A Generalized Linear Model of HIV/AIDS Patients in Kenya: A Case Study of Nyeri County Referral Hospital

With millions of new cases and deaths reported every year, HIV/AIDS is a significant worldwide health concern. Creating successful public health policies and interventions requires an understanding of the dynamics of HIV transmission and progression. WHO predicted that by the end of 2022 roughly 39 million individuals worldwide would be living with HIV, out of which 37.5 million are adults, whereas 1.5 million are children. Despite outstanding global gains in HIV/AIDS prevention, treatment and care, Kenya continues to struggle to effectively handle the HIV epidemic, particularly in areas like Nyeri County. Nyeri County Referral Hospital is a critical healthcare institution for HIV/AIDS patients in the region. However, there is still a lack of understanding about the epidemiological characteristics of HIV/AIDS in this particular population. This study’s aim was to use a GLM on HIV/AIDS data in Nyeri County Referral Hospital in Kenya. To determine the significance of model parameters, Likelihood Ratio Test was used whereas significance of regression coefficients was determined using Wald Chi- Square Test. Deviance was utilized to test for the goodness of fit. R software version 4.4.1 was utilized. This project may help health policymakers in developing or refining HIV/AIDS care programs. Findings from the study can help healthcare planners and policymakers allocate resources more efficiently to meet the requirements of HIV/AIDS patients. The fitted model showed that, only ART use was significant (p-value = 2.684562 × 10<sup>−13</sup>). Because some covariates were not significant, each of them was analyzed separately. Age was a significant predictor (p-value = 0.0001536103). The other variables were not significant. This finding is consistent with previous evidence, which stresses the relevance of ART in lowering viral load, enhancing immunological function, and extending the lives of people living with HIV. To build upon the current findings, future research should explore additional variables that may influence HIV status, for example cultural beliefs, and access to healthcare services. Again, future studies may involve the use of survival analysis through GLM in analyzing similar data.