Assessment of self-rated health: The relative importance of physiological, mental, and socioeconomic factors.

BACKGROUND: 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. METHODOLOGY/PRINCIPAL
FINDINGS: 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.
CONCLUSIONS/SIGNIFICANCE: 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.

Introduction

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.

Materials and methods

Study population and design

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 (R2) 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 (R2). The distribution of this new variable was then reported (see S1 Appendix).

Sample characteristics

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).

Table 1

Sample characteristics.

Age	18–29	22.4
	30–44	29.4
	45–59	26.1
	60+	22.2
	Mean	44.6
Sex	Male	42.3
	Female	57.7
Education	Primary	8.0
	Occupational	23.5
	HS	40.9
	Tertiary	27.6
Marital status	Single	37.4
	Married	39.3
	Cohabiting	23.3
Economic activity	Not in labor force	36.2
	In labor force	63.8
Personal income	No income	5.0
	up to 9 999 CZK	11.3
	10 000–19 999 CZK	36.4
	20 000–29 999 CZK	20.4
	30 000–49 999 CZK	5.5
	50 000+ CZK	1.2
	No answer	20.1

Source: QUALITAS 2016/2017 survey (N = 1021).

As for the other sample characteristics, 8.0% of the respondents did not finish any type of high school, while 27.6% held a tertiary level degree. Compared to the 2011 Census, our sample slightly underrepresented lower educational groups and overrepresented those with general secondary and tertiary educations. Furthermore, 63.8% of the respondents were economically active, whereas 36.2% were out of the labor force. This category incorporated mainly retirees and students, over two-thirds of the non-active population, but also included women on parental leave, housewives, the unemployed, and individuals on disability pensions. Of the respondents, 37.4% were not living with a partner, 39.3% were married and living with their spouse, and 23.3% were living with a partner without being married (see Table 1).

Dependent variable

Self-rated health was assessed using a single item: “How is your health in general? Would you say your health is …” The response categories were 1) very good, 2) good, 3) fair, 4) bad, and 5) very bad. As only two people reported very bad health, the last two categories were merged. In the regression models, the scale was reversed, so higher values indicated better health.

Independent variables

The independent variables were divided into the following categories: self-reported measures of physical health, self-reported measures of mental health, health behaviors, indicators of socioeconomic status, and biomarkers.

To evaluate the respondents’ health status, a list of seven common health conditions was assembled, and the study participants were asked to state whether they had even been diagnosed with the conditions (yes or no). The list comprised the following items: high level of cholesterol; cardiovascular problems (including heart attack or coronary thrombosis); stroke or any kind of cerebrovascular accident; diabetes or high blood sugar; Parkinson’s disease; liver conditions or liver cirrhosis; and cancer or malignant tumor, including leukemia and lymphoma (except for minor skin tumors). Using these items, a summary index indicating the number of diagnoses was produced (min = 0, max = 3; mean = 0.47, and SD = 0.76). In the supplementary analysis, we tested the possibility that the SRH was affected not only by the number of health conditions but also by the specific combination of conditions. However, this hypothesis was not confirmed, and the number of conditions was clearly shown to be the best predictor of SRH.

Furthermore, the respondents were asked to report all the medications they were currently taking. The reported medications were coded into 50 drug classes. This information was used to calculate the number of drug classes that the respondent was being treated with.

In addition, the respondents provided information on their height and weight, from which their body mass index (BMI) was derived (min = 16.0, max = 50.7, mean = 26.1, and SD = 4.9).

Self-reported mental health was assessed with four items adapted from the Centre of Epidemiological Studies Depression (CES-D) scale, which is commonly used to measure depressive symptoms in large population surveys [44]. The respondents were asked: “How much of the time during the past week … you felt depressed; you felt that everything you did was an effort; you felt sad; you felt that you could not get going?” The response categories were 1) none or almost none of the time, 2) some of the time, 3) most of the time, and (4) all or almost all of the time. Cronbach’s alpha confirmed that these items had high internal consistency (alpha = 0.80). The original CES-D scale contains an item on sleep quality. While this indicator was not used in the QUALITAS study, the dataset included the question, “How do you rate the quality of your sleep?” The response categories were 1) very good, 2) rather good, 3) rather bad, and 4) very bad (mean = 2.00; SD = 0.773). Even though this variable differed from the original CES-D sleep quality item, we tested the possibility of including it among the mental health measures. This decision was motivated not only by the full CES-D scale containing a sleep item but also by existing research demonstrating that sleep disturbances and mental health are closely related [45-47]. To validate the scale, we ran a measurement model using SEM confirmatory factor analysis, which confirmed very high internal consistency (RMSEA 0.038; CFI 0.996; TLI 0.989; see Table A1 in S1 Appendix). Thus, to create a single indicator of mental health, we used the predicted values from this model (min = ˗0.713; max = 1.7000; mean = 0; SD = 0.46). The higher the value, the more frequent the participants’ depressive symptoms.

We also included some behavioral indicators likely to be linked to SRH, including, in particular, smoking and alcohol consumption. In the sample, 25.3% of the respondents reported current smoking. Former smokers were coded as non-smokers. Alcohol consumption was measured by the number of events when the respondent had felt strongly under the influence of alcohol in the last 6 months. Given that the variable was highly skewed, it was recorded as a categorical variable with four levels: never (57.5%), once (15.8%), two to five times (18.1%), and six times or more often (8.6%). In addition, BMI was incorporated into the analysis (mean = 26.0; SD = 4.9).

Socioeconomic status was measured using four indicators. The highest level of education was coded using four categories: primary (comparative category), occupational secondary school, general secondary education, and tertiary/university education. These categories reflected the main divisions of the Czech educational system. Marital status was coded using three categories: single as a comparative category (i.e. those currently not living with a partner irrespective of their formal marital status), married and living with a spouse, and unmarried cohabitation (i.e. living with an unmarried partner). Employment status dichotomized respondents into two categories: working (coded as 1) and non-working (coded as 0). Given the very low level of unemployment in the Czech Republic, particularly in the locations where the data were collected (< 2%), it was not possible to further distinguish between various types of inactivity.

The respondents’ monthly net income was measured using 14 categories, which were treated as a linear expression of the underlying income distribution.

Biomarkers

After completing the questionnaire, the participants were invited to the local branch of a commercial laboratory (Synlab) to provide a fasting blood sample. C-reactive protein (CRP), blood glucose, and blood lipids (triglycerides [TG], low-density lipoprotein [LDL], and high-density lipoprotein [HDL]), were determined using routine laboratory analyses.

Two analytical approaches were adopted for the biomarkers. First, we used linear measures for all the biomarkers. The means and standard deviations are reported in Table 2. Graphs showing the distribution of the biomarkers are reported in Fig A1 in S1 Appendix. Second, we produced a set of binary variables that distinguished values under and above the reference level for each indicator.

Table 2

Distribution of biomarkers in the analytical sample as a total and by sex.

