Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.18/7727 |
Resumo: | Background: 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. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experienceSurveyMisclassification biasSelf-reportINSEFMultiple ImputationHypertensionBias CorrectionMIMEMisclassification ErrorEstados de Saúde e de DoençaPortugalBackground: 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.BMC/ Belgian Public Health AssociationRepositório Científico do Instituto Nacional de SaúdeKislaya, IrinaLeite, AndreiaPerelman, JulianMachado, AusendaTorres, Ana RitaTolonen, HannaNunes, Baltazar2021-05-10T15:14:03Z2021-04-082021-04-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.18/7727engArch Public Health. 2021 Apr 8;79(1):45. doi: 10.1186/s13690-021-00562-y0778-736710.1186/s13690-021-00562-yinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-20T15:42:10Zoai:repositorio.insa.pt:10400.18/7727Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:42:21.052492Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
title |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
spellingShingle |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience Kislaya, Irina Survey Misclassification bias Self-report INSEF Multiple Imputation Hypertension Bias Correction MIME Misclassification Error Estados de Saúde e de Doença Portugal |
title_short |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
title_full |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
title_fullStr |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
title_full_unstemmed |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
title_sort |
Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience |
author |
Kislaya, Irina |
author_facet |
Kislaya, Irina Leite, Andreia Perelman, Julian Machado, Ausenda Torres, Ana Rita Tolonen, Hanna Nunes, Baltazar |
author_role |
author |
author2 |
Leite, Andreia Perelman, Julian Machado, Ausenda Torres, Ana Rita Tolonen, Hanna Nunes, Baltazar |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Nacional de Saúde |
dc.contributor.author.fl_str_mv |
Kislaya, Irina Leite, Andreia Perelman, Julian Machado, Ausenda Torres, Ana Rita Tolonen, Hanna Nunes, Baltazar |
dc.subject.por.fl_str_mv |
Survey Misclassification bias Self-report INSEF Multiple Imputation Hypertension Bias Correction MIME Misclassification Error Estados de Saúde e de Doença Portugal |
topic |
Survey Misclassification bias Self-report INSEF Multiple Imputation Hypertension Bias Correction MIME Misclassification Error Estados de Saúde e de Doença Portugal |
description |
Background: 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-05-10T15:14:03Z 2021-04-08 2021-04-08T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.18/7727 |
url |
http://hdl.handle.net/10400.18/7727 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Arch Public Health. 2021 Apr 8;79(1):45. doi: 10.1186/s13690-021-00562-y 0778-7367 10.1186/s13690-021-00562-y |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
BMC/ Belgian Public Health Association |
publisher.none.fl_str_mv |
BMC/ Belgian Public Health Association |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132168275886080 |