is a statistical measure of the average time an
organism is expected to live, based on the year of their birth, their
current age and other demographic factors including gender. The most
commonly used measure of life expectancy is at birth (LEB), which can
be defined in two ways. Cohort LEB is the mean length of life of an
actual birth cohort (all individuals born a given year) and can be
computed only for cohorts born many decades ago, so that all their
members have died. Period LEB is the mean length of life of a
hypothetical cohort assumed to be exposed, from birth through death,
to the mortality rates observed at a given year.
National LEB figures reported by statistical national agencies and
international organizations are indeed estimates of period LEB. In the
and the Iron Age, LEB was 26 years; the 2010 world LEB was
67.2 years. For recent years, in
LEB is about 49, and in
Japan, it is about 83. The combination of high infant mortality and
deaths in young adulthood from accidents, epidemics, plagues, wars,
and childbirth, particularly before modern medicine was widely
available, significantly lowers LEB. But for those who survive early
hazards, a life expectancy of 60 or 70 would not be uncommon. For
example, a society with a LEB of 40 may have few people dying at
precisely 40: most will die before 30 or after 55. In populations with
high infant mortality rates, LEB is highly sensitive to the rate of
death in the first few years of life. Because of this sensitivity to
infant mortality, LEB can be subjected to gross misinterpretation,
leading one to believe that a population with a low LEB will
necessarily have a small proportion of older people. For example,
in a hypothetical stationary population in which half the population
dies before the age of five but everybody else dies at exactly 70
years old, LEB will be about 36, but about 25% of the population will
be between the ages of 50 and 70. Another measure, such as life
expectancy at age 5 (e5), can be used to exclude the effect of infant
mortality to provide a simple measure of overall mortality rates other
than in early childhood; in the hypothetical population above, life
expectancy at 5 would be another 65. Aggregate population measures,
such as the proportion of the population in various age groups, should
also be used along individual-based measures like formal life
expectancy when analyzing population structure and dynamics.
Mathematically, life expectancy is the mean number of years of life
remaining at a given age, assuming age-specific mortality rates remain
at their most recently measured levels. It is denoted by
displaystyle e_ x
,[a] which means the mean number of subsequent years of life for
someone now aged
, according to a particular mortality experience. Longevity, maximum
lifespan, and life expectancy are not synonyms.
Life expectancy is
defined statistically as the mean number of years remaining for an
individual or a group of people at a given age.
Longevity refers to
the characteristics of the relatively long life span of some members
of a population. Maximum lifespan is the age at death for the
longest-lived individual of a species. Moreover, because life
expectancy is an average, a particular person may die many years
before or many years after the "expected" survival. The term "maximum
life span" has a quite different meaning and is more related to
Life expectancy is also used in plant or animal ecology; life
tables (also known as actuarial tables). The term life expectancy may
also be used in the context of manufactured objects, but the
related term shelf life is used for consumer products, and the terms
"mean time to breakdown" (MTTB) and "mean time between failures"
(MTBF) are used in engineering.
1 Human patterns
1.1 Variation over time
1.2 Regional variations
1.3 Economic circumstances
1.4 Sex differences
1.6 Mental illness
1.7 Other illnesses
2 Evolution and aging rate
4 Healthy life expectancy
6 Policy uses
Life expectancy vs. life span
8 See also
8.1 Increasing life expectancy
11 Further reading
12 External links
Human beings are expected to live on average 30–40 years in
Swaziland and 82.6 years in Japan, but the latter's recorded life
expectancy may have been very slightly increased by counting many
infant deaths as stillborn. An analysis published in 2011 in The
Lancet attributes Japanese life expectancy to equal opportunities and
public health as well as diet.
The oldest confirmed recorded age for any human is 122 years, reached
Jeanne Calment who lived between 1875-1997. This is referred to as
the "maximum life span", which is the upper boundary of life, the
maximum number of years any human is known to have lived.
Theoretical study shows that the maximum life expectancy at birth is
limited by the human life characteristic value δ, which is around 104
years. According to a study by biologists Bryan G. Hughes and
Siegfried Hekimi, there is no evidence for limit on human
Variation over time
Further information: Longevity
The following information is derived from the 1961 Encyclopædia
Britannica and other sources, some with questionable accuracy. Unless
otherwise stated, it represents estimates of the life expectancies of
the world population as a whole. In many instances, life expectancy
varied considerably according to class and gender.
