Usually, population aging is measured to inform fiscal and social planning because it is considered to indicate the burden that an elderly population presents to the economic, social security, and health systems of a society. Measures of population aging are expected to indicate shifts in the distribution of individuals’ attributes (e.g., chronological age, health) within a population that are relevant to assessing the burden. We claim that chronological age – even though it is the attribute most broadly used – may frequently not be the best measure to satisfy this purpose. A distribution of chronological age per se does not present a burden. Rather, burdens arise from the characteristics that supposedly or actually accompany chronological ages. We posit that in addition to chronological age, meaningful measures of population aging should reflect, for instance, the distribution of economic productivity, health, functional capacities, or biological age, as these attributes may more directly assess the burden on the socioeconomic and health systems. Here, we illustrate some limitations of measures of population aging based on each kind of measure, including chronological age, and review alternative measures that may better inform fiscal, social, and health planning.

A person’s chronological age is the time since birth, normally measured as the exact number of years or the completed number of years. Demographic measures of population aging using chronological age fall into 2 categories: those, like median age or the old-age dependency ratio (OADR), that are based on a current age structure (the number of people of each age at a given point in time) and others, like life expectancy at birth, or remaining life expectancy (RLE) at any specified age, that are based on a period life table (age-specific death rates in a given time interval) [1].

Measures Based on Current Chronological-Age Structure

Some measures using the current age structure are based on the proportion of the population below a given chronological age or above a threshold considered “old,” or ratios of these measures. For example, the OADR and the potential support ratio frequently use the chronological age of 65 years (or other ages such as 60 or 70 years) as the age above which a person is defined as “old.” In those measures, chronological age is commonly interpreted as an indicator of being active in the workforce, healthy, and a contributor economically – versus being dependent, frail, or a recipient of economic transfers. Such measures can be useful when chronological age is relevant; as, for instance, when a fixed, universal legal retirement age ignores people’s functional, cognitive, or health capacity. In such cases, the chronological age distribution determines the retired share of the population. Some chronological-age measures determine the onset of old age by the relative position (e.g., among the top 15%) in the age distribution (i.e., relative age) instead of a cutoff age [2].

Measures Based on Period Life Tables

Many chronological-age measures are based on a period life table [3]. A life table calculates the number of individuals, per thousand born, who would survive to each chronological age if all individuals experienced the death rates experienced at each chronological age during some well-defined period, like a year or decade. Measures derived from a life table include life expectancy at birth, adult life expectancy (e.g., at chronological ages 30 or 50 years), chronological age through which only 5% of the birth cohort survive, and the chronological age at which only 5% of those who enter adulthood survive [4].

Measures based on RLE calculate how many years the average person of a given chronological age has left to live according to the life table [5]. Countries with high life expectancies (e.g., over 80 years) would have a smaller fraction of people with RLE below 10 [5] or 15 [6] years than the fraction of people with chronological age above 60 or 65 years. Thus, a long-lived population would be “young” (by the criterion of the proportion of the population with RLE below 15 years) even though it is chronologically older (by the criterion of the proportion > 65 years). One weakness of this approach is that many of those with a RLE of 15 years could still be working and healthy.

An intuitively appealing summary of how long the average person in a population can expect to live according to a life table is the population average RLE, defined as the weighted average of age-specific RLE, where the weights are the proportions of the population at each age [7, 8].

The measures based on RLE are informative when functional status strongly relates to RLE, that is, if countries with lower mortality and higher remaining life expectancy have lower disease prevalence and better function by chronological age. However, in some countries, healthy life expectancy (HALE, see below) is diverging from life expectancy, with a greater number of life years spent in poor health [9], whereas in other countries life years spent in good health increase disproportionately [10]. None of the measures based only on chronological age or the life table reflects disease burden or level of physical, cognitive, or economic functioning at given ages.

A measure of population aging that depends on labor force participation is the economic dependency ratio (EDR), defined as the ratio of the number of economically inactive individuals to those employed. The EDR is often used by fiscal planners, governments, and social security administrators as well as labor market officials and employment agencies. It is commonly applied to estimate the fiscal viability of pension plans and indicate national productivity. Dependency is equated with not being engaged in paid work, even though some individuals may not be working for pay but are able to support themselves by other means. The EDR excludes household work (which may be necessary throughout a career of paid work for those who cannot afford to hire help) and voluntary work (which is common in some socioeconomic groups after official retirement) [11]. Further, this ratio is driven not only by the chronological age structure of a population and age-based retirement laws but also by the proportion of women, older individuals, and migrants engaged in paid work as well as the economic situation of the population. If people continue to retire at ages of previous generations, while living longer, the EDR will increase. However, for instance, in Europe over recent decades, increasing numbers of women joined the labor market and thus decreased the EDR despite an increasing mean chronological age of the population. Turkey, which has a chronologically younger population than the Netherlands, has a higher EDR due to low labor force participation rates among Turkish women [12].

