Modern cognitive tests originated through the work of James McKeen Cattell who coined the term "mental tests". They followed Francis Galton's
development of physical and physiological tests. For example, Galton
measured strength of grip and height and weight. He established an
"Anthropometric Laboratory" in the 1880s where patrons paid to have
physical and physiological attributes measured. Galton's measurements
had an enormous influence on psychology. Cattell continued the
measurement approach with simple measurements of perception. Cattell's
tests were eventually abandoned in favor of the battery test approach
developed by Alfred Binet.
Inductive reasoning aptitude:
Also known as abstract reasoning tests and diagrammatic style tests,
are utilized by examining a person's problem-solving skills. This test
is used to "measure the ability to work flexibly with unfamiliar
information to find solutions." These tests are often visualized through
a set of patterns or sequences, with the user determining what does or
does not belong.
Situational judgement test:
A situational judgement test is used to examine how an individual
responds to certain situations. Oftentimes these tests include a
scenario with multiple responses, with the user selecting which response
they feel is the most appropriate given the situation. This is used to
assess how the user would respond to certain situations that may arise
in the future.
Some companies that use situational judgement tests during their hiring
process include Sony, Walmart, Herbert Smith, and much more.
Kohs block design test:
"The Kohs Block Design Test is a non-verbal assessment of executive
functioning, useful with the language and hearing impaired"
Miller Analogies Test:
According to Pearson Assessments, the Miller Analogies Test is used to
determine a students ability to think analytically. The test is 60
minutes long, and is used by schools to determine those who are able to
think analytically, and those who are only "memorizing and repeating
information"
Otis–Lennon School Ability Test:
The OLSAT is a multiple choice exam administered to students anywhere
from Pre-K to 12th grade, used to identify which students are
intellectually gifted. Students will need to be able to: "Follow
directions, detect likenesses and differences, recall words and numbers,
classify items, establish sequences, solve arithmetic problems, and
complete analogies."
The test consists of a mixture between verbal and non-verbal sections,
helping inform the schools of the students "verbal, nonverbal, and
quantitative ability"
Raven's Progressive Matrices:
The Raven's Progressive Matrices is a nonverbal test consisting of 60
multiple choice questions. This test is used to measure the individual's
abstract reasoning, and is considered a nonverbal way to test an
individual's "fluid intelligence."
Stanford–Binet Intelligence Scales:
By measuring the memory, reasoning, knowledge, and processing power of
the user, this test is able to determine "an individual's overall
intelligence, cognitive ability, and detect any cognitive impairment or
learning disabilities."
This test measures five factors of cognitive ability, which are as
follows: "fluid reasoning, knowledge, quantitative reasoning,
visual-spatial processing and working memory."
Wechsler Adult Intelligence Scale:
The Wechsler Adult Intelligence Scale (WAIS) is used to determine and
assess the intelligence of the participant. This is one of the more
common tests used to test an individual's intelligence quotient.
Throughout its history, this test has been revised multiple times since
its creation, starting with the WAIS in 1955, to the WAIS-R in 1981, to
the WAIS-III in 1996, and most recently the WAIS-IV in 2008. This test
helps assess the level of the individuals verbal comprehension,
perceptual reasoning, working memory, and processing speed.
Wechsler Intelligence Scale for Children:
The Wechsler Intelligence Scale for Children (WISC) is for children
within the age range of six to sixteen years old. While this test can be
used to help determine a child's intelligence quotient, it is often
used to determine a child's cognitive abilities. First introduced in
1949, the WSIC is now on its fifth edition (WISC-V), and was most
recently updated in 2014. Similar to the WAIS (Wechsler Adult
Intelligence Scale), this test helps assess the level of the individuals
verbal comprehension, perceptual reasoning, working memory, and
processing speed.
