Associations of alcohol consumption with chronic diseases, lifestyle behaviors and socioeconomic-demographic characteristics in India
Bibliographic record
Abstract
Aims: The objective of the present study was to analyse self-reported and measured chronic diseases and their association with alcohol consumption. Furthermore, the study examined the intensity and patterns of alcohol consumption by lifestyle and socio-demographic characteristics among respondents with chronic diseases.Methods: Secondary data were analysed from the Study on Global AGEing and Adult Health (SAGE), Wave 1 (2007–08), covering respondents aged 18 and older (10,914) in India. Chronic diseases, namely chronic obstructive pulmonary disease (COPD), hypertension, asthma, depression and angina were self-reported as diagnoses and measured using validated epidemiological tools. A multivariable adjusted logistic regression model was used to analyze the association of quantity of alcohol consumed and patterns of alcohol consumption with chronic diseases. A multinomial multivariable regression model was used to examine the risk ratio between alcohol consumption and each lifestyle characteristic among the diseased population.Results: About 17.0% (1,432/10,914) of the population consumed alcohol. At 18.0% (1,967/10,914), the prevalence of self-reported chronic diseases was lower than measured chronic diseases (37.5%; 4091/10,914). Moderate drinking was associated with self-reported hypertension (OR = 1.68; 95% CI = 1.10, 2.55) and measured hypertension (OR = 1.67; 95% CI = 1.16, 2.42). Consumption of three or more alcoholic drinks per session was associated with self-reported depression (OR = 2.68; 95% CI = 1.32, 5.45). Alcohol consumption of more than three drinks per session was associated with vigorous physical activity (RRR = 3.57; 95% CI = 1.25, 10.23). Heavy drinking was associated with the risk of having a body mass index in the overweight range (RRR = 2.29; 95% CI = 1.17, 4.47).Conclusions: The study findings demonstrate that alcohol is a risk factor for hypertension, self-reported depression and being overweight, with these associations varying with the amount of alcohol consumed. A coordinated, targeted multisectoral approach is needed to improve knowledge and awareness of the harmful effects of alcohol consumption and to strengthen alcohol use control policies in India.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".