	All		Men		Women
	Mean	SD	Mean	SD	Mean	SD
Glukose (mmol/L)	5.25	1.16	5.39	1.23	5.13	1.06
CRP (mg/L)	3.05	3.96	2.59	3.64	3.39	4.17
TG (mmol/L)	1.43	1.07	1.66	1.19	1.27	0.95
HDL (mmol/L)	1.46	0.36	1.30	0.29	1.59	0.36
LDL (mmol/L)	3.37	0.86	3.40	0.85	3.35	0.86
LDL/HDL ratio	2.43	0.84	2.73	0.86	2.21	0.74

Source: QUALITAS 2016/2017 survey (N = 1021).

C-reactive protein (CRP) is an indicator of inflammation and cardiovascular disease (mg/L). While some laboratories are limited by their lower levels of detection [14], this was not our case as we were able to detect CRP levels <1 mg/L. CRP levels >5 mg/L are considered to be high risk and CRP levels >10 mg/L suggest recent or ongoing infection [48]. Seven observations of unusually high CRP values (>34) were dropped from the analysis to avoid possible bias in the regression analysis. To indicate the C-reactive protein risk status, we distinguished between CRP ≥ 5 (coded as 1) and CRP < 5 (coded as 0).

Fasting blood glucose is a marker of diabetes or prediabetes (mmol/L). Fasting blood glucose levels >5.6 mmol/L indicate prediabetes, and glucose levels over 7 nmol/L suggest diabetes [49]. To indicate the glucose status risk, we distinguished between those with a fasting blood glucose ≥ 5.6 (coded as 1) and those with lower levels of glucose (coded as 0).

Triglycerides are a type of fat found in the blood, and as converted calories are stored in the fat cells, TG are predictive of cardiovascular disease [50]. Concentrations >2 mmol/L suggest an increased risk of cardiovascular disease, and concentrations >10 mmol/L indicate an increased risk of acute pancreatitis and, possibly, cardiovascular disease. We dropped one observation from the analysis because it was implausibly high (16 mmol/L). The binary indicator for the triglycerides status contrasted TG ≥ 2 mmol/L with lower levels of triglycerides.

High-density lipoprotein is involved in the transport of cholesterol from peripheral tissues to the liver. Also, HDL particles have anti-oxidant, anti-inflammatory, anti-thrombotic, and anti-apoptotic properties [51]. Reduced HDL cholesterol concentration has been correlated with numerous risk factors, including components of metabolic syndrome [52]. The reference value for HDL cholesterol is 1 mmol/L. We distinguished between those with HDL < 1 mmol/L (coded as 1) and those with HDL ≥ 1 mmol/L (coded as 0).

Low-density lipoprotein carries the majority of the cholesterol in the circulation [51]. It is considered to be the ‘bad cholesterol’ and high LDL cholesterol levels are associated with a higher risk of cardiovascular disease. The level considered to be ‘good’ in healthy people is below 3.4 mmol/L. To indicate the LDL risk status, we distinguished those with LDL ≥ 3.4 mmol/L (coded as 1) and those with HDL < 3.4 mmol/L (coded as 0).

The LDL/HDL ratio as a risk indicator has a greater predictive value than the use of isolated parameters (i.e. LDL, HDL, and total cholesterol) [52]. Individuals with a high LDL/HDL ratio have greater cardiovascular risk due to the imbalance between the cholesterol carried by LDL, which carries most of the cholesterol in the circulation, and protective HDL. The LDL/HDL ratio should not be >3.5 for men and >3 for women. Thus, we distinguished between higher and lower LDL/HDL ratios for men and women, respectively.

Using these given reference limits, a composite measure indicating several biomarker levels outside the “normal” range was produced. We were aware that the observed biomarker levels might be affected by the medication the respondents were taking. Thus, we produced an adjusted set of biomarkers that combined information from the blood samples with medications. For example, if a respondent was on anti-diabetes medication, the glucose level was coded as over the limit, even if it was within the healthy range. These adjusted biomarkers were used to produce an adjusted summary index that indicated the number of biomarkers outside the normal healthy range.

Statistical analysis

All the analyses were carried out using STATA, Version 16 (StataCorp). To identify the extent to which health status, mental health, and social factors contribute to variations in SRH, a set of OLS regressions was run to estimate the proportion of variance in SRH explained by each factor. The proportion of explained variance was expressed by R2, which was calculated as 1 minus the proportion of unexplained variance (i.e. the variability of the dependent variable that is not predicted by the model divided by the total variability of the dependent variable).

Our work is inspired by Hiyoshi et al.’s [53] approach to estimating the joint contribution of several variables [also see 54,55]. Similarly to these authors, we analyze contributory factors that are measured by a set of variables, not a single item. The block of variables representing the given contributory factor is entered into the model in a stepwise manner. However, unlike these studies, we are not interested in the question of how various contributory factors attenuate an effect of another explanatory variable. In this study, we focus on the proportion of explained variance of SRH.

Assuming that our SRH measure represented an underlying continuous concept of subjective health and given that dichotomizing the variable would lead to a significant loss of information, we treated SRH as a continuous variable. Moreover, given our aim of estimating the proportion of variance in SRH explained by various factors, we could not use logistic regressions for the binary dependent variable. The pseudo-R2 derived from these models could not be interpreted as a proportion of explained variance because these models were produced using the maximum likelihood method and not calculated to minimize variance. A number of studies on the validity of SRH from other contexts have also used OLS regression [14,40,56-58]. The diagnostics for the reported models are available in Figs A2-A4 in S1 Appendix.

Results

Bivariate analysis results

Fig 1 displays the R2 for each variable and reports the proportion of explained variance from a set of independent models that controlled for age, sex, and location, using binary biomarker measures, both raw and adjusted for medication status. Fig 1 demonstrates that mental health was by far the most important predictor of SRH. By not considering other predictors, mental health explained around 14% of SRH variance. This indicator was followed by the self-reported number of medical conditions (confirmed medical diagnosis) and medications (number of drug classes). Both indicators explained around 7% of the variance. In contrast, socioeconomic characteristics (except for education) only had a weak effect on SRH (around 1% of each explained variance).

Fig 1

The proportion of self-rated health (SRH) variance (R2) explained by each variable after controlling for age, sex, and location (N = 1021).

Note: * binary indicator, ** categorical indicator, Adj. values = values adjusted for medication.

The difference in biomarkers by SRH levels tested using analysis of variance (ANOVA) is presented in Table 3. All models control for age, sex, and location. This table demonstrates that, with exception of triglycerides, respondents reporting different levels of SRH significantly differed in their biomarker levels. Those reporting bad or fair health had higher levels of C-reactive protein and glucose and lower levels of HDL. In contrast, those in very good health had significantly lower LDL/HDL ratios.

Table 3

ANOVA test for biomarker differences by SRH category, predicted values, and contrasts between categories.

	Anova test
	SS	F	Prob>F	R2
CRP—C-reactive protein	553.93	12.28	0.000	0.053
Glucose	22.50	7.50	0.000	0.108
TG—triglycerides	10.99	3.66	0.018	0.058
LDL	10.07	3.36	0.002	0.100
HDL	3.30	1.10	0.000	0.203
LDL/HDL	18.83	6.28	0.000	0.157
	Predicted values				Contrasts (Prob > F)
	1 Bad	2 Fair	3 Good	4 Very good	1:2	2:3	3:4
CRP—C-reactive protein	5.89	3.66	2.76	2.37	0.000	0.003	0.225
Glucose	5.55	5.46	5.14	5.13	0.621	0.000	0.945
TG—triglycerides	1.72	1.53	1.43	1.26	0.275	0.213	0.057
LDL	3.34	3.35	3.47	3.21	0.956	0.068	0.000
HDL	1.39	1.40	1.46	1.57	0.892	0.021	0.000
LDL/HDL	2.57	2.50	2.51	2.17	0.583	0.918	0.000

Note: Df = 3. All models were controlled for age, sex, and location. Multivariate analysis results.

