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Differential Associations of Alcohol Use With Ischemic Heart Disease Mortality by Socioeconomic Status in the US, 1997-2018

2024· article· en· W4391430841 on OpenAlexaff
Yachen Zhu, Laura Llamosas‐Falcón, William C. Kerr, Klajdi Puka, Charlotte Probst

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsUniversity of TorontoWestern UniversityCentre for Addiction and Mental Health
FundersNational Institute on Alcohol Abuse and AlcoholismCenters for Disease Control and PreventionNational Institutes of Health
KeywordsMedicineSocioeconomic statusDemographyMarital statusBody mass indexHazard ratioProportional hazards modelPopulationGerontologyCohortCohort studyNational Death IndexEnvironmental healthConfidence intervalInternal medicine

Abstract

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Importance: People with low socioeconomic status (SES) experience greater burden from alcohol-attributable health conditions and mortality at equal levels of alcohol consumption compared with those with high SES. A U-shaped association has been established between alcohol use and ischemic heart disease (IHD), but no study has explored how such an association differs by SES in the US. Objective: To investigate how the association of alcohol use with ischemic heart disease mortality differs by SES in the general US population. Design, Setting, and Participants: This cohort study used record-linked, cross-sectional National Health Interview Survey data for US adults aged 25 years and older, covering 1997 to 2018 with mortality follow-up until 2019. Data analysis was performed from March to June 2023. Exposures: SES (operationalized using education attainment) and alcohol consumption were obtained from self-reported questionnaires. Main Outcomes and Measures: The outcome was time to IHD mortality or last presumed alive by December 31, 2019. Cox proportional hazard models were applied to evaluate the interaction of SES and alcohol use on IHD mortality, with age as the time scale. Sex-stratified analyses were performed, adjusting for race and ethnicity, marital status, smoking, body mass index, physical activity, and survey year. Fine-Gray subdistribution models were applied to account for competing risks. Results: This cohort study of 524 035 participants (mean [SD] age at baseline, 50.3 [16.2] years; 290 492 women [51.5%]) found a statistically significantly greater protective association of drinking less than 20 g per day (vs lifetime abstinence) with IHD mortality in the high-SES group compared with the low-SES group (interaction term hazard ratio [HR], 1.22 [95% CI, 1.02-1.45] in men; HR, 1.35 [95% CI, 1.09-1.67] in women). In addition, the differential associations of drinking less than 20 g per day with IHD mortality by SES were observed only among people with less than monthly heavy episodic drinking (HED) (interaction term, HR, 1.20 [95% CI, 1.01-1.43] in men; HR, 1.34 [95% CI, 1.08-1.67] in women); no difference was found in people with at least monthly HED. Among women there was a greater protective association of drinking less than 20 g per day with IHD mortality in the high-SES group than the middle-SES group (interaction term, HR, 1.35 [95% CI, 1.06-1.72]). Among men, the harmful association of drinking more than 60 g per day with IHD mortality in the low-SES group was largely explained by other behavioral risk factors (ie, smoking, body mass index, and physical activity). Conclusions and Relevance: This cohort study found a greater protective association between drinking less than 20 g per day with less than monthly HED and IHD mortality in the high-SES group compared with the low-SES group, in both sexes even after adjusting for key covariables and behavioral risk factors. The findings suggest that public health interventions on alcohol use should account for different socioeconomic backgrounds when assessing the level of risk related to alcohol exposure, bearing in mind that levels of consumption deemed safe regarding a specific outcome such as IHD may indeed be less safe or not safe across all sociodemographic groups.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.387
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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