Predictors of chlamydia or gonorrhea among people with HIV in Miami-Dade County Ryan White Program in 2017.

The purpose of this study was to assess the prevalence of chlamydia or gonorrhea and factors associated with the diagnoses among people with HIV (PHIV) in the Ryan White Program Part A (RWP) in Miami-Dade County, Florida. We used 2017 calendar year data to identify factors associated with a chlamydia or gonorrhea diagnoses using logistic regression. About 50% of the 7110 PHIV who were ≥18 years old in active Ryan White care in 2017 reported being screened for chlamydia or gonorrhea. Of those screened, 2.3% reported diagnoses of chlamydia, gonorrhea or both. In the adjusted model, compared to PHIV ≥40 years-old, PHIV aged 18-24 and 25-39 years reported higher odds of diagnoses (adjusted odds ratio [aOR] 4.29; 95% confidence interval [CI]: 1.73-10.63 and aOR 4.58; 95% CI; 2.62-7.99 respectively). Those with multiple sexual partners in the last 12 months reported higher odds of diagnoses (aOR 1.67; (95% CI; 1.04-2.69)). Screening rates for chlamydia or gonorrhea are low, relative to CDC guidelines. Interventions are needed to increase rates of screening and targeted behavioral risk reduction techniques are highly recommended among those 18-39 years of age and those who have multiple sexual partners.

Development and Validation of a Risk Prediction Tool to Identify People with HIV Infection Likely Not to Achieve Viral Suppression.

Identifying people with HIV infection (PHIV), who are at risk of not achieving viral suppression, is important for designing targeted intervention. The aim of this study was to develop and test a risk prediction tool for PHIV who are at risk of not achieving viral suppression after a year of being in care. We used retrospective data to develop an integer-based scoring method using backward stepwise logistic regression. We also developed risk score categories based on the quartiles of the total risk score. The risk prediction tool was internally validated by bootstrapping. We found that nonviral suppression after a year of being in care among PHIV can be predicted using seven variables, namely, age group, race, federal poverty level, current AIDS status, current homelessness status, problematic alcohol/drug use, and current viral suppression status. Those in the high-risk category had about a 23 increase in the odds of nonviral suppression compared with the low-risk group. The risk prediction tool has good discriminative performance and calibration. Our findings suggest that nonviral suppression after a year of being in care can be predicted using easily available variables. In settings with similar demographics, the risk prediction tool can assist health care providers in identifying high-risk individuals to target for intervention. Follow-up studies are required to externally validate this risk prediction tool.