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Background: Health systems research is increasingly being conducted in low and middle-income countries (LMICs). Such research should aim to reduce health disparities between and within countries as a matter of global justice. For such research to do so, ethical guidance that is consistent with egalitarian theories of social justice proposes it ought to (amongst other things) focus on worst-off countries and research populations. Yet who constitutes the worst-off is not well-defined.Methods and Results: By applying existing work on disadvantage from political philosophy, the paper demonstrates that (at least) two options exist for how to define the worst-off upon whom equity-oriented health systems research should focus: those who are worst-off in terms of health or those who are systematically disadvantaged. The paper describes in detail how both concepts can be understood and what metrics can be relied upon to identify worst-off countries and research populations at the sub-national level (groups, communities). To demonstrate how each can be used, the paper considers two real-world cases of health systems research and whether their choice of country (Uganda, India) and research population in 2011 would have been classified as amongst the worst-off according to the proposed concepts.Conclusions: The two proposed concepts can classify different countries and sub-national populations as worst-off. It is recommended that health researchers (or other actors) should use the concept that best reflects their moral commitments-namely, to perform research focused on reducing health inequalities or systematic disadvantage more broadly. If addressing the latter, it is recommended that they rely on the multidimensional poverty approach rather than the income approach to identify worst-off populations. [ABSTRACT FROM AUTHOR]

Background: While community capabilities are recognized as important factors in developing resilient health systems and communities, appropriate metrics for these have not yet been developed. Furthermore, the role of community capabilities on access to maternal health services has been underexplored. In this paper, we summarize the development of a community capability score based on the Future Health System (FHS) project's experience in Bangladesh, India, and Uganda, and, examine the role of community capabilities as determinants of institutional delivery in these three contexts.Methods: We developed a community capability score using a pooled dataset containing cross-sectional household survey data from Bangladesh, India, and Uganda. Our main outcome of interest was whether the woman delivered in an institution. Our predictor variables included the community capability score, as well as a series of previously identified determinants of maternal health. We calculate both population-averaged effects (using GEE logistic regression), as well as sub-national level effects (using a mixed effects model).Results: Our final sample for analysis included 2775 women, of which 1238 were from Bangladesh, 1199 from India, and 338 from Uganda. We found that individual-level determinants of institutional deliveries, such as maternal education, parity, and ante-natal care access were significant in our analysis and had a strong impact on a woman's odds of delivering in an institution. We also found that, in addition to individual-level determinants, greater community capability was significantly associated with higher odds of institutional delivery. For every additional capability, the odds of institutional delivery would increase by up to almost 6 %.Conclusion: Individual-level characteristics are strong determinants of whether a woman delivered in an institution. However, we found that community capability also plays an important role, and should be taken into account when designing programs and interventions to support institutional deliveries. Consideration of individual factors and the capabilities of the communities in which people live would contribute to the vision of supporting people-centered approaches to health. [ABSTRACT FROM AUTHOR]