Source: QUALITAS 2016/2017 survey (N = 1021).

Multivariate analysis results

While Fig 1 is useful for descriptive purposes, it does not provide an answer to the question of how much SRH variance is explained by bodily conditions, mental health, health behavior, and socioeconomic factors. For example, age and the number of health problems were correlated (Spearman’s = 0.36; P < 0.0001), but their contribution could not be interpreted in an additive manner. Therefore, we ran multivariate models. First, we focused on the block of variables independently: socioeconomic characteristics, biomarkers, medication, self-reported health measures, mental health indicators, and health behavior. The results from these regressions and robustness checks are reported in (Tables A2–A4 in S1 Appendix). Second, we estimated models that took all these factors simultaneously (see Table 4).

Table 4

Results of ordinary least squares (OLS) regression models with dependent variable SRH, displaying regression coefficients, standardized coefficients (beta), standard errors (in parentheses), and significance level.

	M1		M2		M3		M4		M5		M6
	Coef.	Beta	Coef.	Beta	Coef.	Beta	Coef.	Beta	Coef.	Beta	Coef.	Beta
Age	-0.020**	-0.395**	-0.021**	-0.414**	-0.019**	-0.380**	-0.012**	-0.246**	-0.014**	-0.274**	-0.013**	-0.253**
	(-0.001)		(-0.002)		(-0.002)		(-0.002)		(-0.002)		(-0.002)
Male	0.019	0.011	0.000	0.000	0.065	0.040	0.041	0.025	-0.028	-0.017	-0.023	-0.014
	(-0.047)		(-0.048)		(-0.052)		(-0.050)		(-0.047)		(-0.048)
Location	0.031	0.019	-0.047	-0.029	-0.028	-0.017	0.014	0.009	0.031	0.019	0.026	0.016
	(-0.047)		(-0.047)		(-0.047)		(-0.045)		(-0.042)		(-0.042)
Married (ref. single)			0.033	0.02	0.062	0.038	0.051	0.031	0.025	0.015	0.009	0.005
			(-0.055)		(-0.054)		(-0.051)		(-0.048)		(-0.048)
Cohabiting (ref. single)			-0.027	-0.014	-0.012	-0.006	-0.012	-0.006	-0.041	-0.021	-0.037	-0.019
			(-0.062)		(-0.061)		(-0.058)		(-0.054)		(-0.054)
Occupational secondary			-0.072	-0.038	-0.084	-0.044	-0.063	-0.033	-0.069	-0.036	-0.074	-0.039
(ref. primary education)			(-0.098)		(-0.097)		(-0.092)		(-0.086)		(-0.086)
General secondary			0.147	0.09	0.116	0.070	0.140	0.085	0.120	0.073	0.107	0.065
(ref. primary education)			(-0.094)		(-0.092)		(-0.088)		(-0.082)		(-0.082)
Tertiary			0.212*	0.118*	0.172	0.095	0.211*	0.117*	0.168	0.093	0.146	0.081
(ref. primary education)			(-0.099)		(-0.097)		(-0.093)		(-0.086)		(-0.086)
Economically active			-0.037	-0.022	-0.028	-0.017	-0.082	-0.049	-0.064	-0.038	-0.034	-0.020
			(-0.060)		(-0.059)		(-0.057)		(-0.053)		(-0.053)
Income (imputed)			0.042**	0.161**	0.037**	0.143**	0.030**	0.115**	0.023**	0.088**	0.022**	0.086**
			(-0.010)		(-0.009)		(-0.009)		(-0.008)		(-0.008)
CRP—C-reactive protein					-0.022**	-0.107**	-0.014*	-0.069*	-0.015**	-0.075**	-0.010	-0.048
					(-0.006)		(-0.006)		(-0.005)		(-0.005)
Glucose					-0.057**	-0.080**	-0.020	-0.028	-0.006	-0.008	0.000	-0.001
					(-0.021)		(-0.021)		(-0.019)		(-0.019)
TG—triglycerides					-0.007	-0.009	0.007	0.010	-0.001	-0.001	0.013	0.018
					(-0.025)		(-0.024)		(-0.022)		(-0.023)
LDL–low density					0.073	0.077	0.036	0.038	0.019	0.020	-0.001	-0.001
lipoprotein					(-0.040)		(-0.038)		(-0.036)		(-0.036)
LDL/HDL RATIO					-0.135**	-0.140**	-0.098*	-0.102*	-0.073	-0.076	-0.032	-0.034
–––					(-0.046)		(-0.044)		(-0.041)		(-0.042)
Medication #							-0.128**	-0.202**	-0.113**	-0.178**	-0.104**	-0.164**
							(-0.020)		(-0.019)		(-0.019)
Diagnoses #							-0.195**	-0.184**	-0.146**	-0.137**	-0.144**	-0.135**
							(-0.033)		(-0.031)		(-0.031)
Depressive									-0.550**	-0.312**	-0.534**	-0.303**
									(-0.045)		(-0.045)
BMI											-0.019**	-0.114**
											(0.005)
Alcohol 1 (ref. 0)											-0.007	-0.003
											(0.058)
Alcohol 2–5 (ref. 0)											0.047	0.022
											(0.058)
Alcohol 6+ (ref. 0)											-0.021	-0.007
											(0.078)
Smoker											-0.109*	-0.059*
											(0.049)
Constant	3.728**		3.419**		3.800**		3.549**		3.613**		3.974**
	(-0.074)		(-0.106)		(-0.154)		(-0.149)		(-0.139)		(-0.168)
R2	0.16		0.20		0.24		0.31		0.40		0.41
Adj. R2	0.15		0.19		0.22		0.30		0.39		0.40
BIC	2316.1		2307.2		2296.3		2206.2		2070.4		2086.2

Standardized (beta) and unstandardized coefficients, standard errors, significance tests (t-test: * p < 0.05; ** p < 0.01), Bayesian information criterion (BIC), the proportion of explained variance (R2), and R2 adjusted for degrees of freedom.

Diagnoses #: The number of conditions respondents was diagnosed with.

Medication #: The number of medication groups respondents was treated with.

Source: QUALITAS 2016/2017 survey (N = 1021).

All models used SRH as the dependent variable, and higher values indicated better health. All tables show standardized (beta) and unstandardized coefficients, standard errors, significance tests (t-test: * p < 0.05; ** p < 0.01), Bayesian information criterion (BIC), the proportion of explained variance (R2), and R2 adjusted for degrees of freedom.

Table 4 integrated all blocks of variables (socioeconomic characteristics, biomarkers, medication, self-reported health measures, mental health indicators, and health behavior) in a stepwise manner. Model 1, serving as a baseline model, controlled for age, sex, and location. Age was the only variable that was significantly linked to SRH, and an age difference of 10 years was associated with a 0.2 shift in SRH. Sex and location (Prague vs. South Bohemia) were not significantly associated with SRH at the 0.05 significance level. Importantly, these controls explained 16% of the total SRH variance.

