Biases in Assertions of Self-Rated Health

Abstract

Comparative analyses frequently examine respondents’ self-rated health (SRH), assuming that it is a valid and comparable measure of generic health. However, given SRH’s vagueness, this assumption is questionable due to (1) manifold non-health influences, such as personal characteristics including optimism, interviewer effects on the rating, and cultural contexts, as well as (2) potential gender, age- or country-specific expectations for one’s health or frames of reference. Conceptually, two major components of SRH can be distinguished: latent health and reporting behavior. While latent health exclusively refers to objective health status, reporting behavior collectively refers to non-health characteristics (NH) affecting SRH. The present paper is primarily concerned with the latter and aims to identify whether and how NH bias SRH, including possible differences by gender, age, and country of residence.
 The presented analyses are based on data from 16,183 participants in five countries drawn from the fifth wave of the Survey of Health, Ageing and Retirement in Europe (SHARE). Latent health is controlled via a wide array of health indicators and the residuals are examined with a model covering NH from three different sources: the interviewer, the respondent, and the country of residence. To identify subgroup-specific response behaviors, all analyses are carried out separately by gender, three age groups (50-64, 65-79, and 80+ years), and country of residence.
 The analyses uncovered influences of – among others–the interviewer’s SRH, the respondent’s life satisfaction, and the country of residence on SRH, while other factors differed by subgroup. The amount of explained variance due to such reporting behavior (with a mean of seven percent) can be deemed meaningful, considering that controlling for latent health already explains around half of SRH’s variance. The greatest source of non-health influences was respondent characteristics, with the interviewer and country having smaller effects.
 These results illustrate the importance of taking NH into account when using SRH measures. Future research on complementing SRH with factual questions in survey design is advisable.
 * This article belongs to a special issue on “Levels and Trends of Health Expectancy: Understanding its Measurement and Estimation Sensitivity”.

Measuring generic health using the minimum european health module: does it work and is it better than self-rated health?

“…The non-health variables used in these analyses were as follows (the distributions of these variables are shown in Table S 3 in the appendix): Education : According to prior research, respondents with higher formal education appear to rate similar health states more positively [ 19 ], possibly due to their ability to use their resources to alleviate negative effects of health problems. However, other research only found this effect for male respondents [ 8 ], highlighting the necessity of separate analyses by gender. In the DEAS data, education was available in the form of three groups that are based on the International Standard Classification of Education (ISCED) representing low (ISCED 0–2), medium (ISCED 3–4), and high education (ISCED 5–6) [ 20 ].…”

“…As the final part of this paper’s analyses, I analyzed the influence of further non-health biases on each health measure by employing a multi-step analytic approach already described in greater detail elsewhere [ 8 ]. In short, the first step of this approach consists of using a linear regression model explaining the generic health measure in question with as many health indicators as available in the data set.…”

“…In order to use a standardized measure of income between the two survey years, I chose a variable representing the respondent’s income position as the percentage points of the mean equivalent income of the German population in the survey year as provided by the DEAS-team [ 17 ]. Optimism : Previous research has shown that optimism mediates the relationship between objective and subjective functioning [ 28 ] and that a similar overall health state was more positively rated by respondents with a greater life satisfaction [ 8 ]. Thus, I included a measure of optimism in this model to investigate whether potential biasing effects of optimism on SRH can be mitigated using a MEHM-based approach.…”

“…Further, recent studies have shown that SRH exhibits some properties that question its suitability to validly and robustly measure generic health, e.g., age-specific health determinants and standards, i.e., lack of measurement invariance [ 4 – 6 ], or systematic influences of non-health characteristics even after controlling for comprehensive health information, i.e., non-health biases [ 7 , 8 ]. Therefore, to efficiently utilize SRH’s potential for inclusive generic health measurement and to increase the validity of research based on SRH, it would be desirable to rectify these drawbacks.…”

The association between social support and self-rated health in midlife: are men more affected than women?

“…Self-rated health may be subject to varying perceptions based on individual characteristics such as culture, age, and gender. Future research could benefit from incorporating objective health measures to validate self-rated health assessments, as proposed by Lazarevič 36 . Similarly, the perception of social support can also be influenced by several factors, including the quality and closeness of interpersonal relationships, the availability and accessibility of social resources, and the person’s ability to seek and use available support.…”