A syndemic approach to assess the effect of substance use and social disparities on the evolution of HIV/HCV infections in British Columbia
Bibliographic record
Abstract
BACKGROUND: Co-occurrence of social conditions and infections may affect HIV/HCV disease risk and progression. We examined the changes in relationship of these social conditions and infections on HIV and hepatitis C virus (HCV) infections over time in British Columbia during 1990-2013. METHODS: The BC Hepatitis Testers Cohort (BC-HTC) includes ~1.5 million individuals tested for HIV or HCV, or reported as a case of HCV, HIV, HBV, or tuberculosis linked to administrative healthcare databases. We classified HCV and HIV infection status into five combinations: HIV-/HCV-, HIV+monoinfected, HIV-/HCV+seroconverters, HIV-/HCV+prevalent, and HIV+/HCV+. RESULTS: Of 1.37 million eligible individuals, 4.1% were HIV-/HCV+prevalent, 0.5% HIV+monoinfected, 0.3% HIV+/HCV+ co-infected and 0.5% HIV-/HCV+seroconverters. Overall, HIV+monoinfected individuals lived in urban areas (92%), had low injection drug use (IDU) (4%), problematic alcohol use (4%) and were materially more privileged than other groups. HIV+/HCV+ co-infected and HIV-/HCV+seroconverters were materially most deprived (37%, 32%), had higher IDU (28%, 49%), problematic alcohol use (14%, 17%) and major mental illnesses (12%, 21%). IDU, opioid substitution therapy, and material deprivation increased in HIV-/HCV+seroconverters over time. In multivariable multinomial regression models, over time, the odds of IDU declined among HIV-/HCV+prevalent and HIV+monoinfected individuals but not in HIV-/HCV+seroconverters. Declines in odds of problematic alcohol use were observed in HIV-/HCV+seroconverters and coinfected individuals over time. CONCLUSIONS: These results highlight need for designing prevention, care and support services for HIV and HCV infected populations based on the evolving syndemics of infections and social conditions which vary across groups.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".