The feasibility of establishing correction factors to adjust self-reported estimates of obesity.
BACKGROUND
This study examines the feasibility of developing correction factors to adjust self-reported measures of body mass index (BMI) to more closely approximate measured values.


METHODS
Data are from the 2005 Canadian Community Health Survey (subsample 2), in which respondents were asked to report their height and weight, and were subsequently measured. Regression analyses were used to determine which socio-demographic and health characteristics were associated with the discrepancies between self-reported and measured values. The sample was then split into two groups. In the first, self-reported BMI and the predictors of the discrepancies were regressed on measured BMI. Correction equations were generated using all predictor variables that were significant at the p < 0.05 level. These correction equations were then tested in the second group to derive estimates of sensitivity, specificity and obesity prevalence. Logistic regression was used to examine relationships between self-reported, measured and corrected BMI and obesity-related health conditions.


RESULTS
Corrected estimates provide more accurate measures of obesity prevalence, mean BMI and sensitivity levels (percentage correctly classified). In almost all cases, associations between BMI and health conditions are more accurate when based on corrected versus self-reported values.
Connor Gorber
Sarah
S
Health Information and Research Division, Statistics Canada, Ottawa. Sarah.ConnorGorber@statcan.gc.ca
Shields
Margot
M
Tremblay
Mark S
MS
McDowell
Ian
I
D000293:Adolescent; D000328:Adult; D000368:Aged; D015992:Body Mass Index; D001837:Body Weights and Measures; D002170:Canada; D003430:Cross-Sectional Studies; D005240:Feasibility Studies; D006304:Health Status; D006801:Humans; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology*; D015995:Prevalence; D012044:Regression Analysis; D012650:Self Disclosure; D011795:Surveys and Questionnaires