Another labor-related measure of population aging considers life expectancy and work conjointly. This measure is based on the ratio of the number of working years to the number of years spent in full retirement [13]. Based on official US death rates for 1935, Olshansky et al. [13] calculated that the proportion of life after age 20 years in 1935 spent working, assuming a full retirement occurred at age 65 years, was 0.7802. Consequently, they defined “old” age in years after 1935 as the age at full retirement that would keep constant this proportion of adult life spent working. According to US death rates in 2009, a full retirement age of 69.1 years would have maintained constant this ratio of working years to retired years.

Similarly, RLE, introduced above, could be relevant economically if the age of eligibility for pensions or if the duration of work depended on RLE rather than on chronological age. In a hybrid demographic-economic measure called the real elderly dependency ratio (REDR), defined as the number of men and women with RLE ≤15 years, divided by the total number of men and women employed, the denominator is not the number of individuals in some age group, but the number, regardless of age, actually employed. In the United States, from 1950 to 2010, the REDR fell from approximately 22 to 17%, while the OADR rose from approximately 13 to 20%. In striking contrast, in Japan, where both female employment and immigration rates are relatively low, the REDR rose from 15% in 1990 to 23% in 2007, and the OADR increased from 17 to 32% [14]. A limitation of the REDR is that some people with RLE < 15 years may still be working.

Rather than focusing only on earnings through paid work, other economic measures of population aging have also considered the difference between earnings and consumption at each chronological age over the life cycle. The “National Transfer Accounts” (NTA) network measured the chronological age-specific schedules of earnings and consumption for countries around the world and summarized them by 2 measures, the fiscal support ratio, which approximates the ratio of total taxes to public transfer inflows, and the support ratio, which approximates the ratio of earnings to consumption. Governments are primarily interested in the fiscal support ratio, while private individuals are primarily interested in the support ratio. NTA finds large variation among countries in these ratios and in the underlying drivers, such as the chronological age when one becomes a net recipient of transfers [15]. Because public transfers go mainly to the elderly, especially in rich countries, while private transfers mostly go to children, “the age structure that favors public finances is much younger than the age structure that favors the combined finances of public and private sectors.”

Economic measures of population aging aim to indicate whether social security and pension funds are sustainable given longer lives but, like chronological-age measures of population aging, these economic indicators do not consider the differences in health and functional status between people of the same chronological age, the same RLE, or the same employment status.

Measures of physical health based on a person’s number of medical diagnoses can be useful indicators of population aging. Such indicators tend to be correlated with increasing chronological age, yet the relations are far from linear. The prevalence of some diseases is strongly associated with RLE and only slightly or not at all with chronological age [16]. It seems very helpful to have health indicators that differentiate between countries with comparable chronological age structures in order to estimate the financial implications of their populations’ health.

An important physical-health measure of population aging is the HALE, which is used widely by the World Health Organization (WHO), the UN, and other international agencies, such as the Institute for Health Metrics Evaluation. It is defined as the average equivalent number of years of full health that a person could expect to live if he or she were to pass through life subject to the age-specific death rates and ill-health rates observed in a given period and country. The WHO has developed methods for calculating the HALE that combine standard life table information on mortality with age- and sex-specific prevalence data for health states using Sullivan’s method [17, 18]. Disease prevalence, incidence, and remission data from the Global Burden of Disease (GBD) project have been used to estimate severity-adjusted prevalence by age and sex for all countries [19]. As data about medical diagnoses are often based on self-report, there are likely unmeasured differences in expectations and norms for health which limit the validity of such measures.

Increases in life expectancy and reductions in morbidity have inspired many discussions on whether extensions to life expectancy have been matched with similar increases in healthy life spans and HALE, or whether morbidity has expanded [20]. The GBD project provided global data on age-specific health from 1990 to 2015. Its analysis comparing the development of HALE and of life expectancy found that, globally, HALE increased less rapidly than life expectancy from 1990 to 2015 [9]. People lived longer on average, but they also lived more years with chronic illnesses. There were, however, large differences in HALE trends between countries. Moreover, living with chronic illnesses does not necessarily compromise functional capacity.

The behavioral and medical sciences use indicators of individuals’ functional capacities and their distribution in a population that are linked with both labor-related productivity and disease. Most of these characteristics are associated with, but not equivalent to, chronological age.

Cognitive Functioning

Cognition refers to the mental functions involved in attention, thinking, understanding, learning, remembering, solving problems, and making decisions [21]. Cognition involves mechanics (or fluid intelligence) and pragmatics (or crystallized intelligence) [22]. The pragmatics are the knowledge, skills, and experiences measured by tests of general world knowledge or vocabulary. During adulthood, the pragmatics stay stable, declining only very late in life. In contrast, the mechanics are strongly linked with the biology of the brain, such as the number of neurons, their connectivity, and the brain metabolism that helps to transport information through the brain, and are measured, for instance, by tests of reaction time or of logical reasoning. The mechanics decline with increasing chronological age starting in the mid-twenties.

