ArticleTobacco induced diseases2025
Risk of all-cause mortality by various cigarette smoking indices: A longitudinal study using the Korea National Health Examination Baseline Cohort in South Korea.
Article in Tobacco induced diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Genetic nurture in intergenerational transmission of substance use.Nature communications · 2026Article
- Cohort profile: the Korean National Health Examination Baseline (KNHEB) cohort for longitudinal health monitoring in South Korea.BMC public health · 2025Article
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Authors and funding
7 authors.
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Abstract
introductionSmoking behaviors can be quantified using various indices. Previous studies have shown that these indices measure and predict health risks differently. Additionally, the choice of measure differs depending on the health outcome of interest. We compared how each smoking index predicted all-cause mortality and assessed the goodness-of-fit of each model.
methodsA population-based retrospective cohort, the Korea National Health Examination Baseline Cohort, was used (N=6001607). Data from 2009 were utilized, and the participants were followed until 2021. Cox proportional hazards regression analyses were performed among all participants and ever smokers, respectively, to estimate all-cause mortality. Model fit was assessed by the Akaike Information Criterion.
resultsFor men, smoking intensity showed the strongest effect size (hazard ratio HR=1.16; 95% CI: 1.14-1.18), while pack-years provided the best model fit for all-cause mortality. Among women, smoking intensity showed both the strongest effect size (HR=1.49; 95% CI: 1.28-1.74) and the best model fit. Smoking status (never/former/current) also showed comparable effect sizes (men, HR=1.14; 95% CI: 1.13-1.15; women, HR=1.14; 95% CI: 1.11- 1.18) with fair model fit. Analyses of people who ever smoked indicated that a model incorporating smoking status, duration, and intensity best described the mortality data.
conclusionsThe smoking indices showed varying effect sizes and model fits by sex, making it challenging to recommend a single optimal measure. Smoking intensity may be preferred for capturing cumulative exposure, whereas smoking status is notable for its simplicity, comparable effect size, and model fit. Further research that includes biochemical measurements, additional health outcomes, and longer follow-up periods is needed to refine these findings.
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