Abstract 15536: Impact of Social Determinants of Health on Atherosclerotic Cardiovascular Diseases Incidence
Bibliographic record
Abstract
INTRODUCTION: Individuals with an unfavorable socioeconomic status have a higher risk of developing chronic diseases such as atherosclerotic cardiovascular disease (ASCVD). However, the shared contribution of socioeconomic status, psychosocial factors and living environment on ASCVD risk remains unknown. We investigated the contribution of these social determinants of health on the incidence of ASCVD. METHODS: We developed a polysocial risk score (PsRS) using latent class analysis based on socioeconomic factors (household income, level/quality of education and employment status), psychosocial factors (living alone, social support, social activities, social isolation, emotional distress, and diagnosis of psychiatric disorders), and living environment (social deprivation score, crime rate, housing quality, housing stability, proximity to green/blue spaces and proximity to nature). The study sample included 319 842 participants of the UK Biobank, free of ASCVD recruited between 2006 and 2010. As of August 2021, 23 840 of them had incident ASCVD (fatal or nonfatal myocardial infarction, ischemic stroke, or cardiac revascularization procedures). Participants were divided into three groups based on latent class analysis adjusted for age. The impact of the PsRS on incident ASCVD was assessed using Cox proportional hazards for all participants, women and men adjusted for age, sex, and ethnicity. RESULTS: Compared to participants with a low PsRS, those with an intermediate score had a slightly higher risk of ASCVD (hazard ratio [HR]=1.05 [95% CI, 1.01-1.09], p=0.024) while those with a high PsRS had a higher risk of ASCVD (HR=1.46 [95% CI, 1.40-1.52], p<0.001). The effect of the PsRS on ASCVD incidence appeared to be more pronounced in women (HR=1,59 [95% CI, 1.48-1.71], p<0.001 and HR=1.39 [95% IC, 1.32-1.46], p<0.001, respectively, for women and men with a high PsRS compared to individuals with a low PsRS within the same sex). CONCLUSION: A PsRS based on the living environment as well as socioeconomic and psychosocial factors was associated with incident ASCVD in participants of the UK Biobank. Multifactor approaches targeting both social determinants of health and traditional risk factors could reduce the burden of ASCVD in the general population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".