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- 1. Correcting self-reported estimates of obesity: Can we more closely approximate measured values? ArchivedArticles and reports: 11-522-X200800011003Description:
This study examined the feasibility of developing correction factors to adjust self-reported measures of Body Mass Index to more closely approximate measured values. Data are from the 2005 Canadian Community Health Survey where 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 reported and measured values. The sample was then split into two groups. In the first, the self-reported BMI and the predictors of the discrepancies were regressed on the 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 of obesity prevalence. Logistic regression was used to examine the relationship between measured, reported and corrected BMI and obesity-related health conditions. Corrected estimates provided more accurate measures of obesity prevalence, mean BMI and sensitivity levels. Self-reported data exaggerated the relationship between BMI and health conditions, while in most cases the corrected estimates provided odds ratios that were more similar to those generated with the measured BMI.
Release date: 2009-12-03 - Articles and reports: 82-003-X200800210569Geography: CanadaDescription:
Based on a representative sample of the Canadian population, this article quantifies the bias resulting from the use of self-reported rather than directly measured height, weight and body mass index. The analysis is based on 4,567 respondents to the 2005 Canadian Community Health Survey, who provided self-reported values for height and weight and were then measured.
Release date: 2008-05-14 - Articles and reports: 11-522-X200600110431Description:
We describe statistical disclosure control methods (SDC) developed for a public release Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) micro-data file. CHIRPP is a national injury surveillance database managed by the Public Health Agency of Canada (PHAC). After describing CHIRPP, the paper includes a brief overview of basic SDC concepts, as an introduction to the process for selecting and developing the appropriate SDC methods for CHIRPP given its specific challenges and requirements. We then summarize some key results. The paper concludes with a discussion of the implication of this work for the health information field and closing remarks with respect to the some methodological issues for consideration.
Release date: 2008-03-17
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- 1. Correcting self-reported estimates of obesity: Can we more closely approximate measured values? ArchivedArticles and reports: 11-522-X200800011003Description:
This study examined the feasibility of developing correction factors to adjust self-reported measures of Body Mass Index to more closely approximate measured values. Data are from the 2005 Canadian Community Health Survey where 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 reported and measured values. The sample was then split into two groups. In the first, the self-reported BMI and the predictors of the discrepancies were regressed on the 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 of obesity prevalence. Logistic regression was used to examine the relationship between measured, reported and corrected BMI and obesity-related health conditions. Corrected estimates provided more accurate measures of obesity prevalence, mean BMI and sensitivity levels. Self-reported data exaggerated the relationship between BMI and health conditions, while in most cases the corrected estimates provided odds ratios that were more similar to those generated with the measured BMI.
Release date: 2009-12-03 - Articles and reports: 82-003-X200800210569Geography: CanadaDescription:
Based on a representative sample of the Canadian population, this article quantifies the bias resulting from the use of self-reported rather than directly measured height, weight and body mass index. The analysis is based on 4,567 respondents to the 2005 Canadian Community Health Survey, who provided self-reported values for height and weight and were then measured.
Release date: 2008-05-14 - Articles and reports: 11-522-X200600110431Description:
We describe statistical disclosure control methods (SDC) developed for a public release Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) micro-data file. CHIRPP is a national injury surveillance database managed by the Public Health Agency of Canada (PHAC). After describing CHIRPP, the paper includes a brief overview of basic SDC concepts, as an introduction to the process for selecting and developing the appropriate SDC methods for CHIRPP given its specific challenges and requirements. We then summarize some key results. The paper concludes with a discussion of the implication of this work for the health information field and closing remarks with respect to the some methodological issues for consideration.
Release date: 2008-03-17
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