Computational Prediction of Subjective Human Immunodeficiency Virus Status in Malawi Using a Random Forest Approach

A longitudinal multilevel analysis of the evermigrated population subjective HIV infection expectation in Malawi

OBJECTIVE

The primary purpose of this study is to assess the variance in the effects of factors influencing ever migrated Malawian population's subjective HIV infection expectation.

DESIGN

Using data from the Malawi Longitudinal Study of Families and Health (MLSFH) survey (1998-2010), 7805 ever migrated Malawian adults were selected for the study. Summary statistics, logistic regression and longitudinal multi-level models were fitted for the study. A binary logistic regression was used to estimate the direction and magnitude of the associations between the variables selected for the study Five multilevel models with random intercepts and coefficients nominal response were fitted.

RESULTS

The study revealed that sexual behaviours had the most significant effect on ever migrated Malawian's subjective HIV infection expectation. All metrics showed that the conditional growth model had the most signficant outcome. The addition of time and other variables as predictors had a significant effect on the conditional growth model.

CONCLUSION

Interventions designed to decrease the spread of HIV should target sexual behaviours and widespread testing among the ever migrated population to reduce subjective HIV infection thoughts.