Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa

• Published in: PloS one 2022-03, Vol.17 (3), p.e0264429
• Main Authors: Orel, Erol, Esra, Rachel, Estill, Janne, Thiabaud, Amaury, Marchand-Maillet, Stéphane, Merzouki, Aziza, Keiser, Olivia
• Format: Article
• Language: English
• Subjects: Africa; Africa, Southern - epidemiology; Age; Algorithms; Biology and Life Sciences; Care and treatment; Circumcision, Male; Computer and Information Sciences; Confidence intervals; Control; Datasets; Disease prevention; Epidemics; Female; Females; Health risks; HIV; HIV infection; HIV Infections - diagnosis; HIV Infections - drug therapy; HIV Infections - epidemiology; HIV patients; HIV Testing; Human immunodeficiency virus; Humans; Male; Males; Medicine and Health Sciences; Methods; Performance prediction; Physical Sciences; Pre-Exposure Prophylaxis; Prevention; Prophylaxis; Regression models; Research and Analysis Methods; Risk factors; Sex; Sexual partners; Sexually transmitted diseases; Statistical analysis; STD; Support vector machines; Trees; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0264429

High yield HIV testing strategies are critical to reach epidemic control in high prevalence and low-resource settings such as East and Southern Africa. In this study, we aimed to predict the HIV status of individuals living in Angola, Burundi, Ethiopia, Lesotho, Malawi, Mozambique, Namibia, Rwanda, Zambia and Zimbabwe with the highest precision and sensitivity for different policy targets and constraints based on a minimal set of socio-behavioural characteristics. We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). The algorithms were trained and validated on 80% of the data, and tested on the remaining 20%. We compared the predictions based on the F1 score, the harmonic mean of sensitivity and positive predictive value (PPV), and we assessed the generalization of our models by testing them against an independent left-out country. The best performing algorithm was trained on a minimal subset of variables which were identified as the most predictive, and used to 1) identify 95% of people living with HIV (PLHIV) while maximising precision and 2) identify groups of individuals by adjusting the probability threshold of being HIV positive (90% in our scenario) for achieving specific testing strategies. Overall 55,151 males and 69,626 females were included in the analysis. The gradient boosting trees algorithm performed best in predicting HIV status with a mean F1 score of 76.8% [95% confidence interval (CI) 76.0%-77.6%] for males (vs [CI 67.8%-70.6%] for SVM) and 78.8% [CI 78.2%-79.4%] for females (vs [CI 73.4%-75.8%] for SVM). Among the ten most predictive variables for each sex, nine were identical: longitude, latitude and, altitude of place of residence, current age, age of most recent partner, total lifetime number of sexual partners, years lived in current place of residence, condom use during last intercourse and, wealth index. Only age at first sex for male (ranked 10th) and Rohrer's index for female (ranked 6th) were not similar for both sexes. Our large-scale scenario, which consisted in identifying 95% of all PLHIV, would have required testing 49.4% of males and 48.1% of females while achieving a precision of 15.4% for males and 22.7% for females. For the second scenario, only 4.6% of males and 6.0% of females would have had to be teste