Additive interactions of smoking with obesity on ischemic heart disease mortality: a national prospective cohort study in the United States
Bibliographic record
Abstract
BACKGROUND: Smoking and obesity are important modifiable risk factors for ischemic heart disease (IHD), often clustering within the same individuals. Previous US studies showed mixed findings regarding their interaction effects on IHD mortality and only investigated the question on the multiplicative scale, while additive scale is better suited to inform public health interventions. METHODS: We linked the 1997-2018 National Health Interview Survey data to the 2019 National Death Index. A total of 579 503 adults aged 18 years and older were included. Mortality status or last presumed alive was assessed until 31 December 2019. We used Aalen's additive hazards models and calculated the relative excess risk due to interaction (RERI) from Cox proportional hazards models and Fine-Gray subdistribution models that accounted for competing risks to comprehensively evaluate the interaction effect of smoking with obesity on IHD mortality. RESULTS: During 10.4 years of follow-up on average, 13 231 IHD deaths occurred. The weighted mortality rate was 177.0 (95% CI: 172.3-181.7) per 100 000 person-years (PY). The combination of current everyday smoking and obesity was associated with 55.56 (95% CI: 30.37-80.74) additional deaths per 100 000 PY compared to the sum of their individual effects. This additive interaction was supported by multiplicative interactions (HR = 1.19, 1.03-1.39; HR = 1.40, 1.22-1.59) and large RERIs of 1.00 (0.59-1.40) and 0.85 (0.6-1.09) from the Cox and Fine-Gray models, respectively. CONCLUSIONS: Our findings highlight the importance of evaluating interactions on multiple scales, which reduces scale-dependence of the interaction effect and can translate better into public health strategies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".