Increasing Prevalence of Neonatal Withdrawal Syndrome: Population Study of Maternal Factors and Child Protection Involvement
Bibliographic record
Abstract
OBJECTIVES: Illicit drug use during pregnancy is an important public health issue, with adverse effects on the newborn and implications for subsequent parenting. The aim of this study was to measure the birth prevalence of neonatal withdrawal syndrome over time, associated maternal characteristics and child protection involvement. METHODS: This is a retrospective cohort study that used linked health and child protection databases for all live births in Western Australia from 1980 to 2005. Maternal characteristics and mental health-and assault-related medical history were assessed by using logistic regression models. RESULTS: The birth prevalence of neonatal withdrawal syndrome increased from 0.97 to a high of 42.2 per 10 000 live births, plateauing after 2002. Mothers with a previous mental health admission, low skill level, Aboriginal status or who smoked during pregnancy were significantly more likely to have an infant with neonatal withdrawal syndrome. These infants were at greater risk for having a substantiated child maltreatment allegation and entering foster care. Increased risk for maltreatment was associated with mothers who were aged <30 years, were from socially disadvantaged backgrounds, Aboriginal status, and had a mental health-or assault-related admission. CONCLUSIONS: There has been a marked increase in neonatal withdrawal syndrome in the past 25 years. Specific maternal characteristics identified should facilitate planning for early identification and intervention for these women. Findings demonstrate an important pathway into child maltreatment and highlight the need for well-supported programs for women who use illicit drugs during pregnancy as well as the need for sustained long-term support after birth.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".