Wechsler Preschool and Primary Scale of Intelligence:
The Wechsler Preschool and Primary Scale of Intelligence (WPPSI) is
used to assess the cognitive ability of children ages two years and six
months old to seven years and seven months old. The current version of
the test is the fourth edition (WPPSI-IV). Children between the ages of
two years and six months old, to three years and 11 months old, are
testing on the following: "block design, information, object assembly,
picture naming, and receptive vocabulary". Children between the ages of
four years old, to seven years and 7 months old, are testing on the
following: "coding, comprehension, matrix reasoning, picture completion,
picture concepts, similarities, symbol search, vocabulary, and word
reasoning."
Wonderlic test:
The Wonderlic test is a multiple choice test consisting of 50 questions
within a 12-minute time frame. Throughout the test, the questions
become more and more difficult. The test is used to determine not only
the individuals intelligence quotient, but also the strengths and
weaknesses of the individual. The test consists of questions ranging
from "English, reading, math, and logic problems"
The Wonderlic test is notoriously used by NFL teams to help gain a
better understanding of college prospects during the NFL combine.
Cambridge Neuropsychological Test Automated Battery:
The Cambridge Neuropsychological Test Automated Battery (CANTAB) is a
test used to assess the "neuro-cognitive dysfunctions associated with
neurologic disorders, phannacologic manipulations, and neuro-cognitive
syndromes."
CANTAB is computer based program from Cambridge Cognition, and can test
for "working memory, learning and executive function; visual, verbal
and episodic memory; attention, information processing and reaction
time; social and emotion recognition, decision making and response
control."
CAT4:
The Cognitive Ability Test was developed by GL Education and is used to
predict student success through the evaluation of verbal, non-verbal,
mathematical, and spatial reasoning. It is being used by many
international schools as part of their admissions process.
CDR computerized assessment system:
The Cognitive Drug Research computerized assessment system is used to
help determine if a drug has "cognitive-impairing properties". It is
also used to "ensure that unwanted interactions with alcohol and other
medications do not occur, or, if they do, to put them in context."
Cognitive pretesting:
Cognitive pretests are used to evaluate the "comprehensibility of
questions", usually given on a survey. This gives the surveyors a better
understanding of how their questions are being perceived, and the
"quality of the data" that is gained from the survey.
Draw-a-Person test:
The Draw-a-Person test can be used on children, adolescents, and
adults. It is most commonly used as a test for children and adolescents
to assess their cognitive and intellectual ability by scoring their
ability to draw human figures.
Knox Cubes: The Knox Cube Imitation Test (KCIT) is a nonverbal test used to assess intelligence.
The creator of the KCIT, Howard A. Knox, described the test as: "Four
1-inch [black] cubes, 4 inches apart, are fastened to a piece of thin
boarding. The movements and tapping are done with a smaller cube. The
operator moves the cube from left to right facing the subject, and after
completing each movement, the latter is asked to do likewise. Line a is
tried first, then b, and so on to e. Three trials are given if
necessary on lines a, b, c, and d, and five trials if needed on line e.
To obtain the correct perspective the subject should be two feet from
the cubes. The movements of the operator should be slow and deliberate."
Multiple choice:
The style of multiple choice examination was expanded upon in 1934 when
IBM introduced a "test scoring machine" that electronically sensed the
location of lead pencil marks on a scanning sheet. This further
increased the efficiency of scoring multiple-choice items and created a
large-scale educational testing method.
Intended test use: placement, admission, fulfilling a requirement, aptitude
Skills tested: listening, grammar, vocabulary
Test length: 50–60 minutes
Test materials: reusable test booklet, consumable answer sheet,
consumable performance chart and report to parents, test administrator
manual, audio CD, scoring stencil for test administrator
Test format: multiple choice
Scoring method: number correct
Results reported: percentile, raw score
Administered by: trained testers, classroom teachers, school administrators
Administration time period: prior to foreign language study, at
discretion of guidance counselor, school psychologist, or other
administration
a supplement to the Stanford-Binet Intelligence Test.
PMT performance seems to be a valid indicator of planning and
behavioral disinhibition across socioeconomic status and culture, can be
administered without the use of language, and is inexpensive. The PMT
also have a relatively short administration time of 10–15 minutes.
Knowledge organization:
Features Ranganathan's PMEST formula: Personality, Matter, Energy,
Space and Time, consisting of five fundamental categories- the
arrangement of which is used to establish the facet order.
