All‐cause and cause‐specific mortality in individuals with an alcohol‐related emergency or hospital inpatient presentation: A retrospective data linkage cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcohol consumption is a leading risk factor for premature mortality globally, but there are limited studies of broader cohorts of people presenting with alcohol-related problems outside of alcohol treatment services. We used linked health administrative data to estimate all-cause and cause-specific mortality among individuals who had an alcohol-related hospital inpatient or emergency department presentation. DESIGN: Observational study using data from the Data linkage Alcohol Cohort Study (DACS), a state-wide retrospective cohort of individuals with an alcohol-related hospital inpatient or emergency department presentation. SETTING: Hospital inpatient or emergency department presentation in New South Wales, Australia, between 2005 and 2014. PARTICIPANTS: Participants comprised 188 770 individuals aged 12 and above, 66% males, median age 39 years at index presentation. MEASUREMENTS: All-cause mortality was estimated up to 2015 and cause-specific mortality (by those attributable to alcohol and by specific cause of death groups) up to 2013 due to data availability. Age-specific and age-sex-specific crude mortality rates (CMRs) were estimated, and standardized mortality ratios (SMRs) were calculated using sex and age-specific deaths rates from the NSW population. FINDINGS: There were 188 770 individuals in the cohort (1 079 249 person-years of observation); 27 855 deaths were recorded (14.8% of the cohort), with a CMR of 25.8 [95% confidence interval (CI) = 25.5, 26.1] per 1000 person-years and SMR of 6.2 (95% CI = 5.4, 7.2). Mortality in the cohort was consistently higher than the general population in all adult age groups and in both sexes. The greatest excess mortality was from mental and behavioural disorders due to alcohol use (SMR = 46.7, 95% CI = 41.4, 52.7), liver cirrhosis (SMR = 39.0, 95% CI = 35.5, 42.9), viral hepatitis (SMR = 29.4, 95% CI = 24.6, 35.2), pancreatic diseases (SMR = 23.8, 95% CI = 17.9, 31.5) and liver cancer (SMR = 18.3, 95% CI = 14.8, 22.5). There were distinct differences between the sexes in causes of excess mortality (all causes fully attributable to alcohol female versus male risk ratio = 2.5 (95% CI = 2.0, 3.1). CONCLUSIONS: In New South Wales, Australia, people who came in contact with an emergency department or hospital for an alcohol-related presentation between 2005 and 2014 were at higher risk of mortality than the general New South Wales population during the same period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".