The pragmatics and the mechanics have been reported [23] as improving over recent decades at all ages in multiple countries [24]. Given historical improvements in mean levels and possibly the shape of the cognitive aging trajectory [24], it seemed useful to calculate a dependency ratio based on a “cognitive age.” One study defined people as cognitively “older” if they remembered fewer than half of the words in a test to recall 10 words [25]. The 10-word list was harmonized for different languages and cultures and administered to representative people from countries accommodating the majority of the world’s population. Some chronologically older countries displayed better memory among people aged > 50 years than some countries with chronologically younger populations. Because cohort replacement is slow, countries whose older adults have higher cognitive levels today are likely to continue to have higher cognitive levels for several decades [26]. A similar measure termed “cognitively intact life expectancy” adjusts life expectancy for the prevalence of cognitive impairment [27].

Cognitive functioning is also measured by using structural and functional neuroimaging techniques [28], which are distinct. Studies use either or sometimes both [29]. Applying machine learning analysis to neuroimaging data from a healthy lifespan sample (n = 2,001, 18–90 years) yielded coefficients that were successfully validated in a second sample of older adults using indicators of functional aging, such as walking speed, grip strength, or cognitive mechanics [30]. The difference between a person’s machine learning-derived brain age and the person’s chronological age was a robust predictor of mortality even after controlling statistically for education, social class, APOEε4, and age-associated illnesses. This difference had a higher sensitivity and specificity than methylation age and telomere length, two prominent biomarkers of aging (see below).

Behavioral assessments of cognitive aging can provide a basis for new measures of population aging comparable over time and across regions. Hence, cognitive age may be a useful complement to chronological age in indices of dependency. Unfortunately, the present costs in time and money of these measures of brain age limit their scalability, and the lack of past measurements limits their historical comparative use.

Sensory Functioning

Sensory functioning, including hearing and vision, affects social and economic participation, independence, and productivity. It has also been found to be closely associated with cognitive performance [31].

Visual acuity declines with increasing chronological age. Refractive errors, cataracts, macular degeneration, and blindness are more prevalent at older chronological ages. Twelve percent of the 60- to 64-year-old women and 34% of the 80- to 84-year-old women worldwide are visually impaired or blind [32]. Later-born cohorts around the world have a lower prevalence of poor eyesight following improved living conditions. Some health trends, such as increases in diabetes, worsen eyesight [33].

Hearing (i.e., auditory acuity) can be measured with high reliability and validity. Hearing tends to decline with increasing chronological age. People who are hearing impaired or deaf may be excluded, or self-exclude, from work and social arenas of interaction, and deafness is more common at older chronological ages [34]. The often affordable corrections of hearing, however, have improved and may compensate for some hearing losses.

Functional Capacity

A person’s functional capacity is usually assessed by measures such as vital (lung) capacity, gait speed, standing balance, grip strength, the chair stand test, and reflex speeds [35]. Balance, strength, and movement restrictions, which can be measured by performance tests, are central for mobility and independent living [36]. These functional capacities decline with increasing chronological age but not necessarily in line with diagnoses of diseases and with great differences among individuals of the same chronological age. They complement other measures of population aging and provide a basis for a widely used measure of disability-free life expectancy [37].

Longitudinal surveys show that individuals of lower socioeconomic status lose functioning and experience an onset of disability earlier in life than people of higher socioeconomic status [38]. Functional abilities have improved in successive cohorts, at least in some countries [39].

In social science and epidemiological surveys, functional limitations are frequently measured by self-reported difficulties with walking, climbing stairs, lifting, and other aspects of mobility, whereas disability is generally indicated by self-reported difficulties performing essential activities of daily living, such as eating and dressing, and instrumental activities of daily living, which require higher cognitive functions, such as grocery shopping and using transportation. Though these measures of functional capacity are easily scalable from individuals to a population, their validity is limited because they rely on self-report (see below). Individuals’ social and demographic characteristics may affect the thresholds at which they acknowledge difficulty in performing activities [40]. Objective measures of physical function obtained by an interviewer – such as grip strength, lung function, and gait speed – avoid such biases [41].

Leonardo da Vinci (1452–1519) wrote that trees form a ring annually and that the conditions of growth affect the thickness and density of the ring. The number of annual growth rings of an individual tree measures the tree’s chronological age in years. Tree rings may have been the earliest known biomarker. In humans, height, weight, body mass index, muscular strength, skin wrinkles or facial features, and hair color are familiar phenotypic traits that are correlated, but imperfectly, with chronological age, RLE, and prospects of health or disease [42].

Biomedical scientists have been seeking markers that predict morbidity and mortality more accurately than chronological age [43], assuming some biological parameters will measure aging more accurately than chronological age. In the last 2 decades, many biomarkers of aging [44] have been proposed, with limited success.

Xia et al. [45] followed 2 earlier major reviews of molecular biomarkers of aging [44, 46] in organizing biomarkers according to the so-called molecular pathways underlying aging: DNA and chromosomes (including telomeres, DNA repair, and epigenetic modification), RNA and the transcriptome (transcriptome profiles, circulating microRNAs, and long noncoding RNAs), metabolism (nutrient sensing, protein metabolism, and lipid metabolism), oxidative stress and mitochondria, cell senescence, inflammation, and intercellular communication. These categories cross many levels of biological organization, from the molecular (e.g., miR-34a) to the phenotypic (e.g., senescence-associated secretory phenotype, as an example of inflammation and intercellular communication). Other reviews of biomarkers of aging focus on parts of this broad spectrum, such as the epigenome [47], metabolism [48], or the interaction between chromatin and metabolism in regulating tissue stem cells during aging [49].