Model 2 entered marital status, education, economic activity, and income. Only higher education (tertiary) and higher income were positively linked to better SRH. Considering standardized coefficients, income is a more important predictor of SRH than education but its effect is still moderate. A one-category shift in income produced a 0.04 shift in SRH. All socioeconomic characteristics together contributed only 4% to the explained variance.

Model 3 incorporated biomarkers. In Table 4, we adopted linear measures of biomarkers. In the Appendix, we report supplementary models that used the binary measures, indicating whether the biomarker was over the reference limit and the binary measures adjusted for the medication (Model 1–3 in Table A3 in Appendix). Irrespective of which measurement was used, data indicated that C-reactive protein, glucose, and LDL/HDL ratio were significantly associated with SRH.

Importantly, adding biomarkers to the model with controls and socioeconomic characteristics increased the proportion of explained variance from 20% to 24% (compare Model 2 with Model 3 in Table 4). Furthermore, once biomarkers were included in the model, education ceased to be significant at the 0.05 level. This means that the observed differences among educational groups can be fully attributed to “objective” biomedical measures of health. Model 4 integrated the number of drug classes and the number of medical conditions that the respondents were diagnosed with. Both variables exerted a strong and independent effect on SRH and contributed another 7 percentage points to the explained SRH variance. Among the biomedical measures, the number of drug classes the respondents were treated with constituted the strongest predictor of SRH, followed by the number of conditions (see standardized coefficients).

Model 5 added self-reported mental health status. Standardized coefficients showed that mental health was the strongest predictor of SRH among all variables in the model and was more important than age. Accordingly, this indicator raised the proportion of explained variance by another 9% to 40% of the total explained variance.

Finally, Model 6 entered BMI and behavioral measures. The association between alcohol consumption and SRH was not significant at the 0.05 level, while both BMI and smoking were negatively linked to SRH. However, the standardized coefficients showed that the importance of smoking was relatively weak. The association between BMI and SRH was approximately twice as large as the link between smoking and SRH. Importantly, although BMI and smoking were negatively associated with SRH, they contributed to only 1% of the explained variance.

Overall, the final model, shown in Table 4, showed that mental health and age were the strongest predictors of SRH. These two indicators were followed by the number of drug classes, medical conditions, and BMI. Among the socioeconomic indicators, only income remained statistically significant at the 0.05 level, but it exerted a lower influence than biomedical measures (see standardized coefficients in Model 6 in Table 4). Altogether, Model 6 (Table 4) explained 41% of the SRH variance.

Discussion and conclusions

This study addressed the question of the concurrent validity of self-rated health in a nationally representative sample from the Czech Republic. Our approach was based on the assumption that an indicator is a valid measure of the outcome if the indicator produces variations in the outcome [41]. In particular, this study addressed the question of the extent to which SRH varies with social conditions compared to health conditions and whether it can be used as an indicator of “true” health in the Czech sample. Thus, we explored how much SRH variance is explained by biomedical, psychological, and social indicators.

The analysis showed that SRH variance can be attributed largely to mental and physical health indicators. This finding suggests that SRH is a valid indicator of “true” health in the Czech sample and that it is a valid and reliable measure of medical burden. At the same time, the results imply that SRH cannot be equated with a narrow biomedical understanding of health. It is rather an indicator of health as a state of complete physical, mental and social well-being. In the bivariate analysis, mental health was by far the most important predictor of SRH (with 14% of variance explained). In the multivariate analysis, biomedical measures (biomarkers, the number of medical conditions, and medication) contributed to around 11% of the variance, while mental health explained around 9% of the variance. Thus, our data suggest that both physical and mental well-being are key dimensions affecting SRH.

The analysis also showed that the social characteristics altogether (marital status, economic activity, education, and income) contributed to only around 5% of explained variance in SRH. However, this does not mean that social inequalities in health are not important in the Czech Republic. Both education and income were significantly linked to SRH and more educated and better off individuals reported better health than those with less education and lower incomes. However, this paper showed that the educational differences ceased to be significant once biomedical and mental health indicators were included in the model. This means that the observed educational differences in SRH reflect differences in “true” health across educational groups. The effect of income persisted after controlling for biomarkers, medication, health conditions, and mental health. Nevertheless, it is important to note that the size of the coefficient for income was reduced significantly once the biomedical and mental status of the individual was considered. The effect was income was particularly visible when the mental health indicator was entered into the model.

This study was not without limitations. First, the data provided only a limited number of biomarkers. Past studies have found that SRH was correlated with markers not available in our dataset, such as hemoglobin and white cell count [15]. Prior research has also reported that heart rate variability was more strongly associated with SRH than inflammatory markers [11]. Such information was also not available in the data. It is possible that more comprehensive measures of respondents’ health status would significantly increase the proportion of explained variance.

Second, this study utilized the WHO version of the SRH item ranging from “very good” to “very bad” that is widely used in European surveys. Compared to the US version ranging from “excellent” to “poor”, the WHO wording of SRH discriminates better at the positive end but generally shows less variation and has a less symmetric distribution [59]. Thus, it is possible that our results are affected by the choice of this particular scale. For example, it is possible that the relative role of sociodemographic characteristics would be more pronounced if the more symmetric US version of SRH was used.

Third, it uses cross-sectional data. Thus, our analysis focuses on associations between SRH and other predictors without addressing the issue of causality. There is an ongoing discussion on stability and change in SRH. On the one hand, SRH might be influenced by the individual’s transitory standing concerning health status. On the other hand, it might be affected by the enduring self-concept of SRH [60].

Fourth, the data were collected using quota sampling. This method selects individuals with a specific demographic profile that matches the target population (i.e., sex, age, education). It is nonprobability (purposive) sampling in which the interviewers have discretion over who is included. Thus, we need to note that the sample might be biased and it is not possible to estimate the sampling error. For example, it is possible that more health-conscious individuals are over-represented in the sample.

Finally, our paper included only a selected number of indicators that were available in the Qualitas data. Thus, some well-known determinants of SRH (such as functional ability, disability) were not included in our model [61]. It is likely that their inclusion would increase the proportion of explained variance but we cannot determine to what extent. Also, functional limitations are likely to reflect both the physical and mental dimensions of health but also socio-economic conditions of life. Thus, future research should aim to include also functional ability in the model.

This appendix contains Figs A1–A4 and Tables A1–A4.

(DOCX)

Click here for additional data file.

11 Aug 2021

PONE-D-21-14590

What contributes to the assessment of Self-Rated Health? The relative importance of physiological, mental, and socio-economic factors

PLOS ONE

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Acknowledgments: This work was supported by the Czech Academy of Sciences, project “QUALITAS - Wellbeing in health and disease”, which is the part of the Strategy of Czech Academy of Sciences AV21: “Top research in the public interest”,. 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 agencies MEDIAN, STEM/MARK and CVVM for questionnaire data collection.

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Reviewer #1: The aim of this study is to determine the relative importance of physical health, mental health, and SES in SRH in a sample of adult respondents from the Czech Republic.

