ArticleDose-response : a publication of International Hormesis Society2012
Low-dose nonlinear effects of smoking on coronary heart disease risk.
Article in Dose-response : a publication of International Hormesis Society, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Emissions from conventional and electronic waterpipes relative to cigarettes and a heated tobacco product.Scientific reports · 2025Article
- Safety and efficacy of e-cigarettes in those with atherosclerotic disease: a review.Open heart · 2023Review
- Impact of secondhand smoke exposure in former smokers on their subsequent risk of coronary heart disease: evidence from the population-based cohort of the Tehran Lipid and Glucose Study.Epidemiology and health · 2020Article
- Effect of Genetic Polymorphisms and Long-Term Tobacco Exposure on the Risk of Breast Cancer.International journal of molecular sciences · 2016Article
- Exposure to ambient air particulate matter and non-alcoholic fatty liver disease.World journal of gastroenterology · 2013Review
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1 author.
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Abstract
Some recent discussions of adverse human health effects of active and passive smoking have suggested that low levels of exposure are disproportionately dangerous, so that "The effects of even brief (minutes to hours) passive smoking are often nearly as large (averaging 80% to 90%) as chronic active smoking" (Barnoya and Glantz, 2005). Recent epidemiological evidence (Teo et al., 2006) suggests a more linear relation. This paper reexamines the empirical relation between self-reported low levels of active smoking and risk of coronary heart disease (CHD) in public-domain data from the National Health and Nutrition Examination Survey (NHANES). Consistent with biological evidence on J-shaped and U-shaped relations between smoking-associated risk factors and CHD risks, we find that low levels of active smoking do not appear to be associated with increased CHD risk. Several methodological challenges in epidemiology may explain how model-derived estimates can predict low-dose linear or concave dose-response estimates, even if the empirical (i.e., data-based) relation does not show a clear increased risk at the lowest doses.
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