Measuring inequities in health service delivery and their impact on health outcomes in low- to middle-income countries

Thesis (Ph.D.)--University of Washington, 2021Scientific evidence increasingly indicates that poor-quality health systems will slow down the tremendous improvements in health outcomes observed in low- to middle-income countries (LMICs) over the past several decades. However, much of this evidence focuses on country-level assessment of health systems performance, derived from measures of excess mortality. This approach does not enable examinations of within-country inequities in access to quality health services. Yet geographical factors, such as rurality, road networks, and elevation, affect accessibility to health services. Moreover, the inputs that condition quality care - drug supply, testing services, qualified health workers - are not uniformly distributed in space. This suggests that access to quality health services varies sub-nationally. Furthermore, there is scant literature on the effect of quality of care improvements on reduced disease burden, such as under-five mortality. Sub-national inequities in health service delivery can materialize as the failure to reach certain groups, populations or geographies in need for care and/or failure to provide care of sufficient quality. This dissertation aimed at presenting statistical methods that could be used to leverage available survey data and estimate metrics of health services delivery, at the scale of programmatic implementation, to inform decision-making. In the first chapter, Where are the missing tuberculosis cases in Bangladesh? Finding gaps in routine surveillance activities in Bangladesh by linking prevalence survey and case notification data, we developed a Bayesian spatial model to jointly analyze two independent data sources: the 2015-16 national tuberculosis sero-prevalence survey and the tuberculosis cases notified yearly to the National Tuberculosis Control Program in Bangladesh. Our study estimated important indicators of the tuberculosis epidemic, including the prevalence-to-notification ratio, a key metric of surveillance activities performance, at the second administrative division level (districts). Our results indicated that despite considerable progress in case notifications and treatment coverage in Bangladesh, substantial within-country inequalities on key indicators of the TB epidemic were still observed. We performed a counter-factual analysis to quantify the potential number of people who develop tuberculosis every year, and could be diagnosed and notified if sub-national disparities were resolved, and found that most people missing from care each year were concentrated in selected districts. Enhanced routine surveillance activities along with active case-finding strategies will be key to reach, diagnose, and treat people with tuberculosis in these areas that likely face barriers in accessing testing and treatment services. Sub-national estimates of prevalence and prevalence-to-notification ratios can guide these efforts, help develop tailored strategies, and ensure that no population is left behind in the fight against tuberculosis. The second chapter, What do we know about readiness and process quality of care in low- to middle-income countries? A statistical framework to measure sub-national inequalities in readiness and process quality metrics using health facility surveys, in Senegal, Kenya, and Tanzania, introduced a statistical framework to characterize levels and trends in readiness and process quality of sick-child care metrics, from health facility surveys, at sub-national resolutions. Large standardized health facility surveys provide the most consistent and comparable description of the level at which patients interact with the health system; yet, analyses of these data sources to date have focused on cross-sectional, national studies, overlooking temporal trends and sub-national inequities. We developed Bayesian hierarchical models to analyze all publicly available survey data in a given country, and explored spatio-temporal variations in metrics of readiness and process quality. We applied our framework to three countries with several rounds of facility surveys- Senegal, Kenya, and Tanzania. Our modelling approach allowed us to supplement direct survey measurements with spatio-temporal smoothing and auxiliary information from spatially resolved covariates (such as health workers density or indicators of urban development) to improve the precision of our estimates for areas with sparse data. We validated our model through out-of-sample predictions. We found evidence of substantial spatio-temporal heterogeneity in health services quality metrics. Improving health system quality requires new methods that enhance the measurements of actionable quality metrics, at a sub-national resolution that can help inform programmatic decision-making. Our study proposed a statistical framework to derive such metrics from health facility surveys. In our third chapter, Metrics of quality-adjusted health service coverage for sick children and child mortality in Senegal, Kenya and Tanzania: an ecological regression study, we introduced statistical methods to estimate quality-adjusted coverage metrics, at sub-national resolution and over time, using household and health facility surveys. Quality-adjusted coverage for sick-child care, a measure of the proportion of children with symptoms of pneumonia, diarrhea, or malaria, that were diagnosed and treated at a health facility according to evidence-based guidelines, is a critical metric to identify and resolve gaps in the availability, readiness and quality of child health services. We applied our framework to three countries with several rounds of household and facility surveys- Senegal, Kenya, and Tanzania, and found overall low levels of coverage, with substantial heterogeneity across sub-national units. We estimated four different metrics of quality-adjusted coverage, corresponding to more or less concise metrics of readiness and process quality of sick-child care, and compared their performance in predicting under-five mortality rates, over time, and at sub-national levels, using an ecological regression framework. Our model included area-level estimates of risk factors (such as diarrhea and lower-respiratory infection incidence) and preventive interventions (such as immunization coverage) to account for differences between regions. We found a negative association between quality-adjusted coverage and under-five mortality rates at the ecological-level, and no evidence of lower predictive performances of more concise quality-adjusted coverage metrics. As quality-adjusted coverage becomes a more common metric of health system performance, our study provides important insights for decision makers. Our analysis finds that spatio-temporal estimates uncovered substantial sub-national disparities. This finding can be used to tailor data collection efforts, as more concise constructs to measure readiness and process quality of care can be performed