This submission was easy to read. I am really sorry to say, however, that there are major methodological weaknesses.

The first contribution, the authors state, is to confirm the validity of SRH in this sample. It not entirely clear either what this means (what type of validity?), nor how exactly it was approached methodologically. Moreover, it is my impression that Czech respondents were included in a number of pan-European surveys of population health where SRH was the focus. I am sorry – I don’t know this literature but a quick scholar google search brings up a number of papers. Perhaps they aren’t 100% relevant, but a brief literature review on SRH in Czech respondents would be helpful.

The second aim was to determine the relative importance of the three factors (physical health, mental health, and SES). However, the authors did this by estimating nested OLS models and looked at the increases in R squared. There are a number of issues.

1. Quite concerning: the authors state in the abstract that “Biomarkers (C-reactive protein, blood glucose, triglyceride, low density lipoprotein, high density lipoprotein), self-reported information on medical conditions, and BMI explain 27% of total variance in SRH.” That’s plain wrong, I am sorry. The physical health indicators explain an additional 11% beyond the 16% explained by age and sex.

2. Similarly: the authors conclude that [SES] “contributed to only around 2% of the explained variance.” This is also misleading: it’s 2% after including all measures of physical and mental health measures, which, one might hypothesize, are the mechanisms through which SES and SRH are linked, at least partly.

3. The methods section raised numerous questions.

a. What was the response rate?

b. Were weights used in the analyses?

c. Who is in this sample, and how does the sample differ from the Czech population overall? What is the age range in particular, given that age is, by far, the most ‘important’ covariate of SRH?

d. In Marital status, does “single” mean “never married” or “not married or cohabiting”?

e. How appropriate is OLS for the analysis of SRH? Did the authors conduct any diagnostics?

f. There appears to be a lot of missingness. First, the authors do not discuss it beyond noting 21% missing values on income. What was missingness on other variables? Then they lay out what appears an irregular set of steps to deal with the missingness, culminating with a nonstandard way of apparently hand-averaging 25 imputations and plugging these averages into the models. That does not appear any better than a random realization from multiply imputed datasets and fundamentally fails to account for the increased uncertainty due to the missingness. Perhaps I misunderstand – in that case, could the authors clarify the procedures?

g. For the biomarkers, how were respondents on medication treated? That is, fasting blood glucose can be normal because it’s normal or because an individual is on diabetes medication. Same with triglycerides and statins or other cholesterol medications.

h. It would be good to add an ANOVA test for difference in biomarkers by SRH levels.

4. The mental health indicator is only the CES-D scale for psychological distress (used to measure depressive symptoms). While sleep quality is included, too, this is not a measure of mental health. Or perhaps, in absence of other indicators, it could serve as a proxy for some dimension of mental health but the authors do not explain or motivate this unusual indicator.

5. Why is there no descriptive table, even in the Appendix?

6. Given that the sample appears to be drawn from two locations, why is there no control for location?

7. The Figure 1 is a clear and compelling way to present the results. However, why is education included as a linear covariate? Table 2 suggests its effects are nonlinear. And why is there no indication about how income was included?

8. The Figure might be more informative if, instead of bivariate models, it was based on age-adjusted models (or age and sex).

9. The analysis includes BMI and sleep quality, and classifies them as physical and mental health measures, respectively. There’s no indicator of smoking or alcohol use, which makes me wonder why these behaviors are not included. The authors may push back that they didn’t set out to study health behaviors, which would be fair. However, it might be helpful to discuss this apparent omission at least in the discussion section.

10. Why are SES measures included in the Table 2 last? That would be very important to justify. I would expect to first control for SES (after age and sex) as the fundamental determinant of the other health measures. If done that way, perhaps the increase in proportion explained would still be modest, but it’s important to show what it would be.

Reviewer #2: The current manuscript describes a study based on data from a survey of adults in the Czech Republic. The participants filled in a questionnaire and provided a blood sample. This enabled the researchers to study the predictors of self-rated health (SRH) status and compare the relative contribution of several types of predictors: biomedical, psychological, and social indicators. While the predictors of SRH have been investigated in many studies, only a few included biomarkers. Those mostly used analyses that did not yield an exact estimate of the amount of variance explain by each type of predictor. Thus, analyses based on linear regression models of data that includes biomarkers may provide a worthwhile contribution to this literature. However, there are several issues which the authors could better address.

First and foremost is the determination of the relative contribution of each type of predictors. The authors focus on the addition to the adjust R2 with each additional step. This is greatly affected by the order of entry of the variables into the model. The actual unique contribution of each set of variables in the final model differs from its contribution in the step when it was first entered and is not reported. There was no trial of a different order of entry into the model. Alternatively, better explain why this order of entry of predictors was chosen. Is the aim to determine what part of SRH reflects physiological markers and disease diagnoses and what part is related to psychological measures, social indicators, and demographics? Why was this order of variables chosen? Or is the aim to identify health inequalities – in which case this aim deserves a more detailed explanation. The analyses are interesting, relevant to the question asked and, as the authors wrote, not often reported in this way (in studies including biomarkers), yet could be better explained.

Several additional issues:

The introduction briefly describes existing research on the topic. Despite the lack of a clear comparison between types of predictors in their contribution to SRH, there are several additional articles that included a range of predictors and could be cited, to provide a fuller picture of the existing literature. For example, Goldman et a. 2009 (doi:10.1016/S1047-2797(03)00077-2), Anreasson et al., 2013 (DOI: 10.1177/1359105311435428).

The study procedure is barely described – much information is missing on the way potential participants were identified, approached and recruited to the study, where they provided the data, and on response rates.

CES-D – the questionnaire originally included 20 items. Shorter 10- or 11-item versions are often used and have been validated. A four item version is not commonly used and it would be appropriated to add information supporting its validity.

Table 2 – if the aim is to compare the contribution of different predictors, it makes more sense to report standardized regression coefficients.

Study limitations – the study is cross-sectional. Longitudinal data may reveal additional information; some well-known correlates of SRH were not included, for example, physical functioning, cognitive functioning, positive emotions.

Corrections

Please correct typos and language errors on lines 45, 58, 93, 137, 156 (+define the abbreviation for CRP), 160, 170, 171, 181, 235, 263, 282, 296, number of diagnoses (in Table 2).

Correct author names in reference 9 (Leshem-Robinow et al.).

**********

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Reviewer #1: No

Reviewer #2: No

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30 Sep 2021

CHANGES IN RESPONSE TO REVIEWS

First of all, we would like to thanks all reviewers for their time spent on reviewing our manuscript and for the valuable and thoughtful comments towards improving our manuscript. Before we address these concerns, point by point, we would like to describe the main conceptual changes.

Both reviewers expressed concerns about the order, in which variables entered the analysis. They argued that the stepwise building of the model and the order of variables block might significantly affect the results. To address these concerns, we changed our analytical strategy. First, we analyze the blocks of variables separately only controlling for age, sex, and location. Second, we built the final model using these blocks. As suggested by Reviewer 1, we start with the sociodemographic variables, and biomedical measures are entered only afterward.

REVIEWER #1:

The aim of this study is to determine the relative importance of physical health, mental health, and SES in SRH in a sample of adult respondents from the Czech Republic.

