<![CDATA[Human Immunodeficiency Virus (HIV) remains a major global public health concern, with sub-Saharan Africa accounting for a substantial proportion of the global burden of infection. In Nigeria, the HIV epidemic shows geographic and demographic variation shaped by age, sex, socioeconomic status, risk behaviors, and access to healthcare services. Understanding the determinants of HIV infection is therefore essential for effective prevention, early detection, and policy formulation. This study aimed to identify significant demographic determinants of HIV infection and determine the best-fitting binary response model among patients tested at General Hospital Takum, Taraba State, Nigeria, between 2018 and 2023. Binary logistic regression models with logit, probit, and complementary log–log link functions were applied to assess the effects of age, sex, and year on HIV infection status. Model performance was evaluated using goodness-of-fit statistics, including deviance, Pearson chi-square, and Hosmer–Lemeshow tests, as well as model selection criteria based on the Akaike Information Criterion and Bayesian Information Criterion. The results indicate a consistent decline in HIV odds across the study years, significantly higher odds among females, and substantially increased odds among adults aged 30–49 years and those aged 50 years and above. Among the three models, the complementary log–log link function demonstrated the best overall fit, with the lowest AIC and BIC values and non-significant goodness-of-fit tests. The study concludes that age, sex, and year are significant predictors of HIV infection, and that the complementary log–log model provides the most reliable framework for predicting HIV status in this population. These findings contribute to epidemiological modelling by supporting more appropriate link-function selection and offer practical implications for localized HIV prevention strategies in Taraba State, Nigeria.]]>
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