Socio-Economic Determinants of HIV-Malaria Co-Infection among Adults in the North Central Zone, Nigeria

Background: Globally, Human Immunodeficiency Virus, and malaria co-infection are responsible for high rates of disease and death predominantly in sub-Saharan Africa. However, the relationship between the socio-economic determinants of the human immunodeficiency virus and malaria co-infection has not been established. Therefore, this study aims to determine the socio-economic variables associated with human immunodeficiency virus and malaria co-infection among adults in peri-urban secondary hospitals in the North Central Zone, Nigeria. Method: A retrospective descriptive cross-sectional study was carried out among human immunodeficiency virus-positive patients at six selected peri-urban secondary hospital facilities in the North Central Zone, Nigeria. Continuos variable was compared using the student t-test, or Wilcoxon test, while the categorical variable was compared using Chi-square and Fisher’s exact test. The significance level was kept at p  0.05. Results: This study showed that patients of 61 years and above, those between 18 and 30 years of age are at risk of HIV/malaria co-infection RR 1.09 (0.92 - 1.31) and (95% CI), 1.02 (0.96 - 1.08). A significant relationship was reported between the likelihood of co-infection and education (p = 0.023), residence (p = 0.001), employment, (p < 0.001) and income (p < 0.001). Similarly, the highest proportion of malaria diagnosis 547 (80.9%) was among the un-employed patient’s contrary to the least proportion reported among employed patients 84 (68.3%). Using a logistic regression model, it was noted that the proportion of co-infection among HIV seropositive patients is negatively associated with their income. Conclusion: Findings from this study revealed a strong association between socio-economic variables and HIV/malaria co-infection among the study population. These socio-economic variables could serve as an essential indicator in any proposed intervention programme and could help to predict future co-infection rates in regions where both infectious diseases are dominant.