1. Background: Self-rated health (SRH) is widely used in research on health inequalities by socioeconomic status. However, researchers must be certain that self-reported health corresponds to disease risk and mortality in comparable ways across SES. The purpose of this paper is to test the comparability of self-rated health across socioeconomic groups with respect to subsequent mortality among U.S. adults. Methods: We use the National Health Interview Survey 1986-1994 linked to Multiple Cause of Death Files 1986-1997 (NHIS-MCD). The analyses are based on adults 25 years and older, non-Hispanic white and black, and only those who completed the interviews in person. Proportional hazard models were used for all multivariate analyses. Findings: Both education and income have an effect on how well SRH predicts mortality. Lower health ratings are more strongly associated with mortality for adults with higher education and/or higher income than for their lower SES counterparts. Conclusion: This is the first study to show an effect of socioeconomic status on the predictive power of SRH on mortality in the U.S. Further analyses need to be conducted to better understand the SES differences, but our findings suggest that individuals across SES strata differ in how they evaluate their health and hence researchers need to exercise caution in using SRH as health indicators for comparative studies. Does the Predictive Power of Self-Rated Health Vary by Socioeconomic Status? Anna Zajacova, PhD, 1 Jennifer Beam Dowd, PhD 2. 1 Population Research Center, University of Texas-Austin 2 Center for Social Epidemiology and Population Health, School of Public Health, University of Michigan ABSTRACT Source of data: NHIS 1986-1994, linked to the National Death Index 1986-1997. Case selection: The analyses are based on data from non-Hispanic Black and White adults 25 years and older, only self-reports (no proxy interviews). Analyses: Cox proportional hazard models were used for all multivariate analyses. All models control for age, sex, race, region of residence, marital status, and household size. SUMMARY & CONCLUSIONS REFERENCES & ACKNOWLEDGEMENTS 1.Banks, J., Michael Marmot, Zoe Oldfield, James P. Smith, The SES Health Gradient on Both Sides of the Atlantic. NBER Working Paper 12674, 2006. 2.Jürges, H., True health vs response styles: exploring cross-country differences in self-reported health. Health Economics, 2007. 16(2): p. 163-178. 3.Schnittker, J., When Mental Health Becomes Health: Age and the Shifting Meaning of Self-Evaluation of General health. The Milbank Quarterly, 83(3): p. 397-423. 4.Benyamini, Y., et al., 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? Gerontologist, 2003. 43(3): p. 396-405. 5.Finch, B.K., et al., Validity of Self-rated Health among Latino(a)s. Am. J. Epidemiol., 2002. 155(8): p. 755-759. 6.van Doorslaer, E. and U.-G. Gerdtham, Does inequality in self-assessed health predict inequality in survival by income? Evidence from Swedish data. Social Science & Medicine, 2003. 57(9): p. 1621- 1629. 7.Burstrom, B. and P. Fredlund, Self rated health: Is it as good a predictor of subsequent mortality among adults in lower as well as in higher social classes? J Epidemiol Community Health, 2001. 55(11): p. 836-840. 8.Franks, P., M.R. Gold, and K. Fiscella, Sociodemographics, Self-Rated Health, and Mortality in the U.S. Social Science and Medicine, 2003. 56(12): p. 2505-14. We gratefully acknowledge the financial support provided by NICHD grant #1 R01 HD053696. Jennifer Dowd thanks the Robert Wood Johnson Health and Society Scholars Program at the University of Michigan for her support. Contact: JENNDOWD@UMICH.EDU or ZAJACOVA@MATH.UTEXAS.EDU OBJECTIVE This study examines whether socioeconomic status affects how self-rated health predicts mortality. Significant differences by SES in the predictive power of health ratings would suggest that individuals across SES strata evaluate their health differently, and hence their subjective health ratings are not directly comparable. INTRODUCTION MATERIALS & METHODS RESULTS Self-rated health is a strong predictor of mortality net of a host of other predictors, including age, sex, race, region of residence, marital status, and socioeconomic status as measured by education or household income. Systematic socioeconomic-status differences appear to exist in the relationship between self-rated health and mortality, which suggest that adults across SES strata vary in how they evaluate their health. In particular, SRH is a stronger predictor of subsequent mortality for high-SES adults, compared to low-SES adults. This systematic difference by SES suggests that studies using self-rated health to quantify and explain health differences by SES in the U.S may be biased and potentially overestimating the magnitude of health inequalities. Self-rated health is a common outcome measure in research on socioeconomic inequalities in health It is therefore crucial to know whether SRH corresponds to objective health the same way for adults across SES. Differences in health evaluation, as determined by differences in how SRH relates to objective health measures such as mortality, have been found across countries 1,2, by age 3, sex 4, and race/ethnicity 5. Fewer studies (with one exception conducted in Europe) examined differences by SES 6,7. They found either no significant differences in health evaluation or a stronger predictive effect of SRH on subsequent mortality for adults of higher SES. The one U.S. study on SES differences in health evaluation process found that health perception scores are a better predictor of future mortality for individuals with higher education 8. The study, however, did not use the traditional 5-point self-rated health scale ** p<.01 *** p<.001 a in the last model, the odds ratio show the effect of poor/fair health for HS graduates in table 1 and for the highest income quartile in table 2, relative to respondents in good, very good, or excellent health. N=338,287. All models adjust for age, sex, race, region of residence, household size, and marital status.