures 3.1 and 3.2 for the country for combined and rural population 32


Level of Coverage Figure 3.1 Coverage rate of public health interventions in India Combined Population

respectively. Perhaps the most remarkable feature of figures 3.1 and 3.2 is the wide variation in the coverage rate of different health interventions. In case of BCG vaccination, the coverage rate is almost universal in the combined as well as rural populations. By contrast, coverage rates in terms of such child health interventions as the use of oral rehydration therapy during diarrhoea proper breastfeeding practices are relatively the poorest. Broadly speaking coverage of child immunisation interventions is relatively better than the coverage of interventions related to maternal health while coverage of maternal health related interventions is better than the coverage of child health related interventions. Because of these variations in the coverage rate of different public health interventions, any effort to measure health system performance requires combining coverage rates of different public health interventions into a single index of performance. 33


Health System Performance Assessment Figure 3.2 Coverage rate of public health interventions in India Rural Population

Moreover, there is little difference in the coverage rate in the combined as compared to rural population, although, coverage rates in rural population are lower than that in the combined population in all but health intervention - exclusive breastfeeding during the first six months. The gap between the coverage rates in the combined population and in the rural population appears to be the widest in case of maternal health related interventions but the narrowest in case of child health related interventions. In case of child immunisation also, the gap in the coverage rate in the combined population and in the rural population is also narrow. One reason for the relatively wide gap in the coverage rate of maternal health related interventions is that maternal health interventions are essentially clinic-based and there is a high concentration of clinics in the urban areas which gets reflected in high coverage in the combined population. 34


Level of Coverage Coverage rates of different health interventions for the constituent States and Union Territories of the country are presented in table 3.2 for the combined population and in table 3.3 for the rural population. Coverage rates of all health interventions vary widely across States/Union Territories which suggests that health system performance varies across States and Union Territories of India. Some idea about the performance of the health system at State/Union Territory level may be made by ranking States/Union Territories in terms of the level of coverage in different public health interventions. This exercise suggests that, for the combined population, the State of Goa and the Union Territory of Lakshadweep rank among the top five States/Union Territories of the country in 11 of the 14 health interventions included in the analysis. Tamil Nadu, on the other hand, ranks among the top five States/Union Territories in 10 interventions whereas Kerala ranks among the top five in seven interventions. In these States and Union Territories, the performance of the health system is not only well above the national average but also highly consistent across different health interventions. Other States and Union Territories where performance of the health system appears to be somewhat satisfactory are: Andaman and Nicobar Islands which rank among top five in six interventions; Daman and Diu which rank among top five in five interventions; Himachal Pradesh and Sikkim which rank among top five in four interventions; and Puducherry which ranks among top five in 3 interventions. In addition, Andhra Pradesh, Arunachal Pradesh, Chhattisgarh, Jammu and Kashmir, Jharkhand, Meghalaya and Mizoram rank among top five States/Union Territories in one intervention only. Rest of the States and Union Territories do not rank among top five in any of the 14 health interventions. On the other hand, Bihar ranks among the bottom five States/Union Territories in 12 of the 14 health interventions. Similarly, Uttar Pradesh ranks among the bottom five in 11 interventions; Meghalaya in 10 interventions; Jharkhand in eight interventions; Tripura in seven interventions; Rajasthan and Arunachal Pradesh in four interventions; Delhi, Madhya Pradesh, Orissa and Uttarakhand in two interventions; and Punjab, Manipur, Haryana and Chhattisgarh in one health intervention. In Bihar and Uttar Pradesh, poor performance of the health system across all health interventions is very much evident from the table. By contrast, performance appears to be highly inconsistent in Arunachal Pradesh, Chhattisgarh, Jharkhand and Meghalaya. These States rank among the top five States/Union Territories of the country in at least one intervention but, at the same time, also rank among the bottom five States/Union Territories in at least one health intervention. 35


Table 3.2 Coverage rates of different public health interventions in India and States/UTs - Total population Country/State/UT

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

India

45.1

50.5

73.1

46.7

47.1

87.3

66.7

64.7

70.1

55.6

36.6

47.8

42.7

26.9

AN Islands

48.2

79.5

93.5

76.4

69.2

96.3

90.9

88.2

92.8

82.2

71.4

75.7

76.1

57.8

Andhra Pradesh

67.3

89.4

92.4

71.8

75.6

97.3

81.8

78.7

88.4

78.9

43.8

75.3

47.5

32.3

Arunachal Pradesh

36.0

46.3

61.5

47.6

34.8

73.8

19.3

54.7

32.5

44.6

64.6

37.6

38.2

38.0

Assam

39.1

45.0

68.3

35.1

30.7

83.8

64.8

60.3

64.2

47.7

34.9

30.4

64.9

34.4

Bihar

24.1

26.3

58.1

27.5

25.6

81.5

53.0

54.4

54.1

49.7

22.0

25.8

16.0

11.8

Chandigarh

66.7

77.6

82.3

73.6

77.8

94.7

82.5

86.0

87.7

46.4

37.5

75.0

49.7

19.1

Chhattisgarh

38.5

51.1

77.7

18.0

38.4

94.8

69.7

71.3

80.0

65.1

36.3

39.9

49.6

43.3

DN Haveli

54.0

63.2

68.9

44.0

52.0

97.3

71.0

70.7

84.7

63.4

50.0

51.1

52.2

24.1

Daman and Diu

82.5

87.4

95.0

64.1

78.7

98.9

94.2

90.5

90.9

80.8

32.2

82.3

38.6

21.0

Delhi

57.7

71.7

90.1

68.7

77.2

91.8

76.5

76.4

83.0

55.0

48.5

79.3

29.1

8.8

Goa

89.6

95.8

98.0

96.4

96.6

95.8

94.1

91.5

94.1

83.9

69.9

96.4

60.9

30.1

Gujarat

52.3

54.8

68.1

56.4

56.3

87.7

71.7

63.4

72.6

56.3

36.7

57.2

48.0

28.8

Haryana

55.0

51.8

85.9

46.8

48.8

86.5

67.8

69.0

69.0

46.2

31.7

49.5

16.5

5.7

Himachal Pradesh

62.1

59.4

85.3

48.3

47.8

98.5

87.4

90.0

94.2

85.6

60.7

48.9

56.5

39.9

36


Country/State/UT

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

Jammu & Kashmir

56.6

73.3

81.8

54.9

52.0

93.6

74.9

70.5

81.3

53.4

53.0

53.5

54.1

62.3

Jharkhand

30.8

30.5

54.5

17.7

29.1

85.0

64.3

62.5

70.6

61.5

21.4

29.4

34.5

49.7

Karnataka

71.9

81.2

86.5

65.1

65.5

96.8

90.3

84.8

85.1

69.2

46.1

64.2

46.5

38.4

Kerala

95.6

95.2

96.6

99.4

99.1

99.1

86.6

87.1

87.9

60.2

45.6

99.0

64.6

22.3

Lakshadweep

78.1

91.4

97.6

90.7

94.0

100.0

92.8

91.1

92.1

47.9

56.7

94.0

69.7

48.0

Madhya Pradesh

33.7

34.0

60.1

46.9

35.8

84.2

55.0

47.2

57.4

39.4

30.0

39.4

42.7

30.9

Maharashtra

61.6

74.4

88.7

63.5

75.5

95.7

86.3

78.8

84.5

70.4

44.2

74.7

52.5

34.8

Manipur

56.9

57.2

73.4

41.0

41.7

81.6

63.5

61.3

58.0

31.5

51.6

40.7

56.8

40.7

Meghalaya

24.6

39.5

51.9

24.5

26.3

77.3

45.9

45.1

51.9

38.4

45.5

26.7

73.6

30.1

Mizoram

43.9

62.4

85.9

55.7

44.7

92.3

66.7

66.2

80.7

71.4

54.9

47.3

77.5

32.0

Nagaland

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Orissa

47.5

54.5

82.0

44.1

27.4

94.2

78.6

73.9

81.0

71.4

49.0

30.2

63.2

42.7

Puducherry

74.8

87.9

88.9

99.0

88.8

96.6

88.0

88.4

91.2

75.9

53.4

90.7

69.6

25.3

Punjab

62.5

64.1

82.5

63.1

78.1

94.7

86.8

86.0

89.1

64.9

52.0

78.8

44.1

9.2

Rajasthan

32.7

27.6

54.8

45.4

37.3

82.6

63.9

55.6

67.3

50.7

30.6

38.8

41.4

25.4

Sikkim

49.5

71.3

95.4

49.5

44.3

98.5

85.5

88.1

92.3

86.5

47.8

44.5

63.6

13.6

37


Country/State/UT

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

Tamil Nadu

76.8

95.6

97.2

94.0

85.9

99.5

90.5

89.5

95.5

72.9

37.5

85.6

76.1

22.3

Tripura

39.6

43.9

62.7

46.2

26.3

69.6

50.9

47.0

51.4

53.6

58.8

22.4

40.8

7.9

Uttar Pradesh

25.0

21.8

62.5

24.5

32.2

73.3

40.2

38.8

46.9

32.1

17.4

32.6

15.1

8.2

Uttarakhand

33.6

32.2

53.0

30.0

30.1

91.2

72.8

72.3

82.3

67.7

43.6

30.7

63.5

37.1

West Bengal

42.5

66.9

94.8

49.1

54.2

96.2

83.8

83.5

82.8

78.3

46.4

53.7

38.5

26.0

Source: IIPS (2010)

38


Table 3.3 Coverage rates of different public health interventions in India and States/UTs - Rural population Country/State/UT

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

India

40.5

45.5

69.7

40.2

41.4

86.2

64.5

62.1

68.0

53.7

34.1

42.2

42.0

27.0

AN Islands

47.4

79.4

92.9

72.5

68.3

95.1

91.6

88.1

90.5

79.7

65.6

73.0

76.0

59.1

Andhra Pradesh

63.4

87.2

90.5

65.7

71.8

96.8

81.1

76.6

87.6

77.9

42.1

71.4

49.3

33.4

Arunachal Pradesh

34.3

42.2

57.6

42.3

32.6

71.2

20.1

49.5

35.8

41.5

59.6

33.3

37.1

38.1

Assam

37.0

42.7

67.2

31.8

27.5

83.4

64.5

59.8

63.5

46.4

34.1

27.7

65.3

33.9

Bihar

22.9

25.3

57.3

25.5

24.4

81.6

52.9

54.4

54.0

49.5

20.9

24.6

15.7

11.7

Chandigarh

58.1

66.7

80.6

51.6

71.0

100.0

100.0

60.0

60.0

28.6

100.0

68.4

42.1

7.6

Chhattisgarh

33.9

47.2

75.7

13.3

34.7

94.6

68.2

69.7

79.2

64.1

35.2

36.2

49.8

44.0

DN Haveli

47.6

58.2

63.9

33.9

43.5

96.6

66.4

67.6

82.3

60.7

45.8

43.6

55.2

24.8

Daman and Diu

78.4

84.1

94.0

60.2

76.9

100.0

96.0

94.4

91.1

78.4

36.3

81.7

37.4

25.6

Delhi

59.3

58.6

87.1

57.0

67.8

88.3

74.8

68.4

83.5

49.8

40.5

71.4

34.4

10.9

Goa

87.7

98.4

99.4

97.8

97.8

100.0

94.8

94.8

100.0

88.3

62.5

98.9

66.5

30.7

Gujarat

45.8

48.0

62.4

48.0

49.6

86.4

69.5

60.2

70.1

54.2

36.2

50.9

47.0

29.4

Haryana

51.9

47.1

83.8

42.1

45.8

85.0

65.0

66.2

66.3

43.8

28.3

47.0

16.0

5.6

Himachal Pradesh

61.5

58.5

85.1

46.2

46.1

98.3

86.8

89.7

94.3

85.5

59.5

47.1

56.4

39.9

39


Country/State/UT

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

Jammu & Kashmir

53.6

70.6

79.2

49.8

47.4

93.0

73.4

68.3

79.9

51.5

51.7

49.3

55.5

63.1

Jharkhand

27.5

26.9

51.7

13.4

26.2

84.1

63.1

60.9

69.1

60.0

20.1

26.6

34.2

48.6

Karnataka

68.3

78.5

84.8

59.7

60.5

97.3

89.6

84.4

85.2

68.9

44.1

58.9

46.0

38.4

Kerala

95.5

95.2

96.3

99.2

99.0

99.0

86.6

87.5

88.1

60.3

43.7

99.0

66.1

23.1

Lakshadweep

76.8

88.0

95.9

88.0

92.1

100.0

91.2

89.6

92.4

42.0

61.6

91.7

77.1

50.4

Madhya Pradesh

27.8

28.1

54.9

40.7

30.7

82.1

51.4

42.6

53.3

35.8

26.0

34.6

42.3

30.8

Maharashtra

57.9

70.5

86.7

54.1

71.0

95.4

85.3

77.8

84.3

69.7

42.6

69.9

52.7

34.7

Manipur

50.8

50.5

69.3

33.7

34.2

79.2

58.9

56.5

54.5

29.7

49.2

33.7

56.7

36.0

Meghalaya

22.3

36.9

48.8

20.7

23.1

76.3

43.5

42.5

50.3

37.3

44.0

23.1

74.4

30.2

Mizoram

37.3

55.6

81.7

40.3

35.0

89.2

59.4

60.4

75.9

67.7

52.1

35.9

76.9

31.2

Nagaland

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

Orissa

45.1

51.9

81.1

40.2

25.4

93.9

77.9

72.9

81.0

71.0

48.0

28.7

63.3

42.7

Puducherry

71.1

96.7

95.7

97.4

87.6

100.0

98.7

97.4

97.4

66.4

31.7

87.9

64.9

21.7

Punjab

60.0

61.4

80.8

59.5

76.0

95.2

88.0

86.7

89.5

64.4

48.3

76.5

43.9

9.4

Rajasthan

28.6

23.3

51.1

40.6

33.1

81.5

62.6

53.4

65.5

48.8

26.5

34.9

39.6

24.8

Sikkim

48.6

70.7

94.6

48.5

42.6

98.4

85.2

87.5

91.9

86.0

47.3

42.9

64.6

12.8

40


Country/State/UT

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

Tamil Nadu

73.6

94.8

96.6

91.8

83.1

99.5

91.2

89.8

95.5

70.3

33.4

82.4

78.7

22.2

Tripura

36.9

41.1

59.2

41.5

24.2

67.6

49.4

44.7

48.5

51.5

58.5

20.9

41.1

8.1

Uttar Pradesh

23.1

20.2

60.8

22.0

29.2

73.0

39.3

37.8

45.7

31.0

16.3

29.8

14.8

8.4

Uttarakhand

30.0

28.2

49.6

25.1

25.8

91.0

71.8

71.3

81.3

66.3

42.9

26.9

63.8

38.0

West Bengal

38.9

63.4

94.4

43.3

50.2

96.3

83.6

82.9

83.1

78.8

46.3

49.8

39.9

26.3

Source: IIPS (2010)

41


Health System Performance Assessment Inter-State variation in the performance of the health system in the rural population is very much similar to that in the combined population, although there are some significant differences in the performance in the two populations. Since the combined population includes both rural and urban population, differential performance in the combined and rural populations actually reflects the difference in the performance in rural and urban populations. In any case, like the combined population, Goa again ranks among top five States/Union Territories of the country in 12 interventions in the rural population. Similarly, Lakshadweep ranks among top five in 11 interventions; Puducherry and Tamil Nadu in nine interventions; Kerala in six interventions; Andaman and Nicobar Islands in five interventions; Daman and Diu in four interventions; Chandigarh and Himachal Pradesh in four interventions; and Arunachal Pradesh, Chhattisgarh, Jammu and Kashmir, Jharkhand, Meghalaya, Mizoram, Sikkim and West Bengal in 1 intervention. By comparison, Uttar Pradesh ranks among poorest five States/Union Territories in 11 interventions, Meghalaya in 10 interventions, Madhya Pradesh in eight interventions, Jharkhand and Tripura in seven interventions, Uttarakhand in four interventions, Rajasthan in 3 interventions, Chandigarh and Manipur in 2 interventions and Chhattisgarh, Delhi, Haryana, Orissa and Punjab in 1 intervention. The case of Bihar and Madhya Pradesh is interesting. Bihar ranks among bottom five States/Union Territories in 12 interventions in the combined population but in only six interventions in the rural population. Madhya Pradesh, by contrast, ranks among bottom five States/Union Territories in only 2 interventions in the combined population but in eight interventions in the rural population. This difference in the ranking of the two States in combined and rural populations suggests that in Bihar, performance of the health system is relatively better in the rural population as compared to the urban population. By the same argument, it appears that performance of the health system is relatively better in urban population in Madhya Pradesh as compared to its rural population. A similar situation appears to prevail in the Union Territory of Puducherry which ranks among top five States/Union Territories in nine interventions in the rural population but in only 3 interventions in the combined population. Obviously, relative performance of the health system of different States/Union Territories varies not only across States/Union Territories and across different health interventions but also across rural and urban populations within the same State/Union Territory. In assessing the performance of the health system, it is therefore necessary that variation in the coverage rate of different health interventions is taken into account and the coverage rate of no single health intervention is sufficient for the purpose. 42


Level of Coverage The coverage rates of different health interventions in the combined and rural populations for the districts of the country have been given in the appendix table 3.1 while summary measures of the distribution of coverage rates of different health interventions are given in table 3.4. There is wide variation in the average coverage rate across different health interventions. At the same time, the shape of the inter-district distribution of the coverage rate of different health interventions is essentially different. In eight out of fourteen health interventions, the inter-district distribution of the coverage rate is positively skewed which means that the number of districts with below the average coverage rate is more than the number of districts with above the average coverage rate. In case of six health interventions, on the other hand, the inter-district distribution of the coverage rate is negatively skewed which implies that the number of districts with more than the average coverage rate is higher than the number of districts with lower than the average coverage rate. Five of these six health interventions are related to child immunisation while the sixth is tetanus toxoid to pregnant women. On the other hand in case of 12 out of 14 health interventions, the inter-district distribution of the coverage rate is negatively skewed which means that the inter-district distribution of the coverage rate of these interventions has a broad peak and short tales. In other words, the inter-district distribution of these health interventions is essentially platokurtic in shape. However, in case of two health interventions - BCG vaccination and use of ORS during diarrhoea, the inter-district distribution of the coverage rate has a narrow peak and a long tale so that the inter-district distribution of the coverage rate is essentially leptokurtic in shape. A leptokurtic distribution curve suggests that in most of the districts of the country, the coverage rate in the two interventions is generally close to the national average so that the deviation from the average level of coverage is generally small for most of the districts, although there are small number of districts where the coverage level deviates substantially from the average level of coverage. These districts constitute the tails of the distribution curve. In the rural population, the situation appears to be very similar to that of the combined population. Like the combined population, the average level of coverage is the highest in child immunisation related health interventions and the coverage of tetanus toxoid injection to pregnant women. On the other hand, the average level is the lowest in case of child health related interventions. Moreover, there is very little difference in both average coverage levels and the shape of the inter-district distribution of the coverage rate of different health interventions in the rural as compared to combined population. 43


Table 3.4 Summary measures of inter-district variation in coverage rates of different public health interventions Summary measure

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

Combined Population Minimum

10.1

7.6

24.0

5.9

5.8

40.9

5.0

14.0

14.1

7.8

0.0

4.0

4.1

0.2

First quartile

29.7

29.9

60.7

29.5

29.0

82.7

54.0

52.3

59.5

44.0

23.5

30.3

29.4

13.7

Median

46.2

52.5

79.8

48.0

44.9

92.8

72.9

70.6

76.5

59.3

37.5

46.1

45.4

26.7

Third quartile

63.6

73.4

92.3

65.8

71.4

97.8

86.6

84.3

88.9

74.1

50.0

71.8

58.4

37.0

Maximum

97.6

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

94.6

100.0

100.0

91.7

84.5

IQR

33.9

43.5

31.6

36.3

42.4

15.1

32.6

32.0

29.4

30.1

26.5

41.5

29.0

23.3

Range

87.5

92.4

76.0

94.1

94.2

59.1

95.0

86.0

85.9

86.8

100.0

96.0

87.6

84.3

Mean

48.0

53.9

75.5

50.1

49.9

88.8

69.0

67.2

72.6

58.1

38.5

50.8

44.8

27.3

Standard deviation

21.4

25.9

18.9

24.5

24.8

11.4

20.7

20.3

19.4

19.5

19.2

24.7

21.0

16.6

CV

0.445

0.482

0.250

0.490

0.498

0.128

0.300

0.302

0.267

0.337

0.498

0.487

0.469

0.609

Skewness

0.360

0.162 -0.577

0.340

0.308 -1.284 -0.650 -0.470 -0.638 -0.236

0.473

0.282

0.022

0.523

Kurtosis

-0.733

-1.140 -0.728 -0.761

-1.022

0.051 -0.989 -0.759 -0.035

Number of districts

601

601

601

601

601

1.241 -0.388 -0.728 601

44

601

601

-0.513 -0.845 601

601

601

601

601

601


Summary measure

V1

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

Rural population Minimum

7.9

4.8

20.0

4.2

4.5

21.4

7.3

9.5

7.1

6.6

0.0

3.8

3.5

0.0

First quartile

26.0

26.4

56.5

25.4

25.2

81.1

51.6

49.8

55.8

40.1

20.0

26.7

27.7

12.6

Median

40.6

47.8

76.7

41.6

39.8

92.2

70.2

67.9

74.2

57.4

34.2

40.9

44.5

26.9

Third quartile

60.2

68.5

90.9

59.4

65.1

97.8

86.7

84.2

89.5

73.5

50.0

66.1

58.7

37.9

Maximum

98.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

94.7

100.0

100.0

92.5

83.4

IQR

34.2

42.1

34.4

34.0

39.9

16.7

35.1

34.4

33.7

33.4

30.0

39.4

31.0

25.3

Range

90.1

95.2

80.0

95.8

95.5

78.6

92.7

90.5

92.9

88.1

100.0

96.2

89.0

83.4

Mean

44.8

50.5

73.2

45.2

46.1

88.4

68.2

65.8

71.7

56.8

36.6

47.1

44.8

27.5

21.5

26.4

20.0

24.1

24.6

12.1

21.6

21.4

20.2

20.4

21.1

24.4

21.6

16.9

CV

0.480

0.522

0.273

0.533

0.533

0.137

0.316

0.326

0.282

0.359

0.578

0.518

0.481

0.617

Skewness

0.505

0.291 -0.486

0.569

0.499

-1.412 -0.552 -0.390 -0.578

-0.171

0.693

0.474

0.047

0.488

Standard deviation

Kurtosis

-0.569 -1.056 -0.852 -0.422 -0.810

Number of districts

590

590

590

590

590

2.191 -0.556 -0.765 -0.557 -0.909 590

590

590

Remarks:

In eight districts of the country, there was no rural population at DLHS 2007-08.

Source:

Author’s calculations.

45

590

590

0.406 -0.789 -0.794 -0.166 590

590

590

590


Health System Performance Assessment Table 3.5 Coverage level of different health interventions across districts Indicator

Very poor

Poor

Average

Good

Very good

Total

Combined population V1

47

187

185

128

54

601

V2

56

157

140

122

126

601

V3

0

32

114

160

295

601

V4

71

151

181

109

89

601

V5

61

195

134

127

84

601

V6

0

0

17

105

479

601

V7

9

59

120

195

218

601

V8

6

67

136

195

197

601

V9

1

44

109

183

264

601

V10

13

109

188

194

97

601

V11

113

222

190

64

12

601

V12

50

187

152

117

95

601

V13

96

146

215

118

26

601

V14

218

259

99

23

2

601

Rural population V1

72

205

161

109

43

590

V2

80

163

133

107

107

590

V3

1

43

120

157

269

590

V4

93

177

171

79

70

590

V5

79

211

118

111

71

590

V6

0

1

21

103

465

590

V7

11

59

127

178

215

590

V8

11

65

159

172

183

590

V9

5

46

117

170

252

590

V10

24

115

171

186

94

590

V11

147

216

156

52

19

590

V12

71

209

127

109

74

590

V13

95

147

205

111

32

590

V14

216

243

112

17

2

590

Source:

Author’s calculations

46


Level of Coverage For the sake of comparison, the level of coverage of different health interventions has been grouped into five categories - very poor when the coverage rate is less than 20 per cent; poor when the coverage rate is between 20-40 per cent; average when the coverage rate is between 40-60 percent; good when the coverage rate is between 60-80 per cent; and very good when the coverage rate is at least 80 per cent. Based on this classification, the inter-district distribution of districts by the level of coverage in different health interventions is presented in table 3.5. In case of BCG vaccination (V6), there is not a single district in the country where the coverage is either poor or very poor in the combined population. Similarly, in case of tetanus toxoid injection to pregnant women (V3), there was not a single district in the country where the coverage rate was very poor in the combined population. By comparison, in case of the proportion of children exclusively breastfed during the first six months of life (V14), the coverage rate is very poor in 218 or more than 25 per cent districts of the country. Similarly, the proportion of the newborns examined within 24 hours of birth (V11) have been found to be less than 20 per cent in 113 or almost 20 per cent districts of the country. The situation in the rural population is very much similar to that in the combined population, although, there is some small but important variations. Unlike the combined population, in the rural population, there is one district in which the coverage is poor in case of BCG vaccination. This district is district Upper Subhanshri in Arunachal Pradesh. Similarly, in district Bharatpur of Rajasthan, the coverage of tetanus toxoid injection to pregnant women has been found to be very poor. It is also clear from the table that the number of above average performing districts is the highest in health interventions related to child immunisation but the lowest in health interventions related to child health. In other words, coverage level of different health interventions varies not only across the districts of the country but also across different health interventions. In other words, coverage levels of different health interventions vary horizontally across different health interventions in the same district as well as vertically across districts in the same intervention. The two-dimensional variation in the coverage level of different health interventions implies that assessment of the performance of the health system based on the coverage level of different health interventions, essentially, requires a multi dimensional approach and there is no single indicator of coverage which can be the basis of assessing the health system performance at any tier of the health care delivery system. At the same time, it is also obvious from the foregoing analysis that there is very substantial disparity or inequality in the performance of the health system with reference to different health interventions which needs to be 47


Health System Performance Assessment analysed and articulated in the context of the performance of the health care delivery system. In any case, an analysis of the observed horizontal and vertical disparity or inequality in coverage of different health interventions is the first step towards assessing the performance of the health care delivery system.

48


4 Coverage Inequality

An important aspect of the performance of the district health system is that its performance should be more or less uniform in all dimensions of health care services delivery. The dimensions of health care services delivery can be defined in many ways. Perhaps the most widely known dimensions of health care services delivery are defined in terms of the type of health care - promotive, preventive or curative health care. Another commonly used approach of defining dimensions of health care services delivery is to link health care services with population groups. Thus, different dimensions of health care services delivery may be defined in terms of services specific to children, services specific to women, services specific to pregnant women, services specific to the aged, etc. Note that dimensions of health care services defined in this manner encompass all - promotive, preventive and curative aspects of health care. More specifically, the delivery of health care services may be characterised on a two-dimensional grid. One dimension of the grid may specify the population group while the other dimension may describe the type of health care service - promotive or preventive or curative health services. Ideally, the health care delivery system should perform uniformly in all cells identified in the grid. The variation in the performance of the health system in different cells of the grid is an indication of the inequality in the performance of the health system in different dimensions of health care services delivery. In any assessment of the health system performance, it is important to analyse the inequality in the performance of the health system in different dimensions of health care services delivery for different groups of the population. 49


Health System Performance Assessment An examination of the coverage rate of different health interventions discussed in the previous chapter clearly suggests that the performance inequality in the health system is quite pervasive in India. It appears that some of the dimensions of health care services delivery receive only a residual attention at the cost of other dimensions which has an impact on the performance of the health system as a whole. An analysis of the inequality in the coverage rates of different health interventions within the system, therefore, provides an insight into the evenness or uniformity in health system performance in delivering health care services to the people. The within-system inequality in the coverage rate of different health interventions can be measured in different ways. Perhaps the simplest measure of inequality is the range (Sen, 1997) and is defined as

where yj is the coverage rate of the health intervention j and ĂŹ is the average coverage rate of all interventions within the district. The major limitation of the range as a measure of inequality is that it depends upon the extreme values only and ignores the distribution of the coverage rate across different health interventions. More refined measures of inequality include relative mean deviation (M), variance (V), Coefficient of variation (C) and mean logarithmic deviation (H). These measures are defined as

, j = 1, 2, .... k;

, j = 1, 2, .... k;

, j = 1, 2, .... k. 50


Coverage Inequality More generally, inequality in the coverage rate of different health interventions within the same administrative unit may be captured through inter-mean difference and interindividual difference. The inter-mean difference in the coverage rate of different health interventions is defined (Gakidou, Murray, Frenk, 2000) as

, j = 1, 2, .... k;

while the inter-individual difference in the coverage rate of different health interventions is defined as

, I = 1, 2, .... k; j = 1, 2, .... k.

Here á and â are arbitrary coefficients to be assigned. Normally, á = 1, 2, and 3, whereas â = 0, 0.5 and 1. The coefficient á is related to the weight given to the extremes of the distribution. When á = 1, all values in the distribution are assigned equal weight. When á > 1, larger weights are assigned to values at the ends or the tails of the distribution. On the hand, the coefficient â reflects the relativeness of the inequality measure with respect to the mean. When â = 0, inequality is measured in absolute terms. When â = 1, inequality is measured relative to the mean. When â is between 0 and 1, mean is increasingly included in the measure of inequality which becomes increasingly more relative as â closes to 1. Notice that when á = 2 and â = 0, both IMD and IID are equal to the variance of the distribution of the coverage rate within the district. On the other hand, when á = 2 and â = 2, IMD is nothing but the square of the coefficient of variation. Similarly when á = 1 and â = 1, IID is nothing but the famous Gini coefficient. IID is calculated on the basis of the difference of the coverage rate of a given health intervention from the coverage rate of all other health interventions by taking into consideration all pairs of the difference. IMD on the other hand, compares the coverage rate of every health intervention with the mean coverage rate of all health interventions within the same administrative unit. As such, IID is more sensitive to the variation in the coverage rate of different health interventions within the district. By contrast, IMD is more sensitive to the mean or average coverage rate of all health interventions in the district. 51


Health System Performance Assessment An application of the foregoing theoretical foundations to within-country, within State/Union Territory and within-district variation in the coverage rate of different health interventions reveal interesting information about the performance of the health system in the country, across States and Union Territories and across districts in the combined as well as rural populations. For India as a whole, coverage rate across the 14 health interventions included in the present analysis varies from more than 87 per cent in BCG vaccination to just around 26 per cent in exclusive breastfeeding during the first six months in the combined population so that the range of coverage is almost around 61. The inter-quartile range (IQR) is however quite narrow (20.7). The inequality or the variation in the coverage rate of different health interventions at the national level is also reflected in terms of the coefficient of variation which is an index of inter-mean difference and in the index IID which is a measure of inter-individual difference in the coverage rate of different health interventions (Table 4.1). This inequality or variation in the coverage rate of different health interventions suggests that the performance of the health system is not the same in all dimensions of health care. Performance appears to be relatively better in some health interventions but, at the same time, substantially poor in other health interventions. More specifically, performance of the health system appears to be poor in two health interventions in which the coverage rate is estimated to range between 20-40 per cent, average in seven health interventions in which the coverage rate is estimated to range between 40-60 per cent, good in four health interventions with coverage rate ranging between 60-80 per cent and very good in only one health intervention in which the coverage rate is estimated to be more than 80 per cent. There is no health intervention in which the performance of the health system at the national level appears to be very poor. In any case, wide variation in the coverage rate of different health interventions has its impact on the coverage rate of all health interventions combined. The simple arithmetic average of the coverage rates of the 14 health interventions is estimated to be around 54 per cent while the median is only about 49 per cent. This implies that the overall performance of the health system in the country can, at best, be rated as only average. Moreover, the very fact that the mean average rate is higher than the median coverage rate suggests that the number of health interventions having lower than the average coverage rate is more than the number of health interventions having higher than the average coverage rate. Obviously, the inequality or the disparity in the coverage rate of different health interventions reflects differential performance of the health system in different dimensions of health care. It is also obvious that this inequality affects the performance of the health system. 52


Coverage Inequality Table 4.1 Inequality in the coverage rate of different health interventions (Combined population) Country/State/UT India AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh D&N Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamilnadu Tripura Uttar Pradesh Uttarakhand West Bengal

Mean 54.4 78.4 72.9 45.0 50.3 37.9 68.3 55.3 60.5 74.1 65.3 85.2 57.9 52.2 68.9 65.4 45.8 70.8 81.3 81.7 45.5 70.4 54.0 43.0 63.0 60.0 79.9 68.3 46.7 66.5 79.9 44.4 33.6 52.9 64.1

Median 49.1 78.0 77.2 41.4 46.4 26.9 76.3 50.4 58.6 82.4 74.1 94.1 56.4 50.7 61.4 59.5 42.1 70.6 91.6 91.3 41.1 74.6 56.9 42.3 64.3 58.9 88.2 71.5 43.4 67.5 87.7 46.6 32.2 48.3 60.6

Remarks:

For definition of IID, see text.

Source:

Author’s calculations.

Range 60.4 48.1 65.0 54.5 53.4 69.7 75.6 76.8 73.2 77.9 83.0 67.9 58.9 80.8 58.6 41.6 67.3 58.4 77.1 52.1 54.2 60.9 50.1 52.8 60.3 66.8 73.7 85.5 57.2 84.9 77.2 61.7 65.1 61.2 70.2

53

IQR 20.7 17.8 18.3 16.5 29.7 29.4 28.5 32.1 18.9 23.1 23.1 10.9 14.0 22.4 36.2 20.9 32.6 20.6 28.3 21.9 18.6 21.0 19.3 22.9 26.8 32.5 15.2 22.5 21.6 39.5 18.8 13.2 17.4 38.6 36.3

CV 0.287 0.171 0.255 0.311 0.329 0.517 0.309 0.374 0.287 0.329 0.348 0.218 0.247 0.431 0.283 0.202 0.438 0.244 0.285 0.218 0.314 0.239 0.248 0.379 0.269 0.333 0.238 0.319 0.344 0.370 0.276 0.359 0.516 0.395 0.339

IID 4.060 3.472 4.484 3.622 4.257 5.003 5.030 5.606 4.380 5.557 5.238 3.471 3.566 5.659 5.094 3.492 5.299 4.512 5.033 4.168 3.598 4.225 3.477 3.925 4.365 5.332 3.923 4.963 4.277 6.198 4.649 3.778 4.439 5.482 5.702


Health System Performance Assessment Table 4.2 Distribution of health interventions by the level of coverage (Combined population) Country/State/UT Very poor India 0 AN Islands 0 Andhra Pradesh 0 Arunachal Pradesh 1 Assam 0 Bihar 2 Chandigarh 1 Chhattisgarh 1 D&N Haveli 0 Daman & Diu 0 Delhi 1 Goa 0 Gujarat 0 Haryana 2 Himachal Pradesh 0 Jammu & Kashmir 0 Jharkhand 1 Karnakata 0 Kerala 0 Lakshadweep 0 Madhya Pradesh 0 Maharashtra 0 Manipur 0 Meghalaya 0 Mizoram 0 Odisha 0 Puducherry 0 Punjab 1 Rajasthan 0 Sikkim 1 Tamilnadu 0 Tripura 1 Uttar Pradesh 3 Uttarakhand 0 West Bengal 0 Source:

Poor 2 0 1 6 6 6 1 4 1 3 1 1 2 1 1 0 6 1 1 0 7 1 1 7 1 2 1 0 6 0 2 3 7 6 2

Average 7 2 2 4 2 5 2 3 6 0 3 0 7 6 5 7 2 2 1 3 5 2 9 5 5 5 1 2 5 5 0 8 2 2 5

Author’s calculations.

54

Good 4 6 6 3 5 0 5 4 5 2 6 2 4 3 2 4 4 5 2 2 1 7 3 2 5 4 3 6 2 2 3 2 2 4 2

Very good 1 6 5 0 1 1 5 2 2 9 3 11 1 2 6 3 1 6 10 9 1 4 1 0 3 3 9 5 1 6 9 0 0 2 5

Total 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14


Coverage Inequality Figure 4.1 Coefficient of variation in the coverage rate of different health interventions

Among different States and Union Territories of the country, the within State/UT inequality or disparity in the coverage rate of different health interventions varies widely. The coefficient of variation in the coverage rate of different health interventions has been found to be the highest in Bihar but the lowest in Andaman and Nicobar Islands. On the other hand the inter-individual difference (IID) in the coverage rate is found to be the highest in Sikkim but the lowest in Andaman and Nicobar Islands. In addition to Bihar, there are eight states - Chhattisgarh, Haryana, Jharkhand, Meghalaya, Sikkim, Tripura, Uttar Pradesh and Uttarakhand - where the coefficient of variation in the coverage rate 55


Health System Performance Assessment Figure 4.2 Inter-individual difference in coverage rate of different health interventions

of different health interventions is found to be quite high. On the other hand, the interindividual difference in the coverage rate has been found to be high in nine states Chhattisgarh, Daman and Diu, Delhi, Haryana, Jharkhand, Orissa, Sikkim, Uttarakhand and West Bengal. This means that in five states of the country - Chattisgarh, Haryana, Jharkhand, Sikkim and Uttarakhand - the within state inequality or the disparity in the coverage rate of different health interventions is found to be well above the national average in terms of both difference from the mean and inter-individual different in the coverage rate. 56


Coverage Inequality Table 4.3 Inequality in the coverage rate of different health interventions (Rural population) Country/State/UT India AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh D&N Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamilnadu Tripura Uttar Pradesh Uttarakhand West Bengal

Mean 51.2 77.1 71.1 42.5 48.9 37.2 63.9 53.3 56.4 73.9 60.8 87.0 54.1 49.6 68.2 63.3 43.7 68.9 81.3 81.2 41.5 68.0 49.5 41.0 57.0 58.8 79.6 67.1 43.9 65.8 78.8 42.4 32.2 50.9 62.7

Median 43.9 77.7 74.2 39.8 44.6 25.4 63.4 48.5 56.7 80.1 63.6 96.3 50.3 47.1 60.5 59.3 41.4 68.6 91.7 88.8 38.3 70.2 50.7 39.9 57.5 57.6 91.8 70.2 40.1 67.7 86.5 43.1 29.5 46.3 56.8

Remarks:

For definition of IID, see text.

Source:

Author’s calculations.

Range 59.0 47.7 63.4 51.1 55.9 69.9 92.4 81.3 71.8 74.4 77.4 69.3 57.0 79.4 58.4 45.6 70.7 58.9 76.1 58.0 56.1 60.7 49.5 55.6 58.0 68.5 78.3 85.8 58.2 85.6 77.3 59.5 64.6 65.9 70.0

57

IQR 23.2 20.6 21.7 13.0 30.3 30.5 25.0 33.9 21.6 28.9 22.4 10.9 14.6 23.4 37.1 21.2 34.0 25.6 27.1 15.1 18.5 27.6 22.0 22.7 35.8 33.4 29.8 25.6 23.1 39.5 19.2 13.0 18.3 41.4 39.0

CV 0.311 0.175 0.254 0.297 0.354 0.535 0.405 0.406 0.322 0.319 0.344 0.224 0.261 0.446 0.292 0.217 0.474 0.258 0.284 0.211 0.352 0.250 0.286 0.417 0.325 0.350 0.310 0.329 0.383 0.378 0.286 0.365 0.538 0.435 0.358

IID 4.155 3.572 4.528 3.189 4.468 5.070 6.185 5.874 4.695 5.617 5.143 3.731 3.593 5.639 5.238 3.655 5.491 4.749 5.007 3.929 3.627 4.376 3.629 4.089 4.780 5.497 5.492 5.215 4.463 6.266 4.825 3.692 4.446 5.847 5.886


Health System Performance Assessment Table 4.4 Distribution of health interventions by the level of coverage (Rural population) Country/State/UT Very poor India 0 AN Islands 0 Andhra Pradesh 0 Arunachal Pradesh 0 Assam 0 Bihar 2 Chandigarh 1 Chhattisgarh 1 D&N Haveli 0 Daman & Diu 0 Delhi 1 Goa 0 Gujarat 0 Haryana 2 Himachal Pradesh 0 Jammu & Kashmir 0 Jharkhand 1 Karnakata 0 Kerala 0 Lakshadweep 0 Madhya Pradesh 0 Maharashtra 0 Manipur 0 Meghalaya 0 Mizoram 0 Odisha 0 Puducherry 0 Punjab 1 Rajasthan 0 Sikkim 1 Tamilnadu 0 Tripura 1 Uttar Pradesh 3 Uttarakhand 0 West Bengal 0 Source:

Poor 2 0 1 7 6 6 1 4 2 3 1 1 2 1 1 0 6 1 1 0 7 1 5 7 4 2 2 0 7 0 2 3 8 6 3

Average 7 2 2 6 3 5 3 3 6 0 5 0 7 6 6 7 2 4 1 2 6 4 7 5 4 5 0 3 4 5 0 9 1 2 4

Author’s calculations.

58

Good 4 7 6 1 4 0 5 5 4 4 4 2 4 3 1 6 4 4 2 3 0 5 2 2 4 4 3 5 2 2 3 1 2 4 2

Very good 1 5 5 0 1 1 4 1 2 7 3 11 1 2 6 1 1 5 10 9 1 4 0 0 2 3 9 5 1 6 9 0 0 2 5

Total 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14


Coverage Inequality Like the combined population, the inequality or diversity in the coverage rate of different health interventions is quite pervasive in the rural population at the country as a whole as well as in states and Union territories. In fact, the variability in the coverage rate of different health interventions is marginally higher in the rural population than in the combined population which suggests that the inequality or disparity in the health system performance is marginally higher in the rural as compared to the combined population. The coefficient of variation has been found to be the highest in Uttar Pradesh and there are seven other states where the coefficient of variation has been found to be well above the national average. These states are Bihar, Chandigarh, Chhattisgarh, Haryana, Jharkhand, Meghalaya and Rajasthan. On the other hand, the inter-individual difference in the coverage rate has been found to be the highest in Sikkim and in eight other states of the country, the inter-individual difference in the coverage rate has been found to be well above the national average. The primary factor behind the observed inequality of disparity in the coverage rate of different health interventions in the country and in different states/UTs is relatively low to very low coverage rates in terms of the use of oral rehydration salt during diarrhoea and the prevalence of exclusive breastfeeding during the first six months of the life of the new born. If these two health interventions are excluded, the diversity or the inequality in the coverage rate of different health interventions reduces sharply. This shows that improving the performance of the health system in terms of the use of oral rehydration therapy in combatting and universalising the practice of exclusive breastfeeding can contribute significantly in improving the performance of the health system as a whole. In general inequality in the coverage rate of different health interventions within a State or Union Territory is higher in rural as compared to the combined population. There are however deviations from this general pattern. In Arunachal Pradesh and the Union Territory of Daman and Diu, the coefficient of variation in the coverage of different health interventions is higher in the combined population compared to the rural population. A similar situation appears to prevail in Delhi also whereas in Andhra Pradesh and Kerala, there appears little difference between the two. In terms of the inter-individual difference in the coverage rate, there are nine States/Union Territories in the country where the inequality in the coverage rate of different health interventions appears to be higher in the combined population that in the rural population as may be seen from figure 4.2. It may however be pointed out here that the inequality in the coverage rate of different health intervention is not related to the average level of coverage. The inequality may be high or low irrespective of whether the average level of coverage is high or low. 59


Health System Performance Assessment Table 4.4 Summary measures of within-district inequality in the coverage rate of different health interventions across the districts of India Summary

Unweighted Median of Range of

measure (Across districts)

IQR of

Coefficient Within a

average of

within

within

within-

of

district

within-

district

district

district

variation

IID in

district

coverage

coverage

coverage

rates

rates

coverage of within- coverage rates

rates

district

rates

coverage rates

Minimum 1st Quartile Median 3rd Quartile Maximum CV IQR

19.7 44.0 56.4 69.7 86.4 0.283 25.7

13.0 40.5 55.0 73.1 97.0 0.360 32.6

Minimum 1st Quartile Median 3rd Quartile Maximum CV IQR

18.3 41.6 54.1 68.2 87.1 0.300 26.6

12.6 37.3 52.2 69.6 97.4 0.381 32.3

Source:

Combined population 37.5 5.7 61.9 18.8 70.1 25.2 78.5 31.8 100.0 62.6 0.167 0.363 16.6 13.0 Rural population 35.3 7.1 62.7 20.3 71.0 27.1 80.5 34.2 100.0 63.6 0.173 0.357 17.8 13.9

0.135 0.292 0.354 0.461 0.958 0.335 0.169

2.418 4.488 5.253 5.930 8.316 0.197 1.441

0.143 0.311 0.388 0.496 1.021 0.331 0.185

2.570 4.767 5.636 6.442 8.617 0.196 1.676

Author’s calculations

District level estimates of different indicators of within-district inequality in the coverage rate of different health interventions are given in appendix table 4.1 for the combined population and in appendix table 4.2 for the rural population. Summary measures of the distribution of different indicators of within-district inequality in the coverage rate of different indicators are given in table 4.4 while the kernel density plots are given in figure 4.3. Wide variations in the within-district inequality or disparity in the coverage rate of different health interventions across the districts of the country in both combined and rural populations is very much evident from the table 4.4. Obviously, disparity in the coverage rate of different health interventions within as well as between districts is quite substantial in the country. 60


Coverage Inequality The pervasive within-district inequality or disparity in the coverage rate of different health interventions suggests that differential performance of the district health system in different dimensions of health care is an important dimension of the performance of the health system as a whole. In other words, not only the level of performance measured in terms of coverage rates but also variation in the coverage rate of different health interventions must be taken into account in measuring the performance of the health system either at the national or State or district level. When the performance of the system varies by health interventions, the most common approach is to take the simple arithmetic mean of the coverage rate of different health interventions to have an idea of the performance of the system as a whole. Use of simple arithmetic mean is however problematic in assessing the overall performance of the health system because of the total compensability of the arithmetic mean in the sense that low or very low performance in one dimension of health care can be compensated totally by high or very high performance in other dimensions of health care. One alternative is to take weighted instead of simple arithmetic mean but this alternative raises the issue of assigning different weights to different health interventions. Ideally, all dimensions of health care should be assigned equal weights in any health system performance assessment exercise. One approach of circumventing the above problem is to use geometric mean instead of arithmetic mean to find the average coverage rate of different health interventions. Geometric mean minimises the effect of compensability of low and high coverage rates. The other approach is to assign a penalty that is relative to low performance in some health interventions and then adjust the simple arithmetic mean according to the penalty so estimated. This means that the health system is not able to perform equally in all dimensions of health care then it should be assigned a penalty for differential performance and this penalty should be subtracted from the average performance of the district. Obviously, if the health system performs equally in all dimensions of health care, the penalty of performing poorly in one or two dimensions of health care will be zero and the simple arithmetic average will be sufficient to reflect the overall performance of the health system. On the other hand, larger is the deviation of the performance from the average, the higher will be the penalty for poor performance. Accounting for unequal performance in different dimensions of health care provides a framework for assessing the district health system performance by taking into account both the level of performance measured through the coverage rate and differential performance in different dimensions of health care as reflected through disparity or inequality in the coverage rate of different health interventions. 61


Health System Performance Assessment Following ___ (), let  denotes the simple arithmetic mean of the coverage rate of different health interventions in an administrative area - country, State or district - then, denoting by P the penalty for differential coverage rates of different health interventions in the administrative area, the index of inequality-adjusted average coverage rate may be defined as C =  - P = Â(1-P/Â) = Â(1-D) where D is the dissimilarity index which measures the inequality in the coverage rate of different health interventions within the administrative area. The index D is calculated as

The dissimilarity index D is proportional to the difference in the coverage rates of different health interventions and the average coverage rate of all health interventions in the district. The larger is this difference, the larger is the index D and hence the smaller is the index C. It may be noted that the penalty P for lower than the average coverage rate in any health intervention is essentially an inequality indicator. When there is no difference in the coverage rate of different health interventions within the district, P = 0 and C = Â irrespective of the level of the coverage rate. On the other hand, the lower or higher is the coverage rate of any health intervention from the average coverage rate, the larger is the value of P and D and the lower is the value of the index C. Obviously, the index C is a better reflection of the performance of the health system in any administrative area than the simple arithmetic mean of the coverage rate of different health interventions. For India as a whole, the inequality-adjusted average coverage rate of 14 health interventions used in the present analysis, the index C, has been estimated to be 47.8 per cent which is substantially lower than the unweighted average coverage rate of 54.4 per cent. On the other hand, the geometric mean of the coverage rate of 14 health interventions has been estimated to be 52.1 per cent. In the rural areas of the country, the index C is estimated to be around 44.3 per cent against the unweighted average coverage rate of 51.2 per cent while the geometric mean is estimated to be 48.9 per cent. In other words, the inequality or disparity in the coverage rate of different health interventions results in a penalty of 6.5 per cent points in the combined population and a penalty of 6.9 per cent points in the rural population and results in a substantial reduction in the simple arithmetic mean of coverage rates of different health interventions. This shows that 62


Coverage Inequality within-country inequality or disparity in the coverage of different health interventions has a strong impact on the performance of the health system. Obviously, substantial improvements in the performance of health system can be achieved by reducing the disparity in the coverage rate of different health interventions. Reduction in coverage disparity bears significance because this disparity is largely due to the poor administrative capacity and organisational efficiency of the health system. Extending the analysis to States and Union Territories of the country, the penalty for within State/UT disparity in the coverage rate of different health interventions has been found to be the highest in Sikkim followed by Daman and Diu. This penalty has also been estimated to be very high in seven States and Union Territories of the country (Table 4.5). On the other hand, the penalty has been found to be the least in Gujarat followed by Andaman and Nicobar Islands and Manipur. The penalty has also been found to be very low in Madhya Pradesh, Arunachal Pradesh and Jharkhand. It may be pointed out here that the penalty resulting from the differential performance of different health systems is relative to the arithmetic mean of the coverage rate of different health interventions and, therefore, is independent of the level of coverage. In the rural population also, the penalty for within State/UT disparity in the coverage rate of different health interventions has been estimated to be the highest in Sikkim. Other States/Union Territories where the penalty has been estimated to be very high are West Bengal, Puducherry and Uttarakhand. By contrast, the penalty resulting from the within State/UT disparity in the coverage rate of different health interventions has been found to be the lowest in Arunachal Pradesh. Other States where the penalty has been estimated to be very low are Madhya Pradesh, Gujarat, Manipur and Tripura. In general, the penalty for within State/UT disparity in the coverage rate of different health interventions has been found to be higher in the rural population as compared to that in the combined population. In Andhra Pradesh, Arunachal Pradesh, Daman and Diu, Delhi, Kerala, Lakshadweep, Madhya Pradesh and Tripura, however, the penalty is estimated to be higher in the combined population as compared to the rural population. A higher penalty reflects larger inequality or disparity in the within State/UT coverage rate of different health interventions. Similarly, a lower penalty reflects smaller disparity or inequality in the within State/UT coverage rate of different health interventions. It therefore appears that within State/UT inequality or disparity in the coverage rate of different health interventions in Andhra Pradesh, Arunachal Pradesh, Daman and Diu, Delhi, Kerala, Lakshadweep, Madhya Pradesh and Tripura is relatively larger in the urban population as the combined population comprises of both rural and urban populations. 63


Health System Performance Assessment Figure 4.4 Penalty for within country and within state/UT inequality in the coverage rate of different health interventions

Adjusting for the inequality in the coverage rate of different health interventions results in a substantial decrease in the average coverage rate of the 14 health interventions. At the national level, the inequality adjusted average coverage rate of 14 health interventions is less than 48 per cent compared to the unadjusted average coverage rate of more than 54 per cent in the combined population. Similarly, the inequality adjusted average coverage rates of 14 health interventions in the rural population is just around 44 per cent compared to unadjusted average coverage rate of more than 51 per 64


Coverage Inequality cent. If there is no inequality in the coverage rate of different health interventions, the adjusted average coverage rate would have been the same as the adjusted coverage rate. In this case, the unadjusted coverage rate can be taken as indicator of the performance of the health system. However, there exists substantial inequality or variability in the coverage rate of different health interventions. As such, the inequality adjusted average coverage rate better reflects the performance of the health system than the unadjusted average coverage rate. Obviously, differential performance of the health system in different dimensions of health care has a substantial impact on the performance of the health system as a whole. In other words, the performance of the health system can be improved substantially by eliminating or minimising the difference in the performance of different dimensions of health care. Among different states and Union Territories of the country, the inequality adjusted average coverage rate of 14 health interventions varies from more than 78 per cent in Goa to less than 27 per cent in Uttar Pradesh in the combined population. In addition to Goa, the adjusted average coverage rate is found to be more than 60 per cent in only 10 states and Union Territories - Andaman and Nicobar Islands, Andhra Pradesh, Chandigarh, Daman and Diu, Karnataka, Kerala, Lakshadweep, Maharashtra, Puducherry and Tamil Nadu. Barring the Union Territories of Chandigarh and Daman and Diu, all these states and Union Territories are located in the southern part of the country. On the other hand, in addition to Uttar Pradesh, there are seven states where the adjusted average coverage rate of 14 health interventions has been found to be less than 40 per cent. Out of these, four states - Bihar, Jharkhand, Madhya Pradesh and Rajasthan - are located in the central part of India whereas the remaining three states - Arunachal Pradesh, Meghalaya and Tripura - are located in the north eastern part of the country. There is no Union Territory in the country where the adjusted coverage rate of 14 health interventions has been found to be less than 40 per cent for the combined population. In the rural population, the inequality adjusted average coverage rate varies from a high of almost 80 per cent in Goa to just around 25 per cent in Uttar Pradesh. In addition to Goa, the adjusted average coverage index is found to be more than 60 per cent in nine states and Union Territories - Andaman and Nicobar, Andhra Pradesh, Daman and Diu, Karnataka, Kerala, Lakshadweep, Maharashtra, Puducherry and Tamil Nadu. In these states, the health system performance may be termed as good. In Arunachal Pradesh, Bihar, Jharkhand, Madhya Pradesh, Meghalaya, Rajasthan, and Tripura, on the other hand, the unadjusted average coverage rate has been found to be less than 40 per cent which reflects the poor performance of the health system. 65


Health System Performance Assessment Figure 4.5 Inequality adjusted coverage rate of different health interventions (Index C)

In general, the inequality adjusted average coverage rate of 14 health interventions is higher in the combined population as compared to rural population in the states and Union Territories of the country. The only exception to this general pattern is the state of Goa and Union Territories of Daman and Diu and Lakshadweep. In Kerala, there is virtually little difference in the inequality adjusted average coverage rate of 14 health interventions. By contrast, the urban-rural difference in the inequality adjusted average coverage rate is found to be the widest in the Union Territory of Chandigarh. 66


Coverage Inequality Table 4.5 Inequality adjusted average coverage rate of different health interventions Country/State/UT

Unweighted average Penalty for coverage

coverage rate Combined Rural India 54.35 51.24 AN Islands 78.44 77.09 Andhra Pradesh 72.89 71.06 Arunachal Pradesh 44.96 42.51 Assam 50.26 48.91 Bihar 37.85 37.19 Chandigarh 69.20 61.16 Chhattisgarh 55.26 53.27 D&N Haveli 60.47 56.44 Daman & Diu 74.08 73.89 Delhi 65.27 60.84 Goa 85.22 86.97 Gujarat 57.88 54.12 Haryana 52.16 49.56 Himachal Pradesh 68.90 68.06 Jammu & Kashmir 65.37 63.31 Jharkhand 45.82 43.74 Karnakata 70.83 68.90 Kerala 81.31 81.33 Lakshadweep 81.72 81.20 Madhya Pradesh 45.48 41.51 Maharashtra 70.40 68.04 Manipur 53.99 49.49 Meghalaya 42.95 40.96 Mizoram 62.97 57.04 Odisha 59.98 58.79 Puducherry 79.89 79.61 Punjab 68.28 67.11 Rajasthan 46.72 43.88 Sikkim 66.46 65.83 Tamilnadu 79.92 78.78 Tripura 44.36 42.37 Uttar Pradesh 33.61 32.24 Uttarakhand 52.86 50.86 West Bengal 64.05 62.66 Source:

inequality Combined Rural 6.53 6.92 5.31 5.55 7.27 7.24 5.62 4.85 7.48 7.87 8.84 9.04 8.69 9.51 9.07 9.42 6.85 7.19 10.03 9.72 9.09 8.24 6.86 7.23 5.29 5.58 8.59 8.63 9.11 9.38 5.94 6.04 9.10 9.38 7.20 7.60 9.47 9.44 7.73 7.01 5.57 5.54 6.81 7.02 5.48 5.75 6.47 6.65 7.14 7.99 8.89 9.25 7.18 10.16 8.43 8.78 6.76 7.12 10.89 10.96 8.14 8.26 6.09 5.91 6.69 6.81 9.48 10.03 9.85 10.28

Author’s calculations.

