WHO/ISBRA Study on State and Trait Markers of Alcohol Use and Dependence: Analysis of Demographic, Behavioral, Physiologic, and Drinking Variables That Contribute to Dependence and Seeking Treatment
Bibliographic record
Abstract
Background Discussions between the World Health Organization (WHO) and the International Society on Biomedical Research on Alcoholism (ISBRA) identified the need for a multiple‐center international study on state and trait markers of alcohol abuse and alcohol dependence. The reasoning behind the generation of such a project included the need to understand the alcohol use characteristics of diverse populations and the performance of biological markers of alcohol use in a variety of settings throughout the world. A second major reason for initiating this study was to collect DNA for well‐structured and stratified association studies between genetic markers and/or “candidate” genes and behavioral/physiological phenotypes of importance to predisposition to alcohol dependence. Methods An extensive interview instrument was developed with leadership from the U.S. National Institute on Alcohol Abuse and Alcoholism (NIAAA). The instrument was translated from English to Finnish, French, German, Japanese, and Portuguese (Brazilian). One thousand eight hundred sixty‐three subjects were recruited at five clinical centers (Montreal, Canada; Helsinki, Finland; Sapporo, Japan; São Paulo, Brazil; and Sydney, Australia). The subjects responded to the structured interview and provided blood and urine samples for biochemical analysis. This article focuses on the demographic characteristics of the study subjects, their drinking habits, alcohol‐dependence characteristics, comorbid psychiatric and other drug variables, and predictors for seeking treatment for alcohol dependence. Multiple logistic regression models were constructed and used to explore variables that contribute to various levels of alcohol consumption, to a diagnosis of alcohol dependence, and to seeking treatment for alcohol dependence. ANOVA with post hoc comparisons, χ 2 , and Pearson moment calculations were used as necessary to assess additional relationships between variables. Results A number of factors previously noted in disparate studies were confirmed in our analysis. Men consumed more alcohol than women, Asians consumed less alcohol than whites or Blacks, alcohol‐dependent subjects consumed more alcohol than nondependent subjects, alcohol consumption increased with age, and an increased level of education (university or postgraduate education) reduced the percentage of such individuals in the category designated as heavy drinkers (>210 g alcohol/week) and in the group who were currently in treatment for dependence. However, our analysis allowed for much more detailed comparisons; for example, although men drank more than women on a g/day basis, the differences were less pronounced on g/kg/day basis, and alcohol‐dependent women drank equal amounts of alcohol as alcohol‐dependent men on a g/kg/day basis. Antisocial personality characteristics or reports of trouble sleeping when an individual stops drinking were associated with higher alcohol intake. The most important of the tested factors that contributed to a DSM‐IV diagnosis of dependence, however, was the report of anxiety if an individual stopped drinking. In terms of the various criteria within the DSM‐IV criteria for alcohol dependence, no one criterion seemed to be prominent for individuals who sought alcohol dependence treatment, but the higher the number of criteria met by the individual, the higher was the probability that he or she would be in treatment. Conclusions This initial report is the beginning of the “data mining” of this rich data set. The cross‐national/cross‐cultural aspects of this study allowed for multiple comparisons of variables across several ethnic/racial groups and allowed for assessment of biochemical markers for alcohol intake and predisposition to alcohol dependence in diverse settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".