In recent years, 3 groups of biomarkers have received considerable research attention: telomere length, algorithms applied to genome-wide DNA methylation data, and algorithms combining information on multiple clinical biomarkers. For example, the Klemera-Doubal method (KDM) biological age [50] and age-related homeostatic dysregulation [51] have been proposed as cross-sectional estimates of biological age. Differences in biological age (based on 10 biomarkers) were found to predict all-cause, cardiovascular disease, and cancer mortality among African-Americans [52]. The “pace of aging” is a longitudinal estimate of biological aging based on changes across repeated measurements of multiple biological measures, such as lipoprotein, cholesterol, triglycerides, indicators of cardiorespiratory fitness, or white blood cell count [53].

The evidence on telomere length and aging is highly equivocal. Variation in measurement procedures and cell types used may explain large differences in results [54]. The evidence on epigenetic age is not much easier to interpret. Different numbers of CpG sites are used to assess methylation-based epigenetic age (i.e., 353 CpG, 99 CpG, and 71 CpG). A CpG site is a region of DNA in which a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases. The 71-CpG sites measure is at least moderately related with aggregate biomarker measures and functional outcomes, such as cognitive performance or grip strength [55]. Unfortunately, the 353-CpG and 99-CpG clocks used chronological age as a validation criterion rather than mortality or morbidity [56], which renders them unsuitable as more precise indicators of functional capacity than chronological age.

One longitudinal study of biomarkers found that although epigenetic clocks correlated with one another and so did biomarker algorithms, correlations between the epigenetic clocks and biomarker algorithms were low, as were correlations of both sets of measures with telomere length. None of the measures of biological aging was strongly associated with health-related characteristics, such as balance, grip strength, motor coordination, physical limitations, cognitive decline, self-rated health, or facial aging [57]. Mitochondrial functioning and morphology have also been explored as biomarkers of individual aging [58]. Mitochondrial health indicators are still in their infancy and demand too much effort to be scaled up to populations, but this limitation may vanish soon [59].

For biomarkers to be useful measures of population aging, it is desirable to demonstrate whether the difference of biomarker age minus chronological age predicts health or future survival. Moreover, biomarkers should also be validated against functional measures, including health and cognitive performance, as has been shown for the brain age measures described above. To date, a major limitation of biomarkers as measures of population aging is that few are available on a population level or across historical time.

Subjective measures of age have been found to have meaning over and above objective indicators, such as chronological age, physical health, and cognitive performance [60]. Subjective age is assessed by asking participants how old they feel using chronological age as a measurement unit [61]. Subjective health is assessed by asking participants: “Overall, how do you rate your health?”, usually using 3- to 10-point Likert scales ranging from “bad” to “excellent” to record responses.

Most studies suggest that people feel younger than their chronological age. This divergence was stronger at older ages: 25% of those in their 30s, 54% in their 40s, and 69% in their 50s reported a youthful age [62]. The difference varies across countries [63, 64]. For instance, Americans feel relatively more youthful than Germans [61].

Subjective age is relevant because people who feel younger than their chronological age generally have higher subjective well-being and positive emotions [61]. Higher well-being and positive emotions are associated with higher levels of cognitive mechanics in later life [65]. Older individuals who perceive themselves as younger than their chronological age display a larger amount of grey matter in the brain and a younger brain age [66]. Depending on their subjective age, 2 persons of the same cognitive age may make less or more use of their cognitive performance potential. Also, under conditions of stress, higher well-being is an important resource [67].

Subjective health, another widely available self-reported measure, predicts mortality after accounting for objective health. People seem to be able to sense changes in their physical health that are not (yet) captured by extant objective health measures [68].

Combining objective and subjective indicators of health with, or instead of, chronological age might yield a more valid assessment of population aging. For example, happy life expectancy assesses how many years of life one can expect to live in a happy state, not counting unhappy life years [69]. A study of increases in US life expectancy between the 1970s and the 2000s suggested that most of the increases in life expectancy were happy life years [70]. An important limitation of subjective measures is that response scales can be difficult to compare across respondents, genders, cohorts, and regions because subjective measures of age and health are culturally influenced and typically refer to a person’s local reference group rather than a global distribution. Nevertheless, such measures tap into a different part of the population variance than objective measures and may be useful for relative comparisons between countries [71]. The diseases now contributing most to the disease burden in aging nations tend to be based on subjective appraisals (such as depression or back and neck pain) [19], suggesting that more attention could be given to subjective measures of aging.