The Sally–Anne test (The ability to attribute false beliefs to others): This test has been used in psychological research to investigate theory of mind.
It has been suggested that lacking a Theory of Mind may be the
reasoning behind some of the communication difficulties accompanied by
individuals with autism.
Neuropsychological tests:
These are standardized test which are given in the same manner to all
examinees and are scored in a similar fashion. The examinees scores on
the tests are interpreted by comparing their score to that of healthy
individuals of a similar demographic background and to standard levels
of operation.
Life expectancy and healthy life expectancy in various countries of the world in 2019, according to WHOLife expectancy and healthy life expectancy by sexLife expectancy development in some big countries of the world since 1960Life expectancy at birth, measured by region, between 1950 and 2050Life expectancy by world region, from 1770 to 2018“Gender Die Gap”: global female life expectancy gap at birth for countries and territories as defined by WHO for 2019. Open the original svg-file
and hover over a bubble to show its data. The square of the bubbles is
proportional to country population based on estimation of the UN.
Life expectancy is a statistical measure of the estimate of the span of a life. The most commonly used measure is life expectancy at birth (LEB), which can be defined in two ways. Cohort LEB is the mean length of life of a birth cohort
(in this case, all individuals born in a given year) and can be
computed only for cohorts born so long ago 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 national agencies and international organizations for human populations are estimates of period LEB.
In the Bronze Age and the Iron Age, human LEB was 26 years; in 2010, world LEB was 67.2 years. In recent years, LEB in Eswatini (formerly Swaziland) is 49, while LEB in Japan is 83. The combination of high infant mortality and deaths in young adulthood from accidents, epidemics,
plagues, wars, and childbirth, before modern medicine was widely
available, significantly lowers LEB. For example, a society with a LEB
of 40 would have relatively few people dying at exactly 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, LEB can be grossly
misinterpreted, leading to the belief that a population with a low LEB
would have a small proportion of older people. A different 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.
For instance, in a society with a life expectancy of 30, it may
nevertheless be common to have a 40-year remaining timespan at age 5
(but perhaps not a 60-year one).
Until the middle of the 20th century, infant mortality was
approximately 40–60% of the total mortality. Excluding child mortality,
the average life expectancy during the 12th–19th centuries was
approximately 55 years. If a person survived childhood, they had about a
50% chance of living 50–55 years, instead of only 25–40 years. As of 2016, the overall worldwide life expectancy had reached the highest level that has been measured in modern times.
Aggregate population measures—such as the proportion of the
population in various age groups—are also used alongside
individual-based measures—such as formal life expectancy—when analyzing
population structure and dynamics. Pre-modern societies had universally
higher mortality rates and lower life expectancies at every age for both
males and females. This example is relatively rare.
Life expectancy, longevity, and maximum lifespan
are not synonymous. Longevity refers to the relatively long lifespan of
some members of a population. Maximum lifespan is the age at death for
the longest-lived individual of a species. Mathematically, life
expectancy is denoted and is the mean number of years of life remaining at a given age , with a particular mortality. Because life expectancy is an average, a particular person may die many years before or after the expected survival.
Life expectancy is also used in plant or animal ecology, and in life tables (also known as actuarial tables). The concept of life expectancy may also be used in the context of manufactured objects, though the related term shelf life is commonly used for consumer products, and the terms "mean time to breakdown" and "mean time between failures" are used in engineering.
The longest verified lifespan for any human is that of Frenchwoman Jeanne Calment,
who is verified as having lived to age 122 years, 164 days, between 21
February 1875 and 4 August 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.
A 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 lifespan. However, this view has been questioned on the basis of error patterns.
Records of human lifespan above age 100 are highly susceptible to errors. For example, the previous world-record holder for human lifespan, Carrie C. White, was uncovered as a simple typographic error after more than two decades.
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.
Based on the data from modern hunter-gatherer populations, it is
estimated that at 15, life expectancy another 39 years (54 years total).
There was a 60% probability of surviving until age 15.
