Does the use of prediction equations to correct self-reported height and weight improve obesity prevalance estimates? A pooled cross-sectional analysis of Health Survey for England data

Abstract

Objective: Adults typically overestimate height and underestimate weight compared with measured values, and such misreporting varies by sociodemographic and health-related factors. Using self-reported and interviewer-measured height and weight, collected from the same participants, we aimed to develop a set of prediction equations to correct bias in self-reported height and weight, and assess whether this adjustment improved the accuracy of obesity prevalence estimates relative to those based only on self-report. Design: Population-based cross-sectional study. Participants: 38,942 participants aged 16+ (Health Survey for England 2011-16) with non-missing self-reported and interviewer-measured height and weight. Main outcome measures: Comparisons between self-reported, interviewer-measured (gold standard) and corrected (based on prediction equations) body mass index (BMI in kg/m2) including (i) difference between means and obesity prevalence, and (ii) measures of agreement for BMI classification. Results: On average, men overestimated height more than women (1.6 and 1.0cm, respectively; p<0.001), whilst women underestimated weight more than men (-2.1 and -1.5kg, respectively; p<0.001). Underestimation of BMI was larger on average for women than for men ( 1.1 and 1.0kg/m2, respectively; p<0.001). Obesity prevalence based on self-reported BMI was 6.8 and 6.0 percentage points (pp) lower than that estimated using measured BMI for men and women, respectively. Corrected BMI (based on models containing all significant predictors of misreporting of height and weight) lowered underestimation of obesity to 0.8pp in both sexes and improved the sensitivity of being classified as obese over self-reported BMI by 15.0pp for men and 12.2pp for women. Results based on models using age alone as a predictor of misreporting were similar. Conclusions: Compared with self-reported data, applying prediction equations improved the accuracy of obesity prevalence estimates and increased sensitivity of being classified as obese. Including additional sociodemographic variables does not add enough predictive power to justify the added complexity of including them in prediction equations.