Repository logo
 
Publication

Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience

2021-04-08Journal article Open access
Simple item page
dc.contributor.authorKislaya, Irina
dc.contributor.authorLeite, Andreia
dc.contributor.authorPerelman, Julian
dc.contributor.authorMachado, Ausenda
dc.contributor.authorTorres, Ana Rita
dc.contributor.authorTolonen, Hanna
dc.contributor.authorNunes, Baltazar
dc.date.accessioned2021-05-10T15:14:03Z
dc.date.available2021-05-10T15:14:03Z
dc.date.issued2021-04-08
dc.description.abstractBackground: Accurate data on hypertension is essential to inform decision-making. Hypertension prevalence may be underestimated by population-based surveys due to misclassification of health status by participants. Therefore, adjustment for misclassification bias is required when relying on self-reports. This study aims to quantify misclassification bias in self-reported hypertension prevalence and prevalence ratios in the Portuguese component of the European Health Interview Survey (INS2014), and illustrate application of multiple imputation (MIME) for bias correction using measured high blood pressure data from the first Portuguese health examination survey (INSEF). Methods: We assumed that objectively measured hypertension status was missing for INS2014 participants (n = 13,937) and imputed it using INSEF (n = 4910) as auxiliary data. Self-reported, objectively measured and MIME-corrected hypertension prevalence and prevalence ratios (PR) by sex, age group and education were estimated. Bias in self-reported and MIME-corrected estimates were computed using objectively measured INSEF data as a gold-standard. Results: Self-reported INS2014 data underestimated hypertension prevalence in all population subgroups, with misclassification bias ranging from 5.2 to 18.6 percentage points (pp). After MIME-correction, prevalence estimates increased and became closer to objectively measured ones, with bias reduction to 0 pp - 5.7 pp. Compared to objectively measured INSEF, self-reported INS2014 data considerably underestimated prevalence ratio by sex (PR = 0.8, 95CI = [0.7, 0.9] vs. PR = 1.2, 95CI = [1.1, 1.4]). MIME successfully corrected direction of association with sex in bivariate (PR = 1.1, 95CI = [1.0, 1.3]) and multivariate analyses (PR = 1.2, 95CI = [1.0, 1.3]). Misclassification bias in hypertension prevalence ratios by education and age group were less pronounced and did not require correction in multivariate analyses. Conclusions: Our results highlight the importance of misclassification bias analysis in self-reported hypertension. Multiple imputation is a feasible approach to adjust for misclassification bias in prevalence estimates and exposure-outcomes associations in survey data.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationArch Public Health. 2021 Apr 8;79(1):45. doi: 10.1186/s13690-021-00562-ypt_PT
dc.identifier.doi10.1186/s13690-021-00562-ypt_PT
dc.identifier.issn0778-7367
dc.identifier.urihttp://hdl.handle.net/10400.18/7727
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherBMC/ Belgian Public Health Associationpt_PT
dc.relation.publisherversionhttps://archpublichealth.biomedcentral.com/articles/10.1186/s13690-021-00562-ypt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectSurveypt_PT
dc.subjectMisclassification biaspt_PT
dc.subjectSelf-reportpt_PT
dc.subjectINSEFpt_PT
dc.subjectMultiple Imputationpt_PT
dc.subjectHypertensionpt_PT
dc.subjectBias Correctionpt_PT
dc.subjectMIMEpt_PT
dc.subjectMisclassification Errorpt_PT
dc.subjectEstados de Saúde e de Doençapt_PT
dc.subjectPortugal
dc.titleCombining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experiencept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue1pt_PT
oaire.citation.startPage45pt_PT
oaire.citation.titleArchives of Public Healthpt_PT
oaire.citation.volume79pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Kislaya et al 2021.pdf
Size:
566.73 KB
Format:
Adobe Portable Document Format
Download

Collections

DEP - Artigos em revistas internacionais