Socio‐economic factors associated with loss to follow‐up among individuals with <scp>HCV</scp>: A Dutch nationwide cross‐sectional study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The path to hepatitis C virus (HCV) elimination is complicated by individuals who become lost to follow-up (LTFU) during care, particularly before receiving effective HCV treatment. We aimed to determine factors contributing to LTFU and whether LTFU is associated with mortality. METHODS: In this secondary analysis, we constructed a database including individuals with HCV who were either LTFU (data from the nationwide HCV retrieval project, CELINE) or treated with directly acting antivirals (DAA) (data from Statistics Netherlands) between 2012 and 2019. This database was linked to mortality data from Statistics Netherlands. Determinants associated with being LTFU versus DAA-treated were assessed using logistic regression, and mortality rates were compared between groups using exponential survival models. These analyses were additionally stratified on calendar periods: 2012-2014, 2015-2017 and 2018-2019. RESULTS: About 254 individuals, LTFU and 5547 DAA-treated were included. Being institutionalized (OR = 5.02, 95% confidence interval (CI) = 3.29-7.65), household income below the social minimum (OR = 1.96, 95% CI = 1.25-3.06), receiving benefits (OR = 1.74, 95% CI = 1.20-2.52) and psychiatric comorbidity (OR = 1.51, 95% CI = 1.09-2.10) were associated with LTFU. Mortality rates were significantly higher in individuals LTFU compared to those DAA-treated (2.99 vs. 1.15/100 person-years (PY), p < .0001), while in those DAA-treated, mortality rates slowly increased between 2012-2014 (.22/100PY) and 2018-2019 (2.25/100PY). CONCLUSION: In the Netherlands, individuals who are incarcerated/institutionalized, with low household income, or with psychiatric comorbidities are prone to being LTFU, which is associated with higher mortality. HCV care needs to be adapted for these vulnerable individuals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".