South Africa has the largest share of people living with HIV in the world and this population is ageing. The social context in which people seek HIV care is often ignored. Apart from clinical interventions, socio-behavioural factors impact successful HIV care outcomes for older adults living with HIV. We use cross-sectional data linked with demographic household surveillance data, consisting of HIV positive adults aged above 40, to identify socio-behavioural predictors of a detectable viral load. Older adults were more likely to have a detectable viral load if they did not disclose their HIV positive status to close family members (aOR 2.56, 95% CI 1.89-3.46), resided in the poorest households (aOR 1.98, 95% CI 1.23-3.18), or were not taking medications other than ART (aOR 1.83, 95% CI 1.02-1.99) likely to have a detectable. Clinical interventions in HIV care must be supported by understanding the socio-behavioural barriers that occur outside the health facility. The importance of community health care workers in bridging this gap may offer more optimum outcomes for older adults ageing with HIV.

There is strong global need for the development of Multipurpose Prevention Technologies (MPTs) that prevent HIV, pregnancy, and/or other sexually transmitted infections (STIs). However, despite decades of research focused on the development of MPTs, numerous research gaps remain, contributing to reproductive health disparities. This commentary will highlight biomedical, socio-behavioral, and implementation science gaps in MPT research. Biomedical gaps and barriers include limited dosage forms, challenges around drug selection and stable coformulation of multiple drugs, and an unclear regulatory pathway. Behavioral, social, and structural gaps include lack of research around MPT preferences for some subgroups of potential end users, lack of knowledge around whether MPTs improve uptake, adherence, and persistence vs. separate products, and a need to further understand how social and cultural factors might impact MPT interest and use. Gaps in implementation science research will need to be addressed to better understand how to implement MPTs to maximize effectiveness and benefit. This commentary will also identify opportunities for integrating biomedical and behavioral science around MPTs.

HIV prevention measures in sub-Saharan Africa are still short of attaining the UNAIDS 90-90-90 fast track targets set in 2014. Identifying predictors for HIV status may facilitate targeted screening interventions that improve health care. We aimed at identifying HIV predictors as well as predicting persons at high risk of the infection.
We applied machine learning approaches for building models using population-based HIV Impact Assessment (PHIA) data for 41,939 male and 45,105 female respondents with 30 and 40 variables respectively from four countries in sub-Saharan countries. We trained and validated the algorithms on 80% of the data and tested on the remaining 20% where we rotated around the left-out country. An algorithm with the best mean f1 score was retained and trained on the most predictive variables. We used the model to identify people living with HIV and individuals with a higher likelihood of contracting the disease.
Application of XGBoost algorithm appeared to significantly improve identification of HIV positivity over the other five algorithms by f1 scoring mean of 90% and 92% for males and females respectively. Amongst the eight most predictor features in both sexes were: age, relationship with family head, the highest level of education, highest grade at that school level, work for payment, avoiding pregnancy, age at the first experience of sex, and wealth quintile. Model performance using these variables increased significantly compared to having all the variables included. We identified five males and 19 females individuals that would require testing to find one HIV positive individual. We also predicted that 4·14% of males and 10.81% of females are at high risk of infection.
Our findings provide a potential use of the XGBoost algorithm with socio-behavioural-driven data at substantially identifying HIV predictors and predicting individuals at high risk of infection for targeted screening.