67

Index C Combined 47.82 73.14 65.62 39.34 42.77 29.01 60.51 46.19 53.62 64.05 56.18 78.36 52.59 43.57 59.79 59.43 36.73 63.63 71.84 73.99 39.91 63.59 48.51 36.48 55.84 51.09 72.72 59.85 39.97 55.57 71.78 38.27 26.93 43.38 54.20

Rural 44.32 71.54 63.82 37.67 41.04 28.15 51.66 43.85 49.25 64.17 52.60 79.74 48.54 40.93 58.68 57.27 34.36 61.30 71.89 74.19 35.97 61.03 43.75 34.31 49.05 49.54 69.45 58.34 36.76 54.86 70.51 36.46 25.43 40.83 52.38


Health System Performance Assessment The adjusted average coverage rates of 14 health interventions (index C) for 601 districts included in the present analysis are given in appendix table 4.3 separately for combined and rural populations along with the estimates of the penalty for within-district inequality or disparity in the coverage rate of different health interventions. Summary measures of the inter-district variation in the index C for the combined and rural populations are given in table 4.6. The inequality adjusted average coverage rate (index C) has been found to be the lowest in district Bahraich in Uttar Pradesh in both combined and rural population (12.8 per cent and 12.0 per cent respectively). On the other hand, the index has been found to be the highest in district Mahe (82.5 per cent) of the Union Territory of Puducherry in the combined population and in district Coimbatore (79.7 per cent) of Tamil Nadu in the rural population. District Mahe in the Union Territory of Puducherry is the only district in the country where the inequality adjusted average coverage rate of the 14 health interventions has been estimated to be more than 80 per cent in the combined population. In the rural population, on the other hand, there is no district in the country with the inequality adjusted average coverage rate of at least 80 per cent. Table 4.6 Summary measures of inter-district distribution of the index C Measure

Unadjusted average

Penalty for

Inequality adjusted

coverage rate

inequality in the

average coverage

coverage rate

rate

Combined

Rural

Combined

Rural

Combined

Rural

Minimum

19.7

18.3

3.4

3.5

12.8

12.0

1st Quartile

44.0

40.9

6.9

7.1

35.6

32.4

Median

56.4

53.6

8.2

8.5

47.8

44.6

3rd Quartile

69.7

68.1

9.6

10.0

62.0

59.5

Maximum

88.3

87.1

13.9

14.0

82.5

79.7

IQR

25.8

27.3

2.7

2.9

26.4

27.1

CV

0.284

0.298

0.221

0.223

0.328

0.346

601

590

601

590

601

590

N Source:

Author’s calculations 68


Coverage Inequality Table 4.7 Distribution of districts by inequality adjusted average coverage rate (Combined population) Country/State/UT Very poor AN Islands 0 Andhra Pradesh 0 Arunachal Pradesh 1 Assam 0 Bihar 1 Chandigarh 0 Chhattisgarh 0 D&N Haveli 0 Daman & Diu 0 Delhi 0 Goa 0 Gujarat 0 Haryana 1 Himachal Pradesh 0 Jammu & Kashmir 0 Jharkhand 0 Karnakata 0 Kerala 0 Lakshadweep 0 Madhya Pradesh 0 Maharashtra 0 Manipur 0 Meghalaya 0 Mizoram 0 Odisha 0 Puducherry 0 Punjab 0 Rajasthan 0 Sikkim 0 Tamilnadu 0 Tripura 0 Uttar Pradesh 11 Uttarakhand 0 West Bengal 0 India 14 Source:

Poor 0 0 8 8 35 0 4 0 0 0 0 1 2 0 1 13 0 0 0 23 1 3 5 1 2 0 0 14 0 0 3 57 3 0 184

Average 0 1 7 18 1 1 12 1 1 8 0 15 17 6 4 9 6 0 0 20 5 2 2 4 26 1 13 18 3 0 1 2 10 15 229

Author’s calculations.

69

Good 2 22 0 1 0 0 0 0 1 1 2 9 0 6 9 0 21 14 1 2 29 4 0 3 2 2 7 0 1 30 0 0 0 4 173

Very good 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

Total 2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601


Health System Performance Assessment Table 4.8 Distribution of districts by inequality adjusted average coverage rate (Rural population) Country/State/UT Very poor AN Islands 0 Andhra Pradesh 0 Arunachal Pradesh 2 Assam 0 Bihar 1 Chandigarh 0 Chhattisgarh 0 D&N Haveli 0 Daman & Diu 0 Delhi 0 Goa 0 Gujarat 0 Haryana 1 Himachal Pradesh 0 Jammu & Kashmir 0 Jharkhand 0 Karnakata 0 Kerala 0 Lakshadweep 0 Madhya Pradesh 1 Maharashtra 0 Manipur 1 Meghalaya 1 Mizoram 0 Odisha 0 Puducherry 0 Punjab 0 Rajasthan 0 Sikkim 0 Tamilnadu 0 Tripura 0 Uttar Pradesh 12 Uttarakhand 0 West Bengal 0 India 19 Source:

Poor 0 0 11 10 36 0 5 0 0 0 0 2 4 0 2 17 0 0 0 31 1 2 4 1 3 0 0 21 0 0 3 57 6 0 216

Average 0 3 3 17 0 1 11 1 1 4 0 18 15 8 4 5 8 0 0 13 7 5 2 6 25 0 14 11 3 0 1 1 7 16 210

Author’s calculations.

70

Good 2 19 0 0 0 0 0 0 1 1 2 5 0 4 8 0 19 14 1 0 25 1 0 1 2 2 6 0 1 29 0 0 0 2 145

Very good 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Total 2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 30 2 20 32 4 29 4 70 13 18 590


Coverage Inequality In all, the inequality adjusted average coverage rate (index C) of 14 health interventions in the combined population has been found to be very poor in 14 districts of the country. Out of these 14 districts, 11 are located in Uttar Pradesh and one each in Arunachal Pradesh, Bihar and Haryana (Table 4.7). In addition to these 14 districts, the inequality adjusted average coverage rate is found to be poor (20-40 per cent) in 184 districts; average (40-60 per cent) in 229 districts; good (60-80 per cent) in 173 districts and very good (80 per cent and above) in only one district of the country - district Mahe in the Union Territory of Puducherry. There are nine states in the country - Arunachal Pradesh, Bihar, Chhattisgarh, Haryana, Jharkhand, Meghalaya, Rajasthan, Uttar Pradesh and Uttarakhand - where there is no district with an inequality adjusted average coverage rate of at least 60 per cent. By contrast, in Goa, Kerala and Tamil Nadu, there is no district with an inequality adjusted average coverage rate of less than 60 per cent. In the rural population, there is no district in the country where the inequality adjusted average coverage rate of 14 health interventions is estimated to be more than 80 per cent. The index C in the rural population is found to range between 60-80 per cent in 145 districts; 40-60 per cent in 210 districts; 20-40 per cent in 216 districts and less than 20 per cent in 19 districts of the country (Table 4.8). Out of these 19 districts, 12 are located in Uttar Pradesh, 2 in Arunachal Pradesh and one each in Bihar, Haryana, Madhya Pradesh, Manipur and Meghalaya. On the other hand, out of 145 districts where the inequality adjusted average coverage rate ranges between 60-80 per cent, 106 districts (73 per cent) are located in the five southern states - Andhra Pradesh, Karnataka, Kerala, Maharashtra and Tamil Nadu. It is obvious from the foregoing analysis that inequality in the coverage rate of different health interventions has a strong impact on the performance of the health system as a whole; the larger is the inequality in the coverage rate of different health interventions, the larger is the performance penalty and lower is the inequality adjusted average coverage rate of the health interventions included in the analysis and hence the lower is the performance of the health system as a whole. Reducing and ultimately minimising the inequality in the coverage rate of different health interventions through evidence-based planning and programming for the delivery of health care services, therefore, can contribute significantly in improving the performance of the health system as a whole. The inequality adjusted average coverage rate of different health interventions may serve as a measure of the performance of the health system. However, it is well known that there exists a strong correlation between the coverage rate of different health 71


Health System Performance Assessment interventions so that a high coverage rate in one health intervention leads to a high coverage rate in other health interventions also. The average coverage rate or the inequality adjusted coverage rate does not take into account the correlation that exists between the coverage rates of different health interventions. In the next chapter, an attempt has been made to develop a composite coverage index that takes into account the observed correlation between coverage rates of different health interventions in search of more refined measure of health system performance.

72


5 Composite Coverage Index

In the previous chapter, an attempt was made to account for the inequality in the coverage rate of different health interventions within an administrative area - country, State/Union Territory/district - by making adjustment in the average coverage rate of different health interventions on the basis of the penalty for the inequality in the coverage rate. One limitation of the approach adopted in the previous chapter is that it does not take into consideration the correlation between the coverage rates of different health interventions across administrative units. If, coverage rates of two or more health interventions are highly correlated then the coverage rate of one intervention influences the coverage rate of the other intervention. This means that the correlation between coverage rates of different health interventions should be taken into consideration in developing any index to measure health system performance so as to make sure that the performance index is not biased by the correlation between coverage rates of different health interventions. In this chapter, an attempt has been made to develop a composite coverage index that takes into account the correlation between coverage rates of different health interventions across the districts of the country. The approach has then been applied to estimate the composite coverage index of 14 health interventions for the country as well as for different States/Union Territories and districts of the country. The composite coverage index so developed may be perceived as a more refined measure of health system performance at national, State/Union Territory and district levels than the average coverage rate or the inequality adjusted average coverage rate presented in the previous chapter. 73


Health System Performance Assessment Composite indexes are now increasingly being recognised as useful tool in benchmarking performance, analysing policies and in public communication(Saltelli, 2007). They have also been found to useful in initiating discussion on the contemporary situation and stimulating public interest in the performance of the health system in meeting the health needs of the people. In the context of the health system, composite coverage indexes depict the ‘big picture’ of the performance encompassing different dimensions of health care delivery. As such, these indexes are useful for policy level discussions and for planning and programming for health care services delivery. Construction of the composite coverage index is also justified in view of the fact that the health care delivery system is essentially a multi dimensional entity and outputs of different dimensions of the health system have their own relevance towards meeting the health care needs of the people. In the context of measuring the performance of the health system as a whole, it is imperative that the performance of all dimensions of the health care delivery system is taking into consideration before arriving at any conclusion or bench marking of the of health system. Construction of the composite coverage index of health system performance essentially involves combining the coverage rate of different health interventions into a single index on the basis of an underlying statistical model. An underlying concept of this approach is that the single index of coverage so arrived at should be able to account for as much variation in the coverage rate of different health interventions as possible. The larger is the variation explained, the more appropriate is the composite coverage index in reflecting health system performance. Construction of the composite index involves a number of steps. These include, according to the order of priority: 1) specification of the underlying model of combining the coverage of different health interventions; 2) selection of statistical indicators reflecting the performance of different health interventions and related data; 3) imputation of missing data; 4) multivariate analysis; 5) normalisation of statistical indicators - the coverage rate in the present case; 6) weighting and aggregation; 7) robustness and sensitivity analysis; 8) transparency and decomposition; 9) links to other variables; and 10) visualisation. The purpose and the rationale of different steps involved in the construction of the composite index of performance assessment are given in table 5.1. The first and perhaps the most important step in the construction of the composite index of health system performance is the development of the theoretical framework to establish causal relationships between different dimensions of the health system and to describe the scope of performance assessment. 74


Composite Coverage Index Table 5.1 Checklist for building a composite indicator Step in construction

Justification

1. Theoretical Framework.

To get a clear understanding of the

Provides the basis for the selection

multidimensional nature of health

and combination of variables into a

care.

meaningful composite index under a

fitness-for-purpose principle.

To structure various subgroups or components of health care services delivery.

To describe the scope of the performance assessment exercise.

To describe the causal relationship between different dimensions of performance assessment.

To decide about the criteria for the selection of indicators to measure the performance.

2. Data Selection.

Should be based on the analytical

To check the quality of the available indicators.

soundness, measurability, country

To discuss the strengths and

coverage, and relevance of the

weaknesses of indicators selected for

indicators to the phenomenon being

performance assessment.

measured and relationship to each

To create a summary table on data

other. The use of proxy variables

characteristics, e.g., availability,

should be considered when data are

source and type.

scarce.

75


Health System Performance Assessment Step in construction

Justification

3. Imputation of Missing Data.

To estimate missing values.

To provide a measure of the

It is needed in order to provide the complete data set.

reliability of each imputed value, so as to assess the impact of the imputation on the composite index results. •

To discuss the presence of outliers in the data set.

4. Multivariate Analysis.

Should be used to study the overall

To check the underlying structure of data.

structure of the data set, assess its

suitability, and guide subsequent

To identify groups that are statistically similar.

methodological choices (e.g.,

weighting, aggregation).

To compare statistically determined structure of data to the theoretical framework.

5. Normalisation.

To be carried out to render the

To select suitable normalisation procedure.

variables comparable.

To discuss presence of outliers in the data set.

To make scale adjustments, if necessary.

To transform highly skewed indicators, if necessary.

6. Weighting and Aggregation.

To be done along the lines of the

To select appropriate weighting and aggregation procedure(s).

underlying theoretical framework.

To discuss whether correlation among indicators should be accounted for.

To discuss whether compensability among indicators should be allowed.

76


Composite Coverage Index Step in construction

Justification

7. Uncertainty and Sensitivity analysis.

To assess the robustness in terms of,

To consider a multi-modelling approach of construction.

e.g., the mechanism for including or

excluding an indicator, the

If possible, develop alternative conceptual scenarios.

normalisation scheme, the

To identify all possible sources of

imputation of missing data, the

uncertainty in the construction of

choice of weights, the aggregation

the index.

method. 8. Back to the Data.

To conduct sensitivity analysis.

To profile performance at the

It is needed to reveal the main

indicator level so as to reveal what is

drivers for an overall good or bad

driving the composite index.

performance. Transparency is

primordial to good analysis and

To check for correlation and causality.

policymaking.

To identify if the composite index results are overly dominated by few indicators and to explain the relative importance of different subcomponents of the composite indicator.

9. Links to other Indicators.

To correlate the composite index

Should be made to correlate the

with other relevant measures, taking

composite indicator (or its

into consideration results of the

dimensions) with existing (simple or

sensitivity analysis.

composite) indicators as well as to

identify linkages through

To develop data-driven narratives based on the results.

regressions.

77


Health System Performance Assessment Step in construction

Justification

10. Visualisation of Results.

•

To identify a coherent set of

Should receive proper attention,

presentation of assessment results

given that the visualisation can

for the targeted audience.

influence (or help to enhance)

•

interpretation

To select the visualisation technique which communicates the most information.

•

To present the composite index results in a clear and accurate manner.

Source: OECD (2008)

Different approaches and methodologies have been developed and proposed to address issues related to the construction of the composite index like imputation of missing data, normalisation of variables included in the analysis, application of multivariate statistical methods and methods of estimating weights for combining different variables into a composite index. A detailed description of many of these approaches is given in a manual prepared by the Organisation of Economic Cooperation and Development (OECD, 2008). For example, missing data often hinder the development of a robust composite index and imputation of missing values is required to ensure that complete data set without missing values is available. There are generally three methods used for dealing with the missing data - case deletion, single imputation and multiple imputation. Similarly, normalisation of variables is required, particular when the unit of measurement of different variables included in the analysis is different. There is a range of methods suggested for the purpose which also includes the well-known zscore. Finally, there are a number of multivariate statistical methods that can be used for the construction of the composite index. These include: principal component analysis, cronbatch coefficient alpha, correspondence analysis, canonical correlation analysis, etc. Similarly, different methodologies for combining different variables have been suggested. It may however be emphasised that the quality of the composite index as well as the soundness of the message it conveys regarding the performance of the health system depends not only on the methodology used in the construction of the composite index but also on the quality of the underlying framework and the relevance and the quality of the data used. A composite index based on a weak theoretical background or on soft data 78


Composite Coverage Index containing large measurement errors can lead to disputable policy messages in spite of the use of state-of-the-art methodology in its construction. Availability of appropriate information is perhaps the most important stumbling block in constructing the composite indicator as well as in measuring the performance of the health system in the developing countries like India. Information required to measure the performance of the health system, in general, is not available through the routine health information system. All efforts to address the health needs of the people can be grouped into three broad components, each having two sub-components, as described in table 5.2. This means that a comprehensive health system performance assessment requires information in terms of the six sub-components of the health system. However, the existing routine health information system in most of the developing countries including India has not been designed to provide this information. Because of these limitations of the routine health information system, the World Health Organization has recommended the use of effective coverage rate of different health care interventions, obtained on the basis of a population-based survey, to assess the performance of the health system in the developing countries. The present analysis follows the approach recommended by the World Health Organization. Table 5.2 Components of the health system Level of health care

Sub-component

Sub-component

First level

Health promotion

Specific protection

Second level

Early detection

Prompt treatment

Third level

Disability limitation

Rehabilitation

The present analysis employs the factor analysis procedure for developing a composite coverage index based on 14 health interventions included in the analysis. Factor analysis procedure first combines different health interventions into factors or domains on the basis of the coverage rate while preserving the maximum possible proportion of the total variation in the original data set. In the factor analysis procedure, the largest factor loading is assigned to that health intervention which has the largest variation in the coverage rate, a desirable property for comparison across administrative units - district in the present case, as health interventions with coverage rate similar across

79


Health System Performance Assessment administrative units are of little interest and cannot possibly explain differences in the performance of the health system. This analytical characterisation is necessary for assessing the health system performance as the health system is essentially a multi dimensional entity and it is not always necessary that performance of the system as measured by coverage rates is the same for all dimensions and all public health interventions. Factor analysis describes a set of q variables x1, x2 ,..., xq in terms of a smaller number of m factors and to highlight the relationship between the variables. The factor analysis model assumes that the data are based on the underlying factors of the model and that the variability in the data set can be decomposed into that accounted by different factors. The model is given by: x1 = á11F1 + á12F2 +...+ á1mFm + e1 x1 = á21F1 + á22F2 +...+ á2mFm + e2 ..

xq = áq1F1 + áq2F2 +...+ áqmFm + eq where xi (i=1,…,q) represents original variables which have been standardized with zero mean and unit variance; á11, á12, ..., á1m are the factor loadings related to the variable xi; F1, F2,...,Fm are m uncorrelated common factors, each with zero mean and unit variance; and ei are the q specific factors supposed to be independently and identically distributed with zero mean. There are several approaches to dealing with the model described above. They include communalities, maximum likelihood factors, centroid method, principal axis method, etc. The most common is the use of principal component analysis to extract the first m principal components and to consider them as factors, neglecting the remaining ones. Principal components factor analysis is most preferred in the development of composite indicators (Nicoletti et al, 2000), as it has the virtue of simplicity and allows for the construction of weights representing the information content of individual variables used in the construction of the composite index. It may however be pointed out that different extraction methods yield different values for the factors and thus for the weights, influencing the composite score. An important issue in the use of factor analysis for the construction of the composite index is to decide about how many factors should be retained. The key consideration is that extraction of the factors should be stopped when there is only very little “random” variability left to be explained. Although, the decision related to stopping the extraction of factors is rather arbitrary, yet various stopping rules have been developed and proposed 80


Composite Coverage Index (Dunteman, 1989). Another method for deciding about the number of factors to be retained combines bootstrapped eigenvalues and eigenvectors (Jackson, 1993). The Kaiser criterion is however the most commonly used yardstick for stopping the extraction of factors in the factor analysis exercise. After deciding the number of factors to be retained for the analysis, it is the standard practice to perform rotation of factors so as to enhance interpretation of the results of factor analysis. The initial factors obtained through factor analysis are always orthogonal and ordered according to the proportion of the variance explained. However, this datacompression comes at the cost of having most variables loaded on the first factor. Moreover, many variables included in the analysis are loaded on more than one factor with substantial loading that makes the interpretation of factors difficult. The purpose of rotation is to improve interpretation of the factor analysis solution. The sum of the eigenvalues is not affected by the process of rotation of factors but changing the axes alters the eigenvalues of different factors and changes the factor loadings of the variables constituting the factor so as to make them more meaningful. A number of strategies have been proposed for rotating factors obtained through factor analysis procedure to enhance their interpretation. The essential idea is to obtain a simple structure to make interpretation of the factors straightforward and unambiguous (Thurstone, 1947). According to Thurstone, the matrix of loadings with rows representing original variables and column representing factors is a simple structure if it follows the following criteria: 1.

Each row contains at least one zero.

2. For each column, there are as many zeros as there are columns. 3. For any pair of factors, there are some variables with zero loadings in one factor and large loading on other factors. 4. For any pair of factors, there are a sizeable proportion of variables having zero loading. 5. For any pair of factors, there is only a small number of large loadings. There are two main types of approaches that have been adopted for the rotation of factors - orthogonal rotation and oblique rotation. In the orthogonal rotation, the new axes are also orthogonal to each other so that the different factors extracted are not correlated. In the oblique rotation, on the other hand, the axes are not required to be orthogonal which allows different factors to be correlated. Different rotation methods imply different loadings to the variables leading to different interpretations of the factor analysis solution. This is a drawback of factor analysis to construct the composite index. 81


Health System Performance Assessment Box 3.1 A sample of stopping rules for extracting factors Kaiser criterion. Drop all factors with eigenvalues less than 1.0. The simplest justification for this rule is that it makes no sense to add a factor that explains less variance than that contained in one individual indicator. Scree plot. This method plots successive eigenvalues which drop sharply and then level off. It suggests retaining all eigenvalues in the sharp descent before the first one on the line where they start to level off. Variance explained criteria. This criteria suggests to keep all those factors which, in combination, account for 90 per cent (sometimes per cent) of the variation. Joliffe criterion. Drop all factors with eigenvalues less than 0.70. This rule may result in twice as many factors as the Kaiser criterion produces and is less often used. Comprehensibility. Though not a strictly mathematical criterion, there is much to be said for limiting the number of factors to those whose dimension of meaning is readily comprehensible.

The most commonly used method of rotating factors is the “varimax rotation.� It is an orthogonal rotation method which maximizes the variance of the squared loadings of a factor on all the variables in a factor matrix or the matrix of factor loadings, which has the effect of differentiating the original variables by extracted factor. In this method, each factor, after rotation, tends to have either large or small factor loadings on any particular variable. The Varimax rotation yields results which make it as easy and possible to identify each variable with a single factor so as to make the interpretation simple. The last step in the construction of composite index deals with the construction of the weights for individual variables constituting different factors. This is done on the basis of square of factor loadings on individual variables which represents the proportion of the total unit variance of the indicator which is explained by the factor. A two-part process is adopted for the purpose. First, individual variables the highest factor loadings are grouped into intermediate composite indicators and then intermediate composite indicators are combined to construct the over all composite index. Weights for the construction of intermediate composite indexes are derived from the factor loadings. First, the factor loading of each variable in a factor is squared; then the squared factor loadings are normalised so that the sum of squared factor loadings of all variables 82


Composite Coverage Index constituting the factor is equal to one. These normalised squared factor loadings serve as weights for combining variables into the intermediate composite index specific to each factor. Only those variables are used which have high factor loadings as these variables explain most of the variance of the factor. Finally, the intermediate composite indexes are combined to construct the overall composite index. The proportion of the total variance explained by the selected factors is used as the weight for combining the intermediate composite indexes representing specific factors into a single composite index combining all the factors extracted which may then be used for bench marking the performance as well as for the purpose of ranking of administrative units on the basis of the joint performance of all variables included in the analysis. It may however be emphasised here that since different methods of the extraction of factors imply different factor loadings on the variables included in the analysis, the value of the composite index is sensitive to the extraction method used for the extraction of factors. Because of this sensitivity, there is a possibility that the ranking of administrative units may change when a different method of extraction of factors is used in the analysis. This is a limitation of the factor analysis-based approach of the construction of the composite index. Application of the factor analysis procedure to inter-district variation in the coverage rate of 14 health interventions covered in the present analysis suggests that these interventions can be grouped into three factors or domains of the health system which account for more than 82 per cent of the total variance in the original data set in the combined population and 81 per cent of the total variance in the original data set in the rural population (Figure 5.3). The Kaiser-Meyer-Olkin (K-M-O) measure of sampling adequacy which reflects the homogeneity of variables, was estimated to be 0.914 in the combined population and 0.911 in the rural population. This means that the partial correlations among the variables included in the analysis are small and patterns of correlations are relatively compact. This also implies that factor analysis procedure has yielded distinct and reliable factors (Sharma, 1996). The Bartlett’s test of sphericity was also found to be statistically significant in the combined population (approximate á2 =113036.847, df=91, p=0.000) as well as in the rural population (approximate á2=10636.353, df=91, p=0.000). This implies that the correlation matrix used in the factor analysis was not the identity matrix. Both these tests confirm the suitability and appropriateness of the factor analysis technique for summarising the coverage of 14 health interventions used in the present analysis. Results of the factor analysis solution have been used for developing the composite coverage index of the 14 health interventions. 83


Health System Performance Assessment Table 5.3 Factor structure Factor 1 Variable

Factor 2

Factor loadings Total

Rural

V1

0.789

0.797

V2

0.761

V3

Variable

Factor 3

Factor loadings Total

Rural

V6

0.850

0.865

0.765

V7

0.845

0.636

0.639

V8

V4

0.861

0.856

V5

0.895

0.900

V12

0.889

0.891

Variable

Factor loadings Total

Rural

V11

0.661

0.663

0.836

V13

0.736

0.717

0.832

0.833

V14

0.799

0.792

V9

0.861

0.868

V10

0.809

0.808

14.11

13.64

Proportion (per cent) of variation explained 34.17 K-M-O Measure

34.00 Total 0.914

34.02

Bartlett’s Test of Sphericity Total 11303.8

Rural 0.911 Remarks:

33.54

Rural 10636.4

Only those health interventions have been shown which have a factor loading of at least 0.60.

Source:

Author’s calculations.

The first of the three domains of the health system revealed through factor analysis has high loadings in six variables, all of which are related to maternal health and account for around 34 per cent of the total variation in both combined and rural populations. This domain, therefore, can be termed as the maternal health domain of the health system. The second domain also accounts for almost 34 per cent of the total variance in both combined and rural populations and has high loadings in interventions related to child immunisation. This domain, therefore can be termed as the child immunisation domain of the health system. Finally, the third domain accounts for around 14 per cent of the total variance and has high loadings child health related interventions. This domain, can therefore be termed as the child health domain of the health system. Specific health interventions having high loadings in the three domains along with the proportion of the total variance accounted by each domain are given in table 5.3. In every domain, 84


Composite Coverage Index interventions having a loading of at least 0.60 are shown. It is also clear from the table that the domain structure is the same for the combined and the rural population. Resulkts of the factor analysis procedure thus suggests that the 14 health interventions used in the present analysis can be grouped into three domains - maternal health domain, child immunisation domain and child health domain - with only a marginal loss in variability and such that the three domains are orthogonal. As such, the composite coverage index has first been developed for the three domains separately and then they are combined to obtain the composite coverage index for the health system as a whole. Since the coverage rate of different health interventions ranges between 0 and 100, it is obvious that the composite coverage index of different domains as well as the composite coverage index of the health system ranges between 0 and 100. It may therefore be argued that the higher is the composite coverage index, the better is the performance of the health system. The composite coverage index, estimated according to the procedure described above, may be used to make an assessment of the performance of the health system of any administrative unit. It is obvious that the composite performance index cannot be more than 100 per cent. Similarly, the composite performance index cannot be less than 0. In other words, based on the level of the composite coverage index, the performance of the health system of any administrative unit can be termed as very good if the composite coverage index is at least 80 per cent; good if the composite coverage index rages between 60-80 per cent; average if the composite coverage index ranges between 40-60 per cent; poor if the composite coverage index ranges between 20-40 per cent; and very poor if the composite coverage index is less than 20 per cent. The above exercise suggests that the composite coverage index of the 14 health interventions for India as a whole is estimated to be around 55 per cent for the combined population and around 52 per cent for the rural population. Among the three domains of the health system, the composite coverage index is estimated to be around 50, 69 and 35 per cent respectively in the domains of maternal health, child immunisation and child health for the combined population and 45, 67 and 34 per cent respectively for the rural population. The exercise also suggests that the composite coverage index is relatively the best in the domain of child immunisation but the poorest in the domain of child health with the coverage in the domain of child health being the poorest (Figure 5.1). The composite coverage index also suggests that the gap in the composite coverage index between the combined and rural populations is the widest in the domain of maternal health but the narrowest in the domain of child health, although this gap has been not found to be very large in any of the three domains. 85


Health System Performance Assessment Figure 5.1 Composite coverage index in India

It may also be seen from the figure 5.1 that the performance of the health system in the combined population is relatively higher in the combined population as compared to the performance of the health system in the rural population. In the three domains of the health system. Although the gap in the composite coverage index between combined and rural populations is found to be the widest in the domain of maternal health but the narrowest in the domain of child health. The very fact that the composite performance index is relatively higher in the combined population as compared to the rural population implies that the performance of the health system as assessed on the basis of the composite coverage rate of 14 health interventions is relatively better in the urban population as compared to the performance of the health system in the rural areas of the country. 86


Composite Coverage Index Table 5.4 Composite coverage indexes in India, States and Union Territories (Combined population) Country/State/ Union Territory India AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal Source:

Health system

Maternal

Child

Child health

55.3 79.0 74.3 43.9 50.4 39.2 71.0 56.0 61.5 76.2 66.8 86.4 59.2 53.3 69.7 65.7 47.4 72.1 82.9 82.9 46.4 71.8 53.6 42.5 62.9 60.1 81.5 70.2 48.2 67.1 81.5 43.8 34.5 54.1 65.1

health 50.0 72.6 77.3 42.4 38.8 29.2 76.7 40.8 54.0 80.2 73.5 95.4 56.9 53.8 55.9 59.8 30.1 70.6 97.8 90.9 40.5 72.1 49.3 30.3 54.0 43.9 88.8 71.4 38.8 55.4 88.5 37.7 31.3 33.4 57.3

immunisation 69.2 90.3 85.2 44.9 64.5 58.8 80.0 76.5 77.8 91.3 77.0 92.1 70.7 68.1 91.3 75.3 69.0 85.6 84.7 85.5 57.1 83.4 59.7 52.1 75.7 80.1 88.3 84.7 64.4 90.3 90.0 54.6 46.6 77.5 85.1

34.8 67.6 40.5 45.2 44.7 16.0 35.2 43.5 40.5 29.9 26.3 51.1 37.3 16.3 51.1 57.1 37.0 43.2 42.7 57.6 34.6 43.3 49.0 48.8 53.4 51.3 47.7 32.4 32.2 39.5 44.4 32.6 13.0 47.7 35.7

Author’s calculations.

87


Health System Performance Assessment Table 5.5 Composite coverage indexes in India, States and Union Territories (Rural population) Country/State/ Union Territory India AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal Source:

Health system

Maternal

Child

Child health

52.2 77.7 72.4 41.7 49.0 38.6 60.8 54.0 57.4 76.0 62.3 88.6 55.4 50.7 68.9 63.5 45.3 70.2 83.0 82.1 42.4 69.4 48.9 40.4 56.6 58.9 81.9 69.2 45.5 66.4 80.4 41.7 33.1 52.1 63.6

health 44.6 70.8 73.4 38.8 36.1 27.8 57.0 36.8 46.6 77.7 65.7 96.7 50.0 50.3 54.5 55.8 26.7 66.3 97.7 88.6 35.0 67.0 42.5 27.1 44.3 41.4 89.1 68.8 34.5 54.1 86.2 34.8 28.8 29.3 53.4

immunisation 67.3 89.2 84.3 43.8 63.9 58.8 70.7 75.5 75.2 92.2 73.5 95.8 68.4 65.7 91.1 73.7 67.7 85.4 84.7 83.7 53.5 82.8 56.2 50.4 70.8 79.6 92.4 85.1 62.7 90.0 89.6 52.5 45.8 76.7 85.1

33.9 66.4 41.0 43.5 44.3 15.5 45.9 43.6 40.5 32.4 26.6 51.0 37.0 15.1 49.9 57.5 36.2 42.4 42.8 62.2 33.3 42.8 46.4 48.5 51.9 50.9 38.6 31.2 30.1 39.1 43.8 32.5 12.6 47.8 36.1

Author’s calculations.

88


Composite Coverage Index Among the constituent States and Union Territories of the country, the composite coverage index is found to be the highest in Goa in both combined and rural populations (86 per cent and 89 per cent respectively). In addition to Goa, the composite coverage index is estimated to be more than 80 per cent in Kerala, Lakshadweep, Puducherry and Tamil Nadu; ranges between 60-80 per cent in 14 States and Union Territories and between 40-60 per cent in 13 States and Union Territories of the country (Table 5.4). This leaves only two States - Uttar Pradesh and Bihar - where the composite coverage index is estimated to be less than 40 per cent. The composite coverage index has been found to be the lowest in Uttar Pradesh among all States and Union Territories of the country (34.5 per cent). On the other hand, in the rural population, the composite coverage index ranges between 60-80 per cent in 12 States and Union Territories while in 15 States and Union Territories of the country, it ranges between 40-60 per cent. This again leaves only two States - Uttar Pradesh and Bihar - where the composite coverage index is estimated to be less than 40 per cent with the lowest index of around 33 per cent in Uttar Pradesh (Table 5.5). In general, the composite coverage index has been found to be higher in the combined as compared to the rural population. However, in three States and Union Territories of the country - Goa, Kerala and Puducherry - the composite coverage index has been found to be higher in the rural population as compared to the combined population. The gap in the composite coverage index in combined and rural populations has been found to be the highest in the Union Territory of Chandigarh followed by States Mizoram, Manipur and Delhi and Union Territory of Dadra and Nagar Haveli. Tables 5.4 and 5.5 also present composite coverage index in the three domains of the health system for the country and for States and Union Territories for combined and rural populations respectively. In both the populations, the index, in general, has been found to be relatively the highest in child immunisation domain but the poorest in child health domains in both combined and rural populations. The exception to this pattern in the combined population are the States of Goa and Kerala and Union Territories of Lakshadweep and Puducherry where the index has been found to be relatively the highest in the maternal health domain and the States of Arunachal Pradesh, Chhattisgarh, Jharkhand, Meghalaya Orissa and Uttarakhand where coverage index has been found to be the lowest among the three domains. In the rural population, on the other hand, the index has been found to be the highest in the child immunisation domain in all States and Union Territories except the States of Goa and Kerala and the Union Territory of Lakshadweep whereas it has been found to be the lowest in the maternal health domain in 10 States - 5 of which are located in the north eastern part of the country. 89


Health System Performance Assessment Among the 601 districts of the country, the composite coverage index has been found to be the best in district Mahe in the Union Territory of Puducherry (89.0 per cent) but the poorest in district Balrampur of Uttar Pradesh (19.6 per cent) In the combined population, there are 63 districts in the country where the composite coverage index has been estimated to be more than 80 per cent whereas in 3 districts, the composite coverage index has been found to be less than 20 per cent. The composite coverage index has been found to range between 60-80 per cent in 216 districts; 40-60 per cent in 219 districts; and 20-40 per cent in 100 districts. Among the three domains of health care, the composite coverage index in the combined population has been found to be the highest in district Malappuram in Kerala but the poorest in district Baharaich of Uttar Pradesh (13.6 per cent) in the maternal health domain. In the child immunisation domain, the index has been estimated to be the highest in district Hamirpur of Himachal Pradesh (97.1 per cent) but the poorest in district Mewat of Haryana (21.3 per cent). Finally, in the child health domain, the index has been found to be the highest in district Nicobars of Andaman and Nicobar Islands (74.2 per cent) but the poorest in district Muzaffarnagar of Uttar Pradesh (4.0 per cent). In the rural population, on the other hand, the composite coverage index has been found to be the highest in district Thiruvananthapuram (88.7 per cent) but the lowest in district Bahraich of Uttar Pradesh (19.1 per cent). There are 49 districts in the country where the composite coverage index in the rural population has been found to be more than 80 per cent whereas in 4 districts, the index has been estimated to be less than 20 per cent. In 191 districts of the country, the index rages between 60-80 per cent; in 232 districts, it ranges between 40-60 per cent whereas in 114 districts, it ranges between 20-40 per cent in the rural population. In the maternal health domain the composite coverage index in the rural population has been found to be the best in district Pathanamthitta of Kerala (99.3 per cent) but the poorest in district Shahjehanpur of Uttar Pradesh (12.1 per cent). In the child immunisation domain, the best and the poorest performing districts are The Nilgiris of Tamil Nadu (98.1 per cent) and Upper Subansiri of Arunachal Pradesh (19.6 per cent) respectively. Lastly, in the child health domain, the composite coverage index has been found to be the highest in district Nicobar of the Union Territory of Andaman and Nicobars Islands ( per cent) whereas in district Muzaffarnagar of Uttar Pradesh the index has been found to be just around 3.5 per cent which is the lowest in the country. Summary measures of the inter-district variation or distribution of the composite coverage index is presented in table 5.5. 90


Composite Coverage Index Table 5.5 Inter-district variation in district health system performance in India Composite

All domains

coverage index

Maternal

Child

health

immunisation

Child health

Combined population 0-20

3

17

0

106

20-40

100

195

33

230

40-60

219

173

127

244

60-80

216

125

202

21

80-100

63

91

239

0

N

601

601

601

601

Summary measures of inter-district distribution Minimum

19.6

13.6

21.3

4.0

Q1

44.7

34.9

58.5

26.5

Median

57.8

49.5

74.5

38.4

Q3

71.2

71.2

85.8

47.0

Maximum

89.0

99.1

97.1

74.2

IQR

26.5

36.3

27.4

20.5

Rural population 0-20

4

22

1

111

20-40

114

233

36

237

40-60

232

150

135

223

60-80

191

112

187

18

80-100

49

73

231

1

N

590

590

590

590

Summary measures of inter-district distribution Minimum

19.1

12.1

19.6

3.5

Q1

41.9

30.6

55.9

23.6

Median

54.2

43.8

72.7

37.2

Q3

69.1

66.2

85.7

46.7

Maximum

88.7

99.3

98.0

82.1

IQR

27.2

35.6

29.8

23.1

91


Health System Performance Assessment The distribution of districts by the composite coverage index in the three domains of the health system is also given in table 5.5 for combined and rural populations. In the child immunisation domain, the composite coverage index is estimated to be more than 60 per cent in 441 or more than 73 per cent districts of the country in the combined population and in 418 or 71 per cent districts in the rural population. By contrast, the composite coverage index is estimated to be more than 60 per cent in only 21 or 3.5 per cent districts in the child health domain and in 216 or about 35.9 per cent districts in the maternal health domain. The corresponding figures in the rural population are 19 (3.2 per cent) and 185 (31.4 per cent) respectively. The districts in India are administratively organised into States and Union Territories. It is therefore logical to carry out an analysis of the distribution of the districts by the level of the composite coverage index for each the state/Union Territory separately. Such an analysis is all the more necessary in view of the fact that health is a state subject in the Constitution of India so that the performance of the district health system depends not only the policies and programmes of the central or the national government but also on the policies and programmes of the state government or Union Territory administration. Moreover, States and Union Territories are primarily responsible for the delivery of health services. At the same time, States and Union Territories of the country are at different levels of social and economic development which may have an impact on the composite coverage index. The State/Union Territory wise distribution of districts by the level of the composite coverage index is given in tables 5.6 and 5.7 respectively for combined and rural populations. In case of combined population, there are 11 States and Union Territories where the composite coverage index has been found to be at least 60 per cent in all the districts. On the other hand, in 5 States, there is not a single district where the composite coverage index has been found to be 60 per cent and above. In the remaining States and Union Territories, districts with very high composite coverage index coexist with districts with low to very low composite coverage index. A similar situation prevails in the rural population of the country. In 9 States and Union Territories, the composite coverage index has been found to be at least 60 per cent in all the districts whereas in 6 States and Union Territories, there is no district where the composite coverage index is estimated to be 60 per cent and more. It is obvious that the composite coverage index reflecting the coverage of 14 health interventions used in the present analysis varies widely across all States/Union Territories as well as across all districts level. 92


Composite Coverage Index Table 5.6 District level composite coverage index by States/Union Territories (Total population) India/State/

0-20

Union Territory AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh Dadra & Nagar Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnataka Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Orissa Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 3

20-40

40-60

60-80

80-100

Total

0 0 3 3 20 0 0 0 0 0 0 1 1 0 0 3 0 0 0 12 0 1 4 0 0 0 0 6 0 0 0 46 0 0 100

0 1 13 17 17 0 10 0 0 1 0 8 11 0 4 16 2 0 0 28 2 4 3 2 15 0 1 24 0 0 4 21 11 4 219

1 19 0 7 0 1 6 1 1 8 0 16 8 11 9 3 16 3 0 5 24 3 0 6 15 1 19 2 4 10 0 0 2 15 216

1 3 0 0 0 0 0 0 1 0 2 0 0 1 1 0 9 11 1 0 9 1 0 0 0 3 0 0 0 20 0 0 0 0 63

2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601

Author’s calculations

93


Health System Performance Assessment Table 5.7 District level composite coverage index by States/Union Territories (Rural population) India/State/

0-20

Union Territory AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh Dadra & Nagar Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnataka Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Orissa Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 4

20-40

40-60

60-80

80-100

Total

0 0 5 4 21 0 0 0 0 0 0 1 2 0 0 4 0 0 0 15 1 1 4 0 0 0 0 7 0 0 1 48 0 0 114

0 1 11 18 16 0 12 1 0 1 0 12 11 1 4 17 5 0 0 28 2 5 3 6 16 0 1 24 0 0 3 18 12 4 232

1 18 0 5 0 1 4 0 1 4 0 12 7 10 10 1 18 3 0 2 24 3 0 2 14 0 19 1 4 12 0 0 1 14 191

1 3 0 0 0 0 0 0 1 0 2 0 0 1 0 0 4 11 1 0 6 0 0 0 0 2 0 0 0 17 0 0 0 0 49

2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 30 2 20 32 4 29 4 70 13 18 590

Author’s calculations

94


6 Health System Performance Index

The composite coverage index presented and discussed in the previous chapter is influenced by both factors endogenous to the health system and factors exogenous to the health system. Factors endogenous to the health system are related to the administrative capacity and organisational efficiency of the health system in delivering services to the people and, therefore, influence the performance of the health system. On the other hand, factors exogenous to the health system are invariant to the administrative capacity and organisational efficiency of the health system but they do affect coverage of different health interventions. Since the observed coverage rate of different health interventions are influenced by both factors endogenous and exogenous to the health system, the observed coverage rate of different health interventions or a combination of coverage rates may not reflect the true performance of the health system. In order to assess the performance of the health system on the basis of observed coverage rate of different health interventions, it is imperative that the effect of exogenous factors is separated from the observed coverage rate or from the composite coverage index to arrive at the adjusted composite coverage index that is influenced primarily by the factors endogenous to the health system. This adjusted composite coverage index may be termed as the health system performance index. Let Cj denotes the composite coverage index in the administrative unit j and let Ej and Xj denote respectively the contribution of endogenous and exogenous factors to Cj, then Cj may be decomposed as Cj = Ej * Xj

(6.1) 95


Health System Performance Assessment or ln Ej = ln Cj - ln Xj

(6.2)

If the contribution of exogenous factors to the composite coverage index is known, then the health system performance index (Pj) for the administrative unit j may be estimated as ln Pj = ln Cj - ln Xj

(6.3)

Stochastically, for any administrative unit j, equation (6.3) may be written as ln Pj = ln Cj - ln Xj + 책j

(6.4)

where 책j is the stochastic error term for the administrative unit j. There are a host of exogenous factors that may influence the coverage rate of different health interventions and hence the composite coverage index. These factors may be grouped into four categories - 1) factors related to availability of resources to the people to use the available health facilities such as poverty and living conditions; 2) factors that influence the knowledge about different health interventions such as literacy and educational status; 3) environmental factors; and 4) social and cultural factors including tradition and religiosity. Many of these factors are interrelated. Elimination or controlling the effect of these factors is necessary to assess the true performance of the health system on the basis of the coverage rate of different health interventions. DLHS 2007-08 provides information about two indicators that can be used to control the effect of most of the exogenous factors on the composite coverage index. The first is the female literacy rate which is defined as the proportion of females at least 7 years of age who can read and write with understanding while the second is the household wealth index. The household wealth index is derived on the basis of the availability of selected household assets, amenities and durables using factor analysis technique. Households are then categorized from the poorest to the richest groups corresponding to the lowest to the highest quintiles of the household wealth index. Households falling in the highest quintiles group have then been classified as households with high standard of living and the proportion of households having high-standard of living to the total number of households has been calculated at national, State and district levels separately for combined and rural populations (IIPS, 2010). The influence of the education of the woman and the living conditions of the household on the coverage rates of different health interventions is well known. Living conditions of the household is a reflection of the resources available at the household level to meet the health needs of household members. On the other hand, education of the woman provides the opportunity to use the resources available for health gains. Since 96


Health System Performance Index coverage rate of different health interventions may be viewed as an indicator of health gain, it may be argued that a part of the inter-district variation in the coverage rate of different health interventions as measured through the composite coverage index is the result of the variation in household level living conditions measured in terms of the proportion of households with high standard of living index and household level opportunities measured in terms of female literacy rate. In order to arrive at a performance index for the health system, we control the effect of female literacy rate and proportion of households with high standard of living index on the composite coverage index using inter-district variation in the composite coverage index and in female literacy rate and proportion of households with high standard of living. More specifically, we first estimate the expected level of the composite coverage index for each district from the prevailing level of female literacy rate and proportion of households with high standard of living index in the district by regressing composite coverage index female literacy rate and proportion of households with high standard of living on the basis of the following regression model (6.5) where Cj is the composite coverage index for district, Lj is the female literacy rate and H j is the proportion of households with high standard of living index in district j. Note that when Lj = Hj = 0, (6.6) The health system performance index may now be defined as (6.7) We have estimated Pj separately for the three domains of the health system - maternal health, child immunisation and child health. The performance indexes of three domains have then been combined to obtain the performance index of the health system in the same way as the composite coverage index has been calculated from the domain specific composite coverage indexes in the previous chapter. The health system performance index has been calculated for the country as a whole as well as for its constituents States and Union Territories and districts separately for combined and rural populations. The performance index may be conceptualised as the composite coverage of the 14 health interventions included in the present analysis after controlling the effect of female literacy rate and proportion of households with high standards of living on the basis of the interdistrict variation in the composite coverage index, female literacy rate and the proportion of households with high standard of living index. 97


Health System Performance Assessment Table 6.1 Results of the regression of the domain specific composite coverage indexes on the female literacy rate (FLI) and households with high standard of living index (SLI) Variable

Maternal health

Child

Child health

immunisation B

â

B

â

B

â

Combined population FLI

0.431

0.067*

0.285

0.052*

0.932

0.106*

SLI

0.280

0.018*

0.097

0.014*

-0.012

-0.029

Intercept

1.357

2.807

-0.302

R

0.525

0.253

0. 161

N

601

601

601

2

Rural population FLI

0.243

0.069*

0.206

0.052*

0.735

0.104*

SLI

0.234

0.016*

0.082

0.012*

0.002

0.024

Intercept

2.383

3.237

0.495

R

0.441

0.203

0.119

N

590

590

590

2

* Statistically significant (P<.001) Source:

Author’s calculations

Table 6.1 presents results of the regression analysis based on the district level data available through DLHS 2007-08 separately for combined and rural populations. The regression coefficient of the composite coverage index on the female literacy rate has been found to be statistically significant in all the three domains of the health system in both combined and rural populations. Moreover, the sign of these regression coefficients is in the expected direction. An increase in the female literacy rate contributes to an increase in the composite coverage index in all the three domains of the health system. This means that coverage of health interventions included in this analysis is influenced statistically significantly by prevailing levels of female literacy rate which needs to be controlled for measuring the health system performance on the basis of these coverage rates. 98


Health System Performance Index On the other hand, the regression coefficient of the composite coverage index on the proportion of the households with high standard of living index has been found to be statistically significant in maternal health and child immunisation domains but not in the domain of child health. Moreover, the sign of the regression coefficient is negative in the combined population. This shows that the proportion of households with high standard of living index has statistically insignificant contribution to the composite coverage index of the maternal health domain and the child immunisation domain but not to the composite coverage index of the child health domain. It is also clear from the table 6.1 that female literacy rate and proportion of households with high standard of living index explain only a part of the variation in the composite coverage index of the three domains of the health system. For example, in the maternal health domain, female literacy rate and proportion of households with high standards of living index account for only around 52 per cent of the total variation in the composite coverage index in the combined population and 44 per cent in the rural population. The corresponding figures in the child immunisation domain are 25 per cent and 20 per cent respectively while these figures are 16 per cent and 12 per cent respectively in case of the child health domain. Rest of the inter-district variation may be attributed to inter-district variation in the health system performance plus inter-district variation in a host of exogenous variables not included in the analysis. On the basis of the foregoing analysis, the health system performance index in India is estimated to be around 27 in the combined population and around 38 in the rural population on the scale of 0 (poorest) to 100 (best). The low level of the index in both rural and urban populations is a reflection of the poor efficiency of the health system public or private - in meeting the health needs of the people. Given the level of the performance index, the health system performance in India may, at best, be characterised as poor in terms of coverage of 14 health interventions. It may also be seen from the figure 6.1 that the health system performance index in the rural population of the country is relatively better than that in the combined population. Since the health system performance index in the combined population is the weighted average of the health system performance index in rural and urban populations, a lower health system performance index in the combined population essentially implies that the health system performance index in the urban population is lower than the health system performance index in the rural population. This observation is in contrast to the earlier observation that the composite coverage index is higher in the combined population relative to the rural population which essentially implies that the composite 99


Health System Performance Assessment Figure 6.1 Health system performance index in India

coverage index in the urban population is higher than that in the rural population. It appears that the higher composite coverage index in the urban population of the country is the result of higher female literacy and a higher proportion of households with high standard of living index in the urban areas. When the effect of these two variables exogenous to the health system is control, the adjusted composite coverage index or the health system performance index in the urban population drops substantially and becomes lower than that in the rural population which means that the performance of the health system in the rural population of the country is relatively better than the performance of the health system in the urban population, particularly in the context of the 14 health interventions included in the present analysis. This observation is in quite contrast to the general belief that health care services in the urban population are comparatively better than those in the rural population of the country. 100


Health System Performance Index Figure 6.2 Health system performance index in States and Union Territories of India

Among the three domains of health system, the performance index in the maternal health domain is estimated to be around 11 in the combined population and around 31 in the rural population. On the other hand the performance index in the child immunisation domain is estimated to be around 55 and 60 respectively in combined and rural populations but and less than 1 and around 3 respectively in the child health domain. Moreover, in all the three domains, the performance index is estimated to be higher in rural than that in the combined population again suggesting relatively better health system performance in rural as compared to urban population in all the three domains. 101


Health System Performance Assessment Table 6.2 Health system performance index in India and States/Union Territories (Combined population) India/States/ Union Territories India AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal Source:

Health system performance index (Per cent) Health system Maternal Child Child health health 10.78 11.46 25.15 6.99 7.30 8.91 9.04 11.09 13.16 13.96 9.51 16.82 11.28 8.93 9.16 13.74 7.09 20.10 18.06 15.01 9.79 17.83 10.45 5.65 8.44 12.01 15.41 11.48 9.00 9.61 24.65 7.57 6.67 4.03 15.00

27.18 33.96 40.04 15.79 23.83 23.89 27.84 31.24 31.91 35.43 27.31 36.49 27.57 25.19 34.21 30.86 26.81 37.57 33.94 33.08 22.75 35.34 23.05 18.59 27.32 32.89 34.40 31.98 25.44 34.15 40.58 20.16 17.35 27.05 35.35

Authorâ&#x20AC;&#x2122;s calculations 102

immunisation 54.66 70.06 71.12 30.85 50.00 48.74 58.11 63.94 63.56 71.54 56.41 71.03 55.06 51.89 73.14 60.12 57.31 70.32 63.77 64.53 44.84 67.24 44.92 38.96 57.31 66.92 67.47 65.64 52.13 72.70 73.07 41.00 35.22 60.98 70.19

0.62 1.33 1.10 0.76 0.73 0.21 0.34 1.14 0.99 0.32 0.22 0.79 0.69 0.13 0.95 1.72 0.98 0.86 0.45 0.94 0.81 0.82 0.79 0.79 0.68 1.35 0.61 0.42 0.87 0.60 0.77 0.39 0.11 0.94 0.57


Health System Performance Index Table 6.3 Health system performance index in India, States/Union Territories (Rural population) India/States/ Union Territories India AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal Source:

Health system performance index (Per cent) Health system Maternal Child Child health health 30.67 44.60 60.30 23.07 23.36 21.14 28.03 28.74 32.65 51.01 36.42 63.62 33.54 30.23 31.94 39.02 20.91 52.08 63.83 54.03 26.11 51.21 28.11 18.16 28.89 31.14 65.13 41.04 24.73 32.42 68.59 23.41 19.83 14.76 41.92

38.29 52.62 58.38 25.30 33.86 31.25 37.41 41.90 42.76 55.97 41.75 62.66 39.87 36.56 47.99 45.02 35.53 54.91 57.76 53.67 31.43 53.35 32.55 26.55 38.68 44.42 62.13 48.76 34.56 47.57 63.27 29.08 25.01 35.34 50.55

Authorâ&#x20AC;&#x2122;s calculations 103

immunisation 60.44 79.65 78.12 36.51 56.84 53.85 60.66 70.11 68.52 83.04 63.69 85.69 61.18 57.66 82.02 66.42 62.99 78.55 74.36 73.18 48.14 75.68 48.93 44.23 62.91 73.46 83.59 75.71 57.16 81.12 82.22 45.88 40.24 68.67 78.71

2.85 6.29 5.43 3.40 3.51 0.88 3.62 5.27 4.62 2.00 1.23 4.03 3.36 0.55 4.24 7.45 4.30 4.07 2.31 5.10 3.66 4.06 3.43 3.95 3.50 6.05 2.35 1.89 3.48 2.75 3.90 1.94 0.50 4.42 2.89


Health System Performance Assessment Figure 6.4 States/Union Territories by the level of health system performance index

Among different states and Union Territories of the country, the health system performance index, varies widely. The index has been estimated to be comparatively the highest in Tamil Nadu closely followed by Andhra Pradesh in the combined population and Tamil Nadu, Goa and Puducherry in the rural population (Tables 6.2 and 6.3). On the other hand, the performance index has been estimated to be the lowest in Arunachal Pradesh followed by Uttar Pradesh and Meghalaya in the combined population and in Uttar Pradesh followed by Arunachal Pradesh and Meghalaya in the rural population. Arunachal Pradesh, Uttar Pradesh and Meghalaya are the only three States of the country where the health system performance index in the combined population is estimated to be less than 20. By contrast, there is no State/Union Territory in the country where the health system performance index in the rural population has been estimated to be less than 20. 104


Health System Performance Index On the other hand, there is no State or Union Territory in the country where the health system performance index is estimated to be more than 50 in the combined population (Figure 6.2). In most of the States/Union Territories of the country, the health system performance index ranges between 20-40 which essentially implies that the health system performance in these States/Union Territories may be characterised as poor. In the rural population, on the other hand, the health system performance index has been estimated to be more than 50 in 11 States/Union Territories, although there is no State/Union Territory where the performance index has been estimated to be more than 70. Moreover, in all States and Union Territories of the country included in the present analysis, the health system performance index has been estimated to be higher in the rural as compared to the combined population which essentially means that the performance of the health system is relatively better in the rural population as compared to that in the urban population in all States/Union Territories of the country. The ruralurban gap in the health system performance index however varies across States and Union Territories. This gap has been found to be the narrowest in Bihar but the widest in Puducherry. The rural-urban gap in the health system performance index has also been found to be very wide in the States of Goa, Kerala, Tamil Nadu and in the Union Territories of Lakshadweep and Daman and Diu. Estimates of the dimension specific health system performance indexes are also presented in tables 6.2 and 6.3 respectively for combined and rural populations. These indexes suggest that the performance of the health system varies by different dimensions of the health care. The health system performance appears to be relatively the best in the child immunisation domain but the poorest in the child health domain in all States and Union Territories in the combined as well as in the rural population. More specifically, the adjusted coverage rate of child health related interventions attributed to factors endogenous to the health system is virtually insignificant in the combined population but, at best, marginal in the rural population in all the States and Union Territories. The highest performance index in this domain is estimated to be only around 7.5 in the rural population of the State of Jammu and Kashmir. Moreover, it is also evident from tables 6.2 and 6.3 that within each State/Union Territory of the country, there is very a wide gap in the performance of the health system in the three dimensions or domains of the health system. The present analysis suggests that the performance of the health system in all States and Union Territories may be characterised as relatively satisfactory in the domain of child immunisation only. In the other two domains, the performance is simply unsatisfactory. 105


Health System Performance Assessment Table 6.4 Summary measures of inter-district variation in health system performance index Summary

Health system

indicators

Maternal

Child

health

immunisation

Child health

Combined population Minimum

8.42

1.98

13.61

0.01

Mean

29.19

13.02

57.20

0.78

Median

29.48

11.15

59.73

0.71

49.19

38.30

83.16

7.59

SD

8.39

7.00

15.05

0.70

IQR

12.28

9.21

23.37

0.67

Skewness

-0.141

0.993

-0.573

3.94

Kurtosis

-0.631

0.546

-0.555

st

1 Quartile

3rd Quartile Maximum

Rural Population Minimum

12.35

6.94

14.34

0.04

Mean

41.87

35.52

63.93

3.54

Median

41.12

31.04

66.46

3.38

Maximum

69.16

76.25

90.30

21.73

SD

13.08

16.89

17.00

2.74

IQR

20.86

23.90

27.07

3.17

Skewness

0.052

0.602

-0.560

2.180

Kurtosis

-0.891

-0.653

-0.593

9.501

1st Quartile

3rd Quartile

Source:

Authorâ&#x20AC;&#x2122;s calculations

The health system performance index for the districts of the country is presented in appendix tables 6.1 and 6.2 respectively for combined and rural populations and for the three domains of the health system. Summary measures of inter-district distribution of the performance index for the health system as a whole and for its three domains are presented in table 6.4 separately for combined and rural populations while kernel density plots are presented in figure 6.5. 106


Health System Performance Index Figure 6.5 Kernel density plot of performance index across districts Combined population

Rural population

Health system

Health system

Maternal health

Maternal health

Child immunisation

Child immunisation

Child health

Child health

107


Health System Performance Assessment Figure 6.6 Performance of the health system across districts (Combined population)