We reviewed measures of population aging that may reflect the economic, social security, and health burdens linked with changes in the distributions of individual attributes in a population and may help inform fiscal, social, and public health planning. Measures that focus on chronological age alone do not always adequately reflect economic, functional, and health characteristics. Such characteristics vary widely among people who have the same chronological age depending on cohort, country or region, and subpopulation. This variability limits the validity of chronological age as a proxy of such characteristics. Likewise, measures of population aging based on labor force participation need not represent the distribution of health or functional ability. Recently developed indicators based on biological aging for the most part are not yet available on a population level and, therefore, have limited applicability. Among measures of health and functional ability, the number of diagnoses and the activities of daily living scale are widely available. Both are, however, self-reported measures. Objectively measured indicators of functioning that are comparable over time, region, and subpopulation include physical performance measures, such as grip strength or walking speed, and are increasingly available. Cognitive performance also remains a viable and scalable candidate measure.

Apart from our quest for adequate measures to assess population aging, several single-number indices of population aging have been proposed to assess how effectively countries respond to challenges of chronological population aging. Examples include the Active Aging Index, the Global Age-Watch Index, and recently the Aging Society Index [72, 73].

Although global chronological aging is undeniable, chronological-age measures are often not relevant to answer concerns related to demographic change. Understanding how chronological aging relates to health, welfare, functioning, and productivity of people and countries around the world requires measures that directly focus on the question at hand. Unfortunately, global data on alternative measures of population aging that incorporate time trends are not (yet) available, and we cannot say whether the world as a whole is aging or not.

All authors contributed equally to this paper. V.F.S. is grateful for support from the Columbia Aging Center and support from the Research Council of Norway through its Centres of Excellence funding scheme, project number 262700. U.M.S. thanks the Alfred P. Sloan Foundation for partial support (G-2015-14132). J.E.C. thanks the US National Science Foundation for partial support through grant DMS-1225529 and Roseanne Benjamin for help.

The authors have no ethical conflicts to disclose.

The authors have no conflicts of interest to declare.

