Geospatial evaluation of trade-offs between equity in physical access to healthcare and health systems efficiency

Abstract

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.

Keywords: geographic information systems; health policy; health services research; health systems evaluation; public health.

Figure 1

The Geographic-Population services access model: a conceptual framework and analytical approach to inform policies for geographical allocation of health services that Optimise equity and efficiency. Note: the model provides a visual display of geographical data that captures proxies for efficiency (population density, x-axis) and equitable geographical access (travel time, y-axis). Efficient quadrants (upper left: low population density, long travel time; lower right: high population density, short travel time) and inefficient quadrants (lower left: low population density, short travel time; upper right: high population density, long travel time) can be visualised. Significant outliers (A–D) can be detected using spatial statistical methods.

Figure 2

Pearson correlation between district-level travel time to the nearest primary care health centre and population per 10 000 m² in Kenya (KEN), Tanzania (TZA), Rwanda (RWA) and Malawi (MWI). Colours correspond to districts with statistically significant bivariate local indicator of spatial autocorrelation clusters of (1): high population density/long travel time, (2): low population density/short travel time, (3): low population density/long travel time and (4): high population density/short travel time. Significance tests for district clusters were conducted using 999 permutation tests with an alpha=0.05. The Benjamini-Hochberg false discovery rate was applied to correct for multiple testing of clusters. Blue horizontal line denotes 120 min travel time. Dotted lines intersect at the median travel time and population density for each country. NS, non-significant.

Figure 3

Pearson correlation between district-level travel time to nearest cancer centre and population per 10 000 m² in Kenya (KEN), Tanzania (TZA), Rwanda (RWA) and Malawi (MWI). Colours correspond to districts with statistically significant bivariate local indicator of spatial autocorrelation clusters of (1): high population density/long travel time, (2): low population density/short travel time, (3): low population density/long travel time and (4): high population density/short travel time. Significance tests for district clusters were conducted using 999 permutation tests with an alpha=0.05. The Benjamini-Hochberg false discovery rate was applied to correct for multiple testing of clusters. Blue horizontal line denotes 120 min travel time. Dotted lines intersect at the median travel time and population density for each country. NS, non-significant.