A QUANTILE REGRESSION ANALYSIS OF THE RELATIONSHIP BETWEEN DEMOGRAPHIC FACTORS AND AGE DISTRIBUTION AMONG HIV/AIDS PATIENTS IN JOS, PLATEAU STATE

Abstract

Human Immunodeficiency Virus (HIV) remains a major public health concern in sub-Saharan Africa, with demographic factors influencing both vulnerability and disease progression. This study employs a quantile regression framework to explore how age interacts with gender, educational attainment, marital status, and employment status among HIV-positive patients in Jos, Plateau State, Nigeria. We conducted a retrospective cross-sectional analysis of 667 available patient records from local treatment centres, fitting quantile regression models at the 25th, 50th, 75th, and 90th percentiles ( = 0.25, 0.50, 0.75, 0.90) using R (v4.3.2). Descriptive statistics indicated a right-skewed age distribution (mean = 33.0 years; skewness = 2.28). Normality tests (KolmogorovSmirnov, ShapiroWilk) confirmed significant departures from normality across most subgroups, justifying the quantile approach. Results revealed that female gender is significantly associated with younger age at diagnosis at the 25th75th percentiles (p < 0.01), whereas lower educational status becomes a significant risk factor at the 50th90th percentiles (p < 0.001). In contrast, marital status and employment status exhibited no significant associations with age across any quantiles. These findings show the disproportionate vulnerability of young women (approximately ages 2640) and less-educated adults to HIV infection. Recommendations include gender-targeted prevention strategies, particularly a comprehensive sexual education and empowerment initiatives for young women and broader application of quantile regression methods in epidemiological research to capture distributional heterogeneity.