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肥胖的体重定义:使用自我报告的体重和身高的敏感性和特异性

Body mass definitions of obesity: sensitivity and specificity using self-reported weight and height.

作者信息

Nieto-García F J, Bush T L, Keyl P M

机构信息

Department of Epidemiology, Johns Hopkins University School of Hygiene and Public Health, Baltimore, MD 21205.

出版信息

Epidemiology. 1990 Mar;1(2):146-52.

PMID:2073502
Abstract

A high correlation between continuous measures of self-reported and measured weight and height has led investigators to infer that self-report of these variables is appropriate in epidemiologic studies. We evaluated the sensitivity and specificity of categorical definitions of body mass defined using self-reported height and weight on 7,455 adult participants of the Lipid Research Clinics Family Study (1975-1978) on whom both self-reported and measured height and weight were available. The categorical definition of obesity used here was a body mass index of at least 30 kg/m2. Overall, the sensitivity of the obese category when defined with self-reported weight and height was 74% (95% CI = 72%-76%), and the specificity was 99%. The sensitivities of the categories defined using self-reported measures varied considerably by sex, age, and educational level. Overall, the sensitivities were higher for women compared with men, as men were less accurate in reporting height. The sensitivity of the categorical definitions of obesity decreased with increasing age and education in both men and women. The sensitivity for elderly obese men was below 50%. These results suggest that substantial misclassification can occur when self-reported information is used to define body mass categories.

摘要

自我报告的体重和身高连续测量值之间的高度相关性,使得研究人员推断在流行病学研究中对这些变量进行自我报告是合适的。我们在脂质研究临床家庭研究(1975 - 1978年)的7455名成年参与者中,评估了使用自我报告的身高和体重定义的体重分类定义的敏感性和特异性,这些参与者同时有自我报告和测量的身高及体重数据。这里使用的肥胖分类定义是体重指数至少为30kg/m²。总体而言,用自我报告的体重和身高定义肥胖类别时,其敏感性为74%(95%置信区间 = 72% - 76%),特异性为99%。使用自我报告测量值定义的类别敏感性因性别、年龄和教育水平而有很大差异。总体而言,女性的敏感性高于男性,因为男性报告身高的准确性较低。肥胖分类定义的敏感性在男性和女性中均随年龄增长和教育程度提高而降低。老年肥胖男性的敏感性低于50%。这些结果表明,当使用自我报告信息来定义体重类别时,可能会出现大量的错误分类。

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