Differences in risk factors for hepatitis B, hepatitis C, and human immunodeficiency virus infection by ethnicity: A large population-based cohort study in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Addressing the needs of ethnic minorities will be key to finding undiagnosed individuals living with hepatitis B (HBV), hepatitis C (HCV), or human immunodeficiency virus (HIV). To inform screening initiatives in British Columbia (BC), Canada, the factors associated with HBV and/or HCV and/or HIV infection among different ethnic groups within a large population-based cohort were assessed. METHODS: Persons diagnosed with HBV, HCV, or HIV in BC between 1990 and 2015 were grouped as East Asian, South Asian, Other Visible Minority (African, Central Asian, Latin American, Pacific Islander, West Asian, unknown ethnicity), and Not a Visible Minority, using a validated name-recognition software. Factors associated with infection within each ethnic group were assessed with multivariable multinomial logistic regression models. RESULTS: Participants included 202 521 East Asians, 126 070 South Asians, 65 210 Other Visible Minorities, and 1 291 561 people who were Not a Visible Minority, 14.4%, 3.3%, 4.5%, and 6.3% of whom had HBV and/or HCV and/or HIV infections, respectively. Injection drug use was most prevalent among infection-positive people who were Not a Visible Minority (22.1%), and was strongly associated with HCV monoinfection, HBV/HCV coinfection, and HCV/HIV coinfection, but not with HBV monoinfection among visible ethnic minorities. Extreme material deprivation and social deprivation were more prevalent than injection drug use or problematic alcohol use among visible ethnic minorities. CONCLUSIONS: Risk factor distributions varied among persons diagnosed with HBV and/or HCV and/or HIV of differing ethnic backgrounds, with lower substance use prevalence among visible minority populations. This highlights the need for tailored approaches to infection screening among different ethnic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".