This submission was easy to read. I am really sorry to say, however, that there are major methodological weaknesses. The first contribution, the authors state, is to confirm the validity of SRH in this sample. It not entirely clear either what this means (what type of validity?), nor how exactly it was approached methodologically.

We specify that we measure concurrent validity of SRH, i.e. the extent to which this indicator correlates with established measures of health. We also specify the measure that we are interested in in the relative contribution of different types of predictors.

Moreover, it is my impression that Czech respondents were included in a number of pan-European surveys of population health where SRH was the focus. I am sorry – I don’t know this literature but a quick scholar google search brings up a number of papers. Perhaps they aren’t 100% relevant, but a brief literature review on SRH in Czech respondents would be helpful.

We added a brief literature review on the Czech Republic. There is a number of studies using SRH but the research on validity of SRH is limited.

The second aim was to determine the relative importance of the three factors (physical health, mental health, and SES). However, the authors did this by estimating nested OLS models and looked at the increases in R squared. There are a number of issues.

1. Quite concerning: the authors state in the abstract that “Biomarkers (C-reactive protein, blood glucose, triglyceride, low density lipoprotein, high density lipoprotein), self-reported information on medical conditions, and BMI explain 27% of total variance in SRH.” That’s plain wrong, I am sorry. The physical health indicators explain an additional 11% beyond the 16% explained by age and sex.

This section was completely changed based on the other comments from both reviewers. The abstract has been re-written.

2. Similarly: the authors conclude that [SES] “contributed to only around 2% of the explained variance.” This is also misleading: it’s 2% after including all measures of physical and mental health measures, which, one might hypothesize, are the mechanisms through which SES and SRH are linked, at least partly.

Again, the section has been re-written and formulation changed.

3. The methods section raised numerous questions.

a. What was the response rate? b. Were weights used in the analyses? c. Who is in this sample, and how does the sample differ from the Czech population overall? What is the age range in particular, given that age is, by far, the most ‘important’ covariate of SRH?

We added a new section with the description of the dataset. As we use quota sampling, it is not possible to determine response rates or use weights.

d. In Marital status, does “single” mean “never married” or “not married or cohabiting”?

We specify that “single” means “currently living without a partner”.

e. How appropriate is OLS for the analysis of SRH? Did the authors conduct any diagnostics?

We report diagnostics of the OLS in the Appendix. We also refer to existing studies using OLS to study SRH.

f. There appears to be a lot of missingness. First, the authors do not discuss it beyond noting 21% missing values on income. What was missingness on other variables? Then they lay out what appears an irregular set of steps to deal with the missingness, culminating with a nonstandard way of apparently hand-averaging 25 imputations and plugging these averages into the models. That does not appear any better than a random realization from multiply imputed datasets and fundamentally fails to account for the increased uncertainty due to the missingness. Perhaps I misunderstand – in that case, could the authors clarify the procedures?

In general, income was the only variable with a larger proportion of missing data. We acknowledge that using a simple mean of imputed values is problematic. Thus, we employ three methods to cope with the missing data. First, we estimate the results only for cases without missing information. Second, we employ multiple imputations and we use the mean value of the imputed income (as it was done in the original version of the paper). Third, we estimate the set of regression models with the imputed data and we report on the distribution of R2 values.

g. For the biomarkers, how were respondents on medication treated? That is, fasting blood glucose can be normal because it’s normal or because an individual is on diabetes medication. Same with triglycerides and statins or other cholesterol medications.

In the revised version of the paper, we included a new variable referring to medications. As for the biomarkers, we distinguished those below and over the reference limit. In the analysis, we used both crude and adjusted measures. The crude measure refers to the number of biomarkers over the reference limits. The adjusted measure considers whether the respondent takes medication for the given condition.

h. It would be good to add an ANOVA test for difference in biomarkers by SRH levels.

We added ANOVA test for differences in biomarkers in Table 2.

4. The mental health indicator is only the CES-D scale for psychological distress (used to measure depressive symptoms). While sleep quality is included, too, this is not a measure of mental health. Or perhaps, in absence of other indicators, it could serve as a proxy for some dimension of mental health but the authors do not explain or motivate this unusual indicator.

We adjusted the analysis. The original CES-D scale contains an item on sleep disturbances, but this item has not been included in the Qualitas questionnaire. Instead, it was replaced by a different measure of sleep quality. In the revised version of the paper, we run a measurement model (confirmatory factor analysis using SEM technique) to test whether our measure of sleep quality could be included in the measure of mental health. Indeed, the measurement model confirmed that all 4 items from CES-D and the sleep quality item load on one latent variable (mental health – the full measurement model is reported in the Appendix). Thus, in the revised version of the paper, we use one indicator of mental health including all 5 items.

5. Why is there no descriptive table, even in the Appendix?

We added a descriptive table (Table 1).

6. Given that the sample appears to be drawn from two locations, why is there no control for location?

All models now control for the location.

7. The Figure 1 is a clear and compelling way to present the results. However, why is education included as a linear covariate? Table 2 suggests its effects are nonlinear. And why is there no indication about how income was included?

Education is now included as a categorical variable. In the figure, we denote which variable is linear, binary, and categorical.

8. The Figure might be more informative if, instead of bivariate models, it was based on age-adjusted models (or age and sex).

We adjusted the results for age, sex, and location.

9. The analysis includes BMI and sleep quality, and classifies them as physical and mental health measures, respectively. There’s no indicator of smoking or alcohol use, which makes me wonder why these behaviors are not included. The authors may push back that they didn’t set out to study health behaviors, which would be fair. However, it might be helpful to discuss this apparent omission at least in the discussion section.

Alcohol intake and smoking are now incorporated into the analysis.

10. Why are SES measures included in the Table 2 last? That would be very important to justify. I would expect to first control for SES (after age and sex) as the fundamental determinant of the other health measures. If done that way, perhaps the increase in proportion explained would still be modest, but it’s important to show what it would be.

In the revised version of the paper, we use a different analytical strategy. We first enter the blocks of variables independently. In the final model, we started with the controls, SES, and then add other health measures.

REVIEWER #2:

The current manuscript describes a study based on data from a survey of adults in the Czech Republic. The participants filled in a questionnaire and provided a blood sample. This enabled the researchers to study the predictors of self-rated health (SRH) status and compare the relative contribution of several types of predictors: biomedical, psychological, and social indicators. While the predictors of SRH have been investigated in many studies, only a few included biomarkers. Those mostly used analyses that did not yield an exact estimate of the amount of variance explain by each type of predictor. Thus, analyses based on linear regression models of data that includes biomarkers may provide a worthwhile contribution to this literature. However, there are several issues which the authors could better address.

First and foremost is the determination of the relative contribution of each type of predictors. The authors focus on the addition to the adjust R2 with each additional step. This is greatly affected by the order of entry of the variables into the model. The actual unique contribution of each set of variables in the final model differs from its contribution in the step when it was first entered and is not reported. There was no trial of a different order of entry into the model. Alternatively, better explain why this order of entry of predictors was chosen. Is the aim to determine what part of SRH reflects physiological markers and disease diagnoses and what part is related to psychological measures, social indicators, and demographics? Why was this order of variables chosen? Or is the aim to identify health inequalities – in which case this aim deserves a more detailed explanation. The analyses are interesting, relevant to the question asked and, as the authors wrote, not often reported in this way (in studies including biomarkers), yet could be better explained.