Based on Athens Agora and Corinth data, total life expectancy at 15 would be 37–41 years.
Most Greeks and Romans died young. About half of all children died
before adolescence. Those who survived to the age of 30 had a reasonable
chance of reaching 50 or 60. The truly elderly, however, were rare.
Because so many died in childhood, life expectancy at birth was probably
between 20 and 30 years.
Data is lacking, but computer models provide the estimate. If a
person survived to age 20, they could expect to live around 30 years
more. Life expectancy was probably slightly longer for women than men.
When infant mortality is factored out (i.e. counting only the 67–75%
who survived the first year), life expectancy is around 34–41 more
years (i.e. expected to live to 35–42). When child mortality is factored
out (i.e. counting only the 55-65% who survived to age 5), life
expectancy is around 40–45 (i.e. age 45–50). The ~50% that reached age 10 could also expect to reach ~45-50; at 15 to ~48–54; at 40 to ~60; at 50 to ~64–68; at 60 to ~70–72; at 70 to ~76–77.
Wang clan of China, 1st c. AD – 1749
35
For the 60% that survived the first year (i.e. excluding infant mortalities), life expectancy rose to ~35.
Early Middle Ages (Europe, from the late 5th or early 6th century to the 10th century)
30–35
Life expectancy for those of both sexes who survived birth averaged
about 30–35 years. However, if a Gaulish boy made it past age 20, he
might expect to live 25 more years, while a woman at age 20 could
normally expect about 17 more years. Anyone who survived until 40 had a
good chance at another 15 to 20 years.
In Europe, around one-third of infants died in their first year.
Once children reached the age of 10, their life expectancy was 32.2
years, and for those who survived to 25, the remaining life expectancy
was 23.3 years. Such estimates reflected the life expectancy of adult
males from the higher ranks of English society in the Middle Ages, and
were similar to that computed for monks of the Christ Church in
Canterbury during the 15th century. At age 21, life expectancy of an aristocrat was an additional 43 years (total age 64).
For males in the 18th century it was 34 years.
For 15-year-old girls: around the 15th – 16th century it was ~33 years
(48 total), and in the 18th century it was ~42 (57 total).
For most of the century it ranged from 35 to 40; however, in the 1720s it dipped as low as 25. For 15-year-old girls, it was ~42 (57 total). During the second half of the century it was ~37, while for the elite it passed 40 and approached 50.
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.
For males: 24.8 years in 1740–1749, 27.9 years in 1750–1759, 33.9 years in 1800–1809.
18th-century American colonies
28
Massachusetts colonists who reached the age of 50 could expect to
live until 71, and those who were still alive at 60 could expect to
reach 75.
Beginning of the 19th century
~29
Demographic research suggests that at the beginning of the 19th
century, no country in the world had a life expectancy longer than 40
years. India was ~25 years, while Belgium was ~40 years. For Europe as a whole, it was ~33 years.
For the 84% who survived the first year (i.e. excluding infant mortality), the average age was ~46–48. If they reached 20, then it was ~60; if 50, then ~70; if 70, then ~80. For a 15-year-old girl it was ~60–65. For the upper-class, LEB rose from ~45 to 50.
Less than half of the people born in the mid-19th century made it
past their 50th birthday. In contrast, 97% of the people born in 21st
century England and Wales can expect to live longer than 50 years.
Over the course of the century: Europe rose from ~33 to 43, the Americas from ~35 to 41, Oceania ~35 to 48, Asia ~28, Africa 26.
In 1820s France, LEB was ~38, and for the 80% that survived, it rose to
~47. For Moscow serfs, LEB was ~34, and for the 66% that survived, it
rose to ~36. Western Europe in 1830 was ~33 years, while for the people of Hau-Lou in China, it was ~40. The LEB for a 10-year-old in Sweden rose from ~44 to ~54.
1900 world average
31–32
Around 48 years in Oceania, 43 in Europe, and 41 in the Americas. Around 47 in the U.S. and around 48 for 15-year-old girls in England.
