Neonatal drug withdrawal syndrome: cross-country comparison using hospital administrative data in England, the USA, Western Australia and Ontario, Canada
Bibliographic record
Abstract
OBJECTIVES: We determined trends over time in the prevalence of neonatal drug withdrawal syndrome (NWS) in England compared with that reported in the USA, Western (W) Australia and Ontario, Canada. We also examined variation in prevalence of NWS according to maternal age, birth weight and across the English NHS by hospital trusts. DESIGN AND SETTING: Retrospective study using national hospital administrative data (Hospital Episode Statistics) for the NHS in England between 1997 and 2011. NWS was identified using international classification of disease codes in hospital admission records. We searched the research literature and contacted researchers to identify studies reporting trends in the prevalence of NWS. MAIN OUTCOME MEASURES: Prevalence of NWS by calendar year per 1000 live births for each country/state. For births in England, prevalence by maternal age group and birth weight group. Prevalence by NHS trust and region at birth, and funnel plot to show outlying English NHS hospital trusts (>3 SD of mean prevalence). MAIN RESULTS: Mean prevalence rates of recorded NWS increased in all four countries. Rates stabilised in England and W. Australia from the early 2000s and rose steeply in the USA and Ontario during the late 2000s. The most recent prevalence rates were 2.7/1000 live births in England (2011; 1544 cases); 2.7/1000 in W. Australia (2009); 3.6/1000 in the USA (2009) and 5.1/1000 in Ontario (2011). The highest prevalence in England was among babies born to mothers aged 25-34 years at delivery and among babies born with low birth weight (1500-2500 g). In England in 2011, 8.6% of hospital trusts had a recorded prevalence outside 3 SD of the overall average (7% above, 1% below). The North East region of England had the highest recorded prevalence of NWS. CONCLUSIONS: Although recorded NWS is stable in England and W. Australia, rising rates in the USA and Ontario may reflect better recognition and/or increased use of prescribed opiate analgesics and highlight the need for surveillance. The extent to which different prevalence rates by hospital trust reflect variation in occurrence, recognition or recording requires further investigation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".