1.
Gavrilov
LA
,
Heuveline
P
:
Aging of population.
The encyclopedia of population
2003
;1:32-37.
2.
d’Albis
H
,
Collard
F
.
Age groups and the measure of population aging
.
Demogr Res
.
2012
;
29
:
617
40
. 1435-9871
3.
United Nations Population Division
. World Population Prospects. In:
York
UN
, editor
.
New York
:
UNPD
;
2017
.
4.
Colchero
F
,
Rau
R
,
Jones
OR
,
Barthold
JA
,
Conde
DA
,
Lenart
A
, et al
The emergence of longevous populations
.
Proc Natl Acad Sci USA
.
2016
Nov
;
113
(
48
):
E7681
90
.
[PubMed]
0027-8424
5.
Ryder
NB
.
Notes on stationary populations
.
Popul Index
.
1975
;
41
(
1
):
3
28
. 0032-4701
6.
Sanderson
WC
,
Scherbov
S
.
Demography. Remeasuring aging
.
Science
.
2010
Sep
;
329
(
5997
):
1287
8
.
[PubMed]
0036-8075
7.
Panush
N
,
Peritz
E
:
Potential demography: A second look.
European Journal of Population/Revue européenne de Démographie
1996
;12:27-39.
8.
Hersch
L
. la de’mographie actuelle a` la de’mographie potentielle. Melange des E’tudes Economiques Offertes a` William Rappard (Georg, Geneva, 1944)
1944
9.
Kassebaum
NJ
,
Arora
M
,
Barber
RM
,
Bhutta
ZA
,
Brown
J
,
Carter
A
, et al;
GBD 2015 DALYs and HALE Collaborators
.
Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015
.
Lancet
.
2016
Oct
;
388
(
10053
):
1603
58
.
[PubMed]
0140-6736
10.
Vaupel
JW
.
Biodemography of human ageing
.
Nature
.
2010
Mar
;
464
(
7288
):
536
42
.
[PubMed]
0028-0836
11.
Maestas
N
.
Back to work expectations and realizations of work after retirement
.
J Hum Resour
.
2010
;
45
(
3
):
718
48
.
[PubMed]
0022-166X
12.
Loichinger
E
,
Skirbekk
V
.
International Variation in Ageing and Economic Dependency: A Cohort Perspective
.
Comp Popul Stud
.
2016
;
•••
:
40
.1869-8980
13.
Olshansky
SJ
,
Goldman
DP
,
Rowe
JW
.
Resetting Social Security
.
Daedalus
.
2015
;
144
(
2
):
68
79
. 0011-5266
14.
Spijker
J
,
MacInnes
J
,
Riffe
T
: Population Aging: How Should It Be Measured?,
2014
,
15.
Lee
R
,
Mason
A
,
Lee
R
,
Mason
A
,
Amporfu
E
,
An
CB
, et al;
members of the NTA Network
.
Is low fertility really a problem? Population aging, dependency, and consumption
.
Science
.
2014
Oct
;
346
(
6206
):
229
34
.
[PubMed]
0036-8075
16.
Riffe
T
,
Chung
PH
,
Spijker
J
,
MacInnes
J
:
Time-to-death patterns in markers of age and dependency.
2015
17.
Mathers
CD
,
Murray
CJ
,
Salomon
JA
:
Methods for measuring healthy life expectancy.
Health systems performance assessment: debates, methods and empiricism
2003
:437-470.
18.
Robine
JM
,
Jagger
C
,
Mathers
CD
,
Crimmins
EM
,
Suzman
RM
.
Determining health expectancies
.
John Wiley & Sons
;
2003
.
19.
Vos
T
,
Abajobir
AA
,
Abate
KH
,
Abbafati
C
,
Abbas
KM
,
Abd-Allah
F
, et al;
GBD 2016 Disease and Injury Incidence and Prevalence Collaborators
.
Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016
.
Lancet
.
2017
Sep
;
390
(
10100
):
1211
59
.
[PubMed]
0140-6736
20.
Solé-Auró
A
,
Alcañiz
M
.
Are we living longer but less healthy? Trends in mortality and morbidity in Catalonia (Spain), 1994-2011
.
Eur J Ageing
.
2014
May
;
12
(
1
):
61
70
.
[PubMed]
1613-9372
21.
Liverman
CT
,
Yaffe
K
,
Blazer
DG
.
Cognitive aging: Progress in understanding and opportunities for action
.
National Academies Press
;
2015
.
22.
Baltes
PB
,
Lindenberger
U
,
Staudinger
UM
. Lifespan theory in developmental psychology; in Damon W, Lerner RM (eds): Handbook of Child Psychology. New York, Wiley,
2006
, vol 1. Theoretical models of human development, pp 569-664.
23.
Flynn
JR
.
Searching for justice: the discovery of IQ gains over time
.
Am Psychol
.
1999
;
54
(
1
):
5
20
. 0003-066X
24.
Staudinger
UM
.
Images of Aging: Outside and Inside Perspectives
.
Annu Rev Gerontol Geriatr
.
2015
;
35
(
1
):
187
209
. 0198-8794
25.
Skirbekk
V
,
Loichinger
E
,
Weber
D
.
Variation in cognitive functioning as a refined approach to comparing aging across countries
.
Proc Natl Acad Sci USA
.
2012
Jan
;
109
(
3
):
770
4
.
[PubMed]
0027-8424
26.
Skirbekk
V
,
Stonawski
M
,
Bonsang
E
,
Staudinger
UM
.
The Flynn effect and population aging
.
Intelligence
.
2013
;
41
(
3
):
169
77
. 0160-2896
27.
Crimmins
EM
,
Saito
Y
,
Kim
JK
.
Change in cognitively healthy and cognitively impaired life expectancy in the United States: 2000–2010
.
SSM Popul Health
.
2016
Dec
;
2
:
793
7
.
[PubMed]
2352-8273
28.
Gabrieli
JD
,
Ghosh
SS
,
Whitfield-Gabrieli
S
.
Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience
.
Neuron
.
2015
Jan
;
85
(
1
):
11
26
.
[PubMed]
0896-6273
