Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys.

Ezzati, Majid; Martin, Hilarie; Skjold, Suzanne; Hoorn, Stephen Vander; Murray, Christopher J L

doi:10.1258/jrsm.99.5.250

Cited by 171Obesity, Physical Activity, DietSurvey Methodology and NonresponseStatistical Methods in Epidemiology

Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys.

Ezzati, Majid, Martin, Hilarie, Skjold, Suzanne, Hoorn, Stephen Vander, Murray, Christopher J L·2006·Journal of the Royal Society of Medicine

DOI 10.1258/jrsm.99.5.250

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Abstract

Objectives: To quantify population-level bias in self-reported weight and height as a function of age, sex, and the mode of self-report, and to estimate unbiased trends in national and state level obesity in the USA. Design: Statistical analysis of repeated cross-sectional health examination surveys (the National Health and Nutrition Examination Survey [NHANES]) and health surveys (the Behavioral Risk Factor Surveillance System [BRFSS]) in the USA. Setting: The 50 states of the USA and the District of Columbia. Results: In the USA, on average, women underreported their weight, but men did not. Young and middle-aged (565 years) adult men over-reported their height more than women of the same age. In older age groups, over-reporting of height was similar in men and women. Population-level bias in self-reported weight was larger in telephone interviews (BRFSS) than in-person interviews (NHANES). Except in older adults, height was over-reported more often in telephone interviews than in-person interviews. Using corrected weight and height in the year 2000, Mississippi (31%) and Texas (30%) had the highest prevalence of obesity for men; Texas (37%), Louisiana (37%), Mississippi (37%), District of Columbia (37%), Alabama (37%), and South Carolina (36%) for women. Conclusions: Population-level bias in self-reported weight and height is larger in telephone interviews than in-person interviews. Telephone interviews are a low-cost method for regular, nationally- and sub-nationally representative monitoring of obesity. It is possible to obtain corrected estimates of trends and geographical distributions of obesity from telephone interviews by using systematic analysis which measure weight and height from an independent sample of the same population.

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Authors: Ezzati, M., Martin, H., Skjold, S., Hoorn, S. V., & Murray, C. J. L.
Affiliations: Harvard University (US)
Initiative for Global Health, Harvard University, Cambridge, USA
Clinical Trials Research Unit, University of Auckland, New Zealand
Harvard School of Public Health, Boston
Corresponding author: Ezzati, Majid
ORCID: Ezzati, Majid
Venue: Journal of the Royal Society of Medicine
Publisher: SAGE Publications
Published: 2006-05
Volume: 99
Issue: 5
Pages: 250-257
ISSN: 0141-0768 (ISSN-L)
1758-1095
DOI: 10.1258/jrsm.99.5.250
OpenAlex: W4322694598
Cited by: 170
References: 14
Citations by year: 2024: 1 · 2023: 1 · 2022: 1 · 2021: 4 · 2018: 2 · 2017: 4 · 2016: 17 · 2015: 16 · 2014: 21 · 2013: 15 · 2012: 19
FWCI: 11.33
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of the Royal Society of Medicine (publishedVersion)
Funders: Centers for Disease Control and Prevention
National Institute on Aging
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: obesity prevalence, self-report bias, self-reported weight, self-reported height, measurement error correction, telephone interviews, face-to-face interviews, sex differences, age differences, obesity trends, NHANES, Behavioral Risk Factor Surveillance System, obesity surveillance

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Trends in national and state-level obesity in the USA after correction for self-report bias: analysis of health surveys.

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