Risk of severe influenza infection in women with a history of pregnancy complications: A longitudinal cohort study
Bibliographic record
Abstract
BACKGROUND: Risk factors for influenza complications in women are poorly understood. We examined the association between pregnancy outcomes and risk of influenza hospitalization up to three decades later. METHODS: We analyzed a cohort of 1,421,531 pregnant women who delivered in Quebec, Canada between 1989 and 2021. Patients were followed over time beginning at the first delivery. The main exposure measures included obstetric complications such as preeclampsia, gestational diabetes, and preterm birth. The main outcome was influenza hospitalization up to 32 years later. We used adjusted Cox regression models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between obstetric complications and risk of influenza hospitalization following pregnancy. RESULTS: A total of 4,016 women were hospitalized for influenza during 32 years of follow-up. Influenza hospitalization was more frequent among women with pregnancy complications than women without complications (18.0 vs 14.1 per 100,000 person-years). Compared with no pregnancy complication, women with gestational diabetes (HR 1.48, 95% CI 1.30-1.69), preeclampsia (HR 1.45, 95% CI 1.28-1.65), placental abruption (HR 1.36, 95% CI 1.12-1.66), preterm birth (HR 1.40, 95% CI 1.27-1.55), cesarean section (HR 1.22, 95% CI 1.13-1.31), and severe maternal morbidity (HR 1.43, 95% CI 1.22-1.68) had a greater risk of influenza hospitalization later in life. These pregnancy outcomes were associated with severe influenza infections requiring critical care. CONCLUSIONS: Women with pregnancy complications have an elevated risk of severe influenza complications later in life and have potential to benefit from seasonal vaccination to prevent influenza hospitalization.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".