Variations by Education Status in Relationships Between Alcohol/Pregnancy Policies and Birth Outcomes and Prenatal Care Utilization: A Legal Epidemiology Study
Bibliographic record
Abstract
CONTEXT: Previous research finds that some state policies regarding alcohol use during pregnancy (alcohol/pregnancy policies) increase low birth weight (LBW) and preterm birth (PTB), decrease prenatal care utilization, and have inconclusive relationships with alcohol use during pregnancy. OBJECTIVE: This research examines whether effects of 8 alcohol/pregnancy policies vary by education status, hypothesizing that health benefits of policies will be concentrated among women with more education and health harms will be concentrated among women with less education. METHODS: This study uses 1972-2015 Vital Statistics data, 1985-2016 Behavioral Risk Factor Surveillance System data, policy data from National Institute on Alcohol Abuse and Alcoholism's Alcohol Policy Information System and original legal research, and state-level control variables. Analyses include multivariable logistic regressions with education-policy interaction terms as main predictors. RESULTS: The impact of alcohol/pregnancy policies varied by education status for PTB and LBW for all policies, for prenatal care use for some policies, and generally did not vary for alcohol use for any policy. Hypotheses were not supported. Five policies had adverse effects on PTB and LBW for high school graduates. Six policies had adverse effects on PTB and LBW for women with more than high school education. In contrast, 2 policies had beneficial effects on PTB and/or LBW for women with less than high school education. For prenatal care, patterns were generally similar, with adverse effects concentrated among women with more education and beneficial effects among women with less education. Although associations between policies and alcohol use during pregnancy varied by education, there was no clear pattern. CONCLUSIONS: Effects of alcohol/pregnancy policies on birth outcomes and prenatal care use vary by education status, with women with more education typically experiencing health harms and women with less education either not experiencing the harms or experiencing health benefits. New policy approaches that reduce harms related to alcohol use during pregnancy are needed. Public health professionals should take the lead on identifying and developing policy approaches that reduce harms related to alcohol use during pregnancy.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".