Is Preconception Substance Use Associated With Unplanned or Poorly Timed Pregnancy?
Bibliographic record
Abstract
OBJECTIVE: Unplanned and poorly timed pregnancies are associated with adverse maternal and neonatal outcomes. Further understanding of preconception substance use with unplanned and poorly timed pregnancy is warranted. METHODS: Data were analyzed from a prospective study enrolling women early in pregnancy. Preconception tobacco, alcohol, marijuana, opioid, and cocaine use was ascertained. Participants reported whether their current pregnancy was planned and whether it was a good time to be pregnant. Multivariable logistic regression modeling generated risk estimates for preconception substance use, and pregnancy planning and timing, adjusting for confounders. RESULTS: Overall, 37.2% reported unplanned pregnancy, 13.0% poorly timed pregnancy, and 39.0% reported either unplanned and/or poorly timed pregnancy. Within 6 months preconception, one-fifth (20.2%) reported nicotine cigarette use. In the month before conception, 71.8% reported alcohol use, 6.5% marijuana, and approximately 1% opioid or cocaine use. Multivariable analysis demonstrated preconception opioid use was associated with increased odds of poorly timed pregnancy (odds ratio [OR] 2.87, 95% confidence interval [CI] 1.03-7.99). Binge drinking the month before conception was associated with increased odds of poorly timed pregnancy and unplanned pregnancy (OR 1.75, 95% CI 1.01-3.05; and OR 1.68, 95% CI 1.01-2.79, respectively). Marijuana use 2 to 3 times in the month preconception was associated with increased risk of unplanned pregnancy, and unplanned and/or poorly timed pregnancy compared with nonuse (OR 1.78, 95% CI 1.03-3.08; and OR 1.79, 95% CI 1.01-3.17, respectively). Preconception tobacco or cocaine use was not associated with unplanned or poorly timed pregnancy following adjustment. CONCLUSIONS: We demonstrate increased odds of unplanned or poorly timed pregnancy among women with preconception binge drinking, marijuana use, and opioid use; however, no association is observed with other substances after multivariable adjustment, including tobacco. Further research to evaluate high-level preconception substance use and substance disorders with pregnancy planning and timing is warranted. Focused efforts optimizing preconception health behaviors and reducing risk of unplanned or poorly timed pregnancy are needed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".