The general self-rated health (SRH) question is the most common health measure employed in large population surveys. This study contributes to research on the concurrent validity of SRH using representative data with biomarkers from the Czech Republic, a population not previously used to assess the SRH measure. This work determines the relative contribution of biomedical and social characteristics to an individual’s SRH assessment. Studies have already explored the associations between SRH and markers of physical health. However, according to a PubMed systematic literature search, the issue of the relative importance of physiological and psychosocial factors that affect individuals’ assessments of their SRH has generally been neglected.
Using data from a specialized epidemiological survey of the Czech population (N = 1021), this study adopted ordinary least squares regression to analyze the extent to which variance in SRH is explained by biomedical measures, mental health, health behavior, and socioeconomic characteristics. This analysis showed that SRH variance can be largely attributed to biomedical and psychological measures. Socioeconomic characteristics (i.e. marital status, education, economic activity, and household income) contributed to around 5% of the total variance. After controlling for age, sex, location, and socioeconomic status, biomarkers (i.e. C-reactive protein, blood glucose, triglyceride, low-density lipoprotein, and high-density lipoprotein), number of medical conditions, and current medications explained 11% of the total SRH variance. Mental health indicators contributed to an additional 9% of the variance. Body mass index and health behaviors (i.e. smoking and alcohol consumption) explained less than 2% of the variance.
The results suggested that SRH was a valid measure of physiological and mental health in the Czech sample, and the observed differences were likely to have reflected inequalities in bodily and mental functions between social groups.
Citation: Hamplová D, Klusáček J, Mráček T (2022) Assessment of self-rated health: The relative importance of physiological, mental, and socioeconomic factors. PLoS ONE 17(4): e0267115. https://doi.org/10.1371/journal.pone.0267115
Editor: Ellen L. Idler, Emory University, School of Public Health, UNITED STATES
Received: May 3, 2021; Accepted: April 3, 2022; Published: April 18, 2022
Copyright: © 2022 Hamplová et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data have been deposited to the public data depository (Czech Social Science Data Archive) that is part of the Consortium of European Social Science Data Archives (CESSDA). It provides open access to social science data for non-commercial use. The data file is accessible at the section “Health Surveys”, data ID: CSDA00288 at http://nesstar.soc.cas.cz/webview/.
Funding: This work was supported by the Czech Science Foundation (project 22-09220S). Data were acquired through and are deposited in the Czech Social Science Data Archive (ČSDA). The ČSDA research infrastructure project is supported by the Ministry of Education, Youth and Sports within the framework of grant LM2018135. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank to all the participants who took part in the study and provided blood samples. They are also grateful to employees of SYNLAB for the blood sampling and analyses of biomarkers and to agency CVVM for questionnaire data collection.
Competing interests: The authors declare no competing interests.
The general self-rated health (SRH) question is the most common health measure employed in large population surveys. One reason for its popularity is the assumption that SRH has high validity as a measure of “objective” health [1]. Studies from various countries and social contexts have demonstrated that SRH is a consistent predictor of mortality as the most objective criterion of “true” health [2,3]. Importantly, the predictive power of SRH with respect to mortality persists even after adjusting for more objective indicators of health, such as biomarkers [4,5]. However, despite the general acceptance of SRH, there is evidence that response styles and validity of SRH might vary across countries [6,7] and that this indicator might be problematic when used as a measure of “true” health in some cases [6–9]. Thus, it is imperative to explore the validity and meaning of SRH in different social contexts and countries and not to assume its validity based on samples from elsewhere.
Moreover, although SRH is widely used, the discussion on its meaning continues. On the one hand, SRH is consistently associated with many indicators of physical health, including cardiovascular diseases, glycemic markers, markers of the autonomic nervous system, hemoglobin, white cell counts, blood pressure, cholesterol levels, BMI, and inflammatory markers [10–19]. For this reason, SRH is viewed as a reliable and valid measure of illness and objective medical burden. On the other hand, growing empirical evidence shows that individuals’ assessments of their own health are contingent on their social experiences [20,21]. Thus, studies have demonstrated that a respondent’s perception of health and how they respond to SRH questions might be affected by their health expectations, sex, culture, personality, education, social norms, believing that their work is meaningful, self-concept of being a healthy or unhealthy person, and other factors [6,22–25].
