Aging in the Context of Cohort Evolution and Mortality Selection

Zheng, Hui

doi:10.1007/s13524-014-0306-9

Cited by 21 publications

(23 citation statements)

References 51 publications

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“…' Costa (2012) found that the processes of both selective mortality (producing robust survivors) and physiological scarring (producing frail survivors) can occur; the outcome depends on the age at which the stressor occurred. Zheng (2014) similarly revealed that the two processes are not mutually exclusive. Young-and old-age mortality rates can be positively associated (i.e., indicative of scarring), while young-age mortality rates are simultaneously negatively associated with mortality acceleration (the rate of increase in the mortality rate) late in life (indicative of selective mortality).…”

Section: Examination Of Frailty and Demographymentioning

confidence: 94%

The Osteological Paradox 20 Years Later: Past Perspectives, Future Directions

2015

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More than 20 years ago, Wood et al. (Curr Anthropol 33:343-370, 1992) published ''The Osteological Paradox: Problems of Inferring Prehistoric Health from Skeletal Samples,'' in which they challenged bioarchaeologists to consider the effects of heterogeneous frailty and selective mortality on health inferences in past populations. Here, we review the paper's impact on bioarchaeology and paleopathology, focusing on recent advancements in studies of ancient health. We find the paper is often cited but infrequently engaged in a meaningful way. Despite an initial decade of limited progress, numerous researchers are now addressing components of the Paradox in more informed ways. We identify four areas of fruitful research: (1) intrasite, contextual perspectives, (2) subadults, (3) associating stress markers with demographic phenomena, and (4) skeletal lesion-formation processes. Although often seen as a problematic assumption, understanding the sources of heterogeneous frailty within human populations is a worthy research question in and of itself, and one that clearly links past and present health research within a global framework.

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Section: Examination Of Frailty and Demographymentioning

confidence: 94%

The Osteological Paradox 20 Years Later: Past Perspectives, Future Directions

2015

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“…These findings support the prediction of increasing prevalence of robust individuals in younger cohorts from technophysio evolution theory and cohort morbidity phenotype theory. All in all, these findings suggest that, although frail individuals are more likely to survive into adulthood in later cohorts due to socioeconomic development and medical advances, which possibly increases the heterogeneity among later cohorts, this selective survival mechanism is not strong enough to reverse the overall reducing mortality variation trend across cohorts due to the continually increasing health capital and decreasing mortality rate at the population level (Zheng 2014). …”

Section: Resultsmentioning

confidence: 99%

“…Recent studies of historical trends of mortality rates also have found patterns of cohort mortality declines that may have resulted from decreased exposures to physiological risk factors for chronic morbidities such as infection and inflammation (Finch and Crimmins 2004), increased health capital brought about by technophysio evolution (Fogel and Costa 1997), or a combination of these and other factors across birth cohorts (Zheng 2014). Period mortality declines may be attributable to a changing mix of socioeconomic development, lifestyle changes, and medical innovations in each period (Omran 1971; Olshansky and Ault 1986).…”

Section: Temporal Changes In Mortality Ratesmentioning

confidence: 99%

Age-Specific Variation in Adult Mortality Rates in Developed Countries

Zheng

Yang

Land

2015

Popul Res Policy Rev

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This paper investigates historical changes in both single-year-of-age adult mortality rates and variation of the single-year mortality rates around expected values within age intervals over the past two centuries in 15 developed countries. We apply an integrated Hierarchical Age-Period-Cohort—Variance Function Regression Model to data from the Human Mortality Database. We find increasing variation of the single-year rates within broader age intervals over the life course for all countries, but the increasing variation slows down at age 90 and then increases again after age 100 for some countries; the variation significantly declined across cohorts born after the early 20th century; and the variation continuously declined over much of the last two centuries but has substantially increased since 1980. Our further analysis finds the recent increases in mortality variation are not due to increasing proportions of older adults in the population, trends in mortality rates, or disproportionate delays in deaths from degenerative and man-made diseases, but rather due to increasing variations in young and middle-age adults.

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“…Because Han Chinese use a lunar calendar (i.e., animal years) to remember his/her birth date and other important life events -such as marriage -age misreporting is relatively lower than among ethnic minorities in China (Zeng and Gu, 2008;Zeng and Vaupel, 2003). It is also possible that less steep slope of mortality at very old ages in less developed western provinces may be due to mortality selection that dropped those frail persons out, leaving more robust ones in the cohort, a common phenomenon at very old ages in general populations (Zheng, 2014).…”

Section: Discussionmentioning

confidence: 99%

Assessments of mortality at oldest-old ages by province in China's 2000 and 2010 censuses

R²,

Andreev³

et al. 2016

IJPS

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This study examined the possible underestimation and age-trajectories of mortality at oldest-old ages in China's 2000 and 2010 censuses. By linking logit-transformed conditional probabilities of dying from 13 countries with the highest data quality in the world, this study found that many Chinese provinces had underestimations of mortality at oldest-old ages when a relatively lenient criterion was applied. When a relatively strict criterion was applied, most provinces had a 30% or more underestimation in the probability of dying. We also investigated age trajectories of death rates after age 80 in these two censuses by applying the Kannisto model. Results showed that the age trajectories were distorted in most provinces after age 95. Overall, eastern-coastal provinces had higher data quality -in terms of low underestimation rates and less distorted age trajectories -whereas western China had provinces with problematic data. Females had greater rates of underestimation yet less distorted age-trajectories than males; and the 2010 census had greater rates of underestimation yet less distorted age-trajectories than the 2000 census. We conclude that appropriate adjustments with simultaneous applications of the Kannisto model are needed for direct estimates of mortality at oldest-old ages in the 2000 and 2010 censuses for China and for its provinces.

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Aging in the Context of Cohort Evolution and Mortality Selection

Cited by 21 publications

References 51 publications

The Osteological Paradox 20 Years Later: Past Perspectives, Future Directions

The Osteological Paradox 20 Years Later: Past Perspectives, Future Directions

Age-Specific Variation in Adult Mortality Rates in Developed Countries

Assessments of mortality at oldest-old ages by province in China's 2000 and 2010 censuses

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