C A S E S T U DY - E L S A LVA D O R
Community Health Disparity and Fatality Rates Exploring the distribution of population under health inequality and access to health-supportive services in El Salvador
Introduction
Analysis Method
About the size of the state of New Jersey, El Salvador has a population of over 6 million and has the highest population density in Central America (CIA, 2016). Although its economic status is improving, about 28% of Salvadorans live below the poverty line. The United Nations Development Programme (UNDP, 2015) ranked El Salvador 116 out of 177 counties based on indicators of life expectancy at birth, adult literacy rate, school enrollment, and GDP per capita. This ranks El Salvador at medium to low on the human development index.
A Geographic Information System (GIS) approach allows the measurement of: 1) the geographic distribution of populations experiencing health disparity, and 2) access to a range of key community resources in municipal and sub-municipal levels across the country.
Since 2009, El Salvador has implemented a series of health reform policies to provide affordable healthcare to the poor. Within this framework, they have approached healthcare as a comprehensive and inter-sectoral service. However, health conditions are poor in El Salvador because of inadequate infrastructure, natural disasters, poverty, and a lack of health care resources. Health inequalities present across multiple dimensions between: 1) socioeconomic status of individuals, 2) infrastructure that supports service of individuals, and 3) spatial or geographic grouping of individuals. What neighborhood contextual factors might be influencing health? One simple categorization differentiates three dimensions: neighborhood characteristics, social characteristics and community resource access (Figure 1).
1. Defining Health Disparity Populations Information for health outcomes (e.g. number of death, also known as mortality) is often used in planning healthcare services, such as determining number of inpatient and outpatient facilities, manage care plans for communities, and identify population-physician demand and supply. As a measurement of the health outcome, this study uses the most recent fatality dataset from 2007 VI Population and V Housing Census (CElADE) that represents occurrences of accidental deaths or due to disease by household (Figure 2). 0
20
40 km
INDIVIDUAL & NEIGOBORHOOD HEALTH OUTCOME
INDIVIDUAL HEALTH FACTOR
Physical Environment
Socioeconomic Environment
Health Service
Quality & Length of Life
Neighborhood Deprivation
Individual Behavior
Figure 2. Fatality distribution in sub-municipal level in El Salvador
2. Defining Service Access Service access considers two types of resources which are plausibly related to health: 1) availability of household service, and 2) accessibility to community services (Table 1, Figure 3).
Biology & Genetics
Indicator Figure 1. Casual framework of neighborhood deprivation’s association with individual and community health outcome
Following the framework, this study aims to identify whether healthsupportive resource access is differentially distributed across El Salvador, and therefore whether it is a potential indicator for observed differences in neighborhood health status.
Availability of household services
Measurement Housing with water supply Housing with sanitation
Method
2007 VI Population & V Housing Census
Cost-distance
2016 Centro Nacional de Registros
Housing with waste management Accessibility to community services
Travel time to mercados Travel time to Unidades de Salud
Table 1. Indicators and measurements used to identify service access
P U B L I C H E A LT H November 2016
Data Source
Spatial interpolation
C A S E S T U D Y P ublic H ealth (a)
• Water Supply
•
Sanitation
Availability: Identifying the distribution of low health outcome, this analysis used Universal Kriging to estimate the distribution of housing that is supported by health-related services. It creates a continuous surface that reflects a spatial correlation. Accessibility: There are 152 mercados and 425 unidades de salud across the country. Travel time is used as a measurement of access at the sub-municipal level and is not constrained by administrative boundaries. An index of accessibility is calculated by using the cost-distance that finds the least cost path or corridor to the destinations
As a second step, Pearson correlation analysis and Ordinary Least Square (OLS) regression analysis tested whether access to community resources varied between deprived neighborhoods.
RESULTS
Table 2 shows the Pearson product-moment correlation coefficient between the characters of population (health disparity) and access index for each service category.
Waste
Mercados
Unidades de salud
0
20
40 km
Populations with a health disparity (high fatality rate) are likely to have a low availability of water, sanitation and waste management systems in their home (negative correlation). Also, they tend to have longer travel times to food resources and primary health care, compared to those who are in neighborhoods with less health disparity. As a result of the OLS regression, it is evident that health disparity populations most likely to live in home without sanitation and waste management system, and experience longer physical accessibility to primary health care based on their strong correlations (Adjusted R2= 37.3%) Implications from the results Fatality Population Built Environmental Factors 1. Robust geo-statistical analysis Distribution identifies key areas where built Housing with water supply -0.035 environmental and service access Housing with sanitation -0.150 variables impact fatality rates. This Housing with waste management -0.130 information can help health authorities 0.080 Travel time to mercados to set up geographic target programs and service improvements. 0.100 Travel time to unidades de salud 2. Regression result shows a statistical Note: Coefficients range between -1, indicating significant negative linear dependency and +1 indicating significant positive linear dependency links between built environmental factors and health-deprived Table 2. Correlation between built environmental factors neighborhood across El Salvador. and the distribution of the health disparity population Especially high residual clusters (>10% difference between observed and predicted) are identified in south San Salvador and San Miguel. This conveys that these areas are overpopulated with factors that relates health disparity. While enhancing health-supportive service delivery is important, these areas particularly need further investigation to determine explanatory variables thus, formulating health policy.
0
20
40 km
(b)
Figure 3. (a) Availability and accessibility of built environmental factors. (b) Identification of key areas for health policy intervention
Figure 4. Residual clusters that shows over and under populated
GeoAdaptive is a global interdisciplinary research consultancy specialized in the development of analysis and territorial strategies that deliver sustainable forms of development. We employ spatial techniquies to convey multi-sectoral and geographically-explicit recommendations to our clients, maximizing their opportunities and reducing their potential risk. Our list of clients, include multilateral organizations, national and regional governments, infrasturucture banks, and foundations in more than 17 countries.
Company contact: 250 Summer Street, 1st Fl Boston, MA 02210. USA phone +1 617-227-8885 www.geoadaptive.com info@geoadaptive.com GeoAdaptive LLC
@GeoAdaptive