1950 world average
45.7–48
Around 60 years in Europe, North America, Oceania, Japan, and parts
of South America; but only 41 in Asia and 36 in Africa. Norway led with
72, while in Mali it was merely 26.
Life expectancy increases with age already achieved.
Life expectancy increases with age as the individual survives the
higher mortality rates associated with childhood. For instance, the
table above gives the life expectancy at birth among 13th-century
English nobles as 30. Having survived to the age of 21, a male member of
the English aristocracy in this period could expect to live:
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% died before reaching adulthood. During the Industrial Revolution, the life expectancy of children increased dramatically. The under-5 mortality rate in London decreased from 74.5% (in 1730–1749) to 31.8% (in 1810–1829).
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,
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.
Life expectancy in 1800, 1950, and 2015 – visualization by Our World in Data
Human beings are expected to live on average 30–40 years in Eswatini and 82.6 years in Japan. An analysis published in 2011 in The Lancet attributes Japanese life expectancy to equal opportunities, public health, and diet.
Plot of life expectancy vs. 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 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 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.
According to the new government reports in the US, life expectancy in
the country dropped again because of the rise in suicide and drug
overdose rates. The Centers for Disease Control (CDC) found nearly 70,000 more Americans died in 2017 than in 2016, with rising rates of death among 25- to 44-year-olds.
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 (5.0 mi) away.
A 2013 study found a pronounced relationship between economic inequality and life expectancy. However, in contrast, a study by José A. Tapia Granados and Ana Diez Roux at the 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 at a more extreme degree during prosperous economic times, they undergo more stress, exposure to pollution, and the 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 expectancies than average. 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.
Sex differences
Pink:
Countries where female life expectancy at birth is higher than males.
Blue: A few countries in southern Africa where females have shorter
lives due to AIDS.
In the present, female human life expectancy is greater than that of males, despite females having higher morbidity rates (see Health survival paradox).
There are many potential reasons for this. 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 wrecks) 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. In the past, mortality rates for females in child-bearing age groups were higher than for males at the same age.
A paper from 2015 found that female foetuses have a higher mortality rate than male foetuses. This finding contradicts papers dating from 2002 and earlier that attribute the male sex to higher in-utero mortality rates.
Among the smallest premature babies (those under 2 pounds (910 grams)),
females 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.
Data from the United Kingdom 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.
Some argue that shorter male life expectancy is merely another
manifestation of the general rule, seen in all mammal species, that
larger-sized 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. However, Kalben's study was restricted to data in Western
Europe alone, where the demographic transition occurred relatively
early. United Nations statistics from mid-twentieth century onward, show
that in all parts of the world, females have a higher life expectancy
at age 60 than males.
Of 72 selected causes of death, only 6 yielded greater female than male
age-adjusted death rates in 1998 in the United States. Except for
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.
One recent suggestion is that mitochondrial
mutations which 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 explanation.
Another explanation is the unguarded X hypothesis.
According to this hypothesis, one reason for why the average lifespan
of males isn't as long as that of females––by 18% on average, according
to the study––is that they have a Y chromosome
which can't protect an individual from harmful genes expressed on the X
chromosome, while a duplicate X chromosome, as present in female
organisms, can ensure harmful genes aren't expressed.
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. 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.
Most of the difference in life expectancy between the sexes is
accounted for by differences in the rate of death by cardiovascular
diseases among persons aged 50–70.
The heritability of lifespan is estimated to be less than 10%, meaning the majority of variation in lifespan is attributable due to differences in environment rather than genetic variation. However, researchers have identified regions of the genome which can influence the length of life and the number of years lived in good health. For example, a genome-wide association study of 1 million lifespans found 12 genetic loci which influenced lifespan by modifying susceptibility to cardiovascular and smoking-related disease. The locus with the largest effect is APOE. Carriers of the APOE ε4 allele live approximately one year less than average (per copy of the ε4 allele), mainly due to increased risk of Alzheimer's disease.
"Healthspan, parental lifespan, and longevity are highly genetically correlated."