The distribution of the health system performance index across the districts of the country suggests that in case of combined population, the performance index ranges between 20-40 in almost three fourth districts on the scale ranging from 0 (poorest) to 100 (best). The performance index has been estimated to be more than 40 in just about 10 per cent of the districts of the country whereas there is no district with a performance index of 60 and above. In the maternal health domain, the health system performance index is estimated to be less than 20 in more than 80 per cent districts whereas in the child health domain, the performance index is estimated to be less than 20 in all districts of the country. On the other hand, the district health system performance appears to be relatively better in the child immunisation domain where the performance index is found to range between 60-80 in almost half of the districts. 108


Health System Performance Index Figure 6.7 Performance of the health system across districts (Rural population)

The performance of the district health system in the rural areas appears to be relatively better as compared to the combined (rural and urban) population. In the rural population, the health system performance index ranges between 40-60 in more than 40 per cent districts of the country and in about 11 per cent districts, the health system performance index is found to range between 60-80 on the scale of 0 to 100. In the maternal health domain, this proportion is around 14 per cent whereas in the child immunisation domain, the performance index in the rural population is estimated to be more than 80 in more than one fifth districts of the country. In case of the child health domain, on the other hand, the performance of the health system in the districts of the country appears to be only a shade better in the rural population as compared to the combined population. 109


Health System Performance Assessment Table 6.5 Performance of the health system in districts of India (Combined population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 0 0 13 5 8 0 0 0 0 0 0 0 2 0 0 1 0 0 0 10 1 2 4 0 0 0 0 6 0 0 2 43 0 0 97 16.1

20-40 2 8 3 22 29 1 16 1 2 9 2 25 18 12 14 21 15 14 1 35 29 7 3 8 28 4 20 25 4 12 2 27 13 13 445 74.1

Authorâ&#x20AC;&#x2122;s calculations 110

Performance index 40-60 60-80 0 0 15 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 18 0 0 0 0 0 0 0 6 0 59 0 9.8 0.0

80+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Total 2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601 100.0


Health System Performance Index Table 6.6 Performance of the health system in districts of India (Rural population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 0 0 3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 2 0 0 0 0 1 0 0 0 13 0 0 22 3.7

20-40 0 0 13 18 35 1 5 0 0 1 0 8 10 0 3 14 0 0 0 36 2 5 5 5 7 0 0 23 0 0 3 56 12 0 262 14.4

Authorâ&#x20AC;&#x2122;s calculations 111

Performance index 40-60 60-80 2 0 10 12 0 0 9 0 2 0 0 0 11 0 1 0 2 0 4 0 0 2 17 0 9 0 12 0 11 0 8 0 16 11 11 3 1 0 8 0 24 7 3 0 0 0 3 0 23 0 0 2 20 0 8 0 4 0 1 28 1 0 1 0 1 0 17 1 240 66 40.7 11.2

80+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Total 2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 30 2 20 32 4 29 4 70 13 18 590 100.0


Health System Performance Assessment Table 6.7 Health system performance in districts of India in maternal health domain (Combined population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 2 3 16 27 37 1 15 1 2 9 2 25 20 12 8 22 7 11 1 43 16 9 7 8 27 4 20 32 4 3 4 69 13 14 494 82.2

20-40 0 20 0 0 0 0 1 0 0 0 0 0 0 0 6 0 20 3 0 2 19 0 0 0 3 0 0 0 0 27 0 1 0 5 107 17.8

Authorâ&#x20AC;&#x2122;s calculations 112

Performance index 40-60 60-80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0

>80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Total 2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601 100.0


Health System Performance Index Table 6.8 Health system performance in districts of India in maternal health domain (Rural population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 0 0 5 6 20 0 3 0 0 0 0 1 2 1 1 6 0 0 0 10 0 1 4 0 1 0 0 7 0 0 0 34 11 0 113 19.1

20-40 0 1 11 21 17 1 11 1 0 2 0 11 15 8 3 16 3 0 0 28 3 5 3 6 24 0 8 24 4 0 4 31 2 5 268 45.4

Authorâ&#x20AC;&#x2122;s calculations 113

Performance index 40-60 60-80 2 0 5 16 0 0 0 0 0 0 0 0 2 0 0 0 1 1 3 0 0 2 13 0 3 0 3 0 10 0 0 0 12 12 0 14 1 0 7 0 23 7 3 0 0 0 2 0 5 0 0 2 12 0 1 0 0 0 0 29 0 0 5 0 0 0 13 0 126 83 21.4 14.1

>80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Total 2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 30 2 20 32 4 29 4 70 13 18 590 100.0


Health System Performance Assessment Table 6.9 Health system performance in districts of India in child immunisation domain (Combined population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 4 0.7

20-40 0 0 13 5 6 0 0 0 0 0 0 0 1 0 0 1 0 0 0 12 1 1 3 0 0 0 0 3 0 0 2 43 0 0 91 15.1

Authorâ&#x20AC;&#x2122;s calculations 114

Performance index 40-60 60-80 0 2 0 23 3 0 15 7 27 4 1 0 3 13 0 1 0 2 7 2 0 2 12 13 10 8 0 12 6 8 9 12 2 24 3 11 0 1 31 2 2 32 6 1 3 0 4 4 5 25 1 3 5 15 20 8 0 4 0 29 2 0 26 0 4 9 1 17 208 294 34.6 48.9

80+ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 4 0.7

Total 2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601 100.0


Health System Performance Index Table 6.10 Health system performance in districts of India in child immunisation domain (Rural population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 3 0.5

20-40 0 0 8 0 4 0 0 0 0 0 0 0 1 0 0 1 0 0 0 9 1 2 4 0 0 0 0 2 0 0 2 30 0 0 64 10.8

Authorâ&#x20AC;&#x2122;s calculations 115

Performance index 40-60 60-80 0 1 0 11 7 0 13 13 21 12 0 1 2 13 0 1 0 0 1 4 0 0 7 17 2 16 0 3 3 9 6 10 1 10 0 10 0 1 29 7 1 14 4 3 1 2 4 4 3 19 0 0 0 13 17 11 0 1 0 8 1 1 38 1 1 12 0 8 162 236 27.5 40.0

80+ 1 11 0 1 0 0 1 0 2 0 2 1 0 9 2 5 16 4 0 0 17 0 0 0 8 2 7 2 3 21 0 0 0 10 125 21.2

Total 2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 30 2 20 32 4 29 4 70 13 18 590 100.0


Health System Performance Assessment Table 6.11 Health system performance in districts of India in child health domain (Combined population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601 100.0

20-40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Authorâ&#x20AC;&#x2122;s calculations 116

Performance index 40-60 60-80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0

>80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Total 2 23 16 27 37 1 16 1 2 9 2 25 20 12 14 22 27 14 1 45 35 9 7 8 30 4 20 32 4 30 4 70 13 19 601 100.0


Health System Performance Index Table 6.12 Health system performance in districts of India in child health domain (Rural population) India/States/ Union Territories AN Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh DN Haveli Daman & Diu Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnakata Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttarakhand West Bengal India Source:

0-20 2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 29 2 20 32 4 29 4 70 13 18 589 99.8

20-40 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0.2

Authorâ&#x20AC;&#x2122;s calculations 117

Performance index 40-60 60-80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.0

>80 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0

Total 2 22 16 27 37 1 16 1 2 5 2 25 20 12 14 22 27 14 1 45 33 9 7 8 30 2 20 32 4 29 4 70 13 18 590 100.0


Health System Performance Assessment Tables 6.5 through 6.12 present State/Union Territory wise distribution of districts by the level of the health system performance index in combined and rural populations. The health system performance index has been estimated to be less than 20 in 97 districts in the combined population and 22 districts in the rural population. Out of the 97 districts with a health system performance of less than 20 in the combined population, 43 (44.3 per cent) are located in Uttar Pradesh alone; 13 (13.4 per cent) are located in Arunachal Pradesh and 10 (10.3 per cent) are located in Madhya Pradesh. Similarly, out of 22 districts with a health system performance index of less than 20 in the rural population, 13 (59 per cent) are located in Uttar Pradesh alone. On the other hand, there are 66 districts in the country where the health system performance index in the rural population ranges between 60-80. Out of these 66 districts, 28 (42.4 per cent) are located in Tamil Nadu alone, 12 (18.2 per cent) in Andhra Pradesh and 11 (16.7 per cent) in Karnataka. At the same time, there is not a single district in 26 States and Union Territories of the country where the health system performance index in the combined population is found to be 60 and more than 60 which suggests that in these States and Union Territories, there is not a single district where the performance of the health system may be termed as good or very good. Finally, table 6.13 presents the best and the worst performing of the country in terms of the district health system and its three domains. In case of combined population, performance of the health system of district Chamrajnagar in Karnataka is found to be the best with a performance index of 49.2 while the performance of district Mewat in Haryana is the poorest with a performance index of 8.4. In the rural population, district Ariyalur in Tamil Nadu with a performance index of 69.2 is the best performing district whereas district East Garo Hills in Meghalaya is the worst performing district with a performance index of 12.4. In case of the maternal health domain, district Chamrajnagar of Karnataka is again the best performing district of the country in the combined population whereas district Bageshwar in Uttarakhand is the worst performing district. The corresponding districts in the rural population are respectively district Ariyalur in Tamil Nadu and district Shahjehanpur in Uttar Pradesh. In case of the child immunisation, the best and worst performing districts are respectively district Bankura in West Bengal and district Mewat in Haryana in the combined population and district Bankura in West Bengal and district Upper Subansiri in Arunachal Pradesh in the rural population. Finally, district Malkangiri in Orissa and district Muzaffarnagar in Uttar Pradesh are respectively the best and the worst performing districts in child health in both combined and rural populations. 118


Table 6.3 Best and worst performing districts in India in terms of health system performance index Performance

Health system

Maternal health

Child immunisation

Child health

Combined population Best

Worst

Chamrajnagar

Chamrajnagar

Bankura

Malkangiri

(Karnataka)

(Karnataka)

(West Bengal)

(Orissa)

49.19

38.30

83.16

7.59

Mewat

Bageshwar

Mewat

Muzaffarnagar

(Haryana)

(Uttarakhand)

(Haryana)

(Uttar Pradesh)

8.42

1.98

13.61

0.01

Rural population Best

Worst

Source:

Ariyalur

Ariyalur

Bankura

Malkangiri

(Tamil Nadu)

(Tamil Nadu)

(West Bengal)

(Orissa)

69.16

76.25

90.30

21.73

East Garo Hills

Shahjehanpur

Upper Subansiri

Muzaffarnagar

(Meghalaya)

(Uttar Pradesh)

(Arunachal Pradesh)

(Uttar Pradesh)

12.35

6.94

14.39

0.04

Authorâ&#x20AC;&#x2122;s calculations

119


Health System Performance Assessment The foregoing analysis suggests that the performance of the health system varies widely not only across the districts of the country but also along different domains of the health system within the same district. Although, there is a substantial gap in the performance in maternal health and child immunisation domains, yet the performance between of the two domains is found to be highly correlated on the basis of inter-district variations in both combined and rural populations. The simple zero order correlation coefficient between the performance index in maternal health and the performance index in the child immunisation domain is found to be 0.630 in the combined population and 0.700 in the rural population. However, performance in the maternal health and child immunisation domains has been found to be only weakly correlated with the performance in the child health domain. The simple zero order correlation between the performance in the maternal health domain and the performance in the child health domain is found to be only 0.218 in the combined population and 0.247 in the rural population. Similarly, the simple zero order correlation coefficient between the performance in the child immunisation domain and the performance in the child health domain has also been found to be only 0.203 in the combined population and 0.141 in the rural population. Obviously, the health system performance in India is, at the best, lop sided. The performance of the health system varies not only across different administrative units of the country but also across different domains within the same administrative unit.

120


7 Epilogue

The present analysis is probably the first comprehensive, multidimensional assessment of the performance of the health system in India in terms of coverage of key health interventions of public health importance. The assessment is based on the beneficiary-based data collected through the population-based survey as recommended by the World Health Organisation and covers both public and private health care delivery institutions. The analysis focusses on the outcomes of the health system in terms of effective coverage rates of selected health interventions and not on the impact of the health system on the health status of the people. The health system is not the only determinant of the health status of the people. There are a host of factors exogenous to the health system that have a telling impact on the health status of the people. On the other hand, outcome indicators such as the effective coverage rate are directly related to inputs to the health system, health care delivery processes and outputs of these processes. The scope of the health system performance assessment exercise presented here is however limited in the sense that it covers only those dimensions and components of the health system for which relevant data and information are available through the DLHS 2007-08 which is the beneficiary-based household survey. The information available through this survey is primarily confined to health interventions that are relevant to reproductive and child health. Other dimensions of the health system are not covered in DLHS 2007-08. As such, results of the health system performance assessment exercise presented here does not takes into account the performance in other domains and dimensions of health care. 121


Health System Performance Assessment The health system performance index presented in the foregoing pages depicts a rather gloomy picture of the performance of the health system in India, particularly in the context of reproductive and child health as revealed through the coverage rate of 14 selected health interventions. There are many concerns. First and the foremost, there are wide variations in the health system performance across the constituent States, Union Territories and districts of the country. Second, within a State/Union Territory or district, there are wide variations in the performance of different domains of the health system. The performance of the health system appears to be relatively the best in the child immunisation domain but the worst in interventions that constitute the child health domain of health care - breast feeding and management of diarrhoea, etc. Obviously, reducing both inter and intra district variation in the coverage rate of health interventions is necessary to improve health system performance and achieve the goal of universal health coverage thereby hastening the pace of health and mortality transition. The analysis also reveals that the performance of the health system is comparatively better in the rural population than in the urban population of the country. This is so when it is well known that there is a heavy concentration of health care facilities, especially private health care facilities, in the urban areas. It appears that a heavy concentration of private health care facilities in the urban areas does not contribute significantly in terms of effective coverage of key reproductive and child health interventions of public health importance. In the rural areas, the health system is primarily focussed on the clinic-based health care as well as community-based services and behaviour change communication efforts whereas the health system in the urban areas is actually limited to institutionbased health care. Organisation of health care services in the rural areas is primarily directed towards universal coverage of key health care interventions that have public health relevance in general and relevance to reproductive and child health in particular. On the contrary, health care services in the urban areas, especially the private health care services, are primarily confined to institution-based treatment and care. Unlike the rural areas of the country, a community-based primary health care services delivery system directed towards universal coverage of key public health interventions such as child immunisation, antenatal care, promotion of scientific approach to breastfeeding and management of diarrhoea, etc. does not exist in the urban areas of the country so that the reach of the health care delivery system in the urban areas is limited. The analysis also reveals that within the same administrative unit - country, State/Union Territory/ district - the performance of the health system is also not the same in the three domains of health care - maternal health, child immunisation and child 122


Epilogue health. The three domains of health care are essentially related to three categories of services that the health care delivery system - public or private - delivers to the people. Maternal health services are essentially clinic-based services. Child immunisation services, on the other hand, are largely organised following a community-based extension approach. Finally, behaviour change communication efforts primarily constitute the basis for child health related interventions. Viewed in this context, it appears that the performance of the health system in India is relatively the best in organising communitybased extension services but the poorest in organising behaviour change communication efforts. The WHO framework for health system performance assessment argues that the delivery of health care services is influenced by the three core functions of the health system - providing stewardship, creating resources for services delivery including human resources development and necessary physical infrastructure, and financing the delivery of health care services. The differential performance of the health system in the matrnel health domain, child immunisation domain and child health domain essentially implies that the stewardship function, the resources creation function and the financing function of the three domains of the health system are not essentially the same. For example, creation of resources for clinic-based health care services may have significant relevance to the delivery of maternal health care services but only limited relevance to the organisation of community-based extension services such as child immunisation or organising behaviour change communication efforts directed, for example, towards promotion of breast feeding and management of diarrhoea in children. Similarly, investments in behaviour change communication efforts may have only a limited impact on child immunisation if community-based extension services are lacking or are poorly organised. However, an analysis of the stewardship function, resources creation function and financing functions of the health system - public or private - has never been carried out in India either in the context of the health system as a whole or in the context of different components or dimensions of the health system. The differential performance of the health system in the three domains of health care revealed through the present analysis suggests the need of analysing the stewardship function, resources creation function and financing function of the health system in the context of different dimensions and domains of the health system. This analysis is expected to provide an understanding of the factors endogenous to the health system that may be attributed to the differential performance of the health system in different domains or dimensions of health care. 123


Health System Performance Assessment The differential performance of the health system in the three domains of health care reflects an interesting chronology of the efforts to improve the health of the people in India. Although, the emphasis on addressing the maternal and child health needs of the people of the country has been articulated in all development plans of the country right since independence, the focus of attention on different components of maternal and child health has varied from time to time. The first component that was given the focussed attention was child immunisation way back in 1978 when the Expanded Programme of Immunisation was launched throughout the country. The Expanded Programme of Immunisation was subsequently expanded into Universal Immunisation Programme in 1985 which had a very strong emphasis on community-based extension services to achieve universal immunisation coverage against six vaccine-preventable diseases. The Universal Immunisation Programme was followed by the Pulse Polio Campaign during the 1990s which was directed towards eradicating polio from the country. In other words, sustained efforts towards child immunisation in the country are now almost 35 years old and the impact of these efforts are reflected in terms of the health system performance index at the national as well as regional and district levels. Compared to child immunisation, a focussed attention on maternal health care in India could be given only in 1990 when the Child Survival and Safe Motherhood Programme was launched. The Child Survival and Safe Motherhood Programme followed the basic philosophy of the Universal Immunisation Programme which had strong underpinnings of community-based extension services to meet the health needs of the people. The Child Survival and Safe Motherhood Programme was followed by the Reproductive and Child Health Programme in 1996 which continues event today in different variants. One of the basic difference between the Child Survival and Safe Motherhood Programme and the Reproductive and Child Health Programme has been the basic orientation of the two programmes. Contrary to the Child Survival and Safe Motherhood Programme, the Reproductive and Child Health Programme is primarily oriented towards clinic and institution-based care in an effort to accelerate the reduction in the risk of death during pregnancy and delivery - maternal mortality. Compared to focussed efforts directed towards child immunisation, focussed efforts towards maternal health care in India are essentially 20 years old and this difference is well reflected in the performance of the health system in the two domains of health care. Finally, focussed efforts directed towards child health related issues had their own evolutionary ups and downs in India. The oral rehydaration therapy to combat deaths in children during diarrhoea was given an impetus under the Child Survival and Safe 124


Epilogue Motherhood Programme but received only a neglected attention under the Reproductive and Child Health Programme. Similarly, promotion of breastfeeding has received attention only recently as the general conviction in the past was that breastfeeding in India was nearly universal. The assessment of the performance of the health system presented here refers to the year 2007. The health system performance index presented in this monograph may serve as the bench mark for assessing the impact of the National Rural Health Mission (2005-12) in terms of how the Mission has contributed towards improving the effective coverage rate of selected health care interventions, especially, interventions related to the reproductive and child health, in the country and in its constituent States/Union Territories and districts so as to achieve the goal of universal access to health care. The Mission has been directed towards architectural corrections in the health care delivery system in the country so as to meet the health care needs of the people in efficient and effective manner. It has followed a decentralised, district-based approach of planning and programming for the delivery of health care services. Although, the framework for the implementation of the Mission has envisaged bench marking of the performance of the health system at local, district, regional and national levels, yet, in practice, there has been little effort in this direction. The framework for the implementation of the Mission is conspicuously silent about the monitoring and evaluation framework necessary to monitor the progress of the Mission towards it stated goals and objectives. In the absence of a well defined monitoring and evaluation framework, monitoring of activities being implemented under the Mission has largely been confined to monitoring the inputs provided and services delivered to the people. Such narrowly focussed monitoring activities have contributed little towards measuring the extent to which the Mission has contributed towards the goal of universal access to health care. The only way to measure the effectiveness of the Mission in this context may be in terms of the increase in the effective coverage rates of key health care interventions but recent estimates of the coverage rate of key health interventions covered under the Mission including reproductive and child health interventions are not available at the district level. Availability of the estimates of key health interventions under the at the most recent date is necessary to assess how the Mission has contributed to the goal of the universal access to health care. One limitation of the health system performance assessment present here is that it does not take into consideration all the dimensions or domains of health care. It takes into account only those domains of health care for which estimates of the coverage rate 125


Health System Performance Assessment of key interventions are available. For example, the present assessment does not take into account institution-based care and treatment services which constitute an important dimension of health care services delivery. Another limitation of the analysis presented here is that it assesses the health system performance in the rural population and in the rural and urban combined population but not in the urban population specifically because of the simple reason that coverage rates of the health interventions included in the analysis are not available for the urban population at the district level.

126


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Appendix Table 3.1 Coverage rates of different health interventions across districts of India (Combined population) State District V1 Andaman & Nicobar Islands Andamans 46.8 Nicobars 50.9 Andhra Pradesh Adilabad 56.7 Nizamabad 83.4 Karimnagar 85.1 Medak 80.3 Hyderabad 86.2 Rangareddi 74.7 Mahbubnagar 62.0 Nalgonda 70.5 Warangal 82.6 Khammam 63.9 Srikakulam 66.6 Vizianagaram 57.8 Visakhapatnam 59.7 East Godavari 53.8 West Godavari 64.8 Krishna 76.6 Guntur 68.1 Prakasam 62.9 Nellore 71.8

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

77.2 83.5

92.7 95.9

67.7 89.1

66.7 72.5

98.5 94.1

93.8 86.3

90.8 84.3

90.8 94.1

77.8 87.4

50.0 84.2

70.1 84.8

71.3 82.8

57.2 60.1

79.2 95.3 97.3 93.2 98.0 97.0 87.3 95.7 96.8 89.8 92.6 82.6 76.4 89.8 95.2 98.3 88.0 84.6 92.3

82.7 96.0 97.7 96.3 100.0 98.6 92.8 98.5 94.6 95.2 92.9 86.5 85.4 94.9 98.1 98.2 97.0 95.1 92.5

43.2 83.4 89.2 75.0 95.6 87.1 66.6 75.0 84.3 69.2 56.6 56.9 59.8 86.6 81.5 84.0 77.8 76.1 88.4

36.7 79.9 93.2 73.9 81.1 90.4 73.7 84.7 81.6 75.0 75.8 70.3 66.4 92.9 89.0 92.4 82.0 78.3 83.5

92.9 98.7 94.8 98.9 98.4 98.0 100.0 100.0 100.0 100.0 97.6 97.2 86.9 99.8 100.0 98.6 97.3 100.0 96.5

74.2 90.4 91.7 91.8 76.8 85.4 81.1 92.6 96.7 75.8 73.3 78.2 77.1 81.5 83.5 89.2 73.0 86.7 69.7

70.9 90.1 92.5 88.4 80.7 92.4 85.7 80.1 87.8 74.5 67.7 65.5 67.6 72.1 79.1 93.2 77.9 62.0 78.3

73.6 92.3 94.8 97.3 90.6 90.2 89.8 99.4 78.2 96.4 90.1 82.2 73.9 90.0 97.8 96.9 92.8 94.1 87.6

50.3 80.0 90.2 85.5 80.1 85.9 80.4 88.1 68.9 87.4 86.2 81.7 65.0 72.8 83.9 87.9 73.1 80.5 75.9

32.1 56.5 66.7 12.5 33.3 12.5 55.6 52.9 24.0 26.3 56.5 70.0 51.7 17.4 53.8 75.0 46.7 20.0 20.0

34.7 85.3 92.6 77.7 82.4 90.0 72.2 83.3 79.0 76.9 70.9 70.9 64.5 92.9 87.2 92.3 76.6 80.0 86.6

35.6 27.2 28.0 46.1 40.7 40.8 40.2 32.1 29.4 58.0 55.0 59.7 54.2 65.2 53.3 52.5 51.6 40.6 53.5

25.6 17.9 17.9 27.1 26.1 31.0 23.1 40.6 36.4 43.7 18.2 32.9 28.4 54.2 38.6 31.1 29.2 36.0 32.9

131


State District Cuddapah Kurnool Anantapur Chittoor Arunachal Pradesh Tawang West Kameng East Kameng Papum Lower Subansiri Upper Subansiri West Siang East Siang Upper Siang Dibang Valley Lohit Changlang Tirap Kurung Kumey Lower Dibang Valley Anjaw Assam Kokrajhar Dhubri Goalpara Bongaigaon

V1 72.1 53.3 64.8 64.3

V2 82.7 81.8 86.1 90.5

V3 98.1 86.6 95.7 94.6

V4 78.6 61.5 66.7 74.1

V5 83.1 65.4 69.6 76.5

V6 98.2 97.7 97.9 99.4

V7 78.8 80.2 85.4 80.0

V8 85.0 68.3 92.8 70.7

V9 82.0 84.9 94.7 88.5

V10 76.6 81.7 79.5 86.3

V11 35.7 25.0 45.5 66.7

V12 73.2 66.8 73.7 75.6

V13 45.3 52.2 54.0 62.8

V14 32.9 45.4 35.8 35.7

44.8 48.9 15.4 49.3 41.0 28.5 33.0 33.1 10.1 47.3 23.2 53.6 29.7 40.0 29.3 43.2

46.4 55.9 25.5 57.8 61.7 51.9 44.1 49.2 13.4 43.5 64.4 62.7 44.6 52.4 37.2 52.5

60.7 64.2 52.9 76.6 67.0 58.1 52.7 65.0 34.5 73.2 68.8 71.7 53.5 53.1 68.5 53.1

40.6 42.9 30.6 71.4 60.7 59.1 54.7 53.9 13.0 56.6 57.9 45.3 49.5 54.7 48.5 33.5

35.7 37.2 23.1 47.0 41.1 42.8 30.8 36.6 9.7 43.1 42.3 45.3 23.8 42.1 35.5 32.7

88.0 88.4 46.8 91.0 62.3 56.5 58.1 73.1 60.0 88.9 91.1 84.5 69.2 54.2 94.8 60.0

25.3 11.7 18.8 21.6 5.0 22.0 28.5 16.2 13.1 15.6 22.2 19.3 53.8 29.2 27.7 20.0

67.5 57.6 38.7 64.6 58.0 31.1 44.4 52.0 27.2 62.2 73.4 75.6 76.9 37.5 31.1 50.0

45.9 44.0 27.7 24.3 14.1 28.5 29.9 28.6 39.9 37.8 28.2 27.2 53.8 33.3 63.2 40.0

50.8 48.7 35.7 40.1 48.5 47.7 37.0 45.1 18.0 64.2 60.5 52.2 38.3 40.8 56.8 26.6

75.0 85.7 18.5 38.5 100.0 75.0 70.0 73.3 70.0 100.0 0.0 100.0 100.0 100.0 78.6 50.0

43.6 40.0 30.3 51.5 36.9 44.1 37.5 32.5 19.5 44.3 42.8 43.0 37.0 31.9 43.5 21.5

35.9 36.4 48.8 58.9 37.7 34.2 31.0 48.2 37.2 20.9 26.9 54.0 37.0 16.7 27.5 39.3

33.6 40.8 22.3 46.0 24.7 30.0 33.3 39.7 16.4 68.1 18.1 45.1 23.5 52.3 37.8 51.5

22.7 20.6 42.0 36.5

27.2 20.7 33.5 35.2

38.2 40.0 65.0 69.6

32.6 15.8 28.2 29.9

35.0 14.5 14.8 27.5

66.8 75.2 68.2 92.1

58.1 57.8 53.0 64.5

41.3 39.1 48.8 58.0

51.9 41.8 47.0 67.4

52.5 32.6 38.6 47.2

71.4 38.5 50.0 30.8

36.8 15.6 17.7 28.8

73.7 71.1 83.3 82.8

31.5 33.2 33.4 38.9

132


State District V1 Barpeta 43.0 Kamrup 59.9 Nalbari 45.0 Darrang 45.8 Marigaon 32.3 Nagaon 42.8 Sonitpur 42.8 Lakhimpur 25.0 Dhemaji 28.1 Tinsukia 47.4 Dibrugarh 53.6 Sibsagar 48.9 Jorhat 53.6 Golaghat 37.0 Karbi Anglong 31.5 North Cachar Hills 48.3 Cachar 41.2 Karimganj 38.0 Hailakandi 43.8 Chirang 28.9 Baska 43.4 Kamrup Metro 50.6 Udalguri 46.9 Bihar Pashchim Champaran 18.8 Purba Champaran 23.5

V2 49.3 69.7 49.9 44.2 43.9 51.2 49.7 37.3 29.9 55.3 58.4 60.6 56.0 42.2 46.4 35.7 51.9 45.6 55.1 33.9 51.8 60.8 53.6

V3 74.6 87.7 77.2 77.9 63.0 70.3 80.8 52.2 46.7 66.9 80.0 75.8 87.6 56.7 62.1 61.6 84.8 81.1 87.2 56.4 69.7 75.6 77.0

V4 28.8 65.5 52.0 38.4 30.7 29.8 42.2 41.3 31.6 43.4 49.9 52.2 47.4 39.4 37.6 40.8 32.2 22.4 22.5 29.3 38.3 56.3 32.8

V5 23.8 64.0 47.8 26.4 25.1 24.3 43.5 35.4 20.8 39.1 50.9 40.1 28.2 33.1 35.1 39.1 31.3 27.5 24.2 26.9 34.9 49.7 18.1

V6 91.0 95.5 95.0 87.1 83.5 82.7 97.2 79.9 77.5 87.2 98.9 97.1 91.0 84.5 81.1 72.4 82.7 74.9 79.4 85.6 90.9 92.2 82.8

V7 67.0 81.5 85.9 66.4 72.8 53.0 79.6 61.4 58.8 70.5 94.5 78.0 76.6 70.4 67.0 49.7 65.0 50.7 47.8 71.0 69.5 86.2 64.0

V8 72.5 65.3 77.4 67.8 60.8 55.6 67.0 57.9 50.2 63.2 91.3 76.8 73.4 61.8 70.3 52.4 64.6 50.8 46.4 60.3 69.6 85.8 62.3

V9 71.7 86.4 83.1 78.7 65.9 58.3 63.0 68.9 58.4 67.2 94.2 76.7 82.7 70.0 73.7 52.2 52.8 48.8 47.4 71.2 71.9 84.8 69.0

V10 56.8 74.0 70.1 52.5 55.6 52.6 59.1 46.5 38.3 50.8 65.2 62.0 69.3 61.9 76.7 48.3 24.5 20.8 20.8 59.7 66.7 67.3 49.1

V11 55.0 44.4 30.0 33.3 40.0 0.0 57.1 29.2 38.8 20.8 32.3 57.1 57.1 23.5 40.0 55.6 31.6 19.8 29.0 35.7 0.0 14.3 50.0

V12 22.8 68.5 51.9 27.6 25.4 21.6 45.0 35.8 20.1 36.1 52.8 41.4 29.8 35.4 34.5 42.6 22.5 25.8 17.4 29.7 42.8 44.1 24.9

V13 57.7 63.6 80.0 88.4 67.9 61.9 85.5 70.4 83.6 69.0 76.4 62.3 86.1 71.3 77.1 23.1 33.4 35.5 17.0 76.1 88.8 73.8 73.2

V14 37.5 46.7 38.9 32.9 43.9 34.6 38.4 48.8 35.8 37.2 50.7 40.9 33.8 43.0 33.1 34.6 18.7 13.3 8.2 30.2 40.3 60.9 44.0

36.3 34.8

70.4 75.9

24.9 27.1

9.6 36.7

73.0 79.1

36.4 46.4

42.0 48.3

38.5 47.1

25.3 45.6

14.7 14.6

9.1 36.3

8.8 7.1

6.4 3.8

133


State District Sheohar Sitamarhi Madhubani Supaul Araria Kishanganj Purnia Katihar Madhepura Saharsa Darbhanga Muzaffarpur Gopalganj Siwan Saran Vaishali Samastipur Begusarai Khagaria Bhagalpur Banka Munger Lakhisarai Sheikhpura Nalanda Patna

V1 12.9 22.0 29.8 16.6 29.6 27.8 19.1 21.4 24.1 21.5 18.5 20.7 32.2 26.9 28.8 15.9 16.8 25.8 33.0 22.8 26.9 40.1 21.6 43.2 25.2 23.8

V2 18.2 24.9 33.6 20.0 41.0 27.7 17.7 32.6 20.1 13.7 28.5 20.2 32.2 33.3 21.5 16.3 23.1 29.4 27.9 20.8 31.9 37.3 25.8 43.7 24.6 24.1

V3 65.0 66.3 74.9 39.4 71.1 57.9 49.1 62.4 52.6 39.2 72.1 55.2 73.0 84.3 62.1 36.3 86.6 76.5 79.9 41.8 57.4 75.7 46.5 83.8 50.3 36.9

V4 12.0 16.3 16.0 23.2 13.7 17.9 21.7 12.5 17.7 20.0 15.2 23.0 36.5 33.4 22.4 28.2 27.6 26.9 25.3 30.3 24.7 48.6 32.6 41.5 39.3 58.3

V5 8.8 17.1 9.9 28.1 16.0 38.2 20.0 15.5 14.5 9.9 37.7 30.0 27.8 37.9 29.3 27.6 32.9 38.1 30.3 23.8 24.6 47.0 19.4 44.2 28.2 31.5

V6 72.8 82.6 83.2 83.7 82.8 60.9 79.4 81.0 85.6 89.0 93.1 91.1 93.1 90.5 92.7 92.2 88.0 81.8 88.5 90.4 77.4 88.1 71.6 81.1 90.8 83.6

134

V7 42.2 42.7 58.6 52.6 46.1 30.0 49.7 48.7 61.8 58.7 55.1 67.2 63.3 68.2 80.9 74.2 62.2 48.8 60.8 62.5 49.4 53.7 43.9 53.6 64.2 53.7

V8 47.0 45.9 55.2 53.1 51.1 32.1 49.1 50.7 64.7 53.2 56.7 68.8 70.6 73.8 78.1 73.2 64.9 54.5 62.3 62.3 54.0 64.0 45.9 54.4 67.9 47.5

V9 35.8 49.9 50.2 52.7 46.4 35.8 52.2 46.1 52.8 62.7 57.0 67.3 68.6 68.1 75.9 70.4 56.3 56.6 60.2 73.2 50.7 63.8 48.2 53.4 69.4 52.9

V10 29.5 44.3 46.9 36.6 46.7 49.9 43.0 50.0 36.8 53.7 62.7 49.1 54.5 69.4 47.0 65.5 62.8 58.3 66.7 66.0 50.8 67.6 41.5 50.7 58.2 52.9

V11 10.5 21.6 6.4 6.5 13.1 14.0 8.1 32.0 18.8 21.8 22.4 19.6 19.5 25.0 25.5 51.7 20.9 22.2 11.0 42.9 24.2 32.4 25.5 25.0 45.5 41.5

V12 9.2 19.1 13.0 26.2 17.9 37.6 20.7 13.8 14.1 10.0 36.3 28.1 29.5 35.2 29.3 31.0 32.7 37.0 26.5 31.8 29.1 42.7 21.0 41.2 28.8 31.9

V13 7.3 11.5 9.6 13.1 20.2 12.2 11.4 14.1 10.6 15.8 16.8 15.2 12.4 13.8 17.1 35.5 12.4 9.9 11.9 23.1 16.7 21.9 13.8 10.4 31.7 18.9

V14 26.2 0.2 3.6 3.6 2.5 5.1 8.2 12.2 23.2 28.3 3.5 3.5 1.1 1.7 14.4 36.4 5.0 2.5 4.4 36.5 3.5 11.9 17.3 9.3 17.2 13.6


State District Bhojpur Buxar Kaimur Rohtas Jehanabad Aurangabad Gaya Nawada Jamui Chandigarh Chandigarh Chhattisgarh Koriya Surguja Jashpur Raigarh Korba Janjgir-Champa Bilaspur Kawardha Rajnandgaon Durg Raipur Mahasamund Dhamtari Kanker

V1 21.7 25.3 26.5 30.8 26.9 20.0 20.8 17.8 25.6

V2 18.4 21.4 24.3 25.6 31.2 20.3 24.2 23.0 27.9

V3 39.3 41.2 57.4 49.8 54.7 43.4 37.3 43.2 48.3

V4 40.5 48.0 42.6 48.5 42.6 30.6 20.7 31.0 17.6

V5 21.7 25.9 32.0 28.4 31.1 21.5 21.1 23.7 19.4

V6 76.4 71.6 68.6 85.1 87.5 93.3 75.2 86.9 55.6

V7 48.4 47.2 37.0 56.4 51.3 77.5 47.6 61.7 27.1

V8 48.7 43.0 38.4 52.3 57.8 76.4 44.3 57.2 29.5

V9 53.4 48.4 33.8 61.1 63.3 76.5 51.2 58.0 33.0

V10 50.7 37.5 29.0 48.1 66.4 70.2 46.5 50.8 28.5

V11 35.1 28.9 17.7 13.7 13.5 23.9 16.7 16.2 20.9

V12 25.4 25.3 28.3 26.8 29.4 23.7 20.1 25.4 20.6

V13 20.9 22.7 30.6 16.2 21.4 14.0 22.2 10.9 16.0

V14 31.3 13.6 9.4 16.4 8.6 18.8 5.8 7.6 22.3

66.7

77.6

82.3

73.6

77.8

94.7

82.5

86.0

87.7

46.4

37.5

75.0

49.7

19.1

37.0 29.5 32.1 45.4 39.1 40.2 36.2 22.4 43.3 50.0 44.2 50.2 55.5 35.9

42.2 28.0 30.9 53.0 41.9 45.9 51.7 49.0 65.4 68.4 55.0 64.2 72.4 62.6

71.0 62.2 59.7 83.8 69.3 79.2 79.7 74.1 87.8 87.7 78.3 90.3 90.0 91.0

18.1 16.6 15.1 25.2 18.2 18.5 13.7 8.2 15.4 19.9 22.7 28.6 24.5 11.8

23.0 22.6 23.4 39.7 26.3 29.6 49.7 46.6 42.2 47.1 41.3 51.7 60.1 41.5

90.4 92.5 95.4 98.3 89.6 92.8 95.8 93.0 94.5 98.1 96.9 98.4 98.0 95.8

69.1 52.8 61.2 64.4 56.2 65.6 74.5 74.8 73.7 77.2 68.2 86.8 74.2 78.9

66.0 52.8 65.7 65.3 61.7 76.0 68.3 71.0 77.2 82.2 72.4 85.5 78.2 78.9

71.4 66.8 78.3 85.2 64.4 71.4 77.6 75.2 79.8 88.5 88.0 86.9 85.3 90.7

57.3 58.8 63.3 60.5 57.6 58.8 63.8 56.8 78.8 70.5 63.0 71.2 67.7 79.0

18.9 32.4 23.5 46.7 25.0 46.2 40.0 28.9 50.0 48.1 50.0 50.0 36.4 42.9

20.9 22.3 22.1 38.1 30.0 37.0 49.5 39.8 47.2 55.2 48.8 52.8 58.6 49.3

27.7 47.3 47.3 42.2 32.4 35.2 38.9 47.9 45.5 55.1 54.8 48.9 77.4 69.2

23.4 37.9 43.7 41.6 34.5 44.6 36.1 27.7 37.8 41.2 48.9 48.6 47.9 51.3

135


State District Bastar Dantewada Dadra & Nagar Haveli Dadra Nagar Haveli Daman & Diu Diu Daman Delhi North Delhi North West Delhi North East Delhi East Delhi New Delhi Central Delhi West Delhi South West Delhi South Delhi Goa North Goa South Goa Gujarat Kachchh Banas Kantha Patan Mahesana Sabar Kantha

V1 28.2 44.8

V2 52.3 55.8

V3 78.8 78.7

V4 17.9 18.7

V5 40.1 33.1

V6 94.8 95.6

V7 66.8 68.5

V8 70.2 69.6

V9 85.6 94.1

V10 65.2 78.6

V11 21.4 18.2

V12 42.6 40.4

V13 65.7 70.1

V14 72.3 74.8

55.4

63.3

69.2

46.2

57.3

97.1

70.9

70.6

84.3

63.4

50.5

54.9

48.8

19.1

77.2 89.8

84.4 91.1

94.4 96.0

42.7 89.0

70.2 89.7

98.3 100.0

94.0 93.2

92.2 87.7

93.0 86.3

83.7 75.3

31.7 35.0

76.3 89.2

27.7 55.2

25.1 15.2

59.2 55.1 54.0 55.9 52.6 59.2 62.9 61.5 57.5

71.5 74.2 64.0 73.2 69.5 79.0 75.7 73.1 69.5

87.6 91.2 87.7 92.3 89.9 95.2 92.3 93.9 91.4

65.4 73.9 59.0 74.5 70.9 88.1 67.5 69.4 59.2

77.1 78.2 68.8 74.7 76.1 91.1 76.6 76.7 78.7

87.8 90.2 85.5 93.7 92.5 94.0 95.3 99.2 88.7

71.9 79.9 62.5 80.4 77.8 82.6 71.5 87.4 76.4

67.2 76.6 65.9 78.6 82.9 83.1 74.7 85.3 75.6

71.7 83.7 73.5 88.4 86.8 87.7 87.1 90.3 83.8

56.7 55.9 46.2 50.8 48.2 66.7 55.2 58.9 57.0

59.6 47.9 49.5 42.7 60.9 50.0 44.2 25.6 53.3

78.0 78.0 75.9 76.3 78.2 93.2 80.1 81.2 81.1

33.5 24.7 25.3 26.9 28.6 29.7 32.6 34.6 29.3

6.5 11.0 7.0 8.4 7.9 13.3 5.0 10.8 11.3

93.8 91.0

96.9 94.4

98.8 96.5

97.8 94.4

96.9 96.6

100.0 96.6

92.9 89.2

90.1 87.3

96.6 89.9

87.7 78.2

76.5 50.0

97.3 94.8

48.7 78.8

31.1 22.4

44.5 26.4 58.2 57.4 44.6

48.5 28.7 53.4 59.0 49.7

54.6 45.7 70.9 68.9 65.4

57.2 61.7 61.7 84.3 61.4

58.0 43.8 68.7 77.0 57.9

77.2 79.2 98.3 94.2 84.5

66.4 59.8 90.1 90.5 64.7

55.2 43.5 82.4 76.6 54.2

62.6 64.0 83.4 88.2 71.0

43.6 43.5 66.5 75.9 43.9

19.2 35.7 40.7 34.7 43.3

58.3 45.6 71.4 75.3 61.5

47.9 49.3 59.5 56.2 36.3

23.1 30.9 12.6 21.5 24.9

136


State District Gandhinagar Ahmadabad Surendranagar Rajkot Jamnagar Porbandar Junagarh Amreli Bhavnagar Anand Kheda Panch Dohad Vadodara Narmada Bharuch Surat The Dangs Navsari Valsad Haryana Panchkula Ambala Yamunanagar Kurukshetra Kaithal

V1 61.5 62.5 36.8 72.6 71.8 76.6 69.4 63.0 54.2 67.1 67.3 45.4 42.7 45.0 37.4 62.9 76.1 20.6 73.0 51.3

V2 59.3 67.3 39.0 82.2 75.0 71.3 74.7 63.8 58.0 68.5 62.2 55.3 46.5 50.2 48.4 64.6 78.4 20.9 77.8 47.0

V3 70.1 79.2 51.6 87.5 88.1 86.5 85.8 82.5 77.0 86.0 77.4 69.2 67.3 60.0 60.8 78.1 85.6 34.6 86.8 70.3

V4 77.1 80.2 49.1 68.3 69.3 68.1 56.3 50.9 58.4 78.4 69.0 52.4 60.4 54.4 28.4 47.9 72.3 9.4 80.9 68.4

V5 74.2 75.5 53.5 74.6 66.9 68.7 68.3 54.5 52.7 71.8 65.8 51.6 47.9 51.9 43.1 53.3 78.7 17.3 81.5 58.1

V6 95.5 94.3 73.3 94.9 94.9 97.0 94.6 92.1 82.3 96.5 96.9 89.8 77.9 93.1 96.4 96.8 97.2 72.1 86.1 85.8

V7 82.0 72.7 67.9 86.8 73.2 89.2 84.9 79.6 70.1 85.3 72.1 67.4 54.6 67.4 77.6 70.5 92.9 47.6 79.1 66.0

V8 73.9 63.9 52.8 80.5 72.9 90.8 77.5 64.7 63.5 78.0 62.3 54.1 44.6 66.7 71.0 66.9 90.6 42.6 75.9 58.3

V9 87.6 77.7 65.0 68.2 74.5 86.7 81.9 69.1 73.6 79.0 82.6 66.0 52.1 78.1 82.2 84.7 93.4 55.9 85.4 75.2

V10 56.6 56.2 44.0 68.9 76.0 73.0 62.8 59.9 55.7 61.4 71.0 52.4 32.7 56.9 64.0 66.5 68.4 49.3 66.9 57.3

V11 50.0 45.2 10.4 80.0 64.7 23.8 52.2 25.0 66.7 26.7 21.3 39.2 30.2 39.5 65.9 26.5 50.0 38.9 42.3 39.0

V12 76.3 74.6 49.8 74.2 63.1 71.8 66.4 57.8 51.5 74.4 76.4 57.8 46.8 51.2 38.7 50.4 78.6 20.3 82.3 68.8

V13 52.5 58.0 37.4 48.4 48.4 51.8 43.4 44.2 48.1 47.7 37.8 43.8 30.3 57.9 58.4 62.8 35.0 47.0 49.7 71.5

V14 37.7 37.0 34.5 18.1 32.5 33.4 29.1 17.3 32.1 21.3 16.6 24.2 19.2 31.2 49.0 27.0 22.2 54.2 25.6 35.0

68.2 59.5 72.5 65.9 65.3

72.3 57.7 60.8 61.5 64.0

90.0 83.0 90.7 90.7 93.1

64.3 55.4 52.3 64.2 48.0

77.0 69.9 68.7 65.8 44.9

94.9 95.2 97.7 93.8 91.9

90.2 86.2 79.6 74.3 83.5

88.7 82.0 82.1 77.9 83.5

82.8 91.9 85.7 82.7 73.7

71.9 62.7 61.5 65.1 49.8

56.1 50.0 41.2 43.0 28.1

74.3 75.8 65.0 71.5 40.3

19.7 19.0 8.5 18.8 18.5

6.9 9.7 3.0 13.8 2.0

137


State District Karnal Panipat Sonipat Jind Fatehabad Sirsa Hisar Bhiwani Rohtak Jhajjar Mahendragarh Rewari Gurgaon Faridabad Mewat Himachal Pradesh Chamba Kangra Lahul & Spiti Kullu Mandi Hamirpur Una Bilaspur Solan Sirmaur

V1 61.9 65.3 69.6 58.0 51.2 58.1 56.0 48.9 66.7 54.5 55.0 68.4 56.1 45.5 17.4

V2 58.9 51.1 57.7 55.4 41.7 56.0 44.3 48.3 60.1 59.7 53.1 70.0 60.1 43.8 17.0

V3 88.2 84.6 95.4 92.3 88.9 92.1 90.6 90.8 96.1 91.0 94.9 95.9 93.1 78.9 52.7

V4 51.3 39.0 53.7 42.1 48.6 53.5 48.6 35.7 52.8 48.0 56.8 65.0 52.3 39.1 14.8

V5 58.3 48.7 61.3 42.6 36.5 57.9 39.4 38.2 40.3 40.5 51.2 62.1 41.4 34.9 33.7

V6 98.1 83.8 95.6 92.5 86.8 94.1 93.7 88.5 95.1 90.6 92.1 94.8 90.7 79.2 48.5

V7 81.8 63.4 78.8 66.4 70.9 71.1 69.6 68.5 86.7 77.7 79.5 80.6 74.7 54.8 14.0

V8 89.9 70.7 81.6 66.4 72.5 69.5 69.6 70.8 86.7 81.2 79.4 82.2 74.7 54.8 14.0

V9 87.1 71.8 83.9 67.3 76.2 76.2 65.7 71.8 80.6 69.6 71.9 74.3 76.5 57.0 20.3

V10 68.7 50.0 66.7 53.8 37.2 54.8 43.2 42.4 44.5 39.5 49.8 51.1 47.2 26.6 7.8

V11 40.1 34.7 47.4 32.3 16.2 15.4 25.0 25.0 50.0 38.5 40.0 40.5 30.2 28.6 7.7

V12 62.4 46.1 62.5 49.6 34.4 50.2 41.3 43.0 46.1 44.2 53.8 65.0 41.9 31.6 33.5

V13 7.8 12.3 8.0 12.1 10.8 27.5 23.0 24.1 20.3 15.7 23.3 33.3 17.6 10.9 7.5

V14 1.4 0.5 2.9 7.4 10.4 5.4 4.8 12.5 9.6 5.2 6.1 2.1 5.9 3.0 0.7

57.1 48.4 63.9 66.4 47.6 74.3 74.2 65.8 60.2 54.5

50.8 56.0 69.0 68.9 38.4 81.0 59.2 68.6 61.0 46.6

83.4 82.5 93.2 87.8 65.1 94.7 91.9 92.6 89.7 76.9

28.4 50.0 60.1 50.3 36.4 63.5 54.3 58.8 54.0 40.6

26.2 60.1 59.5 50.7 35.0 62.3 55.5 65.4 51.1 39.4

94.9 100.0 100.0 100.0 97.5 100.0 100.0 97.8 98.9 98.5

78.6 85.4 84.3 88.9 79.3 95.2 96.1 91.3 83.7 95.5

83.2 88.3 86.0 91.0 84.9 95.2 97.4 89.8 88.5 90.8

91.4 95.7 91.2 100.0 94.4 100.0 95.8 94.1 93.8 93.4

81.7 89.3 79.3 89.8 84.9 94.6 88.6 89.9 79.9 77.0

60.0 75.0 44.4 71.4 40.0 87.5 46.2 69.0 52.6 50.0

31.5 66.3 66.2 51.5 34.4 64.5 54.3 64.0 53.5 32.9

50.2 55.3 40.6 63.0 72.7 81.0 45.2 72.3 41.9 58.3

31.6 39.4 21.9 36.4 36.9 58.2 42.3 51.2 42.4 33.6

138


State District Shimla Kinnaur Jammu & Kashmir Kupwara Baramula Srinagar Badgam Pulwama Anantanag Leh Kargil Doda Udhampur Punch Rajauri Jammu Kathua Jharkhand Garhwa Palamu Chatra Hazaribagh Kodarma Giridih Deoghar Godda

V1 64.4 71.5

V2 68.9 67.4

V3 81.7 93.9

V4 59.3 37.4

V5 51.8 34.5

V6 94.9 100.0

V7 85.0 84.6

V8 87.1 87.3

V9 85.7 89.9

V10 77.0 88.1

V11 54.2 54.5

V12 49.2 34.4

V13 50.5 47.6

V14 46.1 42.4

49.6 64.0 73.4 55.1 60.2 55.7 45.2 49.4 47.8 62.5 43.3 46.8 68.7 75.9

68.6 76.5 92.4 89.1 86.5 71.7 81.0 64.6 58.8 72.9 42.8 58.3 86.5 87.0

77.7 81.7 98.6 93.4 93.1 91.1 89.5 73.5 62.8 87.1 56.1 62.3 92.8 93.5

56.0 54.7 87.3 74.9 77.5 63.3 71.9 54.5 30.9 27.7 23.0 33.9 65.4 55.1

45.3 69.6 84.7 66.3 62.7 70.5 48.2 55.4 33.5 29.7 22.0 23.7 67.2 58.1

89.1 93.5 100.0 98.2 97.5 96.9 98.7 98.7 87.9 98.3 84.8 77.4 97.7 99.4

72.9 66.6 82.9 91.0 83.3 71.7 86.8 89.3 45.9 69.2 56.7 60.8 80.4 94.1

61.3 60.4 87.6 82.4 83.4 66.7 89.9 86.6 51.2 72.6 33.7 52.8 84.8 97.2

71.5 71.6 90.6 82.6 87.3 82.4 88.5 94.5 66.6 84.8 73.3 67.9 93.5 92.8

51.4 61.6 64.9 53.1 61.6 40.1 71.7 64.5 23.3 66.2 32.6 53.4 41.6 64.3

37.8 66.3 56.7 57.6 52.3 56.1 71.8 44.0 30.8 41.9 62.8 48.1 58.8 55.4

45.2 67.0 82.8 69.0 60.0 70.0 53.3 53.7 35.8 29.3 21.0 40.6 73.1 62.1

58.1 68.0 36.7 59.1 50.6 52.6 77.4 73.6 48.1 40.6 64.2 48.8 35.3 36.6

77.9 64.6 65.5 81.5 74.7 84.5 22.9 75.8 38.3 50.5 24.1 74.2 29.8 39.0

20.6 20.5 23.2 50.6 32.2 25.6 29.3 22.3

20.1 22.4 22.9 38.6 33.3 25.0 22.9 24.1

52.8 41.1 43.4 73.3 50.3 37.3 44.3 40.1

10.7 18.0 14.4 25.9 27.0 16.5 16.2 10.6

24.3 31.4 28.1 45.3 49.4 26.9 28.1 16.7

91.2 87.8 82.3 94.3 88.0 59.2 78.1 70.0

71.4 62.9 55.8 80.8 66.7 38.7 57.8 50.9

73.6 60.2 52.8 78.5 61.2 32.3 55.5 40.8

81.9 69.8 62.0 83.6 62.0 47.3 54.9 51.0

75.0 61.1 47.2 74.9 50.9 29.6 56.7 43.4

18.3 26.2 16.5 21.6 27.0 16.7 18.4 10.0

24.3 32.6 31.8 46.0 46.9 26.7 26.9 20.6

36.0 22.3 29.8 23.8 25.4 36.7 39.8 45.0

39.3 64.9 41.8 55.1 47.5 48.0 67.3 66.9

139


State District Sahibganj Pakaur Dumka Dhanbad Bokaro Ranchi Lohardaga Gumla Pashchimi Singhbum Purbi Singhbhum Simdega Seraikela Latehar Jamtara Karnataka Belgaum Bagalkot Bijapur Gulbarga Bidar Raichur Koppal Gadag Dharwad Uttara Kannada Haveri

V1 21.7 18.5 20.5 47.7 41.0 46.8 38.8 37.0 35.1 59.1 35.3 49.3 26.1 33.0

V2 23.2 17.4 27.2 50.9 46.2 40.6 29.4 29.3 32.6 55.0 36.8 49.7 28.7 34.2

V3 45.5 47.4 62.6 68.5 66.8 67.4 63.8 63.2 65.5 82.6 67.0 70.1 59.3 45.6

V4 5.9 10.8 9.2 35.4 31.9 27.5 20.9 10.2 21.9 49.1 10.1 24.4 11.4 17.8

V5 10.1 21.7 25.5 44.0 47.0 38.6 39.4 27.5 25.7 52.4 13.9 30.8 25.0 25.7

V6 75.1 88.5 83.8 81.9 92.0 95.4 98.4 94.0 88.2 100.0 92.4 98.3 97.3 70.6

V7 41.8 51.5 70.7 63.7 77.6 78.0 89.2 77.0 68.1 84.3 66.1 87.0 82.3 53.6

V8 41.5 52.9 61.1 55.3 78.2 79.9 85.6 75.5 66.3 88.6 73.6 89.5 83.8 53.4

V9 62.3 66.3 69.7 65.8 70.9 89.0 95.9 84.7 77.7 97.8 82.4 91.9 88.7 53.7

V10 48.5 61.6 61.9 55.0 71.0 72.7 82.3 76.0 63.9 85.8 74.3 77.0 77.4 46.2

V11 18.4 40.0 5.1 8.3 32.2 27.0 29.2 22.7 34.2 25.0 16.3 25.0 15.3 28.6

V12 8.7 20.0 27.3 43.3 49.6 38.2 43.8 28.8 25.0 48.9 12.1 30.3 25.2 27.9

V13 20.0 31.5 41.5 27.5 31.6 40.5 57.3 38.7 39.4 46.1 24.1 42.4 35.9 39.1

V14 60.7 57.1 42.4 46.6 50.6 53.0 46.7 36.4 57.9 61.6 37.1 52.7 28.9 51.0

61.8 57.6 64.0 58.2 81.4 44.2 52.7 67.0 71.7 81.8 72.9

71.5 62.9 65.2 65.4 81.7 55.7 65.7 80.6 81.0 91.0 89.4

80.0 83.1 73.7 75.8 86.9 60.8 74.0 89.6 90.3 94.2 94.4

75.4 47.1 61.5 47.8 65.2 41.4 24.7 50.9 66.6 82.6 64.1

68.4 53.6 58.4 56.5 56.4 44.9 44.1 70.7 69.5 79.3 53.4

99.4 92.5 91.3 93.4 94.5 89.9 96.1 98.7 98.4 100.0 96.7

88.4 82.1 73.7 85.3 87.2 79.6 84.0 95.2 92.1 86.9 91.0

72.8 75.7 68.2 77.5 84.0 53.4 79.4 90.9 87.2 83.2 83.7

89.7 71.2 67.4 72.7 82.7 70.0 78.5 83.3 90.3 96.4 83.7

65.7 45.7 38.5 61.6 58.0 47.5 57.1 69.7 67.8 83.8 78.2

22.6 52.3 49.0 66.7 60.9 59.3 33.3 41.7 59.0 25.0 45.0

67.6 54.1 56.1 52.1 54.5 50.0 42.2 72.3 68.4 83.6 56.7

45.6 38.6 38.8 50.7 38.7 36.6 32.6 27.3 58.4 56.6 38.4

50.9 57.2 58.9 23.7 45.2 39.8 50.4 33.6 41.6 35.8 46.6

140


State District Bellary Chitradurga Davanagere Shimoga Udupi Chikmagalur Tumkur Kolar Bangalore Bangalore Mandya Hassan Dakshina Kannada Kodagu Mysore Chamarajanagar Kerala Kasaragod Kannur Wayanad Kozhikode Malappuram Palakkad Thrissur Ernakulam Idukki

