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

• Published in: Archives of Public Health
• Main Authors: Kislaya, Irina, Leite, Andreia, Perelman, Julian, Machado, Ausenda, Torres, Ana Rita, Tolonen, Hanna, Nunes, Baltazar
• Other Authors: Comprehensive Health Research Centre (CHRC) - Pólo ENSP, Centro de Investigação em Saúde Pública (CISP/PHRC), Escola Nacional de Saúde Pública (ENSP)
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2021
• Subjects: Bias correction; Hypertension; MIME; Misclassification error; Multiple imputation; Self-reports; Survey; Public Health; Environmental and Occupational Health; SDG 3 - Good Health and Well-being; Norway; Iceland
• Online Access: http://hdl.handle.net/10362/133201; https://doi.org/10.1186/s13690-021-00562-y

Funding Information: The authors are grateful to all the professionals that were involved in the INSEF and INS2014 fieldwork and to all the INSEF and the INS2014 participants. Funding Information: No specific funding was received for this study. The Portuguese National Health Examination Survey 2013–2017 (INSEF) was developed as part of the Pre-defined project of the Public Health Initiatives Program, “Improvement of epidemiological health information to support public health decision and management in Portugal. Towards reduced inequalities, improved health, and bilateral cooperation”, that benefits from a 1.500.000€ Grant from Iceland, Liechtenstein and Norway, through the EEA Grants. Publisher Copyright: © 2021, The Author(s). 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. publishersversion published