Theoretical framework for assessing quality of care

study of nationally representative surveys in 28 LMICs found that health system performance for management of diabetes showed large losses to care at the stage of being tested and low rates of diabetes control (Manne-Goehler et al. 2019). Tuberculosis contributes significantly to the disease and mortality burden in LMICs (Reid et al. 2019). In a pilot study conducted in Delhi, India, researchers used standardized patients—fake patients trained to present with certain symptoms of disease to the health worker—and found that only 21 percent of the tuberculosis cases were correctly managed (Das et al. 2015). Thus, the delivery of poor-quality health care is pervasive across types of services in LMICs.

Three aspects of quality: Structure, process, and outcomes Consistent with the model of quality of care proposed by Donabedian (2003), this chapter distinguishes between three aspects of quality: structure, process, and outcomes. Structural quality refers to the context in which care is provided. This may be the physical health center; the equipment, supplies, and drugs; as well as aspects of human resources and organization, such as training and payment methods. Process quality refers to the actions taken by the care provider in providing the service. Outcomes refer to the end health outcomes, such as maternal mortality and morbidity. Distinguishing these three components of quality allows for understanding what constrains the delivery of high-quality health care. Such an understanding is the first step in determining what the appropriate policy levers are. For instance, if effective coverage is primarily constrained by poor infrastructure, then the policy response would be to invest in infrastructure. In contrast, poor process quality may highlight lacunae in health worker training or effort, which cannot be addressed by infrastructure improvements.

Tying the evidence on poor quality to end outcomes, the recent Lancet Global Health Commission on High Quality Health Systems in the Sustainable Development Goals Era establishes that poor quality of health care is one of the major drivers of excess mortality in LMICs (Kruk et al. 2018). This report also notes that universal health coverage will not lead to sustained improvements in mortality or other intermediate health outcomes unless LMIC health systems can consistently

deliver high-quality services. Thus, a growing consensus highlights the role of poor quality of care in stagnating health outcomes in LMICs and suggests that simply increasing utilization will not improve health outcomes.

It has been well documented that LMIC health systems suffer from poor structural quality (Smith and Hanson 2011; Kruk et al. 2018). In response, since the 1978 Alma Ata Declaration, sustained investments have been made in health care infrastructure in LMICs. In the past few decades, access to health care centers and more sophisticated medical services has expanded across Sub-Saharan African countries (Jamison et al. 2006). Historically, LMICs have had little to no access to the new technological growth in the health sector. However, this is changing as the trends suggest an increase in the supply of such medical technology in LMICs (Howitt et al. 2012). Since 1978, the number of health care professionals has increased significantly, including a growing workforce of community health workers across the world, including in LMICs (Perry, Zulliger, and Rogers 2014). While there is still a long way to go to ensuring universal health coverage, by many measures, these investments may have succeeded. A large body of literature suggests that the availability of health services is no longer the concern it used to be, including in many Sub-Saharan African countries (Leslie et al. 2018; Di Giorgio et al. 2020).

At the same time, the disparity between high coverage rates and poor health outcomes is perhaps clearest in the case of maternal and neonatal health, where sustained gains in health infrastructure have significantly improved access to prenatal care around the world but have had limited impact on birth and delivery outcomes as well as maternal and neonatal mortality (Chou, Walker, and Kanyangarara 2019). A 2013 report found that 75 LMICs account for 95 percent of maternal and child deaths (WHO 2013). High maternal and child deaths in LMICs are taking place despite a greater proportion of births occurring in health facilities (Montagu et al. 2017). A set of researchers constructed deterministic models to project health outcomes if quality of care was improved in a representative sample of 81 LMICs. They found that improving quality of care (in antenatal, intrapartum, and postnatal care) would produce substantial benefits at current levels of utilization, with an estimated decline in the mortality rate of about 21 to 32 percent (Chou, Walker, and Kanyangarara 2019).

Further, consumers of health care in LMICs react strongly to both structural and process quality. For instance, evidence from the Democratic

Republic of Congo shows that consumers whose local public health facility is better provisioned in terms of equipment and consumables are less likely to bypass the local facility (Fink, Kandpal, and Shapira 2022). Similarly, a study in India found that a majority of patients bypassed the local primary health care centers when seeking treatment even though doing so cost them almost twice as much out of pocket (Rao and Sheffel 2018). However, such bypassing decreased with the increase in the competence of the health care provider (Rao and Sheffel 2018). The study found that compared with nonpoor patients, poor patients were less likely to seek treatment by bypassing the local primary health care centers. Therefore, in LMICs, where health systems often face shortages of supplies (AdairRohani et al. 2013) and personnel (Chaudhury et al. 2006), there are great gains to be made by ensuring a basic quality of care (Akachi and Kruk 2017).

An aspect of poor process quality that this chapter does not touch upon is health worker absenteeism. Historically, the absence of health care workers has been thought of as a major hurdle in improving quality of care in LMICs (Belita, Mbindyo, and English 2013). A recent study in Uganda finds that absenteeism can drive patients seeking care away from the public sector, in turn leading to an increase in out-of-pocket expenditures by patients (Zhang, Fink, and Cohen 2021). However, another recent quality of care study in 10 African countries finds that reducing absenteeism would only have a modest impact on average care readiness (Di Giorgio et al. 2020). The study shows that health care workers in LMICs needed to be more knowledgeable to achieve greater care readiness (Di Giorgio et al. 2020). Among other factors, studies have previously documented a significant lack of basic knowledge among health care workers across several African countries on how to diagnose and manage common diseases (Pakenham-Walsh and Bukachi 2009). In addition, health care workers in LMICs often find themselves dealing with complex health issues with limited support and training, which, among other factors, can lead to ineffective quality of care (Vasan et al. 2017). To decompose observed quality of care, this chapter uses a framework that studies the quality of care when the worker is present. On the one hand, absenteeism may reflect an extreme example of mis-adherence to protocol, implying that such estimates of idle capacity present an upper bound on the quality of care available. On the other hand, it may also be the case that poor structural capacity or insufficient knowledge demotivates workers and keeps them away from the facility.