Abstract

Dear Editor,
W
The authors present a study that is based on BMI calculations from self-reported data. 1 Self-reported data are cost-effective, rapid, and easy to administer when sampling large number of individuals; however, their validity is questionable.5–7
Self-reported data are systematically biased compared with those from objectively measured data, leading to substantial underestimation of obesity status. 1 Considering the fact that miscalculations vary by multiple potential confounding factors such as age, race, gender, health, socioeconomic status, region, and language, unmeasured residual confounding is likely.5,6 This confounding probably varies unpredictably across studies and thus can bias not only individual studies but also subgroup comparisons.5,6
Corrected BMI could be useful for estimating the prevalence of obesity in a population, but the use of BMI as a predictor variable for modeling disease is associated with biased estimations.5,6 All in all, predictions of future overweight and obesity based on trends in self-reported data are likely to be inaccurate, as the change in reporting bias will affect the apparent increase in self-reported obesity prevalence. 7
Obesity is associated with lower mortality risk with consistent effects noted in multiple studies and analyses attempting to address residual confounding and reverse causation. Therefore, we should not “fly from the facts” and focus on effective treatments because the paradox is not a paradox at all.
Footnotes
Disclosure Statement
No competing financial interests exist.
