Abstract 4356768: Association of Unemployment Rates and Cardiovascular Disease Mortality in Northeast United States
Bibliographic record
Abstract
Introduction: Unemployment increases the likelihood of cardiovascular disease mortality (CVDM) for middle-aged men in high-income nations. In the United States, both CVDM and unemployment rates have seasonal variations. There is a need to investigate whether the association between CVDM and unemployment varies by season. Hypothesis: Unemployment will increase the risk of CVDM among middle-aged men by season. Methods: CDC population-based study with monthly CVD deaths [ICD-10 (I00-99)] among middle-aged (20-64 years) men in Northeast (NJ, NY, PA) US from Jan 2015 to Dec 2020. For the same period, state-level monthly unemployment rates were ascertained from US Bureau of Labor Statistics; categorized as low (≤10 th percentile: ≤5%), average (5-8%), and high (≥90 th percentile: ≥8%). With population estimates, CVDM rates were associated with unemployment rate categories by season (winter and summer) using negative binomial regression with autocorrelation upon adjusting for occupation type, maximum temperature, smoking, diabetes, asthma prevalence, college education attainment, region, and trend. Results: There were 76,418 CVD deaths recorded. High (compared with low) unemployment rates were associated with increased risk of CVDM (Figure) during the winter [adjusted RR 1.19 (95% CI 1.15-1.24)] and summer [adjusted RR 1.21 (95% CI 1.17-1.25)]. Significant interactions were observed between unemployment rates and maximum temperatures in both seasons. Conclusions: High unemployment rates were associated with an increased risk of CVDM among middle-aged men both in winter and summer. Yet there was no notable strong seasonal variation. Seasonal differences were observed in the association of high unemployment rates with an increased risk of CVDM among middle-aged men. Seasonality and unemployment rates are predisposing risk factors for CVDM. Further studies should assess CVDM risk factors for other susceptible areas in the US.
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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 | 0.002 |
| 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) | 0.004 | 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".