Syndemic Characterization of HCV, HBV, and HIV Co-infections in a Large Population Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Limited data are available on HBV, HCV, and HIV co-infections and triple infection. We characterized co-occurrence of HIV, HBV, and HCV infections at the population level in British Columbia (BC) to identify patterns of predisposing factors unique to co-infection subgroups. METHODS: We analyzed data from the BC Hepatitis Testers Cohort, which includes all individuals tested for HCV or HIV in BC between 1992 and 2013, or included in provincial public health registries of HIV, HCV, HBV, and active tuberculosis. Individuals were classified as negative, mono-, and co-infection groups based on HIV, HBV, and HCV status. We evaluated associations between risk factors (injection drug use, sexual orientation etc.) and co-infection groups using multivariate multinomial logistic regression. FINDINGS: Of a total of 1,376,989 individuals included in the analysis, 1,276,290 were negative and 100,699 were positive for HIV, HBV, and/or HCV. Most cases (91,399, 90.8%) were mono-infected, while 3991 (4.0%) had HBV/HCV, 670 HBV/HIV (0.7%), 3459 HCV/HIV (3.4%), and 1180 HBV/HCV/HIV (1.2%) co-infection. Risk factor and demographic distribution varied across co-infection categories. MSM classification was associated with higher odds of all HIV co-infection groups, particularly HBV/HIV (OR 6.8; 95% CI: 5.6, 8.27), while injection drug use was most strongly associated with triple infection (OR 64.19; 95% CI: 55.11, 74.77) and HIV/HCV (OR 23.23; 95% CI: 21.32, 25.31). INTERPRETATION: Syndemics of substance use, sexual practices, mental illness, socioeconomic marginalization, and co-infections differ among population groups, highlighting avenues for optimal composition and context for health services to meet each population's unique needs. FUNDING: BC Centre for Disease Control and Canadian Institutes of Health Research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".