Mapping spatial and temporal inequalities in utilisation of maternal and newborn care in five East African countries

Abstract

Historically, maternal and newborn health (MNH) outcomes used to monitor progress in achieving global and national targets have been measured at an aggregate level, showing vast inequalities between and within countries. To ensure no one is left behind in improving health, researchers have called for the spatial and temporal disaggregation of MNH data. This thesis aims to generate high spatial resolution data over time that can be used to monitor progress in reducing inequalities amongst utilisation of key MNH services in the East African Community (EAC) region, including Burundi, Kenya, Rwanda, Tanzania, and Uganda.
Following a ‘three-paper’ format, the first paper in this thesis employs a hierarchical mixed effects logistic regression framework, to estimate the odds of: 1) skilled birth attendance (SBA), 2) receiving 4+ antenatal care (ANC) visits, and 3) receiving a postnatal health check-up (PNC) within 48 hours of delivery. Model results are applied to an accessibility surface to visualise the probabilities of obtaining MNH care at both high-resolution and sub-national levels after adjusting for live births in 2015. Across all outcomes, decreasing wealth and education levels are associated with lower odds of obtaining MNH care, while increasing geographic inaccessibility scores are associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance.
The second paper explores temporal trends in absolute and relative spatial inequalities in utilisation of these MNH services between 1990 and 2015. A Bayesian framework is employed to generate sub-national estimates of utilisation of SBA, ANC, and PNC over several time points. Absolute change in estimates over time is reported, as well as relative change in ratios of the best- to-worst performing districts per country. Across all countries, the greatest spatial equality is observed among ANC, while SBA and PNC tend to have greater spatial variability. Lastly, while progress has been made to reduce coverage gaps between districts, improvement in PNC coverage has stagnated and should be monitored closely over the coming decades.
The final paper comprising this work explores the trade-off between increasing spatial resolution in model inputs and resulting model uncertainty, with aims of understanding the optimal spatial resolution to report health outcomes. Prevalence of childbirth via c-section is estimated in Tanzania, using geospatial covariates at varying levels of spatial coarseness within a Bayesian model framework. Uncertainty in posterior outcomes is reported as the distribution of 95% credible intervals at each spatial resolution, and visualised at the spatial resolution with the greatest model precision. Overall, higher spatial resolution input increases model uncertainty, while model precision is best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.
This thesis makes substantive contributions to the literature by outlining where spatial inequalities in key MNH services are occurring within the EAC region and how these disparities are evolving over time. This work also makes methodological contributions by demonstrating how spatial approaches can be used to monitor health indicators, as well as exploring uncertainty in the application of these techniques, with important implications in communicating results to policy makers. These techniques can be applied across health and development outcomes, notably across Sustainable Development Goal indictors, ensuring “no one left behind” by 2030.

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Identifiers

ORCID for Andrew Tatem: orcid.org/0000-0002-7270-941X

ORCID for Zoe Matthews: orcid.org/0000-0003-1533-6618

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