Accurate data on the risk factors of non-communicable diseases is essential to build evidence-based prevention programs. In Belgium, this is assessed through self-reported (SR) data from the Belgian health interview surveys (BHIS) in a wide population sample or through objective measures from small-scale surveys (such as the Belgian health examination survey [BHES]). It has been shown, however, that relying on SR data leads to a prevalence underestimation. The objective of this study is to assess the agreement between SR and measured overweight, hypertension and high cholesterol and to provide information to do a valid correction for measurement error.
The BHIS/BHES 2018 database was used (n = 1184). Kappa coefficient was used to assess the agreement between SR and measured hypertension (systolic BP > 140 mmHg, diastolic BP > 90 mmHg, or reported use of medication for hypertension) and ICC was used to assess the agreement between SR and measured BMI. SR high cholesterol was compared to a measured total serum cholesterol >190 mg/dl.
Risk factor prevalence based on SR data is severely underestimated. The agreement between SR and measured data is high for BMI (ICC: 0.92), moderate for hypertension (Kappa: 0.49) and poor for cholesterol (Kappa: 0.05). Using SR data, 45% of the people with a measured hypertension and 22% of the people with a measured high cholesterol are detected. With regressions based on the SR risk factor, age, sex and education, the measured BMI and hypertension can be predicted with a good accuracy (BMI: R2: 87%, HBP: AUC: 86%). A lower accuracy is observed for the cholesterol model (AUC: 65%). Using predicted values instead of SR data yields higher estimates of people suffering from overweight (+8% relative increase), obesity (+12%), hypertension (+24%) and cholesterol (+36%).
Using SR data yields to an underestimation of the prevalence of obesity, hypertension and high cholesterol in Belgium
Relying on SR data to assess the prevalence of overweight, hypertension and high cholesterol requires a correction for measurement error.
Using the predicted values from regression models based on the SR risk factor, age, sex and education, yields higher estimates of people suffering from overweight, hypertension and cholesterol.