Sex Differences in Validity of Self-Rated Health: A Bayesian Approach

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1 Sex Differences in Validity of Self-Rated Health: A Bayesian Approach Anna Zajacova, University of Wyoming Megan Todd, Columbia University September 25, 215 Abstract A major strength of self-rated health (SRH) is its high overall measurement validity. Recently, however, SRH predictive validity was found to vary by gender, which suggests men and women evaluate their health differently. This could bias our understanding of gender health inequalities measured with SRH. We assess sex differences in concurrent validity of SRH by assessing how well it distinguishes values of other health measures among 6-69-year-old respondents in the NHIS. Using a Bayesian approach, we estimate the predictive posterior distributions of each health measure within each SRH-sex category, then calculate the probability that respondents with a given level of SRH report worse health than respondents with higher SRH. Preliminary results show that SRH corresponds more closely to the level of health problems among men than women. However, the pattern varies across health measures, suggesting that men and women may weight certain health dimensions differently when determining their SRH. Abstract prepared for submission to the 216 Annual Meeting of the Population Association of America, Washington DC. Please do not cite or circulate without permission. 1

2 Sex Differences in Validity of Self-Rated Health: A Bayesian Approach Extended Abstract Self-rated health (SRH) has been used extensively for decades to study health trends and inequalities (Idler & Benyamini 1997; Benyamini & Idler 1999; DeSalvo et al. 26). SRH is a single survey question that asks respondents to rate their own health, typically on a five-point scale ranging from excellent to poor. One of the major strengths of SRH is its high concurrent and predictive validity, meaning that SRH correlates well with concurrent measures of health and also predicts future mortality and health problems. Recent studies, however, have uncovered systematic reporting differences in SRH across population groups. For instance, SRH appears to have a higher predictive validity among men than women (Benyamini et al. 23), among respondents of higher socioeconomic status (Dowd & Zajacova 27), non-hispanic white race/ethnicity (Spencer et al. 29), and younger age (Schnittker 25). These findings are troubling. If population groups systematically differ in their understanding, interpretation, or reporting of SRH, our understanding of health differences based on SRH may be biased Sex differences have long been central to population health research. Sex differences in SRH are particularly challenging to understand: while women consistently report worse SRH than men, they have lower mortality at every age (Idler 23; Case & Paxson 25). Consequently, possible sex differences in SRH reporting have attracted scholarly attention. Most previous studies examined differences in predictive validity of SRH: using survival models, researchers assessed relative mortality differences for very good to poor levels relative to excellent SRH in each sex. Typically the relative mortality differences are larger for men than for women, indicating greater predictive validity of SRH for men. What these models suggest but cannot show directly is that men better distinguish their SRH rating vis-à-vis their underlying health. Figure 1 shows this schematically: the higher predictive validity of SRH for men suggests that the distributions of underlying health are well-separated across SRH levels as in the top panel of the figure, whereas for women the health levels in different SRH levels are more mixed. Mathematically, the probability that an individual with excellent health has worse level of a health measure such as the number of chronic conditions or mental health score than an individual with very good Fig 1. Schematic diagram of health distributions at two SRH levels A hypothetical health measure Excellent SRH Very good SRH 2

3 health is virtually zero in the top plot. In the bottom plot, it is much more likely that a better SRH category will be associated with worse health than a less good SRH category. We can address this question directly, using concurrently collected measures of health such as chronic conditions, mental health, and physical limitations. Using a Bayesian approach, we simulate posterior predictive probabilities for the various health measures for each level of SRH. We then calculate the overlap of these distributions, which gives us a direct measure of how well SRH distinguishes select dimensions of health status for men and women. Our hypothesis, based on previous research, is that men s SRH will have more distinct, less overlapping distributions of health measures compared to women, as shown in Figure 1. This approach is an extension of previous work using standard frequentist methods where the means of the health distributions were compared. The major advantage of the Bayesian approach is that we can describe the complete distributions, capturing not only the central tendencies in each category but also behaviors at the tails of the health distributions. This is a novel application of a basic Bayesian estimation, which will yield unique results about the SRH evaluation process for men and women. DATA AND METHODS Data We use National Health Interview Survey (NHIS) data. The NHIS is an annual cross-sectional survey representative of the civilian non-institutionalized population of the United States. The NHIS is conducted by the National Center for Health Statistics in order to monitor the health of Americans. We focus on older adults because of the increasing prevalence of health problems at older ages. Thus, we limit our sample to respondents aged between 6 and 69 years old. The upper age boundary was selected to minimize reporting differences across age and other issues inherent in analyses of elderly samples, such as institutionalized adults and mortality selection. Excluding respondents with missing information on key variables yields an analytic sample of 66,846 adults aged 6-69 (56% female). Measures SRH is determined from a respondent s answer to the following question: would you say your health in general is excellent, very good, good, fair, or poor? We include a range of concurrent measures: a count of chronic conditions, the Kessler 6 (K6) scale, physical limitations and disability, and hospitalization episodes. The preliminary results here include the first two of these. The K6 measures psychological distress were based on symptoms over the past 3 days (Kessler et al. 22). Respondents were asked how often they felt nervous, hopeless, restless/fidgety, 3

