Introduction Decisions regarding the geographical placement of healthcare services require consideration of trade-offs between equity and efficiency, but few empirical assessments are available. We applied a novel geospatial framework to study these trade-offs in four African countries.
Methods Geolocation data on population density (a surrogate for efficiency), health centres and cancer referral centres in Kenya, Malawi, Tanzania and Rwanda were obtained from online databases. Travel time to the closest facility (a surrogate for equity) was estimated with 1 km resolution using the Access Mod 5 least cost distance algorithm. We studied associations between district-level average population density and travel time to closest facility for each country using Pearson’s correlation, and spatial autocorrelation using the Global Moran’s I statistic. Geographical clusters of districts with inefficient resource allocation were identified using the bivariate local indicator of spatial autocorrelation.
Results Population density was inversely associated with travel time for all countries and levels of the health system (Pearson’s correlation range, health centres: −0.89 to −0.71; cancer referral centres: −0.92 to −0.43), favouring efficiency. For health centres, negative spatial autocorrelation (geographical clustering of dissimilar values of population density and travel time) was weaker in Rwanda (−0.310) and Tanzania (−0.292), countries with explicit policies supporting equitable access to rural healthcare, relative to Kenya (−0.579) and Malawi (−0.543). Stronger spatial autocorrelation was observed for cancer referral centres (Rwanda: −0.341; Tanzania: −0.259; Kenya: −0.595; Malawi: −0.666). Significant geographical clusters of sparsely populated districts with long travel times to care were identified across countries.
Conclusion Negative spatial correlations suggested that the geographical distribution of health services favoured efficiency over equity, but spatial autocorrelation measures revealed more equitable geographical distribution of facilities in certain countries. These findings suggest that even when prioritising efficiency, thoughtful decisions regarding geographical allocation could increase equitable physical access to services.
- geographic information systems
- health services research
- health policy
- health systems evaluation
- public health
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Handling editor Seye Abimbola
Twitter @hiyer_epi, @marciacastrorj
Contributors HSI, JF and TRR conceived the study and design. JF and TRR facilitated acquisition of the data. HSI and NGW analysed the data. HSI, NGW, MCC, SH, LFS, JF and TRR interpreted the data. HSI drafted the manuscript. HSI, JF, NGW, SH, LFS, MCC and TRR provided critical review and final approval of the manuscript.
Funding We are grateful for administrative support from the Zhu Family Global Center for Cancer Prevention at the Harvard T. H. Chan School of Public Health and the Department of Medical Oncology at the Dana-Farber Cancer Institute. This study grew out of related geographical analytical work as part of the Lancet Commission on Diagnostics, and we are grateful for the support of our colleagues in that group. HSI was supported by NIH T32 CA 009001 and the Harvey V. Fineberg Fellowship in Cancer Prevention. TRR was supported by NIH U01-CA184374.
Disclaimer This study would not have been possible without the researchers, private companies, government workers, and non-profits who have made vast amounts of satellite, demographic, and government geospatial data publicly available.
Competing interests LFS reports holding stocks from InheRET and personal fees from Roche Diagnostics, outside the submitted work.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data for preparing the scatter plots presented in this study are available in a public, open access repository. Additional data are available upon request to the corresponding author. Data used in this study include tables with health centre and cancer referral centre locations and gridded image files with travel time data (500m resolution) and population density data (100m resolution). These data were generated using publicly available datasets and so no conditions apply constraining their use. We have made selected data and code used to generate the major figures in the paper available at the following link: https://github.com/hiyer09/geopsa_paper.
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