A comparison of national estimates of obesity prevalence from the behavioral risk factor surveillance system and the national health and nutrition examination survey

Abstract

Background: Obesity interventions are implemented at state or sub-state level in the United States (US), where only selfreported weight and height data for adults are available from the Behavioral Risk Factor Surveillance System (BRFSS). The prevalence estimates of overweight and obesity generated from self-reported weight and height from BRFSS are known to underestimate the true prevalence. However, whether this underestimation is consistent across different demographic groups has not been fully investigated. Methods: In this study, we compared the prevalence estimates of obesity (body mass index (BMI) X30 kg/m 2) and overweight (BMI X25 kg/m 2) in different demographic groups in the US from the National Health and Nutrition Examination Survey (NHANES) and BRFSS during 1999-2000. We also compared the rank orders of the obesity and overweight prevalence across different demographic groups from the two data sources. Results: Compared to NHANES, BRFSS underestimated the overall prevalence of obesity and overweight by 9.5 and 5.7 percentage points, respectively. The underestimation differed across different demographic groups: the underestimation of obesity and overweight prevalence was higher among women (13.1 and 12.2 percentage points, respectively) than among men (5.8 and À0.6 percentage points, respectively). The variation of underestimation was higher among men. A clear inverse association between educational attainment and obesity prevalence among non-Hispanic African American women was observed from BRFSS data. However, no such association was found from NHANES. While BRFSS can identify correctly the population with the highest obesity and overweight burden, it did not accurately rank the obesity and overweight prevalence across different demographic groups. Conclusion: Compared to NHANES, BRFSS disproportionately underestimates the prevalence of obesity and overweight across different gender, race, age, and education subgroups.

Depression and anxiety among US adults: associations with body mass index

“…First, all information including BMI, chronic disease status and mental disorders was self-reported, thus subject to recall bias. Second, it has been reported that the prevalence of overweight or obesity was underestimated by self-reported BMI compared with that by measured BMI, 41 which is consistent with our finding that the prevalence of obesity was lower in this study (26.0%) than that reported by Ogden et al (32.2%, based on the measured BMI in the National Health and Nutrition Examination Survey). 6 Therefore, we may have underestimated the associations of BMI with mental disorders.…”

Regression models to predict corrected weight, height and obesity prevalence from self-reported data: data from BRFSS 1999–2007

“…Because of the nonavailability of measured data by trained technicians in such important surveys as BRFSS, 31 which is the biggest source of health risk data at state and local levels, self-reported prevalence of obesity as compared with corrected data has been consistently lower over the years by up to 5.9% (Table 6). Similar results have been reported elsewhere 34 when comparisons are made between the prevalence rates of those surveys that collect data over the telephone, such as BRFSS, 32 and those that collect data in person, such as NHANES. 33 It should be of concern that, for females, self-reported data on obesity prevalence rates were lower by as much as 5.1% compared with the corrected prevalence rates.…”

“…BMI, calculated as weight in kilograms divided by the square of height in meters, is then used to classify those with a BMIX30 kg m À2 as obese and those with BMIo30 kg m À2 as nonobese. However, because of biased self-reporting of both weight and height, obesity prevalence determined from surveys such as BRFSS underestimates prevalence by 9.5% 32 for US adults aged 20 years and above for BRFSS 1999-2000, compared with the National Health and Nutrition and Examination Survey (NHANES), 33 and the degree of underestimation varies among different demographic groups. 32 As part of NHANES, 33 trained technologists measure weight and height in a mobile examination center, as well as collect self-reported weight and height in the home interview portion of the survey.…”

“…Data collected in both ways provide an opportunity to model a relationship between self-reported and measured data. The parameters determined from this model can then be applied to BRFSS 32 data, enabling computations of corrected obesity prevalence rates.…”

Tradução e adaptação da versão brasileira do Night Eating Questionnaire

“…Além disso, poder-se-ia cogitar que entram nesse escopo fatores que podem ser determinados pelo poder aquisitivo, como o acesso aos alimentos em termos quantitativos e qualitativos, fato que poderia induzir o consumo de alimentos mais calóricos em indivíduos com menor poder aquisitivo, por serem mais baratos. Esses aspectos corroboram a idéia de se realizar novos estudos em populações com diferentes níveis de escolaridade, para apreciar de modo mais detalhado a adequação deste instrumento, que poderá viabilizar estudos que explorem com maior profundidade dados de evidências recentes, que sugerem forte associação entre a síndrome do comer noturno, obesidade, diabetes do tipo II e pobre qualidade de vida 38,39,40 . Apesar disso, os resultados deste estudo de adaptação lingüística do Questionário sobre Hábitos do Comer Noturno (Tabela 2), obtido por meio de um cuidadoso processo de tradução e adaptação compatível com as recomendações atuais para este processo 21,22,23,24,41,42,43,44 , sugerem que se trata de um instrumento de fácil compreensão e uso, o que estimula o estudo da aplicação desta versão traduzida e adaptada em populações com níveis de escolaridade diversos.…”