Life expectancy at birth takes account of infant mortality but not
Life expectancy at birth in years
Life expectancy at older age
Bronze Age data, the total life expectancy at
15 would not exceed 34 years. Based on the data from modern
hunter-gatherer populations, it is estimated that at 15, life
expectancy was an additional 39 years (total 54), with a 0.60
probability of reaching 15.
20 to 33
Based on Early
Neolithic data, total life expectancy at 15 would be
Bronze Age and Iron Age
Based on Early and Middle
Bronze Age data, total life expectancy at 15
would be 28–36 years
25 to 28
Based on Athens Agora and Corinth data, total life expectancy at 15
would be 37–41 years
If a child survived to age 10, life expectancy was an additional 37.5
years (total age 47.5 years).
Pre-Columbian Southern United States
Medieval Islamic Caliphate
Average lifespan of scholars was 59–84.3 years in the Middle
East and 69–75 in Islamic Spain.
Late medieval English peerage
At age 21, life expectancy was an additional 43 years (total age
Early modern England
34 years for males in the 18th century.
Pre-Champlain Canadian Maritimes
Samuel de Champlain
Samuel de Champlain wrote that in his visits to
Mi'kmaq and Huron
communities, he met people over 100 years old. Daniel Paul attributes
the incredible lifespan in the region to low stress and a healthy diet
of lean meats, diverse vegetables and legumes.
18th-century Qing China
18th-century Edo Japan
Early 19th-century England
1900 world average
1950 world average
2014 world average
Life expectancy increases with age as the individual survives the
higher mortality rates associated with childhood. For instance, the
table above listed the life expectancy at birth among 13th-century
English nobles at 30. Having survived until the age of 21, a male
member of the English aristocracy in this period could expect to
1200–1300: to age 64
1300–1400: to age 45 (because of the bubonic plague)
1400–1500: to age 69
1500–1550: to age 71
17th-century English life expectancy was only about 35 years, largely
because infant and child mortality remained high.
Life expectancy was
under 25 years in the early Colony of Virginia, and in
seventeenth-century New England, about 40 per cent died before
reaching adulthood. During the Industrial Revolution, the life
expectancy of children increased dramatically. The under-5
mortality rate in London decreased from 745 in 1730–1749 to 318 in
Public health measures are credited with much of the recent increase
in life expectancy. During the 20th century, despite a brief drop due
to the 1918 flu pandemic starting around that time the average
lifespan in the
United States increased by more than 30 years, of
which 25 years can be attributed to advances in public health.
Further information: List of countries by life expectancy
Plot of life expectancy vs.
GDP per capita
GDP per capita in 2009. This phenomenon is
known as the Preston curve.
Graphs of life expectancy at birth for some sub-Saharan countries
showing the fall in the 1990s primarily due to the HIV pandemic.
There are great variations in life expectancy between different parts
of the world, mostly caused by differences in public health, medical
care, and diet. The impact of
AIDS on life expectancy is particularly
notable in many African countries. According to projections made by
the United Nations (UN) in 2002, the life expectancy at birth for
2010–2015 (if HIV/
AIDS did not exist) would have been:
70.7 years instead of 31.6 years Botswana
69.9 years instead of 41.5 years South Africa
70.5 years instead of 31.8 years Zimbabwe
Actual life expectancy in
Botswana declined from 65 in 1990 to 49 in
2000 before increasing to 66 in 2011. In South Africa, life expectancy
was 63 in 1990, 57 in 2000, and 58 in 2011. And in Zimbabwe, life
expectancy was 60 in 1990, 43 in 2000, and 54 in 2011.
During the last 200 years, African countries have generally not had
the same improvements in mortality rates that have been enjoyed by
countries in Asia, Latin America, and Europe.
In the United States, African-American people have shorter life
expectancies than their European-American counterparts. For example,
white Americans born in 2010 are expected to live until age 78.9, but
black Americans only until age 75.1. This 3.8-year gap, however, is
the lowest it has been since 1975 at the latest. The greatest
difference was 7.1 years in 1993. In contrast, Asian-American
women live the longest of all ethnic groups in the United States, with
a life expectancy of 85.8 years. The life expectancy of Hispanic
Americans is 81.2 years.