In July 2020, scientists identified 10 genomic loci with consistent effects across multiple lifespan-related traits, including healthspan, lifespan, and longevity. The genes affected by variation in these loci highlighted haem metabolism
as a promising candidate for further research within the field. This
study suggests that high levels of iron in the blood likely reduce, and
genes involved in metabolising iron likely increase healthy years of
life in humans.
A follow-up study which investigated the genetics of frailty
and self-rated health in addition to healthspan, lifespan, and
longevity also highlighted haem metabolism as an important pathway, and
found genetic variants which lower blood protein levels of LPA and VCAM1 were associated with increased healthy lifespan.
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 inhabitants.
In the United States, the number of centenarians grew from 32,194
in 1980 to 71,944 in November 2010 (232 centenarians per million
inhabitants).
Mental illness
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 general population.
The mentally ill have been shown to have a 10- to 25-year reduction in life expectancy.
Generally, the reduction of lifespan in the mentally ill population
compared to the mentally stable population has been studied and
documented.
The greater mortality of people with mental disorders may be due to death from injury, from co-morbid conditions, or medication side effects. For instance, psychiatric medications can increase the risk of developing diabetes. It has been shown that the psychiatric medication olanzapine can increase risk of developing agranulocytosis, among other comorbidities. Psychiatric medicines also affect the gastrointestinal tract; the mentally ill have a four times risk of gastrointestinal disease.
As of 2020 and the COVID-19 pandemic, researchers have found an increased risk of death in the mentally ill.
Other illnesses
Post-COVID life expectancy in the US, UK, Netherlands, and Austria
The life expectancy of people with diabetes, which is 9.3% of the U.S. population, is reduced by roughly 10–20 years. People over 60 years old with Alzheimer's disease have about a 50% life expectancy of 3–10 years. Other demographics that tend to have a lower life expectancy than average include transplant recipients and the obese.
Education
Education on all levels has been shown to be strongly associated with increased life expectancy. This association may be due partly to higher income,
which can lead to increased life expectancy. Despite the association,
among identical twin pairs with different education levels, there is
only weak evidence of a relationship between educational attainment and
adult mortality.
According to a paper from 2015, the mortality rate for the
Caucasian population in the United States from 1993 to 2001 is four
times higher for those who did not complete high school compared to those who have at least 16 years of education. In fact, within the U.S. adult population, people with less than a high school education have the shortest life expectancies.
Preschool education also plays a large role in life expectancy.
It was found that high-quality early-stage childhood education had
positive effects on health. Researchers discovered this by analyzing the
results of the Carolina Abecedarian Project,
finding that the disadvantaged children who were randomly assigned to
treatment had lower instances of risk factors for cardiovascular and
metabolic diseases in their mid-30s.
Various species of plants and animals, including humans, have
different lifespans. Evolutionary theory states that organisms which—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 restriction.
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.
The ability of skin fibroblasts to perform DNA repair after UV irradiation was measured in shrew, mouse, rat, hamster, cow, elephant and human. It was found that DNA repair capability increased systematically with species life span. Since this original study in 1974, at least 14 additional studies were performed on mammals to test this correlation.
In all, but two of these studies, lifespan correlated with DNA repair
levels, suggesting that DNA repair capability contributes to life
expectancy.
In a broad survey of zoo animals, no relationship was found between investment of the animal in reproduction and its life span.
A
survival tree to explain the calculations of life-expectancy. Red
numbers indicate a chance of survival at a specific age, and blue ones
indicate age-specific death rates.
In actuarial notation, the probability of surviving from age to age is denoted and the probability of dying during age (i.e. between ages and ) is denoted
. 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%. This probability describes the likelihood of dying at that age, and is not the rate at which people of that age die. It can be shown that
(1)
The curtate future lifetime, denoted , is a discrete random variable representing the remaining lifetime at age , rounded down to whole years. Life expectancy, more technically called the curtate expected lifetime and denoted , is the mean of —that is to say, the expected number of whole years of life remaining, assuming survival to age . So,
(2)
Substituting (1) into the sum and simplifying gives the final result
(3)
If the assumption is made that, on average, people live a half year
on the year of their death, the complete life expectancy at age would be .
