Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

• Published in: Communications medicine 2022-09, Vol.2 (1), p.117-13, Article 117
• Main Authors: Hierink, Fleur, Boo, Gianluca, Macharia, Peter M., Ouma, Paul O., Timoner, Pablo, Levy, Marc, Tschirhart, Kevin, Leyk, Stefan, Oliphant, Nicholas, Tatem, Andrew J., Ray, Nicolas
• Format: Article
• Language: English
• Subjects: 692/700/228; 692/700/478; Medicine; Medicine & Public Health
• ISSN: 2730-664X; 2730-664X
• DOI: 10.1038/s43856-022-00179-4

Background Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels. Methods Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min). Results Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels. Conclusions The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed. Plain Language Summary Knowing where people reside and what health services are accessible to them in a timely manner can make a difference in life-or-death situations. Geographic models that mimic the journey of patients can help understand where people cannot access healthcare and can provide valuable insights for policy and research. Population distribution data is essential for these models, as it determines the relative coverage provided by the existing health system. However, there are several datasets available on population distribution that vary widely. In this study, we quantify the impact of using six different population data sets to calculate healthcare coverage in sub-Saharan Africa. Our results show large continental, national,