ArticleBMC medical research methodology2023
Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey.
Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Assessing the benefits of hypothetical air pollution reduction scenarios on stroke in belgium: a g-computation approach.BMC medical research methodology · 2025Article
- Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease.Statistics in medicine · 2025Article
- Development and validation of intracranial hypertension prediction models based on radiomic features in patients with traumatic brain injury: an exploratory study based on CENTER-TBI data.Critical care (London, England) · 2025Article
- Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.Journal of medical Internet research · 2025Article
- Burden of disease attributable to high body mass index in Belgium: a comparative risk assessment analysis.BMJ public health · 2025Article
- Precision prediction of hyperhomocysteinemia development in perimenopausal women using LASSO regression.Frontiers in reproductive health · 2025Article
- Moderate tea consumption and dementia-related neuroimaging markers.Frontiers in neurology · 2025Article
- Rising socioeconomic disparities in childhood overweight and obesity in Belgium.Archives of public health = Archives belges de sante publique · 2024Article
- The potential impact fraction of population weight reduction scenarios on non-communicable diseases in Belgium: application of the g-computation approach.BMC medical research methodology · 2024Article
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Abstract
backgroundIn many countries, the prevalence of non-communicable diseases risk factors is commonly assessed through self-reported information from health interview surveys. It has been shown, however, that self-reported instead of objective data lead to an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia. This study aimed to assess the agreement between self-reported and measured height, weight, hypertension and hypercholesterolemia and to identify an adequate approach for valid measurement error correction.
methodsNine thousand four hundred thirty-nine participants of the 2018 Belgian health interview survey (BHIS) older than 18 years, of which 1184 participated in the 2018 Belgian health examination survey (BELHES), were included in the analysis. Regression calibration was compared with multiple imputation by chained equations based on parametric and non-parametric techniques.
resultsThis study confirmed the underestimation of risk factor prevalence based on self-reported data. With both regression calibration and multiple imputation, adjusted estimation of these variables in the BHIS allowed to generate national prevalence estimates that were closer to their BELHES clinical counterparts. For overweight, obesity and hypertension, all methods provided smaller standard errors than those obtained with clinical data. However, for hypercholesterolemia, for which the regression model's accuracy was poor, multiple imputation was the only approach which provided smaller standard errors than those based on clinical data.
conclusionsThe random-forest multiple imputation proves to be the method of choice to correct the bias related to self-reported data in the BHIS. This method is particularly useful to enable improved secondary analysis of self-reported data by using information included in the BELHES. Whenever feasible, combined information from HIS and objective measurements should be used in risk factor monitoring.
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