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

Abstract

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.

Prevalence of arterial hypertension and risk factors among people with acquired immunodeficiency syndrome

“…People with higher education level may have more access to relevant health information, presenting a broader perception on cardiovascular risk factors and the need to maintain a healthy lifestyle 22 . Singles were the majority of patients and, given this, our study showed that single people are more likely to have multiple partners, thus becoming more vulnerable to HIV 23 . However, the increase in the number of HIV infection cases among people in stable relationships must be highlighted; this derives from the lack of negotiation about condom use, especially by females 24 .…”

Predictors of HIV, HIV Risk Perception, and HIV Worry Among Adolescent Girls and Young Women in Lilongwe, Malawi

“…15 Determining which AGYW in SSA meet these definitions is challenging, yet important. 10,16 Much of the analysis-to-date has been conducted in South Africa among trial participants, 17-19 and understanding regional variation is critical.…”

Perceived Versus Calculated HIV Risk: Implications for Pre-exposure Prophylaxis Uptake in a Randomized Trial of Men Who Have Sex With Men

“…[15][16][17][18][19] Reconciling perceived risk, real-life behaviors, and validated risk indices is essential to effectively implement preventive measures such as PrEP. [20][21][22][23] Throughout the course of biomedicine, this reconciliation has often been achieved through the development of objective risk calculators. Incidence of cardiovascular disease and osteoporosis, for example, have been drastically reduced through the use of Framingham and DXA scores, respectively.…”