V1 61.6 51.6 70.0 84.2 91.8 88.3 79.8 87.2 89.7 73.9 95.0 92.0 89.0 95.1 84.2 90.9

V2 64.3 81.1 82.9 91.8 96.4 92.9 93.9 92.5 98.1 95.6 97.2 94.0 97.0 94.5 91.5 97.4

V3 64.8 85.1 90.3 96.7 96.6 97.7 94.5 95.7 98.0 97.6 97.8 94.4 97.8 97.1 97.0 96.0

V4 45.5 63.9 64.8 71.2 95.3 83.4 72.8 62.2 93.8 84.9 86.7 80.1 96.0 81.9 80.4 76.5

V5 54.9 63.8 69.4 78.0 92.2 79.6 66.8 57.1 89.4 78.8 71.6 67.8 86.3 85.4 71.4 79.0

V6 94.1 97.0 95.6 98.7 100.0 97.9 97.1 100.0 100.0 100.0 100.0 100.0 99.0 100.0 100.0 100.0

V7 85.9 89.6 89.7 90.3 98.6 96.3 95.1 98.3 97.6 96.8 93.5 94.5 96.4 100.0 99.0 93.1

V8 76.1 84.3 88.6 89.3 89.4 96.3 97.1 96.9 92.6 95.8 89.9 93.2 93.4 98.0 99.0 96.8

V9 79.6 82.5 84.2 90.1 97.4 95.8 92.8 95.3 96.7 90.7 95.2 90.9 95.0 96.1 93.9 92.0

V10 60.5 74.4 60.9 83.5 83.8 82.6 81.4 80.7 84.8 79.7 83.8 93.2 87.8 82.8 87.7 81.7

V11 46.4 83.3 48.1 35.7 0.0 60.0 34.4 30.8 50.0 71.4 47.1 58.8 47.1 38.5 37.9 37.5

V12 51.9 57.2 68.6 85.0 95.3 83.6 61.6 55.3 92.1 75.7 68.9 71.3 90.6 86.3 78.0 78.4

V13 44.4 55.7 47.9 48.2 65.3 63.8 48.5 38.3 46.4 52.9 54.5 42.1 66.3 56.3 45.1 55.2

V14 32.3 22.9 41.4 44.1 26.9 28.0 35.2 26.3 19.5 17.7 27.3 31.8 33.1 27.3 24.2 33.8

95.6 96.4 93.7 91.0 96.2 94.9 96.0 96.3 97.6

99.7 89.0 97.9 98.9 99.1 87.7 98.0 95.7 96.1

99.3 99.5 95.8 98.4 98.5 98.6 95.3 96.2 100.0

98.6 100.0 95.5 100.0 100.0 99.2 100.0 100.0 99.5

98.9 99.6 95.5 99.3 100.0 99.6 98.5 98.9 99.0

100.0 97.3 97.8 98.8 98.2 100.0 100.0 100.0 100.0

91.9 93.1 82.9 79.5 72.4 73.7 87.2 90.2 88.7

91.9 91.7 85.1 79.7 72.5 78.7 90.9 85.9 90.5

92.4 88.4 82.7 75.6 75.7 81.9 92.2 94.1 96.7

57.7 57.0 55.1 59.6 35.7 47.5 66.4 60.5 67.2

36.4 42.9 50.0 38.9 45.5 53.8 61.1 60.0 30.0

97.6 99.1 95.5 98.3 100.0 99.6 100.0 99.4 99.4

80.2 57.1 83.5 57.6 65.1 57.1 54.6 50.0 73.4

16.5 23.0 30.6 22.8 23.4 10.4 10.2 10.9 31.1

141


State District Kottayam Alappuzha Pathanamthitta Kollam Thiruvananthapuram Lakshadweep Lakshadweep Madhya Pradesh Sheopur Morena Bhind Gwalior Datia Shivpuri Guna Tikamgarh Chhatarpur Panna Sagar Damoh Satna Rewa Umaria Shahdol Sidhi Neemuch

V1 97.2 94.4 97.6 96.5 97.1

V2 86.4 95.0 98.2 91.2 99.5

V3 99.6 100.0 99.4 98.7 98.1

V4 100.0 99.5 100.0 99.5 99.0

V5 100.0 99.0 100.0 100.0 98.5

V6 100.0 100.0 100.0 98.3 100.0

V7 95.2 92.5 93.2 91.6 94.6

V8 96.4 94.3 91.2 94.8 91.5

V9 94.5 95.9 95.1 92.6 96.9

V10 73.7 75.0 72.2 75.6 72.7

V11 54.5 54.5 28.6 16.7 69.2

V12 100.0 100.0 99.2 98.1 100.0

V13 63.8 57.1 61.1 60.0 65.8

V14 29.8 21.9 19.5 26.4 27.0

77.7

91.1

97.4

90.2

93.8

100.0

92.4

90.8

92.4

47.9

56.7

93.6

69.3

49.6

14.7 20.2 28.7 29.3 33.9 15.2 20.0 25.0 35.5 15.8 32.7 30.4 24.8 29.7 20.8 37.2 12.4 50.4

14.4 12.5 18.6 25.8 24.8 14.3 16.6 21.6 29.2 20.0 40.3 35.8 28.6 23.7 30.5 42.3 15.6 51.9

32.0 38.9 45.2 54.0 63.9 30.0 33.3 60.7 65.1 46.7 64.2 57.0 54.2 55.3 55.2 70.5 32.1 78.7

41.4 59.2 51.7 65.5 45.3 44.4 50.6 51.6 50.5 38.4 46.9 31.4 42.4 46.0 34.7 43.6 23.4 54.0

27.2 31.6 25.6 36.4 20.1 20.5 20.7 22.1 23.9 39.9 41.5 33.9 37.7 37.7 33.5 44.8 19.4 50.6

81.4 87.0 85.3 82.5 78.9 80.3 69.8 73.6 81.7 80.2 86.8 82.4 84.2 87.0 84.4 90.4 72.5 91.2

51.7 52.0 51.5 59.8 38.7 42.3 35.7 43.1 53.7 53.5 58.1 30.1 50.7 49.7 51.0 56.6 39.1 70.2

39.3 45.5 52.2 54.2 34.5 24.8 26.7 22.1 33.1 38.1 43.4 29.7 42.1 44.1 26.0 53.0 28.8 70.2

48.3 51.5 64.0 54.6 41.5 28.5 43.5 33.8 51.2 53.5 67.4 50.7 55.6 65.5 46.8 67.5 42.9 71.4

24.2 34.2 34.9 30.3 23.2 15.2 20.6 19.9 27.5 38.6 51.2 31.3 48.9 41.6 38.5 39.1 25.3 48.7

13.4 15.4 17.5 31.7 34.2 15.2 17.5 9.5 12.3 25.0 31.5 27.5 44.2 33.3 34.6 27.8 3.8 31.5

27.7 32.8 32.5 40.7 23.5 23.9 22.2 27.4 26.8 42.8 49.2 29.8 39.6 43.8 40.4 48.6 23.8 55.4

27.2 32.4 43.7 50.5 47.4 41.9 39.1 24.8 32.8 37.2 41.8 51.4 40.0 40.5 33.7 47.2 34.9 33.3

26.7 25.1 23.1 33.9 29.6 27.5 13.3 19.8 9.4 32.4 32.5 37.0 60.4 29.5 47.2 24.0 35.1 19.1

142


State District Mandsaur Ratlam Ujjain Shajapur Dewas Jhabua Dhar Indore West Nimar Barwani East Nimar Rajgarh Vidisha Bhopal Sehore Raisen Betul Harda Hoshangabad Katni Jabalpur Narsimhapur Dindori Mandla Chhindwara Seoni

V1 51.3 40.8 60.4 43.9 52.5 24.5 34.0 64.7 41.6 21.8 37.0 41.7 42.4 50.2 47.3 29.2 35.5 39.1 48.4 17.6 57.7 43.8 11.9 28.3 41.6 36.3

V2 57.5 50.2 65.8 56.1 53.4 23.5 37.3 67.8 38.2 21.6 27.2 42.0 33.6 59.4 45.0 25.9 43.2 42.0 45.6 19.7 61.1 50.4 16.9 28.4 44.3 48.4

V3 82.6 81.9 90.0 82.7 76.9 41.7 62.8 86.9 66.4 42.1 55.9 68.3 57.3 78.1 79.0 51.1 68.2 65.5 74.6 51.2 84.6 80.2 35.7 57.7 68.3 76.0

V4 52.6 59.9 68.3 69.8 67.2 40.4 42.8 79.7 42.5 29.1 41.5 49.4 48.5 59.0 56.1 36.1 40.8 46.0 65.4 44.1 63.0 48.0 13.1 28.4 40.1 52.2

V5 44.1 44.1 62.7 57.3 49.9 31.3 36.5 75.2 30.9 30.4 20.4 39.7 25.9 47.8 46.3 22.6 37.4 33.4 63.6 39.5 47.4 47.3 15.3 32.1 41.9 45.7

V6 92.5 94.4 97.0 82.9 89.8 60.4 80.0 92.4 81.3 65.4 77.9 80.8 79.1 97.1 91.0 92.4 93.2 82.0 96.2 85.4 96.7 91.7 65.3 86.3 94.3 96.1

143

V7 52.9 65.3 86.6 63.2 72.6 24.0 61.7 77.3 64.8 38.2 51.3 44.2 43.1 74.0 64.2 60.2 59.0 60.4 74.7 67.7 68.1 63.5 27.6 49.5 73.9 63.1

V8 59.7 65.3 86.6 60.5 61.5 18.8 52.1 74.2 61.7 30.7 48.1 48.1 47.3 76.7 63.4 49.2 61.9 52.3 64.6 53.1 49.5 61.2 28.3 43.2 60.2 48.5

V9 62.7 68.1 77.6 64.5 60.2 26.0 55.5 77.6 64.1 40.4 57.9 51.9 39.5 76.9 65.6 64.4 71.8 59.5 77.2 70.8 60.0 61.2 41.8 68.4 64.8 75.5

V10 31.4 49.4 70.3 41.6 46.3 16.7 32.1 50.9 54.3 25.3 50.3 21.4 23.4 59.3 48.7 41.4 55.0 45.0 53.1 50.9 60.0 45.4 39.8 51.7 50.4 45.3

V11 24.3 29.0 46.7 26.9 32.9 32.8 23.0 60.3 48.1 38.7 37.5 23.4 22.3 59.8 28.4 34.3 30.6 44.4 22.2 21.4 44.1 35.1 26.5 32.8 35.7 39.1

V12 52.2 46.5 63.6 65.3 54.4 37.2 39.2 83.5 31.4 33.1 24.5 43.3 24.8 48.9 53.9 24.5 38.4 43.5 66.7 40.2 58.2 54.8 16.3 31.7 49.7 53.6

V13 40.0 31.9 56.7 51.2 51.0 50.0 37.4 41.2 30.6 32.8 46.7 28.0 39.2 48.1 47.4 49.5 69.0 57.1 49.0 49.5 56.0 31.9 56.7 56.4 41.1 50.0

V14 17.7 28.7 34.5 29.6 22.3 45.7 26.7 33.2 20.4 21.0 18.4 16.1 29.5 32.9 24.1 31.7 57.7 39.2 26.3 51.7 29.4 21.3 48.5 40.7 35.7 31.3


State District Balaghat Maharashtra Nandurbar Dhule Jalgaon Buldana Akola Washim Amravati Wardha Nagpur Bhandara Gondiya Gadchiroli Chandrapur Yavatmal Nanded Hingoli Parbhani Jalna Aurangabad Nashik Thane Mumbai (Suburban) Mumbai Raigarh

V1 63.6

V2 64.0

V3 86.4

V4 48.3

V5 47.5

V6 100.0

V7 79.5

V8 75.6

V9 88.3

V10 67.2

V11 34.8

V12 55.3

V13 54.1

V14 31.2

29.4 40.6 46.6 61.0 60.8 66.6 70.2 80.4 74.7 65.8 62.4 46.7 71.0 61.3 59.8 47.9 60.9 58.1 51.0 62.2 67.8 69.6 71.1 63.2

38.3 49.5 58.2 66.7 69.1 68.6 77.9 88.6 95.5 84.5 83.0 72.5 83.2 71.5 76.2 69.3 69.0 69.3 57.2 71.6 84.7 89.7 92.7 83.8

55.4 71.9 79.2 89.1 89.6 93.5 93.8 99.2 97.3 95.7 92.0 83.2 91.2 87.2 97.8 90.8 83.9 88.3 84.2 86.1 93.9 98.1 97.5 94.1

25.4 50.5 53.1 66.6 74.3 65.2 63.6 81.4 82.2 56.9 53.6 23.5 54.9 53.4 55.9 41.5 64.6 65.5 65.8 63.5 71.7 93.5 92.1 69.2

37.6 62.8 68.3 77.4 76.3 69.6 76.0 87.0 88.4 70.9 76.7 61.9 74.8 74.5 65.4 70.6 77.5 71.8 66.4 73.9 76.2 90.7 92.3 79.3

75.5 83.0 90.0 98.5 97.9 94.8 95.4 100.0 98.0 98.8 98.7 92.2 100.0 97.4 96.4 88.1 95.3 94.2 98.4 98.1 100.0 97.9 100.0 98.6

47.4 60.4 73.8 92.7 93.4 84.8 86.0 87.5 92.8 86.9 94.2 76.8 78.2 92.8 88.0 76.1 89.6 92.5 85.7 85.7 90.9 91.0 92.2 91.3

31.2 45.2 62.0 87.4 84.1 80.0 77.8 90.7 94.9 79.7 90.7 52.8 79.7 83.9 83.1 67.1 86.0 85.6 74.6 78.9 86.4 86.8 84.9 80.2

46.0 66.5 79.4 85.2 83.0 85.8 82.4 94.5 96.4 92.4 90.6 88.4 95.2 87.2 83.0 67.5 80.8 82.3 76.9 83.8 81.9 95.5 86.7 92.6

34.8 44.9 51.7 60.0 63.3 58.2 69.2 88.7 81.5 80.7 82.8 65.4 90.0 73.0 72.5 60.6 70.1 73.1 59.1 69.2 73.7 77.7 84.6 73.8

39.6 41.4 43.2 39.8 42.9 35.6 44.9 61.8 43.1 51.7 52.9 58.8 37.0 43.4 48.2 46.0 38.7 49.2 41.4 41.1 30.2 44.4 53.6 45.7

38.1 58.5 65.0 74.8 82.6 65.1 80.2 91.3 86.2 73.1 81.3 64.0 72.6 71.7 67.0 64.5 77.2 67.3 70.0 67.5 80.1 92.6 97.2 81.0

37.7 42.2 44.3 48.8 60.2 50.6 62.4 68.4 60.8 61.8 56.7 49.7 63.1 58.3 45.3 46.4 62.3 52.6 40.0 54.4 57.4 49.6 50.6 53.9

47.6 45.5 36.9 44.8 34.8 30.2 40.8 52.7 45.7 44.7 50.6 48.5 29.6 36.1 29.4 18.8 37.8 18.5 35.8 46.7 30.8 30.7 33.0 27.7

144


State District Pune Ahmadnagar Bid Latur Osmanabad Solapur Satara Ratnagiri Sindhudurg Kolhapur Sangli Manipur Senapati Tamenglong Churachandpur Bishnupur Thoubal Imphal West Imphal East Ukhrul Chandel Meghalaya West Garo Hills East Garo Hills South Garo Hills West Khasi Hills

V1 75.2 76.8 58.8 62.6 60.5 72.5 65.4 73.8 81.0 79.2 71.1

V2 89.0 83.6 61.5 76.2 71.9 86.6 92.6 83.3 92.5 82.8 81.6

V3 97.1 95.9 86.9 90.0 91.7 96.8 98.8 95.0 99.5 95.6 97.3

V4 83.2 80.1 68.3 63.9 58.9 67.1 87.4 73.3 92.7 89.0 76.1

V5 90.1 92.3 79.2 80.3 75.6 79.7 94.9 79.5 96.8 92.5 84.8

V6 97.8 97.8 97.9 94.9 98.0 100.0 100.0 98.3 100.0 99.7 95.7

V7 95.5 91.4 86.8 89.3 86.4 89.8 98.0 98.6 92.6 91.6 93.4

V8 92.1 93.7 82.3 82.3 77.3 94.9 92.7 87.7 90.6 83.9 93.5

V9 93.7 92.7 87.8 82.4 77.9 91.0 99.0 93.3 97.9 93.6 96.1

V10 85.4 82.8 69.0 74.3 69.9 66.3 90.8 83.2 81.4 83.1 82.5

V11 37.0 42.4 45.3 38.2 37.4 57.1 47.9 30.4 65.4 60.5 35.9

V12 90.5 87.2 77.8 75.3 71.8 80.0 92.8 81.2 97.9 94.9 89.4

V13 58.1 48.8 49.5 46.0 65.4 43.1 55.9 45.4 74.0 50.8 50.5

V14 28.8 40.9 25.2 21.5 22.1 17.0 35.9 16.0 22.7 24.5 25.5

47.7 26.7 45.9 73.5 79.9 78.0 68.8 41.8 59.2

56.2 21.6 45.9 78.7 80.3 80.9 73.4 30.1 57.4

74.5 43.8 63.5 87.9 90.1 88.1 84.2 58.9 78.6

24.4 14.2 30.5 57.5 59.4 87.2 66.0 13.4 27.6

24.9 12.4 34.1 56.8 61.6 85.0 61.9 17.4 36.2

94.0 40.9 64.5 89.8 92.4 97.6 85.9 81.4 82.5

69.6 27.9 49.8 79.3 82.2 88.0 65.0 51.6 57.2

69.1 23.4 46.7 77.5 77.8 85.1 61.1 54.1 51.5

69.5 27.4 34.4 68.4 67.1 86.3 59.0 49.5 53.6

30.2 9.3 22.8 42.3 38.1 58.4 35.3 26.4 28.7

47.1 20.5 40.0 75.9 51.0 75.9 73.6 46.2 54.8

23.6 14.4 30.4 55.1 67.2 82.0 59.1 17.8 33.7

61.6 51.4 51.0 63.6 61.3 55.2 53.3 62.0 50.6

36.6 14.9 29.7 48.2 55.3 65.0 63.2 33.6 30.0

26.7 22.7 23.9 14.3

29.6 29.3 26.5 27.6

41.5 39.0 32.0 41.2

17.5 9.3 26.5 21.2

15.6 8.0 20.4 29.0

73.0 63.8 66.7 64.3

42.6 24.3 34.7 32.6

33.6 15.9 20.4 31.0

46.7 22.9 41.7 37.0

22.7 21.0 29.9 24.8

52.2 22.2 73.3 38.3

17.8 7.1 18.4 23.6

75.5 79.3 90.2 80.6

31.0 38.4 16.7 33.2

145


State District Ri Bhoi East Khasi Hills Jaintia Hills Mizoram Mamit Kolasib Aizawl Champhai Serchhip Lunglei Lawngtlai Saiha Orissa Bargarh Jharsuguda Sambalpur Debagarh Sundargarh Kendujhar Mayurbhanj Baleshwar Bhadrak Kendrapara Jagatsinghapur Cuttack Jajapur

V1 26.5 43.0 21.9

V2 49.4 64.2 46.8

V3 66.6 81.9 52.7

V4 28.0 44.1 25.8

V5 28.9 46.2 30.8

V6 86.8 96.0 83.3

V7 68.4 65.6 42.4

V8 65.1 68.8 54.3

V9 69.1 67.2 63.3

V10 53.9 53.1 53.9

V11 54.9 47.2 60.7

V12 32.0 48.5 30.0

V13 65.8 65.2 70.6

V14 34.2 33.5 19.6

36.3 47.9 60.8 45.7 54.1 43.5 30.6 46.5

55.4 64.3 75.0 64.0 78.9 55.7 48.7 63.3

76.7 82.4 92.4 87.8 99.6 83.5 75.6 93.8

41.1 70.5 88.5 55.0 73.8 55.6 28.8 47.8

30.4 51.3 71.3 41.5 56.0 51.7 28.9 36.7

89.1 87.8 95.7 92.8 97.1 93.1 81.1 96.5

52.2 71.0 77.0 61.1 76.1 73.1 41.5 69.2

55.7 65.7 74.2 59.5 74.4 73.8 41.5 72.2

70.2 79.6 87.9 74.2 90.9 85.9 78.1 80.1

72.8 71.6 87.4 61.2 74.1 76.6 56.6 72.7

63.1 67.0 57.6 49.0 47.8 36.2 25.0 51.0

32.5 55.8 74.5 43.9 57.6 53.4 30.9 39.8

87.1 82.0 72.2 78.8 70.9 74.0 88.1 69.7

25.0 39.3 35.8 32.3 22.6 34.0 35.3 29.0

54.2 44.8 47.7 25.6 38.9 54.6 55.5 70.0 53.6 57.1 77.1 51.3 58.6

59.2 57.9 57.3 38.1 53.3 47.5 63.5 64.2 52.4 59.4 79.1 62.3 63.8

89.4 87.3 82.9 57.7 82.0 80.0 95.2 97.7 77.8 89.9 92.2 85.1 91.0

43.7 64.9 56.6 44.5 45.3 34.3 43.1 52.6 42.7 46.9 79.7 68.3 61.6

51.7 54.7 44.5 29.0 36.7 13.3 26.7 21.2 22.3 25.8 40.1 42.5 32.3

97.3 99.1 98.5 91.1 97.0 90.9 97.0 98.1 99.1 96.9 98.3 98.2 97.4

94.2 90.7 82.9 73.5 78.9 81.4 84.1 91.4 90.9 95.6 95.1 87.2 92.1

89.1 86.6 82.4 59.7 74.0 74.7 80.6 88.6 84.0 93.8 85.7 82.7 89.9

79.1 94.4 86.1 84.8 86.9 74.2 81.6 86.1 84.8 83.7 96.7 83.3 87.1

77.2 77.8 78.3 71.7 80.4 65.8 74.4 81.2 68.2 72.8 85.6 74.1 80.7

46.2 51.9 62.1 37.5 42.1 6.3 37.5 75.0 68.3 52.8 68.4 29.0 53.6

57.8 46.8 45.2 27.6 31.8 15.1 33.3 27.5 26.1 27.3 36.7 46.6 39.8

40.0 48.4 53.4 50.6 54.9 66.3 76.9 75.5 84.2 68.4 69.3 62.4 69.9

60.1 46.9 57.7 52.6 49.2 43.6 39.7 38.7 35.5 36.6 26.9 32.7 29.9

146


State District Dhenkanal Anugul Nayagarh Khordha Puri Ganjam Gajapati Kandhamal Baudh Sonapur Balangir Nuapada Kalahandi Rayagada Nabarangapur Koraput Malkangiri Puducherry Yanam Pondicherry Mahe Karaikal Punjab Gurdaspur Amritsar Kapurthala

V1 42.4 45.7 38.5 50.5 56.2 56.6 38.4 34.6 24.0 54.8 32.8 52.6 33.1 30.8 45.3 40.9 47.1

V2 50.5 48.8 41.8 67.4 63.3 54.7 44.5 34.3 50.8 60.6 48.3 52.6 48.1 49.0 65.8 44.9 34.9

V3 77.2 71.4 71.9 84.5 87.7 80.2 59.5 68.2 84.9 85.0 100.0 86.5 74.6 80.8 89.4 79.9 81.4

V4 46.9 40.7 44.1 70.8 63.6 55.4 19.7 25.3 28.8 40.9 51.7 28.8 27.5 18.3 15.9 18.9 14.8

V5 25.8 22.6 19.8 33.6 39.8 17.8 12.6 14.4 22.6 52.8 37.9 35.4 25.3 8.0 6.9 9.3 11.0

V6 94.3 97.1 96.4 95.8 96.0 83.2 75.2 96.5 91.2 96.7 91.9 86.8 85.4 87.5 92.7 95.1 98.3

V7 78.6 84.6 78.7 79.4 87.1 61.7 57.5 71.4 75.2 91.1 78.4 80.1 58.1 45.7 62.6 80.6 49.3

V8 74.3 73.3 66.9 77.9 81.3 61.2 52.8 69.5 66.2 86.9 73.8 74.0 50.8 45.1 56.5 80.7 48.8

V9 81.6 87.4 64.9 84.6 89.9 65.7 61.3 84.5 79.8 91.4 76.6 64.7 68.0 65.0 77.7 79.9 81.4

V10 81.5 77.1 63.2 78.1 83.7 53.1 41.5 67.2 76.8 78.7 90.0 62.6 73.2 53.6 52.0 67.8 50.0

V11 52.3 49.0 48.9 47.4 46.2 41.3 0.0 36.6 36.5 60.9 75.0 58.5 42.1 46.7 66.7 39.1 57.1

V12 30.3 22.0 23.9 37.7 45.4 22.1 12.0 20.4 29.3 65.9 38.5 46.4 26.1 5.5 4.0 9.2 11.1

V13 61.7 46.6 54.8 67.5 77.3 56.8 72.7 56.0 49.0 50.0 38.5 51.1 60.4 82.3 78.9 67.3 78.5

V14 22.9 24.0 36.7 37.4 36.2 32.5 34.4 59.9 47.9 65.2 48.4 63.6 62.4 44.9 48.2 46.9 52.5

45.1 76.8

43.2 98.6

51.2 98.7

98.4 97.3

71.2 96.0

82.8 100.0

53.4 100.0

53.4 100.0

69.0 100.0

71.0 62.5

69.2 30.0

67.6 94.1

72.4 75.4

32.0 18.4

73.3

97.7

96.6

99.6

87.4

100.0

92.6

92.6

96.8

77.5

26.7

92.9

68.1

21.4

65.0 80.2 64.8

63.9 80.8 58.3

82.7 88.4 86.2

49.8 65.2 65.7

76.4 79.7 81.9

93.6 96.6 95.5

79.5 94.0 92.2

78.7 94.0 88.3

86.6 92.8 88.8

50.0 87.4 61.6

31.3 38.3 53.6

71.2 80.1 88.6

28.3 47.5 35.5

6.1 4.7 7.5

147


State District Jalandhar Hoshiarpur Nawanshahr Rupnagar Fatehgarh Sahib Ludhiana Moga Firozpur Muktsar Faridkot Bathinda Mansa Sangrur Patiala Tarn Taran SAS Nagar Barnala Rajasthan Ganganagar Hamumangarh Bikaner Churu Jhunjhunun Alwar Bharatpur Dhaulpur

V1 69.5 62.8 56.1 55.6 53.0 49.5 72.0 69.2 72.5 75.4 63.7 24.4 53.0 66.4 81.6 57.9 68.0

V2 62.5 61.9 64.5 69.7 62.0 59.0 70.8 71.2 70.7 71.3 65.9 36.2 51.0 64.3 77.2 69.5 57.7

V3 88.6 91.6 80.8 80.2 74.5 75.8 84.3 82.9 83.6 84.1 85.7 57.9 82.1 85.7 86.1 78.4 89.1

V4 60.4 55.1 54.5 64.9 67.7 61.0 63.7 65.1 56.0 57.3 66.4 59.0 72.4 67.3 57.6 73.7 64.8

V5 81.0 82.1 74.6 77.9 72.7 64.8 83.5 81.4 66.1 71.6 77.2 75.3 86.6 79.4 76.0 76.2 83.4

V6 97.6 99.0 97.2 96.2 96.3 88.7 98.2 94.7 98.4 91.3 99.8 93.2 84.8 96.2 87.5 93.7 89.4

V7 89.2 86.6 84.9 84.7 86.2 80.9 97.5 89.0 96.6 92.5 92.4 82.2 68.0 86.5 83.4 79.6 72.8

V8 84.2 93.8 83.8 84.3 87.3 83.5 97.5 89.0 96.6 92.5 93.6 74.9 66.3 83.0 85.0 77.1 73.8

V9 94.1 94.8 91.8 89.6 89.6 79.7 95.2 93.3 97.7 86.5 97.9 79.3 71.9 88.8 86.0 87.9 82.8

V10 65.4 58.5 59.5 52.2 43.9 50.6 84.6 88.5 90.7 85.5 77.3 46.7 49.3 57.4 85.5 50.0 51.0

V11 71.4 71.2 68.2 58.5 60.0 44.4 52.5 42.2 50.0 61.5 45.5 19.8 72.9 15.0 10.0 57.1 21.9

V12 87.3 84.5 77.9 73.9 71.2 64.9 80.1 80.0 65.3 74.1 77.1 77.7 89.0 83.2 78.2 72.5 83.3

V13 30.6 27.9 44.3 47.5 45.3 45.9 45.5 48.1 75.9 62.8 60.5 54.6 42.3 35.4 39.2 42.9 24.5

V14 11.9 9.9 13.3 12.7 14.8 15.5 5.0 4.6 6.7 5.1 13.9 7.1 8.2 10.4 6.4 15.0 4.7

38.6 38.2 25.1 27.0 40.8 21.9 16.9 18.5

39.0 34.7 24.9 22.4 30.3 14.4 8.0 7.6

64.0 55.5 46.3 43.7 54.6 36.8 24.0 26.6

40.9 33.5 29.9 30.5 59.0 46.0 44.2 48.5

36.4 23.1 29.1 20.2 39.1 28.6 23.9 23.6

90.5 83.3 71.8 72.2 92.8 74.8 55.3 75.2

67.6 61.0 54.6 54.4 75.9 56.1 33.1 57.9

74.3 73.2 46.6 46.1 68.7 29.6 28.0 35.4

82.7 75.6 60.3 61.7 85.5 54.4 40.4 53.7

62.0 63.3 37.5 41.7 49.7 30.8 20.2 31.3

21.4 0.0 18.2 12.5 35.7 22.7 21.7 14.7

37.1 27.0 32.2 20.7 44.5 28.7 24.7 27.5

40.3 28.1 42.6 51.5 45.9 36.4 36.6 28.4

31.5 25.6 33.5 38.7 32.7 36.3 44.3 31.9

148


State District Karauli Sawai Dausa Jaipur Sikar Nagaur Jodhpur Jaisalmer Barmer Jalor Sirohi Pali Ajmer Tonk Bundi Bhilwara Rajsamand Udaipur Dungarpur Banswara Chittaurgarh Kota Baran Jhalawar Sikkim Sikkim North

V1 22.0 26.9 25.5 32.2 46.2 32.0 31.9 24.2 26.3 38.2 41.1 52.0 55.4 49.5 44.3 27.7 28.3 25.9 30.1 20.4 30.9 47.1 46.9 27.5

V2 13.4 18.2 22.3 26.8 33.6 27.6 32.1 18.5 19.8 30.4 35.9 45.1 47.1 38.9 32.8 34.7 32.1 27.0 30.1 19.2 34.5 45.5 38.0 22.5

V3 36.8 41.1 47.4 52.0 67.0 59.5 56.8 51.8 53.9 62.9 68.8 77.1 79.6 71.6 63.7 60.2 60.7 56.5 64.8 55.1 58.8 70.8 70.8 38.9

V4 52.0 49.0 60.6 62.5 59.4 39.3 38.2 26.5 21.4 35.2 46.3 38.3 48.9 47.7 53.8 38.7 41.2 39.2 46.3 46.4 45.0 64.9 58.7 44.4

V5 42.2 29.2 45.1 47.8 52.5 43.6 35.8 23.1 22.8 35.6 39.4 34.0 40.1 34.4 41.0 39.2 44.9 40.0 47.5 44.4 45.3 55.6 41.3 37.2

V6 81.5 66.2 85.0 92.5 87.7 87.9 85.8 78.6 83.5 78.8 87.5 92.6 85.4 94.2 78.2 94.3 92.7 84.0 93.5 96.8 87.7 95.6 80.9 76.9

V7 59.0 49.6 70.7 72.9 72.4 66.4 68.4 57.3 59.8 59.9 66.7 64.9 55.1 56.7 72.4 72.1 68.5 81.5 89.2 96.8 67.9 74.4 58.8 58.7

V8 41.4 35.3 50.0 60.2 54.5 52.1 50.9 46.2 58.8 57.7 67.8 67.0 53.4 51.5 57.5 70.5 67.9 81.5 91.4 96.8 67.9 71.9 56.9 44.2

V9 49.7 47.8 63.2 77.4 75.6 67.9 68.4 59.0 64.9 66.4 72.7 67.0 71.9 62.5 64.4 80.3 77.8 84.6 93.5 90.3 79.0 88.9 64.7 68.3

V10 37.8 24.8 39.2 44.4 51.8 60.9 58.8 38.8 50.0 60.0 59.4 50.9 49.8 46.0 48.3 69.2 75.4 84.6 89.2 84.2 70.3 71.8 47.7 51.6

V11 18.5 44.3 29.2 55.3 25.8 42.3 12.5 27.3 34.8 20.0 18.6 20.8 50.0 45.5 41.7 65.0 47.9 43.1 47.4 17.6 26.5 55.2 20.8 40.0

V12 44.6 27.1 48.6 51.4 52.3 41.8 37.3 23.5 25.2 38.3 40.7 38.6 43.7 36.2 44.9 40.8 45.3 41.3 45.2 50.2 44.3 58.1 43.7 34.3

V13 40.5 40.8 41.2 43.0 37.6 29.1 31.2 24.2 34.2 44.4 41.5 38.2 43.1 42.3 54.8 50.1 51.4 38.8 49.6 43.5 53.7 54.7 47.5 50.9

V14 20.4 39.7 23.9 32.8 36.5 31.3 32.8 30.9 13.2 12.5 12.5 19.4 28.6 20.7 24.0 15.3 13.4 13.7 16.4 17.9 12.8 24.2 26.8 20.3

47.6

67.9

97.9

47.4

43.4

99.0

89.0

89.5

95.8

89.1

53.3

46.0

72.3

12.6

149


State District Sikkim West Sikkim South Sikkim East Tamil Nadu Thiruvallur Chennai Kancheepuram Vellore Dharmapuri Tiruvannamalai Viluppuram Salem Namakkal Erode The Nilgiris Coimbatore Dindigul Karur Trichy Ariyalur Krishnagiri Cuddalore Nagapattinam Thiruvarur Thanjavur Pudukottai

V1 46.5 45.1 61.5

V2 62.9 73.8 72.9

V3 92.9 94.7 95.3

V4 41.8 47.5 66.3

V5 32.4 46.0 61.4

V6 99.0 97.7 98.9

V7 75.7 83.0 94.3

V8 88.3 84.8 93.0

V9 95.5 91.4 89.0

V10 88.3 85.7 86.7

V11 45.2 38.6 58.3

V12 33.9 43.6 59.5

V13 72.8 55.6 52.9

V14 17.0 10.5 12.9

85.1 89.8 80.7 76.7 75.2 83.7 81.7 73.7 75.7 82.7 93.3 84.9 83.3 81.8 63.5 74.3 80.2 74.4 71.3 67.1 61.2 72.2

99.2 99.5 93.2 88.9 95.4 96.3 92.0 96.2 96.7 99.6 99.1 96.4 97.1 98.3 91.9 98.5 93.9 97.1 98.2 96.3 95.4 98.0

98.7 100.0 100.0 96.3 98.2 96.6 97.9 97.0 97.8 99.7 100.0 99.2 97.3 98.9 98.8 98.2 96.2 95.6 99.5 97.9 98.7 99.2

97.4 100.0 94.7 93.0 92.0 75.9 94.3 95.6 94.8 98.5 90.7 96.8 92.5 91.7 95.1 92.3 86.9 92.3 98.4 96.5 99.1 97.9

93.6 98.5 92.6 86.6 72.6 71.1 73.5 90.6 88.3 95.2 93.2 94.5 80.3 86.4 91.4 92.5 73.2 88.1 98.0 86.9 93.5 72.5

99.5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 95.2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 99.8 100.0 100.0 100.0 100.0 100.0

92.5 85.6 92.1 86.9 91.4 90.8 99.5 82.2 91.6 94.4 95.9 91.1 96.0 87.7 91.8 95.3 90.9 92.6 96.6 98.6 86.9 99.0

94.2 90.4 95.9 87.7 91.5 90.2 99.3 74.1 85.2 95.8 93.7 81.5 88.8 89.3 91.8 90.0 88.6 90.6 96.8 96.7 91.4 98.4

96.7 94.7 95.8 98.5 98.3 97.6 96.0 95.6 95.2 89.7 98.9 95.3 98.9 94.5 95.2 96.0 90.2 95.8 99.9 98.6 90.9 93.3

84.5 94.2 75.9 76.3 59.2 74.6 70.0 83.9 84.0 80.6 87.6 73.3 61.9 88.2 62.7 88.2 60.3 50.0 37.0 70.3 65.7 68.4

50.0 37.5 33.3 40.0 22.2 46.7 0.0 100.0 50.0 66.7 66.7 30.0 27.8 66.7 16.7 53.8 75.0 13.3 26.7 23.5 31.6 9.1

92.2 97.6 96.7 92.3 68.4 74.0 65.7 91.0 90.3 96.9 93.0 95.7 72.4 87.6 92.0 94.3 70.8 89.0 93.7 81.3 93.8 74.0

82.6 88.6 57.3 85.8 86.4 90.8 91.7 70.2 75.5 72.3 74.9 64.8 78.5 69.9 79.0 77.6 84.5 74.7 76.1 77.5 75.0 75.4

28.3 22.3 16.8 32.6 28.1 17.0 28.7 15.3 18.6 19.7 15.6 12.1 22.0 20.0 20.8 22.8 21.8 23.5 18.3 22.1 24.0 20.4

150


State District Sivganga Madurai Theni Virudhunagar Ramanathpuram Thoothukudi Thirunelveli Kanniyakumari Tripura West Tripura South Tripura Dhalai North Tripura Uttar Pradesh Saharanpur Muzaffarnagar Bijnor Moradabad Rampur Jyotiba Phule Nagar Meerut Baghpat Ghaziabad Gautam Budh Nagar Bulandshahar Aligarh

V1 61.0 68.4 77.9 67.3 86.4 84.8 73.0 86.2

V2 97.7 92.4 95.0 90.5 95.9 96.3 94.5 98.0

V3 95.8 93.8 96.2 98.4 97.9 93.2 91.8 92.8

V4 94.0 95.4 93.3 91.7 97.5 97.8 98.4 98.9

V5 92.7 87.4 81.0 78.2 77.6 74.8 92.2 90.0

V6 100.0 100.0 95.9 100.0 96.5 100.0 100.0 98.7

V7 92.1 77.1 87.9 81.9 92.9 90.6 87.1 86.5

V8 86.9 86.5 76.1 69.5 91.7 98.4 77.3 83.8

V9 94.6 92.9 93.1 93.6 96.1 94.8 91.7 97.6

V10 72.7 73.1 67.2 83.1 69.1 76.5 86.5 82.7

V11 29.2 45.8 29.4 36.1 37.9 33.3 37.9 36.0

V12 92.6 89.5 76.0 85.4 76.1 77.3 95.6 93.4

V13 66.1 67.7 76.5 73.7 67.9 77.8 63.5 64.4

V14 18.5 16.4 16.7 20.4 22.6 40.9 15.2 25.7

47.8 45.9 38.0 41.5

50.6 40.2 37.2 51.1

83.4 69.9 57.3 70.1

66.7 45.8 43.4 52.6

32.7 13.6 34.8 42.0

92.8 77.2 65.5 56.6

67.0 65.6 43.4 38.1

66.9 58.0 39.9 37.4

71.2 66.5 39.2 34.8

66.8 61.8 46.0 60.8

70.6 83.3 57.1 55.2

20.4 11.1 30.1 38.0

34.2 40.8 42.0 47.3

13.1 11.4 4.8 3.8

33.9 45.4 20.4 23.7 16.1 19.6 48.9 53.5 46.9 40.5 44.5 36.4

25.4 32.5 27.5 20.8 15.6 18.5 28.2 34.4 38.6 23.7 25.3 20.2

71.4 72.4 73.5 56.7 55.6 57.7 72.8 78.2 69.8 66.4 72.7 69.3

31.9 31.3 36.5 24.3 23.5 27.2 38.4 32.2 39.3 27.5 29.5 32.1

58.6 88.0 94.2 38.2 67.9 25.0 59.4 86.3 55.6 53.0 87.3 62.0

79.3 79.7 79.1 75.8 69.8 70.2 77.8 72.1 72.3 79.9 78.4 77.5

49.6 44.1 43.1 33.8 35.1 39.0 45.2 38.4 34.2 40.5 34.1 38.8

46.3 39.6 41.5 36.7 35.3 36.6 40.0 37.8 39.5 40.5 40.1 32.2

58.7 49.3 53.8 44.9 50.7 49.3 60.0 47.7 47.0 52.3 46.7 49.4

38.5 33.5 37.7 32.9 35.7 27.1 49.6 38.5 42.2 42.4 34.5 30.9

31.7 5.7 29.3 17.0 9.8 15.3 18.2 25.7 18.4 14.3 11.4 8.2

50.1 81.8 91.0 37.6 63.2 22.6 60.1 84.8 51.7 56.1 83.4 56.6

9.5 4.6 11.1 14.2 13.1 4.1 15.2 13.3 21.6 19.5 9.6 9.6

4.1 2.3 7.0 4.6 8.4 1.9 8.1 10.4 4.4 6.1 4.1 5.1

151


State District Hathras Mathura Agra Firozabad Etah Mainpuri Budaun Bareilly Pilibhit Shahjahanpur Kheri Sitapur Hardoi Unnao Lucknow Rae Bareli Farrukhabad Kannauj Etawah Auraiya Kanpur Kanpur Jalaun Jhansi Lalitpur Hamirpur

V1 27.4 31.8 33.0 36.2 19.3 18.6 14.4 18.0 18.7 15.3 16.8 19.9 17.6 20.6 43.9 27.8 17.6 23.8 27.3 20.9 18.7 31.8 18.3 27.1 23.9 32.0

V2 16.5 20.9 19.5 21.4 8.3 8.2 8.1 16.2 17.9 10.9 14.6 15.6 8.8 15.4 41.2 27.3 9.6 11.6 17.5 13.3 21.8 28.1 19.0 24.8 18.8 32.7

V3 46.7 58.6 54.0 65.4 46.1 31.9 32.0 48.8 57.8 43.6 50.5 58.7 32.8 59.8 83.9 70.4 33.5 44.7 47.3 46.5 32.3 79.8 37.0 73.6 70.1 63.4

V4 29.3 39.9 36.2 25.9 20.0 20.9 10.9 15.4 18.9 8.9 14.6 21.3 13.4 17.4 47.9 24.4 13.3 14.0 26.7 14.5 21.9 40.8 33.5 40.9 33.1 32.0

V5 47.5 51.1 41.1 48.4 66.3 29.1 18.9 55.0 14.0 12.1 13.2 14.2 39.1 17.4 45.6 20.6 18.5 48.8 33.3 39.1 17.1 38.2 24.0 35.7 22.0 18.0

V6 73.6 73.0 78.4 75.0 50.0 74.4 62.5 66.0 71.8 64.5 57.3 60.7 63.9 81.5 82.9 85.3 63.1 71.3 82.8 78.5 80.7 88.0 79.5 94.4 82.7 97.3

152

V7 36.4 30.3 34.5 45.3 26.3 42.3 23.9 38.2 34.5 27.8 20.9 24.6 33.3 44.5 69.5 45.4 44.2 36.1 44.5 47.6 48.7 61.3 39.5 46.5 31.0 50.7

V8 38.8 28.9 29.9 35.8 16.7 44.5 17.8 38.2 33.0 27.0 20.9 24.2 30.1 47.1 67.1 48.1 21.9 38.5 45.7 43.4 46.7 52.6 39.7 42.3 30.5 52.0

V9 50.4 30.9 39.7 41.2 24.4 44.5 33.2 43.6 44.1 40.0 30.0 31.3 41.6 49.6 65.9 53.7 34.5 44.3 49.1 54.9 64.0 76.0 50.3 63.4 51.8 60.0

V10 31.4 18.7 26.3 32.4 17.6 30.4 21.1 30.0 26.0 18.1 20.9 18.8 26.4 37.1 38.2 42.8 21.1 30.0 36.3 32.6 53.5 55.3 30.7 41.3 44.0 33.9

V11 21.7 9.4 14.4 13.7 9.6 9.7 11.3 19.8 19.2 14.8 22.1 10.3 20.2 22.9 38.7 44.3 23.7 23.7 12.5 21.3 23.9 30.0 13.1 33.3 27.3 26.3

V12 47.8 50.3 45.9 45.5 65.0 27.4 14.3 51.7 13.8 9.5 15.0 17.7 41.4 21.5 46.6 23.9 15.5 46.2 34.3 40.9 18.7 38.2 22.6 34.0 22.7 30.6

V13 9.8 19.4 8.6 7.2 6.4 19.8 4.2 8.4 8.0 4.9 11.9 13.1 18.8 15.3 24.8 20.0 8.1 6.4 11.4 10.1 31.9 32.5 29.3 40.9 33.5 38.1

V14 8.1 6.7 4.8 1.6 1.7 12.0 2.8 4.6 6.6 4.1 19.5 4.1 8.4 3.7 7.3 26.0 2.6 4.4 11.1 7.4 16.4 19.0 20.6 5.3 28.4 9.2


State District Mahoba Banda Chitrakoot Fatehpur Pratapgarh Kaushambi Allahabad Barabanki Faizabad Ambedaker Nagar Sultanpur Bahraich Shrawasti Balrampur Gonda Siddharthnagar Basti Sant Kabir Nagar Maharajganj Gorakhpur Kushinagar Deoria Azamgarh Mau Ballia Jaunpur

V1 20.4 23.6 18.6 23.6 40.5 26.0 30.2 21.2 28.0 22.1 38.5 11.4 11.4 11.4 16.7 13.6 24.4 16.9 30.2 42.1 30.8 39.3 30.1 27.7 29.1 27.9

V2 16.3 21.7 25.2 16.0 29.8 20.0 27.1 24.3 28.7 24.2 28.2 14.5 18.8 23.9 17.3 21.2 27.9 24.7 29.2 43.1 33.9 46.1 35.1 34.8 42.3 26.5

V3 69.5 57.1 57.7 46.5 84.6 48.2 53.1 60.4 77.3 79.3 82.4 58.4 58.6 66.3 71.3 75.5 84.7 83.6 81.5 85.9 80.4 93.2 84.4 90.0 82.7 85.4

V4 44.3 17.6 18.1 15.4 29.4 14.5 25.1 21.1 29.2 30.9 36.8 7.1 11.2 8.3 19.6 9.8 28.2 26.0 15.1 31.1 26.4 41.3 49.0 39.8 34.9 32.6

V5 39.8 19.7 18.6 27.7 21.7 31.4 34.0 17.1 28.2 25.5 15.1 5.8 6.4 6.2 11.5 6.1 15.1 10.4 39.7 34.0 53.8 61.0 23.3 26.1 23.7 14.0

V6 90.0 61.1 71.2 80.4 86.4 62.8 57.7 69.1 75.0 84.1 86.3 67.3 61.3 54.7 64.4 71.9 86.0 78.5 85.9 92.6 80.6 95.2 79.2 84.1 79.4 74.5

153

V7 44.5 34.6 32.4 28.8 61.9 24.9 39.8 39.4 50.8 59.5 55.1 21.9 20.3 23.9 28.9 46.7 59.6 54.0 49.7 68.6 46.9 77.6 46.4 52.6 55.1 53.9

V8 42.2 27.2 31.4 31.4 63.3 24.0 32.9 41.6 51.1 54.8 55.1 21.9 19.4 24.9 28.9 47.2 59.2 54.6 47.8 62.5 49.2 73.6 48.8 51.9 56.3 53.2

V9 59.1 36.2 43.2 47.7 63.3 35.0 42.2 38.8 54.1 64.0 59.2 29.2 30.2 24.3 32.0 49.2 65.9 57.1 49.7 64.2 46.2 64.0 55.3 64.7 64.7 48.1

V10 43.1 27.1 27.5 31.4 46.6 25.1 36.9 27.7 43.4 45.2 41.4 16.8 17.7 14.9 22.3 33.0 43.2 39.2 36.1 44.5 33.0 44.6 29.5 25.6 22.6 40.5

V11 14.3 34.4 26.8 21.4 25.6 19.0 26.6 27.7 26.8 14.1 15.4 9.5 15.0 10.5 21.0 12.2 15.9 18.0 13.9 25.2 11.0 18.2 10.8 20.0 11.4 10.7

V12 44.6 22.4 20.0 32.4 19.1 36.4 36.4 21.3 30.1 21.6 16.5 6.0 7.0 5.8 10.4 5.3 13.7 7.8 41.9 31.8 53.8 60.3 19.9 22.4 21.9 12.6

V13 41.1 20.1 28.3 15.2 20.2 10.4 29.4 18.9 14.5 16.5 15.1 6.0 4.6 6.3 4.8 10.0 13.3 6.7 25.3 29.6 19.2 24.7 15.7 16.2 14.4 15.2

V14 6.4 6.8 9.8 3.2 16.3 1.4 10.2 24.1 6.2 6.4 5.7 2.8 1.7 2.2 4.0 6.4 9.1 6.3 14.7 25.0 10.5 10.2 10.1 6.8 6.3 5.8


State District Ghazipur Chandauli Varanasi Sant Ravidas Nagar Mirzapur Sonbhadra Uttarakhand Uttarkashi Chamoli Rudraprayag Tehri Garhwal Dehradun Garhwal Pithoragarh Bageshwar Almora Champawat Nainital Udham Singh Nagar Haridwar West Bengal Darjiling Jalpaiguri Koch Behar Uttar Dinajpur Dakshin Dinajpur

V1 29.8 19.7 32.1 21.7 19.1 13.8

V2 28.7 17.1 22.7 16.6 18.4 15.0

V3 80.2 52.7 86.3 64.4 62.7 68.0

V4 30.1 33.8 54.1 26.1 25.5 21.4

V5 14.9 23.0 40.4 17.8 29.8 27.6

V6 75.1 74.1 88.1 66.7 56.2 71.0

V7 46.5 35.6 65.2 32.0 32.9 49.7

V8 46.8 37.8 65.2 36.8 32.0 51.0

V9 57.3 45.9 64.2 38.3 33.3 47.7

V10 35.5 36.7 48.1 30.1 29.4 29.5

V11 13.5 22.2 32.8 24.7 18.8 22.6

V12 14.1 26.2 39.0 18.5 38.3 42.5

V13 27.1 21.7 12.6 13.8 13.4 18.4

V14 15.4 26.1 4.1 10.2 3.8 9.4

23.0 42.4 31.4 32.4 50.5 34.6 34.2 21.1 27.8 26.7 30.3 37.3 37.4

22.2 42.5 37.3 33.6 52.4 34.3 29.3 20.1 27.4 23.4 31.6 37.1 31.0

37.4 66.1 60.0 49.1 70.3 54.2 52.9 37.5 48.5 50.9 48.8 56.1 59.4

24.3 31.7 20.9 25.1 54.7 29.5 24.3 17.0 24.4 23.2 31.9 38.8 36.9

21.7 38.8 21.3 19.5 52.6 28.3 28.4 18.2 24.8 19.1 36.0 35.8 37.7

91.1 96.1 95.4 94.9 94.8 89.5 94.9 85.9 99.0 87.8 87.9 93.4 84.6

76.6 67.9 76.0 85.4 78.2 75.4 72.8 63.5 81.5 69.9 73.8 76.0 61.6

72.5 66.8 78.1 83.4 77.1 75.0 75.6 65.5 78.0 70.7 66.7 80.7 57.2

79.0 95.1 91.8 83.6 84.8 84.6 88.8 72.6 94.9 74.4 78.2 84.6 71.8

66.5 86.7 76.5 69.4 64.8 72.2 77.6 57.6 67.3 59.9 62.3 76.0 58.7

76.2 44.8 44.7 36.6 44.2 29.4 41.7 33.7 73.3 40.6 57.1 71.4 34.9

20.6 40.2 23.1 21.1 51.1 31.8 33.3 18.9 25.4 21.2 37.6 35.5 35.3

79.8 75.6 80.5 73.1 49.3 75.5 73.1 50.5 78.7 57.9 66.4 58.6 37.4

34.2 52.4 42.2 32.4 47.4 30.1 44.7 20.1 34.1 50.1 26.0 31.8 31.5

47.0 40.3 41.0 39.2 40.7

70.4 72.9 55.6 53.3 78.1

97.9 97.8 96.0 89.2 96.1

72.5 48.4 46.4 27.6 40.4

72.7 52.9 40.9 65.6 43.5

100.0 98.4 97.9 87.6 98.7

89.0 83.2 87.9 69.0 92.6

90.5 90.3 84.8 64.3 94.3

87.6 93.0 87.2 61.2 89.4

78.5 81.0 80.2 61.3 87.1

63.0 47.6 63.4 34.4 47.6

70.3 50.4 37.2 64.7 43.9

42.2 36.1 35.0 26.0 44.8

18.0 20.7 17.1 23.8 29.4

154


State District Maldah Murshidabad Birbhum Barddhaman Nadia North 24 Pargnas Hugli Bankura Puruliya Pachim Medinipur Haora Kolkata South 24 Parganas Purab Medinipur Source:

V1 41.0 47.5 36.9 50.9 45.9 52.1 65.5 33.6 32.3 33.2 55.2 63.0 36.1 38.8

V2 58.4 63.5 59.1 68.1 76.3 85.1 81.1 65.7 66.1 48.0 80.4 91.8 67.5 74.9

V3 94.0 97.6 90.7 95.3 98.4 98.4 98.7 93.7 91.5 88.6 98.5 97.6 99.0 98.4

V4 28.5 41.6 48.7 57.9 70.0 62.8 80.2 61.4 39.9 45.1 65.8 87.6 36.4 40.8

V5 45.7 44.7 41.8 54.2 68.4 70.0 75.6 54.5 48.7 49.5 77.5 70.9 38.4 51.3

V6 97.3 94.1 94.1 91.9 96.7 96.7 97.4 100.0 96.1 99.0 98.8 98.2 95.4 98.1

IIPS (2010)

155

V7 75.0 74.1 76.7 73.2 96.7 89.7 97.4 96.6 82.8 93.1 79.4 81.2 76.7 89.5

V8 76.9 71.7 79.0 74.7 92.8 90.8 97.4 96.6 81.6 91.0 79.0 81.2 80.1 89.8

V9 76.4 77.4 74.3 79.3 96.1 85.6 97.4 92.7 85.2 91.0 78.1 82.0 81.0 91.7

V10 70.5 68.3 75.5 76.5 84.8 82.3 89.0 87.9 79.7 84.0 71.9 74.1 82.0 91.0

V11 23.8 35.8 48.1 54.5 42.9 46.2 35.7 46.7 50.0 51.2 50.0 42.9 30.4 62.1

V12 41.6 42.1 39.8 55.2 65.3 65.0 76.2 56.0 47.3 53.3 76.4 79.1 39.6 55.2

V13 42.6 41.5 48.3 42.4 52.7 32.9 25.1 40.7 44.8 41.0 31.3 31.1 30.9 43.0

V14 44.8 31.0 29.8 24.4 25.1 13.8 18.1 42.1 47.4 29.1 12.6 26.7 8.5 25.1


156


Appendix Table 3.2 Coverage rates of different health interventions across districts of India (Rural population) State District V1 Andaman & Nicobar Islands Andamans 46.8 Nicobars 48.6 Andhra Pradesh Adilabad 53.0 Nizamabad 64.5 Karimnagar 67.2 Medak 70.5 Hyderabad Rangareddi 49.6 Mahbubnagar 64.5 Nalgonda 82.7 Warangal 62.5 Khammam 75.9 Srikakulam 46.8 Vizianagaram 59.6 Visakhapatnam 78.6 East Godavari 70.3 West Godavari 69.9 Krishna 85.7 Guntur 60.4 Prakasam 65.5 Nellore 62.6

V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

V14

77.2 85.7

92.7 94.9

67.7 87.6

66.7 73.1

98.5 87.0

93.8 83.3

90.8 78.3

90.8 87.0

77.8 85.9

50.0 100.0

70.1 82.5

71.3 87.7

57.2 65.6

76.4 80.3 94.3 83.3

82.2 94.7 95.9 98.8

37.6 63.8 72.4 73.4

31.6 68.6 73.0 80.3

91.2 97.1 100.0 100.0

72.1 88.2 79.4 77.3

68.1 91.2 69.7 80.0

70.6 91.4 90.9 82.2

46.5 78.3 90.1 76.0

39.1 37.5 66.7 36.4

30.5 72.9 73.7 69.5

38.6 55.3 60.7 42.3

23.0 37.5 39.3 39.6

91.3 84.5 95.7 86.3 98.3 77.4 86.3 93.3 95.1 90.8 97.5 81.1 99.1 94.0

95.2 96.1 97.8 94.6 98.3 84.2 92.2 95.6 98.4 91.4 96.8 94.3 98.3 92.5

84.3 76.8 85.7 66.1 78.7 54.0 64.4 72.6 72.0 87.0 81.4 73.6 82.9 54.1

92.1 80.6 90.6 72.0 95.4 59.6 73.7 70.6 83.1 81.0 77.6 78.6 88.9 72.9

100.0 96.2 93.2 100.0 98.5 97.6 100.0 98.7 100.0 95.5 98.0 100.0 100.0 98.0

81.4 71.2 93.2 72.6 87.7 77.8 80.5 90.7 93.4 70.5 91.8 87.0 93.0 71.4

67.4 77.4 93.3 71.0 92.3 64.2 84.4 86.7 80.3 77.3 93.9 64.8 97.7 65.3

88.4 92.5 93.2 95.2 95.5 82.7 89.6 97.3 100.0 88.6 93.9 92.6 90.9 89.8

75.0 72.2 90.5 87.8 89.3 79.3 80.1 83.3 88.3 74.2 81.0 79.4 87.0 84.7

19.0 50.0 80.0 12.5 81.3 22.7 50.0 14.3 38.5 20.0 52.9 23.5 0.0 50.0

91.3 76.5 91.9 74.8 92.1 61.9 72.2 75.0 82.9 82.9 84.8 77.5 85.7 68.9

68.1 51.5 33.3 58.6 53.3 51.8 41.8 48.3 31.1 53.7 25.4 40.4 47.1 53.6

55.6 31.9 23.3 42.4 32.3 47.8 23.3 29.3 38.7 31.4 16.1 38.2 33.7 18.3

157


State District Cuddapah Kurnool Anantapur Chittoor Arunachal Pradesh Tawang West Kameng East Kameng Papum Lower Subansiri Upper Subansiri West Siang East Siang Upper Siang Dibang Valley Lohit Changlang Tirap Kurung Kumey Lower Dibang Valley Anjaw Assam Kokrajhar Dhubri Goalpara Bongaigaon

