Alcohol‐attributable cancer deaths under 80 years of age in New Zealand
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Cancer deaths made up 30% of all alcohol-attributable deaths in New Zealanders aged 15-79 years in 2007, more than all other chronic diseases combined. We aimed to estimate alcohol-attributable cancer mortality and years of life lost by cancer site and identify differences between Māori and non-Māori New Zealanders. DESIGN AND METHODS: We applied the World Health Organization's comparative risk assessment methodology at the level of Māori and non-Māori subpopulations. Proportions of specific alcohol-related cancers attributable to alcohol were calculated by combining alcohol consumption estimates from representative surveys with relative risks from recent meta-analyses. These proportions were applied to both 2007 and 2012 mortality data. RESULTS: Alcohol consumption was responsible for 4.2% of all cancer deaths under 80 years of age in 2007. An average of 10.4 years of life was lost per person; 12.7 years for Māori and 10.1 years for non-Māori. Half of the deaths were attributable to average consumption of <4 standard drinks per day. Breast cancer comprised 61% of alcohol-attributable cancer deaths in women, and more than one-third of breast cancer deaths were attributable to average consumption of <2 standard drinks per day. Mortality data from 2012 produced very similar findings. DISCUSSION AND CONCLUSIONS: Alcohol is an important and modifiable cause of cancer. Risk of cancer increases with higher alcohol consumption, but there is no safe level of drinking. Reduction in population alcohol consumption would reduce cancer deaths. Additional strategies to reduce ethnic disparities in risk and outcome are needed in New Zealand. [Connor J, Kydd R, Maclennan B, Shield K, Rehm J. Alcohol-attributable cancer deaths under 80 years of age in New Zealand. Drug Alcohol Rev 2017;36:415-423].
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".