Cities also experience a wide range of life expectancy based on
neighborhood breakdowns. This is largely due to economic clustering
and poverty conditions that tend to associate based on geographic
location. Multi-generational poverty found in struggling neighborhoods
also contributes. In
United States cities such as Cincinnati, the life
expectancy gap between low income and high income neighborhoods
touches 20 years.
Economic circumstances also affect life expectancy. For example, in
the United Kingdom, life expectancy in the wealthiest and richest
areas is several years higher than in the poorest areas. This may
reflect factors such as diet and lifestyle, as well as access to
medical care. It may also reflect a selective effect: people with
chronic life-threatening illnesses are less likely to become wealthy
or to reside in affluent areas. In Glasgow, the disparity is
amongst the highest in the world: life expectancy for males in the
heavily deprived Calton area stands at 54, which is 28 years less than
in the affluent area of Lenzie, which is only 8 km away.
A 2013 study found a pronounced relationship between economic
inequality and life expectancy. However, a study by José A. Tapia
Granados and Ana Diez Roux at the
University of Michigan
University of Michigan found that
life expectancy actually increased during the Great Depression, and
during recessions and depressions in general. The authors suggest
that when people are working extra hard during good economic times,
they undergo more stress, exposure to pollution, and likelihood of
injury among other longevity-limiting factors.
Life expectancy is also likely to be affected by exposure to high
levels of highway air pollution or industrial air pollution. This is
one way that occupation can have a major effect on life expectancy.
Coal miners (and in prior generations, asbestos cutters) often have
lower life expediencies than average life expediencies. Other factors
affecting an individual's life expectancy are genetic disorders, drug
use, tobacco smoking, excessive alcohol consumption, obesity, access
to health care, diet and exercise.
Pink: Countries where females life expectancy at birth is higher than
males. Blue: A few countries in the south of Africa where females have
shorter lives due to AIDS
Comparison of male and female life expectancy at birth for countries
and territories as defined in the 2011 CIA Factbook, with selected
bubbles labelled. The dotted line corresponds to equal female and male
life expectancy. The apparent 3D volumes of the bubbles are linearly
proportional to their population. (In the SVG file, hover over
a bubble to highlight it and show its data.)
In the uterus, male fetuses have a higher mortality rate (babies are
conceived in a ratio estimated to be from 107 to 170 males to 100
females, but the ratio at birth in the
United States is only 105 males
to 100 females). Among the smallest pre-mature babies (those under
2 pounds or 900 g), females again have a higher survival rate. At the
other extreme, about 90% of individuals aged 110 are female. The
difference in life expectancy between men and women in the United
States dropped from 7.8 years in 1979 to 5.3 years in 2005,
with women expected to live to age 80.1 in 2005. Also, data
from the UK shows the gap in life expectancy between men and women
decreasing in later life. This may be attributable to the effects of
infant mortality and young adult death rates.
In the past, mortality rates for females in child-bearing age groups
were higher than for males at the same age. This is no longer the
case, and female human life expectancy is considerably higher than
that of males. The reasons for this are not entirely certain.
Traditional arguments tend to favor sociology-environmental factors:
historically, men have generally consumed more tobacco, alcohol and
drugs than women in most societies, and are more likely to die from
many associated diseases such as lung cancer, tuberculosis and
cirrhosis of the liver. Men are also more likely to die from
injuries, whether unintentional (such as occupational, war or car
accidents) or intentional (suicide). Men are also more likely to
die from most of the leading causes of death (some already stated
above) than women. Some of these in the
United States include: cancer
of the respiratory system, motor vehicle accidents, suicide, cirrhosis
of the liver, emphysema, prostate cancer, and coronary heart
disease. These far outweigh the female mortality rate from breast
cancer and cervical cancer.
Some argue that shorter male life expectancy is merely another
manifestation of the general rule, seen in all mammal species, that
larger (size) individuals (within a species) tend, on average, to have
shorter lives. This biological difference occurs because women
have more resistance to infections and degenerative diseases.
In her extensive review of the existing literature, Kalben concluded
that the fact that women live longer than men was observed at least as
far back as 1750 and that, with relatively equal treatment, today
males in all parts of the world experience greater mortality than
females. Kalben's study, however, was restricted to data in Western
Europe alone, where demographic transition occurred relatively early.
