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The article focuses on subjective versus objective measures of economic distress. Topics include socioeconomic disparities are typically measured in terms of objective criteria such as education, income, wealth, and unemployment, and status anxiety may further explain an apparent discordance between objective and subjective measures.

We consider a broad set of variables used by social scientists and clinicians to identify the leading predictors of five-year survival among American adults. We address a question not considered in earlier research: Do the strongest predictors of survival vary by age, sex or race/ethnicity? The analysis uses hazard models with 30 well-established predictors to examine five-year survival in the National Health and Nutrition Examination Survey. We find that the simple measure of self-assessed health and self-reported measures of functional ability and disability are the strongest predictors in all demographic groups, and are generally ranked considerably higher than biomarkers. Among the biomarkers, serum albumin is highly ranked in most demographic groups, whereas clinical measures of cardiovascular and metabolic function are consistently among the weakest predictors. Despite these similarities, there is substantial variation in the leading predictors across demographic groups, most notably by race and ethnicity [ABSTRACT FROM AUTHOR]

The proliferation of biosocial surveys has increased the importance of weighing the costs and benefits of adding biomarker collection to population-based surveys. A crucial question is whether biomarkers offer incremental value beyond self-reported measures, which are easier to collect and impose less respondent burden. We use longitudinal data from a nationally representative sample of older Taiwanese (aged 54+ in 2000, examined in 2000 and 2006 with mortality follow-up through 2011) to address that question with respect to predicting all-cause mortality. A summary measure of biomarkers improves mortality prediction (as measured by the area under the receiver operating characteristic curve) compared with self-reports alone, but individual biomarkers perform better than the summary score. We find that incorporating change in biomarkers over a six-year period yields a small improvement in mortality prediction compared with one-time measurement. But, is the incremental value worth the costs? [ABSTRACT FROM AUTHOR]

Researchers have had a longstanding interest in understanding the determinants of mortality. This article examines the impact of a broad array of biological markers, together with self-reports of physical and mental health status, on the probability of dying for older adults. The estimates are derived from logistic regression models based on data from a national survey in Taiwan. The analysis confirms previous studies demonstrating the effects of clinical measures related to metabolic syndrome on mortality and identifies detrimental effects of neuroendocrine and immune-system markers. The results reveal that biomarkers provide independent explanatory power in the presence of self-reported health measures. The associations between biomarkers and mortality found here provide new avenues for projecting future mortality and elucidating differences in longevity across populations. [ABSTRACT FROM AUTHOR]

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