Self-Rated Health and Subjective Survival Probabilities as Predictors of Mortality

[Healthy life expectancy: a critical look at the benefits and potential of the demographic health indicator]

Demographic ageing yields many societal consequences that depend strongly on the health status of the population. Special indicators have been developed for tracking and assessing population health, which are referred to with the overarching term healthy life expectancy (HLE). The derivation of HLE is intuitive and easily comprehensible. However, an overly simplistic interpretation hides the extreme complexity inherent in adding the health dimension to the life table. This makes HLE compared to classic life expectancy (LE) extremely sensitive to certain conceptual and methodological features. In the article, this is presented in more detail for three aspects: the underlying definition of health, the choice of survey data as a basis for estimating health status, and the reporting behavior of survey participants. It is shown that the impact on HLE can be enormous, leading to considerable bias in the interpretation of levels and trends, but also in the analysis of differences between populations. Nevertheless, the extension of classical LE to HLE is an important achievement that must not be abandoned. Therefore, the article also discusses ways in which the HLE indicator could be made more robust and reliable. Until this is achieved, however, the high methodological sensitivity of HLE must not be ignored if it is used to assess the health status of populations and as a basis for health policy measures.

[Regional differences and trends in healthy life expectancy in Germany]

BACKGROUND

Against the background of increasing life expectancy, the question arises in which state of health the additional years of life are spent. The aim of this study is to assess for the first time regional differences in healthy life expectancy for Germany.

METHODS

The concept of healthy life expectancy allows for the combination of regional differences in health status and mortality in a single measure. This article uses the concept of partial healthy life expectancy. We use official data on deaths and population numbers to calculate abridged life tables. Data from the Socio-Economic Panel (SOEP) are used to determine the age- and sex-specific prevalences of health status. Regional differences are analyzed from 2002 to 2019 by dividing Germany into four regions (North, South, East, West).

RESULTS

The regional differences in healthy life expectancy in Germany are greater than differences in life expectancy, and trends in healthy life expectancy partly differ from the corresponding trends in mortality. These differences over time also vary according to age: while healthy life expectancy has tended to stagnate and, in some cases, decline among the population aged between 20 and 64, the number and proportion of years in good health has increased among older adults up to the age of 79.

CONCLUSION

There are striking regional differences and trends in the distribution of expected years in good health in Germany. The timely identification of regionally divergent developments could facilitate the implementation of targeted health-promoting measures.

The impact of interpersonal reporting heterogeneity on cross-country differences in Healthy Life Years in Europe

BACKGROUND

The European Union has used Healthy Life Years (HLY) as an indicator to monitor the health of its aging populations. Scholarly and popular interest in HLY across countries has grown, particularly regarding the ranking of countries. It is important to note that HLY is based on self-assessments of activity limitations, raising the possibility that it might be influenced by differences in health reporting behaviours between populations, a phenomenon known as differential item functioning (DIF).

METHODS

We estimated DIF-adjusted HLY at age 50 for Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, and Sweden to determine the extent to which differences in HLY might be influenced by reporting heterogeneity across countries. We used anchoring vignettes, taken from the 2004 Survey of Health, Ageing and Retirement in Europe, to estimate DIF-adjusted prevalence rates of activity limitations measured by the Global Activity Limitations Indicator (GALI). The Sullivan method was used to calculate DIF-adjusted HLY.

RESULTS

Changes in HLY before and after adjustment ranged from a 1.20-year decrease for men in Italy to a 1.61-year increase for women in Spain. Adjustment for DIF produced changes in the rankings of the countries by HLY, with upward and downward movements of up to three positions.

CONCLUSION

Our results show that DIF is likely to affect HLY estimates, thereby posing a challenge to the validity of comparisons of HLY across European countries. The findings suggest that HLY should be used to monitor population health status within a country, rather than to make comparisons across countries.

Who live longer than their age peers: individual predictors of longevity among older individuals

BACKGROUND

There are a very few studies focusing on the individual-based survival with a long follow-up time.

AIM

To identify predictors and determine their joint predictive value for longevity using individual-based outcome measures.

