Health care risk adjustment models have traditionally relied on demographic and clinical variables such as age, gender, and disease conditions to predict health expenditures. However, these conventional approaches often fail to capture the influence of broader socioeconomic and environmental factors that shape population health outcomes, particularly in developing countries. This study investigated the impact of non-traditional variables such as income level, educational attainment, housing quality, employment type, and environmental exposure on health care risk adjustment in Nigeria. Using a cross-sectional quantitative design, data were collected from both national health databases and household surveys covering 2,100 respondents across six geopolitical zones. Multiple regression and variance analyses were conducted using SPSS to determine the predictive significance of these non-traditional variables on health expenditure risk scores. The results reveal that education, income inequality, and environmental conditions have statistically significant effects on health risk adjustment, improving model accuracy by approximately 18% compared to conventional demographic only models. The findings highlight the need for Nigeria’s health financing frameworks to incorporate non-traditional variables into risk adjustment algorithms to promote fairness and efficiency in resource allocation. Policymakers are encouraged to adopt a multidimensional health risk model that integrates social determinants of health to strengthen the equity of Nigeria’s healthcare reimbursement system.
Adamu Daniel Kamaru (Thu,) studied this question.