Risk Prediction Score for HIV Infection: Development and Internal Validation with Cross-Sectional Data from Men Who Have Sex with Men in China

AIDS Behav. 2018 Jul;22(7):2267-2276. doi: 10.1007/s10461-018-2129-y.

Abstract

Receptive anal intercourse, multiple partners, condomless sex, sexually transmitted infections (STIs), and drug/alcohol addiction are familiar factors that correlate with increased human immunodeficiency virus (HIV) risk among men who have sex with men (MSM). To improve estimation to HIV acquisition, we created a composite score using questions from routine survey of 3588 MSM in Beijing, China. The HIV prevalence was 13.4%. A risk scoring tool using penalized maximum likelihood multivariable logistic regression modeling was developed, deploying backward step-down variable selection to obtain a reduced-form model. The full penalized model included 19 sexual predictors, while the reduced-form model had 12 predictors. Both models calibrated well; bootstrap-corrected c-indices were 0.70 (full model) and 0.71 (reduced-form model). Non-Beijing residence, short-term living in Beijing, illegal drug use, multiple male sexual partners, receptive anal sex, inconsistent condom use, alcohol consumption before sex, and syphilis infection were the strongest predictors of HIV infection. Discriminating higher-risk MSM for targeted HIV prevention programming using a validated risk score could improve the efficiency of resource deployment for educational and risk reduction programs. A valid risk score can also identify higher risk persons into prevention and vaccine clinical trials, which would improve trial cost-efficiency.

Keywords: China; HIV; HIV prevention; Men who have sex with men (MSM); Risk score.

Publication types

  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Alcohol Drinking / epidemiology*
  • China / epidemiology
  • Condoms
  • Cross-Sectional Studies
  • HIV Infections / epidemiology*
  • HIV Infections / prevention & control
  • Homosexuality, Male / statistics & numerical data
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Prevalence
  • Risk Assessment
  • Risk Factors
  • Risk-Taking
  • Safe Sex
  • Sexual Behavior / statistics & numerical data*
  • Sexual Partners
  • Sexual and Gender Minorities / statistics & numerical data*
  • Surveys and Questionnaires
  • Syphilis / epidemiology*
  • Unsafe Sex / statistics & numerical data*
  • Young Adult