METHODS

Data were drawn from Tampere Longitudinal Study on Aging (TamELSA), a study of individuals' age 60-89 years (N = 1450) with a mortality follow-up of up to 35 years. Two measures of longevity were used: the longevity difference (LD) and realized probability of dying (RPD), both of which compare each individual's longevity with their life expectancy as derived from population life tables. Independent variables were categorized into five domains: sociodemographic, health and functioning, subjective experiences, social activities, and living conditions. Linear regression models were used in three steps: bivariate analysis for each variable, multivariate analysis based on backward elimination for each domain, and one final model.

RESULTS

The most important predictors of both outcomes were marital status, years smoked regularly, mobility, self-rated health, endocrine and metabolic diseases, respiratory diseases, and unwillingness to do things or lack of energy. The explained variance in longevity was 13.8% for LD and 14.1% for RPD. This demonstrated a large proportion of unexplained error margins for the prediction of individual longevity, even though many known predictors were used.

DISCUSSION AND CONCLUSIONS

Several predictors associated with longer life were found. Yet, on an individual level, it remains difficult to predict who will live longer than their age peers. The stochastic element in the process of aging and in death may affect this prediction.

Subjective life expectancy and actual mortality: results of a 10-year panel study among older workers

This research examined the judgemental process underlying subjective life expectancy (SLE) and the predictive value of SLE on actual mortality in older adults in the Netherlands. We integrated theoretical insights from life satisfaction research with existing models of SLE. Our model differentiates between bottom-up (objective data of any type) and top-down factors (psychological variables). The study used data from the first wave of the Netherlands Interdisciplinary Demographic Institute Work and Retirement Panel. This is a prospective cohort study among Dutch older workers. The analytical sample included 2278 individuals, assessed at age 50-64 in 2001, with vital statistics tracked through 2011. We used a linear regression model to estimate the impact of bottom-up and top-down factors on SLE. Cox proportional hazard regression was used to determine the impact of SLE on the timing of mortality, crude and adjusted for actuarial correlates of general life expectancy, family history, health and trait-like dispositions. Results reveal that psychological variables play a role in the formation of SLE. Further, the results indicate that SLE predicts actual mortality, crude and adjusted for socio-demographic, biomedical and psychological confounders. Education has an additional effect on mortality. Those with higher educational attainment were less likely to die within the follow-up period. This SES gradient in mortality was not captured in SLE. The findings indicate that SLE is an independent predictor of mortality in a pre-retirement cohort in the Netherlands. SLE does not fully capture educational differences in mortality. Particularly, higher-educated individuals underestimate their life expectancy.

Subjective life expectancy is a risk factor for perceived health status and mortality

BACKGROUND

The purpose of this study was to investigate the association between subjective life expectancy (SLE) and self-rated health and further SLE will predict higher risk for mortality.

METHODS

Data from the Korean Longitudinal Study of Aging (KLoSA) from 2006 to 2014 was assessed using longitudinal data analysis and 10,244 research subjects were included at baseline in 2006. Our modeling approach was based on generalized estimating equation (GEE) for self-rated health and Cox proportional hazards models for mortality.

RESULTS

SLE was significantly associated with mortality (p for trend <0.0001), with the following ORs predicting mortality (yes vs. no): HR = 2.133 (p < .0001) for 0%, HR = 1.805 (p < .0001) for 10-20%, HR = 1.494 (p 0.002) for 30-40%, HR = 1.423 (p 0.002) for 50-60%, HR = 1.157 (p 0.235) for 70-80%, vs. 90-100%. In terms of age-specific association with SLE for self-rated health and mortality, as subjects got older, self-rated health tended to lean more toward poor self-rated health, but as for mortality, the probability of dying increased for people who are younger and HR also tended to increase.

CONCLUSION

This study has shown that SLE is associated with self-rated health and further is a powerful predictor of mortality after adjusting for self-rated health as well as sociodemographic factors and health risk status and behavior factors in a representative population of Koreans.