V1 51.4 52.1 80.5 62.5

V2 74.0 82.3 98.3 95.9

V3 79.4 83.6 93.1 97.9

V4 46.6 51.0 79.5 82.1

V5 56.5 66.1 75.2 91.0

V6 81.6 96.7 100.0 100.0

V7 76.3 76.7 96.6 81.1

V8 60.5 58.3 89.7 78.9

V9 68.4 78.7 79.3 97.4

V10 59.4 79.2 66.2 84.1

V11 42.9 69.6 25.0 50.0

V12 55.2 67.5 69.1 89.7

V13 60.0 62.7 35.1 54.2

V14 39.2 34.1 41.0 41.7

43.2 53.4 51.3 7.9 30.9 40.0 16.5 24.3 42.0 44.1 38.8 19.5 10.2 33.7 47.9 22.4

52.5 62.2 40.1 10.5 41.5 52.4 60.3 30.9 64.3 49.6 40.8 39.0 13.4 44.9 53.0 35.2

53.1 70.9 74.6 42.9 58.6 53.1 66.9 63.9 69.0 70.3 55.7 49.4 34.5 52.2 62.1 38.7

34.0 43.9 57.4 20.0 48.2 54.5 56.1 41.1 63.6 57.7 35.5 42.9 13.2 52.2 40.2 41.1

32.7 43.2 44.2 15.7 31.3 42.1 42.9 31.7 48.3 37.5 31.5 20.8 9.7 37.8 35.3 23.6

60.0 83.8 87.2 53.8 62.5 54.2 86.2 96.8 58.5 80.0 85.0 60.0 60.0 21.4 86.1 64.3

20.0 21.6 17.9 16.7 15.0 29.2 25.0 22.6 7.3 25.0 30.0 60.0 12.2 28.6 11.1 21.4

50.0 73.0 69.2 50.0 42.5 37.5 67.9 19.4 50.0 50.0 60.0 70.0 26.5 14.3 50.0 50.0

40.0 24.3 30.8 30.8 27.5 33.3 35.7 71.0 17.5 40.0 52.5 60.0 40.0 7.1 48.6 26.7

26.6 52.9 68.4 39.1 37.6 40.8 60.2 50.0 49.4 31.7 43.0 27.3 18.0 27.9 47.1 33.9

50.0 100.0 100.0 4.5 63.6 100.0 0.0 72.7 100.0 20.0 66.7 100.0 70.0 60.0 85.7 100.0

21.5 40.2 43.5 20.0 27.5 31.9 43.4 39.5 44.6 38.6 37.9 32.4 19.5 29.4 37.0 30.0

39.3 52.3 21.4 47.0 52.4 16.7 23.6 29.4 39.1 44.6 35.9 37.8 37.2 35.3 36.0 40.0

51.5 43.3 66.8 12.7 45.2 52.3 15.6 34.6 19.7 44.5 33.9 23.9 16.4 34.9 41.3 28.3

41.4 43.3 35.4 39.6

48.2 52.0 33.1 51.9

73.8 69.6 67.5 84.6

26.1 38.3 25.0 30.7

21.6 34.9 22.9 28.9

90.6 90.6 91.2 82.5

65.9 69.8 66.7 63.6

71.4 69.8 59.6 63.4

70.6 71.7 65.5 51.9

56.4 66.7 45.6 24.6

55.0 0.0 30.8 32.1

20.9 42.8 25.3 21.2

56.4 88.8 83.9 33.7

38.9 40.3 37.2 19.1

158


State District Barpeta Kamrup Nalbari Darrang Marigaon Nagaon Sonitpur Lakhimpur Dhemaji Tinsukia Dibrugarh Sibsagar Jorhat Golaghat Karbi Anglong North Cachar Hills Cachar Karimganj Hailakandi Chirang Baska Kamrup Metro Udalguri Bihar Pashchim Champaran Purba Champaran

V1 28.9 43.7 27.1 17.2 50.8 41.9 36.6 42.7 46.5 40.4 49.7 33.1 36.3 20.3 23.6 32.3 40.3 44.0 33.6 49.7 40.5 44.4 46.2

V2 34.3 39.5 28.5 19.1 54.0 33.3 41.6 53.2 54.6 56.1 53.4 37.6 42.8 25.4 36.0 45.0 46.6 48.9 22.4 57.9 49.2 50.8 52.7

V3 56.5 75.9 45.3 37.4 78.3 65.5 55.5 86.9 85.8 78.9 72.1 53.4 80.3 36.7 51.1 62.6 68.5 77.5 49.3 74.5 80.3 64.9 76.4

V4 29.4 34.3 30.5 13.4 47.4 28.1 39.3 20.1 43.5 40.4 53.7 33.6 19.6 30.3 40.2 29.5 26.5 51.1 22.6 49.8 38.5 40.2 31.9

V5 26.9 22.6 19.4 12.3 46.1 14.5 32.1 21.7 23.8 33.3 47.6 34.4 24.9 33.1 34.4 24.3 21.9 47.1 24.7 35.9 40.5 35.7 15.5

V6 85.4 87.2 76.6 73.6 99.0 67.9 86.1 78.8 90.7 100.0 92.7 92.9 74.7 67.8 79.5 83.9 82.6 94.4 55.3 96.3 96.8 86.0 81.8

V7 70.8 66.7 57.3 55.4 94.1 54.5 70.9 47.7 76.7 81.8 87.8 85.7 50.0 59.1 60.7 73.2 53.2 85.6 39.5 80.2 79.3 66.7 63.6

V8 60.4 67.9 48.5 38.0 90.1 50.0 62.5 45.5 73.3 36.4 85.4 85.7 50.0 42.2 57.3 61.0 56.0 78.7 44.7 79.0 65.6 58.1 60.6

V9 70.8 79.5 57.3 42.1 96.0 48.2 71.3 47.4 82.6 100.0 82.9 85.7 48.1 50.9 68.6 66.7 58.7 83.1 47.4 76.5 62.4 62.4 68.2

V10 59.7 51.5 38.0 32.7 66.7 40.1 60.6 20.3 67.9 58.6 62.8 80.4 19.0 51.8 45.2 55.1 51.6 69.4 51.8 61.6 57.8 50.2 47.8

V11 35.7 33.3 42.2 38.5 33.3 100.0 26.7 30.0 57.1 0.0 14.3 50.0 19.8 71.4 29.2 40.0 0.0 30.0 20.0 53.8 57.1 20.8 50.0

V12 29.7 24.0 18.6 13.4 48.2 17.8 36.1 15.2 28.2 36.6 42.7 33.8 24.7 35.0 35.7 24.5 19.9 51.8 21.9 36.6 42.9 33.5 23.0

V13 76.1 91.0 83.3 70.9 79.9 84.0 73.7 16.5 85.7 59.5 73.2 76.6 35.4 73.3 70.1 68.5 61.4 80.0 22.4 61.7 87.2 70.4 72.5

V14 30.2 33.5 36.0 33.1 52.2 33.4 44.9 8.7 34.1 47.1 63.5 31.9 12.4 30.2 49.1 43.9 33.6 37.8 26.3 39.1 38.0 39.2 44.9

29.6 20.4

41.9 19.4

70.9 43.3

12.6 29.5

15.4 20.5

82.4 92.8

45.1 77.1

49.8 76.4

45.5 77.1

45.6 70.4

12.5 23.9

16.4 21.9

19.8 13.7

2.5 18.6

159


State District Sheohar Sitamarhi Madhubani Supaul Araria Kishanganj Purnia Katihar Madhepura Saharsa Darbhanga Muzaffarpur Gopalganj Siwan Saran Vaishali Samastipur Begusarai Khagaria Bhagalpur Banka Munger Lakhisarai Sheikhpura Nalanda Patna

V1 26.5 25.6 20.2 18.6 21.7 17.7 18.2 32.1 23.3 27.1 25.6 19.7 32.8 25.5 19.7 23.2 30.2 35.7 18.3 22.3 16.2 17.8 22.9 23.0 17.6 26.2

V2 32.3 29.0 16.2 16.9 18.2 27.6 21.3 32.5 26.5 31.7 24.1 30.8 26.7 26.1 24.6 19.3 34.3 36.0 19.0 21.5 22.5 37.1 14.5 34.6 16.4 21.1

V3 56.3 76.2 38.8 36.7 37.5 71.3 35.1 74.0 46.3 54.5 56.8 61.1 78.8 55.1 45.3 51.8 75.9 79.0 52.7 47.7 41.3 70.1 31.2 75.6 47.5 45.7

V4 23.6 26.4 25.4 38.9 45.0 14.0 15.3 35.0 13.8 42.8 42.8 9.8 24.3 15.1 31.1 17.3 16.0 43.9 21.1 34.6 30.2 23.4 53.6 26.5 19.8 45.3

V5 24.5 37.9 18.4 21.6 23.9 37.9 18.2 28.4 18.7 31.5 31.9 12.9 27.9 35.8 19.2 14.6 10.2 47.2 24.5 24.4 22.7 8.4 21.6 36.4 18.2 24.9

V6 76.6 81.9 92.6 78.0 70.3 93.1 74.2 93.9 56.1 88.8 67.7 81.6 88.2 61.5 72.8 85.2 84.9 87.8 91.6 91.3 87.4 73.3 87.9 79.6 79.1 85.2

160

V7 48.2 49.4 66.9 51.7 46.3 53.8 47.5 64.3 27.2 52.8 36.0 49.5 61.1 29.3 46.2 61.6 60.1 52.0 66.7 63.6 62.5 37.0 52.2 46.3 50.5 57.5

V8 52.6 54.4 66.9 50.3 42.6 55.0 43.6 69.6 30.0 59.2 37.5 50.3 62.9 31.7 48.4 64.6 56.6 63.3 68.7 67.1 58.1 43.0 44.6 48.2 50.0 55.5

V9 49.6 57.1 77.9 55.1 45.7 57.2 49.7 70.4 34.4 64.2 33.5 44.7 61.1 35.9 50.5 52.1 51.6 67.3 67.9 70.2 57.5 39.4 48.4 46.5 51.6 59.4

V10 49.7 58.5 70.3 52.1 35.9 62.4 45.7 54.9 28.1 66.8 29.0 49.3 67.6 50.7 39.9 37.1 47.7 69.8 50.7 57.3 51.6 24.6 47.6 45.6 41.9 46.7

V11 25.0 22.4 40.0 34.8 28.7 19.6 14.6 19.5 18.3 14.3 18.1 33.3 11.3 15.4 25.6 18.9 6.6 19.0 20.0 46.6 16.2 16.1 31.8 15.0 6.9 10.1

V12 29.0 37.3 27.3 25.1 22.5 36.6 16.3 30.1 21.1 29.9 28.2 11.1 24.3 35.6 21.5 14.1 13.0 42.8 22.7 26.3 24.4 8.7 25.4 36.4 18.9 24.9

V13 15.9 9.8 22.4 21.0 21.8 17.0 21.7 11.4 16.3 21.7 31.0 14.6 11.7 12.2 14.3 10.3 10.1 21.7 15.8 33.3 10.7 8.9 23.6 7.0 11.6 15.7

V14 2.9 2.4 38.9 31.3 12.8 3.2 6.0 0.8 23.4 9.0 9.6 12.2 4.1 4.9 17.8 23.2 3.8 12.2 2.9 17.3 7.7 6.0 17.3 3.9 8.1 17.3


State District Bhojpur Buxar Kaimur Rohtas Jehanabad Aurangabad Gaya Nawada Jamui Chandigarh Chandigarh Chhattisgarh Koriya Surguja Jashpur Raigarh Korba Janjgir-Champa Bilaspur Kawardha Rajnandgaon Durg Raipur Mahasamund Dhamtari Kanker

V1 20.0 17.5 28.2 43.7 12.1 20.9 26.7 17.1 14.1

V2 13.5 22.6 21.2 43.4 17.6 24.2 32.3 20.5 15.2

V3 38.1 86.0 62.7 84.6 64.2 65.1 84.1 40.3 34.9

V4 18.3 26.9 20.3 41.7 11.5 14.7 33.6 22.1 26.3

V5 8.9 33.2 28.2 45.2 8.3 15.2 38.0 28.0 26.7

V6 89.1 88.4 92.3 78.3 72.4 82.2 92.6 84.3 91.9

V7 57.3 62.1 81.4 53.7 41.1 40.6 69.4 51.6 75.2

V8 53.1 64.7 78.9 54.9 46.7 44.1 75.0 52.4 74.5

V9 62.3 56.3 75.8 53.1 36.4 48.0 69.4 52.2 71.4

V10 53.1 63.3 46.1 51.8 29.4 43.3 70.4 36.7 64.8

V11 21.3 18.7 24.1 25.9 11.3 21.8 25.0 6.7 51.7

V12 9.0 33.1 27.4 42.5 8.7 17.5 35.4 26.3 28.8

V13 15.3 11.2 16.3 11.4 7.3 11.7 13.9 13.1 35.5

V14 29.4 4.7 13.4 9.7 26.0 0.2 1.7 3.8 37.8

58.1

66.7

80.6

51.6

71.0

100.0

100.0

60.0

60.0

28.6

100.0

68.4

42.1

7.6

26.8 30.5 42.3 51.1 45.0 36.7 31.0 35.0 19.3 25.2 28.2 45.6 43.5 36.4

49.5 46.2 52.8 68.3 61.9 43.6 29.5 61.1 47.3 30.7 32.1 61.2 49.8 47.4

77.4 79.3 77.1 88.7 87.2 78.5 58.9 90.8 72.6 59.2 65.9 89.2 82.3 73.5

14.0 8.4 15.0 19.0 10.6 17.2 14.0 11.2 6.6 9.2 7.8 22.7 19.9 13.2

36.9 47.3 29.7 56.7 42.2 27.9 21.9 40.8 44.7 14.3 14.3 47.3 36.2 34.4

94.6 97.2 95.7 97.8 97.0 92.8 95.0 95.8 92.2 89.8 88.7 97.6 98.5 96.9

65.6 72.9 67.6 71.0 74.2 62.7 60.3 78.9 73.9 52.3 67.3 90.4 63.1 65.3

68.7 65.4 69.0 76.1 77.3 74.7 65.3 78.9 69.9 58.0 62.3 89.0 63.1 69.1

86.9 77.8 93.0 83.9 89.4 71.1 77.7 90.5 73.7 62.5 69.2 84.1 83.1 86.6

64.7 60.0 78.4 67.3 73.3 59.2 62.0 78.7 54.7 50.7 54.6 68.9 59.0 62.4

20.0 33.3 22.2 27.8 59.1 41.7 23.3 40.0 28.9 20.0 18.5 47.1 50.0 37.5

39.7 46.7 37.4 56.4 53.4 34.8 20.6 48.4 37.1 16.8 13.8 50.2 34.1 40.9

66.1 39.0 68.7 79.1 59.0 33.1 47.4 68.7 48.9 29.4 25.8 45.9 39.2 54.3

72.3 35.8 75.0 49.1 42.1 43.8 44.1 51.7 28.0 35.4 22.8 48.2 43.1 51.6

161


State District Bastar Dantewada Dadra & Nagar Haveli Dadra Nagar Haveli Daman & Diu Diu Daman Delhi North Delhi North West Delhi North East Delhi East Delhi New Delhi Central Delhi West Delhi South West Delhi South Delhi Goa North Goa South Goa Gujarat Kachchh Banas Kantha Patan Mahesana Sabar Kantha

V1 39.3 27.8

V2 62.1 26.7

V3 86.9 61.2

V4 10.8 15.5

V5 38.9 20.6

V6 93.3 92.0

V7 73.3 50.7

V8 77.8 50.7

V9 77.8 65.9

V10 77.2 58.5

V11 55.6 33.3

V12 44.8 21.3

V13 46.2 47.0

V14 37.5 37.3

48.8

58.2

63.9

36.0

49.1

96.3

66.3

67.5

81.9

60.9

45.7

47.3

51.6

18.8

87.4 69.2

89.3 78.8

95.5 93.0

89.3 29.3

88.9 64.1

100.0 100.0

98.1 94.6

94.4 94.6

87.0 94.6

71.9 83.3

42.9 32.4

91.2 72.3

58.2 19.2

17.3 30.7

55.6 23.8 66.7 63.9

22.2 33.3 66.7 63.9

55.6 52.4 86.7 89.2

22.2 23.8 66.7 69.4

55.6 23.8 73.3 81.1

0.0 50.0 100.0 91.7

0.0 0.0 87.5 69.2

0.0 0.0 75.0 53.8

0.0 0.0 100.0 83.3

0.0 33.3 45.8 43.5

50.0 46.2 27.3 50.0

50.0 32.1 91.9 84.0

14.3 16.7 35.7 51.4

0.0 4.0 11.8 0.0

53.3 59.6 61.5

63.3 69.6 38.5

100.0 95.7 92.3

56.7 72.3 15.4

73.3 89.1 38.5

92.3 100.0 100.0

84.6 100.0 50.0

92.3 81.3 50.0

92.3 88.2 100.0

52.4 66.7 70.0

33.3 30.0 20.0

76.3 88.1 40.0

25.0 41.0 35.7

20.0 16.7 14.1

95.4 86.7

99.1 97.6

100.0 100.0

99.1 97.6

99.1 100.0

100.0 100.0

90.0 93.1

90.0 93.1

100.0 100.0

89.7 88.7

71.4 37.5

100.0 100.0

52.5 87.8

30.3 18.2

33.3 62.6 60.8 25.9 56.2

30.6 64.5 63.8 28.0 60.3

42.4 83.2 83.1 44.6 74.6

36.1 48.6 74.5 60.5 36.8

50.0 50.9 67.0 42.1 44.6

100.0 90.1 95.0 78.1 97.2

81.8 80.6 85.0 58.1 70.0

70.0 62.5 75.0 41.5 64.3

80.0 66.7 76.7 61.9 84.3

34.8 57.1 60.4 42.5 63.8

66.7 25.6 34.8 34.2 29.2

47.1 53.7 70.8 44.4 40.0

45.5 42.2 52.3 50.3 61.2

12.8 17.6 21.9 29.8 28.9

162


State District Gandhinagar Ahmadabad Surendranagar Rajkot Jamnagar Porbandar Junagarh Amreli Bhavnagar Anand Kheda Panch Dohad Vadodara Narmada Bharuch Surat The Dangs Navsari Valsad Haryana Panchkula Ambala Yamunanagar Kurukshetra Kaithal

V1 47.8 42.6 56.8 69.1 63.9 39.2 63.8 51.4 34.4 74.6 41.9 54.6 70.2 71.4 42.9 87.0 30.2 20.7 37.1 49.0

V2 50.0 48.5 61.6 73.2 71.2 38.1 63.1 53.7 42.5 82.7 51.6 52.6 65.3 71.4 44.6 81.5 31.6 19.4 32.9 57.8

V3 71.8 67.9 66.5 85.4 82.4 49.1 75.1 65.6 59.1 90.6 66.1 68.8 81.5 84.8 64.1 85.9 42.9 34.5 48.8 69.6

V4 48.3 59.4 68.9 61.8 48.6 52.1 66.5 81.8 23.6 80.6 48.9 57.0 53.2 57.1 57.8 56.5 38.1 9.5 38.3 64.3

V5 44.1 47.4 67.8 61.2 63.4 51.3 63.8 74.3 40.7 85.6 47.0 67.2 64.5 65.9 55.4 69.6 46.9 17.3 41.9 55.7

V6 86.6 77.4 93.3 93.3 93.8 78.6 96.5 93.1 97.1 92.6 89.7 97.6 95.3 95.5 83.3 96.6 74.7 71.9 91.2 85.5

V7 68.2 52.8 78.3 69.5 82.8 68.0 68.4 93.0 75.5 88.5 67.0 89.3 93.0 90.9 64.9 93.1 69.3 47.8 61.4 63.8

V8 62.1 43.4 73.3 73.3 73.4 55.3 59.6 77.5 70.6 85.2 53.1 81.0 90.7 84.1 53.1 93.1 50.7 42.7 59.6 61.8

V9 77.3 51.3 88.3 70.0 81.5 62.1 80.7 90.1 81.6 92.6 64.9 82.1 81.4 67.4 69.8 96.6 66.2 56.2 69.0 78.3

V10 54.6 31.3 64.8 79.4 61.1 45.0 71.1 79.2 63.1 75.3 50.5 66.5 64.8 60.2 41.7 71.2 43.2 49.3 54.7 57.7

V11 63.6 31.7 58.8 55.6 55.6 19.7 22.5 34.8 64.1 50.0 37.7 47.8 9.1 70.0 38.7 88.9 8.3 38.9 41.4 40.0

V12 47.1 46.2 70.9 60.4 61.1 52.0 75.1 72.0 36.6 84.0 54.2 68.1 66.4 66.9 58.6 72.9 43.9 20.3 37.6 64.9

V13 43.9 29.5 51.8 45.2 41.5 49.0 37.6 55.9 57.6 50.4 41.9 58.5 51.8 52.0 32.0 39.5 36.6 47.0 55.7 72.9

V14 32.8 20.1 33.1 28.1 30.4 28.3 16.5 23.9 49.9 25.7 24.0 13.2 39.9 22.9 22.6 35.6 33.3 54.2 24.0 34.9

49.4 48.4 33.9 53.2 54.5

44.8 47.2 34.4 37.8 56.0

77.3 93.4 71.7 90.6 92.1

52.3 35.3 31.1 47.2 41.5

65.1 40.5 27.2 34.7 31.4

93.0 87.5 68.5 93.2 87.7

79.1 65.9 43.6 76.3 69.1

73.8 69.0 43.6 76.3 69.1

88.1 70.1 48.1 79.7 74.1

60.6 43.9 18.7 35.9 42.3

52.9 21.3 27.5 13.8 21.9

71.0 43.3 24.7 32.1 34.7

21.7 26.6 10.5 8.6 14.9

12.2 12.8 4.3 10.5 5.5

163


State District Karnal Panipat Sonipat Jind Fatehabad Sirsa Hisar Bhiwani Rohtak Jhajjar Mahendragarh Rewari Gurgaon Faridabad Mewat Himachal Pradesh Chamba Kangra Lahul & Spiti Kullu Mandi Hamirpur Una Bilaspur Solan Sirmaur

V1 57.8 53.8 54.5 60.6 61.5 60.1 54.5 16.3 64.5 66.1 64.8 67.2 55.0 68.3 64.4

V2 43.1 55.3 54.2 59.5 57.5 56.7 52.0 15.9 66.2 48.1 70.1 60.0 51.9 53.5 50.8

V3 90.4 93.7 91.9 92.6 86.3 87.9 94.5 50.5 87.3 86.7 95.2 95.3 91.1 94.7 85.6

V4 42.7 49.6 35.6 41.1 46.7 60.7 54.7 14.0 57.2 38.8 61.4 42.1 44.1 48.9 43.9

V5 33.0 41.0 41.1 38.4 55.3 61.5 50.8 34.5 71.9 49.1 57.9 31.1 49.5 57.1 62.2

V6 93.7 89.9 92.9 90.7 98.8 94.5 92.4 47.4 94.3 89.4 94.3 90.6 94.4 95.1 100.0

V7 71.9 76.8 58.6 82.5 80.2 71.4 78.5 12.6 94.3 63.6 81.6 81.3 68.5 79.3 78.7

V8 71.9 78.3 58.6 82.5 90.7 76.9 78.5 12.6 90.1 72.3 83.9 81.3 66.0 79.3 78.7

V9 59.4 72.5 64.3 70.1 88.4 82.2 72.2 19.1 85.7 80.0 72.7 73.0 72.2 80.5 83.6

V10 42.4 39.9 50.0 47.3 66.0 62.6 48.6 6.6 71.2 57.3 48.3 32.5 51.0 66.1 60.8

V11 15.2 44.4 28.0 30.2 37.5 43.3 23.7 6.3 44.2 33.8 33.3 44.7 12.5 50.0 38.0

V12 31.9 44.8 47.4 35.8 56.7 70.3 55.0 34.5 68.0 46.2 63.8 37.1 41.0 58.7 64.7

V13 25.8 17.6 10.8 18.4 8.6 21.7 23.0 7.6 21.6 15.5 33.0 19.0 22.8 7.9 9.1

V14 5.7 5.8 5.6 2.3 1.8 17.0 6.6 0.7 5.1 0.7 1.3 12.5 5.5 0.6 2.1

67.5 55.0 73.1 48.3 71.7 63.2 63.7 45.6 58.6 54.8

68.8 49.3 82.1 54.7 66.9 67.9 64.9 37.5 65.4 41.6

94.0 82.7 94.5 81.7 93.8 86.8 93.5 63.5 77.8 75.6

58.1 26.6 64.8 49.8 37.9 48.2 60.1 33.1 53.1 37.4

65.4 23.4 62.1 60.6 34.5 47.1 59.5 31.9 42.0 36.1

97.7 95.1 100.0 100.0 100.0 100.0 100.0 96.9 93.1 98.3

90.7 77.8 93.9 84.1 84.6 88.9 83.3 78.1 80.7 95.1

89.4 82.7 93.9 87.0 87.2 91.1 86.1 85.7 84.2 90.0

94.1 91.4 100.0 95.6 89.7 100.0 91.7 93.8 82.5 96.7

92.1 81.7 94.3 88.7 88.1 89.9 79.3 84.2 76.3 80.1

76.3 57.1 87.5 76.7 54.5 71.4 44.4 35.7 45.0 47.4

64.2 28.5 64.3 67.2 34.4 48.0 66.2 32.4 40.3 30.7

73.4 48.8 80.7 53.9 47.6 62.1 40.6 73.5 49.7 57.8

52.9 33.0 57.3 39.6 42.4 35.2 21.9 36.4 46.7 34.5

164


State District Shimla Kinnaur Jammu & Kashmir Kupwara Baramula Srinagar Badgam Pulwama Anantanag Leh Kargil Doda Udhampur Punch Rajauri Jammu Kathua Jharkhand Garhwa Palamu Chatra Hazaribagh Kodarma Giridih Deoghar Godda

V1 63.5 74.7

V2 59.4 59.8

V3 90.4 91.3

V4 48.7 50.7

V5 48.2 52.4

V6 98.8 100.0

V7 87.5 96.9

V8 92.5 98.5

V9 95.0 95.4

V10 80.1 87.6

V11 59.2 41.7

V12 51.1 51.5

V13 37.7 42.6

V14 40.1 41.7

54.6 53.9 61.7 46.6 65.7 47.0 73.4 48.5 42.0 61.3 41.8 45.1 63.5 58.8

71.2 88.1 75.7 57.6 80.8 62.5 85.5 67.7 79.8 86.8 42.1 56.4 90.7 69.9

90.9 92.8 80.1 61.2 88.7 71.8 92.8 77.0 87.2 93.2 54.3 60.2 97.3 85.7

60.4 72.2 50.8 27.7 53.8 53.0 50.9 54.5 67.4 76.6 20.9 31.8 73.0 21.9

67.0 64.9 68.2 30.4 57.0 54.6 55.0 45.4 44.8 62.0 19.9 21.7 63.5 23.3

96.9 98.4 93.3 86.8 100.0 99.1 98.9 88.5 98.0 97.4 83.6 77.3 100.0 98.7

70.2 91.1 67.8 43.4 80.0 89.0 93.1 73.2 87.8 84.3 53.6 60.4 73.7 67.1

63.8 82.1 60.0 48.7 86.7 85.5 96.6 61.3 91.8 84.3 31.2 52.3 84.2 71.1

82.3 80.6 71.1 68.0 91.1 94.5 92.0 70.5 89.8 87.8 71.8 67.6 95.0 82.9

37.7 50.8 61.3 22.6 49.5 62.1 62.1 51.8 69.9 60.4 32.6 52.4 64.6 65.6

53.4 55.6 64.9 30.8 58.8 44.0 54.7 38.7 70.6 51.8 61.0 48.1 64.3 41.9

66.5 67.8 65.3 33.7 65.9 52.2 60.0 45.5 49.2 59.9 18.7 39.1 57.1 23.3

54.3 59.9 69.9 48.0 37.0 76.0 36.6 58.2 77.4 49.8 65.0 47.8 47.6 38.5

83.3 83.4 64.7 37.2 40.6 75.8 41.4 77.5 25.3 74.9 25.4 73.0 70.7 48.8

29.7 21.5 27.2 37.9 18.1 19.7 24.6 22.5

34.5 21.5 25.7 39.8 25.1 19.5 24.0 23.2

58.5 41.9 42.5 61.5 61.3 52.3 35.9 39.6

20.1 14.0 13.8 25.6 7.3 10.4 14.3 9.9

38.1 27.7 26.5 40.8 23.4 24.0 25.2 16.2

88.5 81.2 77.2 88.3 82.7 91.3 58.8 69.4

82.6 53.6 56.8 76.3 69.2 71.3 38.2 50.9

76.7 50.0 54.3 65.0 59.4 73.3 31.6 41.0

65.1 60.2 53.8 61.7 68.4 82.0 46.9 50.6

66.5 45.1 56.0 53.3 60.7 74.7 28.3 43.3

24.4 16.5 20.0 5.0 5.4 18.3 11.9 10.0

39.9 31.4 25.3 42.2 26.2 24.0 24.7 19.5

19.8 29.3 40.3 22.8 41.7 35.8 36.7 45.3

48.6 40.6 67.6 27.3 42.4 38.6 47.4 66.2

165


State District Sahibganj Pakaur Dumka Dhanbad Bokaro Ranchi Lohardaga Gumla Pashchimi Singhbum Purbi Singhbhum Simdega Seraikela Latehar Jamtara Karnataka Belgaum Bagalkot Bijapur Gulbarga Bidar Raichur Koppal Gadag Dharwad Uttara Kannada Haveri

V1 36.1 48.4 31.9 29.3 25.4 36.8 17.2 19.2 31.4 45.3 41.2 20.8 36.4 34.1

V2 29.2 35.6 33.1 29.2 26.9 27.7 16.3 21.1 36.8 48.9 34.2 19.2 40.9 32.4

V3 62.7 72.0 43.8 45.5 58.1 61.5 45.9 39.8 63.1 80.9 61.3 43.8 61.7 65.6

V4 9.2 20.7 15.5 22.6 9.0 17.8 9.0 17.0 16.1 29.7 14.4 4.2 20.8 6.8

V5 26.5 44.0 23.6 46.5 24.0 37.5 20.6 30.9 21.7 37.2 29.9 8.8 28.3 12.7

V6 93.9 94.1 69.0 87.6 97.1 98.3 87.7 87.0 87.8 100.0 95.1 75.5 97.9 92.0

V7 76.4 83.2 51.3 63.5 82.0 88.2 50.0 61.1 65.6 90.2 85.2 40.4 85.6 65.4

V8 74.5 79.8 50.8 56.9 83.4 84.0 51.3 58.0 63.6 92.7 82.0 40.4 88.7 73.0

V9 84.2 83.1 51.8 62.8 88.3 95.8 65.6 68.9 76.3 95.2 91.8 60.3 89.6 82.1

V10 75.2 76.2 45.2 50.5 76.8 82.2 61.6 60.1 62.3 83.7 73.5 47.6 77.0 73.7

V11 22.7 21.4 26.1 27.0 15.9 29.2 39.3 25.0 28.6 0.0 18.8 20.6 22.2 15.7

V12 27.8 43.8 25.9 45.5 24.2 41.0 18.9 32.1 21.5 37.3 27.8 8.2 29.4 11.0

V13 38.9 24.2 39.8 24.3 36.5 58.0 31.6 21.6 38.6 50.4 41.9 20.5 45.3 24.5

V14 36.6 53.7 51.6 46.9 29.8 44.8 57.1 64.4 58.7 45.6 53.6 58.8 56.5 38.0

52.8 71.1 75.7 57.0 52.4 79.1 59.2 89.0 86.2 46.6 87.5

58.0 94.7 95.5 67.1 55.2 78.7 60.1 97.1 91.2 79.4 97.6

81.2 94.7 97.8 76.7 54.3 84.6 70.0 95.9 96.9 84.1 99.5

39.0 82.1 83.1 71.7 36.2 58.7 52.5 72.7 80.5 60.1 96.6

45.0 71.8 79.1 63.9 46.1 53.4 50.7 76.2 76.9 60.3 83.2

95.2 100.0 100.0 100.0 94.3 96.8 89.6 100.0 97.8 97.0 100.0

77.4 100.0 95.7 87.7 82.8 89.4 65.7 95.1 95.6 89.4 96.9

71.1 100.0 94.2 69.2 74.4 88.4 64.2 95.1 95.6 83.3 96.9

73.8 100.0 89.9 90.8 80.5 87.4 64.2 90.2 95.6 81.8 95.4

43.0 79.2 79.9 64.6 63.8 61.6 34.2 78.8 79.5 73.6 91.6

48.5 100.0 66.7 22.2 55.0 66.7 48.8 35.7 50.0 77.8 50.0

45.9 69.2 75.8 64.7 44.0 53.9 47.0 76.6 80.7 53.5 87.3

36.3 11.5 49.2 47.1 39.9 35.6 34.9 54.2 65.8 52.9 75.5

58.9 6.8 12.4 52.1 29.8 43.9 56.0 35.3 27.8 21.0 40.5

166


State District Bellary Chitradurga Davanagere Shimoga Udupi Chikmagalur Tumkur Kolar Bangalore Bangalore Mandya Hassan Dakshina Kannada Kodagu Mysore Chamarajanagar Kerala Kasaragod Kannur Wayanad Kozhikode Malappuram Palakkad Thrissur Ernakulam Idukki

V1 65.8 65.6 61.2 53.6 93.0 70.1 94.6 87.2 50.9 93.8 80.8 36.8 80.0 78.1 91.5 79.9

V2 78.9 78.6 77.0 60.3 93.0 89.7 94.2 90.9 64.4 97.1 92.7 48.8 87.0 92.7 98.4 90.6

V3 88.9 91.1 90.4 73.6 94.7 95.3 96.6 94.9 73.2 97.2 98.0 54.3 94.8 93.8 96.3 94.3

V4 60.3 55.0 48.3 38.7 78.9 61.1 80.6 56.1 20.2 85.7 72.7 34.3 63.6 70.2 94.7 81.6

V5 63.6 61.9 71.3 49.2 64.7 50.0 84.5 49.5 41.2 69.3 64.0 38.4 71.6 62.7 91.5 79.2

V6 98.4 97.7 98.2 96.7 100.0 98.6 100.0 100.0 95.2 100.0 100.0 90.5 98.0 97.9 100.0 100.0

V7 88.7 90.7 96.6 83.1 93.5 95.9 100.0 98.2 83.8 92.7 97.8 79.7 88.2 95.7 98.3 87.8

V8 88.7 85.7 91.4 77.5 91.3 86.5 98.4 96.4 78.1 90.9 97.8 56.8 88.2 97.9 90.0 83.7

V9 83.6 90.7 81.0 73.0 89.4 89.0 96.7 92.9 77.1 94.6 97.8 68.9 90.0 93.6 98.3 95.9

V10 59.1 61.8 70.3 62.9 93.1 81.5 81.5 80.0 57.3 83.5 86.5 46.4 80.4 82.5 86.5 85.7

V11 47.6 53.6 55.6 64.3 60.0 60.0 38.5 26.1 33.3 42.9 35.3 55.6 37.5 28.6 0.0 18.2

V12 63.7 61.6 69.3 43.8 65.6 55.6 84.9 47.3 38.1 65.3 74.5 43.9 79.0 56.3 94.7 81.4

V13 50.0 61.9 27.7 47.8 45.1 41.4 56.0 41.4 31.7 58.7 40.2 38.2 47.7 53.4 68.7 55.7

V14 45.0 39.9 32.8 23.3 32.1 51.5 26.8 28.1 54.2 30.1 24.2 42.9 44.1 36.1 27.9 33.4

94.3 95.6 97.4 95.8 95.4 95.6 96.6 92.4 95.6

95.7 93.4 95.8 85.5 99.6 91.7 88.1 98.4 99.0

100.0 91.7 100.0 100.0 99.2 99.4 99.4 98.3 98.3

100.0 100.0 99.5 100.0 98.3 100.0 100.0 100.0 100.0

98.6 98.9 98.9 100.0 98.8 100.0 100.0 98.9 100.0

100.0 100.0 100.0 97.3 100.0 98.0 100.0 98.1 97.7

93.9 86.2 87.5 97.3 92.2 90.0 93.8 79.2 72.7

93.9 86.2 89.3 97.3 92.2 94.0 95.3 79.2 72.7

93.9 96.6 96.4 84.2 92.2 94.0 93.8 73.1 77.3

74.7 70.7 67.8 53.6 57.9 75.5 75.0 58.0 36.2

28.6 50.0 31.6 33.3 40.0 12.5 75.0 36.4 44.4

100.0 98.6 99.4 100.0 97.1 100.0 100.0 100.0 100.0

55.3 55.4 72.3 54.3 80.3 61.2 65.6 65.4 64.7

24.0 7.1 31.4 29.0 16.9 25.9 30.8 22.9 24.4

167


State District Kottayam Alappuzha Pathanamthitta Kollam Thiruvananthapuram Lakshadweep Lakshadweep Madhya Pradesh Sheopur Morena Bhind Gwalior Datia Shivpuri Guna Tikamgarh Chhatarpur Panna Sagar Damoh Satna Rewa Umaria Shahdol Sidhi Neemuch

V1 94.9 98.0 95.3 96.1 93.8

V2 87.0 99.3 99.2 98.1 97.8

V3 99.1 99.3 96.8 94.7 95.5

V4 99.1 100.0 98.4 100.0 95.6

V5 99.5 100.0 98.4 98.1 96.0

V6 100.0 100.0 100.0 100.0 97.7

V7 78.7 92.5 97.1 84.4 83.9

V8 78.7 90.0 97.1 88.9 86.2

V9 82.6 94.9 97.1 91.1 83.9

V10 50.0 72.6 80.0 66.3 54.5

V11 53.8 33.3 55.6 60.0 48.8

V12 99.5 99.1 100.0 100.0 96.1

V13 56.5 57.8 70.0 55.2 83.1

V14 11.4 20.5 33.3 8.1 30.8

75.6

88.0

95.8

87.2

91.7

100.0

91.0

89.6

92.5

41.8

61.5

91.3

76.0

52.1

60.6 15.9 29.4 24.8 21.7 29.8 34.7 23.4 31.4 43.9 26.2 11.7 35.4 14.1 13.9 31.7 39.2 31.0

60.8 16.9 35.7 15.0 21.9 24.1 35.6 26.8 23.0 45.6 30.8 16.6 21.7 11.4 15.9 36.5 36.2 37.6

85.4 37.1 63.6 39.5 51.4 60.6 62.1 47.4 61.4 72.5 58.9 34.6 55.6 27.6 37.8 59.8 68.1 67.5

44.6 24.5 34.6 47.7 33.3 43.1 33.1 24.3 42.0 61.4 38.3 12.0 36.3 43.2 54.2 36.2 57.6 65.5

44.1 25.5 31.6 26.1 21.4 19.5 34.6 26.6 18.0 43.5 32.8 14.6 16.1 17.4 29.2 23.6 55.3 50.6

100.0 61.4 91.8 86.5 95.7 80.6 92.1 79.6 79.4 86.4 77.4 65.3 76.7 64.6 74.5 78.7 95.4 78.3

82.4 28.1 53.6 52.1 56.5 51.1 67.2 30.2 32.7 68.2 57.5 27.4 45.0 29.3 60.9 55.3 72.3 47.8

78.3 21.9 55.7 55.2 60.9 29.0 54.7 24.1 29.0 58.0 46.7 27.1 41.7 18.2 50.0 45.7 60.0 43.5

88.4 33.3 66.7 68.8 56.5 47.8 54.7 42.6 33.6 58.0 52.8 41.1 51.7 35.4 51.1 52.1 75.4 52.2

67.5 19.4 48.5 37.0 29.3 23.7 41.0 26.8 17.7 41.5 28.6 39.3 45.6 15.8 27.1 40.7 46.4 44.6

28.6 38.3 21.1 14.5 47.4 4.9 31.4 9.8 39.3 29.9 19.0 24.2 37.5 18.0 28.1 34.9 20.6 54.2

51.5 28.0 33.8 33.8 20.5 24.5 41.7 21.4 22.4 47.0 33.9 15.0 22.9 17.7 30.7 34.9 62.1 65.1

58.8 32.2 69.9 46.0 41.9 31.4 38.5 51.8 50.0 51.5 38.9 57.1 42.4 34.6 52.5 55.6 48.6 48.8

31.7 19.8 59.9 21.8 26.0 9.0 34.8 35.4 31.0 23.1 26.6 48.0 18.3 12.5 36.8 38.4 27.2 46.7

168


State District Mandsaur Ratlam Ujjain Shajapur Dewas Jhabua Dhar Indore West Nimar Barwani East Nimar Rajgarh Vidisha Bhopal Sehore Raisen Betul Harda Hoshangabad Katni Jabalpur Narsimhapur Dindori Mandla Chhindwara Seoni

V1 34.8 21.8 11.5 23.7 45.8 14.3 40.0 46.1 12.3 26.0 40.5 30.8 29.2 26.5 19.9 42.9 33.3 28.1 39.6 11.3 10.2 12.4 22.5 53.5 17.0 34.9

V2 43.5 20.7 14.9 23.9 56.0 5.6 44.6 48.2 18.2 23.2 40.3 45.6 22.5 31.4 23.7 38.9 46.2 33.9 55.0 12.7 10.8 14.3 17.8 58.8 25.9 26.4

V3 72.8 38.7 43.7 54.0 81.0 33.2 77.4 76.4 42.9 48.8 66.1 78.1 53.1 56.2 50.7 77.7 74.9 65.9 81.7 28.8 23.9 32.6 57.2 85.9 51.3 49.6

V4 47.8 37.6 39.0 22.2 47.7 53.8 43.6 46.1 35.1 33.9 45.9 56.2 44.8 38.0 39.1 50.0 48.7 37.7 66.0 34.4 39.1 25.3 51.1 56.7 32.1 41.6

V5 35.1 29.1 33.4 27.4 41.3 27.3 42.6 47.0 35.9 19.7 35.3 44.4 35.9 31.8 35.4 42.3 42.8 39.4 53.3 26.3 17.0 20.6 21.7 52.0 30.9 20.2

V6 92.7 57.8 82.7 85.4 89.8 85.9 90.1 92.1 80.3 92.1 83.8 94.6 85.9 84.8 81.0 89.7 96.8 91.3 80.3 79.8 79.1 68.7 71.0 97.7 86.7 79.2

169

V7 71.4 20.0 65.9 46.7 49.0 50.0 59.7 68.4 53.0 57.8 40.5 58.9 49.2 52.7 55.2 60.4 63.8 50.7 61.7 52.1 40.9 42.4 44.7 84.4 48.4 38.0

V8 53.7 14.6 49.4 39.3 50.0 42.4 58.3 78.9 35.9 45.1 44.6 58.9 41.7 39.1 41.0 59.4 48.4 49.3 57.4 40.4 22.6 35.2 18.5 86.4 23.4 41.6

V9 69.0 21.3 67.1 68.5 54.0 49.4 55.6 71.1 53.0 60.8 50.0 64.3 63.3 62.0 55.2 61.3 76.3 65.2 62.3 41.5 27.8 46.4 31.5 72.7 47.7 31.7

V10 48.8 12.9 51.1 49.5 26.8 32.1 43.7 47.7 36.3 36.7 17.1 49.1 38.0 47.6 46.8 46.5 45.2 34.2 37.5 23.8 13.0 29.8 18.9 67.5 39.0 16.5

V11 33.3 26.5 21.4 27.6 17.5 8.2 33.3 23.8 24.5 35.2 20.6 15.8 31.7 27.5 39.5 29.6 39.5 21.4 32.0 14.9 15.1 4.0 8.0 47.1 34.2 20.4

V12 38.9 35.1 34.8 27.4 47.4 30.7 50.5 51.7 38.1 23.4 39.0 44.3 41.7 41.0 37.2 50.8 51.6 43.3 63.9 27.0 18.7 27.1 26.6 56.1 36.9 21.7

V13 45.9 47.6 47.8 57.1 35.5 29.4 35.0 30.6 35.6 47.9 26.5 32.9 40.9 41.8 37.2 47.0 50.0 50.7 52.6 24.3 42.2 33.1 23.4 61.4 34.7 39.9

V14 34.6 47.9 53.6 42.2 16.7 24.4 21.6 16.8 32.9 28.4 14.8 33.7 29.2 33.9 55.0 21.7 30.2 23.7 29.4 27.9 26.9 33.3 19.6 37.9 48.2 29.1


State District Balaghat Maharashtra Nandurbar Dhule Jalgaon Buldana Akola Washim Amravati Wardha Nagpur Bhandara Gondiya Gadchiroli Chandrapur Yavatmal Nanded Hingoli Parbhani Jalna Aurangabad Nashik Thane Mumbai (Suburban) Mumbai Raigarh

V1 37.7

V2 34.8

V3 62.6

V4 40.1

V5 31.3

V6 81.1

V7 62.2

V8 58.6

V9 64.5

V10 53.4

V11 50.0

V12 31.5

V13 30.5

V14 18.3

76.5 53.3 63.0 46.5 62.9 53.8 56.2 68.0 35.5 45.5 61.7 46.6 40.4 55.2 77.8 60.9 82.9 56.6 24.5 49.5 59.7

81.7 62.4 73.9 53.0 83.7 62.6 63.3 80.0 42.7 72.3 81.7 67.5 52.7 67.9 83.6 73.7 97.3 73.3 34.5 62.7 70.0

95.8 87.9 92.2 81.6 94.8 87.7 86.8 89.0 69.5 82.2 90.8 90.4 77.0 86.8 94.9 88.4 97.2 97.5 51.3 82.4 91.5

76.5 67.0 49.7 50.0 51.2 63.8 60.1 44.5 43.2 19.8 49.0 37.5 43.9 61.6 85.2 60.5 71.4 47.7 16.5 47.7 56.1

92.0 74.7 71.3 61.4 68.4 77.4 71.7 69.8 59.8 60.1 75.0 69.9 64.1 69.9 90.4 79.6 84.5 61.3 32.0 64.6 74.7

97.3 100.0 94.4 97.0 98.3 98.7 98.3 100.0 82.4 92.9 98.6 91.2 89.3 95.1 100.0 94.6 100.0 100.0 74.2 96.9 97.8

89.2 91.4 83.3 86.4 90.0 86.7 90.2 75.5 57.0 74.8 97.2 81.5 69.3 92.2 93.3 89.2 100.0 87.8 43.8 86.2 84.3

91.9 86.2 73.6 69.7 88.3 82.9 90.0 77.4 43.5 50.3 93.1 71.7 57.3 86.3 86.7 82.0 100.0 86.5 27.1 76.9 75.6

90.5 87.9 83.3 74.6 96.7 86.8 83.3 94.3 65.9 87.7 93.1 70.7 76.0 87.3 91.1 82.9 94.1 86.5 41.7 83.1 77.8

84.3 62.2 67.7 57.4 80.3 73.5 63.2 89.0 43.5 64.9 85.3 64.9 49.7 73.7 84.0 74.9 82.1 71.6 30.2 69.5 69.3

42.6 46.6 51.0 41.5 50.0 44.4 33.0 41.2 39.6 58.6 54.2 45.3 37.1 44.2 43.3 38.3 47.1 36.8 38.6 35.6 36.5

86.1 78.5 78.0 60.3 71.8 76.1 71.4 67.1 53.0 62.6 79.0 62.0 58.4 67.1 92.8 74.1 80.0 59.7 31.9 54.8 70.5

48.6 62.3 65.1 36.3 61.9 48.8 50.5 64.5 40.5 49.2 57.7 50.7 42.9 57.3 54.2 46.7 71.7 46.2 34.4 52.6 66.3

39.1 38.6 41.3 34.4 49.3 27.1 42.4 26.9 48.4 46.8 51.1 19.4 37.8 19.3 29.8 22.7 40.2 32.2 48.8 49.5 20.5

59.5

63.9

83.6

60.5

72.8

97.1

89.9

85.3

83.8

70.9

32.8

74.5

65.5

41.5

170


State District Pune Ahmadnagar Bid Latur Osmanabad Solapur Satara Ratnagiri Sindhudurg Kolhapur Sangli Manipur Senapati Tamenglong Churachandpur Bishnupur Thoubal Imphal West Imphal East Ukhrul Chandel Meghalaya West Garo Hills East Garo Hills South Garo Hills West Khasi Hills

V1 72.8 62.4 71.7 67.6 63.3 80.7 73.4 71.3 78.7 65.8 57.7

V2 83.0 80.1 81.9 79.6 92.8 92.3 83.4 83.0 88.5 65.6 68.6

V3 97.1 93.3 94.5 97.3 98.6 99.4 95.2 93.5 98.8 92.5 86.6

V4 73.8 62.7 70.9 70.8 86.0 92.4 54.4 47.2 74.7 61.2 46.9

V5 82.5 78.0 78.4 81.5 94.6 96.5 72.8 63.9 89.7 68.1 71.1

V6 94.4 98.2 98.1 96.5 100.0 100.0 100.0 100.0 100.0 95.5 96.8

V7 94.4 90.9 98.1 94.6 97.4 95.0 88.5 96.6 91.3 85.2 91.6

V8 86.1 81.8 86.5 96.5 94.9 92.5 95.1 85.7 95.7 83.0 84.2

V9 97.1 92.7 92.3 98.2 98.7 97.5 90.2 79.3 93.5 86.4 85.3

V10 86.5 70.6 84.5 85.2 90.5 82.7 65.4 71.4 86.0 60.4 71.8

V11 44.0 37.9 30.4 33.3 45.7 65.2 55.6 34.8 58.2 34.8 41.1

V12 84.6 77.5 79.7 87.8 93.0 97.7 73.7 67.9 90.1 64.0 70.1

V13 54.8 51.9 45.3 50.0 54.7 76.1 38.6 59.8 69.7 52.6 58.5

V14 28.9 29.9 15.0 23.0 37.0 21.4 16.4 29.3 52.9 31.3 35.4

67.3 58.8 45.9 63.8 65.4 47.6 26.7 74.9 41.8

71.7 56.1 46.6 68.1 73.6 56.6 21.7 74.9 29.5

83.2 79.7 63.4 81.1 82.4 74.6 43.8 88.3 58.9

54.9 28.8 31.0 61.7 83.6 24.0 14.3 50.8 13.5

53.1 34.8 34.1 55.4 81.8 24.9 12.4 54.0 17.4

92.0 83.9 64.8 83.1 100.0 94.1 40.7 94.0 81.3

77.3 59.8 49.3 57.1 90.2 69.5 27.7 82.1 51.6

73.3 55.2 46.5 51.9 78.0 68.9 23.2 80.6 53.9

65.3 58.6 34.3 54.5 90.5 69.5 27.4 66.7 49.6

44.4 31.5 22.8 34.4 63.4 30.2 9.3 39.5 26.4

72.3 53.3 40.0 73.8 79.2 47.1 20.5 45.5 46.2

50.5 32.1 30.4 52.4 78.8 23.6 14.4 60.8 17.8

52.6 52.9 51.0 51.9 60.4 61.6 51.4 67.1 62.0

48.0 27.7 29.7 57.2 55.8 36.6 14.9 54.5 33.6

21.1 37.9 21.6 23.4

24.9 60.4 45.2 47.3

34.8 77.3 51.1 65.0

7.4 29.6 23.6 27.7

6.0 34.3 28.6 28.7

60.3 95.7 82.5 87.9

17.5 58.5 41.0 68.3

9.5 64.5 52.8 64.5

19.0 65.6 62.5 68.8

17.9 50.2 53.0 54.2

22.2 45.5 60.0 55.1

6.5 35.4 28.2 30.8

80.3 68.4 71.8 66.9

36.5 34.4 19.2 34.8

171


State District Ri Bhoi East Khasi Hills Jaintia Hills Mizoram Mamit Kolasib Aizawl Champhai Serchhip Lunglei Lawngtlai Saiha Orissa Bargarh Jharsuguda Sambalpur Debagarh Sundargarh Kendujhar Mayurbhanj Baleshwar Bhadrak Kendrapara Jagatsinghapur Cuttack Jajapur

V1 23.8 25.7 12.5

V2 26.2 27.8 25.3

V3 31.9 39.6 37.7

V4 24.4 14.6 17.1

V5 21.1 13.7 25.1

V6 69.6 70.4 62.0

V7 36.2 38.0 29.4

V8 21.3 28.7 26.8

V9 43.5 44.0 33.7

V10 29.2 22.5 21.6

V11 73.3 37.5 35.9

V12 17.1 15.1 21.0

V13 90.8 75.2 81.4

V14 17.1 33.5 35.7

52.4 39.3 33.5 30.6 34.8 33.5 40.6 50.0

69.1 61.3 47.8 47.8 45.7 50.0 56.7 82.5

89.9 87.0 73.7 75.5 76.9 73.6 91.9 100.0

70.4 44.8 47.2 28.8 36.8 30.5 34.5 56.7

51.9 36.3 35.4 28.9 37.7 26.9 27.4 50.0

95.0 89.9 80.4 81.1 89.1 87.6 95.5 97.1

66.7 57.3 60.7 42.1 63.1 50.4 69.7 73.5

71.4 55.1 60.7 42.1 66.2 53.1 74.6 67.6

90.0 70.8 66.7 78.4 80.0 69.0 77.3 91.2

85.5 55.3 61.9 56.6 68.9 74.5 69.8 83.5

36.8 49.0 58.3 25.0 26.7 71.2 52.6 60.0

57.3 37.5 30.3 30.9 37.4 28.0 32.7 46.7

80.5 77.6 78.1 88.1 70.0 87.1 66.4 68.9

33.2 30.5 43.1 35.3 35.7 26.4 27.1 21.5

41.9 34.6 65.0 53.2 23.7 50.5 48.3 24.0 38.6 34.4 53.5 76.7 58.2

44.1 50.0 61.5 57.1 49.8 50.5 58.3 37.4 47.5 39.9 49.6 79.2 63.5

68.7 100.0 97.5 89.1 84.9 75.5 84.2 58.4 75.6 57.1 78.0 92.7 90.9

36.5 50.0 49.7 42.3 28.2 41.8 65.0 40.4 44.2 16.8 51.3 80.3 61.8

19.8 38.5 23.9 50.4 22.1 21.2 39.8 26.0 23.0 9.4 17.2 39.7 32.5

96.5 92.9 98.1 97.1 91.4 98.9 98.1 90.4 94.1 75.0 83.0 98.1 97.7

83.7 78.6 90.7 94.1 75.2 90.5 85.2 72.3 76.5 56.8 61.7 96.4 92.4

71.8 71.4 87.0 88.2 66.0 83.2 81.1 56.6 71.8 52.3 62.8 88.9 90.2

87.1 78.6 85.2 77.9 80.0 84.2 81.5 83.1 80.0 61.4 67.0 96.4 87.1

79.1 94.7 80.6 77.8 76.5 65.8 75.0 72.9 80.2 40.0 52.7 85.8 81.0

47.9 75.0 68.8 50.0 36.5 68.3 25.0 35.9 48.7 0.0 37.5 68.4 53.6

17.6 37.5 28.8 57.1 29.2 25.4 47.6 25.6 28.2 12.1 18.9 36.4 40.3

42.5 33.3 75.8 40.4 49.1 82.8 61.7 52.0 61.1 73.7 57.3 70.1 69.9

24.3 51.4 37.7 60.0 47.0 32.2 32.3 52.4 20.5 33.8 33.8 27.5 29.8

172


State District Dhenkanal Anugul Nayagarh Khordha Puri Ganjam Gajapati Kandhamal Baudh Sonapur Balangir Nuapada Kalahandi Rayagada Nabarangapur Koraput Malkangiri Puducherry Yanam Pondicherry Mahe Karaikal Punjab Gurdaspur Amritsar Kapurthala

