HCV-infected individuals have higher prevalence of comorbidity and multimorbidity: a retrospective cohort study
Bibliographic record
Abstract
Abstract Background Almost 1% of Canadians are hepatitis C (HCV)-infected. The liver-specific complications of HCV are established but the extra-hepatic comorbidity, multimorbidity, and its relationship with HCV treatment, is less well known. We describe the morbidity burden for people with HCV and the relationship between multimorbidity and HCV treatment uptake and cure in the pre- and post-direct acting antiviral (DAA) era. Methods We linked adults with HCV at The Ottawa Hospital Viral Hepatitis Program as of April 1, 2017 to provincial health administrative data and matched on age and sex to 5 Ottawa-area residents for comparison. We used validated algorithms to identify the prevalence of mental and physical health comorbidities, as well as multimorbidity (2+ comorbidities). We calculated direct age- and sex-standardized rates of comorbidity and comparisons were made by interferon-based and interferon-free, DAA HCV treatments. Results The mean age of the study population was 54.5 years (SD 11.4), 65% were male. Among those with HCV, 4% were HIV co-infected, 26% had liver cirrhosis, 47% received DAA treatment, and 57% were cured of HCV. After accounting for age and sex differences, the HCV group had greater multimorbidity (prevalence ratio (PR) 1.38, 95% confidence interval (CI) 1.20 to 1.58) and physical-mental health multimorbidity (PR 2.71, 95% CI 2.29–3.20) compared to the general population. Specifically, prevalence ratios for people with HCV were significantly higher for diabetes, renal failure, cancer, asthma, chronic obstructive pulmonary disease, substance use disorder, mood and anxiety disorders and liver failure. HCV treatment and cure were not associated with multimorbidity, but treatment prevalence was significantly lower among middle-aged individuals with substance use disorders despite no differences in prevalence of cure among those treated. Conclusion People with HCV have a higher prevalence of comorbidity and multimorbidity compared to the general population. While HCV treatment was not associated with multimorbidity, people with substance use disorder were less likely to be treated. Our results point to the need for integrated, comprehensive models of care delivery for people with HCV.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".