In the revised version of the paper, we adopted a new analytical strategy that uses a different order of entering variables into the model (see the description above)

Several additional issues:

The introduction briefly describes existing research on the topic. Despite the lack of a clear comparison between types of predictors in their contribution to SRH, there are several additional articles that included a range of predictors and could be cited, to provide a fuller picture of the existing literature. For example, Goldman et a. 2009 (doi:10.1016/S1047-2797(03)00077-2), Anreasson et al., 2013 (DOI: 10.1177/1359105311435428).

The articles are added to the literature review.

The study procedure is barely described – much information is missing on the way potential participants were identified, approached and recruited to the study, where they provided the data, and on response rates.

We added better description of survey and participants’ characteristics.

CES-D – the questionnaire originally included 20 items. Shorter 10- or 11-item versions are often used and have been validated. A four item version is not commonly used and it would be appropriated to add information supporting its validity.

In the Appendix, we report a measurement model (SEM) and we show that the 4-item scale has a very good internal consistency. We comment on this fact in the main body of the paper.

Table 2 – if the aim is to compare the contribution of different predictors, it makes more sense to report standardized regression coefficients.

Standardized coefficients are included in all tables.

Study limitations – the study is cross-sectional. Longitudinal data may reveal additional information; some well-known correlates of SRH were not included, for example, physical functioning, cognitive functioning, positive emotions.

We acknowledged this limitation in the study limitations.

Corrections

Please correct typos and language errors on lines 45, 58, 93, 137, 156 (+define the abbreviation for CRP), 160, 170, 171, 181, 235, 263, 282, 296, number of diagnoses (in Table 2).

The article has been edited by a professional service specializing in academic writing, all abbreviations are defined.

Correct author names in reference 9 (Leshem-Robinow et al.).

The name as it is printed on the publication is used.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

15 Nov 2021

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: (No Response)

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Reviewer #1: Yes

Reviewer #3: Partly

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #3: No

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Reviewer #1: Yes

Reviewer #3: No

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors were highly responsive to all comments and the extensive revision yielded a much stronger paper. I am sorry, however, to pose several residual questions.

Introduction last paragraph: what is “this social context” in line 83? And in lines 88-90, do the authors refer to Czech republic?

Methods: I didn’t understand why the quota sampling invalidates the question about response rate. Presumably some individuals who were approach did not participate: what was the response rate?

Approach: about the multiple imputation, page 6 lines 131-2. How or where is the distribution of the 25 R squareds reported?

Minor: I would suggest referring to the two locations as “location,” not “region.”

Results: the fact that only 2 out of >1,000 people assessed their health as “very bad” suggests that the SRH item, or the labels used for its 5 categories in the Czech language, capture something different than the English labels of excellent, very good, good, fair, or poor. This should be discussed as a limitation.

Results: more importantly, the results are now much expanded but a bit difficult to follow. It seems the authors incorporated a lot of different approaches and robustness checks. I think the results section would be much stronger if they only retained one approach/specification and told the story with it; all the robustness checks could be in the appendix.

There is little discussion in general. One issue that I was hoping to see was to engage with the issue that socioeconomic factors are more distal characteristics, that may influence SRH via the more proximate factors, such as health conditions or biological risk variables.

More broadly, there is little discussion that would help readers understand the results. What do the findings mean for SRH, or for the Czech population health, or for population-health research? Why might it be that this study found such a weak relationship between SES and SRH? Etc.

Reviewer #3: The study examines associations of physiological, mental and socioeconomic factors with self-rated health, specifically “the relative contribution of biomedical and social characteristics to an individual´s SRH assessment” in a population sample from the Czech republic. The topic is basically relevant. Yet the paper has a major flaw in that the basic idea of the study seems to be missing. It remains unclear why these associations are important to know, why they are analyzed here, and what should we learn from the results. It is striking that the crucial part in all studies, interpretation of findings, is largely missing. The Discussion only repeats the statistical results and discusses limitations, concerning mainly data.

There are several starts in the Introduction that let the reader believe that this is may be the theme of the study, the perspective from which the associations/ relative explanatory powers are studied, but none of these themes seems to have guided the analyses, nor are they discussed in Discussion. The validity of SRH is mentioned in several places in the paper. Yet is remains unclear, what validity the authors have in their minds; the validity of SRH as a measure of objective health?; its validity as predictor of death?, or something else. On p 88-90 validity against biomedical data is mentioned. Also, there are references in Introduction to the extent to which SRH may rely on social experience, to what extent it is a measure of social determination of health, and also a reference to health inequalities. These perspectives would, if followed more, lead to different specific research question and in all likelihood also to different analytic designs. For instance, if SRH were considered as a measure of health inequalities, it would be important to analyze, or at least discuss, whether it reflects different objective health (disease, functioning…) between different (socioeconomic?) groups, or whether and to what extent it reflects difference in the way different groups evaluate their health. The conclusion in the Abstract concludes that SRH likely is a valid measure of physiological and mental health in the Czech sample and the observed differences were likely to reflect inequalities in bodily and mental functions between social groups. But in the analyses, there is no comparison whatsoever between social groups. If this were the topic of the study, one proper design for the analysis would be to see whether the differences in SRH between different social groups follow the differences in physical and mental health in these groups.

The Introduction says that the aim of the analyses is to determine how much variance of SRH can be explained by biomedical (sometimes called physiological) , psychological, and social indicators. This, again, is a different perspective from the perspectives of validity and inequality, but this perspective either is clearly justified; the reader expects the authors to explain why we need to know this. Another big question is, what is the authors´ understanding on the possible mechanisms on the associations of the selected factor groups with SRH. It is justified to say that the mechanisms on why diseases and depression explain variance of SRH are different from the mechanisms on why income explains it.

The problems described above lead me to suggest that the authors would plan the thematization, research questions, and analytic design again from the beginning, in order to use the data available in a way that could give a real contribution to the present literature.

If the authors decide to continue their work on this paper and maintain the focus on the explanatory power on different variable groups, they may also consider the following comments:

• It is problematic to claim (p3) that ”what contributes to an individual´s assessment of SRH remains largely unknown”. Today there are several studies combining empiric data with conceptual understanding and creating useful frameworks on what and how contributes to self-assessments of health. It is true that there is no exhaustive and comprehensive list of contributing factors, but then, there cannot be, this is an inherent and necessary characteristic of the self-rated health which is a subjective construction of everything that a person considers as belonging to his/her “health”.

• As the authors speak about the relative contribution of biomedical and social characteristics, I am sure many readers expect to learn about the relative contribution of these variable groups. Yet the biomedical and social variables are not analyzed as groups but only as individual variables. As such, the empirical findings are not novel. The authors write (“p4 ..”no study has tested the concurrent validity of SRH, ie the extent to which this indicator correlates with established measures of health, using biomedical data”. This is clearly wrong. There are numerous studies that analyze the associations of diseases, functioning, symptoms, medication with SRH, and recently also several studies that analyze the association of SRH with indicators measured from blood. Also the associations with a variety of different social variables, including socio-economic, and behavioral variables with SRH have been studied. Mainly the findings of this manuscript are in line with earlier studies in that highest associations are found for diseases, other health variables, and also socioeconomic factors.