In countries such as Hungary, Bulgaria, India and China, males
continued to outlive females into the twentieth century. Of 72
selected causes of death, only 6 yielded greater female than male
age-adjusted death rates in 1998 in the United States. With the
exception of birds, for almost all of the animal species studied,
males have higher mortality than females. Evidence suggests that the
sex mortality differential in people is due to both biological/genetic
and environmental/behavioral risk and protective factors.
There is a recent suggestion that mitochondrial mutations that shorten
lifespan continue to be expressed in males (but less so in females)
because mitochondria are inherited only through the mother. By
contrast, natural selection weeds out mitochondria that reduce female
survival; therefore such mitochondria are less likely to be passed on
to the next generation. This thus suggests that females tend to live
longer than males. The authors claim that this is a partial
In developed countries, starting around 1880, death rates decreased
faster among women, leading to differences in mortality rates between
males and females. Before 1880 death rates were the same. In people
born after 1900, the death rate of 50- to 70-year-old men was double
that of women of the same age. Cardiovascular disease was the main
cause of the higher death rates among men. Men may be more vulnerable
to cardiovascular disease than women, but this susceptibility was
evident only after deaths from other causes, such as infections,
started to decline.
Main article: Centenarian
In developed countries, the number of centenarians is increasing at
approximately 5.5% per year, which means doubling the centenarian
population every 13 years, pushing it from some 455,000 in 2009 to 4.1
million in 2050.
Japan is the country with the highest ratio of
centenarians (347 for every 1 million inhabitants in September 2010).
Shimane prefecture had an estimated 743 centenarians per million
In the United States, the number of centenarians grew from 32,194 in
1980 to 71,944 in November 2010 (232 centenarians per million
Mental illness is reported to occur in approximately 18% of the
average American population.
Life expectancy in the seriously mentally ill is much shorter than the
The seriously mentally ill have a 10 to 25 year reduction in life
expectancy. The reduction of lifespan has been studied and
The greater mortality of people with mental disorders may be due to
death from injury, from co-morbid conditions, or from medication side
effects. Psychiatric medicines can increase the chance of
developing diabetes. Psychiatric medicine can also
cause Agranulocytosis. Psychiatric medicines also affect the
stomach, where the mentally ill have a four times risk of
The life expectancy of people with diabetes, which is 9.3% of the U.S.
population, is reduced by roughly ten to twenty years. Other
demographics that tend to have a lower life expectancy than average
include transplant recipients, and the obese.
Evolution and aging rate
Main article: Life history theory
Various species of plants and animals, including humans, have
different lifespans. Evolutionary theory states that organisms that,
by virtue of their defenses or lifestyle, live for long periods and
avoid accidents, disease, predation, etc. are likely to have genes
that code for slow aging, which often translates to good cellular
repair. One theory is that if predation or accidental deaths prevent
most individuals from living to an old age, there will be less natural
selection to increase the intrinsic life span. That finding was
supported in a classic study of opossums by Austad; however, the
opposite relationship was found in an equally prominent study of
guppies by Reznick.
One prominent and very popular theory states that lifespan can be
lengthened by a tight budget for food energy called caloric
Caloric restriction observed in many animals (most
notably mice and rats) shows a near doubling of life span from a very
limited calorific intake. Support for the theory has been bolstered by
several new studies linking lower basal metabolic rate to increased
life expectancy. That is the key to why animals like
giant tortoises can live so long. Studies of humans with life
spans of at least 100 have shown a link to decreased thyroid activity,
resulting in their lowered metabolic rate.
In a broad survey of zoo animals, no relationship was found between
the fertility of the animal and its life span.
Life table § The mathematics
A survival tree to explain the calculations of life-expectancy. Red
numbers indicate chance of survival at a specific age, and blue ones
indicate age-specific death rates.
The starting point for calculating life expectancy is the age-specific
death rates of the population members. If a large number of data is
available, a statistical population can be created that allow the
age-specific death rates to be simply taken as the mortality rates
actually experienced at each age (the number of deaths divided by the
number of years "exposed to risk" in each data cell). However, it is
customary to apply smoothing to iron out, as much as possible, the
random statistical fluctuations from one year of age to the next. In
the past, a very simple model used for this purpose was the Gompertz
function, but more sophisticated methods are now used.
