A Simple Risk Prediction Algorithm for HIV Transmission: Results from HIV Prevention Trials in KwaZulu Natal, South Africa (2002–2012)

• Published in: AIDS and behavior Vol. 22; no. 1; pp. 325 - 336
• Main Authors: Wand, Handan, Reddy, Tarylee, Naidoo, Sarita, Moonsamy, Suri, Siva, Samantha, Morar, Neetha S., Ramjee, Gita
• Format: Journal Article
• Language: English
• Published: New York Springer US 2018; Springer Nature B.V
• Subjects: Adult; Algorithms; Biomedical data; Biomedicine; Communicable Disease Control; Disease prevention; Disease transmission; Family Characteristics; Female; Health Psychology; Health risks; Health services; HIV; HIV Infections - diagnosis; HIV Infections - epidemiology; HIV Infections - prevention & control; HIV Infections - transmission; Human immunodeficiency virus; Humans; Infectious Diseases; Medicine; Medicine & Public Health; Original Paper; Population Surveillance; Pre-Exposure Prophylaxis; Predictions; Prevention; Program Evaluation - methods; Prophylaxis; Public Health; Recruitment; Regression analysis; Regression models; Research design; Risk; Risk Assessment - methods; Scores; Sexual Behavior; Sexual Partners; Sexually transmitted diseases; South Africa; Statistical analysis; STD; Tests; Young Adult; South Africa; Risk prediction tool; Sudáfrica; herramienta de predicción del riesgo; Population attributable risk; South Africa; KwaZulu-Natal; riesgo atribuible a la población; HIV prevention; Infección por VIH; HIV infection; prevención del VIH
• ISSN: 1090-7165; 1573-3254
• DOI: 10.1007/s10461-017-1785-7

We aimed to develop a HIV risk scoring algorithm for targeted screening among women in South Africa. We used data from five biomedical intervention trials (N = 8982 Cox regression models were used to create a risk prediction algorithm and it was internally and externally validated using standard statistical measures; 7-factors were identified as significant predictors of HIV infection: <25 years old, being single/not cohabiting, parity (<3), age at sexual debut (<16), 3+ sexual partners, using injectables and diagnosis with a sexually transmitted infection(s). A score of ≥25 (out of 50) was the optimum cut point with 83% (80%) sensitivity in the development (validation) dataset. Our tool can be used in designing future HIV prevention research and guiding recruitment strategies as well as in health care settings. Identifying, targeting and prioritising women at highest risk will have significant impact on preventing new HIV infections by scaling up testing and pre-exposure prophylaxis in conjunction with other HIV prevention modalities.