Relationship between Future Time Orientation and Item Nonresponse on Subjective Probability Questions: A Cross-Cultural Analysis

Time orientation is an unconscious yet fundamental cognitive process that provides a framework for organizing personal experiences in temporal categories of past, present and future, reflecting the relative emphasis given to these categories. Culture lies central to individuals' time orientation, leading to cultural variations in time orientation. For example, people from future-oriented cultures tend to emphasize the future and store information relevant for the future more than those from present- or past-oriented cultures. For survey questions that ask respondents to report expected probabilities of future events, this may translate into culture-specific question difficulties, manifested through systematically varying "I don't know" item nonresponse rates. This study drew on the time orientation theory and examined culture-specific nonresponse patterns on subjective probability questions using methodologically comparable population-based surveys from multiple countries. The results supported our hypothesis. Item nonresponse rates on these questions varied significantly in the way that future-orientation at the group as well as individual level was associated with lower nonresponse rates. This pattern did not apply to non-probability questions. Our study also suggested potential nonresponse bias. Examining culture-specific constructs, such as time orientation, as a framework for measurement mechanisms may contribute to improving cross-cultural research.

Methodological Aspects of Subjective Life Expectancy: Effects of Culture-Specific Reporting Heterogeneity Among Older Adults in the United States

OBJECTIVES

Subjective life expectancy (SLE) has been suggested as a predictor of mortality and mortality-related behaviors. Although critical for culturally diverse societies, these findings do not consider cross-cultural methodological comparability. Culture-specific reporting heterogeneity is a well-known phenomenon introducing biases, and research on this issue with SLE is not established.

METHOD

Using data from the Health and Retirement Study, we examined reporting heterogeneity in SLE focusing on item nonresponse, focal points, and reports over time for five ethnic-cultural groups: non-Hispanic Whites, non-Hispanic Blacks, non-Hispanic other races, English-interviewed Hispanics, and Spanish-interviewed Hispanics.

RESULTS

On item nonresponse, Spanish-interviewed Hispanics said, "I don't know," to SLE significantly more than any other groups. Nearly half of the respondents chose 0, 50, or 100, making them focal points. However, the focal points differed: 50 for Whites, 100 for Blacks, and 0 for Spanish-interviewed Hispanics. The relationship of SLE measured at two time points was higher for Whites than minorities. Moreover, those who said "I don't know" to SLE showed higher subsequent mortality than those who gave an answer. SLE was not a significant mortality predictor for Hispanics.

DISCUSSION

Overall, SLE is not free from culture-specific reporting heterogeneity. This warrants further research about its culture-relevant measurement mechanisms.

Self-reported health and subsequent mortality: an analysis of 767 deaths from a large Thai cohort study

Background

Few studies have examined the link between self-reported health (SRH) and subsequent mortality in developing countries, and very few considered mortality effects of changes in SRH. We examined the relationship between SRH and subsequent all cause or cause-specific mortality in Thailand. We also noted if mortality varied after people changed their SRH.

Methods

We used longitudinal data including SRH from a nationwide Thai Cohort Study (baseline 2005 - follow-up 2009) and linked to official death records (2005–2012). Cox regression examined the association between SRH in 2005 and subsequent all-cause mortality or cause-specific mortality, with results given as confounder-adjusted hazard ratios (HR). We further assessed association between changes in SRH during 2005–2009 and mortality from 2009 to 2012.

Results

Poor SRH at baseline independently relates strongly with subsequent cardiovascular disease (CVD) mortality (HR = 2.8, CI: 1.3-5.9) and “other” causes of death (HR = 1.9, CI: 1.1-3.3) but moderately with cancer mortality (HR = 1.4, CI: 0.7-3.0). SRH did not exhibit a relationship with injury mortality (HR = 1.0, CI: 0.5-2.1). Worsening SRH from 2005 to 2009 associated with increased mortality in 2009–2012 for females but not for males.

Conclusions

In Thailand, SRH is a good predictor of population mortality due to internal causes (e.g. CVD). SRH is holistic, simple to measure and low cost; when repeated it measures dynamic health status. In many developing countries chronic diseases are emerging and morbidity information is limited. SRH could help monitor such transitions in burdens and trends of population health.

Socioeconomic position and the association between anticipated and actual survival in older English adults

BACKGROUND

Socioeconomic disadvantage may cause individuals to have lower expectations of longevity and not engage in healthy behaviours because they judge the long-term health benefits of these to be minimal. We explored demographic, health behaviour, health and socioeconomic correlates of subjectively estimated lifespan ('anticipated survival'); the ability of anticipated survival to predict actual survival; and whether the predictive ability of anticipated survival differed by other variables, particularly socioeconomic position.

