title: The usefulness of "corrected" body mass index vs. self-reported body mass index: comparing the population distributions, sensitivity, specificity, and predictive utility of three correction equations using Canadian population-based data.
abstract: BACKGROUND
  National data on body mass index (BMI), computed from self-reported height and weight, is readily available for many populations including the Canadian population. Because self-reported weight is found to be systematically under-reported, it has been proposed that the bias in self-reported BMI can be corrected using equations derived from data sets which include both self-reported and measured height and weight. Such correction equations have been developed and adopted. We aim to evaluate the usefulness (i.e., distributional similarity; sensitivity and specificity; and predictive utility vis-à-vis disease outcomes) of existing and new correction equations in population-based research.
  
  
  METHODS
  The Canadian Community Health Surveys from 2005 and 2008 include both measured and self-reported values of height and weight, which allows for construction and evaluation of correction equations. We focused on adults age 18-65, and compared three correction equations (two correcting weight only, and one correcting BMI) against self-reported and measured BMI. We first compared population distributions of BMI. Second, we compared the sensitivity and specificity of self-reported BMI and corrected BMI against measured BMI. Third, we compared the self-reported and corrected BMI in terms of association with health outcomes using logistic regression.
  
  
  RESULTS
  All corrections outperformed self-report when estimating the full BMI distribution; the weight-only correction outperformed the BMI-only correction for females in the 23-28 kg/m2 BMI range. In terms of sensitivity/specificity, when estimating obesity prevalence, corrected values of BMI (from any equation) were superior to self-report. In terms of modelling BMI-disease outcome associations, findings were mixed, with no correction proving consistently superior to self-report.
  
  
  CONCLUSIONS
  If researchers are interested in modelling the full population distribution of BMI, or estimating the prevalence of obesity in a population, then a correction of any kind included in this study is recommended. If the researcher is interested in using BMI as a predictor variable for modelling disease, then both self-reported and corrected BMI result in biased estimates of association.
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada. djdutton@ucalgary.ca.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Dutton
authors.forename: Lindsay
authors.initials: L
authors.lastname: McLaren
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D001835:Body Weight; D001837:Body Weights and Measures / Q000379:methods* / Q000706:statistics & numerical data; D002170:Canada / Q000453:epidemiology; D003710:Demography / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D006306:Health Surveys / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology*; D016017:Odds Ratio; D015995:Prevalence; D057566:Self Report; D012680:Sensitivity and Specificity; D055815:Young Adult