Demographics and medical disorders associated with smoking: a population-based study

Abstract Background Few studies have investigated factors associated with smoking behaviors. In this population-based study, we investigated demographics and medical comorbid diseases to establish a prediction model for smoking behaviors by using the National Health Interview Survey (NHIS) and Natio...

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Main Authors: Wei-Sheng Chung, Pei-Tseng Kung, Hui-Yun Chang, Wen-Chen Tsai
Format: Article
Language:English
Published: BMC 2020-05-01
Series:BMC Public Health
Subjects:
Sex
Age
Online Access:http://link.springer.com/article/10.1186/s12889-020-08858-4
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spelling doaj-a1b1b639b8f241a2990291bbdadc0bf62020-11-25T02:05:55ZengBMCBMC Public Health1471-24582020-05-012011810.1186/s12889-020-08858-4Demographics and medical disorders associated with smoking: a population-based studyWei-Sheng Chung0Pei-Tseng Kung1Hui-Yun Chang2Wen-Chen Tsai3Department of Internal Medicine, Taichung Hospital, Ministry of Health and WelfareDepartment of Healthcare Administration, Asia UniversityDepartment of Health Services Administration, China Medical UniversityDepartment of Health Services Administration, China Medical UniversityAbstract Background Few studies have investigated factors associated with smoking behaviors. In this population-based study, we investigated demographics and medical comorbid diseases to establish a prediction model for smoking behaviors by using the National Health Interview Survey (NHIS) and National Health Insurance Research Database (NHIRD). Methods We enrolled individuals aged ≥40 years who had participated in the NHIS in 2001, 2005, and 2009. We identified the smoking behaviors of the study participants in the NHIS. Smoking behaviors were divided into ever smokers (current smokers and ex-smokers) and nonsmokers (never smokers).We defined medical comorbid disorders of the study participants by using medical claim data from the NHIRD. We used multivariable logistic regression models to calculate the adjusted odds ratio and 95% confidence interval for variables associated with smoking. The significant variables in the multivariable model were included in the receiver operating characteristic curves (ROC) to predict the sensitivity and specificity of the model. Results In total, 26,375 participants (12,779 men and 13,596 women) were included in the analysis. The prevalence of smoking was 39.29%. The mean ages of the 16,012 nonsmokers were higher than those of the 10,363 smokers (57.86 ± 12.92 years vs. 53.59 ± 10.82 years). Men outnumbered women among smokers (68.18% vs. 31.82%). Male sex, young age and middle age, being insured categories, residence in suburban areas, and chronic obstructive pulmonary disease (COPD) were independent factors associated with smoking. The area under the ROC curve of these significant factors to predict smoking behaviors was 71.63%. Conclusion Sex, age, insured categories, residence in suburban areas, and COPD were associated with smoking in people.http://link.springer.com/article/10.1186/s12889-020-08858-4SmokingSexAgeChronic obstructive pulmonary diseaseReceiver operating curve
collection DOAJ
language English
format Article
sources DOAJ
author Wei-Sheng Chung
Pei-Tseng Kung
Hui-Yun Chang
Wen-Chen Tsai
spellingShingle Wei-Sheng Chung
Pei-Tseng Kung
Hui-Yun Chang
Wen-Chen Tsai
Demographics and medical disorders associated with smoking: a population-based study
BMC Public Health
Smoking
Sex
Age
Chronic obstructive pulmonary disease
Receiver operating curve
author_facet Wei-Sheng Chung
Pei-Tseng Kung
Hui-Yun Chang
Wen-Chen Tsai
author_sort Wei-Sheng Chung
title Demographics and medical disorders associated with smoking: a population-based study
title_short Demographics and medical disorders associated with smoking: a population-based study
title_full Demographics and medical disorders associated with smoking: a population-based study
title_fullStr Demographics and medical disorders associated with smoking: a population-based study
title_full_unstemmed Demographics and medical disorders associated with smoking: a population-based study
title_sort demographics and medical disorders associated with smoking: a population-based study
publisher BMC
series BMC Public Health
issn 1471-2458
publishDate 2020-05-01
description Abstract Background Few studies have investigated factors associated with smoking behaviors. In this population-based study, we investigated demographics and medical comorbid diseases to establish a prediction model for smoking behaviors by using the National Health Interview Survey (NHIS) and National Health Insurance Research Database (NHIRD). Methods We enrolled individuals aged ≥40 years who had participated in the NHIS in 2001, 2005, and 2009. We identified the smoking behaviors of the study participants in the NHIS. Smoking behaviors were divided into ever smokers (current smokers and ex-smokers) and nonsmokers (never smokers).We defined medical comorbid disorders of the study participants by using medical claim data from the NHIRD. We used multivariable logistic regression models to calculate the adjusted odds ratio and 95% confidence interval for variables associated with smoking. The significant variables in the multivariable model were included in the receiver operating characteristic curves (ROC) to predict the sensitivity and specificity of the model. Results In total, 26,375 participants (12,779 men and 13,596 women) were included in the analysis. The prevalence of smoking was 39.29%. The mean ages of the 16,012 nonsmokers were higher than those of the 10,363 smokers (57.86 ± 12.92 years vs. 53.59 ± 10.82 years). Men outnumbered women among smokers (68.18% vs. 31.82%). Male sex, young age and middle age, being insured categories, residence in suburban areas, and chronic obstructive pulmonary disease (COPD) were independent factors associated with smoking. The area under the ROC curve of these significant factors to predict smoking behaviors was 71.63%. Conclusion Sex, age, insured categories, residence in suburban areas, and COPD were associated with smoking in people.
topic Smoking
Sex
Age
Chronic obstructive pulmonary disease
Receiver operating curve
url http://link.springer.com/article/10.1186/s12889-020-08858-4
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