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Effects of measurement on obesity and morbidity

Margot Shields, Sarah Connor Gorber and Mark S. Tremblay

Abstract
Keywords
Findings
Authors
Why is this study important?
What else is known on this topic?
What does this study add?

Abstract

Objectives

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This article compares associations between body mass index (BMI) categories based on self-reported versus measured data with selected health conditions. The goal is to see if the misclassifications resulting from the use of self-reported data alters associations between excess body weight and these health conditions.

Methods

The analysis is based on 2,667 respondents aged 40 years or older from the 2005 Canadian Community Health Survey (CCHS) who, during a face-to-face interview, provided self-reported values for height and weight and were then measured by trained interviewers. Multiple logistic regression analysis was used to examine associations between BMI categories (based on self-reported and measured data) and obesity-related health conditions.

Results

On average, BMI based on self-reported height and weight was 1.3 kg/m² lower than BMI based on measured values. Consequently, based on self-reported data, a substantial proportion of individuals with excess body weight were erroneously placed in lower BMI categories. This misclassification resulted in elevated associations between overweight/obesity and morbidity.

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Keywords

body mass index, measurement error, misclassification, self-report, sensitivity and specificity, validity

Findings

Numerous studies from around the world have documented associations between excess body weight and a wide range of chronic conditions, including type 2 diabetes, cardiovascular disease, hypertension, gallbladder disease and certain types of cancer. In these studies, it is common practice to use body mass index (BMI) categories to examine health risks of excess weight. BMI is a measure of an individual's weight in relation to height and is a simple way of measuring excess weight in population health surveys. [Full text]

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Authors

Margot Shields (613-961-4177; Margot.Shields@statcan.gc.ca) and Sarah Connor Gorber (613-951-1193; Sarah.Connorgorber@statcan.gc.ca) are with the Health Information and Research Division, and Mark S. Tremblay (613-951-4385; Mark.Tremblay@statcan.gc.ca) is with the Physical Health Measures Division at Statistics Canada, Ottawa, Ontario, K1A 0T6.

Why is this study important?

• The practice of collecting self-reported data for height and weight is a fiscal necessity for large-scale health surveys conducted at Statistics Canada.
• It is important to examine the extent to which the use of self-reported data alters our understanding of the associations between excess body weight and morbidity.

What else is known on this topic?

• Many studies have found that self-reported data yield lower estimates of the prevalence of obesity, compared with estimates based on measured data, but few studies have examined the effect of the misclassification bias on the relationship between BMI categories and obesity-related health conditions.

What does this study add?

• Misclassification that occurred when BMI categories were derived from self-reported data resulted in erroneously elevated associations between overweight and obesity and obesity-related health conditions.

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