Mortality of mothers with opioid-use during pregnancy: an international comparison using linked mother-baby records for England and Ontario
Bibliographic record
Abstract
IntroductionOpioid-use during pregnancy is indicative of future adversity for mothers and children. We aimed to investigate neonatal abstinence syndrome (NAS) as a marker of drug-use in pregnancy and to compare maternal all-cause mortality relative to those with infants born without NAS in two high prevalence jurisdictions; England and Ontario. Objectives and ApproachWe developed two parallel cohorts using linked mother-baby records for all births in hospitals in England and Ontario between 2002 and 2013. Mothers with opioid-use were identified based on NAS recorded in the baby’s record. Maternal mortality within 10 years of delivery was compared for mothers with and without a NAS-pregnancy. The association between opioid-use in pregnancy and all-cause mortality was modelled using Cox regression. In addition, we estimated the unadjusted cumulative incidence of cause-specific mortality within a competing risks framework. Harmonised clinical codes were used to define all exposures, outcomes and comorbidities. ResultsThe study population comprised 13,581 and 4,966 NAS mothers in England and Ontario, respectively. Controls totalled 4,205,941 for England and 929,985 for Ontario. The crude hazard ratios for all-cause mortality were 12.1 (95% CI; 11.1-13.2) for England and 11.4 (9.7, 13.4) for Ontario, which were attenuated to 9.8 (9.0-10.6) and 9.0 (7.6, 10.7) respectively, after adjustment for maternal age at delivery and Charlson comorbidity index. The cumulative incidence of death was much higher among NAS mothers than controls for all causes except cancer in the English cohort, where estimates were similar. The majority of deaths within the NAS group were attributable to avoidable causes, particularly reflecting unintentional injuries (cumulative incidence of 19.6 (16.6-23.1) for England and 13.9 (9.7-19.3) for Ontario). Conclusion/ImplicationsOur approach demonstrates the utility of mother-baby record linkage to examine long-term health outcomes of vulnerable families. We identified similarly high mortality rates among the mothers of NAS babies in both England and Ontario, indicating a need for public health programs to target support to mothers as well and infants.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".