Information on cross-cultural variation between SR and measured BMI values has the potential to identify key social factors that shape how individual height and weight is perceived and reported in different locations. Italians overestimated their height more than the other two countries in all decile groups, but this effect was less distinct for the lowest two deciles 10th and 20th percentiles. The ANOVAs on height difference showed four significant main effects by country, gender, age groups and height deciles. Sex, age, the education dummy variables, and income as a continuous variable were entered in the first step to control for the effects of socioeconomic factors shown to influence accuracy of SR height and weight values [ 8 , 9 , 11 ]. There were also two interaction effects associated with the country factor. Older people tend to relatively over-report height and under-report weight, but the magnitude differs between countries and gender. Depending on the country, reported weights of each age group were underestimated differently. Differences between measured and self-reported values are always presented as reported — measured , thus giving over-reported values a positive and under-reported values a negative sign.
Reported annual household income was combined with an index of durable goods ownership, dwelling characteristics, and access to services to create a continuous variable based on long-term wealth status for the household [ 29 ]. Information on cross-cultural variation between SR and measured BMI values has the potential to identify key social factors that shape how individual height and weight is perceived and reported in different locations. In the questionnaire, participants filled in gender, age, stature and weight. This article investigates the combined effects of categorization and self-report bias on the estimated association between obesity and mortality. There were two significant interaction effects associated with the country factor: The scanning garment for males was a short that covers from the waist to mid-thigh. There were four derived variables, including BMI and three difference scores. Sampling was based on a stratified, multistage cluster sample design to ensure the full range of living conditions in each country were represented [ 25 ]. This misreporting results in lower obesity prevalence rates when SR data are used to calculate BMI, and these inaccurate values have considerable policy and public health implications. Total PAL, smoking frequency, and drinking frequency were entered in the second step of the regression. Participants were sorted according to age group and sex, and all analyses were then conducted separately by country. Cox models and age-standardized death rates were used to evaluate the effects of categorization and self-report bias on the mortality risks and percent of deaths attributable to obesity. Confirming the precision of SR measures has particularly important implications in the ongoing struggle to accurately document global increases in obesity. In contrast, the percent of deaths attributable to excess weight was lower using self-reported versus measured data because self-reports led to systematic downward bias in the BMI distribution. However, individuals frequently overestimate their height and underestimate their weight, resulting in artificially lower obesity prevalence rates. Paired t-tests Hypothesis One — BMI calculated from SR height and weight will be significantly lower than BMI calculated from measured height and weight in both older and younger adults. In other words, only younger males in the Netherlands and, especially, in Italy reported their weights more or less correctly. Paired samples t-tests on each cell categorized by gender, age and country showed that the reported BMI was statistically smaller than measured BMI in every case. Sex, age, the education dummy variables, and income as a continuous variable were entered in the first step to control for the effects of socioeconomic factors shown to influence accuracy of SR height and weight values [ 8 , 9 , 11 ]. Weight deciles or obesity status did not statistically affect the overestimation of height. BMI variables Participants were first asked to report their height and weight during the interview. Depending on the country, reported weights of each age group were underestimated differently. A second linear regression assessed if age contributed to discrepancies between SR and measured BMI values. The present study investigates whether differences between self-reported and measured values are the same for populations from different regions, and the influences of gender and age. Volume 25, Issue 12 , December , Pages
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