METHODS

Data were from wave 1 of the English Longitudinal Study of Ageing. Anticipated survival for up to 25 years was measured on a scale of 0-100. Actual survival was measured over a mean of 6 years, and socioeconomic position using education, household income, occupational class and area deprivation.

RESULTS

Of 10 768 participants, 2255 (21%) died during follow-up. Anticipated survival was positively associated with socioeconomic position, and was greater in women, younger individuals, non-smokers and those who were not widowed, consumed more alcohol, were more physically active, and reported better physical and mental health. After full adjustment, anticipated survival remained positively associated with actual survival. Those reporting low anticipated survival were more likely to die over time than those reporting moderate anticipated survival (HR (95% CIs 1.11 (1.00 to 1.23). The relationship differed significantly by income and age, being strongest in younger individuals and those with higher household income.

CONCLUSIONS

Anticipated survival varied with other variables as expected and reflected actual survival. Younger individuals and those with higher household income were better able to identify subtle differences associated with actual survival.

Culture-Sensitive Question Order Effects of Self-Rated Health Between Older Hispanic and Non-Hispanic Adults in the United States

Objective: The aim of this study is to examine context effects created by the question order for self-rated health (SRH) by race/ethnicity and language. Method: Differences in SRH estimates for non-Hispanic Whites and Hispanics were first examined with multiple observational data that asked SRH in different contexts. To examine context effects by socio-demographics and health-related characteristics, we conducted experiments on SRH question order. Results: While Hispanics reported poorer health than non-Hispanic Whites, this difference, in part, depended on question contexts. With SRH asked after rather than before specific health questions, Hispanics, especially Spanish-speaking Hispanics, reported better health, while non-Hispanic Whites’ reports remained consistent. Among Spanish-speaking Hispanics, the context effect was larger for unmarried and less educated persons and those with comorbidities. Discussion: Question contexts influence SRH reports by Spanish-speaking older adults. Cross-cultural inquiries on the meaning of health and its dynamics with question contexts may explain what SRH measures for increasingly diverse populations.

NONDRINKER MORTALITY RISK IN THE UNITED STATES

The literature has shown that people who do not drink alcohol are at greater risk for death than light to moderate drinkers, yet the reasons for this remain largely unexplained. We examine whether variation in people's reasons for nondrinking explains the increased mortality. Our data come from the 1988-2006 National Health Interview Survey Linked Mortality File (N= 41,076 individuals age 21 and above, of whom 10,421 died over the follow-up period). The results indicate that nondrinkers include several different groups that have unique mortality risks. Among abstainers and light drinkers the risk of mortality is the same as light drinkers for a subgroup who report that they do not drink because of their family upbringing, and moral/religious reasons. In contrast, the risk of mortality is higher than light drinkers for former drinkers who cite health problems or who report problematic drinking behaviors. Our findings address a notable gap in the literature and may inform social policies to reduce or prevent alcohol abuse, increase health, and lengthen life.

Education and health among U.S. working-age adults: a detailed portrait across the full educational attainment spectrum

This article presents detailed estimates of relative and absolute health inequalities among U.S. working-age adults by educational attainment, including six postsecondary schooling levels. We also estimate the impact of several sets of mediating variables on the education-health gradient. Data from the 1997-2009 National Health Interview Survey (N = 178,103) show remarkable health differentials. For example, high school graduates have 3.5 times the odds of reporting "worse" health than do adults with professional or doctoral degrees. The probability of fair or poor health in mid-adulthood is less than 5 percent for adults with the highest levels of education but over 20 percent for adults without a high school diploma. The probability of reporting excellent health in the mid-forties is below 25 percent among high school graduates but over 50 percent for those adults who have professional degrees. These health differences characterize all the demographic subgroups examined in this study. Our results show that economic indicators and health behaviors explain about 40 percent of the education-health relationship. In the United States, adults with the highest educational degrees enjoy a wide array of benefits, including much more favorable self-rated health, compared to their less-educated counterparts.