These are the most common methods now used for that purpose:
to fit a mathematical formula, such as an extension of the Gompertz
function, to the data,
for relatively small amounts of data, to look at an established
mortality table that was previously derived for a larger population
and make a simple adjustment to it (as multiply by a constant factor)
to fit the data.
with a large number of data, one looks at the mortality rates actually
experienced at each age, and applies smoothing (as by cubic splines).
While the data required are easily identified in the case of humans,
the computation of life expectancy of industrial products and wild
animals involves more indirect techniques. The life expectancy and
demography of wild animals are often estimated by capturing, marking,
and recapturing them. The life of a product, more often termed
shelf life, is also computed using similar methods. In the case of
long-lived components, such as those used in critical applications: in
aircraft, methods like accelerated aging are used to model the life
expectancy of a component.
The age-specific death rates are calculated separately for separate
groups of data that are believed to have different mortality rates
(such as males and females, and perhaps smokers and non-smokers if
data are available separately for those groups) and are then used to
calculate a life table from which one can calculate the probability of
surviving to each age. In actuarial notation, the probability of
surviving from age
displaystyle ,_ n p_ x !
and the probability of dying during age
) is denoted
displaystyle q_ x !
. For example, if 10% of a group of people alive at their 90th
birthday die before their 91st birthday, the age-specific death
probability at 90 would be 10%. That is a probability, not a mortality
The expected future lifetime of a life age
in whole years (the curtate expected lifetime of (x)) is denoted by
displaystyle ,e_ x !
.[a] It is the conditional expected future lifetime (in whole years),
assuming survival to age
denotes the curtate future lifetime at
displaystyle e_ x =E[K(x)]=sum _ k=0 ^ infty k,Pr(K(x)=k)=sum _
k=0 ^ infty k,,_ k p_ x ,,q_ x+k .
displaystyle _ k p_ x ,q_ x+k = _ k p_ x - _ k+1 p_ x
in the sum and simplifying gives the equivalent formula:
displaystyle e_ x =sum _ k=1 ^ infty ,_ k p_ x .
If the assumption is made that on average, people live a half year in
the year of death, the complete expectation of future lifetime at age
displaystyle e_ x +1/2
Life expectancy is by definition an arithmetic mean. It can also be
calculated by integrating the survival curve from 0 to positive
infinity (or equivalently to the maximum lifespan, sometimes called
'omega'). For an extinct or completed cohort (all people born in year
1850, for example), it can of course simply be calculated by averaging
the ages at death. For cohorts with some survivors, it is estimated by
using mortality experience in recent years. The estimates are called
period cohort life expectancies.
It is important to note that the statistic is usually based on past
mortality experience and assumes that the same age-specific mortality
rates will continue into the future. Thus, such life expectancy
figures need to be adjusted for temporal trends before calculating how
long a currently living individual of a particular age is expected to
live. Period life expectancy remains a commonly used statistic to
summarize the current health status of a population.
However, for some purposes, such as pensions calculations, it is usual
to adjust the life table used by assuming that age-specific death
rates will continue to decrease over the years, as they have usually
done in the past. That is often done by simply extrapolating past
trends; but some models exist to account for the evolution of
mortality like the Lee–Carter model.
As discussed above, on an individual basis, a number of factors
correlate with a longer life. Factors that are associated with
variations in life expectancy include family history, marital status,
economic status, physique, exercise, diet, drug use including smoking
and alcohol consumption, disposition, education, environment, sleep,
climate, and health care.
Healthy life expectancy
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In order to assess the quality of these additional years of life,
'healthy life expectancy' has been calculated for the last 30 years.
Since 2001, the World Health Organization has published statistics
called Healthy life expectancy (HALE), defined as the average number
of years that a person can expect to live in "full health" excluding
the years lived in less than full health due to disease and/or
injury. Since 2004,
Eurostat publishes annual statistics called
Healthy Life Years
Healthy Life Years (HLY) based on reported activity limitations. The
United States uses similar indicators in the framework of the national
health promotion and disease prevention plan "Healthy People 2010".
More and more countries are using health expectancy indicators to
monitor the health of their population.
Forecasting life expectancy and mortality forms an important
subdivision of demography. Future trends in life expectancy have huge
implications for old-age support programs like U.S. Social Security
and pension since the cash flow in these systems depends on the number
of recipients who are still living (along with the rate of return on
the investments or the tax rate in pay-as-you-go systems). With longer
life expectancies, the systems see increased cash outflow; if the
systems underestimate increases in life-expectancies, they will be
unprepared for the large payments that will occur, as humans live
longer and longer.
