Abstract
To apply a novel method to adjust for HIV knowledge as an unmeasured confounder for the effect of unsafe injection on future HIV testing. The data were collected from 601 HIV-negative persons who inject drugs (PWID) from a cohort in San Francisco. The panel-data generalized estimating equations (GEE) technique was used to estimate the adjusted risk ratio (RR) for the effect of unsafe injection on not being tested (NBT) for HIV. Expert opinion quantified the bias parameters to adjust for insufficient knowledge about HIV transmission as an unmeasured confounder using Bayesian bias analysis. Expert opinion estimated that 2.5%-40.0% of PWID with unsafe injection had insufficient HIV knowledge; whereas 1.0%-20.0% who practiced safe injection had insufficient knowledge. Experts also estimated the RR for the association between insufficient knowledge and NBT for HIV as 1.1-5.0. The RR estimate for the association between unsafe injection and NBT for HIV, adjusted for measured confounders, was 0.96 (95% confidence interval: 0.89,1.03). However, the RR estimate decreased to 0.82 (95% credible interval: 0.64, 0.99) after adjusting for insufficient knowledge as an unmeasured confounder. Our Bayesian approach that uses expert opinion to adjust for unmeasured confounders revealed that PWID who practice unsafe injection are more likely to be tested for HIV - an association that was not seen by conventional analysis.
Highlights
Unsafe drug injection is a major risk factor for HIV and other blood-borne illnesses globally.[1]
The bias analysis revealed that persons who inject drugs (PWID) who are practicing unsafe injection are more likely to be tested for HIV
We found that if the insufficient HIV knowledge was considered as an unmeasured confounder, PWID who practice unsafe injection are more likely to be tested for HIV – an association that was not seen by conventional analysis
Summary
Unsafe drug injection is a major risk factor for HIV and other blood-borne illnesses globally.[1]. Any factors not conceived at the design stage or measured during data collection (hereafter called “unmeasured confounders”) make adjustment impossible in conventional analysis. This limitation to conventional frequentist analytic methods is a common critique of observational studies.[19] virtually any observational study may be rightly or wrongly criticized for failure to adjust for unmeasured confounding. Adjustment for Unmeasured Confounding in Longitudinal Data likelihood of not receiving an HIV test, if all PWID were low-risk drug injectors, compared with the 3-month likelihood of not receiving an HIV test if all PWID were unsafe drug injectors. The analysis methodology developed for this case study can be used in other studies with few modifications
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