Disparities in health utilities among hepatitis C patients receiving care in different settings
Bibliographic record
Abstract
BACKGROUND: Although chronic hepatitis C (CHC) disproportionately affects marginalized individuals, most health utility studies are conducted in hospital settings which are difficult for marginalized patients to access. We compared health utilities in CHC patients receiving care at hospital-based clinics and socio-economically marginalized CHC patients receiving care through a community-based program. METHODS: We recruited CHC patients from hospital-based clinics at the University Health Network and community-based sites of the Toronto Community Hep C Program, which provides treatment, support, and education to patients who have difficulty accessing mainstream health care. We elicited utilities using six standardized instruments (EuroQol-5D-3L [EQ-5D], Health Utilities Index Mark 2/Mark 3 [HUI2/HUI3], Short Form-6D [SF-6D], time trade-off [TTO], and Visual Analogue Scale [VAS]). Multivariable regression analysis was performed to examine factors associated with differences in health utility. RESULTS: Compared with patients recruited from the hospital setting ( n = 190), patients recruited from the community setting ( n = 101) had higher unemployment (87% versus 67%), history of injection drug use (88% versus 42%), and history of mental health issue(s) (79% versus 46%). Unadjusted health utilities were lower in community than hospital patients (e.g., EQ-5D: 0.722 [SD 0.209] versus 0.806 [SD 0.195]). Unemployment and a history of mental health issue(s) were significant predictors of low health utility. CONCLUSIONS: Socio-economically marginalized CHC patients have lower health utilities than patients typically represented in the CHC utility literature. Their utilities should be incorporated into future cost-utility analyses to better represent the population living with CHC in health policy decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".