ArticleBMJ global health2019
The predictive power of health system environments: a novel approach for explaining inequalities in access to maternal healthcare.
Article in BMJ global health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Contraceptive access in displacement settings: a quantitative study of Syrians displaced to Türkiye.Sexual and reproductive health matters · 2025Article
- Geosocial inequalities in walking for leisure at older ages.BMC public health · 2025Article
- How Zambia reduced inequalities in under-five mortality rates over the last two decades: a mixed-methods study.BMC health services research · 2023Article
- Intersectional social identities and loneliness: Evidence from a municipality in Switzerland.Journal of community psychology · 2022Article
- Universal health coverage and the poor: to what extent are health financing policies making a difference? Evidence from a benefit incidence analysis in Zambia.BMC public health · 2022Article
- Understanding maternal choices and experiences of care by skilled providers: Voices of mothers who delivered at home in selected communities of Lusaka city, Zambia.Frontiers in global women's health · 2022Article
- Assessment of health facility quality improvements, United Republic of Tanzania.Bulletin of the World Health Organization · 2020Article
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Abstract
introductionThe growing use of Geographic Information Systems (GIS) to link population-level data to health facility data is key for the inclusion of health system environments in analyses of health disparities. However, such approaches commonly focus on just a couple of aspects of the health system environment and only report on the average and independent effect of each dimension.
methodsUsing GIS to link Demographic and Health Survey data on births (2008-13/14) to Service Availability and Readiness Assessment data on health facilities (2010) in Zambia, this paper rigorously measures the multiple dimensions of an accessible health system environment. Using multilevel Bayesian methods (multilevel analysis of individual heterogeneity and discriminatory accuracy), it investigates whether multidimensional health system environments defined with reference to both geographic and social location cut across individual-level and community-level heterogeneity to reliably predict facility delivery.
resultsRandom intercepts representing different health system environments have an intraclass correlation coefficient of 25%, which demonstrates high levels of discriminatory accuracy. Health system environments with four or more access barriers are particularly likely to predict lower than average access to facility delivery. Including barriers related to geographic location in the non-random part of the model results in a proportional change in variance of 74% relative to only 27% for barriers related to social discrimination.
conclusionsHealth system environments defined as a combination of geographic and social location can effectively distinguish between population groups with high versus low probabilities of access. Barriers related to geographic location appear more important than social discrimination in the context of Zambian maternal healthcare access. Under a progressive universalism approach, resources should be disproportionately invested in the worst health system environments.
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