Objectives: The main objective of this study was to compare results from two approaches for estimating the effect of different factors on the risk of HIV infection and determine the best fitting model.Study design: We performed secondary data analysis on cross-sectional data which was collected from the Zimbabwe Demographic Health Survey (ZDHS) from 2005 to 2015.Methods: Survey and cluster adjusted logistic regression was used to determine variables for use in survival analysis with HIV status as the outcome variable. Covariates found significant in the logistic regression were used in survival analysis to determine the factors associated with HIV infection over the 10 years. The data for the survival analysis were modeled assuming age at survey imputation (Model 1) and interval-censoring (Model 2).Results: Model goodness of fit test based on the Cox-Snell residuals against the cumulative hazard indicated that Model 1 was the best model. On the contrary, the Akaike Information Criterion (AIC) indicated that Model 2 was the best model. Factors associated with a high risk of HIV infection were being female, number of sexual partners, and having had an STI in the past year prior to the survey.Conclusion: The difference between the results from the Cox-Snell residuals graphical method and the model estimates and AIC value maybe due to the lack of adequate methods to test the goodness-of -fit of interval-censored data. We concluded that Model 2 with interval-censoring gave better estimates due to its consistency with the published results from literature. Even though we consider the interval-censoring model as the superior model with regards to our specific data, the method had its own set of limitations.
The burden of HIV and related diseases have been areas of great concern pre and post the emergence of COVID-19 in Zimbabwe. Machine learning models have been used to predict the risk of diseases, including HIV accurately. Therefore, this paper aimed to determine common risk factors of HIV positivity in Zimbabwe between the decade 2005 to 2015. The data were from three two staged population five-yearly surveys conducted between 2005 and 2015. The outcome variable was HIV status. The prediction model was fit by adopting 80% of the data for learning/training and 20% for testing/prediction. Resampling was done using the stratified 5-fold cross-validation procedure repeatedly. Feature selection was done using Lasso regression, and the best combination of selected features was determined using Sequential Forward Floating Selection. We compared six algorithms in both sexes based on the F1 score, which is the harmonic mean of precision and recall. The overall HIV prevalence for the combined dataset was 22.5% and 15.3% for females and males, respectively. The best-performing algorithm to identify individuals with a higher likelihood of HIV infection was XGBoost, with a high F1 score of 91.4% for males and 90.1% for females based on the combined surveys. The results from the prediction model identified six common features associated with HIV, with total number of lifetime sexual partners and cohabitation duration being the most influential variables for females and males, respectively. In addition to other risk reduction techniques, machine learning may aid in identifying those who might require Pre-exposure prophylaxis, particularly women who experience intimate partner violence. Furthermore, compared to traditional statistical approaches, machine learning uncovered patterns in predicting HIV infection with comparatively reduced uncertainty and, therefore, crucial for effective decision-making.
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