Gaps in the Hepatitis C Prenatal and Postpartum Care Cascade: Rationale for Treatment in Pregnancy
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) infections have increased among younger populations, including pregnant people. While universal screening guidelines have improved case-finding, studies suggest low postpartum linkage-to-care rates. Better understanding of the peripartum HCV care cascade at the population level is needed to inform optimal management, including the role of treatment in pregnancy. METHODS: A retrospective cohort study linking pregnant individuals with HCV test records to health administrative data in Ontario, Canada. We examined the HCV care continuum, including during subsequent pregnancies. We used Andersen-Gill models to examine predictors of missed opportunities for treatment, defined as pregnancies occurring before/in absence of treatment, and initiating treatment. RESULTS: From 2003 to 2021, we identified 42 797 pregnancies in 16 888 people who tested HCV antibody-positive between 1999 and 2021. Of antibody-positive individuals, 14 538 (86.1%) had RNA testing and 7457 (51.3%) tested RNA-positive. Treatment uptake was 1.1%, 2.5%, and 5.2% at 1, 2, and 5 years after RNA positivity. Ultimately, 3861 (51.8%) initiated treatment and 2277 (30.5%) demonstrated sustained virologic response. Among those with confirmed chronic HCV, 47.5% (n = 3025) experienced a missed opportunity for treatment, with 19.2% (n = 1221) having multiple missed opportunities. Numbers of pregnancies and diagnosis year pre-2012 were associated with higher likelihood of a missed opportunity for treatment, while diagnoses of substance use disorder, HIV, and chronic disease were associated with lower likelihood. CONCLUSIONS: Despite diagnosis, <10% initiated treatment within 5 years of RNA positivity and multiple pregnancies occurred before treatment, resulting in repeated exposures. Additional efforts will be required to ensure linkage-to-care and treatment either during pregnancy or postpartum.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".