By definition, life expectancy is 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 the 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.
The starting point for calculating life expectancy is the age-specific death rates of the population members. If a large amount 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 remove
(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. The most common modern methods include:
fitting a mathematical formula (such as the Gompertz function, or an extension of it) to the data.
looking at an established mortality table
derived from a larger population and making a simple adjustment to it
(such as multiplying by a constant factor) to fit the data. (In cases of
relatively small amounts of data.)
looking at the mortality rates actually experienced at each age and applying a piecewise model (such as by cubic splines) to fit the data. (In cases of relatively large amounts of data.)
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, or smokers and non-smokers) and are then used to
calculate a life table
from which one can calculate the probability of surviving to each age.
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 (e.g. aircraft),
methods like accelerated aging are used to model the life expectancy of a component.
It is important to note that the life expectancy statistic is
usually based on past mortality experience and assumes that the same
age-specific mortality rates will continue. 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, some factors
correlate with 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
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 (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.
The long-standing quest for longer life
led in the 2010s to a more promising focus on increasing HALE, also
known as a person's "healthspan". Besides the benefits of keeping people
healthier longer, a goal is to reduce health-care expenses on the many
diseases associated with cellular senescence. Approaches being explored include fasting, exercise, and senolytic drugs.
Forecasting
Forecasting life expectancy and mortality form 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 one of two different approaches:
Forecasting the life expectancy directly, generally using ARIMA
or other time-series extrapolation procedures. This 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. This 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 in 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. The software includes Professor Rob J. Hyndman's R package called 'demography' and UC Berkeley's LCFIT system.
Policy uses
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 used in describing the physical quality of life
of an area. It is also used for an individual when the value of a life
settlement is determined a life insurance policy is sold for a cash
asset.
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 U.S. (r = −0.620).
Life expectancy vs. other measures of longevity
"Remaining" life expectancy—expected number of remaining years of life as a function of current age—is used in retirement income planning.
Life expectancy is commonly confused with the average age an adult
could expect to live. This confusion may create the expectation that an
adult would be unlikely to exceed an life expectancy, even though, with
all statistical probability, an adult, who has already avoided many
statistical causes of adolescent mortality, should be expected to
outlive the life expectancy calculated from birth. One must compare the life expectancy of the period after childhood to estimate also the life expectancy of an adult.
Life expectancy can change dramatically after childhood. In the table above, note the life expectancy at birth
in the Paleolithic is 22–33 years, but life expectancy at 15 is 54
years. Additional studies similarly show a dramatic increase in life
expectancy once adulthood was reached.
Maximum life span is an individual-specific concept, and therefore is an upper bound rather than an average. Science author Christopher Wanjek
writes, "[H]as the human race increased its life span? Not at all. This
is one of the biggest misconceptions about old age: we are not living
any longer." The maximum life span, or oldest age a human can live, may
be constant.
Further, there are many examples of people living significantly longer
than the average life expectancy of their time period, such as Socrates (71), Saint Anthony the Great (105), Michelangelo (88), and John Adams (90).
However, anthropologist John D. Hawks criticizes the popular conflation of life span (life expectancy) and maximum life span
when popular science writers falsely imply that the average adult human
does not live longer than their ancestors. He writes, "[a]ge-specific
mortality rates have declined across the adult lifespan. A smaller
fraction of adults die at 20, at 30, at 40, at 50, and so on across the
lifespan. As a result, we live longer on average... In every way we can
measure, human lifespans are longer today than in the immediate past,
and longer today than they were 2000 years ago... age-specific mortality
rates in adults really have reduced substantially."
Life expectancy can also create popular misconceptions about at
what age a certain population may expect to die at. The modal age at
death is the age when most deaths occur in a population, and is
sometimes used instead of life expectancy for this kind of
understanding. For example, in the table above, life expectancy in the
Paleolithic is listed as 22–33 years. For many, this implies that most
people in the Paleolithic died in their late twenties. However, in the
Paleolithic, most adults in the Paleolithic died at 72 years (the average modal adult life span).
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