V1 34.8 31.8 33.1 56.4 54.0 35.6 35.5 46.9 53.9 43.8 38.9 51.8 55.1 28.9 38.6 53.3 36.4

V2 57.8 47.0 32.3 57.8 48.0 57.5 39.6 34.4 62.2 65.0 41.2 51.5 61.8 46.4 47.6 58.8 45.1

V3 88.3 72.6 67.9 89.9 81.4 81.3 77.7 81.1 95.0 89.0 72.4 86.3 86.2 79.1 78.9 84.4 77.5

V4 57.8 25.4 24.0 45.3 33.9 63.8 11.5 14.3 40.7 14.4 42.6 27.9 61.2 14.2 45.3 39.8 34.0

V5 51.6 24.4 13.2 24.8 14.0 29.9 6.1 11.4 23.6 6.2 20.4 34.3 38.8 6.8 38.6 53.6 28.4

V6 100.0 87.5 96.6 96.8 89.9 95.3 94.4 98.3 96.7 92.1 96.4 85.5 95.6 86.1 98.3 96.7 95.1

V7 88.7 57.8 70.5 96.8 79.7 79.1 79.6 48.3 83.1 63.6 78.6 79.6 88.2 43.1 81.0 91.2 78.3

V8 86.5 50.8 68.2 93.6 72.2 83.7 79.6 47.5 79.7 56.2 66.7 72.7 79.4 40.6 81.0 86.8 75.0

V9 96.2 67.7 84.1 84.0 73.4 88.4 77.8 81.4 80.0 77.5 64.7 64.8 89.7 63.4 86.2 91.2 81.7

V10 75.7 73.0 66.8 72.3 65.1 77.8 67.9 52.0 74.7 51.6 63.2 63.5 84.4 51.5 77.6 78.6 77.1

V11 60.0 43.5 37.5 52.9 6.7 45.2 36.4 57.1 33.3 66.7 48.9 58.5 45.8 45.5 56.5 60.9 41.3

V12 45.5 26.3 19.6 26.4 17.0 33.3 7.7 11.7 30.8 3.8 24.1 46.0 45.3 5.1 37.3 66.1 26.7

V13 55.7 62.1 56.5 68.4 66.0 71.1 69.0 77.3 77.8 79.0 53.4 51.4 75.8 82.0 50.3 49.2 54.1

V14 53.5 62.6 59.4 37.5 40.8 33.0 46.6 52.6 40.4 48.4 35.6 62.8 34.6 48.3 57.1 64.5 45.7

70.5

95.4

93.2

99.2

81.1

100.0

97.8

95.7

97.9

74.3

12.5

87.0

59.3

20.8

70.1

98.6

98.7

93.6

96.2

100.0

100.0

100.0

100.0

56.9

100.0

86.1

75.6

25.5

73.3 65.1 53.8

75.6 52.9 61.3

83.3 86.4 80.4

53.0 59.9 61.6

70.5 80.6 73.5

93.3 85.2 100.0

93.3 67.7 92.0

93.3 72.1 93.3

91.1 75.4 100.0

86.3 45.3 80.6

35.5 20.0 45.9

68.8 78.7 73.7

43.7 25.1 56.8

6.3 4.6 17.5

173


State District Jalandhar Hoshiarpur Nawanshahr Rupnagar Fatehgarh Sahib Ludhiana Moga Firozpur Muktsar Faridkot Bathinda Mansa Sangrur Patiala Tarn Taran SAS Nagar Barnala Rajasthan Ganganagar Hamumangarh Bikaner Churu Jhunjhunun Alwar Bharatpur Dhaulpur

V1 70.0 54.0 69.0 62.6 59.5 60.4 58.8 54.2 18.3 69.6 70.2 55.7 68.7 55.8 53.0 54.8 80.8

V2 67.5 62.2 68.5 59.6 58.7 56.7 53.3 57.6 29.2 66.8 66.5 63.6 65.4 68.0 52.6 68.2 77.5

V3 80.0 77.7 82.9 80.2 92.1 88.1 85.8 82.2 49.8 82.9 80.1 80.7 83.7 79.2 81.2 75.0 86.0

V4 51.2 67.5 61.0 46.1 49.8 63.5 61.2 55.2 57.1 57.7 48.6 52.2 68.7 62.4 70.5 71.2 57.2

V5 73.0 73.1 80.8 74.3 78.7 87.6 81.4 63.8 70.5 79.5 61.0 73.7 76.0 78.0 84.8 75.5 74.0

V6 96.4 98.4 94.1 91.4 98.6 95.7 98.6 97.4 91.0 98.3 98.4 97.1 96.2 100.0 91.9 95.2 93.1

V7 96.4 91.8 89.7 79.7 87.5 87.2 94.2 94.7 80.3 98.3 95.1 82.6 86.5 93.0 79.0 87.3 91.7

V8 96.4 91.7 89.7 78.3 94.4 76.6 92.8 92.1 71.6 98.3 95.1 82.6 82.7 87.5 69.4 87.1 90.3

V9 90.9 93.4 92.6 88.4 94.4 89.4 88.6 89.5 77.3 94.8 96.7 91.2 86.5 94.4 74.2 90.3 91.7

V10 88.9 47.2 89.8 51.4 54.6 61.1 60.9 41.5 47.5 82.4 92.0 60.1 48.8 57.6 56.0 50.3 87.1

V11 57.1 45.2 37.5 33.3 68.1 86.2 46.3 44.0 13.3 49.1 39.3 70.7 22.2 60.6 73.6 56.7 10.0

V12 73.7 71.8 82.3 69.0 82.5 91.4 87.3 64.5 73.0 75.5 59.1 76.7 83.4 76.7 85.2 70.8 75.3

V13 69.3 48.0 49.0 30.9 28.4 29.7 34.1 40.7 54.0 42.2 75.0 42.7 35.0 49.8 40.9 47.2 39.1

V14 6.3 14.8 5.0 4.8 7.9 7.1 6.7 12.6 9.0 5.5 7.0 13.0 10.3 14.0 10.0 15.1 7.1

40.5 19.0 19.9 44.0 25.8 13.9 21.5 17.1

32.7 11.5 18.3 35.1 18.8 6.2 29.6 14.1

71.6 34.4 53.0 67.7 52.9 20.0 55.5 37.0

37.1 42.8 43.8 54.5 19.7 40.3 33.9 19.8

26.3 26.1 42.2 38.0 21.5 22.6 35.2 19.0

85.2 73.1 96.7 79.8 83.3 53.1 93.1 66.3

47.5 56.5 96.7 59.6 59.4 29.7 69.3 53.3

44.3 28.7 96.7 58.3 58.3 25.3 67.3 45.7

65.6 51.9 90.2 62.6 64.2 39.0 77.2 57.6

37.3 29.7 84.5 45.3 49.6 18.2 64.6 35.2

44.4 19.0 17.6 17.1 34.8 19.5 68.8 10.0

31.2 26.5 48.7 39.8 23.6 23.4 37.0 23.6

35.0 34.3 41.1 45.2 34.1 35.5 51.6 38.4

27.1 37.9 15.5 23.0 13.1 43.2 15.5 31.5

174


State District Karauli Sawai Dausa Jaipur Sikar Nagaur Jodhpur Jaisalmer Barmer Jalor Sirohi Pali Ajmer Tonk Bundi Bhilwara Rajsamand Udaipur Dungarpur Banswara Chittaurgarh Kota Baran Jhalawar Sikkim Sikkim North

V1 40.0 27.0 24.2 23.8 16.4 29.7 35.0 38.0 23.5 22.8 37.0 22.0 40.9 25.6 19.3 40.3 26.6 45.3 25.7 21.8 46.7 38.0 42.7 22.8

V2 26.8 29.3 20.3 20.6 4.8 27.8 37.7 35.0 21.3 16.6 30.2 17.4 28.3 24.4 11.1 38.6 20.5 38.8 27.5 13.8 32.1 32.9 32.5 23.1

V3 60.1 54.9 41.7 46.3 24.1 62.8 61.7 56.2 43.2 50.7 62.0 33.5 54.9 50.0 33.1 64.2 55.0 73.6 58.4 35.8 67.0 66.8 66.3 53.9

V4 49.3 40.8 25.3 59.1 45.2 43.5 34.4 35.0 56.6 24.0 33.4 41.1 58.2 31.0 48.7 63.5 32.0 34.7 36.8 45.6 57.4 40.1 39.4 32.7

V5 37.1 40.5 16.6 42.6 21.2 44.8 33.3 22.6 41.2 21.5 34.9 33.9 37.8 26.9 38.8 46.5 35.9 31.1 40.6 26.1 49.5 35.3 28.8 34.1

V6 75.7 87.0 83.3 85.6 75.5 93.3 90.7 86.2 88.9 77.9 78.3 74.5 95.7 82.6 78.6 91.1 86.8 91.8 91.4 61.3 89.4 86.0 93.3 82.1

V7 67.6 68.8 61.6 72.0 58.7 88.9 71.7 58.6 69.0 55.8 57.8 56.4 77.1 66.3 62.3 66.7 62.0 68.5 63.4 44.1 76.9 65.4 50.7 80.4

V8 51.4 68.8 52.1 49.6 36.0 91.1 77.4 70.0 50.7 50.0 55.8 40.9 68.6 43.0 37.7 63.6 47.9 69.9 62.8 29.7 58.7 67.3 43.4 80.4

V9 59.2 77.9 68.1 63.2 54.3 93.3 81.1 75.9 74.6 58.7 64.8 65.6 88.6 65.1 45.8 88.6 66.1 68.5 74.2 42.3 77.9 71.0 57.9 83.9

V10 46.0 69.4 43.3 37.6 31.6 89.4 59.7 67.9 39.6 38.7 60.2 47.2 50.3 55.8 33.9 66.0 59.1 50.0 74.1 21.0 50.9 59.0 40.7 84.3

V11 41.4 24.2 15.4 29.7 11.5 42.9 0.0 0.0 21.4 25.0 20.7 38.5 25.0 5.0 18.3 29.4 35.3 25.0 43.2 41.8 21.4 19.5 28.6 35.4

V12 42.1 40.4 17.2 46.2 25.5 43.5 34.9 26.9 46.6 22.0 38.1 30.2 43.7 29.4 40.6 52.4 36.9 37.8 40.9 25.7 48.9 36.7 31.5 35.9

V13 50.0 53.3 49.6 40.2 26.3 49.3 37.0 28.3 42.8 24.4 43.6 48.3 47.5 28.1 38.8 47.6 28.3 39.1 52.0 42.9 34.7 39.1 42.1 35.0

V14 23.8 12.2 41.7 24.3 30.3 17.1 35.1 22.6 30.5 29.8 11.8 20.4 31.9 31.8 19.2 26.0 33.8 22.5 11.7 38.8 35.3 12.7 18.0 12.2

61.3

70.8

94.8

62.1

57.3

98.5

93.9

92.4

87.9

84.7

57.6

56.3

56.2

12.6

175


State District Sikkim West Sikkim South Sikkim East Tamil Nadu Thiruvallur Chennai Kancheepuram Vellore Dharmapuri Tiruvannamalai Viluppuram Salem Namakkal Erode The Nilgiris Coimbatore Dindigul Karur Trichy Ariyalur Krishnagiri Cuddalore Nagapattinam Thiruvarur Thanjavur Pudukottai

V1 46.8 44.8 45.5

V2 68.0 73.5 63.0

V3 98.2 94.8 92.7

V4 46.4 47.3 40.9

V5 43.0 45.5 31.4

V6 99.2 97.7 98.6

V7 88.4 82.6 76.5

V8 89.3 84.7 88.2

V9 95.9 91.6 95.7

V10 89.2 85.6 88.4

V11 52.5 38.6 45.0

V12 45.9 43.1 31.2

V13 71.8 55.8 73.1

V14 12.2 10.6 15.7

72.6

98.0

98.0

91.0

91.5

100.0

96.9

89.2

96.9

90.4

50.0

93.5

79.8

25.0

87.1 72.1 72.7 81.7 82.5 77.4 85.9 79.2 82.7 57.3 73.4 73.6 70.3 86.5 67.5 55.3 58.3 92.7 70.1 65.1

98.5 97.2 94.7 95.5 98.8 89.6 98.5 99.2 92.9 90.5 98.4 94.7 97.7 94.7 93.5 97.4 92.0 98.9 93.5 92.4

100.0 95.2 98.3 97.5 100.0 100.0 87.1 98.3 96.6 87.0 99.5 98.1 99.1 97.0 97.3 95.6 98.7 100.0 93.4 91.5

96.7 89.0 90.7 89.9 98.8 91.3 98.4 87.5 85.3 90.3 98.5 91.6 97.4 97.1 95.6 91.9 98.7 89.6 85.9 98.1

96.8 86.4 70.9 78.3 95.1 91.3 95.2 80.7 70.0 79.6 98.0 86.8 75.9 73.1 85.1 90.6 92.3 92.7 76.6 93.5

100.0 100.0 100.0 100.0 100.0 100.0 96.2 100.0 100.0 100.0 100.0 96.3 100.0 100.0 100.0 100.0 100.0 100.0 93.3 100.0

100.0 89.1 88.4 97.4 100.0 90.2 88.5 91.2 93.2 82.1 94.8 96.3 98.8 95.1 76.9 88.9 86.0 100.0 86.2 86.5

92.9 86.7 88.4 87.2 94.7 97.6 84.6 91.2 88.6 80.0 94.8 89.3 97.6 95.1 66.7 81.8 95.3 100.0 76.7 72.2

92.3 95.7 100.0 100.0 94.7 92.7 96.2 97.1 95.5 92.3 100.0 96.3 92.9 98.4 94.9 93.2 93.0 100.0 86.7 88.9

67.6 51.0 56.0 61.6 80.9 71.4 88.9 87.3 57.1 68.1 37.7 81.0 65.7 71.9 85.7 71.3 71.0 89.6 62.7 87.3

100.0 20.0 0.0 30.0 0.0 60.0 16.7 66.7 66.7 40.0 18.2 0.0 10.0 35.0 100.0 21.1 25.0 25.0 10.0 52.6

98.0 87.3 64.8 70.6 95.1 96.2 98.2 83.7 67.9 83.9 93.4 88.5 76.4 72.1 87.4 92.0 92.8 92.0 70.8 94.7

76.0 75.2 86.7 84.6 80.6 56.6 63.8 72.1 81.8 73.6 76.3 74.2 75.2 67.3 73.3 67.1 79.9 77.3 77.0 67.4

13.8 25.1 30.4 28.0 18.4 10.9 24.6 20.8 21.4 21.5 19.5 13.3 20.2 20.7 15.0 15.7 27.0 19.2 17.6 12.5

176


State District Sivganga Madurai Theni Virudhunagar Ramanathpuram Thoothukudi Thirunelveli Kanniyakumari Tripura West Tripura South Tripura Dhalai North Tripura Uttar Pradesh Saharanpur Muzaffarnagar Bijnor Moradabad Rampur Jyotiba Phule Nagar Meerut Baghpat Ghaziabad Gautam Budh Nagar Bulandshahar Aligarh

V1 79.8 67.2 85.8 82.6 61.3 77.9 81.5 70.5

V2 100.0 96.4 93.0 96.5 90.8 87.6 91.7 83.7

V3 100.0 97.4 92.9 95.5 98.1 97.8 98.1 97.8

V4 95.8 95.3 96.1 74.2 90.7 91.8 93.4 90.9

V5 89.9 83.9 77.8 72.1 87.9 83.4 70.1 76.1

V6 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

V7 88.6 98.5 86.4 92.0 88.6 89.1 100.0 87.0

V8 94.3 98.5 97.7 88.0 88.6 91.5 98.6 73.9

V9 94.3 98.5 95.5 98.0 93.0 100.0 95.7 91.3

V10 81.2 67.9 76.1 73.0 60.4 77.2 68.2 90.2

V11 0.0 8.3 33.3 40.0 16.7 33.3 0.0 21.1

V12 87.5 78.8 78.3 74.1 88.2 90.6 63.0 85.1

V13 76.1 80.0 80.6 91.5 85.0 89.3 92.5 84.1

V14 9.6 21.8 42.6 15.4 21.9 34.4 27.7 16.2

36.6 38.8 43.4 48.1

43.2 52.5 40.4 50.6

54.9 67.2 68.4 80.7

41.5 48.2 42.7 59.2

32.3 41.7 13.5 27.1

64.5 55.2 76.5 92.7

42.7 34.5 65.0 72.0

37.9 34.5 56.9 68.3

37.6 31.0 64.7 74.4

44.2 58.6 60.8 67.6

55.6 59.3 80.0 75.0

28.1 35.5 11.2 19.5

41.7 47.0 41.1 35.1

4.9 4.1 12.1 12.7

30.2 34.3 22.4 22.2 19.3 30.6 52.6 10.8 26.7 11.0 20.2 22.1

14.8 15.8 21.1 22.4 9.6 34.5 35.1 14.4 40.8 24.0 17.6 24.1

50.2 66.5 47.0 77.6 42.9 84.2 76.8 57.5 81.8 65.0 55.6 57.3

30.1 28.5 17.6 29.4 12.8 49.4 31.4 6.3 32.7 8.3 15.7 18.8

35.3 57.7 30.1 25.1 34.6 23.4 84.5 4.5 23.0 6.1 16.8 15.6

80.3 79.1 55.3 84.8 77.9 81.0 72.7 67.6 79.7 54.6 59.8 67.8

32.9 37.7 36.3 64.9 47.7 47.7 37.9 20.6 53.4 24.3 33.1 37.4

30.3 31.2 28.4 58.6 44.3 50.0 37.1 21.0 55.1 25.2 26.8 39.7

39.0 48.6 38.6 66.7 55.3 56.9 47.0 29.1 64.4 23.9 32.7 39.0

24.6 30.4 35.5 46.4 32.6 31.0 35.2 16.2 23.4 14.2 24.8 26.7

12.2 2.5 23.4 15.8 22.0 11.9 25.7 7.0 9.4 9.6 19.6 27.1

38.3 51.5 31.8 21.3 37.4 19.8 82.9 4.6 21.8 5.6 19.2 18.5

6.8 9.1 31.3 16.3 9.7 16.2 14.3 5.6 14.7 5.9 20.4 20.7

3.7 3.0 11.1 6.8 8.1 10.9 10.6 2.5 6.0 1.7 6.5 26.4

177


State District Hathras Mathura Agra Firozabad Etah Mainpuri Budaun Bareilly Pilibhit Shahjahanpur Kheri Sitapur Hardoi Unnao Lucknow Rae Bareli Farrukhabad Kannauj Etawah Auraiya Kanpur Kanpur Jalaun Jhansi Lalitpur Hamirpur

V1 15.4 23.8 20.3 11.7 43.2 20.9 17.7 40.1 18.1 21.8 27.8 15.1 22.7 33.7 40.1 40.2 29.8 15.4 42.2 29.2 16.0 23.9 14.8 27.2 31.1 18.5

V2 13.5 26.7 27.7 5.6 23.9 18.9 23.9 46.9 6.8 13.8 28.3 7.2 15.6 15.0 25.5 37.1 27.6 15.4 41.5 32.1 7.3 16.4 14.2 25.7 26.2 17.5

V3 47.0 84.4 70.2 27.0 72.1 49.2 57.4 94.1 43.3 41.1 75.6 30.3 45.2 62.2 63.2 63.5 79.4 69.8 85.5 60.8 29.2 43.9 31.2 85.5 74.9 58.3

V4 12.6 26.0 36.8 9.7 26.6 32.7 17.7 42.0 19.4 21.5 28.2 9.9 14.2 17.1 25.8 36.5 28.7 18.1 25.1 28.6 10.0 27.6 28.0 32.3 41.3 27.1

V5 55.2 13.5 94.0 18.3 84.6 19.3 18.0 62.2 65.1 30.1 27.2 15.1 26.0 41.2 52.3 50.6 14.4 11.3 28.5 17.8 34.6 46.3 19.4 13.8 34.1 26.7

V6 63.5 85.5 81.9 61.9 80.3 75.4 70.8 96.6 47.3 86.6 74.2 63.9 81.3 77.0 80.2 74.2 74.7 63.6 91.8 98.4 60.8 73.3 77.3 74.8 93.0 70.7

178

V7 37.9 59.4 43.6 23.0 30.5 35.0 30.8 78.6 22.8 43.8 50.8 43.8 27.1 47.8 42.1 19.6 45.1 27.5 66.0 50.8 32.0 34.3 36.1 53.7 51.2 39.5

V8 37.0 58.9 41.5 16.0 38.2 36.9 30.3 74.4 11.9 44.1 50.0 19.2 28.6 38.5 40.6 25.8 45.4 27.5 61.2 50.8 30.4 36.2 35.3 53.1 53.5 36.9

V9 42.3 65.2 58.5 31.2 45.0 45.9 42.6 64.1 19.6 52.8 54.2 32.9 47.4 40.4 58.5 38.1 58.3 31.0 59.5 58.7 40.7 50.5 47.9 48.3 55.8 49.4

V10 27.9 43.4 38.9 19.2 34.0 35.4 26.8 44.2 15.3 34.9 42.4 20.2 31.0 32.1 47.4 35.6 35.8 20.5 41.2 31.0 24.5 30.8 29.3 39.5 38.9 27.2

V11 19.0 16.4 27.9 11.4 12.0 21.3 24.6 16.7 8.1 13.3 21.9 21.6 21.1 10.8 9.8 16.5 13.2 20.0 22.9 17.6 19.0 23.6 13.8 10.7 36.4 13.1

V12 52.2 12.4 90.7 14.0 81.4 23.0 19.1 62.6 64.8 29.1 28.2 14.1 30.9 39.9 53.9 46.0 13.6 10.0 30.4 27.8 37.8 47.4 18.8 12.2 35.2 24.5

V13 7.1 12.5 10.1 4.2 9.1 23.2 28.7 24.7 6.7 10.1 13.8 8.1 15.3 5.3 16.4 21.5 28.1 3.5 27.5 38.2 17.7 8.3 27.5 15.8 38.6 4.2

V14 4.7 8.9 8.4 3.3 2.9 27.9 9.8 10.8 1.5 13.7 5.5 2.5 2.9 0.6 8.4 5.7 16.1 4.1 20.8 9.7 9.5 7.5 22.1 5.2 7.3 1.3


State District Mahoba Banda Chitrakoot Fatehpur Pratapgarh Kaushambi Allahabad Barabanki Faizabad Ambedaker Nagar Sultanpur Bahraich Shrawasti Balrampur Gonda Siddharthnagar Basti Sant Kabir Nagar Maharajganj Gorakhpur Kushinagar Deoria Azamgarh Mau Ballia Jaunpur

V1 21.7 27.2 18.0 24.2 16.2 30.6 20.7 35.3 28.4 18.6 16.9 26.7 28.3 42.9 18.2 21.2 43.6 19.2 39.4 26.3 15.2 33.3 19.6 16.0 13.6 11.5

V2 10.3 19.6 20.3 18.9 12.8 33.4 16.9 21.3 28.8 12.9 6.2 18.3 33.2 21.6 16.0 19.3 30.5 18.5 29.2 26.1 14.5 24.2 17.2 24.4 7.8 18.9

V3 40.9 72.8 30.4 45.7 49.3 80.3 68.2 81.9 81.2 68.0 29.6 53.2 91.2 65.2 59.7 54.2 71.6 60.8 84.3 70.0 51.6 71.0 62.3 83.2 40.5 58.2

V4 12.8 21.7 22.7 12.0 14.1 26.3 29.0 33.8 12.9 41.7 19.3 33.3 37.3 29.9 23.2 19.1 32.3 15.8 27.8 23.8 21.8 30.3 25.5 25.1 6.2 11.2

V5 45.5 23.9 17.0 28.5 12.9 53.7 21.1 41.7 38.6 37.1 26.4 46.7 26.0 52.9 27.3 33.2 87.9 13.6 20.5 19.7 65.8 56.3 18.1 10.7 10.3 6.3

V6 74.1 83.9 80.4 61.4 57.9 80.6 81.5 84.1 85.3 93.1 74.0 72.1 87.8 72.6 52.3 75.2 79.9 71.6 86.9 85.5 68.0 80.2 68.8 78.6 63.3 61.5

179

V7 36.5 56.7 47.2 21.9 20.6 46.9 27.4 67.4 49.3 44.8 41.6 26.6 54.6 48.2 31.5 33.3 41.9 32.5 61.3 45.1 33.5 47.3 32.0 53.9 26.0 20.2

V8 40.3 41.9 44.8 21.1 20.6 49.2 26.7 69.8 46.4 42.5 42.9 25.6 52.3 39.3 29.7 35.9 39.0 32.5 62.0 47.9 33.7 43.5 38.1 54.5 26.0 19.3

V9 44.5 71.0 62.9 34.3 28.5 46.2 50.0 63.6 48.3 56.8 43.4 27.1 67.7 55.4 31.5 42.5 46.8 45.0 62.5 54.2 50.9 58.7 38.9 55.8 39.8 30.3

V10 30.2 41.9 51.3 23.8 19.4 33.1 42.5 34.0 35.7 42.9 30.2 15.3 27.6 48.4 30.3 31.8 32.7 28.3 46.1 44.4 36.1 38.0 30.0 38.0 17.7 17.6

V11 23.5 0.0 23.1 16.7 22.3 10.2 28.4 20.0 14.6 15.6 9.4 8.5 23.6 11.8 20.3 18.5 3.6 18.9 26.2 41.7 10.4 37.0 22.3 16.7 16.3 14.8

V12 43.9 20.2 18.5 34.2 14.5 53.8 21.7 41.3 40.4 40.5 24.7 46.3 23.7 53.1 36.5 34.5 80.6 12.7 18.2 23.5 62.3 46.4 18.3 7.4 7.2 6.5

V13 6.5 22.2 31.7 10.3 11.9 19.1 33.0 16.4 24.0 41.0 18.7 21.2 17.2 9.0 12.9 14.3 4.9 9.2 20.5 20.0 13.5 10.0 14.5 6.3 5.3 4.2

V14 5.1 8.8 16.8 1.5 20.8 10.5 27.8 8.3 13.6 6.4 12.7 5.5 7.4 9.3 3.5 5.1 2.3 7.5 15.3 27.2 9.1 4.6 11.2 5.3 4.7 1.7


State District Ghazipur Chandauli Varanasi Sant Ravidas Nagar Mirzapur Sonbhadra Uttarakhand Uttarkashi Chamoli Rudraprayag Tehri Garhwal Dehradun Garhwal Pithoragarh Bageshwar Almora Champawat Nainital Udham Singh Nagar Haridwar West Bengal Darjiling Jalpaiguri Koch Behar Uttar Dinajpur Dakshin Dinajpur

V1 13.2 18.6 12.4 37.4 17.2 27.8

V2 20.7 13.7 14.3 27.6 12.9 17.6

V3 74.8 57.8 65.7 81.7 58.2 84.2

V4 9.0 19.8 20.5 35.6 17.2 47.4

V5 5.7 12.4 28.4 14.2 13.8 33.3

V6 71.3 58.7 68.5 85.8 80.8 90.4

V7 46.7 21.2 46.2 55.0 44.0 64.5

V8 47.2 20.7 48.3 54.7 45.5 62.8

V9 49.2 28.3 44.8 57.7 46.5 61.7

V10 32.9 15.5 27.7 40.3 36.2 48.7

V11 11.7 9.1 23.3 13.9 19.2 27.0

V12 5.5 16.8 43.7 14.5 17.2 30.3

V13 9.8 12.9 18.4 14.6 14.9 11.2

V14 6.4 4.4 9.4 5.7 3.1 2.2

25.5 20.7 40.4 24.3 36.8 33.0 34.9 26.3 32.8 30.4 31.6 36.5 21.0

24.0 18.4 35.1 20.4 43.2 31.5 27.7 26.3 26.4 36.3 29.9 34.3 19.4

46.1 36.6 62.5 48.0 59.6 53.1 55.5 43.8 52.1 59.3 47.8 50.0 35.7

21.0 16.4 27.3 22.7 38.0 27.9 30.0 23.0 22.9 20.1 22.5 30.8 22.0

23.7 18.7 35.9 17.8 34.2 26.9 32.3 30.1 27.6 20.5 18.2 27.4 19.1

98.9 85.7 96.2 87.1 91.5 89.4 84.9 87.5 94.8 95.1 94.7 93.5 91.0

84.3 63.9 65.8 66.7 75.6 74.2 62.6 72.2 70.8 75.6 84.2 71.0 76.2

78.7 66.0 64.6 68.1 75.3 74.2 57.6 63.9 74.0 78.0 82.9 77.4 72.1

94.4 71.4 96.2 71.3 80.5 83.3 69.8 75.0 88.5 91.5 82.9 85.2 78.0

65.9 57.4 87.0 57.6 60.4 72.4 55.1 63.7 77.5 76.1 67.7 74.5 65.4

73.3 33.7 48.0 41.1 40.8 29.4 35.6 57.1 42.0 43.2 35.1 81.8 73.7

25.1 19.5 39.1 20.2 36.9 30.3 30.4 30.1 31.8 22.7 20.4 29.2 18.7

79.0 49.3 74.9 56.9 47.6 75.7 33.3 68.9 72.2 80.2 73.0 65.2 81.6

35.3 20.7 53.7 49.9 58.2 30.5 28.3 27.8 45.6 41.9 32.0 30.7 33.5

31.3 47.6 37.5 37.9 42.6

63.2 67.5 57.9 76.5 64.8

93.3 95.6 90.5 96.3 97.7

57.9 55.3 47.4 36.6 64.2

50.5 51.9 40.3 43.2 65.3

100.0 95.7 94.4 98.5 100.0

97.7 78.7 76.9 92.3 91.5

97.7 78.3 79.6 93.8 91.5

95.5 83.0 75.9 89.2 89.8

88.9 83.5 75.4 88.0 82.5

46.2 55.0 50.0 50.0 61.9

52.0 53.5 38.9 44.6 63.2

42.9 50.6 49.5 44.7 44.5

44.1 23.9 31.7 29.9 14.0

180


State District Maldah Murshidabad Birbhum Barddhaman Nadia North 24 Pargnas Hugli Bankura Puruliya Pachim Medinipur Haora Kolkata South 24 Parganas Purab Medinipur Source:

V1 45.3 60.2 37.1 39.5 40.6 46.8 41.4 45.3 33.2 38.2 29.6

V2 79.2 79.5 69.9 57.1 58.9 62.1 72.8 83.4 49.5 74.4 64.3

V3 98.7 98.9 97.8 95.7 93.8 97.9 98.5 99.4 88.6 98.3 90.9

V4 67.1 76.6 41.9 43.3 26.5 41.7 67.3 42.6 44.9 40.2 37.1

V5 70.9 70.9 46.2 38.8 44.8 42.7 64.4 62.9 49.2 50.6 46.7

V6 100.0 97.0 98.7 98.1 96.8 95.9 97.1 94.6 98.9 97.8 95.8

V7 82.7 97.0 85.3 86.5 74.4 74.4 97.1 80.6 93.2 89.0 83.3

V8 80.8 97.0 90.7 83.5 76.6 71.9 92.8 80.6 90.9 90.1 82.4

V9 86.5 97.0 92.0 85.6 76.8 80.2 95.7 83.3 90.9 91.2 85.8

V10 83.0 91.2 83.2 79.9 70.5 69.2 87.1 78.8 83.9 91.0 79.8

V11 50.0 50.0 47.6 60.5 23.8 34.1 42.9 40.0 51.2 62.1 50.0

V12 69.5 71.9 44.7 34.9 40.6 39.7 60.5 57.2 53.3 54.5 46.5

V13 31.8 31.0 35.8 35.9 42.8 43.8 51.2 42.1 41.0 43.3 44.2

V14 8.5 17.4 19.6 17.3 45.6 29.6 20.6 12.3 29.4 25.3 47.9

34.3 37.7

66.7 51.0

98.7 88.6

33.3 23.5

38.7 63.9

95.4 87.2

76.1 67.4

78.9 62.6

80.7 59.4

80.8 59.3

30.4 36.2

40.5 63.0

28.2 26.4

7.4 24.9

IIPS (2010)

181


182


Appendix Table 4.1 Within district inequality in coverage rates of different health interventions State District Andaman & Nicobar Islands Andamans Nicobars Andhra Pradesh Adilabad Nizamabad Karimnagar Medak Hyderabad Rangareddi Mahbubnagar Nalgonda Warangal Khammam Srikakulam Vizianagaram Visakhapatnam East Godavari West Godavari Krishna Guntur Prakasam Nellore

Mean

Combined population Median IQR CV

IID

Mean

Rural population Median IQR CV

IID

75.1 82.1

74.3 84.6

23.9 5.7

0.214 0.151

4.467 2.934

75.1 81.9

74.3 85.8

23.9 8.1

0.214 0.150

4.558 3.114

56.3 76.9 80.8 74.6 76.4 76.7 72.2 78.1 74.3 73.7 71.4 70.9 65.5 76.0 79.0 83.3 73.7 71.2 73.5

53.5 84.4 92.1 82.9 81.8 88.6 77.1 84.0 82.1 75.4 72.1 70.6 65.7 84.1 83.7 90.8 77.2 79.2 80.9

38.2 11.9 8.3 18.7 16.7 14.5 23.8 23.3 21.7 24.0 30.0 20.9 16.1 25.3 25.3 17.5 17.2 23.9 18.0

0.380 0.317 0.307 0.349 0.311 0.347 0.285 0.271 0.332 0.272 0.284 0.218 0.221 0.285 0.234 0.226 0.257 0.326 0.302

5.994 5.469 4.975 6.346 5.803 6.098 5.259 5.395 6.136 5.278 5.075 3.917 3.673 5.349 4.731 4.154 4.839 6.030 5.424

54.3 76.9 81.7 73.9

49.8 85.3 91.3 81.0

33.9 15.5 9.8 21.6

0.390 0.333 0.274 0.339

6.004 6.285 4.834 6.531

76.4 71.3 76.6 73.5 71.2 69.7 68.5 60.8 75.6 79.0 83.5 73.0 70.8 72.4

88.0 76.9 83.0 79.4 72.3 70.2 68.6 59.7 82.9 83.1 90.7 76.7 78.1 79.2

26.7 25.0 24.0 25.3 24.0 32.3 19.7 20.3 23.7 28.1 16.1 17.4 24.0 18.1

0.375 0.290 0.301 0.317 0.314 0.301 0.228 0.214 0.283 0.235 0.221 0.244 0.311 0.302

7.076 5.679 6.159 6.321 5.950 5.755 4.348 3.716 5.636 5.091 4.495 4.885 6.040 5.660

183


State District Cuddapah Kurnool Anantapur Chittoor Arunachal Pradesh Tawang West Kameng East Kameng Papum Lower Subansiri Upper Subansiri West Siang East Siang Upper Siang Dibang Valley Lohit Changlang Tirap Kurung Kumey Lower Dibang Valley Anjaw Assam Kokrajhar Dhubri Goalpara

Mean 73.0 67.9 74.4 76.1

Combined population Median IQR CV 78.7 10.6 0.272 67.6 26.4 0.276 76.6 25.8 0.253 76.1 20.3 0.206

IID 4.975 5.040 5.068 3.959

Mean 72.1 64.8 73.0 76.7

Rural population Median IQR CV 76.7 12.0 0.265 63.1 26.6 0.292 75.6 26.5 0.260 73.4 22.9 0.208

IID 5.117 5.271 5.305 4.373

49.6 50.2 31.1 52.8 47.1 43.5 41.8 46.2 27.3 54.7 44.3 55.7 49.3 45.6 48.6 41.0

45.4 46.4 29.0 50.4 44.8 43.5 37.3 46.7 18.8 52.0 42.6 52.9 47.1 41.5 40.7 41.6

21.2 17.0 15.5 21.6 24.4 25.1 19.1 19.5 23.4 23.9 39.3 24.3 16.8 18.6 29.4 18.2

0.339 0.382 0.370 0.352 0.494 0.339 0.293 0.348 0.670 0.417 0.561 0.376 0.417 0.408 0.416 0.297

4.412 4.975 3.091 5.130 6.125 3.954 3.206 4.371 4.561 6.069 6.864 5.604 5.313 4.328 5.320 3.287

46.2 48.7 26.5 45.3 48.1 34.3 39.7 41.7 27.2 55.2 42.9 54.6 45.9 45.6 44.9 41.0

39.8 47.5 20.0 44.3 48.9 34.3 34.6 42.0 18.8 54.4 43.2 52.6 41.0 41.5 37.1 41.6

19.3 14.5 28.5 12.1 22.5 15.1 13.7 20.4 23.3 28.1 36.3 25.5 31.4 18.6 30.7 18.1

0.330 0.388 0.616 0.344 0.474 0.415 0.508 0.334 0.673 0.421 0.553 0.384 0.472 0.408 0.494 0.296

4.028 4.969 4.600 4.258 6.235 3.994 4.865 3.968 4.814 6.601 6.745 5.833 5.914 4.626 5.989 3.438

45.7 36.9 44.5

39.8 35.9 44.5

23.5 20.7 18.8

0.353 0.516 0.414

4.227 4.926 4.808

44.8 35.5 48.5

39.5 35.3 45.1

26.3 23.5 29.4

0.379 0.548 0.484

4.800 5.354 6.543

184


State District Bongaigaon Barpeta Kamrup Nalbari Darrang Marigaon Nagaon Sonitpur Lakhimpur Dhemaji Tinsukia Dibrugarh Sibsagar Jorhat Golaghat Karbi Anglong North Cachar Hills Cachar Karimganj Hailakandi Chirang Baska Kamrup Metro Udalguri

Mean 50.7 53.7 69.5 63.2 54.8 50.8 45.6 60.8 49.3 44.2 53.9 67.8 62.1 62.3 52.2 54.7 46.9 45.5 39.6 39.0 49.6 55.6 64.5 53.4

Combined population Median IQR CV 43.1 34.8 0.411 55.9 31.7 0.372 67.0 15.9 0.204 61.1 31.0 0.309 49.2 40.8 0.394 49.8 31.0 0.357 51.9 26.6 0.459 58.1 32.6 0.298 47.7 24.4 0.324 38.6 26.0 0.428 53.1 26.9 0.318 61.8 37.1 0.301 61.3 26.7 0.264 63.2 32.2 0.338 49.9 30.4 0.337 54.3 37.1 0.341 48.3 12.8 0.253 37.3 30.3 0.462 36.8 27.0 0.497 36.4 26.5 0.584 46.1 38.5 0.408 59.3 28.7 0.422 64.1 30.5 0.315 51.8 23.0 0.350

185

IID 5.486 5.454 3.825 5.282 5.824 4.947 5.537 4.782 4.329 4.901 4.622 5.541 4.434 5.647 4.727 4.959 3.134 5.739 5.297 6.144 5.354 6.053 5.296 5.048

Mean 49.3 52.7 54.9 62.8 53.6 50.8 44.3 59.7 48.6 43.5 51.7 66.9 60.9 60.8 52.7 58.2 34.4 44.8 38.4 38.2 49.6 55.6 63.0 52.5

Rural population Median IQR CV 41.4 35.0 0.447 55.7 29.9 0.386 51.6 36.5 0.488 60.6 32.1 0.315 47.6 40.2 0.429 50.1 31.5 0.365 49.1 29.8 0.480 57.5 34.8 0.318 47.2 24.1 0.332 40.1 26.1 0.436 50.5 24.8 0.326 60.4 38.7 0.319 59.8 26.3 0.290 62.5 36.9 0.362 50.2 31.5 0.336 51.7 50.4 0.408 30.0 24.3 0.365 36.7 31.2 0.469 35.9 28.5 0.520 36.4 27.5 0.600 46.1 38.4 0.407 59.4 28.9 0.421 63.2 29.9 0.328 51.4 21.8 0.362

IID 6.148 5.761 7.392 5.589 6.516 5.298 5.982 5.316 4.598 5.270 4.758 6.003 5.006 6.274 5.026 6.516 3.488 5.841 5.490 6.274 5.582 6.394 5.721 5.372


State District Bihar Pashchim Champaran Purba Champaran Sheohar Sitamarhi Madhubani Supaul Araria Kishanganj Purnia Katihar Madhepura Saharsa Darbhanga Muzaffarpur Gopalganj Siwan Saran Vaishali Samastipur Begusarai Khagaria Bhagalpur Banka Munger

Mean 29.6 37.6 28.4 33.2 35.1 32.5 35.6 31.9 32.1 35.2 35.5 35.5 41.1 39.9 43.9 47.3 44.6 46.7 42.3 40.6 42.1 44.9 37.2 49.6

Combined population Median IQR CV 25.1 36.5 22.2 23.5 31.7 27.2 35.3 31.1 21.2 32.3 23.7 25.1 37.0 29.1 34.4 36.6 29.3 36.4 32.8 37.6 31.7 39.2 30.5 47.8

27.1 22.5 29.7 27.9 43.3 31.9 30.2 17.7 31.1 35.2 34.8 36.7 37.5 43.9 39.0 40.6 49.3 40.3 41.2 30.0 36.3 37.0 26.2 26.0

0.702 0.571 0.732 0.665 0.731 0.655 0.635 0.501 0.644 0.595 0.642 0.658 0.601 0.631 0.587 0.567 0.593 0.500 0.623 0.555 0.620 0.476 0.511 0.406

186

IID 5.585 5.759 5.498 5.895 7.161 5.748 6.171 4.324 5.536 5.724 6.106 6.278 6.739 6.822 7.074 7.290 7.144 6.453 7.224 6.155 7.203 5.820 5.072 5.503

Mean 29.6 37.5 28.1 32.1 35.8 32.5 35.0 31.1 31.3 34.4 35.2 34.9 40.5 38.8 44.1 47.7 44.0 46.3 42.1 40.6 41.6 44.4 36.6 48.4

Rural population Median IQR CV 24.0 36.4 21.8 23.0 32.3 27.2 35.8 30.5 19.4 32.1 23.2 25.4 37.3 23.6 33.8 36.7 28.2 36.7 33.2 37.6 30.4 38.9 30.7 45.6

28.1 22.6 28.6 28.1 44.5 30.8 29.9 18.0 32.7 36.1 34.3 37.1 38.5 44.0 39.4 42.1 50.6 42.5 43.3 30.6 38.2 43.8 25.1 30.5

0.712 0.574 0.736 0.683 0.731 0.654 0.647 0.513 0.671 0.626 0.646 0.675 0.614 0.669 0.594 0.573 0.615 0.520 0.633 0.557 0.634 0.551 0.511 0.455

IID 5.713 5.954 5.604 5.984 7.356 5.834 6.278 4.474 5.673 5.953 6.157 6.439 6.992 7.065 7.366 7.667 7.399 6.762 7.484 6.406 7.401 6.775 5.182 6.286


State District Lakhisarai Sheikhpura Nalanda Patna Bhojpur Buxar Kaimur Rohtas Jehanabad Aurangabad Gaya Nawada Jamui Chandigarh Chandigarh Chhattisgarh Koriya Surguja Jashpur Raigarh Korba Janjgir-Champa Bilaspur Kawardha Rajnandgaon

Mean 33.9 45.4 45.8 40.8 38.0 35.7 34.0 39.9 41.8 43.6 32.4 36.7 28.0

Combined population Median IQR CV 29.2 24.3 0.462 44.0 12.3 0.455 42.4 34.4 0.456 39.2 27.0 0.445 37.2 26.0 0.418 33.2 20.9 0.413 31.3 11.1 0.427 39.5 25.8 0.498 36.9 29.5 0.519 27.3 54.3 0.629 23.2 25.2 0.551 28.2 36.5 0.613 26.4 8.6 0.390

IID 4.217 5.359 5.708 4.867 4.295 3.919 3.732 5.453 6.003 7.303 4.679 6.124 2.754

Mean 34.1 45.7 44.5 37.3 38.0 33.8 33.7 38.3 42.5 43.2 30.5 36.4 27.4

Rural population Median IQR CV 28.4 25.8 0.477 44.5 11.7 0.442 40.6 37.2 0.488 31.5 25.1 0.511 35.8 28.9 0.447 32.3 22.4 0.439 31.5 10.9 0.422 35.8 31.2 0.543 37.3 30.2 0.516 26.7 54.5 0.639 21.5 28.4 0.603 27.3 38.3 0.629 25.0 10.2 0.411

IID 4.436 5.445 6.085 5.065 4.686 4.040 3.791 5.740 6.174 7.404 4.965 6.324 2.943

68.3

76.3

28.5

0.309

5.316

63.9

63.4

25.0

0.405

7.161

45.5 44.5 47.3 56.4 46.2 52.9 55.4 51.1 59.9

39.6 42.6 45.5 49.9 40.5 46.1 50.7 48.5 57.7

45.2 28.9 37.4 23.3 30.1 32.2 33.8 41.7 34.6

0.523 0.462 0.491 0.359 0.430 0.390 0.386 0.460 0.367

6.581 5.561 6.349 5.563 5.499 5.788 5.930 6.555 6.091

40.8 43.5 46.5 54.6 39.5 51.3 52.8 49.8 58.7

30.2 42.2 45.8 49.9 33.1 43.7 47.0 48.1 58.9

45.4 29.6 36.7 22.9 35.3 33.7 34.4 41.0 37.0

0.617 0.476 0.503 0.384 0.563 0.415 0.434 0.474 0.385

7.035 5.742 6.591 5.841 6.198 5.996 6.442 6.699 6.340

187


State District Durg Raipur Mahasamund Dhamtari Kanker Bastar Dantewada Dadra & Nagar Haveli Dadra Nagar Haveli Daman & Diu Diu Daman Delhi North Delhi North West Delhi North East Delhi East Delhi New Delhi Central Delhi West Delhi South West Delhi South Delhi Goa North Goa South Goa

Mean 63.5 59.5 65.3 66.2 62.8 57.3 60.1

Combined population Median IQR CV 61.8 32.4 0.333 54.9 22.5 0.321 58.5 36.4 0.312 70.1 21.7 0.299 65.9 34.5 0.380 65.5 31.1 0.404 69.1 36.2 0.408

IID 5.871 5.288 5.618 5.385 6.589 6.271 6.587

Mean 62.3 55.0 63.4 63.7 62.2 55.9 58.8

Rural population Median IQR CV 60.5 29.4 0.357 53.0 29.8 0.393 55.7 40.6 0.350 67.8 25.9 0.336 64.9 36.2 0.390 65.2 33.8 0.432 68.2 38.0 0.423

IID 6.184 6.090 6.139 5.984 6.826 6.843 7.034

60.8

60.3

18.7

0.293

4.518

56.6

54.9

18.0

0.324

4.998

70.8 78.1

80.5 89.1

43.2 12.7

0.368 0.310

6.709 5.311

68.3 79.4

75.6 89.1

53.9 17.9

0.405 0.292

7.583 5.619

63.8 65.8 58.9 65.5 65.9 72.4 65.8 67.7 65.2

69.4 75.4 63.3 74.6 73.5 82.9 73.1 74.9 72.6

16.5 24.2 21.7 27.9 27.1 29.3 22.1 27.3 23.4

0.325 0.353 0.361 0.373 0.356 0.341 0.362 0.383 0.337

4.739 5.648 5.261 6.122 5.757 5.921 5.862 6.642 5.408

63.9

66.6

30.8

0.362

6.078

66.8

70.0

36.3

0.395

7.335

51.9 71.3 65.4

45.0 76.8 68.3

31.5 27.5 37.8

0.546 0.355 0.389

7.902 6.948 7.183

86.1 82.9

95.2 90.5

8.9 13.8

0.232 0.248

4.022 4.168

86.9 85.7

97.3 95.4

10.0 12.0

0.235 0.284

4.680 5.180

188


State District Gujarat Kachchh Banas Kantha Patan Mahesana Sabar Kantha Gandhinagar Ahmadabad Surendranagar Rajkot Jamnagar Porbandar Junagarh Amreli Bhavnagar Anand Kheda Panch Dohad Vadodara Narmada Bharuch Surat The Dangs Navsari

Mean 51.2 47.0 65.6 68.6 54.5 68.2 67.5 47.5 71.8 69.4 70.6 67.7 58.9 60.3 67.3 62.8 54.9 46.7 57.4 58.7 61.4 72.8 37.9 71.0

Combined population Median IQR CV 54.9 44.7 67.6 75.6 56.1 72.0 70.0 49.5 74.4 72.4 72.4 68.9 61.5 58.2 73.1 68.2 53.3 46.7 55.7 59.6 63.8 78.5 40.8 78.5

12.9 19.5 21.1 24.7 19.8 19.6 18.0 15.5 13.3 9.6 18.4 22.9 16.2 16.2 16.0 13.1 17.0 18.8 14.6 25.3 18.5 20.0 28.2 13.7

0.292 0.306 0.316 0.295 0.272 0.225 0.215 0.324 0.256 0.211 0.292 0.255 0.335 0.210 0.312 0.347 0.273 0.324 0.263 0.315 0.308 0.297 0.459 0.253

189

IID 3.829 3.868 5.205 5.174 3.910 4.111 3.811 4.055 4.081 3.555 5.279 4.574 5.190 3.327 5.274 5.398 3.823 4.047 3.952 5.104 5.025 5.339 4.686 4.171

Mean 49.1 45.9 64.6 67.6 52.1 66.7 52.2 44.0 68.6 66.1 66.2 65.1 57.6 57.0 65.8 61.5 52.8 46.4 49.5 56.9 58.0 76.3 37.8 75.6

Rural population Median IQR CV 50.2 43.5 66.9 73.2 54.3 67.2 46.3 43.1 68.7 69.3 65.9 63.7 59.8 52.3 68.9 65.2 51.1 46.8 45.4 58.4 60.8 83.7 40.8 83.4

13.8 20.1 22.8 26.9 20.7 13.2 34.1 15.6 19.3 12.7 25.4 22.5 17.0 19.8 15.8 13.6 19.1 18.0 20.8 27.8 27.4 22.1 28.5 13.0

0.297 0.311 0.309 0.303 0.298 0.216 0.445 0.382 0.256 0.239 0.337 0.259 0.342 0.254 0.286 0.344 0.288 0.321 0.337 0.345 0.338 0.254 0.462 0.252

IID 4.025 3.950 5.338 5.713 4.382 3.927 6.482 4.581 4.673 4.284 6.007 4.735 5.481 4.053 5.104 5.568 4.094 4.153 4.534 5.569 5.527 5.230 4.929 4.755


State District Valsad Haryana Panchkula Ambala Yamunanagar Kurukshetra Kaithal Karnal Panipat Sonipat Jind Fatehabad Sirsa Hisar Bhiwani Rohtak Jhajjar Mahendragarh Rewari Gurgaon Faridabad Mewat Himachal Pradesh Chamba Kangra

Mean 60.9

Combined population Median IQR CV 62.2 17.1 0.225

IID 3.651

Mean 61.2

Rural population Median IQR CV 62.8 12.2 0.217

IID 3.696

68.4 64.1 62.1 63.5 56.2 61.1 51.6 61.8 52.7 48.7 55.8 51.1 50.6 59.7 54.0 57.6 63.2 54.5 42.1 20.7

73.3 66.3 66.9 65.9 56.9 62.2 50.6 64.6 54.6 45.2 57.0 46.5 45.7 56.5 51.3 54.4 66.7 54.2 41.5 15.9

22.0 26.8 27.1 14.8 39.6 32.7 28.6 26.2 24.2 37.2 19.7 28.8 33.9 40.3 35.9 27.4 25.2 33.2 25.4 20.9

0.365 0.382 0.439 0.360 0.481 0.460 0.460 0.436 0.457 0.531 0.445 0.476 0.459 0.437 0.467 0.415 0.383 0.461 0.508 0.730

5.872 6.218 6.921 5.684 7.398 7.220 6.254 6.789 6.403 7.220 6.565 6.591 6.367 7.084 6.832 6.277 6.104 6.882 5.779 3.858

65.8 60.1 58.8 61.9 53.7 59.7 53.4 60.0 49.5 49.3 51.8 48.9 50.4 54.8 54.5 56.1 61.5 49.6 34.8 19.9

69.6 62.9 63.3 62.1 53.4 59.5 53.2 62.4 52.1 42.5 51.5 42.9 45.6 52.4 51.7 54.6 64.3 48.4 32.5 15.0

27.9 26.3 33.1 18.0 43.0 35.9 30.1 28.4 21.6 43.6 26.1 36.6 31.6 45.6 33.9 27.8 28.7 36.9 18.3 21.8

0.392 0.374 0.462 0.349 0.500 0.472 0.466 0.450 0.491 0.576 0.489 0.523 0.458 0.476 0.453 0.446 0.406 0.519 0.533 0.756

6.912 6.253 7.453 5.902 7.643 7.795 6.992 7.252 6.703 8.043 7.103 7.257 6.503 7.468 6.914 6.951 6.848 7.367 5.144 4.042

60.6 70.8

58.6 70.7

46.6 32.1

0.395 0.267

6.613 5.169

59.5 70.6

56.1 72.0

45.5 32.2

0.414 0.267

6.971 5.375

190


State District Lahul & Spiti Kullu Mandi Hamirpur Una Bilaspur Solan Sirmaur Shimla Kinnaur Jammu & Kashmir Kupwara Baramula Srinagar Badgam Pulwama Anantanag Leh Kargil Doda Udhampur Punch Rajauri Jammu Kathua

Mean 68.5 72.6 60.5 82.3 71.5 76.5 67.9 63.4 68.3 66.7 61.6 69.0 78.9 75.2 73.6 69.5 71.2 69.9 47.3 59.5 45.7 53.5 69.7 72.2

Combined population Median IQR CV 67.6 25.9 0.314 70.2 35.2 0.273 56.4 46.2 0.389 84.3 28.1 0.178 66.7 40.5 0.303 70.7 25.5 0.194 60.6 34.5 0.286 56.4 45.3 0.371 66.7 31.8 0.236 69.5 44.2 0.351 59.7 66.8 83.8 78.2 76.1 70.3 74.7 69.1 46.9 64.4 43.1 53.1 70.9 70.1

22.5 6.9 22.4 26.6 25.2 21.8 30.2 29.2 22.8 31.9 35.1 14.8 25.6 37.3

0.236 0.135 0.215 0.195 0.203 0.217 0.290 0.242 0.355 0.375 0.439 0.269 0.303 0.293

191

IID 5.764 5.389 6.343 3.910 5.965 3.980 5.360 6.481 4.538 6.513

Mean 68.2 71.4 59.2 82.0 70.3 77.5 68.0 62.6 64.0 66.7

3.871 2.418 4.317 4.025 4.059 4.006 5.219 4.629 4.526 6.219 5.353 3.814 5.620 5.782

61.3 68.2 74.7 74.4 73.6 68.0 70.1 69.1 45.9 57.0 44.4 52.4 68.3 70.9

Rural population Median IQR CV 65.6 25.8 0.316 69.7 38.0 0.291 54.6 46.8 0.414 84.8 27.3 0.177 67.3 43.5 0.326 74.9 25.8 0.188 61.5 40.4 0.310 56.3 49.1 0.397 62.0 32.5 0.273 69.3 44.2 0.350 59.8 66.6 71.9 76.4 75.8 66.8 74.0 67.2 45.0 62.2 42.0 52.4 65.8 67.8

23.2 8.3 25.4 25.8 25.6 23.5 33.9 29.7 23.9 31.4 32.5 14.6 30.6 37.8

0.232 0.143 0.210 0.204 0.207 0.229 0.302 0.253 0.374 0.418 0.453 0.282 0.280 0.298

IID 6.054 5.899 6.805 4.080 6.387 4.118 5.963 6.996 4.957 6.593 4.064 2.570 4.404 4.356 4.321 4.364 5.877 4.987 4.728 6.763 5.713 4.149 5.460 5.960


State District Jharkhand Garhwa Palamu Chatra Hazaribagh Kodarma Giridih Deoghar Godda Sahibganj Pakaur Dumka Dhanbad Bokaro Ranchi Lohardaga Gumla Pashchimi Singhbum Purbi Singhbhum Simdega Seraikela Latehar Jamtara Karnataka Belgaum

Mean

Combined population Median IQR CV

IID

Mean

Rural population Median IQR CV

IID

45.7 44.4 39.4 56.6 47.7 33.3 42.6 36.6 34.5 41.8 43.5 49.6 56.2 56.8 58.6 50.1 50.1 66.9 45.8 58.5 49.0 41.5

37.7 36.9 36.8 52.9 48.5 31.0 42.1 40.5 32.4 43.7 42.0 49.3 50.1 49.9 52.0 37.9 48.7 60.4 37.0 51.2 32.4 42.4

51.5 39.1 27.0 37.3 26.1 12.5 29.2 28.4 29.0 35.6 36.5 18.1 28.7 37.6 45.8 47.0 33.1 35.5 53.7 50.8 55.7 23.1

0.586 0.493 0.472 0.414 0.363 0.352 0.441 0.516 0.618 0.525 0.553 0.358 0.337 0.392 0.444 0.522 0.420 0.327 0.612 0.440 0.607 0.335

7.358 5.771 5.060 6.415 4.602 3.069 4.984 4.956 5.651 5.938 6.659 4.675 5.206 6.145 7.264 7.211 5.691 5.957 7.715 7.114 8.095 3.733

45.4 43.3 38.2 55.7 45.6 32.0 41.9 36.3 33.5 40.9 42.2 46.2 49.5 53.6 57.3 49.6 48.0 59.8 44.8 55.7 48.4 40.0

37.2 36.0 36.0 51.1 46.0 30.0 41.4 40.3 30.6 42.6 42.1 41.5 44.3 47.8 51.4 37.8 48.7 49.7 36.1 50.9 33.2 41.8

52.0 38.4 25.7 41.3 26.1 13.2 29.7 29.0 27.1 36.3 37.3 31.7 35.3 48.9 46.6 46.9 34.2 49.3 53.3 52.3 56.2 23.6

0.593 0.505 0.476 0.439 0.389 0.392 0.446 0.522 0.635 0.542 0.570 0.474 0.458 0.501 0.464 0.528 0.458 0.490 0.634 0.477 0.617 0.352

7.522 6.090 5.043 6.951 4.856 3.496 5.266 5.351 5.980 6.244 6.842 6.191 6.459 7.658 7.539 7.314 6.199 8.137 8.031 7.544 8.261 3.949

68.6

70.0

16.1

0.276

4.957

66.8

65.9

16.7

0.282

5.116

192


State District Bagalkot Bijapur Gulbarga Bidar Raichur Koppal Gadag Dharwad Uttara Kannada Haveri Bellary Chitradurga Davanagere Shimoga Udupi Chikmagalur Tumkur Kolar Bangalore Bangalore Mandya Hassan Dakshina Kannada Kodagu Mysore

Mean 62.4 61.8 63.4 69.8 55.2 58.2 69.4 74.5 77.2 71.0 61.6 70.9 71.6 77.6 80.6 81.9 75.1 72.6 82.1 79.4 79.2 78.9 83.9 81.4 77.8

