Indices to measure risk of HIV acquisition in Rakai, Uganda

Abstract

Introduction: Targeting most-at-risk individuals with HIV preventive interventions is cost-effective. We developed gender-specific indices to measure risk of HIV among sexually active individuals in Rakai, Uganda.

Methods: We used multivariable Cox proportional hazards models to estimate time-to-HIV infection associated with candidate predictors. Reduced models were determined using backward selection procedures with Akaike's information criterion (AIC) as the stopping rule. Model discrimination was determined using Harrell's concordance index (c index). Model calibration was determined graphically. Nomograms were used to present the final prediction models.

Results: We used samples of 7,497 women and 5,783 men. 342 new infections occurred among females (incidence 1.11/100 person years,) and 225 among the males (incidence 1.00/100 person years). The final model for men included age, education, circumcision status, number of sexual partners, genital ulcer disease symptoms, alcohol use before sex, partner in high risk employment, community type, being unaware of a partner's HIV status and community HIV prevalence. The Model's optimism-corrected c index was 69.1 percent (95% CI = 0.66, 0.73). The final women's model included age, marital status, education, number of sex partners, new sex partner, alcohol consumption by self or partner before sex, concurrent sexual partners, being employed in a high-risk occupation, having genital ulcer disease symptoms, community HIV prevalence, and perceiving oneself or partner to be exposed to HIV. The models optimism-corrected c index was 0.67 (95% CI = 0.64, 0.70). Both models were well calibrated.

Conclusion: These indices were discriminative and well calibrated. This provides proof-of-concept that population-based HIV risk indices can be developed. Further research to validate these indices for other populations is needed.

Figure 1. Cumulative HIV incidence by predicted quartile of HIV risk score for sexually active men.

Figure 2. Cumulative HIV incidence by predicted quartile of HIV risk score for sexually active women.

Figure 3. Observed vs predicted probabilities of being HIV-free at 4 years of follow-up.

The figure provides bias-corrected calibration of the men's prediction model.

Figure 4. Observed vs predicted probabilities of being HIV-free at 4 years of follow-up.

The figure provides bias-corrected calibration of the women's prediction model.

Figure 5. Nomogram of HIV risk for sexually active men developed using the Rakai cohort, Uganda 2003–2011.

Figure 6. Nomogram of HIV risk for sexually active women developed using the Rakai cohort, Uganda 2003–2011.