08 June 2018 - 10 June 2018
Under the conference theme “Surviving and thriving in times of change”, the Asian Conference on Aging & Gerontology 2018 (AGen2018) aims to integrate results from different disciplines, fields, and model systems, as well as innovative technological and experimental approaches, into groundbreaking new ideas that challenge existing paradigms regarding aging. AGen2018 discusses analysis which address the ways in which change is impacting the lives of the elderly, and the live of the current generations who will join the ranks of the aged in years to come.
Sergei Scherbov will present latest research from the Reassessing Ageing from a Population Perspective (Re-Ageing) project, entitled "Aging in the World: Aging Demographic Datasheet 2018", coauthored by Warren Sanderson and Stefanie Andruchowitz, during Session III "Aging & Gerontology" on Saturday, 9 Jun 3:15-3:45PM. In his presentation, he will show how redefining old age based on changing characteristics of people provide a more accurate assessment of the challenges of population aging and the effects of policies to overcome them. For many years Scherbov together with Warren Sanderson have developed new measures of age and aging in demographic research. They suggest to broaden research methods to account for significant increases in life expectancy, as the focus on chronological age of people alone provides a limited picture of the process, one that is often not appropriate for either scientific study or policy analysis. Their groundbreaking results have been published in Nature and Science and other high level journals. Scherbov is also PI of the Re-Aging project at IIASA that, among other things, ascertains the extent to which advanced societies are actually aging in multiple dimensions, including health, cognitive abilities, and longevity.
The AGen2018 will be held between 08 – 11 June 2018 at the Art Center Kobe in Japan.
For more information please visit the event website.
Aging in the World: Aging Demographic Datasheet 2018
Most studies of population aging focus on only one characteristic of people: their chronological age. For example, the Old Age Dependency Ratio categorizes people as 'old' at age 65, regardless of whether they were living 50 years ago or likely to be living 50 years in the future. But 65-year-olds today generally have higher remaining life expectancies and are healthier than their counterparts in previous generations.
age-specific characteristics vary over time and place. Focusing on only one aspect of the changes entailed in population aging but not on all the others provides a limited picture that is often not appropriate for scientific study or policy analysis.
The presentation is devoted to new ways of measuring aging that more accurately represent the real world. It will be shown that once more adequate measures of aging are used past aging looks very different and in countries with high life expectancy almost no aging was observed. Future aging trends look much less gloomy when new indicators of aging are used compared to traditional approaches.
The recently developed characteristics approach for the study of population aging will be introduced and used in evaluating differences in aging across space and time. The main idea of the approach is the conversion of different characteristics that reflect people's physical, cognitive or health conditions to a single metric. The hallmark of the approach is the consistent use of changing characteristic schedules together with changing age structures, regardless of the exact way in which the two are combined.
Last edited: 06 June 2018
Aging Demographic Data Sheet
Analyzing Population Aging from a New Perspective
Ediev D, Sanderson W, & Scherbov S (2019). The inverse relationship between life expectancy-induced changes in the old-age dependency ratio and the prospective old-age dependency ratio. Theoretical Population Biology 125: 1-10. DOI:10.1016/j.tpb.2018.10.001.
Ghislandi S, Sanderson W, & Scherbov S (2018). A Simple Measure of Human Development: the Human Life Indicator. Population and Development Review DOI:10.1111/padr.12205. (In Press)
Arpino B, Bordone V, & Scherbov S (2018). Smoking, education and the ability to predict own survival probabilities. Advances in Life Course Research 37: 23-30. DOI:10.1016/j.alcr.2018.06.001.
International Institute for Applied Systems Analysis (IIASA)
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