Inequality, validity of self-reported height, and its implications for BMI estimates: An analysis of randomly selected primary sampling units' data

Gugushvili, Alexi; Jarosz, Ewa

doi:10.1016/j.pmedr.2019.100974

Cited by 10 publications

(7 citation statements)

References 38 publications

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“…Consistent with previous literature, this study showed an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia based on self-reported data [ 3 , 5 – 7 , 10 , 13 , 14 , 16 , 46 – 50 ]. The observed under-reporting for weight and over-reporting for height, resulting in a underestimation of the BMI, is a general trend observed in many studies although the degree of the trend varies for men and women and the characteristics of the population being examined [ 6 ].…”

Section: Discussionsupporting

confidence: 91%

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

Pelgrims

Devleesschauwer

Vandevijvere

et al. 2023

BMC Med Res Methodol

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Background In many countries, the prevalence of non-communicable diseases risk factors is commonly assessed through self-reported information from health interview surveys. It has been shown, however, that self-reported instead of objective data lead to an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia. This study aimed to assess the agreement between self-reported and measured height, weight, hypertension and hypercholesterolemia and to identify an adequate approach for valid measurement error correction. Methods Nine thousand four hundred thirty-nine participants of the 2018 Belgian health interview survey (BHIS) older than 18 years, of which 1184 participated in the 2018 Belgian health examination survey (BELHES), were included in the analysis. Regression calibration was compared with multiple imputation by chained equations based on parametric and non-parametric techniques. Results This study confirmed the underestimation of risk factor prevalence based on self-reported data. With both regression calibration and multiple imputation, adjusted estimation of these variables in the BHIS allowed to generate national prevalence estimates that were closer to their BELHES clinical counterparts. For overweight, obesity and hypertension, all methods provided smaller standard errors than those obtained with clinical data. However, for hypercholesterolemia, for which the regression model’s accuracy was poor, multiple imputation was the only approach which provided smaller standard errors than those based on clinical data. Conclusions The random-forest multiple imputation proves to be the method of choice to correct the bias related to self-reported data in the BHIS. This method is particularly useful to enable improved secondary analysis of self-reported data by using information included in the BELHES. Whenever feasible, combined information from HIS and objective measurements should be used in risk factor monitoring.

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Section: Discussionsupporting

confidence: 91%

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

Pelgrims

Devleesschauwer

Vandevijvere

et al. 2023

BMC Med Res Methodol

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“…This is consistent with results for China that indicated overestimates of height were significantly larger for individuals living in urban areas than rural areas [ 41 ]. However, a multi-country study found individuals living in rural areas were likely to overestimate their height by a greater amount than urban residents, attributing the difference to having their height measured more often [ 42 ]. Having musculoskeletal disorders was associated with greater underreporting of height and this could be related to cognitive decline (as mentioned earlier), postural issues and spinal compression.…”

Section: Discussionmentioning

confidence: 99%

Discrepancies in self-reported and measured anthropometric measurements and indices among older Australians: prevalence and correlates

Fry

Temple

2022

BMC Public Health

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Background Anthropometric measurements and indices such as weight, height and Body Mass Index (BMI) are often used to assess overall health and nutritional status. Clinicians and epidemiologists often rely on self-reported weight and height to measure BMI. Differences between self-reported and measured weight and height can lead to differences between self-reported and measured BMI, biasing relative risks of diseases associated with differential BMI. Methods Applying regression analysis to a large nationally representative survey data with contemporaneous self-reports and measurements on 3412 individuals aged 65 or over, we provided estimates of the difference between self-reports and measurements of weight, height and BMI for older Australians, analysing demographic, socioeconomic and health correlates of estimated differences. Results We found both males and females underestimated weight, overestimated height and underestimated BMI and there was some evidence these differences increased with age. There was also evidence that these differences were associated with high levels of education and household composition. Conclusion Although average differences were small, for many individuals the differences may be significant, indicating measurements should be taken in clinically focused research and practice. This is important as systematic underestimation of BMI in older adults can have implications for estimating the size of populations at risk of many health conditions, including diabetes, hypertension and functional limitations.

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“…To understand specific mechanisms related to social comparison, future studies should account for reference groups’ mean BMI levels. The previous analysis of randomly selected primary sampling units using the same dataset suggests that reported and measured height estimates are not significantly different from each other [ 36 ], but individuals, and particularly women, might still underreport their weight [ 54 ]. Furthermore, at a macro level, we were able to generate aggregated BMI data only for regional level within the analysed countries, but it might be the case that more localised BMI context (at municipality or even at neighbourhood level) might matter more for individuals.…”

Section: Discussionmentioning

confidence: 99%

“…We created the BMI categories using individuals’ self-reported weight and height information. By comparing actual and self-reported anthropometric measures, the previous research demonstrates that self-reported estimates are good proxies for actual height and weight [ 35 , 36 ]. After dividing individuals’ weight in kilogrammes by their height in metres squared, we classified the derived BMI scores as underweight (below 18.5, men 0.9%, women 2.7%), normal weight (18.5–24.9, men 33.6%, women 39.2%), overweight (25.0–29.9, men 39.7%, women 28.8%), and obese (30.0 and above, men 25.7%, women 29.3%).…”

Section: Methodsmentioning

confidence: 99%

BMI and dissatisfaction with life: contextual factors and socioemotional costs of obesity

Jarosz

Gugushvili

2021

Qual Life Res

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Purpose In this study, we investigate whether individuals’ BMI categories are associated with being dissatisfied with one’s life, how this association is affected by the social comparison that individuals make, and what the role of the overall BMI levels in this process is. Methods We use data for 21,577 men and 27,415 women, collected in 2016 by the European Bank for Reconstruction and Development, from 34 countries in Europe, the Middle East, and Central Asia. To understand the moderating effect of contextual environment, we use multilevel mixed effect logistic regression models and data for national, regional, and cohort-specific BMI levels. Result We find that the association of BMI and dissatisfaction with life differs by gender, with overweight men being less likely to be dissatisfied with life than men with normal weight and obese women being more likely to be dissatisfied with life compared to women with normal weight. For contextual effects, we find that obese women in regions with low BMI levels are more likely to be dissatisfied with life. The effect of obesity on female life dissatisfaction is not observed in regions with high BMI levels. As for men, regional BMI levels affect the levels of life dissatisfaction but only for underweight men. Conclusions Our study adds additional nuance to the quality-of-life research by showing that the association between BMI and decreased life satisfaction is, at least partially, moderated by the contextual environment, and that the character of these effects differs by gender.

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Inequality, validity of self-reported height, and its implications for BMI estimates: An analysis of randomly selected primary sampling units' data

Cited by 10 publications

References 38 publications

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

Discrepancies in self-reported and measured anthropometric measurements and indices among older Australians: prevalence and correlates

BMI and dissatisfaction with life: contextual factors and socioemotional costs of obesity

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