Dataset Information

Challenging the role of social norms regarding body weight as an explanation for weight, height, and BMI misreporting biases: development and application of a new approach to examining misreporting and misclassification bias in surveys.

ABSTRACT:

Background

Cultural pressures to be thin and tall are postulated to cause people to misreport their body weight and height towards more socially normative (i.e., desirable) values, but a paucity of direct evidence supports this idea. We developed a novel non-linear approach to examining weight, height, and BMI misreporting biases and used this approach to examine the association between socially non-normative weight and misreporting biases in adults.

Methods

The Survey of Lifestyles, Attitudes, and Nutrition 2007 (SLÁN 2007), a nationally representative survey of the Republic of Ireland (N = 1942 analyzed) was used. Self-reported weight (height) was classified as under-reported by ? 2.0 kg (2.0 cm), over-reported by ? 2.0 kg (2.0 cm), or accurately reported within 2.0 kg (2.0 cm) to account for technical errors of measurement and short-term fluctuations in measured weight (height). A simulation strategy was used to define self-report-based BMI as under-estimated by more than 1.40 kg/m2, over-estimated by more than 1.40 kg/m2, or accurately estimated within 1.40 kg/m2. Patterns of biases in self-reported weight, height, and BMI were explored. Logistic regression was used to identify factors associated with mis-estimated BMI and to calculate adjusted odds ratios (AOR) and 99% confidence intervals (99%CI).

Results

The patterns of bias contributing the most to BMI mis-estimation were consistently, in decreasing order of influence, (1) under-reported weight combined with over-reported height, (2) under-reported weight with accurately reported height, and (3) accurately reported weight with over-reported height. Average bias in self-report-based BMI was -1.34 kg/m2 overall and -0.49, -1.33, and -2.66 kg/m2 in normal, overweight, and obese categories, respectively. Despite the increasing degree of bias with progressively higher BMI categories, persons describing themselves as too heavy were, within any given BMI category, less likely to have under-estimated BMI (AOR 0.5, 99%CI: 0.3-0.8, P < 0.001), to be misclassified in a lower BMI category (AOR 0.3, 99%CI: 0.2-0.5, P < 0.001), to under-report weight (AOR 0.5, 99%CI: 0.3-0.7, P < 0.001), and to over-report height (OR 0.7, 99%CI: 0.6-1.0, P = 0.007).

Conclusions

A novel non-linear approach to examining weight, height, and BMI misreporting biases was developed. Perceiving oneself as too heavy appears to reduce rather than exacerbate weight, height, and BMI misreporting biases.

SUBMITTER: Brestoff JR

PROVIDER: S-EPMC3121629 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Publications

Challenging the role of social norms regarding body weight as an explanation for weight, height, and BMI misreporting biases: development and application of a new approach to examining misreporting and misclassification bias in surveys.

Brestoff Jonathan R JR Perry Ivan J IJ Van den Broeck Jan J

BMC public health 20110518

<h4>Background</h4>Cultural pressures to be thin and tall are postulated to cause people to misreport their body weight and height towards more socially normative (i.e., desirable) values, but a paucity of direct evidence supports this idea. We developed a novel non-linear approach to examining weight, height, and BMI misreporting biases and used this approach to examine the association between socially non-normative weight and misreporting biases in adults.<h4>Methods</h4>The Survey of Lifestyl ...[more]

PMID: 21592341

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Project description:Using imperfect tests may lead to biased estimates of disease frequency and measures of association. Many studies have looked into the effect of misclassification on statistical inferences. These evaluations were either within a cross-sectional study framework, assessing biased prevalence, or for cohort study designs, evaluating biased incidence rate or risk ratio estimates based on misclassification at one of the two time-points (initial assessment or follow-up). However, both observations at risk and incident cases can be wrongly identified in longitudinal studies, leading to selection and misclassification biases, respectively. The objective of this paper was to evaluate the relative impact of selection and misclassification biases resulting from misclassification, together, on measures of incidence and risk ratio. To investigate impact on measure of disease frequency, data sets from a hypothetical cohort study with two samples collected one month apart were simulated and analyzed based on specific test and disease characteristics, with no elimination of disease during the sampling interval or clustering of observations. Direction and magnitude of bias due to selection, misclassification, and total bias was assessed for diagnostic test sensitivity and specificity ranging from 0.7 to 1.0 and 0.8 to 1.0, respectively, and for specific disease contexts, i.e., disease prevalences of 5 and 20%, and disease incidences of 0.01, 0.05, and 0.1 cases/animal-month. A hypothetical exposure with known strength of association was also generated. A total of 1,000 cohort studies of 1,000 observations each were simulated for these six disease contexts where the same diagnostic test was used to identify observations at risk at beginning of the cohort and incident cases at its end. Our results indicated that the departure of the estimates of disease incidence and risk ratio from their true value were mainly a function of test specificity, and disease prevalence and incidence. The combination of the two biases, at baseline and follow-up, revealed the importance of a good to excellent specificity relative to sensitivity for the diagnostic test. Small divergence from perfect specificity extended quickly to disease incidence over-estimation as true prevalence increased and true incidence decreased. A highly sensitive test to exclude diseased subjects at baseline was of less importance to minimize bias than using a highly specific one at baseline. Near perfect diagnostic test attributes were even more important to obtain a measure of association close to the true risk ratio, according to specific disease characteristics, especially its prevalence. Low prevalent and high incident disease lead to minimal bias if disease is diagnosed with high sensitivity and close to perfect specificity at baseline and follow-up. For more prevalent diseases we observed large risk ratio biases towards the null value, even with near perfect diagnosis.

Dataset Information

Challenging the role of social norms regarding body weight as an explanation for weight, height, and BMI misreporting biases: development and application of a new approach to examining misreporting and misclassification bias in surveys.

Background

Methods

Results

Conclusions

Publications

Challenging the role of social norms regarding body weight as an explanation for weight, height, and BMI misreporting biases: development and application of a new approach to examining misreporting and misclassification bias in surveys.

Similar Datasets

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