Robin Nance1, Joseph Delaney2, John W McEvoy3, Michael J Blaha4, Gregory L Burke5, Ana Navas-Acien6, Joel D Kaufman7, Elizabeth C Oelsner8, Robyn L McClelland9. 1. Department of Biostatistics, Collaborative Health Studies Coordinating Center, University of Washington, Building 29, Suite 210, 6200 NE 74th Street, Box 354922, Seattle, WA 98115, USA. Electronic address: rmnance@uw.edu. 2. Department of Epidemiology, Collaborative Health Studies Coordinating Center, University of Washington, Box 354922, Building 29, Suite 310, 6200 NE 74th Street, Seattle, WA 98115-8160, USA. 3. Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Baltimore, MD 21287, USA. 4. Department of Medicine, Johns Hopkins Hospital, Johns Hopkins University, 600 N. Wolfe Street, Blalock 524C, Baltimore, MD 21287, USA. 5. Division of Public Health Sciences, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157-1063, USA. 6. Department of Environmental Health Sciences, Columbia University, 722 168th W St, Office 1105, New York, NY 10032, USA. 7. Departments of Environmental & Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, 4225 Roosevelt Way NE, Suite 100, Seattle, WA 98105, USA. 8. Department of Medicine, Division of General Medicine, Presbyterian Hospital, Columbia University, 630 West 168th Street, 9-105E, New York, NY 10032, USA. 9. Department of Biostatistics, Collaborative Health Studies Coordinating Center, University of Washington, Building 29, Suite 210, 6200 NE 74th Street, Box 354922, Seattle, WA 98115, USA.
Abstract
OBJECTIVES: Smoking as an epidemiological exposure can be quantified in many ways including duration, intensity, pack-years, recency, and age at initiation. However, it is not clear which of these are most important for cardiovascular disease (CVD) and how they should be modeled. STUDY DESIGN AND SETTING: Using the Multi-Ethnic Study of Atherosclerosis, Cox models for time to incident CVD adjusted for age, sex, race/ethnicity, education category, and income category were compared which included various characterizations of smoking history. RESULTS: Duration, age at starting, time since quitting, and noncigarette forms of smoking were not independently associated with CVD, whereas baseline current intensity was associated with CVD [e.g., hard CVD hazard ratio 1 pack/d of 1.85 95% confidence interval (1.33, 2.57)]. Former smokers, regardless of duration, intensity, or recency, were not at increased risk, suggesting that risk may risk may drop precipitously from the time of quitting. For CVD events, representing smoking exposure as baseline smoking intensity produced better model fit as measured by Akaike information criterion than models using smoking status or pack-years. CONCLUSION: The association of smoking with incident CVD events was well captured by including a simple term for baseline smoking intensity.
OBJECTIVES: Smoking as an epidemiological exposure can be quantified in many ways including duration, intensity, pack-years, recency, and age at initiation. However, it is not clear which of these are most important for cardiovascular disease (CVD) and how they should be modeled. STUDY DESIGN AND SETTING: Using the Multi-Ethnic Study of Atherosclerosis, Cox models for time to incident CVD adjusted for age, sex, race/ethnicity, education category, and income category were compared which included various characterizations of smoking history. RESULTS: Duration, age at starting, time since quitting, and noncigarette forms of smoking were not independently associated with CVD, whereas baseline current intensity was associated with CVD [e.g., hard CVD hazard ratio 1 pack/d of 1.85 95% confidence interval (1.33, 2.57)]. Former smokers, regardless of duration, intensity, or recency, were not at increased risk, suggesting that risk may risk may drop precipitously from the time of quitting. For CVD events, representing smoking exposure as baseline smoking intensity produced better model fit as measured by Akaike information criterion than models using smoking status or pack-years. CONCLUSION: The association of smoking with incident CVD events was well captured by including a simple term for baseline smoking intensity.
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