Foetal alcohol spectrum disorder in Aotearoa, New Zealand: Estimates of prevalence and indications of inequity
Bibliographic record
Abstract
INTRODUCTION: Foetal alcohol spectrum disorder (FASD) is 100% caused by alcohol. The lifelong disability caused by prenatal alcohol exposure cannot be reversed. Lack of reliable national prevalence estimates of FASD is common internationally and true of Aotearoa, New Zealand. This study modelled the national prevalence of FASD and differences by ethnicity. METHODS: FASD prevalence was estimated from self-reported data on any alcohol use during pregnancy for 2012/2013 and 2018/2019, combined with risk estimates for FASD from a meta-analysis of case-ascertainment or clinic-based studies in seven other countries. A sensitivity analysis using four more recent active case ascertainment studies was performed to account for the possibility of underestimation. RESULTS: We estimated FASD prevalence in the general population to be 1.7% (95% confidence interval [CI] 1.0%; 2.7%) in the 2012/2013 year. For Māori, the prevalence was significantly higher than for Pasifika and Asian populations. In the 2018/2019 year, FASD prevalence was 1.3% (95% CI 0.9%; 1.9%). For Māori, the prevalence was significantly higher than for Pasifika and Asian populations. The sensitivity analysis estimated the prevalence of FASD in the 2018/2019 year to range between 1.1% and 3.9% and for Māori, from 1.7% to 6.3%. DISCUSSION AND CONCLUSIONS: This study used methodology from comparative risk assessments, using the best available national data. These findings are probably underestimates but indicate a disproportionate experience of FASD by Māori compared with some ethnicities. The findings support the need for policy and prevention initiatives to support alcohol-free pregnancies to reduce lifelong disability caused by prenatal alcohol exposure.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".