Evaluating urban–rural access to pathology and laboratory medicine services in Tanzania

Abstract

Abstract Placement of pathology and laboratory medicine (PALM) services requires balancing efficiency (maximizing test volume) with equitable urban–rural access. We compared the association between population density (proxy for efficiency) and travel time to the closest facility (proxy for equitable access) across levels of Tanzania’s public sector health system. We linked geospatial data for Tanzania from multiple sources. Data on facility locations and other geographic measures were collected from government and non-governmental databases. We classified facilities assuming increasing PALM availability by tier: (1) dispensaries, (2) health centres, (3) district hospitals and (4) regional/referral hospitals. We used the AccessMod 5 algorithm to estimate travel time to the closest facility for each tier across Tanzania with 500-m resolution. District-level average population density and travel time to the closest facility were calculated and presented using medians and interquartile ranges. Spatial correlations between these variables were estimated using the global Moran’s I and bivariate Local Indicator of Spatial Autocorrelation, specifying a queen’s neighbourhood matrix. Spatial analysis was restricted to 171 contiguous districts. The study included 5406 dispensaries, 675 health centres, 186 district hospitals and 37 regional/referral hospitals. District-level travel times were shortest for Tier 1 (median: [IQR]: 45.4 min [30.0–74.7]) and longest for Tier 4 facilities (160.2 min [107.3–260.0]). There was a weak spatial autocorrelation across tiers (Tier 1: −0.289, Tier 2: −0.292, Tier 3: −0.271 and Tier 4: −0.258) and few districts were classified as significant spatial outliers. Across tiers, geographic patterns of populated districts surrounded by neighbours with short travel time and sparsely populated districts surrounded by neighbours with long travel time were observed. Similar spatial correlation measures across health system levels suggest that Tanzania’s health system reflects equitable urban–rural access to different PALM services. Longer travel times to hospital-based care could be ameliorated by shifting specialized diagnostics to more accessible lower tiers.