29.
Liem
F
,
Varoquaux
G
,
Kynast
J
,
Beyer
F
,
Kharabian Masouleh
S
,
Huntenburg
JM
, et al
Predicting brain-age from multimodal imaging data captures cognitive impairment
.
Neuroimage
.
2017
Mar
;
148
:
179
88
.
[PubMed]
1053-8119
30.
Cole
JH
,
Franke
K
.
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
.
Trends Neurosci
.
2017
Dec
;
40
(
12
):
681
90
.
[PubMed]
0166-2236
31.
Lindenberger
U
,
Baltes
PB
.
Sensory functioning and intelligence in old age: a strong connection
.
Psychol Aging
.
1994
Sep
;
9
(
3
):
339
55
.
[PubMed]
0882-7974
32.
Leasher
JL
,
Bourne
RR
,
Flaxman
SR
,
Jonas
JB
,
Keeffe
J
,
Naidoo
K
, et al;
Vision Loss Expert Group of the Global Burden of Disease Study
.
Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990 to 2010
.
Diabetes Care
.
2016
Sep
;
39
(
9
):
1643
9
.
[PubMed]
0149-5992
33.
Ackland
P
,
Resnikoff
S
,
Bourne
R
.
World blindness and visual impairment: despite many successes, the problem is growing
.
Community Eye Health
.
2017
;
30
(
100
):
71
3
.
[PubMed]
0953-6833
34.
Homans
NC
,
Metselaar
RM
,
Dingemanse
JG
,
van der Schroeff
MP
,
Brocaar
MP
,
Wieringa
MH
, et al
Prevalence of age-related hearing loss, including sex differences, in older adults in a large cohort study
.
Laryngoscope
.
2017
Mar
;
127
(
3
):
725
30
.
[PubMed]
0023-852X
35.
Haas
SA
,
Krueger
PM
,
Rohlfsen
L
.
Race/ethnic and nativity disparities in later life physical performance: the role of health and socioeconomic status over the life course
.
J Gerontol B Psychol Sci Soc Sci
.
2012
Mar
;
67
(
2
):
238
48
.
[PubMed]
1079-5014
36.
Dal Bello-Haas
VP
,
Thorpe
LU
,
Lix
LM
,
Scudds
R
,
Hadjistavropoulos
T
.
The effects of a long-term care walking program on balance, falls and well-being
.
BMC Geriatr
.
2012
Dec
;
12
(
1
):
76
.
[PubMed]
1471-2318
37.
Robine
JM
,
Brouard
N
,
Colvez
A
.
Les indicateurs d’espérance de vie sans incapacité (EVSI). Des indicateurs globaux de l’état de santé des populations
.
Rev Epidemiol Sante Publique
.
1987
;
35
(
3-4
):
206
24
.
[PubMed]
0398-7620
38.
House
JS
,
Lantz
PM
,
Herd
P
.
Continuity and change in the social stratification of aging and health over the life course: evidence from a nationally representative longitudinal study from 1986 to 2001/2002 (Americans’ Changing Lives Study)
.
J Gerontol B Psychol Sci Soc Sci
.
2005
Oct
;
60
(
Spec No 2
):
15
26
.
[PubMed]
1079-5014
39.
Heikkinen
E
,
Kauppinen
M
,
Rantanen
T
,
Leinonen
R
,
Lyyra
TM
,
Suutama
T
, et al
Cohort differences in health, functioning and physical activity in the young-old Finnish population
.
Aging Clin Exp Res
.
2011
Apr
;
23
(
2
):
126
34
.
[PubMed]
1594-0667
40.
Freedman
VA
,
Agree
EM
,
Cornman
JC
,
Spillman
BC
,
Kasper
JD
.
Reliability and validity of self-care and mobility accommodations measures in the National Health and Aging Trends Study
.
Gerontologist
.
2014
Dec
;
54
(
6
):
944
51
.
[PubMed]
0016-9013
41.
Goldman
N
,
Glei
DA
,
Rosero-Bixby
L
,
Chiou
ST
,
Weinstein
M
.
Performance-based measures of physical function as mortality predictors: incremental value beyond self-reports
.
Demogr Res
.
2014
Jan
;
30
(
7
):
227
52
.
[PubMed]
1435-9871
42.
Chen
W
,
Qian
W
,
Wu
G
,
Chen
W
,
Xian
B
,
Chen
X
, et al
Three-dimensional human facial morphologies as robust aging markers
.
Cell Res
.
2015
May
;
25
(
5
):
574
87
.
[PubMed]
1001-0602
43.
Butler
RN
,
Sprott
R
,
Warner
H
,
Bland
J
,
Feuers
R
,
Forster
M
, et al
Biomarkers of aging: from primitive organisms to humans
.
J Gerontol A Biol Sci Med Sci
.
2004
Jun
;
59
(
6
):
B560
7
.
[PubMed]
1079-5006
44.
López-Otín
C
,
Blasco
MA
,
Partridge
L
,
Serrano
M
,
Kroemer
G
.
The hallmarks of aging
.
Cell
.
2013
Jun
;
153
(
6
):
1194
217
.
[PubMed]
0092-8674
45.
Xia
X
,
Chen
W
,
McDermott
J
,
Han
JJ
.
Molecular and phenotypic biomarkers of aging
.
F1000 Res
.
2017
Jun
;
6
:
860
.
[PubMed]
2046-1402
46.
Engelfriet
PM
,
Jansen
EH
,
Picavet
HS
,
Dollé
ME
.
Biochemical markers of aging for longitudinal studies in humans
.
Epidemiol Rev
.
2013
;
35
(
1
):
132
51
.
[PubMed]
0193-936X
47.
Sen
P
,
Shah
PP
,
Nativio
R
,
Berger
SL
.
Epigenetic mechanisms of longevity and aging
.
Cell
.
2016
Aug
;
166
(
4
):
822
39
.
[PubMed]
0092-8674
48.
López-Otín
C
,
Galluzzi
L
,
Freije
JM
,
Madeo
F
,
Kroemer
G
.
Metabolic control of longevity
.
Cell
.
2016
Aug
;
166
(
4
):
802
21
.
[PubMed]
0092-8674
49.
Brunet
A
,
Rando
TA
.
Interaction between epigenetic and metabolism in aging stem cells
.
Curr Opin Cell Biol