Life expectancy forecasting is usually based on two different
Forecasting the life expectancy directly, generally using
other time series extrapolation procedures: that has the advantage of
simplicity, but it cannot account for changes in mortality at specific
ages, and the forecast number cannot be used to derive other life
table results. Analyses and forecasts using this approach can be done
with any common statistical/mathematical software package, like
EViews, R, SAS, Stata, Matlab, or SPSS.
Forecasting age specific death rates and computing the life expectancy
from the results with life table methods: that is usually more complex
than simply forecasting life expectancy because the analyst must deal
with correlated age-specific mortality rates, but it seems to be more
robust than simple one-dimensional time series approaches. It also
yields a set of age specific-rates that may be used to derive other
measures, such as survival curves or life expectancies at different
ages. The most important approach within this group is the Lee-Carter
model, which uses the singular value decomposition on a set of
transformed age-specific mortality rates to reduce their
dimensionality to a single time series, forecasts that time series and
then recovers a full set of age-specific mortality rates from that
forecasted value. Software includes Professor Rob J. Hyndman's R
package called `demography` and UC Berkeley's LCFIT system.
Life expectancy is one of the factors in measuring the Human
Development Index (HDI) of each nation along with adult literacy,
education, and standard of living.
Life expectancy is also used in describing the physical quality of
life of an area or, for an individual when the value of a life
settlement is determined a life insurance policy sold for a cash
Disparities in life expectancy are often cited as demonstrating the
need for better medical care or increased social support. A strongly
associated indirect measure is income inequality. For the top 21
industrialized countries, if each person is counted equally, life
expectancy is lower in more unequal countries (r = −0.907).
There is a similar relationship among states in the US (r =
Life expectancy vs. life span
Life expectancy differs from maximum life span.
Life expectancy is an
average for all people in the population — including those who
die shortly after birth, those who die in early adulthood (e.g.
childbirth, war), and those who live unimpeded until old age. Lifespan
is an individual-specific concept — maximum lifespan is therefore an
upper bound rather than an average.
However, these two terms are often confused with each other to the
point that when people hear "life expectancy was 35 years" they often
interpret this as meaning that people of that time or place had short
maximum life spans. One such example can be seen in the In Search
of... episode "The Man Who Would Not Die" (About Count of St. Germain)
where it is stated "Evidence recently discovered in the British Museum
indicates that St. Germain may have well been the long lost third son
of Rákóczi born in Transylvania in 1694. If he died in Germany in
1784, he lived 90 years. The average life expectancy in the 18th
century was 35 years. Fifty was a ripe old age. Ninety... was
In reality, there are other examples of people living significantly
longer than the life expectancy of their time period, such as
Socrates, Saint Anthony, Michelangelo, and Benjamin Franklin.
It can be argued that it is better to compare life expectancy of the
period after childhood to get a better handle on life span. Life
expectancy can change dramatically after childhood, as is demonstrated
by the Roman Life Expectancy table in which at birth, the life
expectancy was 21, but by the age of 5, it jumped to 42. Studies
like Plymouth Plantation; "Dead at Forty" and Life Expectancy by Age,
1850–2004 similarly show a dramatic increase in life expectancy once
adulthood was reached.
DNA damage theory of aging
List of countries by life expectancy
List of longest-living organisms
Maximum life span
Increasing life expectancy
Strategies for Engineered Negligible
a. ^ ^ In standard actuarial notation, ex refers to the expected
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Wikimedia Commons has media related to Life expectancy.
Charts for all countries
Our World In Data – Life Expectancy—Visualizations of how life
expectancy around the world has changed historically (by Max Roser).
Includes life expectancy for different age groups. Charts for all
countries, world maps, and links to more data sources.
Global Agewatch has the latest internationally comparable statistics
on life expectancy from 195 countries.
Life expectancy at birth from the CIA's World Factbook.
CDC year-by-year life expectancy figures for USA from the USA Centers
for Disease Controls and Prevention, National Center for Health
Life expectancy in Roman times from the University of Texas.
Animal lifespans: Animal Lifespans from Tesarta Online (Internet
Archive); The Life Span of Animals from Dr Bob's All Creatures Site.
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