Temporal changes in alcohol‐related mortality and morbidity in <scp>Australia</scp>
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcohol consumption is an avoidable risk factor for morbidity and mortality. Studies have examined relative risks and outcomes of alcohol-related harms in Australia at discrete times, limiting the ability to examine changes across time. This paper examined alcohol consumption and its contribution to deaths, illness and injury at two time-points, 2001 and 2010. DESIGN: Alcohol consumption was modelled based on the 2001 and 2010 National Drug Strategy Household Survey, upshifted to reflect alcohol sales data. SETTING: All data reported are from Australian sources. MEASUREMENTS: Based on relative risk estimates obtained from meta-analysis, alcohol-attributable fractions were estimated for 42 disease and injury categories in 2001 and 2010 separately for conditions that were not 100% alcohol-attributable. Deaths and hospital separations attributable to alcohol were calculated in 2001 and 2010. FINDINGS: There was a relatively stable per capita consumption of alcohol across time, with males reporting higher levels of consumption compared with females. While there were increases in the number of abstainers from alcohol across time, the proportion of heavy alcohol consumers also increased. This corresponded with an observed increase in alcohol-attributable burden. For example, alcohol-attributable deaths increased from 4957 [95% confidence interval (CI) = 2867-8770] to 5610 (95% CI = 3398-9408) during the study period. CONCLUSION: The findings demonstrate that there has been an increase in alcohol-attributable harms between 2001 and 2010 in Australia without a corresponding increase in per capita consumption.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".