4 so sad that nothing could cheer him/her up, that everything was an effort, and worthless. For each symptom, respondents answer all (5), most (4), some (3), a little (2), or none (1) of the time. Responses for the six questions are summed to create the K6 scale, with a possible range of zero to 3. The count of chronic conditions is a summation index from -13, adding up all conditions reported by the respondents. The conditions included arthritis, asthma, cancer, COPD, diabetes, hypertension, kidney problems, stroke, angina pectoris, heart attack, coronary heart disease, and other heart conditions. Analytic strategy Our goal is to calculate how well different SRH levels distinguish between different values of health measures such as the K6 depression score and chronic condition count. We estimate the predictive posterior distribution of each health measure within each SRH level and sex. We then calculate the probability that an individual will report worse health (higher K6 score or more chronic conditions) for all adjacent pairs of SRH levels: excellent vs. very good, very good vs. good, good vs. fair, and fair vs. poor. The distribution of both health measures used in the preliminary analyses, K6 and the count of chronic conditions, was specified as negative binomial truncated at the respective maximum score for each measure (24 for K6 and 12 for the count of chronic conditions). We used vague priors for the negative binomial distributions -- Uniform(,1) for parameter p and gamma(.1,.1) for parameter r. We estimated the models using JAGS with 1, iterations and 5 burn-in values. PRELIMINARY RESULTS In preliminary analyses, we examined two health measures: the K6 depression score and the sum of chronic conditions. If SRH sorted perfectly into non-overlapping distribution of a health measure, then the probability that an individual with a given SRH will have a worse score on some health measure than an individual with a lower (worse) SRH would be 1 for all pairs of SRH levels. If SRH were reported at random (no association with any objective health measure), then this probability would be.5. In other words, a higher probability that an individual reports a worse level of a health measure like the K6 if they also report one level worse SRH corresponds to SRH ratings that distinguish health differences better. As Table 2 shows, for instance, among men there is a.71 probability that a respondent who reports very good SRH will also have a higher K6 score (higher depressive symptoms) than a respondent who reports excellent SRH. Among women, the probability is 2, suggesting that women s SRH ratings (at the excellent to very good levels) correspond less closely correspond to their K6 score. This gender comparison is also valid when we compare very good to good SRH and good to fair SRH. At the fair vs. poor levels, women s K6 scores correspond to their SRH rating more closely than men s. 4

5 With respect to chronic conditions, women s SRH offers a greater probability of correct sorting at the excellent versus very good level; men s SRH differentiates better at the very good vs. good, and fair vs. poor levels. PRELIMINARY SUMMARY The preliminary results indicate that men s health ratings correspond somewhat better to the level of specific health problems than women s health ratings, indicating a higher concurrent validity of SRH ratings for men. However, the sex patterns vary across different health measures, suggesting that men and women incorporate various dimensions of their health into their SRH judgment in different ways. 5

6 REFERENCES Benyamini, Y. et al., 23. Gender Differences in the Self-Rated Health-Mortality Association: Is It Poor Self-Rated Health That Predicts Mortality or Excellent Self- Rated Health That Predicts Survival? The Gerontologist, 43(3), pp Benyamini, Y. & Idler, E.L., Community Studies Reporting Association between Self-Rated Health and Mortality: Additional Studies, 1995 to Research on Aging, 21(3), pp Case, A. & Paxson, C., 25. Sex Differences in Morbidity and Mortality. Demography, 42, p6. DeSalvo, K.B. et al., 26. Mortality Prediction with a Single General Self-Rated Health Question. A Meta-Analysis. Journal of General Internal Medicine, 21(3), pp Dowd, J.B. & Zajacova, A., 21. Does Self-Rated Health Mean the Same Thing Across Socioeconomic Groups? Evidence From Biomarker Data. Annals of Epidemiology, 2(1), pp Dowd, J.B. & Zajacova, A., 27. Does the predictive power of self-rated health for subsequent mortality risk vary by socioeconomic status in the US? Int. J. Epidemiol., 36(6), pp Idler, E.L., 23. Discussion: Gender Differences in Self-Rated Health, in Mortality, and in the Relationship Between the Two. The Gerontologist, 43(3), pp Idler, E.L. & Benyamini, Y., Self-Rated Health and Mortality: A Review of Twenty- Seven Community Studies. Journal of Health and Social Behavior, 38(1), p.17. Kessler, R.C. et al., 22. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(6), pp Layes, A., Asada, Y. & Kephart, G., 212. Whiners and deniers What does self-rated health measure? Social Science & Medicine, 75(1), pp.1 9. Schnittker, J., 25. When Mental Health Becomes Health: Age and the Shifting Meaning of Self-Evaluations of General Health. The Milbank Quarterly, 83(3), pp Spencer, S.M. et al., 29. Racial Differences in Self-Rated Health at Similar Levels of Physical Functioning: An Examination of Health Pessimism in the Health, Aging, and Body Composition Study. Journal of Gerontology: Psychological and Social Sciences., 64B(1), pp Spiers, N. et al., 23. Are Gender Differences in the Relationship Between Self-Rated Health and Mortality Enduring? Results From Three Birth Cohorts in Melton Mowbray, United Kingdom. The Gerontologist, 43(3), pp

7 Distribution of K6 Depression Score by Sex and SRH Excellent, Men Excellent, Women Very Good, Men Very Good, Women Good, Men Good, Women Fair, Men Fair, Women Poor, Men Poor, Women NHIS , Respondents age

8 Distribution of the Number of Conditions by Sex and SRH Excellent, Men Excellent, Women Very Good, Men Very Good, Women Good, Men Good, Women Fair, Men Fair, Women Poor, Men Poor, Women NHIS , Respondents age

9 Table 2. Distribution of health measures within each SRH level, by gender. Men Women K6 depression score Excellent vs. very good.71 2 Very good vs. good 5.59 Good vs. fair.52 5 Fair vs. poor Sum of chronic conditions Excellent vs. very good Very good vs. good 9 8 Good vs. fair 2 2 Fair vs. poor 5 2 The values in this table represent the probability that a worse SRH category is associated with a higher score on a health measure. The values are calculated as the arithmetic difference of pairs of random samples from posterior predictive probability distributions of each health outcome within any SRH level, by gender. Bolded values indicate a higher probability and thus a better concurrent validity of SRH with respect to a specific health measure. 9