• The sequence of analyses is hard to follow. As it is not groups of variables but individual variables belonging to different groups that are studied, why is it necessary to show the basic associations in separate tables? Why not first show the individual associations in one table and then try to analyze the contributions of different groups of variables? As to the individual explanatory factors, it would be fair to take into account and at least discuss the fact that the associations and explanatory powers depend not only to the content of the indicator but also on the number and categories of the variables included in the indicator.

• To an international audience it is not a very good justification of a study that these associations have not earlier been studied in a Czech population. Numerous studies show that SRH as a variable behaves largely in the same way in different countries, different population groups etc. If there is no good reason to believe that in Czech the association would be different, the justification of the study should be based on its scientific novelty and message. Of course, Czech population can work as well as any other for this purpose.

.

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2 Feb 2022

We would like to thank both reviewers for their time and helpful comments that helped to improve the manuscript. We hope that we address all points raised in the reviews.

Before we address each point in detail, we would like to describe the major changes to the paper. First, we revised the introductory part to make it more focused and to explain the research agenda more clearly. Second, we followed the advice of Reviewer 1 who suggested to move much of the results to the Appendix. Thus, the result section is more concise and we believe it is easier to follow. Third, we expanded the discussion and we point out the major implications of our findings.

Reviewer #1:

The authors were highly responsive to all comments and the extensive revision yielded a much stronger paper. I am sorry, however, to pose several residual questions.

Introduction last paragraph: what is “this social context” in line 83? And in lines 88-90, do the authors refer to Czech republic?

The sentence has been reformulated to make it clear that we talk about the Czech Republic.

Methods: I didn’t understand why the quota sampling invalidates the question about response rate. Presumably some individuals who were approach did not participate: what was the response rate?

The information on how many respondents from the originally selected sample is provided.

Approach: about the multiple imputation, page 6 lines 131-2. How or where is the distribution of the 25 R squareds reported?

The section is moved to the Appendix.

Minor: I would suggest referring to the two locations as “location,” not “region.”

The term region has been replaced by “location” in the whole text.

Results: the fact that only 2 out of >1,000 people assessed their health as “very bad” suggests that the SRH item, or the labels used for its 5 categories in the Czech language, capture something different than the English labels of excellent, very good, good, fair, or poor. This should be discussed as a limitation.

In the discussion, we point out that this study utilized the WHO version of the SRH item ranging from “very good” to “very bad” that is widely used in European surveys. This item has generally better discrimination power at the positive end.

Results: more importantly, the results are now much expanded but a bit difficult to follow. It seems the authors incorporated a lot of different approaches and robustness checks. I think the results section would be much stronger if they only retained one approach/specification and told the story with it; all the robustness checks could be in the appendix.

As suggested, we moved a significant part of the result section to the Appendix and we believe that the flow of the text is easier to follow in this version of the paper.

There is little discussion in general. One issue that I was hoping to see was to engage with the issue that socioeconomic factors are more distal characteristics, that may influence SRH via the more proximate factors, such as health conditions or biological risk variables.

More broadly, there is little discussion that would help readers understand the results. What do the findings mean for SRH, or for the Czech population health, or for population-health research? Why might it be that this study found such a weak relationship between SES and SRH? Etc.

We expanded the discussion in the suggested direction.

Reviewer #3:

The study examines associations of physiological, mental and socioeconomic factors with self-rated health, specifically “the relative contribution of biomedical and social characteristics to an individual´s SRH assessment” in a population sample from the Czech republic. The topic is basically relevant. Yet the paper has a major flaw in that the basic idea of the study seems to be missing. It remains unclear why these associations are important to know, why they are analyzed here, and what should we learn from the results.

We revised the introductory part of the paper and we hope that the basic idea of the study is expressed better. We try to explain as to why it is relevant to validate the SRH item in different contexts.

It is striking that the crucial part in all studies, interpretation of findings, is largely missing. The Discussion only repeats the statistical results and discusses limitations, concerning mainly data.

The results section is completely revised. Much of the detailed findings have been moved to the appendix. We also completely revised the discussion to make explain the contribution and implications of the study.

There are several starts in the Introduction that let the reader believe that this is may be the theme of the study, the perspective from which the associations/ relative explanatory powers are studied, but none of these themes seems to have guided the analyses, nor are they discussed in Discussion. The validity of SRH is mentioned in several places in the paper. Yet is remains unclear, what validity the authors have in their minds; the validity of SRH as a measure of objective health?; its validity as predictor of death?, or something else. …..

We completely revised the introductory section and we hope that the aim of the manuscript and the issue of validity is clearer.

If the authors decide to continue their work on this paper and maintain the focus on the explanatory power on different variable groups, they may also consider the following comments:

• It is problematic to claim (p3) that ”what contributes to an individual´s assessment of SRH remains largely unknown”. …

The section is revised.

• As the authors speak about the relative contribution of biomedical and social characteristics, I am sure many readers expect to learn about the relative contribution of these variable groups. Yet the biomedical and social variables are not analyzed as groups but only as individual variables.

We explain our approach in the method section. Our work is inspired by Hiyoshi et al.’s approach to estimating the joint contribution of several variables. Thus, we do not focus on individual coefficients but the joint contribution to the explained variance.

As such, the empirical findings are not novel. The authors write (“p4 ..”no study has tested the concurrent validity of SRH, ie the extent to which this indicator correlates with established measures of health, using biomedical data”. This is clearly wrong. ….

Here, we refer to the national context. Hopefully, it is clearer in the revised version.

• To an international audience it is not a very good justification of a study that these associations have not earlier been studied in a Czech population. Numerous studies show that SRH as a variable behaves largely in the same way in different countries, different population groups etc. If there is no good reason to believe that in Czech the association would be different, the justification of the study should be based on its scientific novelty and message. Of course, Czech population can work as well as any other for this purpose.

We hope that we explain the contribution of the manuscript clearer.

Submitted filename: Response to reviewers.docx

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25 Feb 2022

31 Mar 2022

** P4 says that face-to-face interviews were conducted, later a questionnaire is mentioned several times. Please clarify.

We clarify that a questionnaire was administered by face-to-face mode of data collection.

Due to sampling method, response rate could not be calculated.

The comment on response rate was included as a reaction to a reviewer comment. It is deleted from the revised manuscript.

** In all likelihood the sample is not representative for the population in the study areas. This does not hamper the results but would be worth mentioning in Discussion.

It is mentioned in the discussion.

** The limitation - A major well-known factor associated with SRH was missing, functioning, functional ability, disability... It would be good to mention this in the Discussion.

It is mentioned in the discussion among the study limitations.

Submitted filename: Response to Reviews.docx

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4 Apr 2022

Assessment of self-rated health: The relative importance of physiological, mental, and socioeconomic factors

PONE-D-21-14590R3

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8 Apr 2022

PONE-D-21-14590R3

Assessment of self-rated health: The relative importance of physiological, mental, and socioeconomic factors

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