Perinatal Substance Use and Factors Associated With Maternal and Neonatal Morbidity and Mortality
Bibliographic record
Abstract
BACKGROUND: Pregnant people who use substances face barriers accessing care. There are limited data on maternal and neonatal health in this population. Our objective was to determine the association of clinical and demographic factors with maternal and neonatal morbidity and mortality among people with perinatal substance use disorder (PSUD). METHODS: This population-based retrospective cohort study using provincial health administrative data within a publicly funded universal health care setting (British Columbia [BC], Canada) included all dyads with PSUD indicated within the past year of first-prenatal care contact to delivery (2010-2021). Exposures included sociodemographic, pregnancy, and clinical factors. Primary outcomes included maternal and neonatal morbidity. We employed regression models to estimate adjusted odds ratios (aORs) and predicted probabilities. RESULTS: Among 22 856 people with PSUD with 27 637 deliveries, maternal morbidity rate was 1.5 times higher than all BC deliveries and neonatal morbidity and mortality rates were over 2 times higher. Relative to other BC regions, residing in Vancouver Coastal increased maternal morbidity odds (aOR, 1.35; 95% CI, 1.15-1.59). Residing in Interior or Vancouver Island increased neonatal morbidity odds (aOR, 1.16; 95% CI, 1.06-1.26; aOR, 1.10; 95% CI, 1.01-1.21, respectively). Delivery during more recent years increased maternal mortality odds by 11% (aOR, 1.11; 95% CI, 1.00-1.24). CONCLUSION: People with PSUD had 2 times higher rates of maternal and neonatal morbidity and mortality than provincial rates. Odds of maternal mortality increased 11% annually. Region of residence, SUD type, demographic, and clinical factors were independently associated with maternal and neonatal morbidity. Enhanced care and health surveillance are needed to address disparities in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".