Multiple Chronic Conditions Before Pregnancy and Risk of Adverse Maternal Health Outcomes: Population‐Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the risks of perinatal emergency department (ED) use, hospitalisation and severe maternal morbidity or mortality (SMM-M) associated with preconception MCC, according to the number of chronic conditions, complex MCC and co-occurring cardiometabolic conditions. DESIGN: Population-based cohort study. SETTING: Ontario, Canada. POPULATION: Females aged 13-54 years, with a recognised pregnancy, 2012-2021. METHODS: Modified Poisson regression was used to generate adjusted relative risks (aRRs) according to the number of chronic conditions, complex MCC (≥ 3 chronic conditions affecting ≥ 3 body systems) and co-occurring cardiometabolic conditions. aRRs were adjusted for age, parity, income quintile, rurality and immigrant/refugee status. MAIN OUTCOME MEASURES: ED use, hospitalisation and SMM-M from the estimated date of conception to 42 days postpartum. RESULTS: In total, 894 042 individuals had no pre-pregnancy chronic condition; 357 398 had 1; 94 427 had 2; and 27 326 had ≥ 3 chronic conditions. Relative to those without a chronic condition, the aRR for ED use increased with 1 (1.26, 95% CI 1.25-1.27), 2 (1.55, 1.54-1.56) and ≥ 3 (1.86, 1.85-1.88) conditions. For hospitalisations, the corresponding aRRs were 1.45 (1.43-1.47), 2.06 (2.02-2.10) and 3.18 (3.09-3.27). For SMM-M, the corresponding aRRs were 1.38 (1.35-1.42), 1.82 (1.75-1.90) and 2.75 (2.59-2.92). SMM-M risk was even more pronounced with complex MCC (aRR 2.92, 95% CI 2.72-3.14), and ≥ 3 cardiometabolic conditions (aRR 5.45, 95% CI 4.29-6.91). CONCLUSIONS: MCC, especially complex or cardiometabolic MCC, is associated with elevated risk of maternal morbidity. Multidisciplinary patient-centred care may mitigate these risks.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".