Modelling the non-linear force of infection for HIV in Malawi in 2015

Abstract

Background HIV/AIDS is one of the leading causes of deaths in Sub-Saharan Africa among individuals in the reproductive ages. Malawi is one of the countries in this hardest-hit region. This study aimed to model the age-specific Force of infection of HIV infection for Malawi in 2015. Methods The report is a secondary analysis of cross-sectional study data from the 2015 Malawi Demographic and Health Survey. The outcome variable was HIV status which was a binary variable coded, positive and negative. Statistical analysis was done using survey logistic and multilevel logistic models, survival models. Modelling the age-specific force of infection was done using Farrington models, log-logistic model, non-linear model using maximum likelihood estimation and Susceptible Infected model. Results There were 14,010 male and female individuals aged 15-49 years in our analysis, where 9.1% were HIV positive. Place of residence, age in years, gender, marital status and total number sexual partners showed a significant association with HIV infection. Individuals living in urban areas were more likely to have HIV infection than those living in the rural areas. The risk of having HIV infection was less likely among males than females. Furthermore, the risk of having HIV infection increased as the age in years increased. This was also observed with the total number of sexual partners. Individuals who reported to have not been married before were less likely to have the infection unlike those who reported living together, married, divorced and widowed. The Farrington’s two parameter model was the best fit for the Force of Infection. The Force of infection for HIV decreased as the ages increased. Conclusion Modelling the age-specific forces of infections of HIV is very important as its estimates help in targeting the specific groups that need more interventions. Modelling also helps in predictive models such as those used on the 90:90:90 for HIV by year 2020. It also helps in evaluating disease control programs, comparing prevention and therapy programs and informing policies.