Inaccurate self-report of height and its impact on misclassification of body mass index in postmenopausal women

Agreement between self-reported and measured weight, height, and derived BMI by educational attainment across racial and ethnic groups of US women

OBJECTIVE

Underreporting of weight and overreporting of height is consistently shown among women, thereby reducing accuracy of estimation of body mass index-and thus obesity-in epidemiologic studies that rely on self-reported data. Additionally, misreporting has been shown to differ by socioeconomic status and race and ethnicity, which can result in differential misclassification and bias that can obfuscate associations with obesity across groups in multiethnic and socioeconomically varying populations. Therefore, we sought to assess agreement between self-reported and objectively measured weight, height, and derived body mass index (BMI) across levels of educational attainment within racial and ethnic groups in a population-based cohort of US women.

METHODS

Among self-identified White, Black, and Latina women enrolled in the Sister Study (2003-2009), we assessed mean differences in self-reported vs. objectively measured weight, height, and derived BMI. Using adjusted linear and multinomial logistic regression, we compared measurement error among participants reporting some college/vocational school or ≥college vs. ≤high school. We assessed BMI agreement using Bland-Altman plots and weighted kappa (k) statistics.

RESULTS

Among 18,638 participants (age: mean ± standard deviation = 56 ± 9.0 years), 84% identified as White, 10% Black, and 5% Latina. Approximately half (49%) attained a college education. Weight and height were generally underreported. Higher underreporting of weight among participants with ≥college vs. ≤high school was of larger magnitude among Black and Latina vs. White participants. Bland-Altman results revealed that agreement in continuous BMI was good among White participants but generally fair among Black and Latina participants. Categorical BMI agreement was consistently high with minor variation by race and ethnicity and educational attainment (weighted k range = 0.92-0.93).

CONCLUSIONS

Despite higher measurement error in weight among Black and Latina participants with ≥college education, self-reported and objectively measured BMI categories showed high agreement across groups. Results support the utility of self-reported data that reliably estimate BMI category across socioeconomic, racial, and ethnic groups in this cohort.

Relationship between BMI trajectories and cardiometabolic outcomes in postmenopausal women: a growth mixture modeling approach

PURPOSE

The objective of this manuscript is to identify longitudinal trajectories of change in body mass index (BMI) after menopause and investigate the association of BMI trajectories with risk of diabetes and cardiovascular disease (CVD) among postmenopausal women.

METHODS

Using data from 54,073 participants in the Women's Health Initiative (WHI) clinical trials, we used growth mixture modeling (GMM) to develop BMI trajectories. Cox proportional hazards models were used to examine the relationship between BMI trajectories with incident diabetes and CVD. Further, we stratified by hormone therapy trial arm and time since menopause.

RESULTS

Using GMM, we identified five BMI trajectories. We did not find evidence of substantial change in BMI over time; the trajectories were stable over the study follow-up period in this sample of postmenopausal women. Risk of diabetes and CVD increased by BMI trajectory; risk was greater for women in moderate-high, high, and very high BMI trajectories compared to those in the lowest trajectory group.

CONCLUSIONS

Despite minimal change in BMI over the follow-up period, our results demonstrate a strong association of high BMI with diabetes and CVD. These results highlight the importance of further longitudinal research focused on adverse health effects of BMI in older women.

Stratified Probabilistic Bias Analysis for Body Mass Index-related Exposure Misclassification in Postmenopausal Women

BACKGROUND

There is widespread concern about the use of body mass index (BMI) to define obesity status in postmenopausal women because it may not accurately represent an individual's true obesity status. The objective of the present study is to examine and adjust for exposure misclassification bias from using an indirect measure of obesity (BMI) compared with a direct measure of obesity (percent body fat).

METHODS

We used data from postmenopausal non-Hispanic black and non-Hispanic white women in the Women's Health Initiative (n=126,459). Within the Women's Health Initiative, a sample of 11,018 women were invited to participate in a sub-study involving dual-energy x-ray absorptiometry scans. We examined indices of validity comparing BMI-defined obesity (≥30 kg/m), with obesity defined by percent body fat. We then used probabilistic bias analysis models stratified by age and race to explore the effect of exposure misclassification on the obesity-mortality relationship.

RESULTS

Validation analyses highlight that using a BMI cutpoint of 30 kg/m to define obesity in postmenopausal women is associated with poor validity. There were notable differences in sensitivity by age and race. Results from the stratified bias analysis demonstrated that failing to adjust for exposure misclassification bias results in attenuated estimates of the obesity-mortality relationship. For example, in non-Hispanic white women 50-59 years of age, the conventional risk difference was 0.017 (95% confidence interval = 0.01, 0.023) and the bias-adjusted risk difference was 0.035 (95% simulation interval = 0.028, 0.043).

CONCLUSIONS

These results demonstrate the importance of using quantitative bias analysis techniques to account for nondifferential exposure misclassification of BMI-defined obesity. See video abstract at, http://links.lww.com/EDE/B385.

The impact of maternal smoking during pregnancy on childhood asthma: adjusted for exposure misclassification; results from the National Health and Nutrition Examination Survey, 2011-2012

PURPOSE

We sought to examine the association between childhood asthma and self-reported maternal smoking during pregnancy (MSDP) after adjusting for a range of exposure misclassification scenarios using a Bayesian approach that incorporated exposure misclassification probability estimates from the literature.

METHODS

Self-reported MSDP and asthma data were extracted from National Health and Nutrition Examination Survey 2011-2012. The association between self-reported MSDP and asthma was adjusted for exposure misclassification using a Bayesian bias model approach.

RESULTS

We included 3074 subjects who were 1-15 years of age, including 492 asthma cases. The mean (SD) of age of the participants was 8.5 (4.1) and 7.1 (4.2) years and the number (percentage) of female was 205 (42%) and 1314 (51%) among asthmatic and nonasthmatic groups, respectively. The odds ratio (OR) for the association between self-reported MSDP and asthma in logistic regression adjusted for confounders was 1.28 (95% confidence interval: 0.92, 1.77). In a Bayesian analysis that adjusted for exposure misclassification using external data, we found different ORs between MSDP and asthma by applying different priors (posterior ORs 0.90 [95% credible interval {CRI}: 0.47, 1.60] to 3.05 [95% CRI: 1.73, 5.53] in differential and 1.22 [CRI 95%: 0.62, 2.25] to 1.60 CRI: 1.18, 2.19) in nondifferential misclassification settings.

CONCLUSIONS

Given the assumptions and the accuracy of the bias model, the estimated effect of MSDP on asthma after adjusting for misclassification was strengthened in many scenarios.