Combined population Median IQR CV 57.4 22.0 0.248 62.8 11.3 0.218 63.5 21.8 0.267 73.3 26.9 0.244 51.7 16.1 0.272 54.9 34.7 0.362 71.5 33.1 0.320 70.6 22.6 0.208 83.4 10.0 0.279 75.6 33.8 0.272 61.1 25.5 0.274 77.8 25.2 0.267 69.7 25.6 0.237 84.6 17.3 0.253 93.8 11.3 0.361 86.0 15.8 0.231 80.6 31.4 0.291 84.0 39.9 0.358 92.4 11.4 0.291 82.3 21.4 0.269 88.3 25.6 0.272 91.5 25.1 0.263 92.0 9.6 0.235 90.4 14.7 0.279 86.0 23.2 0.306

193

IID 4.244 3.555 4.429 4.661 3.974 5.892 5.962 4.140 5.064 5.392 4.631 4.762 4.640 4.878 6.000 4.308 5.840 6.981 5.129 4.883 5.402 5.129 4.322 5.301 5.910

Mean 59.0 56.9 60.6 69.9 52.5 57.1 69.4 71.1 76.2 73.3 57.8 68.6 70.2 75.0 81.2 80.0 74.3 70.6 77.2 78.2 78.7 78.2 85.6 81.0 75.9

Rural population Median IQR CV 55.4 27.9 0.293 57.6 14.9 0.242 61.6 25.3 0.310 72.7 31.6 0.263 47.6 17.0 0.309 55.8 37.3 0.379 70.8 31.1 0.310 63.8 27.8 0.239 82.7 10.5 0.303 75.8 32.8 0.260 54.7 27.2 0.318 75.7 27.8 0.288 64.8 28.0 0.240 80.2 22.6 0.251 93.1 10.4 0.357 83.5 18.0 0.244 80.3 35.9 0.306 83.6 46.6 0.378 88.4 28.7 0.389 81.5 19.5 0.290 88.3 28.1 0.270 90.4 28.1 0.261 93.5 12.7 0.209 89.6 15.8 0.282 83.7 31.6 0.328

IID 4.856 3.765 5.307 5.203 4.349 6.194 6.053 4.751 5.695 5.393 5.171 5.404 4.767 5.114 6.298 4.957 6.251 7.369 7.308 5.614 5.643 5.382 4.316 5.708 6.673


State District Chamarajanagar Kerala Kasaragod Kannur Wayanad Kozhikode Malappuram Palakkad Thrissur Ernakulam Idukki Kottayam Alappuzha Pathanamthitta Kollam Thiruvananthapuram Lakshadweep Lakshadweep Madhya Pradesh Sheopur Morena Bhind Gwalior Datia Shivpuri

Mean 79.2

Combined population Median IQR CV 86.3 18.3 0.267

IID 5.253

Mean 78.0

Rural population Median IQR CV 83.9 21.5 0.271

IID 5.597

82.6 81.0 81.5 78.5 77.3 77.3 82.2 81.3 83.5 85.1 84.2 82.5 81.4 86.4

94.0 92.4 89.4 85.4 86.0 84.8 93.8 94.9 96.4 95.8 94.7 96.4 93.7 97.0

15.7 33.7 12.8 35.1 32.0 37.8 26.8 31.4 22.1 23.0 20.0 22.4 18.8 21.5

0.310 0.299 0.249 0.307 0.330 0.332 0.303 0.313 0.284 0.246 0.271 0.320 0.328 0.234

5.507 5.605 4.630 5.941 6.338 6.127 5.315 5.554 5.297 4.677 4.904 5.769 5.848 4.182

82.9 80.5 81.7 78.6 77.4 77.9 81.5 80.7 83.4 86.7 82.4 82.7 81.3 87.0

93.8 96.6 90.0 85.8 86.5 84.8 92.9 92.6 96.1 94.6 94.1 96.5 94.0 97.1

15.4 37.6 12.6 31.0 32.1 37.1 27.3 23.5 23.2 21.6 20.2 22.4 19.9 14.6

0.302 0.314 0.251 0.308 0.327 0.325 0.309 0.319 0.280 0.218 0.313 0.311 0.339 0.226

5.830 6.369 5.037 6.370 6.751 6.533 6.030 6.190 5.615 4.450 6.077 6.131 6.431 4.499

81.6

91.0

21.9

0.215

4.317

81.0

88.8

15.9

0.209

4.384

33.5 38.5 41.0 46.4 38.5 30.3

27.5 33.5 39.3 45.6 34.4 26.2

16.1 23.3 25.3 22.3 18.3 22.4

0.523 0.494 0.448 0.342 0.412 0.565

4.540 5.068 4.948 4.311 4.063 4.147

31.8 34.8 40.6 40.2 36.5 27.7

27.5 31.4 38.3 37.3 32.1 23.3

15.0 22.5 25.9 23.8 16.8 20.7

0.542 0.579 0.490 0.420 0.457 0.638

4.435 5.434 5.527 4.760 4.392 4.439

194


State District Guna Tikamgarh Chhatarpur Panna Sagar Damoh Satna Rewa Umaria Shahdol Sidhi Neemuch Mandsaur Ratlam Ujjain Shajapur Dewas Jhabua Dhar Indore West Nimar Barwani East Nimar Rajgarh Vidisha

Mean 30.7 32.5 38.1 40.2 49.1 39.9 46.7 44.8 41.2 49.5 29.2 55.5 51.5 54.0 69.1 56.8 56.5 33.8 44.4 68.9 48.3 33.6 42.5 42.7 39.7

Combined population Median IQR CV 24.5 18.1 0.498 24.9 19.1 0.539 33.0 24.1 0.510 38.5 12.1 0.383 45.2 15.8 0.305 32.7 17.1 0.373 43.3 13.6 0.302 42.7 14.4 0.351 36.6 13.5 0.366 46.0 15.8 0.341 27.1 14.6 0.543 53.0 21.1 0.341 52.4 18.1 0.379 49.8 23.7 0.351 67.1 23.4 0.242 58.9 19.4 0.289 53.9 15.6 0.294 32.1 17.3 0.363 38.3 20.0 0.347 74.7 17.8 0.242 45.3 30.4 0.351 31.8 12.3 0.330 44.1 21.4 0.374 42.7 18.2 0.397 39.4 19.5 0.377

195

IID 3.924 4.510 5.215 3.999 3.956 3.441 3.580 4.046 3.764 4.454 4.036 5.052 5.178 5.215 4.510 4.420 4.391 3.345 4.139 4.255 4.671 2.749 4.248 4.470 3.911

Mean 25.7 30.9 34.2 38.1 43.9 33.6 44.1 43.4 39.7 45.3 30.4 53.2 47.0 50.5 65.6 55.2 52.2 30.8 40.6 52.4 46.9 28.7 39.1 40.4 35.1

Rural population Median IQR CV 18.1 17.1 0.557 23.0 22.3 0.564 29.4 22.8 0.578 35.9 8.3 0.417 40.1 19.1 0.349 26.8 16.6 0.495 40.3 16.7 0.333 41.3 15.4 0.360 35.8 16.9 0.410 41.4 16.7 0.404 31.2 13.0 0.507 48.0 24.3 0.394 47.6 16.1 0.421 47.4 22.6 0.387 60.1 27.3 0.256 56.2 20.7 0.282 49.3 17.0 0.310 27.8 17.6 0.427 36.1 22.1 0.383 49.7 17.3 0.235 45.1 29.0 0.360 26.8 12.7 0.396 39.6 19.4 0.404 40.4 16.9 0.444 33.3 18.3 0.442

IID 3.679 4.596 5.411 4.108 4.053 4.218 3.918 4.065 4.161 4.963 4.161 5.863 5.343 5.397 4.717 4.372 4.514 3.695 4.257 3.413 4.753 2.922 4.330 4.815 3.995


State District Bhopal Sehore Raisen Betul Harda Hoshangabad Katni Jabalpur Narsimhapur Dindori Mandla Chhindwara Seoni Balaghat Maharashtra Nandurbar Dhule Jalgaon Buldana Akola Washim Amravati Wardha Nagpur Bhandara

Mean 62.0 54.3 43.8 54.4 50.7 59.1 47.3 59.7 52.6 31.7 45.4 53.0 54.4 64.0 41.7 54.5 60.8 70.9 72.3 67.8 72.9 83.7 81.3 74.5

Combined population Median IQR CV 59.4 26.8 0.261 51.3 17.5 0.316 38.8 20.9 0.424 56.4 27.6 0.314 45.5 16.5 0.247 64.1 24.1 0.326 50.2 13.1 0.397 59.1 11.4 0.265 49.2 17.0 0.340 28.0 24.8 0.516 42.0 23.4 0.370 47.0 22.4 0.312 49.3 15.3 0.314 63.8 28.8 0.307 38.2 50.0 60.1 70.8 75.3 67.6 76.9 88.1 87.3 76.4

11.6 17.2 24.6 26.6 22.4 23.7 16.9 10.5 18.9 23.5

0.290 0.230 0.252 0.253 0.246 0.279 0.212 0.159 0.222 0.217

196

IID 4.297 4.553 4.833 4.524 3.284 5.124 5.057 3.987 4.712 4.314 4.398 4.331 4.417 5.467

Mean 41.7 51.3 41.4 49.7 44.6 54.6 44.0 51.6 49.7 31.0 42.5 46.9 53.4 63.1

3.044 3.342 4.217 4.952 4.696 5.087 4.053 3.326 4.502 4.460

37.8 51.8 56.9 68.6 71.4 67.6 70.6 83.4 82.0 74.8

Rural population Median IQR CV 37.6 32.3 0.495 48.5 17.7 0.334 36.0 22.0 0.456 51.1 28.7 0.382 39.6 19.3 0.310 56.5 25.6 0.355 45.8 19.2 0.443 46.9 29.1 0.340 44.2 17.0 0.345 27.3 25.3 0.536 40.8 25.5 0.432 39.8 20.0 0.358 48.6 17.4 0.332 60.7 35.1 0.333 34.5 46.0 55.0 67.4 70.9 65.7 72.5 89.1 83.7 76.1

12.7 16.3 24.8 28.8 25.3 24.1 18.5 17.3 23.5 27.4

0.358 0.252 0.278 0.274 0.246 0.280 0.214 0.169 0.225 0.230

IID 5.504 4.693 4.991 5.308 3.734 5.471 5.506 4.722 4.624 4.646 5.026 4.265 4.715 5.991 3.582 3.544 4.456 5.342 4.994 5.277 4.276 3.829 4.897 4.901


State District Gondiya Gadchiroli Chandrapur Yavatmal Nanded Hingoli Parbhani Jalna Aurangabad Nashik Thane Mumbai (Suburban) Mumbai Raigarh Pune Ahmadnagar Bid Latur Osmanabad Solapur Satara Ratnagiri Sindhudurg Kolhapur Sangli

Mean 76.2 63.2 72.9 70.8 69.1 61.1 71.0 69.2 64.7 70.2 73.3 79.1 80.6 73.9 79.5 79.0 69.7 69.8 68.9 74.4 82.3 74.2 84.6 80.1 76.7

Combined population Median IQR CV 82.1 32.6 0.219 63.0 25.3 0.285 76.5 23.2 0.275 72.4 27.3 0.251 69.8 26.2 0.280 65.8 23.5 0.305 73.7 20.3 0.234 70.6 24.8 0.283 66.1 23.8 0.277 70.4 20.1 0.219 78.2 17.2 0.278 90.2 21.7 0.269 89.4 18.1 0.247 79.8 24.7 0.265 89.6 16.1 0.271 85.4 15.0 0.244 73.4 26.2 0.276 75.8 19.4 0.295 71.9 16.0 0.281 79.9 24.2 0.300 92.7 26.3 0.252 82.2 18.5 0.333 92.6 16.5 0.235 86.5 13.2 0.253 83.7 21.1 0.292

197

IID 4.492 4.931 5.262 4.826 5.255 4.848 4.384 4.974 4.922 4.231 5.172 5.091 4.712 5.026 4.983 4.694 5.014 5.201 4.886 5.543 4.917 5.983 4.091 4.549 5.442

Mean 76.3 62.0 70.5 69.0 67.4 62.1 70.1 68.9 60.7 65.1 70.3

72.0 77.1 78.0 69.3 69.2 67.9 71.6 81.9 73.4 85.0 79.1 75.9

Rural population Median IQR CV 80.4 33.8 0.229 61.4 24.7 0.302 72.7 21.6 0.288 70.6 27.1 0.267 66.5 36.6 0.317 66.2 23.8 0.315 71.9 22.4 0.248 68.9 28.3 0.292 58.9 26.0 0.303 63.7 30.8 0.265 71.4 24.2 0.300

77.8 83.8 85.2 74.8 74.5 70.3 73.6 92.9 80.8 92.5 86.0 83.4

26.1 19.4 15.1 29.8 22.1 15.9 31.7 27.8 19.7 16.1 13.1 27.6

0.280 0.260 0.245 0.280 0.292 0.289 0.321 0.257 0.336 0.237 0.258 0.311

IID 4.921 5.214 5.624 5.213 6.111 5.467 4.824 5.495 5.227 4.899 5.935

5.583 5.240 4.935 5.419 5.486 5.239 6.290 5.339 6.408 4.559 5.090 6.249


State District Manipur Senapati Tamenglong Churachandpur Bishnupur Thoubal Imphal West Imphal East Ukhrul Chandel Meghalaya West Garo Hills East Garo Hills South Garo Hills West Khasi Hills Ri Bhoi East Khasi Hills Jaintia Hills Mizoram Mamit Kolasib Aizawl Champhai Serchhip Lunglei

Mean

Combined population Median IQR CV

IID

Mean

Rural population Median IQR CV

IID

52.1 24.9 42.1 68.2 68.8 79.5 65.0 41.7 50.1

52.0 22.5 43.0 71.0 67.2 83.5 64.1 44.0 52.6

37.6 13.2 17.6 21.4 20.3 10.5 12.7 26.2 23.0

0.409 0.491 0.289 0.206 0.219 0.147 0.188 0.454 0.334

5.795 3.140 3.278 3.849 4.179 2.951 3.193 5.094 4.452

52.1 24.9 42.1 64.7 66.7 77.4 60.5 41.7 50.9

52.1 22.5 43.0 66.3 66.9 79.0 57.2 44.0 54.3

37.6 13.1 16.9 20.3 25.1 15.8 14.1 26.2 26.0

0.410 0.490 0.289 0.215 0.239 0.156 0.206 0.454 0.340

6.030 3.332 3.406 3.930 4.571 3.393 3.385 5.350 4.752

37.6 28.8 37.2 35.6 52.1 58.9 46.9

32.3 22.8 28.2 31.8 54.4 58.7 49.8

22.0 19.0 18.7 12.5 33.8 20.3 28.9

0.491 0.696 0.593 0.472 0.357 0.278 0.398

4.714 4.757 5.091 3.906 4.978 4.301 4.926

34.7 26.0 37.5 33.2 51.7 54.1 45.8

31.1 20.1 27.7 28.1 54.7 54.4 48.2

15.9 20.8 19.7 13.4 34.6 29.3 29.9

0.521 0.792 0.602 0.530 0.370 0.344 0.415

4.818 5.226 5.754 4.390 5.365 5.193 5.385

56.3 66.9 75.0 60.5 69.6 63.6

55.6 68.8 74.8 60.3 74.0 64.4

34.7 19.7 16.3 25.1 21.8 23.8

0.362 0.204 0.205 0.284 0.286 0.286

5.425 3.560 3.859 4.579 5.144 4.954

54.4 55.6 67.9 56.6 67.8 54.9

51.8 59.5 69.8 55.2 68.3 54.4

41.8 21.4 30.6 27.7 31.6 32.8

0.403 0.284 0.278 0.323 0.314 0.360

6.189 4.506 5.341 5.150 5.997 5.541

198


State District Lawngtlai Saiha Orissa Bargarh Jharsuguda Sambalpur Debagarh Sundargarh Kendujhar Mayurbhanj Baleshwar Bhadrak Kendrapara Jagatsinghapur Cuttack Jajapur Dhenkanal Anugul Nayagarh Khordha Puri Ganjam Gajapati Kandhamal Baudh

Mean 49.3 62.0 67.1 68.0 66.8 53.1 60.8 53.4 63.5 69.1 63.6 64.8 73.6 64.7 67.7 58.6 56.5 53.6 65.2 68.1 53.0 41.6 52.8 54.5

Combined population Median IQR CV 41.5 40.2 0.438 66.3 25.8 0.324 59.7 61.4 59.9 51.6 54.1 60.2 69.0 75.3 68.3 63.9 79.4 65.4 66.9 57.0 48.9 51.9 69.2 70.5 56.0 43.0 58.0 49.9

34.3 37.9 28.6 31.1 37.1 38.0 40.8 32.5 39.0 40.0 21.9 35.4 34.4 34.7 34.2 27.1 30.9 37.6 17.3 35.6 34.8 45.3

0.287 0.288 0.255 0.383 0.342 0.498 0.363 0.352 0.379 0.373 0.304 0.323 0.327 0.381 0.431 0.386 0.299 0.291 0.347 0.545 0.460 0.428

199

IID 5.606 5.440

Mean 49.4 58.3

5.307 5.453 4.699 5.625 5.731 7.134 6.328 6.402 6.447 6.642 5.693 5.803 6.001 6.075 6.703 5.660 5.295 5.379 4.849 6.041 6.620 6.512

66.8 68.0 62.5 52.0 56.9 53.0 62.3 67.9 62.2 64.5 74.0 63.1 67.8 56.4 54.4 53.4 62.5 67.3 51.7 40.2 52.1 54.3

Rural population Median IQR CV 42.1 40.1 0.437 61.6 37.4 0.382 58.6 58.9 56.8 52.2 49.9 59.6 68.5 72.3 67.1 63.1 79.8 63.4 66.7 54.9 46.0 51.2 67.5 68.8 53.1 40.0 58.0 49.5

34.5 33.8 34.6 32.6 39.8 37.5 39.4 33.9 39.1 41.2 22.9 33.6 34.7 36.3 39.4 26.7 42.8 37.6 22.0 36.0 35.6 45.1

0.287 0.293 0.318 0.402 0.390 0.491 0.385 0.351 0.393 0.380 0.306 0.339 0.327 0.407 0.467 0.388 0.357 0.299 0.364 0.575 0.472 0.433

IID 5.922 6.346 5.388 5.594 5.579 5.935 6.222 7.362 6.789 6.711 6.946 6.993 6.073 6.088 6.280 6.530 7.221 5.860 6.303 5.706 5.257 6.574 6.960 6.690


State District Sonapur Balangir Nuapada Kalahandi Rayagada Nabarangapur Koraput Malkangiri Puducherry Yanam Pondicherry Mahe Karaikal Punjab Gurdaspur Amritsar Kapurthala Jalandhar Hoshiarpur Nawanshahr Rupnagar Fatehgarh Sahib Ludhiana Moga Firozpur

Mean 70.1 63.0 60.3 52.5 47.4 54.5 54.3 51.2

Combined population Median IQR CV 65.6 30.2 0.247 62.8 37.0 0.353 60.6 20.2 0.281 54.5 31.2 0.360 46.2 27.8 0.527 59.6 28.9 0.505 57.1 40.4 0.506 49.7 35.2 0.514

IID 4.822 6.141 4.688 5.161 6.597 7.209 7.406 6.978

62.9 82.0 88.3 80.2

68.3 96.7 93.2 92.6

19.4 23.9 9.8 22.4

0.267 0.320 0.193 0.310

4.345 5.872 3.386 5.659

61.7 73.6 69.2 71.0 70.0 68.0 67.7 66.0 61.7 73.6 71.4

68.1 80.5 73.8 76.2 76.7 71.4 71.8 69.5 62.9 81.8 80.7

29.5 22.9 29.4 25.1 31.0 26.1 27.0 28.5 28.9 27.1 22.8

0.398 0.346 0.350 0.332 0.362 0.310 0.308 0.320 0.309 0.338 0.338

6.329 5.869 5.873 5.631 6.284 5.229 5.167 5.415 4.918 5.920 5.646

200

Mean 69.7 63.3 59.8 52.3 45.8 54.1 52.1 51.0

Rural population Median IQR CV 65.3 31.3 0.252 61.4 37.2 0.363 60.7 19.3 0.284 54.3 31.7 0.370 46.0 28.6 0.550 59.9 29.8 0.512 57.3 42.1 0.552 50.2 34.7 0.512

IID 4.995 6.494 4.795 5.509 7.084 7.692 8.071 7.316

85.8

97.4

21.8

0.248

5.025

77.5

90.1

25.8

0.356

6.874

60.7 69.1 67.9 70.1 68.2 67.3 69.8 66.9 63.6 71.5 70.9

65.8 74.5 71.3 81.4 73.4 72.2 72.4 69.7 60.7 77.5 81.6

31.9 33.0 33.6 27.4 35.3 25.3 27.1 38.7 41.1 31.9 26.8

0.396 0.362 0.380 0.356 0.381 0.313 0.310 0.340 0.379 0.354 0.348

6.634 6.653 7.038 6.459 7.124 5.699 5.870 6.326 6.746 6.757 6.331


State District Muktsar Faridkot Bathinda Mansa Sangrur Patiala Tarn Taran SAS Nagar Barnala Rajasthan Ganganagar Hamumangarh Bikaner Churu Jhunjhunun Alwar Bharatpur Dhaulpur Karauli Sawai Dausa Jaipur Sikar Nagaur Jodhpur

Mean 73.3 72.3 72.6 56.3 64.1 65.6 67.1 66.5 61.9 51.9 44.4 39.5 38.8 53.9 37.0 30.1 34.3 40.0 38.6 46.6 53.7 53.8 48.7 45.8

Combined population Median IQR CV 74.2 29.6 0.329 74.8 21.3 0.302 77.2 26.5 0.306 58.5 38.3 0.450 70.0 28.3 0.329 73.4 26.0 0.401 79.9 23.2 0.406 73.1 20.8 0.297 70.4 30.5 0.422 40.6 36.5 35.5 40.2 47.8 33.6 26.4 29.9 41.0 40.3 46.3 51.7 52.4 43.0 37.8

29.2 35.5 17.2 26.6 26.8 15.1 17.2 20.9 22.7 19.3 26.3 18.6 25.3 26.7 26.0

0.394 0.527 0.370 0.434 0.355 0.419 0.410 0.518 0.435 0.317 0.384 0.331 0.314 0.352 0.414

201

IID 5.828 4.778 5.525 6.738 5.026 6.611 6.193 4.831 6.580

Mean 70.3 72.7 70.7 53.0 65.9 65.3 68.6 67.5 58.5

5.574 6.496 3.968 4.566 5.250 4.097 3.305 4.794 4.622 3.323 4.880 4.824 4.662 4.657 5.131

49.3 44.5 33.5 40.0 53.5 35.1 27.9 33.0 37.6 35.1 45.8 46.4 53.3 44.7 40.4

Rural population Median IQR CV 72.6 34.7 0.360 73.4 22.5 0.318 73.6 31.2 0.316 55.6 38.9 0.483 72.1 26.9 0.319 72.4 30.7 0.389 79.2 28.1 0.414 71.0 28.9 0.305 66.4 30.7 0.426 37.4 36.5 33.4 41.7 48.9 32.0 24.4 28.3 38.3 37.3 44.4 43.0 50.2 36.4 31.4

34.3 38.3 24.7 30.2 27.4 15.4 18.5 21.0 21.8 16.9 25.1 22.4 26.8 25.6 27.1

0.488 0.537 0.501 0.513 0.396 0.454 0.446 0.570 0.465 0.348 0.401 0.422 0.351 0.403 0.502

IID 6.864 5.905 6.180 7.210 5.540 6.814 7.201 5.585 6.800 6.524 6.731 4.745 5.746 5.938 4.349 3.490 5.177 4.760 3.392 5.135 5.429 5.297 4.934 5.558


State District Jaisalmer Barmer Jalor Sirohi Pali Ajmer Tonk Bundi Bhilwara Rajsamand Udaipur Dungarpur Banswara Chittaurgarh Kota Baran Jhalawar Sikkim Sikkim North Sikkim West Sikkim South Sikkim East Tamil Nadu Thiruvallur Chennai

Mean 37.9 40.6 45.7 49.9 50.4 53.7 49.8 51.6 54.2 53.4 53.0 59.6 55.7 51.8 62.8 50.3 44.0

Combined population Median IQR CV 29.1 26.2 0.458 34.5 34.2 0.500 41.4 24.7 0.400 43.9 27.8 0.412 48.0 28.3 0.405 49.9 10.8 0.279 46.9 15.7 0.345 51.1 19.8 0.281 55.2 31.3 0.398 49.7 26.2 0.392 42.2 42.6 0.461 48.6 43.7 0.437 48.3 62.6 0.546 49.5 31.0 0.399 61.5 17.1 0.281 47.6 16.9 0.314 42.1 16.4 0.358

IID 4.606 5.508 4.983 5.588 5.634 3.868 4.382 3.895 5.812 5.639 6.524 6.966 8.316 5.643 4.613 4.301 4.209

Mean 37.0 39.9 44.9 47.8 49.8 44.7 44.0 47.9 51.4 50.2 49.7 58.4 54.6 49.6 56.0 47.9 40.7

Rural population Median IQR CV 27.4 27.4 0.483 34.5 34.9 0.514 40.9 25.8 0.402 39.6 30.8 0.431 42.2 33.0 0.408 38.9 13.4 0.379 41.4 17.1 0.414 47.7 16.9 0.292 53.6 34.2 0.432 47.6 25.5 0.421 35.7 47.4 0.518 47.1 46.2 0.454 46.3 63.6 0.566 47.1 36.7 0.437 58.0 23.7 0.338 45.3 20.8 0.346 39.7 17.0 0.395

IID 4.804 5.728 5.102 5.758 5.649 4.439 4.734 3.933 6.302 5.989 7.048 7.343 8.617 6.171 5.301 4.667 4.511

67.9 63.7 64.1 71.6

70.1 67.9 64.7 69.6

42.0 45.7 40.2 32.0

0.378 0.413 0.398 0.316

6.695 7.117 6.800 5.675

67.6 63.3 64.0 70.5

69.9 68.1 64.7 66.5

42.8 46.4 40.4 33.9

0.383 0.425 0.400 0.321

7.173 7.629 7.123 6.010

85.3 85.6

93.1 94.5

12.6 10.3

0.234 0.273

4.127 4.600

78.4

89.3

15.3

0.395

6.910

202


State District Kancheepuram Vellore Dharmapuri Tiruvannamalai Viluppuram Salem Namakkal Erode The Nilgiris Coimbatore Dindigul Karur Trichy Ariyalur Krishnagiri Cuddalore Nagapattinam Thiruvarur Thanjavur Pudukottai Sivganga Madurai Theni Virudhunagar Ramanathpuram

Mean 80.4 81.5 77.1 79.0 77.9 83.2 81.4 85.1 85.9 79.7 78.3 82.9 77.9 83.8 79.5 76.9 79.3 79.5 79.1 77.0 78.1 77.6 75.9 76.4 79.0

Combined population Median IQR CV 92.9 18.8 0.315 87.3 13.8 0.242 88.9 25.1 0.316 87.0 20.8 0.280 91.9 26.6 0.369 90.8 19.9 0.255 89.3 17.3 0.261 94.8 17.0 0.245 93.3 9.8 0.251 92.8 20.9 0.328 86.1 22.9 0.311 88.0 10.8 0.239 91.8 26.9 0.342 92.4 15.6 0.248 85.7 17.1 0.241 89.8 20.4 0.351 96.7 25.9 0.361 91.6 25.5 0.320 91.2 27.0 0.304 84.4 26.0 0.365 92.4 26.7 0.321 87.0 23.2 0.284 79.5 17.2 0.309 82.5 20.9 0.289 89.1 25.2 0.285

203

IID 5.657 4.406 6.000 5.049 6.562 4.234 4.483 4.252 4.038 5.801 5.863 4.053 6.058 4.372 4.249 6.275 6.592 5.986 5.722 6.641 5.845 5.096 5.424 5.289 5.380

Mean 80.4 81.7 74.4 78.1 77.2 81.4 77.1 81.4 84.1 87.1 78.7 82.5 76.5 83.8 78.6 76.4 78.8 78.0 79.3 76.9 75.9 74.7 71.5 76.3 78.9

Rural population Median IQR CV 90.8 22.4 0.293 89.2 12.5 0.253 87.6 27.4 0.377 85.3 21.3 0.296 92.1 28.8 0.375 86.6 21.2 0.264 88.9 20.0 0.387 94.9 17.5 0.374 92.7 10.4 0.310 96.8 11.2 0.257 85.9 24.4 0.293 87.4 16.1 0.239 88.4 23.6 0.338 91.3 14.8 0.248 84.0 24.7 0.257 87.0 20.8 0.329 94.8 24.3 0.373 89.6 27.6 0.360 92.2 21.5 0.309 84.7 26.1 0.363 89.8 24.8 0.349 81.1 20.0 0.281 76.9 16.3 0.354 84.6 16.3 0.326 90.6 24.6 0.301

IID 5.690 4.901 7.152 5.770 7.116 5.134 6.712 6.583 5.677 4.668 5.944 4.652 6.255 4.846 5.179 6.314 7.156 6.850 6.081 6.940 6.674 5.439 6.164 5.920 6.024


State District Thoothukudi Thirunelveli Kanniyakumari Tripura West Tripura South Tripura Dhalai North Tripura Uttar Pradesh Saharanpur Muzaffarnagar Bijnor Moradabad Rampur Jyotiba Phule Nagar Meerut Baghpat Ghaziabad Gautam Budh Nagar Bulandshahar Aligarh Hathras Mathura Agra Firozabad

Mean 81.2 78.9 81.1

Combined population Median IQR CV 87.7 19.2 0.246 89.4 19.8 0.303 88.3 13.6 0.276

IID 4.855 5.405 5.035

Mean 81.2 78.8 80.2

Rural population Median IQR CV 86.1 16.9 0.238 88.1 24.6 0.290 88.7 11.3 0.322

IID 4.817 5.624 5.975

56.0 49.4 41.3 45.0

66.8 52.0 41.0 44.7

32.1 25.9 7.9 16.5

0.406 0.469 0.333 0.336

6.060 6.138 3.229 3.595

55.9 48.3 40.4 43.4

63.4 50.2 41.6 44.4

35.5 24.3 7.1 19.8

0.421 0.464 0.335 0.351

6.634 6.263 3.511 4.112

42.1 43.6 46.1 32.9 35.7 29.6 44.4 46.7 41.5 40.2 43.0 37.7 34.7 33.6 33.3 35.4

42.4 41.9 39.6 33.4 35.2 26.1 47.1 38.5 40.9 40.5 37.3 34.3 33.9 30.6 33.8 36.0

24.7 35.0 40.6 16.5 38.7 19.6 29.1 34.7 15.2 28.2 39.9 31.9 24.2 27.9 19.6 22.9

0.492 0.633 0.593 0.534 0.601 0.632 0.461 0.517 0.431 0.494 0.623 0.584 0.498 0.548 0.554 0.564

5.543 7.344 7.190 4.580 5.783 5.033 5.517 6.342 4.682 5.328 7.105 5.996 4.585 4.912 4.812 5.355

41.5 42.7 46.5 31.3 34.7 29.6 40.0 46.0 36.5 40.3 41.7 35.4 33.6 30.5 30.6 33.0

40.8 40.5 40.2 32.5 33.6 26.9 45.6 37.5 36.8 41.4 36.1 32.8 32.6 26.7 30.3 36.1

23.0 34.4 39.5 16.4 36.8 21.1 29.4 35.4 22.0 27.9 40.8 31.8 22.0 24.0 20.3 25.5

0.492 0.647 0.590 0.550 0.598 0.643 0.499 0.514 0.487 0.504 0.642 0.636 0.515 0.582 0.613 0.633

5.740 7.731 7.723 4.585 5.856 5.302 5.618 6.602 4.944 5.751 7.510 6.396 4.813 4.850 4.951 5.817

204


State District Etah Mainpuri Budaun Bareilly Pilibhit Shahjahanpur Kheri Sitapur Hardoi Unnao Lucknow Rae Bareli Farrukhabad Kannauj Etawah Auraiya Kanpur Kanpur Jalaun Jhansi Lalitpur Hamirpur Mahoba Banda Chitrakoot

Mean 27.0 29.5 19.7 32.4 27.5 21.5 23.4 23.9 28.3 32.4 50.3 40.0 23.4 31.7 34.3 33.6 35.5 48.0 32.7 43.1 37.1 41.2 41.1 29.3 30.6

Combined population Median IQR CV 19.7 29.8 0.762 28.3 20.8 0.578 16.1 12.2 0.751 34.1 30.9 0.568 19.1 19.2 0.670 15.1 17.8 0.778 20.2 7.1 0.566 19.4 10.0 0.668 28.3 19.7 0.522 22.2 29.1 0.641 46.1 27.5 0.413 35.3 22.6 0.476 19.8 17.2 0.659 33.1 28.2 0.578 33.8 25.6 0.546 35.9 29.6 0.581 27.9 28.7 0.548 39.5 27.8 0.430 30.0 17.8 0.500 40.9 12.0 0.490 30.8 16.6 0.491 33.3 20.7 0.520 42.7 19.3 0.525 25.4 14.0 0.488 27.2 13.2 0.523

205

IID 5.229 4.488 3.663 4.969 4.802 4.328 3.105 3.894 3.880 5.480 5.464 5.009 3.976 4.879 4.915 5.221 5.103 5.604 4.109 5.289 4.554 5.449 5.608 3.605 3.992

Mean 25.1 28.3 18.3 31.1 27.6 20.3 23.0 22.1 26.4 30.5 44.2 39.7 21.7 31.1 32.6 32.4 34.7 36.6 29.7 44.1 35.4 39.4 40.1 26.3 29.9

Rural population Median IQR CV 18.8 29.1 0.820 25.6 21.4 0.607 15.0 11.9 0.791 32.5 31.9 0.599 19.1 18.4 0.680 15.0 18.7 0.817 20.0 7.7 0.581 17.7 8.0 0.712 26.9 17.5 0.536 18.2 29.7 0.692 38.3 42.0 0.543 34.5 22.8 0.485 17.2 17.2 0.731 33.4 28.1 0.595 29.6 27.4 0.608 33.6 29.5 0.605 26.8 27.7 0.551 25.6 32.4 0.676 27.8 15.3 0.546 38.8 18.6 0.460 28.1 17.2 0.519 31.6 22.8 0.563 41.4 21.1 0.551 20.3 13.2 0.547 25.7 12.4 0.539

IID 5.473 4.567 3.585 5.318 4.942 4.334 3.171 3.807 3.902 5.638 6.773 5.138 4.142 5.113 5.232 5.456 5.074 6.827 4.042 5.365 4.604 5.858 5.926 3.668 4.122


State District Fatehpur Pratapgarh Kaushambi Allahabad Barabanki Faizabad Ambedaker Nagar Sultanpur Bahraich Shrawasti Balrampur Gonda Siddharthnagar Basti Sant Kabir Nagar Maharajganj Gorakhpur Kushinagar Deoria Azamgarh Mau Ballia Jaunpur Ghazipur Chandauli

Mean 30.1 43.5 27.1 34.4 32.3 38.8 39.2 39.3 19.9 20.3 20.3 25.2 29.2 39.0 34.6 40.1 48.6 41.1 53.5 38.4 40.2 38.9 35.8 36.8 33.8

Combined population Median IQR CV 28.3 14.8 0.603 35.2 40.3 0.540 25.0 14.9 0.551 33.5 11.4 0.332 26.0 18.0 0.473 29.7 23.0 0.517 28.2 36.6 0.619 37.7 39.4 0.626 13.0 15.6 0.958 16.4 12.0 0.876 13.2 17.4 0.899 20.3 16.1 0.764 17.4 37.2 0.819 28.1 44.2 0.672 25.4 42.4 0.745 37.9 23.0 0.537 42.6 32.5 0.444 40.1 25.2 0.516 53.2 31.4 0.472 32.6 28.2 0.589 31.3 29.2 0.606 32.0 33.9 0.619 30.3 37.6 0.663 30.0 28.4 0.573 30.0 15.1 0.444

206

IID 4.474 6.473 3.823 2.916 3.908 5.323 6.668 6.801 4.651 4.383 4.517 4.832 6.433 7.203 7.101 5.688 5.871 5.706 6.926 6.228 6.575 6.690 6.557 5.687 3.884

Mean 29.2 42.9 25.3 30.7 31.5 37.7 39.9 38.5 19.1 20.2 20.0 24.1 28.9 38.4 34.0 39.1 46.0 41.0 54.1 39.1 41.3 38.1 35.6 36.4 33.2

Rural population Median IQR CV 26.6 14.0 0.628 34.3 39.9 0.552 22.9 15.5 0.579 30.7 13.5 0.366 26.6 17.6 0.469 28.3 23.2 0.536 27.3 41.8 0.625 36.5 40.4 0.643 12.6 15.1 1.010 16.2 12.3 0.882 12.6 17.6 0.908 19.1 15.2 0.793 17.0 37.9 0.825 26.4 45.1 0.688 24.8 42.3 0.760 37.2 22.7 0.553 41.4 33.0 0.487 39.8 25.2 0.520 54.6 31.3 0.476 32.8 29.2 0.582 30.8 29.7 0.603 29.7 32.6 0.633 29.8 37.6 0.671 29.3 26.4 0.575 30.3 14.8 0.451

IID 4.666 6.548 3.955 3.124 3.845 5.575 6.916 6.894 4.746 4.461 4.560 4.838 6.456 7.284 7.154 5.854 6.139 5.955 7.329 6.315 6.747 6.758 6.649 5.726 3.826


State District Varanasi Sant Ravidas Nagar Mirzapur Sonbhadra Uttarakhand Uttarkashi Chamoli Rudraprayag Tehri Garhwal Dehradun Garhwal Pithoragarh Bageshwar Almora Champawat Nainital Udham Singh Nagar Haridwar West Bengal Darjiling Jalpaiguri Koch Behar Uttar Dinajpur Dakshin Dinajpur Maldah

Mean 46.8 29.8 29.5 34.8

Combined population Median IQR CV 44.3 32.7 0.523 25.4 17.6 0.558 29.6 14.3 0.512 28.6 30.1 0.558

IID 6.705 4.292 3.883 5.239

Mean 43.5 29.8 28.1 33.7

Rural population Median IQR CV 40.4 35.3 0.591 23.9 18.4 0.562 28.5 12.8 0.509 28.1 26.9 0.551

IID 7.299 4.369 3.881 5.201

51.8 60.5 55.7 52.8 62.3 53.2 55.1 41.6 56.1 48.3 52.5 58.1 48.2

52.0 59.3 52.4 42.9 53.7 44.4 48.8 35.6 57.9 50.5 53.0 57.4 37.6

53.2 31.3 44.8 48.4 24.8 44.8 41.5 41.9 51.0 43.2 33.7 38.9 23.5

0.510 0.347 0.469 0.500 0.248 0.437 0.429 0.553 0.488 0.457 0.376 0.363 0.340

6.850 5.703 7.099 7.108 4.184 6.101 6.445 6.173 7.261 6.039 5.300 5.658 4.343

50.5 59.1 55.1 51.6 55.6 52.3 54.2 41.3 55.4 46.6 49.7 56.3 45.6

50.6 58.1 51.3 41.5 52.9 43.1 48.9 35.2 56.0 49.0 50.5 57.6 35.3

54.3 33.2 45.7 50.1 32.9 43.9 41.5 42.5 53.7 41.3 39.3 45.0 26.2

0.540 0.376 0.478 0.520 0.328 0.451 0.441 0.556 0.512 0.469 0.433 0.418 0.388

7.513 6.285 7.456 7.494 5.084 6.396 6.766 6.359 7.934 6.164 6.016 6.584 4.760

71.4 65.2 62.2 54.8 66.2 58.3

72.6 62.9 59.5 61.3 62.9 52.1

23.8 40.7 45.7 29.8 48.2 34.2

0.311 0.380 0.411 0.379 0.384 0.387

5.647 6.790 6.999 5.579 6.826 6.294

69.5 63.6 61.2 53.7 65.8 58.0

65.1 58.8 58.8 59.4 63.3 52.3

28.8 46.8 46.1 27.1 48.0 34.9

0.341 0.412 0.419 0.383 0.390 0.392

6.486 7.353 7.233 5.755 7.078 6.417

207


State District Murshidabad Birbhum Barddhaman Nadia North 24 Pargnas Hugli Bankura Puruliya Pachim Medinipur Haora Kolkata South 24 Parganas Purab Medinipur Source:

Mean 59.4 60.2 64.2 72.3 69.4 73.9 69.2 63.8 64.1 68.2 72.0 57.3 67.8

Combined population Median IQR CV 55.5 31.8 0.352 53.9 33.0 0.338 63.0 21.8 0.291 73.2 39.4 0.315 76.2 33.9 0.354 80.7 29.4 0.365 63.6 44.8 0.340 58.1 35.2 0.327 52.3 44.6 0.381 77.0 21.5 0.339 80.2 21.2 0.310 53.6 44.6 0.483 68.5 45.6 0.362

Authorâ&#x20AC;&#x2122;s calculations

208

IID 5.779 5.599 4.978 6.124 6.423 6.725 6.514 5.820 6.574 5.672 5.692 7.507 6.659

Mean 59.3 60.4 65.7 70.7 64.5 74.0 68.7 63.2 64.2 68.1 56.4 67.6

Rural population Median IQR CV 54.5 31.8 0.367 54.0 34.6 0.336 61.4 29.6 0.304 70.1 41.5 0.343 70.9 39.4 0.377 78.1 34.1 0.341 60.6 47.7 0.355 57.2 36.5 0.342 52.3 44.4 0.379 75.1 28.7 0.367 53.6 68.3

46.7 45.7

0.496 0.364

IID 6.119 5.736 5.570 6.785 6.766 6.779 6.769 6.053 6.727 6.754 7.839 6.915


Appendix Table 4.2 Inequality adjusted average coverage rate of different health interventions State District

Andaman & Nicobar Islands Andamans Nicobars Andhra Pradesh Adilabad Nizamabad Karimnagar Medak Hyderabad Rangareddi Mahbubnagar Nalgonda Warangal Khammam Srikakulam Vizianagaram Visakhapatnam East Godavari West Godavari Krishna Guntur Prakasam

Average

Combined Penalty for

Index

Average

Rural Penalty for

Index

coverage rate

inequality

C

coverage rate

inequality

C

75.1 82.1

6.8 4.5

68.3 77.6

75.1 81.9

6.8 4.4

68.3 77.5

56.3 76.9 80.8 74.6 76.4 76.7 72.2 78.1 74.3 73.7 71.4 70.9 65.5 76.0 79.0 83.3 73.7 71.2

9.7 9.2 9.3 9.9 9.2 10.6 8.1 8.5 9.9 7.7 7.8 5.9 5.4 8.6 7.5 7.0 7.2 9.6

46.6 67.7 71.6 64.7 67.2 66.2 64.1 69.6 64.4 66.0 63.7 65.0 60.1 67.4 71.5 76.3 66.5 61.6

54.3 76.9 81.7 73.9

9.6 9.7 7.8 9.6

44.7 67.2 74.0 64.3

76.4 71.3 76.6 73.5 71.2 69.7 68.5 60.8 75.6 79.0 83.5 73.0 70.8

11.4 8.4 9.4 9.4 8.1 8.2 6.3 5.4 8.5 7.7 6.9 6.9 9.1

65.0 62.9 67.1 64.1 63.0 61.5 62.2 55.4 67.1 71.3 76.6 66.1 61.8

209


State District

Nellore Cuddapah Kurnool Anantapur Chittoor Arunachal Pradesh Tawang West Kameng East Kameng Papum Lower Subansiri Upper Subansiri West Siang East Siang Upper Siang Dibang Valley Lohit Changlang Tirap Kurung Kumey Lower Dibang Valley Anjaw Assam Kokrajhar

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 73.5 73.0 67.9 74.4 76.1

inequality 8.6 7.6 7.6 7.9 5.9

C 65.0 65.4 60.4 66.5 70.2

coverage rate 72.4 72.1 64.8 73.0 76.7

inequality 8.3 7.3 7.9 7.9 6.5

C 64.1 64.8 57.0 65.1 70.2

49.6 50.2 31.1 52.8 47.1 43.5 41.8 46.2 27.3 54.7 44.3 55.7 49.3 45.6 48.6 41.0

6.7 7.2 4.8 7.4 9.2 6.3 5.2 6.5 7.5 9.3 10.7 8.3 8.0 6.7 8.5 5.2

42.8 43.0 26.3 45.3 37.8 37.3 36.6 39.6 19.8 45.4 33.5 47.4 41.4 38.9 40.1 35.8

46.2 48.7 26.5 45.3 48.1 34.3 39.7 41.7 27.2 55.2 42.9 54.6 45.9 45.6 44.9 41.0

6.3 6.7 7.5 5.8 8.5 5.5 6.9 5.8 7.6 9.8 10.1 8.3 8.8 6.6 9.3 5.2

39.9 42.0 19.1 39.4 39.6 28.7 32.8 36.0 19.6 45.4 32.8 46.3 37.1 38.9 35.6 35.8

45.7

7.2

38.5

44.8

7.5

37.3

210


State District

Dhubri Goalpara Bongaigaon Barpeta Kamrup Nalbari Darrang Marigaon Nagaon Sonitpur Lakhimpur Dhemaji Tinsukia Dibrugarh Sibsagar Jorhat Golaghat Karbi Anglong North Cachar Hills Cachar Karimganj Hailakandi Chirang Baska

Average

Combined Penalty for

Index

coverage rate 36.9 44.5 50.7 53.7 69.5 63.2 54.8 50.8 45.6 60.8 49.3 44.2 53.9 67.8 62.1 62.3 52.2 54.7 46.9 45.5 39.6 39.0 49.6 55.6

inequality 7.5 7.4 9.3 8.3 5.6 9.0 9.8 8.2 8.6 7.7 6.8 7.9 7.3 9.2 6.7 9.3 8.0 8.9 4.7 9.2 8.1 9.6 9.5 9.8

C 29.4 37.1 41.3 45.3 63.9 54.1 45.0 42.6 37.0 53.0 42.5 36.3 46.6 58.6 55.4 53.0 44.2 45.8 42.2 36.3 31.5 29.4 40.1 45.8 211

Average

Rural Penalty for

Index

coverage rate 35.5 48.5 49.3 52.7 54.9 62.8 53.6 50.8 44.3 59.7 48.6 43.5 51.7 66.9 60.9 60.8 52.7 58.2 34.4 44.8 38.4 38.2 49.6 55.6

inequality 7.7 9.3 9.9 8.5 10.7 9.2 10.5 8.3 8.8 8.1 6.9 7.7 7.0 9.7 7.4 9.8 8.0 11.3 5.8 9.2 8.2 9.6 9.4 9.8

C 27.8 39.2 39.3 44.2 44.2 53.6 43.1 42.5 35.5 51.6 41.8 35.8 44.6 57.1 53.5 50.9 44.7 46.9 28.6 35.6 30.2 28.6 40.2 45.8


State District

Kamrup Metro Udalguri

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 64.5 53.4

inequality 8.2 7.7

C 56.3 45.7

coverage rate 63.0 52.5

inequality 8.3 7.7

C 54.7 44.8

29.6 37.6 28.4 33.2 35.1 32.5 35.6 31.9 32.1 35.2 35.5 35.5 41.1 39.9 43.9 47.3 44.6 46.7 42.3 40.6 42.1

8.5 8.3 8.7 9.5 11.3 8.8 9.7 6.3 9.3 9.1 10.1 10.2 10.7 11.4 11.4 12.2 12.1 10.5 11.9 9.5 11.9

21.1 29.2 19.7 23.7 23.7 23.7 25.9 25.6 22.8 26.1 25.4 25.3 30.4 28.6 32.5 35.1 32.6 36.3 30.4 31.1 30.2

29.6 37.5 28.1 32.1 35.8 32.5 35.0 31.1 31.3 34.4 35.2 34.9 40.5 38.8 44.1 47.7 44.0 46.3 42.1 40.6 41.6

8.8 8.4 8.7 9.3 11.6 8.7 9.7 6.4 9.5 9.3 10.1 10.3 10.7 11.8 11.6 12.5 12.4 10.8 12.0 9.6 12.1

20.8 29.1 19.4 22.8 24.2 23.8 25.3 24.7 21.8 25.0 25.2 24.7 29.7 26.9 32.4 35.2 31.7 35.5 30.0 31.0 29.5

Bihar Pashchim Champaran Purba Champaran Sheohar Sitamarhi Madhubani Supaul Araria Kishanganj Purnia Katihar Madhepura Saharsa Darbhanga Muzaffarpur Gopalganj Siwan Saran Vaishali Samastipur Begusarai Khagaria

212


State District

Bhagalpur Banka Munger Lakhisarai Sheikhpura Nalanda Patna Bhojpur Buxar Kaimur Rohtas Jehanabad Aurangabad Gaya Nawada Jamui Chandigarh Chandigarh Chhattisgarh Koriya Surguja Jashpur Raigarh Korba

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 44.9 37.2 49.6 33.9 45.4 45.8 40.8 38.0 35.7 34.0 39.9 41.8 43.6 32.4 36.7 28.0

inequality 9.3 8.3 8.2 6.7 7.5 9.0 7.5 6.5 6.2 5.3 8.7 9.3 12.6 7.7 9.8 3.9

C 35.6 28.9 41.4 27.2 37.9 36.8 33.3 31.5 29.5 28.7 31.3 32.5 31.0 24.7 26.8 24.1

coverage rate 44.4 36.6 48.4 34.1 45.7 44.5 37.3 38.0 33.8 33.7 38.3 42.5 43.2 30.5 36.4 27.4

inequality 10.9 8.1 9.2 7.1 7.3 9.4 7.9 7.0 6.2 5.2 9.1 9.4 12.7 8.0 10.0 4.1

C 33.6 28.5 39.2 27.0 38.4 35.1 29.4 31.0 27.6 28.5 29.1 33.0 30.5 22.5 26.3 23.3

68.3

8.7

59.6

63.9

10.0

54.0

45.5 44.5 47.3 56.4 46.2

10.9 8.7 10.0 8.5 8.7

34.6 35.8 37.3 47.9 37.5

40.8 43.5 46.5 54.6 39.5

11.7 8.7 10.1 8.7 9.7

29.2 34.8 36.4 46.0 29.9

213


State District

Janjgir-Champa Bilaspur Kawardha Rajnandgaon Durg Raipur Mahasamund Dhamtari Kanker Bastar Dantewada Dadra & Nagar Haveli Dadra Nagar Haveli Daman & Diu Diu Daman Delhi North Delhi North West Delhi North East Delhi East Delhi New Delhi Central Delhi West Delhi

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 52.9 55.4 51.1 59.9 63.5 59.5 65.3 66.2 62.8 57.3 60.1

inequality 9.0 9.1 9.9 9.9 9.1 7.9 9.1 8.1 10.3 10.1 10.7

C 43.9 46.3 41.2 50.0 54.4 51.6 56.2 58.0 52.5 47.2 49.4

coverage rate 51.3 52.8 49.8 58.7 62.3 55.0 63.4 63.7 62.2 55.9 58.8

inequality 9.4 9.7 9.9 9.8 8.9 8.9 9.9 8.7 10.5 10.6 11.0

C 41.9 43.2 40.0 48.8 53.3 46.1 53.5 55.0 51.7 45.3 47.9

60.8

6.7

54.1

56.6

7.1

49.5

70.8 78.1

11.2 9.4

59.6 68.7

68.3 79.4

11.8 9.1

56.5 70.3

63.8 65.8 58.9 65.5 65.9 72.4 65.8

7.4 9.6 8.0 10.2 9.4 10.2 9.2

56.4 56.2 50.9 55.3 56.5 62.1 56.6

63.9

8.6

55.3

66.8

10.5

56.3

51.9

11.8

40.1

214


State District

South West Delhi South Delhi

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 67.7 65.2

inequality 10.5 8.8

C 57.2 56.4

coverage rate 71.3 65.4

inequality 10.3 11.0

C 61.0 54.4

86.1 82.9

7.3 7.3

78.8 75.6

86.9 85.7

7.6 8.3

79.3 77.5

51.2 47.0 65.6 68.6 54.5 68.2 67.5 47.5 71.8 69.4 70.6 67.7 58.9 60.3 67.3 62.8 54.9 46.7

5.7 5.6 7.7 8.1 6.1 6.5 5.9 5.9 6.2 5.1 7.7 6.8 7.4 5.1 8.0 8.1 5.4 5.7

45.4 41.3 57.9 60.4 48.5 61.6 61.6 41.6 65.6 64.3 63.0 60.8 51.5 55.2 59.3 54.6 49.5 40.9

49.1 45.9 64.6 67.6 52.1 66.7 52.2 44.0 68.6 66.1 66.2 65.1 57.6 57.0 65.8 61.5 52.8 46.4

5.4 5.7 7.4 8.6 6.4 5.3 9.8 6.3 6.3 6.0 8.2 6.8 7.7 6.3 7.2 7.8 5.6 5.7

43.7 40.2 57.2 59.0 45.7 61.5 42.4 37.7 62.3 60.1 58.0 58.3 49.9 50.8 58.6 53.6 47.1 40.7

Goa North Goa South Goa Gujarat Kachchh Banas Kantha Patan Mahesana Sabar Kantha Gandhinagar Ahmadabad Surendranagar Rajkot Jamnagar Porbandar Junagarh Amreli Bhavnagar Anand Kheda Panch Dohad

215


State District

Vadodara Narmada Bharuch Surat The Dangs Navsari Valsad Haryana Panchkula Ambala Yamunanagar Kurukshetra Kaithal Karnal Panipat Sonipat Jind Fatehabad Sirsa Hisar Bhiwani Rohtak Jhajjar Mahendragarh

Average

Combined Penalty for

Index

coverage rate 57.4 58.7 61.4 72.8 37.9 71.0 60.9

inequality 5.6 7.7 7.3 8.3 7.5 7.1 5.7

68.4 64.1 62.1 63.5 56.2 61.1 51.6 61.8 52.7 48.7 55.8 51.1 50.6 59.7 54.0 57.6

9.0 9.6 10.4 8.4 11.6 10.6 9.3 10.0 9.3 11.0 9.2 9.9 9.8 11.0 10.5 9.3 216

Average

Rural Penalty for

Index

C 51.8 51.0 54.1 64.5 30.5 63.9 55.1

coverage rate 49.5 56.9 58.0 76.3 37.8 75.6 61.2

inequality 6.7 8.1 8.0 8.0 7.5 7.3 5.1

C 42.8 48.8 50.0 68.3 30.3 68.3 56.0

59.3 54.5 51.7 55.2 44.6 50.5 42.3 51.8 43.4 37.7 46.7 41.1 40.8 48.7 43.5 48.4

65.8 60.1 58.8 61.9 53.7 59.7 53.4 60.0 49.5 49.3 51.8 48.9 50.4 54.8 54.5 56.1

9.8 9.1 10.7 8.0 11.6 11.0 10.1 10.2 9.2 12.4 9.7 10.8 9.6 11.8 10.0 9.7

56.1 51.0 48.1 53.9 42.1 48.7 43.3 49.8 40.3 36.9 42.1 38.1 40.8 43.1 44.6 46.4


State District

Rewari Gurgaon Faridabad Mewat Himachal Pradesh Chamba Kangra Lahul & Spiti Kullu Mandi Hamirpur Una Bilaspur Solan Sirmaur Shimla Kinnaur Jammu & Kashmir Kupwara Baramula Srinagar Badgam Pulwama Anantanag

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 63.2 54.5 42.1 20.7

inequality 9.1 10.3 8.5 6.1

C 54.2 44.1 33.5 14.6

coverage rate 61.5 49.6 34.8 19.9

inequality 9.6 11.1 7.2 6.2

C 52.0 38.5 27.6 13.7

60.6 70.8 68.5 72.6 60.5 82.3 71.5 76.5 67.9 63.4 68.3 66.7

10.7 8.6 8.8 8.7 11.1 6.5 10.3 6.9 9.1 10.8 7.3 10.7

50.0 62.2 59.7 63.9 49.5 75.8 61.3 69.6 58.9 52.6 61.0 56.0

59.5 70.6 68.2 71.4 59.2 82.0 70.3 77.5 68.0 62.6 64.0 66.7

11.0 8.6 8.9 9.2 11.5 6.4 10.9 6.7 9.7 11.5 8.0 10.6

48.5 62.0 59.3 62.3 47.6 75.6 59.5 70.8 58.3 51.1 55.9 56.0

61.6 69.0 78.9 75.2 73.6 69.5

6.3 3.4 6.9 6.5 6.7 5.9

55.3 65.6 71.9 68.7 66.9 63.6

61.3 68.2 74.7 74.4 73.6 68.0

6.2 3.5 6.7 6.8 6.9 6.2

55.1 64.7 67.9 67.6 66.7 61.9

217


State District

Leh Kargil Doda Udhampur Punch Rajauri Jammu Kathua Jharkhand Garhwa Palamu Chatra Hazaribagh Kodarma Giridih Deoghar Godda Sahibganj Pakaur Dumka Dhanbad Bokaro Ranchi Lohardaga

