On the impact of inequity on the attainment of health results in the African Region: a methodological exploration.

Abstract

Background: The core of Universal Health Coverage (UHC) is ensuring everyone can access essential health services without financial hardship, shifting focus from specific interventions to individual outcomes. Yet, in the WHO African region, there remains a critical gap in understanding the interrelations of the dimensions of health inequities with each other, and with health outcomes. This paper explores the interrelations among inequity drivers to inform their prioritization in each country of the region.

Methods: This study analysed the association of the commonly used eight drivers of health inequity (independent variable): residence, ethnicity, occupation, gender, religion, education, socioeconomic status, and social capital, with differences in health results across countries (dependent variable). UHC indices served as the indicators for the dependent variable. Data came from the Demographic and Health Surveys with normalized indices for ethnicity and religion from prior studies. Missing values were imputed. Principal Component Analysis generated weighted composite indices, and inequity gaps were normalized to a 0-1 scale. Partial Least Squares regression examined relationships while addressing multicollinearity. Model validation used Root Mean Square Error of Prediction and explained variance, verified against known country-level inequity patterns.

Results: Health inequity in the WHO African Region shows marked disparities, with an average composite gap of 0.49 (± 0.038) on a 0-1 scale. Angola (0.79), Kenya (0.68), and Chad (0.67) record the highest inequities, while Algeria and Equatorial Guinea (0.31) show greater equity. Ethnicity is the strongest driver (0.65), creating 30% more disparity than wealth (0.55) or social capital (0.56). Four latent variables emerged as drivers of inequity and explained up to 77% of the variance in UHC service coverage across countries. These are personal characteristics, geographic residence, social networks, and socioeconomic position.

Conclusion: The major emerging drivers of inequity are not the same drivers of inequality. It is more targeted to focus on deciphering the four emerging latent variables as drivers of inequities to align with the need for person-centred health services required in the PHC approach for attaining UHC results - a shift away from the common programmatic focus of the eight drivers of inequality.