Closing the Universal Health Coverage Gap through Efficiency: A Bias-Corrected Bootstrap DEA Analysis of Health-System Performance in Sub-Saharan Africa

Although there is a significant gap in universal health coverage (UHC) in Sub-Saharan Africa, it is unclear how much of the region's current health resources could be used to increase service coverage through increased efficiency. An output-oriented variable-returns-to-scale data envelopment analysis (DEA) was estimated at a 2022 benchmark of 46 countries using an unbalanced panel of 48 Sub-Saharan African countries (2000-2023) without imputed values. Smoothed bootstrap bias correction was then applied (1,000 replications) which founded structural correlates using truncated-bootstrap regression, and cross-checked the frontier using scale-efficiency, jackknife, governance-PCA, stochastic-frontier, matched-year, and two-way fixed-effects analyzes. The average UHC service coverage was 40.33. The mean country-specific corrected radial output-expansion potential was 21.8%, and the bias-corrected efficiency was 0.834 as opposed to a traditional 0.889. Efficiency varied not by income (p=0.488) but by financing mode (external 0.884, government 0.863, private/out-of-pocket 0.784; p=0.005). Lower inefficiency was linked to government health spending as a percentage of the government budget (-0.152; 95% bootstrap CI -0.330 to -0.008). The stochastic-frontier cross-check yielded a mean efficiency of 0.858 with significant concordance with DEA rankings (ρ=0.651; p<0.001), and efficiency was stable between matched 2018 and 2022 countries (Wilcoxon p=0.318; Spearman ρ=0.752). In conclusion, the estimated efficiency opportunity is larger after bias correction. Efficient systems are distinguished from inefficient ones by institutional capacity, operating scale, and financing architecture rather than just revenue. UHC service coverage could be increased without solely depending on new funding by lowering point-of-service payments, bolstering domestic pooled financing, enhancing public budget allocation, and investing in health-data systems.