Syndemic profiles of people living with hepatitis C virus using population-level latent class analysis to optimize health services
Bibliographic record
Abstract
BACKGROUND: Hepatitis C (HCV) affects diverse populations such as people who inject drugs (PWID), 'baby boomers,' gay/bisexual men who have sex with men (gbMSM), and people from HCV endemic regions. Assessing HCV syndemics (i.e.relationships with mental health/chronic diseases) among subpopulations using Latent Class Analysis (LCA) may facilitate targeted program planning. METHODS: The BC Hepatitis Testers Cohort(BC-HTC) includes all HCV cases identified in BC between 1990 and 2015, integrated with medical administrative data. LCA grouped all BC-HTC HCV diagnosed people(n = 73,665) by socio-demographic/clinical indicators previously determined to be relevant for HCV outcomes. The final model was chosen based on fit statistics, epidemiological meaningfulness, and posterior probability. Classes were named by most defining characteristics. RESULTS: The six-class model was the best fit and had the following names and characteristics: 'Younger PWID'(n =11,563): recent IDU (67%), people born >1974 (48%), mental illness (62%), material deprivation (59%). 'Older PWID'(n =15,266): past IDU (78%), HIV (17%), HBV (17%) coinfections, alcohol misuse(68%). 'Other Middle-Aged People'(n = 9019): gbMSM (26%), material privilege (31%), people born between 1965-1974 (47%). 'People of Asian backgrounds' (n = 4718): East/South Asians (92%), no alcohol misuse (97%) or mental illness (93%), people born <1945 (26%), social privilege (66%). 'Rural baby boomers' (n = 20,401): rural dwellers (32%), baby boomers (79%), heterosexuals (99%), no HIV (100%). 'Urban socially deprived baby boomers' (n = 12,698): urban dwellers (99%), no IDU (100%), liver disease (22%), social deprivation (94%). CONCLUSIONS: Differences between classes suggest variability in patients' service needs. Further analysis of health service utilization patterns may inform optimal service layout.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".