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Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey

Ingrid Pelgrims (UGent) , Brecht Devleesschauwer (UGent) , Stefanie Vandevijvere, Eva M. De Clercq, Stijn Vansteelandt (UGent) , Vanessa Gorasso (UGent) and Johan Van Der Heyden (UGent)
(2023) BMC MEDICAL RESEARCH METHODOLOGY. 23(1).
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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.
Keywords
Measurement error, Multiple imputation, Health interview survey, Health examination survey, Obesity, Hypertension, Hypercholesterolemia

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MLA
Pelgrims, Ingrid, et al. “Using Random-Forest Multiple Imputation to Address Bias of Self-Reported Anthropometric Measures, Hypertension and Hypercholesterolemia in the Belgian Health Interview Survey.” BMC MEDICAL RESEARCH METHODOLOGY, vol. 23, no. 1, Springer Science and Business Media LLC, 2023, doi:10.1186/s12874-023-01892-x.
APA
Pelgrims, I., Devleesschauwer, B., Vandevijvere, S., De Clercq, E. M., Vansteelandt, S., Gorasso, V., & Van Der Heyden, J. (2023). Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey. BMC MEDICAL RESEARCH METHODOLOGY, 23(1). https://doi.org/10.1186/s12874-023-01892-x
Chicago author-date
Pelgrims, Ingrid, Brecht Devleesschauwer, Stefanie Vandevijvere, Eva M. De Clercq, Stijn Vansteelandt, Vanessa Gorasso, and Johan Van Der Heyden. 2023. “Using Random-Forest Multiple Imputation to Address Bias of Self-Reported Anthropometric Measures, Hypertension and Hypercholesterolemia in the Belgian Health Interview Survey.” BMC MEDICAL RESEARCH METHODOLOGY 23 (1). https://doi.org/10.1186/s12874-023-01892-x.
Chicago author-date (all authors)
Pelgrims, Ingrid, Brecht Devleesschauwer, Stefanie Vandevijvere, Eva M. De Clercq, Stijn Vansteelandt, Vanessa Gorasso, and Johan Van Der Heyden. 2023. “Using Random-Forest Multiple Imputation to Address Bias of Self-Reported Anthropometric Measures, Hypertension and Hypercholesterolemia in the Belgian Health Interview Survey.” BMC MEDICAL RESEARCH METHODOLOGY 23 (1). doi:10.1186/s12874-023-01892-x.
Vancouver
1.
Pelgrims I, Devleesschauwer B, Vandevijvere S, De Clercq EM, Vansteelandt S, Gorasso V, et al. Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey. BMC MEDICAL RESEARCH METHODOLOGY. 2023;23(1).
IEEE
[1]
I. Pelgrims et al., “Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey,” BMC MEDICAL RESEARCH METHODOLOGY, vol. 23, no. 1, 2023.
@article{01GWF9G6XFDJE1KCNEC4XB20V4,
  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.}},
  articleno    = {{69}},
  author       = {{Pelgrims, Ingrid and Devleesschauwer, Brecht and Vandevijvere, Stefanie and De Clercq, Eva M. and Vansteelandt, Stijn and Gorasso, Vanessa and Van Der Heyden, Johan}},
  issn         = {{1471-2288}},
  journal      = {{BMC MEDICAL RESEARCH METHODOLOGY}},
  keywords     = {{Measurement error,Multiple imputation,Health interview survey,Health examination survey,Obesity,Hypertension,Hypercholesterolemia}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{15}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey}},
  url          = {{http://doi.org/10.1186/s12874-023-01892-x}},
  volume       = {{23}},
  year         = {{2023}},
}
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