Injection Drug Use and Hepatitis C as Risk Factors for Mortality in HIV-Infected Individuals
Bibliographic record
Abstract
BACKGROUND: HIV-infected individuals with a history of transmission through injection drug use (IDU) have poorer survival than other risk groups. The extent to which higher rates of hepatitis C (HCV) infection in IDU explain survival differences is unclear. METHODS: Adults who started antiretroviral therapy between 2000 and 2009 in 16 European and North American cohorts with >70% complete data on HCV status were followed for 3 years. We estimated unadjusted and adjusted (for age, sex, baseline CD4 count and HIV-1 RNA, AIDS diagnosis before antiretroviral therapy, and stratified by cohort) mortality hazard ratios for IDU (versus non-IDU) and for HCV-infected (versus HCV uninfected). RESULTS: Of 32,703 patients, 3374 (10%) were IDU; 4630 (14%) were HCV+; 1116 (3.4%) died. Mortality was higher in IDU compared with non-IDU [adjusted HR 2.71; 95% confidence interval (CI): 2.32 to 3.16] and in HCV+ compared with HCV- (adjusted HR 2.65; 95% CI: 2.31 to 3.04). The effect of IDU was substantially attenuated (adjusted HR 1.57; 95% CI: 1.27 to 1.94) after adjustment for HCV, while attenuation of the effect of HCV was less substantial (adjusted HR 2.04; 95% CI: 1.68 to 2.47) after adjustment for IDU. Both IDU and HCV were strongly associated with liver-related mortality (adjusted HR 10.89; 95% CI: 6.47 to 18.3 for IDU and adjusted HR 14.0; 95% CI: 8.05 to 24.5 for HCV) with greater attenuation of the effect of IDU (adjusted HR 2.43; 95% CI: 1.24 to 4.78) than for HCV (adjusted HR 7.97; 95% CI: 3.83 to 16.6). Rates of CNS, respiratory and violent deaths remained elevated in IDU after adjustment for HCV. CONCLUSIONS: A substantial proportion of the excess mortality in HIV-infected IDU is explained by HCV coinfection. These findings underscore the potential impact on mortality of new treatments for HCV in HIV-infected people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".