Changing contribution of area-level deprivation to total variance in age at death: a population-based decomposition analysis

Socioeconomic status, lifestyle and risk of incident dementia: a prospective cohort study of 276730 participants

Healthy lifestyle might alleviate the socioeconomic inequities in health, but the extent of the joint and interactive effects of these two factors on dementia are unclear. This study aimed to detect the joint and interactive associations of socioeconomic status (SES) and lifestyle factors with incident dementia risk, and the underlying brain imaging alterations. Cox proportional hazards analysis was performed to test the joint and interactive associations. Partial correlation analysis was performed to reflect the brain imaging alterations. A total of 276,730 participants with a mean age of 55.9 (±8.0) years old from UK biobank were included. Over 8.5 (±2.6) years of follow-up, 3013 participants were diagnosed with dementia. Participants with high SES and most healthy lifestyle had a significantly lower risk of incident dementia (HR=0.19, 95% CI=0.14 to 0.26, P<2×10-16), Alzheimer's disease (AD, HR=0.19, 95% CI=0.13 to 0.29, P=8.94×10-15), and vascular dementia (HR=0.24, 95% CI=0.12 to 0.48, P=7.57×10-05) compared with participants with low SES and an unhealthy lifestyle. Significant interactions were found between SES and lifestyle on dementia (P=0.002) and AD (P=0.001) risks; the association between lifestyle and dementia was stronger among those of high SES. The combination of high SES and healthy lifestyle was positively associated with higher volumes in brain regions vulnerable to dementia-related atrophy. These findings suggest that SES and lifestyle significantly interact and influence dementia with its related brain structure phenotypes.

Cause-of-Death Determinants of Lifespan Inequality

We propose a novel decomposition approach that breaks down the levels and trends of lifespan inequality as the sum of cause-of-death contributions. The suggested method shows whether the levels and changes in lifespan inequality are attributable to the levels and changes in (1) the extent of inequality in the cause-specific age-at-death distribution (the "Inequality" component), (2) the total share of deaths attributable to each cause (the "Proportion" component), or (3) the cause-specific mean age at death (the "Mean" component). This so-called Inequality-Proportion-Mean (or IPM) method is applied to 10 low-mortality countries in Europe. Our findings suggest that the most prevalent causes of death (in our setting, "circulatory system" and "neoplasms") do not necessarily contribute the most to overall levels of lifespan inequality. In fact, "perinatal and congenital" causes are the strongest drivers of lifespan inequality declines. The contribution of the IPM components to changes in lifespan inequality varies considerably across causes, genders, and countries. Among the three components, the Mean one explains the least lifespan inequality dynamics, suggesting that shifts in cause-specific mean ages at death alone contributed little to changes in lifespan inequality.

Individual stochasticity in the life history strategies of animals and plants

The life histories of organisms are expressed as rates of development, reproduction, and survival. However, individuals may experience differential outcomes for the same set of rates. Such individual stochasticity generates variance around familiar mean measures of life history traits, such as life expectancy and the reproductive number R0. By writing life cycles as Markov chains, we calculate variance and other indices of variability for longevity, lifetime reproductive output (LRO), age at offspring production, and age at maturity for 83 animal and 332 plant populations from the Comadre and Compadre matrix databases. We find that the magnitude within and variability between populations in variance indices in LRO, especially, are surprisingly high. We furthermore use principal components analysis to assess how the inclusion of variance indices of different demographic outcomes affects life history constraints. We find that these indices, to a similar or greater degree than the mean, explain the variation in life history strategies among plants and animals.

From convergence to divergence: Lifespan variation in US states, 1959-2017

BACKGROUND

Large disparities in life expectancy exist across US states and the gaps have been widening in recent decades. Less is known about the lifespan variability - a measure that can provide important insights into mortality inequalities both between and within states.

METHOD

Using yearly lifetables from the United States Mortality Database, we explore geographic and temporal patterns in lifespan variation (unconditional and conditional on survival to age 10, 35 and 65) across US states between 1959 and 2017. We also examine the contribution of state differences in life expectancy to overall lifespan variation using standard decomposition techniques.

