Prescription Opioid Use and Concurrent Psychotropic Drug Use During Pregnancy: A Population-Based Retrospective Cohort Study Utilizing Linked Administrative Data
Bibliographic record
Abstract
IntroductionIt is important to investigate the use of prescription opioids during pregnancy to gain insight into the potential impact of maternal opioid exposure during pregnancy on children. We report the prevalence of prescription opioid use and concurrent psychotropic drug use in a large, Canadian population-based cohort of pregnant women. Objectives and ApproachUsing population-level linked administrative data from a universal health care system, this study included all women with a live birth in Manitoba from 1996 to 2014. Dispensing of opioids was determined from prescription drug data. Patterns of prescription opioids dispensed to pregnant women were investigated by demographic characteristics, region of residence, and socioeconomic status. Concurrent psychotropic therapies were also measured. These data address limitations associated with re-call bias, cilitate longitudinal analaysis, and allow the investigation of rare outcomes, difficult to study using other data sources. ResultsIn a large population level sample of pregnancies (N=245,784), 2.43% of pregnancies were exposed to 2+ dispensations of opioids. An additional 4.95% of pregnancies recorded at a single opioid dispensation. Compared to women who were not dispensed any opioid prescriptions, the proportion of opioid exposed pregnancies who were also prescribed anti-depressants (SSRI/SNRI) was sevenfold higher (22.5% vs 3.05%). The same pattern was found for anxiolytics (37.2% vs 1.5%) and antipsychotics (3.5% vs 0.34%). Conclusion/ImplicationsThese data demonstrate high proportions of women were dispensed opioids during pregnancy. Further research should be done on the short term and long term effects of these medications on infants and children. Moreover, these results highlight the need for further investigation into the effects of exposure to multiple psychotropic drugs
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".