The Politics of AntibioticsAn interview with Ramanan Laxminarayan

International audienc

The Politics of AntibioticsAn interview with Ramanan Laxminarayan

International audienc

Heirs, corporate aristocrats and ‘Meritocrats’: the social space of top CEOs and Chairmen in India

This article proposes an analysis of the social space inhabited by the CEOs and Chairmen of the top 100 Indian companies in 2012, using a Multiple Correspondence Analysis (MCA). The analysis aims to understand the internal divisions to be found in the field of economic power, by looking at the divisions along the lines of educational capital, inherited capital (family capital), caste and social capital (drawing on a network analysis of interlocking directorates). Our results point to a very peculiar structuration of the economic field; we find that credentialism has a very weak influence, there is a clear and massive cleavage between owners and managers of capital, social capital carries decisive weight and the actors closest to the state apparatus occupy a marginal role. We argue that it is possible to identify three poles among business leaders: the multipositional family business owners, the unipositional family business owners and the managerial galaxy

Temporal Trends and Determinants of HIV Testing at Antenatal Care in Sub-Saharan Africa: A Pooled Analysis of Population-Based Surveys (2005-2021).

In sub-Saharan Africa (SSA), integrating HIV testing into antenatal care (ANC) has been crucial toward reducing mother-to-child transmission of HIV. With the introduction of new testing modalities, we explored temporal trends in HIV testing within and outside of ANC and identified sociodemographic determinants of testing during ANC.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

A small area model to assess temporal trends and sub-national disparities in healthcare quality

Monitoring subnational healthcare quality is important for identifying and addressing geographic inequities. Yet, health facility surveys are rarely powered to support the generation of estimates at more local levels. With this study, we propose an analytical approach for estimating both temporal and subnational patterns of healthcare quality indicators from health facility survey data. This method uses random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We applied this method for three countries in which at least four health facility surveys had been conducted since 1999 - Kenya, Senegal, and Tanzania - and estimated measures of sick-child care quality per WHO Service Availability and Readiness Assessment (SARA) guidelines at programmatic subnational level, between 1999 and 2020. Model performance metrics indicated good out-of-sample predictive validity, illustrating the potential utility of geospatial statistical models for health facility data. This method offers a way to jointly estimate indicators of healthcare quality over space and time, which could then provide insights to decision-makers and health service program managers

A small area model to assess temporal trends and sub-national disparities in healthcare quality

Abstract Monitoring subnational healthcare quality is important for identifying and addressing geographic inequities. Yet, health facility surveys are rarely powered to support the generation of estimates at more local levels. With this study, we propose an analytical approach for estimating both temporal and subnational patterns of healthcare quality indicators from health facility survey data. This method uses random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We applied this method for three countries in which at least four health facility surveys had been conducted since 1999 – Kenya, Senegal, and Tanzania – and estimated measures of sick-child care quality per WHO Service Availability and Readiness Assessment (SARA) guidelines at programmatic subnational level, between 1999 and 2020. Model performance metrics indicated good out-of-sample predictive validity, illustrating the potential utility of geospatial statistical models for health facility data. This method offers a way to jointly estimate indicators of healthcare quality over space and time, which could then provide insights to decision-makers and health service program managers

Estimating global, regional, and national daily and cumulative infections with SARS-CoV-2 through Nov 14, 2021: a statistical analysis

Timely, accurate, and comprehensive estimates of SARS-CoV-2 daily infection rates, cumulative infections, the proportion of the population that has been infected at least once, and the effective reproductive number (Reffective) are essential for understanding the determinants of past infection, current transmission patterns, and a population’s susceptibility to future infection with the same variant. Although several studies have estimated cumulative SARS-CoV-2 infections in select locations at specific points in time, all of these analyses have relied on biased data inputs that were not adequately corrected for. In this study, we aimed to provide a novel approach to estimating past SARS-CoV-2 daily infections, cumulative infections, and the proportion of the population infected, for 190 countries and territories from the start of the pandemic to Nov 14, 2021. This approach combines data from reported cases, reported deaths, excess deaths attributable to COVID-19, hospitalisations, and seroprevalence surveys to produce more robust estimates that minimise constituent biases