The estimated health impact of alcohol interventions in New Zealand: A modelling study
Bibliographic record
Abstract
AIMS: To estimate the health impacts of key modelled alcohol interventions among Māori (indigenous peoples) and non-Māori in New Zealand (NZ). DESIGN: Multi-stage life-table intervention modelling study. We modelled two scenarios: (1) business-as-usual (BAU); and (2) an intervention package scenario that included a 50% alcohol tax increase, outlet density reduction from 63 to five outlets per 100 000 people, outlet hours reduction from 112 to 50 per week and a complete ban on all forms of alcohol marketing. SETTING AND PARTICIPANTS: The model's population replicates the 2018 NZ population by ethnicity (Māori/non-Māori), age and sex. MEASUREMENTS: Alcohol consumption was estimated using nationally representative survey data combined with sales data and corrected for tourist and unrecorded consumption. Disease incidence, prevalence and mortality were calculated using Ministry of Health data. We used dose-response relationships between alcohol and illness from the 2016 Global Burden of Disease study and calculated disability rates for each illness. Changes in consumption were based on the following effect sizes: total intervention package [-30.3%, standard deviation (SD) = 0.02); tax (-7.60%, SD = 0.01); outlet density (-8.64%, SD = 0.01); outlet hours (-9.24%, SD = 0.01); and marketing (-8.98%, SD = 0.02). We measured health gain using health-adjusted life years (HALYs) and life expectancy. FINDINGS: Compared with the BAU scenario, the total alcohol intervention package resulted in 726 000 [95% uncertainty interval (UI) = 492 000-913 000] HALYs gained during the life-time of the modelled population. Māori experienced greater HALY gains compared with non-Māori (0.21, 95% UI = 0.14-0.26 and 0.16, 95% UI = 0.11-0.20, respectively). When modelled individually, each alcohol intervention within the intervention package produced similar health gains (~200 000 HALYs per intervention) owing to the similar effect sizes. CONCLUSIONS: Modelled interventions for increased alcohol tax, reduced availability of alcohol and a ban on alcohol marketing among Māori and non-Māori in New Zealand (NZ) suggest substantial population-wide health gains and reduced health inequities between Māori and non-Māori.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".