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Prediction equations do not eliminate systematic error in self-reported body mass index
M W Plankey1, J Stevens, K M Flegal
1National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, MD 20782, USA.
Obesity Research
|July 1, 1997
Summary
Self-reported body mass index (BMI) data contains systematic errors that vary with actual BMI. Linear regression models struggle to correct these reporting inaccuracies in obesity research.
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Epidemiological studies frequently rely on self-reported height and weight to calculate body mass index (BMI).
- Accurate BMI measurement is crucial for understanding obesity risks and related health outcomes.
Purpose of the Study:
- To investigate the systematic errors in self-reported BMI values.
- To assess the effectiveness of linear regression models in correcting these self-reporting errors and predicting measured BMI.
Main Methods:
- Utilized data from 5079 adults (30-64 years) from the second National Health and Nutrition Examination Survey.
- Compared measured and self-reported height and weight to calculate BMI (kg/m2).
- Employed multiple linear regression to predict measured BMI from self-reported BMI and analyze error patterns.
Main Results:
- Self-reported BMI errors were not random but systematically varied with actual BMI (correlation of -0.37 for men, -0.38 for women).
- The error pattern showed weak association with self-reported BMI itself.
- Linear regression models demonstrated limited success in accurately predicting measured BMI, failing to eliminate systematic reporting errors.
Conclusions:
- The inherent systematic error in self-reported BMI poses significant challenges for epidemiological research.
- Linear regression models are insufficient to fully correct for the characteristic reporting errors in BMI data.
- Future research may need alternative methods to obtain accurate BMI measurements for obesity studies.