Self-Reported HIV and HCV Screening Rates and Serostatus Among Substance Abuse Treatment Patients

A simple risk-based strategy for hepatitis C virus screening among incarcerated people in a low- to middle-income setting

Background

Hepatitis C virus (HCV) is among the highest priority diseases in custodial settings; however, the diagnosis remains suboptimal among people in custody. This study aimed to validate a short survey for identifying people with HCV infection in a provincial prison in Iran.

Methods

Between July and December 2018, residents and newly admitted inmates of Gorgan central prison completed a questionnaire, including data on the history of HCV testing, drug use, injecting drug use, sharing injecting equipment, and imprisonment. Participants received rapid HCV antibody testing, followed by venipuncture for RNA testing (antibody-positive only). Each enrollment question (yes/no) was compared with the testing results (positive/negative).

Results

Overall, 1892 people completed the questionnaire, including 621 (34%) who were currently on opioid agonist therapy (OAT); 30% of participants had been tested for HCV previously. About 71% had a history of drug use, of whom 13% had ever injected drugs; 52% had ever shared injecting equipment. The prevalence of HCV antibody and RNA was 6.9% (n = 130) and 4.8% (n = 90), respectively. The antibody prevalence was higher among people on OAT compared to those with no history of OAT (11.4% vs. 4.0%). History of drug use was the most accurate predictor of having a positive HCV antibody (sensitivity: 95.2%, negative predictive value: 98.9%) and RNA testing (sensitivity: 96.7%, negative predictive value: 99.5%). The sensitivity of the drug use question was lowest among people with no OAT history and new inmates (87% and 89%, respectively). Among all participants, sensitivity and negative predictive value of the other questions were low and ranged from 34 to 54% and 94 to 97%, respectively.

Conclusions

In resource-limited settings, HCV screening based on having a history of drug use could replace universal screening in prisons to reduce costs. Developing tailored screening strategies together with further cost studies are crucial to address the current HCV epidemic in low- to middle-income countries.

Comparisons of New HIV Rapid Test Kit Performance

The development of rapid point-of-care tests for HIV infection has greatly reduced the problem of failure to return for test results. Test manufacturers are now developing test kits that can test for two or even three diseases at the same time, multiple-disease test kits. This study reports on the sensitivity and specificity of HIV tests when included on multi-disease test kits. 1029 participants were recruited from 2011 to 2014. HIV test kit sensitivities ranged from 91.1 to 100%, and the HIV test kit specificities from 99.5 to 100%. The two HIV kits which used oral fluid instead of blood performed well.

Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model, which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework we estimate individual treatment effects by directly modeling the response. We find that accurate estimation of individual treatment effects is possible even in complex heterogenous settings but that the type of RF approach plays an important role in accuracy. Methods designed to be adaptive to confounding, when used in parallel with out-of-sample estimation, do best. One method found to be especially promising is counterfactual synthetic forests. We illustrate this new methodology by applying it to a large comparative effectiveness trial, Project Aware, to explore the role drug use plays in sexual risk. The analysis reveals important connections between risky behavior, drug usage, and sexual risk.