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

Abstract

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.

Examination of Sexual Identity Differences in the Prevalence of Hypertension and Antihypertensive Medication Use Among US Adults: Findings From the Behavioral Risk Factor Surveillance System

“…Although several studies have found a moderate association between self-reported and objective measures of hypertension, 43 the use of objective measures is preferred to reduce misclassification of participants with undiagnosed hypertension. 44,45 Further, the BRFSS does not include information about important social determinants, such as experiences of discrimination and violence, that are hypothesized to contribute to hypertension risk in sexual minority adults. 5 Therefore, residual confounding may have biased findings.…”

Why some treated patients with arterial hypertension do not report of having it: Findings from epidemiological studies

“…Proper public health decision-making requires reliable and well-documented data based on extensive research. 2 Misreporting of the prevalence and awareness of HT can lead to inappropriate prevention planning, access to health care, and misallocation of public funding [1][2][3]. Therefore, precise analysis and confirmation of the diagnosis of HT in epidemiological studies is important and often requires a thorough clinical assessment.…”

“…In survey-based research, the respondents may fail to provide the correct answer if they do not understand the question, or if a diagnosis has not been made previously, and may withhold information for other personal reasons [3][4][5][6].…”

“…The percentage of patients who declare the presence of the disease is usually lower than its real prevalence. Therefore, to increase the credibility of data, when preparing population studies, it is recommended to supplement the subjective response of the examined person regarding their health condition with an objective measurement that is easy to interpret by the research team, to confirm the information obtained [3].…”

“…[5]. More frequent contact with healthcare may also contribute to patients' greater awareness of their health condition and the disease they suffer from [3,6]. On the other hand, male gender [3,4,6,7] as well as lower education or lower socio-economic status may be a factor contributing to the unawareness of HT [8].…”