A model of errors in BMI based on self-reported and measured anthropometrics with evidence from Brazilian data

The economics of obesity literature implicitly assumes that measured anthropometrics are error-free and they are often treated as a gold standard when compared to self-reported data. We use factor mixture models to analyse measurement error in both self-reported and measured anthropometrics with nationally representative data from the 2013 National Health Survey in Brazil. A small but statistically significant fraction of measured anthropometrics are attributed to recording errors, while, as they are imprecisely recorded and due to reporting behaviour, only between 10 and 23% of our self-reported anthropometrics are free from any measurement error. Post-estimation analysis allows us to calculate hybrid anthropometric predictions that best approximate the true body weight and height distribution. BMI distributions based on the hybrid measures do not differ between our factor mixture models, with and without covariates, and are generally close to those based on measured data, while BMI based on self-reported data under-estimates the true BMI distribution. “Corrected self-reported BMI” measures, based on common methods to mitigate reporting error in self-reports using predictions from corrective equations, do not seem to be a good alternative to our “hybrid” BMI measures. Analysis of regression models for the association between BMI and health care utilization shows only small differences, concentrated at the far-right tails of the BMI distribution, when they are based on our hybrid measure as opposed to measured BMI. However, more pronounced differences are observed, at the lower and higher tails of BMI, when these are compared to self-reported or “corrected self-reported” BMI.