RESULTS

Despite overall convergence in lifespan variation across states over the last six decades, in more recent years there has been notable divergence. Gender-specific analyses show that lifespan variation was generally greater among males than among females; but this pattern reverses for mortality past age 65. Much of the state disparities in lifespan variation, unconditional and conditional on survival to age 10 and 35, were due to mortality differences under the age 65. Decomposition analysis shows that while within-state variability remains the primary driver of overall lifespan variation, the contribution of cross-state differences in life expectancy is growing.

CONCLUSIONS

Variation in longevity is greater within US States than between them, yet cross-states disparities in mortality are increasing. This likely reflects the long-term consequences of rising social, economic, and political stratification for health inequalities both within and across states.

The Cross-sectional Average Inequality in Lifespan (CAL†): A Lifespan Variation Measure That Reflects the Mortality Histories of Cohorts

Lifespan variation is a key metric of mortality that describes both individual uncertainty about the length of life and heterogeneity in population health. We propose a novel and timely lifespan variation measure, which we call the cross-sectional average inequality in lifespan, or CAL†. This new index provides an alternative perspective on the analysis of lifespan inequality by combining the mortality histories of all cohorts present in a cross-sectional approach. We demonstrate how differences in the CAL† measure can be decomposed between populations by age and cohort to explore the compression or expansion of mortality in a cohort perspective. We apply these new methods using data from 10 low-mortality countries or regions from 1879 to 2013. CAL† reveals greater uncertainty in the timing of death than the period life table-based indices of variation indicate. Also, country rankings of lifespan inequality vary considerably between period and cross-sectional measures. These differences raise intriguing questions as to which temporal dimension is the most relevant to individuals when considering the uncertainty in the timing of death in planning their life courses.

Declining regional disparities in mortality in the context of persisting large inequalities in economic conditions: the case of Germany

BACKGROUND

Subnational regional mortality inequalities are large and appear to be mostly increasing within industrialized countries, although comparative studies across high-income countries are scarce. Germany is an important country to examine because it continues to experience considerable economic disparities between its federal states, in part resulting from its former division.

METHODS

We analyse state-level mortality in Germany utilizing data from a newly constructed regional database based on the methodology of the Human Mortality Database. We compare time trends (1991-2015) in the German state-level standard deviation in life expectancy to that of other large, wealthy countries and examine the association between mortality and economic inequalities at the regional level. Finally, using contour-decomposition methods, we investigate the degree to which age patterns of mortality are converging across German federal states.

RESULTS

Regional inequalities in life expectancy in Germany are comparatively low internationally, particularly among women, despite high state-level inequalities in economic conditions. These low regional mortality inequalities emerged 5-10 years after reunification. Mortality is converging over most ages between the longest- and shortest-living German state populations and across the former East-West political border, with the exception of an emerging East-West divergence in mortality among working-aged men.

CONCLUSIONS

The German example shows that large regional economic inequalities are not necessarily paralleled with large regional mortality disparities. Future research should investigate the factors that fostered the emergence of this unusual pattern in Germany.

Variance as a life history outcome: Sensitivity analysis of the contributions of stochasticity and heterogeneity

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Individuals vary in traits or in luck; both cause variance in life history outcomes.

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The variance components are calculated from a multistate group-stage cohort model.

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Sensitivity analysis shows how variance components relate to demographic parameters.

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Both mortality and fertility affect the variance components.

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Effects depend on life history timing, and the nature and mixture of differences.

Variance in life history outcomes among individuals is a requirement for natural selection, and a determinant of the ecological dynamics of populations. Heterogeneity among individuals will cause such variance, but so will the inherently stochastic nature of their demography. The relative contributions of these variance components – stochasticity and heterogeneity – to life history outcomes are presented here in a general, demographic calculation. A general formulation of sensitivity analysis is provided for the relationship between the variance components and the demographic rates within the life cycle. We illustrate these novel methods with two examples; the variance in longevity within and between frailty groups in a laboratory population of fruit flies, and the variance in lifetime reproductive output within and between initial environment states in a perennial herb in a stochastic fire environment. In fruit flies, an increase in mortality would increase the variance due to stochasticity and reduce that due to heterogeneity. In the plant example, increasing mortality reduces, and increasing fertility increases both variance components. Sensitivity analyses such as these can provide a powerful tool in identifying patterns among life history stages and heterogeneity groups and their contributions to variance in life history outcomes.