.
2017
Apr
;
45
:
1
7
.
[PubMed]
0955-0674
50.
Levine
ME
.
Modeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?
J Gerontol A Biol Sci Med Sci
.
2013
Jun
;
68
(
6
):
667
74
.
[PubMed]
1079-5006
51.
Li
Q
,
Wang
S
,
Milot
E
,
Bergeron
P
,
Ferrucci
L
,
Fried
LP
, et al
Homeostatic dysregulation proceeds in parallel in multiple physiological systems
.
Aging Cell
.
2015
Dec
;
14
(
6
):
1103
12
.
[PubMed]
1474-9718
52.
Levine
ME
,
Crimmins
EM
.
Evidence of accelerated aging among African Americans and its implications for mortality
.
Soc Sci Med
.
2014
Oct
;
118
:
27
32
.
[PubMed]
0277-9536
53.
Belsky
DW
,
Caspi
A
,
Houts
R
,
Cohen
HJ
,
Corcoran
DL
,
Danese
A
, et al
Quantification of biological aging in young adults
.
Proc Natl Acad Sci USA
.
2015
Jul
;
112
(
30
):
E4104
10
.
[PubMed]
0027-8424
54.
Henriques
CM
,
Ferreira
MG
.
Consequences of telomere shortening during lifespan
.
Curr Opin Cell Biol
.
2012
Dec
;
24
(
6
):
804
8
.
[PubMed]
0955-0674
55.
Belsky
DW
,
Caspi
A
,
Cohen
HJ
,
Kraus
WE
,
Ramrakha
S
,
Poulton
R
, et al
Impact of early personal-history characteristics on the Pace of Aging: implications for clinical trials of therapies to slow aging and extend healthspan
.
Aging Cell
.
2017
Aug
;
16
(
4
):
644
51
.
[PubMed]
1474-9718
56.
Horvath
S
.
DNA methylation age of human tissues and cell types
.
Genome Biol
.
2013
;
14
(
10
):
R115
.
[PubMed]
1474-7596
57.
Belsky
DW
,
Moffitt
TE
,
Cohen
AA
,
Corcoran
DL
,
Levine
ME
,
Prinz
JA
, et al
Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing?
Am J Epidemiol
.
2017
.
[PubMed]
0002-9262
58.
Picard
M
:
Pathways to aging: the mitochondrion at the intersection of biological and psychosocial sciences.
Journal of aging research
2011
;2011
59.
Picard
M
,
Prather
AA
,
Puterman
E
,
Cuillerier
A
,
Coccia
M
,
Aschbacher
K
, et al
A mitochondrial health index sensitive to mood and caregiving stress
.
Biol Psychiatry
.
2018
Jul
;
84
(
1
):
9
17
.
[PubMed]
0006-3223
60.
Stephan
Y
,
Caudroit
J
,
Jaconelli
A
,
Terracciano
A
.
Subjective age and cognitive functioning: a 10-year prospective study
.
Am J Geriatr Psychiatry
.
2014
Nov
;
22
(
11
):
1180
7
.
[PubMed]
1064-7481
61.
Westerhof
GJ
,
Barrett
AE
.
Age identity and subjective well-being: a comparison of the United States and Germany
.
J Gerontol B Psychol Sci Soc Sci
.
2005
May
;
60
(
3
):
S129
36
.
[PubMed]
1079-5014
62.
Goldsmith
RE
,
Heiens
RA
.
Subjective age: a test of five hypotheses
.
Gerontologist
.
1992
Jun
;
32
(
3
):
312
7
.
[PubMed]
0016-9013
63.
McCann
RM
,
Kellermann
K
,
Giles
H
,
Gallois
C
,
Viladot
MA
.
Cultural and gender influences on age identification
.
Commun Stud
.
2004
;
55
(
1
):
88
105
. 1051-0974
64.
Kaufman
G
,
Elder
GH
 Jr
.
Revisiting age identity: A research note
.
J Aging Stud
.
2002
;
16
(
2
):
169
76
. 0890-4065
65.
Kessler
EM
,
Staudinger
UM
.
Intergenerational potential: effects of social interaction between older adults and adolescents
.
Psychol Aging
.
2007
Dec
;
22
(
4
):
690
704
.
[PubMed]
0882-7974
66.
Kwak
S
,
Kim
H
,
Chey
J
,
Youm
Y
.
Feeling how old I am: subjective age is associated with estimated brain age
.
Front Aging Neurosci
.
2018
Jun
;
10
:
168
.
[PubMed]
1663-4365
67.
Staudinger
UM
,
Marsiske
M
,
Baltes
PB
.
Resilience and reserve capacity in later adulthood: potentials and limits of development across the life span
.
Dev Psychopathol
.
1995
;
2
:
801
47
.0954-5794
68.
Idler
EL
,
Benyamini
Y
.
Self-rated health and mortality: a review of twenty-seven community studies
.
J Health Soc Behav
.
1997
Mar
;
38
(
1
):
21
37
.
[PubMed]
0022-1465
69.
Veenhoven
R
.
Happy life-expectancy
.
Soc Indic Res
.
1996
;
39
(
1
):
1
58
. 0303-8300
70.
Yang
Y
.
Long and happy living: trends and patterns of happy life expectancy in the U.S., 1970-2000
.
Soc Sci Res
.
2008
Dec
;
37
(
4
):
1235
52
.
[PubMed]
0049-089X
71.
Bonsang
E
,
Skirbekk
V
,
Staudinger
UM
.
As You Sow, So Shall You Reap: Gender-Role Attitudes and Late-Life Cognition
.
Psychol Sci
.
2017
Sep
;
28
(
9
):
1201
13
.
[PubMed]
0956-7976
72.
Zaidi
A
,
Gasior
K
,
Zolyomi
E
,
Schmidt
A
,
Rodrigues
R
,
Marin
B
.
Measuring active and healthy ageing in Europe
.
J Eur Soc Policy
.
2017
;
27
(
2
):
138
57
. 0958-9287
73.
Chen
C
,
Goldman
DP
,
Zissimopoulos
J
,
Rowe
JW
.
Multidimensional comparison of countries’ adaptation to societal aging.
Proceedings of the National Academy of Sciences
.
2018
:
201806260
.
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.