Average

Combined Penalty for

Index

coverage rate 71.2 69.9 47.3 59.5 45.7 53.5 69.7 72.2

inequality 8.2 7.4 6.6 9.8 8.8 5.7 8.6 9.6

45.7 44.4 39.4 56.6 47.7 33.3 42.6 36.6 34.5 41.8 43.5 49.6 56.2 56.8 58.6

12.3 10.0 7.8 10.4 6.8 4.8 8.3 8.2 9.6 9.5 10.6 6.7 8.5 10.1 11.7 218

Average

Rural Penalty for

Index

C 63.0 62.5 40.7 49.7 36.9 47.8 61.0 62.6

coverage rate 70.1 69.1 45.9 57.0 44.4 52.4 68.3 70.9

inequality 8.7 7.7 6.8 10.3 8.8 5.8 8.4 9.7

C 61.4 61.4 39.1 46.7 35.7 46.6 59.8 61.2

33.4 34.3 31.6 46.2 40.9 28.5 34.3 28.4 25.0 32.3 32.8 42.8 47.7 46.6 46.9

45.4 43.3 38.2 55.7 45.6 32.0 41.9 36.3 33.5 40.9 42.2 46.2 49.5 53.6 57.3

12.3 10.0 7.5 11.0 6.8 5.1 8.2 8.3 9.4 9.5 10.6 9.2 10.1 11.9 11.9

33.0 33.3 30.7 44.7 38.7 26.9 33.7 28.0 24.1 31.4 31.6 37.1 39.4 41.7 45.4


State District

Gumla Pashchimi Singhbum Purbi Singhbhum Simdega Seraikela Latehar Jamtara Karnataka Belgaum Bagalkot Bijapur Gulbarga Bidar Raichur Koppal Gadag Dharwad Uttara Kannada Haveri Bellary Chitradurga Davanagere Shimoga Udupi

Average

Combined Penalty for

Index

coverage rate 50.1 50.1 66.9 45.8 58.5 49.0 41.5

inequality 12.1 9.8 9.8 12.9 11.6 13.9 6.0

68.6 62.4 61.8 63.4 69.8 55.2 58.2 69.4 74.5 77.2 71.0 61.6 70.9 71.6 77.6 80.6

6.9 6.6 5.1 6.7 7.8 6.0 9.2 9.0 6.6 8.1 8.7 6.8 7.9 7.3 8.0 10.7 219

Average

Rural Penalty for

Index

C 37.9 40.3 57.0 32.9 46.8 35.0 35.5

coverage rate 49.6 48.0 59.8 44.8 55.7 48.4 40.0

inequality 12.1 10.1 13.1 13.1 11.9 14.0 6.0

C 37.5 37.9 46.7 31.7 43.8 34.4 34.0

61.6 55.8 56.7 56.7 62.0 49.2 49.0 60.4 67.8 69.0 62.3 54.8 63.0 64.3 69.7 69.9

66.8 59.0 56.9 60.6 69.9 52.5 57.1 69.4 71.1 76.2 73.3 57.8 68.6 70.2 75.0 81.2

6.8 7.4 5.3 7.7 8.2 6.5 9.3 8.7 7.7 8.7 8.8 7.6 8.4 7.6 7.9 10.5

59.9 51.6 51.6 52.9 61.6 46.1 47.8 60.7 63.4 67.6 64.5 50.1 60.2 62.6 67.1 70.7


State District

Chikmagalur Tumkur Kolar Bangalore Bangalore Mandya Hassan Dakshina Kannada Kodagu Mysore Chamarajanagar Kerala Kasaragod Kannur Wayanad Kozhikode Malappuram Palakkad Thrissur Ernakulam Idukki Kottayam Alappuzha Pathanamthitta

Average

Combined Penalty for

Index

coverage rate 81.9 75.1 72.6 82.1 79.4 79.2 78.9 83.9 81.4 77.8 79.2

inequality 6.9 9.4 11.8 9.3 7.6 9.0 8.8 7.5 8.7 9.5 8.2

82.6 81.0 81.5 78.5 77.3 77.3 82.2 81.3 83.5 85.1 84.2 82.5

10.0 10.3 7.8 9.8 10.8 10.3 9.7 10.3 9.5 8.5 9.2 10.6 220

Average

Rural Penalty for

Index

C 75.0 65.7 60.8 72.7 71.8 70.1 70.1 76.4 72.7 68.3 71.0

coverage rate 80.0 74.3 70.6 77.2 78.2 78.7 78.2 85.6 81.0 75.9 78.0

inequality 7.1 9.9 12.5 11.1 7.9 9.1 8.8 6.7 8.7 10.3 8.4

C 72.9 64.4 58.1 66.1 70.3 69.6 69.4 78.9 72.2 65.6 69.6

72.6 70.7 73.8 68.6 66.5 67.0 72.4 71.0 74.1 76.6 75.1 71.9

82.9 80.5 81.7 78.6 77.4 77.9 81.5 80.7 83.4 86.7 82.4 82.7

9.7 10.9 7.9 9.8 10.7 10.0 9.7 10.0 9.3 7.2 10.5 10.5

73.1 69.7 73.8 68.8 66.7 67.9 71.8 70.8 74.1 79.5 71.9 72.2


State District

Kollam Thiruvananthapuram Lakshadweep Lakshadweep Madhya Pradesh Sheopur Morena Bhind Gwalior Datia Shivpuri Guna Tikamgarh Chhatarpur Panna Sagar Damoh Satna Rewa Umaria Shahdol Sidhi Neemuch Mandsaur

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 81.4 86.4

inequality 10.5 7.9

C 70.9 78.5

coverage rate 81.3 87.0

inequality 10.7 7.8

C 70.6 79.2

81.6

7.6

74.0

81.0

7.0

74.0

33.5 38.5 41.0 46.4 38.5 30.3 30.7 32.5 38.1 40.2 49.1 39.9 46.7 44.8 41.2 49.5 29.2 55.5 51.5

6.7 7.4 7.6 6.9 6.0 6.3 6.3 7.2 8.0 5.4 5.9 5.9 5.3 5.7 5.6 6.5 5.8 7.5 7.2

26.8 31.1 33.4 39.5 32.5 24.0 24.4 25.3 30.1 34.7 43.2 34.0 41.4 39.1 35.6 43.0 23.4 48.0 44.4

31.8 34.8 40.6 40.2 36.5 27.7 25.7 30.9 34.2 38.1 43.9 33.6 44.1 43.4 39.7 45.3 30.4 53.2 47.0

6.4 7.7 8.0 7.3 6.4 6.5 5.8 7.2 8.0 5.5 6.0 6.3 5.7 5.7 6.0 7.2 5.7 8.6 7.0

25.4 27.1 32.6 32.9 30.1 21.2 20.0 23.7 26.2 32.7 37.9 27.2 38.4 37.7 33.8 38.1 24.7 44.6 40.0

221


State District

Ratlam Ujjain Shajapur Dewas Jhabua Dhar Indore West Nimar Barwani East Nimar Rajgarh Vidisha Bhopal Sehore Raisen Betul Harda Hoshangabad Katni Jabalpur Narsimhapur Dindori Mandla Chhindwara

Average

Combined Penalty for

Index

coverage rate 54.0 69.1 56.8 56.5 33.8 44.4 68.9 48.3 33.6 42.5 42.7 39.7 62.0 54.3 43.8 54.4 50.7 59.1 47.3 59.7 52.6 31.7 45.4 53.0

inequality 7.9 6.7 6.5 6.4 5.2 6.4 6.8 7.3 4.1 6.5 6.2 5.7 6.6 6.7 7.5 7.2 5.2 7.9 7.3 5.4 6.9 7.0 7.0 6.9

C 46.0 62.4 50.3 50.1 28.6 37.9 62.1 41.0 29.6 36.0 36.5 34.0 55.4 47.6 36.3 47.2 45.5 51.3 40.1 54.3 45.6 24.7 38.4 46.1 222

Average

Rural Penalty for

Index

coverage rate 50.5 65.6 55.2 52.2 30.8 40.6 52.4 46.9 28.7 39.1 40.4 35.1 41.7 51.3 41.4 49.7 44.6 54.6 44.0 51.6 49.7 31.0 42.5 46.9

inequality 7.7 7.2 6.2 6.5 5.7 6.4 4.9 7.4 4.2 6.1 6.4 5.7 8.4 6.6 7.5 8.1 5.7 7.8 7.8 7.3 6.7 7.1 7.6 6.9

C 42.8 58.4 49.0 45.6 25.1 34.2 47.5 39.5 24.5 33.0 34.0 29.4 33.3 44.7 33.9 41.6 38.9 46.8 36.2 44.3 43.0 23.9 34.9 40.0


State District

Seoni Balaghat Maharashtra Nandurbar Dhule Jalgaon Buldana Akola Washim Amravati Wardha Nagpur Bhandara Gondiya Gadchiroli Chandrapur Yavatmal Nanded Hingoli Parbhani Jalna Aurangabad Nashik Thane

Average

Combined Penalty for

Index

coverage rate 54.4 64.0

inequality 6.7 8.1

41.7 54.5 60.8 70.9 72.3 67.8 72.9 83.7 81.3 74.5 76.2 63.2 72.9 70.8 69.1 61.1 71.0 69.2 64.7 70.2 73.3

4.5 5.4 6.6 7.8 7.3 7.3 6.2 5.3 7.2 6.9 7.5 7.2 7.8 7.3 8.1 7.5 6.7 7.4 7.4 6.2 7.7 223

Average

Rural Penalty for

Index

C 47.7 55.9

coverage rate 53.4 63.1

inequality 7.0 8.8

C 46.4 54.2

37.2 49.1 54.3 63.1 65.0 60.4 66.7 78.4 74.1 67.6 68.7 56.0 65.1 63.6 61.1 53.6 64.3 61.8 57.3 64.0 65.5

37.8 51.8 56.9 68.6 71.4 67.6 70.6 83.4 82.0 74.8 76.3 62.0 70.5 69.0 67.4 62.1 70.1 68.9 60.7 65.1 70.3

5.1 5.5 6.7 8.0 7.6 7.5 6.1 5.9 7.1 7.7 7.8 7.4 8.0 7.5 9.4 7.9 6.9 7.8 7.6 7.4 8.5

32.7 46.2 50.2 60.6 63.7 60.1 64.4 77.5 74.9 67.1 68.5 54.6 62.5 61.4 58.0 54.2 63.2 61.0 53.1 57.7 61.8


State District

Mumbai (Suburban) Mumbai Raigarh Pune Ahmadnagar Bid Latur Osmanabad Solapur Satara Ratnagiri Sindhudurg Kolhapur Sangli Manipur Senapati Tamenglong Churachandpur Bishnupur Thoubal Imphal West Imphal East Ukhrul Chandel

Average

Combined Penalty for

Index

coverage rate 79.1 80.6 73.9 79.5 79.0 69.7 69.8 68.9 74.4 82.3 74.2 84.6 80.1 76.7

inequality 8.8 8.2 7.8 8.5 7.7 7.9 8.3 7.2 8.8 8.9 9.4 7.1 7.5 8.9

52.1 24.9 42.1 68.2 68.8 79.5 65.0 41.7 50.1

9.3 4.9 5.2 6.1 6.4 4.6 4.4 8.0 6.7 224

Average

Rural Penalty for

Index

C 70.3 72.5 66.0 71.0 71.4 61.8 61.5 61.8 65.6 73.4 64.8 77.6 72.6 67.8

coverage rate

inequality

C

72.0 77.1 78.0 69.3 69.2 67.9 71.6 81.9 73.4 85.0 79.1 75.9

8.3 8.0 7.6 8.2 8.3 7.2 9.1 9.1 9.5 7.0 7.9 9.6

63.7 69.2 70.4 61.1 60.8 60.7 62.5 72.9 63.8 77.9 71.1 66.2

42.8 20.0 36.9 62.1 62.4 74.8 60.6 33.8 43.4

52.1 24.9 42.1 64.7 66.7 77.4 60.5 41.7 50.9

9.3 4.9 5.2 6.1 6.8 4.9 4.9 8.0 7.1

42.7 20.0 36.9 58.7 59.9 72.5 55.5 33.7 43.8


State District

Meghalaya West Garo Hills East Garo Hills South Garo Hills West Khasi Hills Ri Bhoi East Khasi Hills Jaintia Hills Mizoram Mamit Kolasib Aizawl Champhai Serchhip Lunglei Lawngtlai Saiha Orissa Bargarh Jharsuguda Sambalpur Debagarh Sundargarh Kendujhar

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate

inequality

C

coverage rate

inequality

C

37.6 28.8 37.2 35.6 52.1 58.9 46.9

7.6 7.6 8.8 5.9 8.1 6.9 7.9

30.0 21.2 28.5 29.7 44.0 52.0 39.0

34.7 26.0 37.5 33.2 51.7 54.1 45.8

6.9 7.7 9.1 6.2 8.4 8.0 8.1

27.9 18.3 28.5 27.0 43.3 46.2 37.7

56.3 66.9 75.0 60.5 69.6 63.6 49.3 62.0

8.7 5.5 5.6 6.9 7.8 8.2 9.5 8.7

47.6 61.4 69.4 53.6 61.7 55.4 39.8 53.4

54.4 55.6 67.9 56.6 67.8 54.9 49.4 58.3

9.8 6.9 7.8 7.5 8.7 9.3 9.5 9.8

44.7 48.7 60.1 49.1 59.1 45.7 39.9 48.6

67.1 68.0 66.8 53.1 60.8 53.4

8.8 9.1 7.9 8.5 9.6 11.5

58.2 58.9 59.0 44.6 51.2 42.0

66.8 68.0 62.5 52.0 56.9 53.0

8.8 9.1 9.2 8.7 10.2 11.3

57.9 58.9 53.3 43.2 46.6 41.7

225


State District

Mayurbhanj Baleshwar Bhadrak Kendrapara Jagatsinghapur Cuttack Jajapur Dhenkanal Anugul Nayagarh Khordha Puri Ganjam Gajapati Kandhamal Baudh Sonapur Balangir Nuapada Kalahandi Rayagada Nabarangapur Koraput Malkangiri

Average

Combined Penalty for

Index

coverage rate 63.5 69.1 63.6 64.8 73.6 64.7 67.7 58.6 56.5 53.6 65.2 68.1 53.0 41.6 52.8 54.5 70.1 63.0 60.3 52.5 47.4 54.5 54.3 51.2

inequality 10.4 10.1 10.6 10.5 9.1 9.0 9.6 9.9 10.9 8.7 8.5 9.0 7.0 9.5 10.8 10.5 7.8 10.3 6.9 8.2 9.6 11.0 12.2 10.2

C 53.1 59.0 52.9 54.2 64.6 55.7 58.1 48.7 45.6 44.9 56.7 59.1 46.0 32.1 42.0 44.0 62.2 52.6 53.4 44.3 37.8 43.4 42.1 41.0 226

Average

Rural Penalty for

Index

coverage rate 62.3 67.9 62.2 64.5 74.0 63.1 67.8 56.4 54.4 53.4 62.5 67.3 51.7 40.2 52.1 54.3 69.7 63.3 59.8 52.3 45.8 54.1 52.1 51.0

inequality 10.8 10.0 10.8 10.7 9.2 9.2 9.6 10.3 11.5 8.7 10.0 9.2 7.3 9.7 10.9 10.6 7.9 10.6 6.9 8.4 9.7 11.2 13.0 10.2

C 51.5 57.8 51.4 53.8 64.9 53.9 58.2 46.1 42.9 44.7 52.5 58.1 44.4 30.5 41.2 43.7 61.7 52.8 52.8 44.0 36.1 42.9 39.2 40.8


State District

Puducherry Yanam Pondicherry Mahe Karaikal Punjab Gurdaspur Amritsar Kapurthala Jalandhar Hoshiarpur Nawanshahr Rupnagar Fatehgarh Sahib Ludhiana Moga Firozpur Muktsar Faridkot Bathinda Mansa Sangrur Patiala Tarn Taran

Average

Combined Penalty for

Index

coverage rate

inequality

C

62.9 82.0 88.3 80.2

7.1 10.5 5.8 9.6

55.8 71.5 82.5 70.6

61.7 73.6 69.2 71.0 70.0 68.0 67.7 66.0 61.7 73.6 71.4 73.3 72.3 72.6 56.3 64.1 65.6 67.1

10.2 9.9 9.8 9.0 10.3 8.3 8.2 8.4 7.6 9.4 9.1 9.0 7.4 8.6 10.6 8.3 10.4 11.1

51.5 63.7 59.4 62.0 59.7 59.7 59.5 57.7 54.1 64.2 62.2 64.3 64.8 64.1 45.7 55.8 55.2 56.0 227

Average

Rural Penalty for

Index

coverage rate

inequality

C

85.8

8.2

77.6

77.5

10.7

66.8

60.7 69.1 67.9 70.1 68.2 67.3 69.8 66.9 63.6 71.5 70.9 70.3 72.7 70.7 53.0 65.9 65.3 68.6

9.9 9.9 11.0 10.1 10.8 8.3 8.6 9.3 9.9 9.9 9.7 10.0 8.2 9.1 10.8 8.3 10.3 11.5

50.8 59.2 56.9 59.9 57.5 59.0 61.2 57.6 53.6 61.6 61.2 60.3 64.5 61.6 42.2 57.5 54.9 57.1


State District

SAS Nagar Barnala Rajasthan Ganganagar Hamumangarh Bikaner Churu Jhunjhunun Alwar Bharatpur Dhaulpur Karauli Sawai Dausa Jaipur Sikar Nagaur Jodhpur Jaisalmer Barmer Jalor Sirohi Pali Ajmer

Average

Combined Penalty for

Index

coverage rate 66.5 61.9

inequality 7.8 10.7

51.9 44.4 39.5 38.8 53.9 37.0 30.1 34.3 40.0 38.6 46.6 53.7 53.8 48.7 45.8 37.9 40.6 45.7 49.9 50.4 53.7

9.3 10.4 6.1 7.1 8.1 6.0 5.2 7.1 6.5 5.0 7.1 7.1 6.7 7.3 8.2 7.5 9.1 7.9 8.8 8.5 5.6 228

Average

Rural Penalty for

Index

C 58.7 51.2

coverage rate 67.5 58.5

inequality 8.1 10.3

C 59.4 48.2

42.6 34.1 33.4 31.7 45.9 31.0 24.9 27.3 33.5 33.6 39.5 46.6 47.1 41.4 37.6 30.4 31.5 37.8 41.1 42.0 48.1

49.3 44.5 33.5 40.0 53.5 35.1 27.9 33.0 37.6 35.1 45.8 46.4 53.3 44.7 40.4 37.0 39.9 44.9 47.8 49.8 44.7

10.5 10.6 7.1 8.7 8.7 6.2 5.3 7.5 6.5 5.2 7.3 7.7 7.7 7.8 8.6 7.8 9.1 7.8 9.2 8.8 6.5

38.8 34.0 26.4 31.4 44.7 28.9 22.6 25.5 31.1 29.9 38.5 38.7 45.7 37.0 31.7 29.1 30.8 37.1 38.7 40.9 38.2


State District

Tonk Bundi Bhilwara Rajsamand Udaipur Dungarpur Banswara Chittaurgarh Kota Baran Jhalawar Sikkim Sikkim North Sikkim West Sikkim South Sikkim East Tamil Nadu Thiruvallur Chennai Kancheepuram Vellore Dharmapuri Tiruvannamalai Viluppuram

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 49.8 51.6 54.2 53.4 53.0 59.6 55.7 51.8 62.8 50.3 44.0

inequality 6.2 6.0 9.5 8.8 11.1 11.7 13.3 8.8 7.1 6.4 6.2

C 43.6 45.6 44.7 44.6 41.9 47.9 42.4 43.0 55.7 43.9 37.8

coverage rate 44.0 47.9 51.4 50.2 49.7 58.4 54.6 49.6 56.0 47.9 40.7

inequality 6.6 5.6 9.7 8.9 11.9 12.0 13.7 9.5 8.0 6.8 6.4

C 37.4 42.3 41.7 41.3 37.8 46.4 40.9 40.1 48.1 41.0 34.3

67.9 63.7 64.1 71.6

11.2 11.9 11.6 9.2

56.7 51.8 52.6 62.4

67.6 63.3 64.0 70.5

11.4 12.2 11.6 9.3

56.3 51.1 52.4 61.2

85.3 85.6 80.4 81.5 77.1 79.0 77.9

6.9 8.0 9.9 7.2 9.8 8.2 10.8

78.5 77.7 70.5 74.4 67.3 70.8 67.1

78.4

10.7

67.7

80.4 81.7 74.4 78.1 77.2

9.0 7.4 10.8 8.5 11.2

71.4 74.3 63.6 69.5 66.0

229


State District

Salem Namakkal Erode The Nilgiris Coimbatore Dindigul Karur Trichy Ariyalur Krishnagiri Cuddalore Nagapattinam Thiruvarur Thanjavur Pudukottai Sivganga Madurai Theni Virudhunagar Ramanathpuram Thoothukudi Thirunelveli Kanniyakumari

Average

Combined Penalty for

Index

coverage rate 83.2 81.4 85.1 85.9 79.7 78.3 82.9 77.9 83.8 79.5 76.9 79.3 79.5 79.1 77.0 78.1 77.6 75.9 76.4 79.0 81.2 78.9 81.1

inequality 7.2 7.5 7.4 7.2 9.9 9.2 6.7 10.6 7.6 6.9 10.6 11.9 9.8 9.9 10.5 10.2 8.4 8.2 8.2 8.8 7.6 9.1 8.4

C 76.0 73.8 77.7 78.7 69.8 69.1 76.3 67.3 76.2 72.6 66.3 67.4 69.7 69.2 66.5 67.9 69.2 67.7 68.2 70.2 73.6 69.8 72.7

230

Average

Rural Penalty for

Index

coverage rate 81.4 77.1 81.4 84.1 87.1 78.7 82.5 76.5 83.8 78.6 76.4 78.8 78.0 79.3 76.9 75.9 74.7 71.5 76.3 78.9 81.2 78.8 80.2

inequality 7.7 10.5 10.4 9.3 7.4 8.9 6.6 10.4 7.7 7.8 9.9 12.0 10.5 9.7 10.3 10.6 8.1 9.0 8.8 9.5 7.0 8.9 9.7

C 73.7 66.6 71.0 74.7 79.7 69.8 75.9 66.1 76.1 70.7 66.5 66.7 67.5 69.6 66.6 65.2 66.6 62.5 67.5 69.4 74.1 69.9 70.5


State District

Tripura West Tripura South Tripura Dhalai North Tripura Uttar Pradesh Saharanpur Muzaffarnagar Bijnor Moradabad Rampur Jyotiba Phule Nagar Meerut Baghpat Ghaziabad Gautam Budh Nagar Bulandshahar Aligarh Hathras Mathura Agra Firozabad Etah Mainpuri

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate

inequality

C

coverage rate

inequality

C

56.0 49.4 41.3 45.0

9.8 9.8 4.7 5.6

46.2 39.6 36.7 39.3

55.9 48.3 40.4 43.4

10.2 9.6 4.6 6.0

45.8 38.8 35.8 37.4

42.1 43.6 46.1 32.9 35.7 29.6 44.4 46.7 41.5 40.2 43.0 37.7 34.7 33.6 33.3 35.4 27.0 29.5

8.5 11.1 11.5 6.6 9.2 7.5 8.5 10.2 6.8 7.8 11.1 9.1 7.0 7.5 6.9 7.9 8.5 6.5

33.5 32.5 34.6 26.3 26.5 22.1 36.0 36.5 34.8 32.3 31.9 28.7 27.6 26.1 26.4 27.5 18.4 23.1

41.5 42.7 46.5 31.3 34.7 29.6 40.0 46.0 36.5 40.3 41.7 35.4 33.6 30.5 30.6 33.0 25.1 28.3

8.1 11.0 11.6 6.4 9.0 7.6 8.5 10.0 6.7 8.3 11.2 9.2 6.9 7.1 6.6 8.4 8.6 6.6

33.4 31.7 34.8 24.8 25.7 22.0 31.5 35.9 29.8 32.0 30.5 26.2 26.7 23.4 24.0 24.6 16.5 21.7

231


State District

Budaun Bareilly Pilibhit Shahjahanpur Kheri Sitapur Hardoi Unnao Lucknow Rae Bareli Farrukhabad Kannauj Etawah Auraiya Kanpur Kanpur Jalaun Jhansi Lalitpur Hamirpur Mahoba Banda Chitrakoot Fatehpur

Average

Combined Penalty for

Index

coverage rate 19.7 32.4 27.5 21.5 23.4 23.9 28.3 32.4 50.3 40.0 23.4 31.7 34.3 33.6 35.5 48.0 32.7 43.1 37.1 41.2 41.1 29.3 30.6 30.1

inequality 5.3 8.2 7.4 6.8 4.8 5.7 6.0 8.9 8.4 7.9 5.9 7.7 7.2 8.2 8.3 8.9 6.0 7.5 7.1 8.4 7.7 5.5 5.9 6.4

C 14.4 24.2 20.0 14.7 18.6 18.2 22.2 23.5 41.8 32.1 17.5 24.0 27.1 25.4 27.1 39.0 26.7 35.6 30.0 32.8 33.4 23.7 24.7 23.7 232

Average

Rural Penalty for

Index

coverage rate 18.3 31.1 27.6 20.3 23.0 22.1 26.4 30.5 44.2 39.7 21.7 31.1 32.6 32.4 34.7 36.6 29.7 44.1 35.4 39.4 40.1 26.3 29.9 29.2

inequality 5.0 8.4 7.5 6.7 4.8 5.6 5.8 9.2 10.4 7.9 6.0 7.7 7.7 8.1 8.1 10.6 5.7 7.7 7.2 8.8 7.9 5.4 5.9 6.4

C 13.3 22.7 20.1 13.6 18.2 16.5 20.6 21.3 33.8 31.7 15.7 23.4 24.9 24.4 26.6 25.9 24.0 36.4 28.2 30.6 32.3 20.9 24.0 22.8


State District

Pratapgarh Kaushambi Allahabad Barabanki Faizabad Ambedaker Nagar Sultanpur Bahraich Shrawasti Balrampur Gonda Siddharthnagar Basti Sant Kabir Nagar Maharajganj Gorakhpur Kushinagar Deoria Azamgarh Mau Ballia Jaunpur Ghazipur Chandauli

Average

Combined Penalty for

Index

coverage rate 43.5 27.1 34.4 32.3 38.8 39.2 39.3 19.9 20.3 20.3 25.2 29.2 39.0 34.6 40.1 48.6 41.1 53.5 38.4 40.2 38.9 35.8 36.8 33.8

inequality 10.4 5.6 4.3 6.3 8.5 10.9 10.2 7.1 6.4 6.9 7.1 10.6 11.8 11.4 8.3 9.3 8.8 10.7 9.5 10.2 10.5 10.1 8.7 5.7

C 33.1 21.5 30.1 26.1 30.3 28.3 29.1 12.8 13.9 13.4 18.1 18.5 27.3 23.2 31.8 39.2 32.3 42.8 28.9 30.0 28.4 25.7 28.1 28.0 233

Average

Rural Penalty for

Index

coverage rate 42.9 25.3 30.7 31.5 37.7 39.9 38.5 19.1 20.2 20.0 24.1 28.9 38.4 34.0 39.1 46.0 41.0 54.1 39.1 41.3 38.1 35.6 36.4 33.2

inequality 10.4 5.5 4.3 6.0 8.6 11.4 10.3 7.2 6.4 6.9 7.1 10.6 11.9 11.4 8.3 9.6 8.8 11.0 9.6 10.5 10.5 10.1 8.6 5.6

C 32.5 19.8 26.4 25.5 29.1 28.5 28.2 12.0 13.8 13.0 17.1 18.2 26.5 22.6 30.8 36.4 32.2 43.2 29.5 30.8 27.6 25.4 27.8 27.6


State District

Varanasi Sant Ravidas Nagar Mirzapur Sonbhadra Uttarakhand Uttarkashi Chamoli Rudraprayag Tehri Garhwal Dehradun Garhwal Pithoragarh Bageshwar Almora Champawat Nainital Udham Singh Nagar Haridwar West Bengal Darjiling Jalpaiguri Koch Behar Uttar Dinajpur Dakshin Dinajpur

Average

Combined Penalty for

Average

Rural Penalty for

Index

Index

coverage rate 46.8 29.8 29.5 34.8

inequality 10.3 6.4 5.6 8.6

C 36.5 23.5 23.9 26.2

coverage rate 43.5 29.8 28.1 33.7

inequality 11.1 6.5 5.4 8.2

C 32.4 23.2 22.7 25.5

51.8 60.5 55.7 52.8 62.3 53.2 55.1 41.6 56.1 48.3 52.5 58.1 48.2

12.8 9.3 12.0 12.3 6.9 11.0 10.9 10.4 12.9 9.7 8.9 9.6 7.4

39.0 51.2 43.6 40.5 55.4 42.2 44.2 31.2 43.2 38.6 43.5 48.5 40.8

50.5 59.1 55.1 51.6 55.6 52.3 54.2 41.3 55.4 46.6 49.7 56.3 45.6

13.2 9.6 12.2 12.5 8.0 11.2 10.9 10.4 13.4 9.5 10.0 11.1 8.0

37.4 49.5 42.9 39.1 47.6 41.1 43.3 30.9 42.0 37.1 39.7 45.2 37.6

71.4 65.2 62.2 54.8 66.2

8.4 11.4 11.6 8.9 12.4

63.0 53.8 50.6 45.9 53.8

69.5 63.6 61.2 53.7 65.8

9.7 12.3 11.6 8.7 12.4

59.8 51.3 49.6 44.9 53.4

234


State District

Maldah Murshidabad Birbhum Barddhaman Nadia North 24 Pargnas Hugli Bankura Puruliya Pachim Medinipur Haora Kolkata South 24 Parganas Purab Medinipur Source:

Average

Combined Penalty for

Index

coverage rate 58.3 59.4 60.2 64.2 72.3 69.4 73.9 69.2 63.8 64.1 68.2 72.0 57.3 67.8

inequality 10.0 9.4 9.2 7.8 9.7 10.3 10.8 10.9 9.7 11.6 9.0 8.9 12.9 11.3

C 48.3 50.0 51.0 56.3 62.6 59.1 63.1 58.3 54.1 52.5 59.2 63.0 44.4 56.5

Authorâ&#x20AC;&#x2122;s calculations

235

Average

Rural Penalty for

Index

coverage rate 58.0 59.3 60.4 65.7 70.7 64.5 74.0 68.7 63.2 64.2 68.1

inequality 10.1 9.8 9.3 8.7 10.5 10.7 10.2 11.5 10.0 11.5 9.9

C 47.9 49.5 51.1 57 60.2 53.9 63.8 57.1 53.2 52.6 58.3

56.4 67.6

13.0 11.3

43.4 56.2


236


Appendix Table 5.1 Composite coverage index in districts of India State District Health Andaman & Nicobar Islands Andamans Nicobars Andhra Pradesh Adilabad Nizamabad Karimnagar Medak Hyderabad Rangareddi Mahbubnagar Nalgonda Warangal Khammam Srikakulam Vizianagaram Visakhapatnam E Godavari W Godavari Krishna Guntur Prakasam

Combined population Maternal Child immunisation

Rural population Maternal Child

Child

Health

health

system

health

system

health

76.2 82.3

68.6 78.7

90.6 89.4

60.0 74.2

76.2 81.3

68.6 77.9

90.5 84.4

59.9 82.1

56.8 78.6 82.5 77.0 78.1 79.4 73.4 79.8 76.5 75.4 72.1 71.2 66.0 78.2 80.4 84.4 74.8 73.4

51.6 86.1 92.1 81.0 89.3 89.0 74.2 83.4 85.3 76.8 74.0 69.4 67.1 85.3 85.2 89.8 80.3 78.4

72.8 90.5 92.9 92.6 85.5 90.5 87.6 92.3 86.6 87.0 83.1 81.1 74.3 83.6 89.1 93.3 83.1 85.0

30.7 31.4 34.4 29.5 32.9 29.3 37.6 41.1 30.7 43.8 40.8 51.8 43.3 48.0 47.6 50.1 41.4 33.2

54.3 78.7 83.0 76.2

47.5 85.9 90.2 79.0

70.1 91.9 92.7 91.6

32.5 29.0 41.8 31.6

79.5 72.7 78.7 75.4 73.1 70.5 68.6 61.1 77.7 80.6 84.4 74.1 73.0

85.9 73.1 82.2 80.8 74.3 72.0 65.4 58.4 84.0 86.0 89.2 78.6 76.5

93.8 87.2 92.6 86.6 85.6 82.1 78.1 69.4 82.8 88.6 92.8 82.3 85.1

29.1 36.5 36.1 34.8 39.8 38.4 53.0 47.1 49.9 48.0 52.3 43.2 35.0

237

immunisation

Child health


State District Health Nellore Cuddapah Kurnool Anantapur Chittoor Arunachal Pradesh Tawang West Kameng East Kameng Papum Pare Lower Subansiri Upper Subansiri West Siang East Siang Upper Siang Dibang Valley Lohit Changlang Tirap Kurung Kumey Lower Dibang Valley Anjaw Assam Kokrajhar

Combined population Maternal Child immunisation 81.8 84.3 82.7 90.3 85.1

Rural population Maternal Child

Child

Health

health 36.3 37.8 42.2 44.5 53.1

system 74.5 73.5 66.4 74.5 77.1

health 83.2 77.6 62.1 72.2 77.5

system 75.5 74.5 69.5 75.8 76.6

health 85.4 80.0 67.6 74.2 77.9

48.9 48.7 30.7 52.1 44.4 42.2 40.7 44.5 26.1 53.2 45.5 53.4 48.5 43.8 48.0 39.9

43.8 46.2 28.1 57.5 49.6 46.8 41.2 43.2 15.4 49.7 48.3 51.4 38.3 44.7 42.2 37.3

55.5 50.1 33.5 48.3 37.1 37.0 39.6 42.9 32.0 53.5 54.8 51.5 58.6 39.0 55.0 39.5

45.5 51.4 30.1 48.3 49.3 43.5 42.4 51.6 37.8 60.9 16.2 62.8 48.6 53.2 45.3 47.0

45.9 47.3 27.0 45.2 45.6 32.1 38.0 40.1 26.0 53.8 44.4 52.3 45.0 43.8 44.6 39.9

38.5 43.8 18.1 47.6 53.9 40.7 31.2 37.9 15.4 50.1 46.3 49.9 32.6 44.7 37.0 37.3

54.5 49.0 38.1 45.8 36.4 19.6 39.4 37.2 31.9 54.6 55.0 51.1 55.8 39.1 52.8 39.7

43.3 51.4 21.8 38.0 47.6 41.7 51.3 52.5 37.6 60.7 14.1 61.4 48.8 53.3 43.1 47.1

45.4

32.1

54.2

56.3

44.5

30.2

54.5

55.4

238

immunisation 81.5 83.3 80.6 89.5 86.2

Child health 35.7 39.6 42.4 43.4 53.7


State District Health Dhubri Goalpara Bongaigaon Barpeta Kamrup Nalbari Darrang Marigaon Nagaon Sonitpur Lakhimpur Dhemaji Tinsukia Dibrugarh Sibsagar Jorhat Golaghat Karbi Anglong North Cachar Hills Cachar Karimganj Hailakandi Chirang Baska

system 36.7 43.1 50.8 53.4 70.4 64.5 54.7 51.0 46.1 60.5 50.2 44.2 54.7 69.3 62.5 61.7 53.3 55.3 47.1 45.3 39.4 38.3 50.3 56.9

Combined population Maternal Child health 19.5 30.1 35.1 36.5 67.8 52.3 39.9 34.0 36.4 48.2 36.9 27.8 45.9 55.6 50.7 46.3 39.1 39.3 43.4 39.8 36.0 36.6 32.3 44.5

immunisation 49.7 51.4 66.3 72.1 80.8 82.6 70.9 68.0 60.7 73.5 63.3 57.1 68.2 89.3 78.5 78.9 70.0 73.7 55.2 58.5 49.8 48.9 69.9 73.9 239

Child

Health

health 47.3 54.5 51.4 49.0 51.7 50.2 51.6 50.9 34.4 59.2 50.7 52.6 43.4 54.3 52.4 57.6 47.2 49.7 36.4 27.1 22.5 16.7 47.0 45.7

system 35.2 45.7 49.2 52.2 56.5 64.1 53.4 50.8 44.9 59.2 49.5 43.2 52.2 68.3 61.3 60.0 53.7 59.5 35.1 44.5 38.1 37.4 50.3 56.8

Rural population Maternal Child health 17.0 29.9 31.7 34.6 44.3 51.6 36.2 33.5 33.6 45.9 36.0 26.5 42.6 51.9 47.9 42.7 38.7 36.3 27.3 38.3 33.8 34.6 32.3 44.4

immunisation 48.7 52.4 66.2 71.3 76.1 82.5 71.0 68.2 60.7 72.6 62.7 56.0 65.0 89.6 79.0 78.5 70.6 86.2 47.8 57.7 48.9 48.5 69.7 73.9

Child health 47.0 67.9 50.9 49.0 38.6 49.6 52.4 51.0 33.8 59.3 50.7 53.3 44.6 56.3 50.5 57.3 49.5 51.5 23.3 27.4 22.0 17.0 46.8 45.5


State District Health

Combined population Maternal Child

Child

Health

health 52.7 55.4

system 64.7 51.4

health 51.4 36.5

immunisation 82.6 64.8

Child

system 66.1 52.5

health 54.2 38.0

Pashchim

29.5

24.0

43.4

9.4

29.5

23.2

43.9

9.7

Champaran Purba Champaran Sheohar Sitamarhi Madhubani Supaul Araria Kishanganj Purnia Katihar Madhepura Saharsa Darbhanga Muzaffarpur Gopalganj Siwan Saran Vaishali Samastipur Begusarai

38.6 28.8 33.8 35.9 34.9 36.0 32.7 33.8 35.5 37.0 37.3 42.5 42.2 45.6 48.8 46.8 48.7 43.5 41.8

36.5 17.3 24.3 25.0 24.9 27.4 33.1 23.0 22.8 21.3 17.2 32.3 28.0 35.8 38.9 30.1 25.8 33.4 36.5

53.5 45.7 53.3 59.1 56.1 54.8 41.7 55.0 55.4 60.7 63.8 65.0 69.1 70.3 74.1 75.5 75.3 66.9 60.1

7.8 15.7 9.7 6.4 7.6 11.3 9.9 9.2 18.2 17.8 22.4 13.0 11.7 9.8 12.0 18.3 40.2 11.8 10.3

38.5 28.4 32.7 36.6 34.8 35.3 31.8 33.1 34.6 36.7 36.8 42.0 41.0 45.9 49.4 46.2 48.4 43.4 41.8

36.1 16.5 22.8 25.1 25.0 26.8 30.7 21.2 20.5 20.8 16.0 31.8 24.5 35.7 38.6 29.0 24.2 33.2 36.2

53.5 45.5 52.0 60.4 55.9 53.9 41.9 55.0 55.3 60.5 63.3 64.5 69.5 71.0 75.5 75.4 75.8 67.1 60.5

7.9 15.9 9.8 6.6 7.6 10.9 10.1 8.9 18.6 17.8 22.6 12.1 11.7 9.3 12.0 17.2 40.8 10.6 10.2

Kamrup Metro Udalguri

immunisation 83.6 65.8

Rural population Maternal Child

health 53.6 55.4

Bihar

240


State District Health Khagaria Bhagalpur Banka Munger Lakhisarai Sheikhpura Nalanda Patna Bhojpur Buxar Kaimur Rohtas Jehanabad Aurangabad Gaya Nawada Jamui Chandigarh Chandigarh Chhattisgarh Koriya Surguja Jashpur Raigarh

system 43.5 46.8 38.3 50.9 34.9 46.2 47.3 42.5 39.3 36.9 34.5 42.0 43.8 46.4 33.9 39.0 28.0

Combined population Maternal Child health 33.8 27.9 30.3 46.9 26.3 46.9 31.7 34.9 27.3 30.8 33.9 34.0 34.7 25.5 22.9 26.4 24.5

immunisation 67.7 71.1 56.6 67.5 50.4 58.8 70.4 58.3 55.7 49.8 41.6 60.9 65.3 79.0 53.2 63.2 34.9

Rural population Maternal Child

Child

Health

health 8.7 33.7 13.5 20.8 18.3 13.9 29.7 22.9 28.8 20.8 18.7 15.6 14.2 18.6 14.2 11.0 19.8

system 43.1 46.7 37.7 50.1 35.2 46.5 46.0 39.2 39.6 35.0 34.2 40.6 44.6 46.1 32.1 38.8 27.5

health 32.2 23.5 29.9 45.4 25.4 47.4 28.3 28.5 25.9 27.8 33.6 30.5 35.0 24.6 19.4 25.3 22.9

immunisation 68.3 75.2 55.5 68.2 51.9 58.6 70.2 56.4 57.7 48.5 41.0 61.2 66.6 79.0 52.4 63.7 35.4

Child health 8.5 33.7 13.1 17.2 18.7 14.6 30.4 23.3 28.8 20.0 18.9 14.8 14.6 18.4 13.5 11.0 19.7

70.2

75.1

80.1

34.3

63.6

65.1

70.3

43.7

46.6 45.0 48.5 56.8

31.6 27.4 27.8 44.0

71.2 65.0 73.1 75.2

23.6 39.6 39.5 43.2

41.9 43.9 47.7 54.9

22.9 25.9 26.4 40.5

68.8 64.0 72.5 73.9

22.6 39.4 39.6 43.7

241


State District Health Korba Janjgir-Champa Bilaspur Kawardha Rajnandgaon Durg Raipur Mahasamund Dhamtari Kanker Bastar Dantewada Dadra and Nagar Haveli DN Haveli Daman and Diu Diu Daman Delhi North West North North East East New Delhi Central

Combined population Maternal Child immunisation 66.1 73.2 76.3 74.5 80.9 83.6 78.1 86.1 81.0 84.9 76.9 81.5

Rural population Maternal Child

Child

Health

health 31.2 41.9 38.1 34.8 43.7 47.7 51.2 49.1 54.7 55.0 56.4 58.0

system 40.4 51.7 54.0 50.9 58.7 62.3 55.7 64.2 64.3 62.6 56.8 59.6

health 22.5 36.0 40.2 35.1 43.0 46.3 37.8 49.2 53.5 43.8 37.4 38.6

system 46.9 53.3 56.4 52.3 60.2 64.1 60.0 66.1 66.6 63.1 58.2 61.0

health 34.2 38.2 44.1 37.4 46.4 51.4 45.6 53.1 57.2 44.8 40.3 41.7

62.0

56.5

77.7

37.5

57.7

49.0

75.0

36.8

72.8 80.0

71.9 90.3

92.4 88.8

27.7 33.9

70.5 81.3

65.0 89.9

93.6 90.5

27.4 37.6

64.7 67.4 59.9 67.3 67.3 74.4

72.5 74.4 67.3 73.6 72.3 84.6

71.4 77.7 67.2 79.0 78.2 83.2

29.8 25.5 24.6 23.8 29.1 28.7

65.0

75.0

69.0

30.3

69.5

75.1

82.4

23.8

242

immunisation 63.1 72.4 75.1 73.3 80.0 82.6 76.6 86.2 79.6 84.8 76.6 81.1

Child health 29.3 39.7 36.2 35.1 45.2 52.2 48.7 47.1 53.3 54.2 56.3 58.8


State District Health West South West South

Combined population Maternal Child immunisation 77.3 84.8 76.7

Rural population Maternal Child

Child

Health

health 24.8 22.7 28.6

system 52.9 73.9 67.4

health 43.4 78.9 68.8

immunisation 74.7 87.6 83.3

Child

system 67.3 70.1 66.6

health 74.9 75.1 72.1

health 22.8 28.3 25.2

87.3 84.2

96.9 94.6

93.6 88.5

49.2 48.7

88.4 87.7

98.8 97.1

94.1 95.2

48.6 46.3

53.0 48.5 67.8 71.2 55.9 70.1 68.7 49.7 72.1 69.7 72.7 68.7 60.1 60.4 69.3 65.0 56.0

54.0 42.8 64.2 71.5 56.7 70.6 73.3 47.0 75.5 70.7 72.7 68.6 60.1 57.0 73.8 69.4 54.4

61.4 58.5 84.5 85.3 64.2 79.7 73.4 61.0 80.0 78.4 87.6 80.7 73.4 69.4 80.4 77.3 66.3

30.3 38.3 35.8 36.7 33.7 46.0 46.2 29.0 44.9 46.5 37.0 40.1 28.4 46.8 31.6 25.0 34.8

51.1 47.3 66.6 70.5 53.6 68.4 54.0 46.4 69.0 66.6 68.6 65.9 58.6 57.3 67.5 63.7 53.9

47.5 41.7 61.5 67.8 53.8 65.9 40.5 39.4 68.0 66.6 65.5 63.3 58.1 49.7 69.3 67.6 50.6

62.1 56.9 83.6 86.8 63.1 80.0 74.0 61.2 79.8 77.2 85.3 78.9 71.7 70.2 78.7 75.6 65.4

32.8 37.7 37.4 37.4 30.0 46.1 38.0 27.7 45.1 41.1 35.6 40.8 27.9 44.7 35.4 25.1 33.6

Goa North Goa South Goa Gujarat Kachchh Banaskantha Patan Mahesana Sabarkantha Gandhinagar Ahmadabad Surendranagar Rajkot Jamnagar Porbandar Junagarh Amreli Bhavnagar Anand Kheda Panch Mahals

243


State District Health Dohad Vadodara Narmada Bharuch Surat The Dangs Navsari Valsad Haryana Panchkula Ambala Yamunanagar Kurukshetra Kaithal Karnal Panipat Sonipat Jind Fatehabad Sirsa Hisar Bhiwani Rohtak Jhajjar

Combined population Maternal Child

system 47.5 58.9 59.3 62.5 74.8 38.4 72.5 62.0

health 51.0 51.7 41.1 57.4 77.7 19.2 80.2 60.7

70.2 66.3 64.0 65.2 57.4 63.3 52.6 63.3 53.6 50.2 57.5 52.1 51.5 60.4 54.9

73.3 66.3 66.6 68.6 55.5 61.5 53.0 64.4 53.3 46.8 58.8 50.1 47.3 56.6 52.9

immunisation 52.8 72.9 78.6 77.4 88.9 53.7 79.0 68.9 85.9 84.1 81.7 79.1 76.9 85.4 68.3 81.6 69.6 69.4 73.6 68.8 68.9 79.3 72.3

Child

Health

health 25.9 42.4 56.7 38.8 34.0 47.7 38.1 48.3

system 47.1 50.8 57.6 58.9 76.9 38.3 77.3 62.1

health 50.9 39.0 37.8 49.3 73.9 19.0 82.7 59.8

68.0 62.1 60.9 63.6 54.6 61.9 54.7 61.4 50.3 51.0 53.3 49.8 51.2 55.0 55.2

67.9 59.6 60.3 64.8 50.4 58.4 52.9 60.9 50.4 45.5 52.2 45.8 47.6 50.7 52.8

24.4 23.7 15.1 23.3 14.5 14.0 13.6 16.6 15.7 12.1 15.5 16.3 19.7 24.0 17.7 244

Rural population Maternal Child immunisation 51.7 67.6 78.0 76.4 90.5 53.9 87.1 69.8 87.3 79.4 80.8 77.9 75.0 85.2 72.9 80.4 65.3 72.9 70.9 68.2 67.7 72.3 72.0

Child health 26.3 39.1 56.2 39.6 51.1 47.7 40.3 48.8 21.0 26.2 14.0 25.6 15.1 13.6 14.4 16.2 13.3 10.8 13.1 14.9 19.6 23.3 20.0


State District Health Mahendragarh Rewari Gurgaon Faridabad Mewat Himachal Pradesh Chamba Kangra Lahul & Spiti Kullu Mandi Hamirpur Una Bilaspur Solan Sirmaur Shimla Kinnaur Jammu & Kashmir Kupwara Baramula Srinagar Badgam Pulwama

Combined population Maternal Child immunisation 75.0 77.1 73.3 55.1 21.3

Rural population Maternal Child

Child

Health

health 21.0 22.9 16.3 12.5 4.9

system 57.6 62.8 50.5 34.9 20.1

health 57.5 66.4 47.3 34.0 26.6

system 58.7 64.4 55.5 42.6 20.8

health 58.1 68.9 53.9 42.5 27.0

60.9 71.9 70.0 73.1 62.0 82.2 73.0 77.0 69.1 64.6 69.1 66.9

42.0 59.3 66.5 59.7 40.7 70.9 62.3 67.2 59.0 45.6 60.3 51.8

86.1 91.9 88.4 94.1 88.4 97.1 95.7 92.7 89.2 91.3 86.1 90.1

45.5 54.3 34.2 54.7 49.7 73.7 44.3 63.0 45.0 46.3 49.8 47.4

59.7 71.6 69.8 71.9 60.6 81.9 71.9 77.7 69.0 63.9 64.8 66.8

39.7 59.2 65.8 57.1 38.4 71.0 60.4 67.5 57.2 43.0 53.4 51.7

86.0 91.3 88.3 94.2 88.0 96.5 95.8 92.9 91.0 92.3 83.5 90.0

44.7 54.2 34.1 53.8 48.5 73.1 42.0 65.9 44.4 45.6 47.2 47.4

61.9 68.8 80.1 75.7 74.3

54.7 67.6 85.6 72.8 71.2

69.7 71.0 85.6 82.0 83.1

60.5 66.2 53.5 67.6 60.6

61.6 68.0 74.9 74.8 74.3

54.1 65.6 71.4 71.3 71.0

69.4 71.0 84.0 81.1 83.3

60.8 66.5 61.4 68.2 60.4

245

immunisation 74.4 76.5 69.0 45.0 20.1

Child health 16.6 20.3 13.0 12.5 4.5


State District Health Anantanag Leh Kargil Doda Udhampur Punch Rajauri Jammu Kathua Jharkhand Garhwa Palamu Chatra Hazaribagh Kodarma Giridih Deoghar Godda Sahibganj Pakaur Dumka Dhanbad Bokaro Ranchi

Combined population Maternal Child

system 69.9 71.3 71.2 47.5 59.6 45.0 53.3 70.6 73.2

health 69.0 62.3 57.1 42.5 46.7 31.9 41.7 73.7 69.0

47.9 46.5 41.2 58.7 49.7 34.0 43.9 37.5 35.3 42.8 45.1 51.1 58.0 58.6

23.5 27.1 26.3 44.5 39.7 25.5 26.7 20.7 16.4 20.8 26.1 46.4 45.6 41.0

immunisation 72.3 87.4 87.2 55.7 78.6 57.0 62.8 80.4 90.0 78.8 68.6 60.4 82.6 66.1 41.8 60.7 51.5 54.1 64.4 69.7 64.6 78.0 83.3

Child

Health

health 66.2 54.3 66.5 39.6 44.9 47.9 58.7 39.4 42.6

system 68.3 70.1 70.4 46.0 56.9 43.5 52.2 69.1 71.8

health 66.7 58.9 55.5 40.2 41.7 30.0 39.7 66.2 66.4

47.6 45.5 39.9 58.0 47.7 32.8 43.1 37.1 34.3 41.9 43.8 48.5 51.7 55.8

22.9 26.1 25.4 41.8 36.4 23.8 25.4 20.0 14.7 19.4 24.1 39.7 35.2 32.1

32.5 40.2 31.0 35.6 34.6 35.8 44.9 44.3 35.7 43.9 32.1 29.9 39.3 41.8 246

Rural population Maternal Child immunisation 70.9 87.8 86.5 54.7 77.5 55.3 62.3 82.1 88.9 78.7 67.3 58.4 83.5 64.6 41.2 59.8 51.3 53.2 63.6 68.3 69.2 75.9 85.8

Child health 65.7 54.6 67.4 39.1 43.6 48.0 58.0 44.3 43.4 32.3 39.7 30.4 35.3 34.1 34.4 45.9 44.3 35.9 43.9 32.3 19.9 32.6 40.4


State District Health Lohardaga Gumla Pashchimi Singhbhum Purbi Singhbhum Simdega Seraikela Latehar Jamtara Karnataka Belgaum Bagalkot Bijapur Gulbarga Bidar Raichur Koppal Gadag Dharwad Uttara Kannada Haveri Bellary Chitradurga Davanagere

Combined population Maternal Child immunisation 90.5 81.6 73.1

Rural population Maternal Child

Child

Health

health 45.5 33.5 45.3

system 59.6 51.4 48.9

health 35.3 29.0 28.5

system 60.9 51.9 51.1

health 37.7 29.9 31.4

68.9 47.3 60.2 51.4 42.1

55.6 25.0 39.2 26.7 29.1

91.5 77.9 89.0 86.1 55.7

46.6 27.2 41.8 27.6 41.0

62.2 46.3 57.8 50.8 40.6

43.2 22.8 33.6 25.3 27.3

92.6 77.5 88.0 85.8 53.8

35.0 27.6 43.6 28.3 40.9

70.8 63.0 62.5 63.9 70.3 56.0 59.5 71.2 75.4 79.2 71.8 63.0 71.2 72.7

70.2 57.5 62.0 57.5 68.5 48.4 47.7 70.1 72.9 84.4 68.7 56.0 65.3 72.6

83.7 74.0 68.4 78.4 81.7 68.6 79.5 87.9 87.6 90.3 86.8 79.6 85.8 84.2

41.5 49.7 49.5 44.3 47.3 44.0 39.8 33.7 51.9 39.8 43.4 40.1 50.1 45.4

69.1 59.5 57.4 61.0 70.5 53.4 58.2 70.8 71.8 78.6 73.8 59.2 69.0 71.2

66.2 50.8 54.9 50.9 65.4 41.6 44.7 67.7 66.5 83.4 66.8 47.0 61.9 68.1

82.9 72.6 64.1 78.9 85.1 68.9 78.6 87.7 85.7 90.8 90.4 79.5 85.2 84.1

42.5 48.7 47.1 42.3 47.3 44.7 41.2 37.2 50.8 36.7 50.4 39.9 46.7 47.3

247

immunisation 90.0 81.1 71.5

Child health 45.0 33.6 44.0


State District Health Shimoga Udupi Chikmagalur Tumkur Kolar Bangalore Bangalore Rural Mandya Hassan Dakshin Kannada Kodagu Mysore Chamarajanagar Kerala Kasaragod Kannur Wayanad Kozhikode Malappuram Palakkad Thrissur Ernakulam Idukki Kottayam

Combined population Maternal Child

system 79.3 83.7 83.0 76.6 74.2 84.0 80.3 80.4 80.0 85.5 83.3 79.7 80.8

health 83.1 94.4 86.3 75.8 71.6 93.0 82.9 84.0 81.2 92.2 89.0 82.0 84.7

84.3 82.9 82.4 79.9 78.2 78.5 83.7 82.9 85.5 86.8

98.2 97.5 95.6 97.8 99.1 96.9 98.3 98.0 98.6 97.5

immunisation 90.5 94.1 94.0 92.9 94.5 94.6 92.8 92.7 94.4 94.5 95.6 96.0 92.9 87.3 86.0 81.2 79.0 71.6 77.0 87.8 86.7 89.1 92.3

Child

Health

health 43.2 32.5 48.6 39.4 31.5 36.7 43.9 41.7 42.5 48.0 40.0 34.9 41.9

system 76.6 84.2 81.3 75.8 72.1 78.2 79.3 79.9 79.1 87.0 82.9 77.8 79.6

health 77.5 94.2 83.9 72.8 66.9 78.8 82.9 82.3 79.2 91.0 88.1 78.0 82.7

84.4 82.7 82.7 80.0 78.3 79.2 83.1 82.8 85.3 87.9

98.0 97.2 95.8 98.2 99.0 96.8 98.2 97.0 98.5 97.7

43.1 39.8 53.5 38.8 43.3 37.7 38.7 37.2 44.9 47.8 248

Rural population Maternal Child immunisation 89.1 94.8 93.0 93.7 93.7 96.1 92.1 92.5 93.5 96.2 95.5 96.1 92.0 87.4 86.3 81.7 77.8 72.0 78.6 86.5 88.4 88.6 91.8

Child health 43.5 33.9 46.3 39.8 32.0 33.3 39.1 43.0 43.8 54.6 39.6 32.4 41.6 44.0 38.5 52.9 40.5 43.0 37.6 37.5 34.5 45.0 54.1


State District Health Alappuzha Pathanamthitta Kollam Thiruvananthapuram Lakshadweep Lakshadweep Madhya Pradesh Sheopur Morena Bhind Gwalior Datia Shivpuri Guna Tikamgarh Chhatarpur Panna Sagar Damoh Satna Rewa Umaria Shahdol Sidhi

Combined population Maternal Child immunisation 91.9 90.7 90.9 91.5

Rural population Maternal Child

Child

Health

health 42.4 35.9 35.0 51.3

system 84.8 84.9 84.1 88.7

health 98.2 99.3 98.0 98.2

system 86.0 84.8 84.1 87.6

health 98.1 99.1 97.5 98.8

82.8

90.5

85.4

58.1

81.9

88.1

83.7

62.5

35.5 40.6 42.8 47.5 38.1 31.3 31.7 33.1 38.9 41.7 50.2 39.9 47.3 46.1 41.8 50.5 30.6

26.6 33.2 33.4 41.9 32.9 24.9 27.2 33.1 36.3 34.2 44.8 34.7 37.3 39.0 34.9 46.4 20.7

49.6 54.5 58.1 56.8 43.9 38.8 39.8 39.0 50.0 53.2 61.8 45.3 56.6 58.1 49.8 61.9 42.2

23.3 24.9 28.5 38.9 36.8 29.0 23.1 18.7 18.0 32.0 35.3 39.3 49.2 34.2 39.3 32.8 26.6

33.8 37.2 42.8 41.5 35.6 28.7 26.5 31.5 35.1 39.7 44.9 34.0 45.0 44.6 40.4 46.4 32.0

23.9 28.4 31.2 30.7 30.6 20.2 22.0 31.4 31.4 30.6 36.4 26.7 33.8 37.4 31.4 39.9 21.8

48.0 52.5 60.5 53.2 39.0 37.2 33.2 37.4 47.0 52.2 57.7 41.2 56.2 56.2 49.5 58.8 44.9

23.2 21.7 27.8 39.7 39.5 28.8 21.3 17.8 15.3 31.5 34.8 34.0 45.0 33.7 40.0 32.0 25.4

249

immunisation 91.6 90.3 90.6 94.5

Child health 35.6 36.2 34.0 51