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

被引：7

作者：

Pelgrims, Ingrid ^{[1
,2
,3
]}

Devleesschauwer, Brecht ^{[3
,4
]}

Vandevijvere, Stefanie ^{[3
]}

De Clercq, Eva M. ^{[1
]}

Vansteelandt, Stijn ^{[2
]}

Gorasso, Vanessa ^{[3
,5
]}

van der Heyden, Johan ^{[3
]}

机构：

[1] Sciensano, Serv Risk & Hlth Impact Assessment, Rue Juliette Wytsman 14, B-1050 Brussels, Belgium

[2] Univ Ghent, Appl Math Comp Sci & Stat, Krijgslaan 281,S9, BE-9000 Ghent, Belgium

[3] Sciensano, Dept Epidemiol & Publ Hlth, Rue Juliette Wytsman 14, B-1050 Brussels, Belgium

[4] Univ Ghent, Dept Translat Physiol Infectiol & Publ Hlth, Salisburylaan 133, B-9820 Merelbeke, Belgium

[5] Univ Ghent, Dept Publ Hlth & Primary Care, Corneel Heymanslaan 10, B-9000 Ghent, Belgium

来源：

BMC MEDICAL RESEARCH METHODOLOGY | 2023年 / 23卷 / 01期

关键词：

Measurement error; Multiple imputation; Health interview survey; Health examination survey; Obesity; Hypertension; Hypercholesterolemia; BODY-MASS INDEX; MEASUREMENT-ERROR; HIGH CHOLESTEROL; MISSING DATA; WEIGHT; VALIDITY; OBESITY; HEIGHT; BMI; CLASSIFICATION;

D O I：

10.1186/s12874-023-01892-x

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

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.

引用

页数：15