This paper offers two contributions to the existing literature. First, it raises a fundamental question of the validity of SRH using a wide range of indicators. Although studies have tested the concurrent validity of SRH based on physiological or psychosocial correlates, there is a dearth of research on the relative importance of these domains. In general, studies have tended to focus on either the association between SRH and “objective” markers of physical health [10–16] or the association between SRH and various socio-demographic characteristics [14,20,26]. However, according to a systematic PubMed literature search, the relative importance of the physiological and psychosocial factors that affect individuals’ assessments of their SRH has not yet been investigated. Therefore, the following analyses were conducted to determine how much variance in SRH can be explained by biomedical, psychological, and social indicators. This is an important issue, as SRH has been widely used in previous studies as a measure of the social determinants of health and as an indicator for measuring social inequalities in health [27–31]. The common assumption of these studies was that differences in SRH reflect inequalities in “true” health [32]. If this analysis showed that SRH is predicted more by some social characteristics rather than by direct measures of health, such a result would warrant caution in dealing with SRH.
Second, the study used data from the Czech Republic. To our knowledge, no study has tested the concurrent validity of SRH, i.e. the extent to which this indicator correlates with established measures of health, using biomedical data in this country. We believe that it is important to analyze the validity of SRH in different contexts as response styles and predictive power of SRH significantly vary across countries. In the Czech Republic, SRH has been studied in various national and comparative studies, including studies of the general population [33–37], immigrants [38], and school-aged children [39] but these studies did not address the issue of the validity of this indicator. The validity of SRH was tested by Baćak and Ólafsdóttir [40] using data from the 2014 European Social Survey, which included data from the Czech Republic. However, the study observed relied exclusively on self-reports of health problems and did not estimate the relative contribution of biomedical, mental, and social correlates. In the current study, we report associations between SRH and various measures of health, including biomarkers, and we focus on the proportion of SRH variance explained by these measures. Thus, we adopt Borsboom et al.’s [41] concept of validity maintaining that an indicator is a valid measure of the outcome if the indicator produces variations in the outcome.
The study population was defined based on the QUALITAS—Wellbeing in health and disease survey. A total of 1056 individuals, aged 18 years or older, residing in Prague (capital, 1.4 million inhabitants) and České Budějovice and surroundings (100,000 inhabitants) in Southern Bohemia, participated in the study. They were selected for the face-to-face interviews via quota sampling (i.e. sex, age, education, place of residence, and community size) based on the 2011 Czech population and housing census. The study followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Institute for Clinical and Experimental Medicine and Thomayer Hospital in Prague (study number G-16–05–02). Written informed consent was obtained from each participant who provided blood samples prior to enrolment in the study after an explanation of the study procedures.
The participants were asked to provide a fasting blood sample and to participate in the survey related to their health and socioeconomic status. The questionnaires were administered via face-to-face interviews. Because the participants were selected by quota sampling, there were no missing values for sex, age, education, place of residence, and community size. As for other covariates (i.e. biomarkers, reported health problems, economic activity, sleep quality, alcohol consumption, and smoking), the proportion of missing values was small (< 1%). In total, 35 respondents (< 3.5% of the sample) were dropped from the analysis because of missing information for at least one of these variables.
Personal income, the only variable with a large number of missing values (21%), was dealt with as follows. Initially, the model was only estimated for respondents who had answered the question. However, to acknowledge that a subsample with non-missing values differed from the full sample—the refusal was more common among men and the economically active population—two other strategies were employed. To deal with a large amount of missing data, the multiple imputation method was employed. This method, an iterative form of stochastic imputation, uses the distribution of observed data to estimate multiple values for missing information. Multiple plausible values are produced to reflect the uncertainty of the true value [42]. However, as this study primarily addressed how much SRH variance is explained by various sets of indicators, the standard method of applying multiple imputations cannot be used due to limitations in calculating the share of explained variance (R 2 ) in imputed datasets. Thus, we did not use the full imputation model. Instead, we used the multiple imputation method to produce 25 plausible values for the missing responses for personal income and calculated the mean of these plausible values, which was subsequently entered into an ordinary least squares (OLS) regression. The disadvantage of this approach is that it fails to account for uncertainty due to the missing information. Thus, for the final step, we used all 25 imputed values to estimate 25 regression models to produce 25 “plausible” values for the explained variance (R 2 ). The distribution of this new variable was then reported (see S1 Appendix).
The characteristics of the analytical sample are shown in Table 1. The ages ranged from 18 to 94, with a mean age of 44.6 (SD 16.0). Compared with the 2016 population statistics [43], where the mean age of the adult population in Prague and South Bohemia was 48.5, our sample was slightly younger. This might be partly due to not targeting an institutionalized population, only those living in private dwellings. In a supplementary analysis (not included here), we re-ran the models with an upper age limit of 80, but there was no difference in the results compared with using the age-restricted sample. Compared to the population statistics [43], women were overrepresented in our sample (57.7% in the QUALITAS sample and 51.8% in the population statistics). All the models controlled for age, sex, and location (i.e. Prague vs. South Bohemia).