[Relation between passive smoking at home and dietary intake]. 1998

Y Nishino, and A Fukao, and Y Tsubono, and I Tsuji, and A Kuwahara, and S Hisamichi
Department of Social Medicine, Tohoku University Graduate School of Medicine.

To investigate confounding factors in the relation of passive smoking to diseases, we compared the dietary intake of passive smokers, non-smokers without passive smoke exposure, and smokers. The subjects were female respondents to a baseline survey, which was conducted as part of a collaborative cohort study in a rural area. Of the subjects, 101 females were smokers. A total of 1978 female non-smokers answered the question about passive smoking exposure at home, including 1,392 (70.4%) passive smokers and 586 (29.6%) non-passive smokers. Among these three groups, the dietary intake of 36 foods (frequency and amount) was compared by odds ratios calculated with a logistic regression model. The percentages of subjects reporting frequent intake of milk or milk products, carrot or pumpkin, tomatoes, oranges, and fruits except oranges, were significantly lower in passive smokers than in non-passive smokers (OR = 0.80, 0.74, 0.80, 0.77, 0.79). On the other hand, more subjects in passive smokers reported frequent or large intake of pork, salt pickled vegetables, soy sauce pickled foods, soft drinks, coffee, and moso soup (OR = 1.38, 1.53, 1.32, 1.73, 1.30, 1.33). The dietary pattern of passive smokers was similar to that of smokers. In conclusion, in this study, passive smokers had different dietary patterns from non-passive smokers'. In future research regarding disease with passive smoking exposure, dietary factors should be considered as a confounder.

UI MeSH Term Description Entries
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D004032 Diet Regular course of eating and drinking adopted by a person or animal. Diets
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328 Adult A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available. Adults
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly
D000369 Aged, 80 and over Persons 80 years of age and older. Oldest Old
D014028 Tobacco Smoke Pollution Contamination of the air by tobacco smoke. Air Pollution, Tobacco Smoke,Environmental Pollution, Tobacco Smoke,Passive Smoking,Smoking, Passive,Environmental Smoke Pollution, Tobacco,Environmental Tobacco Smoke Pollution,Involuntary Smoking,Second Hand Smoke,Secondhand Smoke,Secondhand Smoking,Hand Smoke, Second,Hand Smokes, Second,Involuntary Smokings,Passive Smokings,Pollution, Tobacco Smoke,Pollutions, Tobacco Smoke,Second Hand Smokes,Secondhand Smokes,Secondhand Smokings,Smoke Pollution, Tobacco,Smoke Pollutions, Tobacco,Smoke, Second Hand,Smoke, Secondhand,Smokes, Second Hand,Smokes, Secondhand,Smoking, Involuntary,Smoking, Secondhand,Smokings, Involuntary,Smokings, Passive,Smokings, Secondhand,Tobacco Smoke Pollutions
D016015 Logistic Models Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor. Logistic Regression,Logit Models,Models, Logistic,Logistic Model,Logistic Regressions,Logit Model,Model, Logistic,Model, Logit,Models, Logit,Regression, Logistic,Regressions, Logistic

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