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Life expectancy is the expected (in the statistical sense) number of years of life remaining at a given age. It is denoted by ex, which means the average number of subsequent years of life for someone now aged x, according to a particular mortality experience. (In technical literature, this symbol means the average number of complete years of life remaining, ie excluding fractions of a year. The corresponding statistic including fractions of a year, ie the normal meaning of life expectancy, has a symbol with a small circle over the e.) (see for example [2745])

The term is most often used in the human context, but is also used in plant or animal ecology; it is calculated by the analysis of life tables (also known as actuarial tables). The term life expectancy may also be used in the context of manufactured objects although the related term shelf life is used for consumer products and the term mean time to breakdown (MTTB) is used in engineering literature.

Life expectancy is heavily dependent on the criteria used to select the group. For example, in countries with high infant mortality rates, the life expectancy at birth is highly sensitive to the rate of death in the first few years of life. In these cases, another measure such as life expectancy at age 5 (e5) can be used to exclude the effects of infant mortality to provide a simple measure of overall mortality rates other than in early childhood.

Life expectancy is usually calculated separately for males and females.


Humans live on average 39.5 years in Swazilandmarker and 81 years in Japanmarker (2008 est.), although Japan's recorded life expectancy may have been very slightly increased by counting many infant deaths as stillborn. The oldest confirmed recorded age for any human is 122 years (see Jeanne Calment), though some people are reported to have lived longer. This is referred to as the "maximum life span", which is the upper boundary of life, the maximum number of years a human can live.

Lifespan variation over time

The following information is derived from Encyclopaedia Britannica, 1961 and other sources, and unless otherwise stated represents estimates of the life expectancies of the population as a whole. In many instances life expectancy varied considerably according to class and gender.

Sometimes, mainly in the past, life expectancy increased during the years of childhood, as the individual survived the high mortality rates then associated with childhood. A pre-20th century individual who lived past the teenage years could expect to live to an age comparable to the life expectancy of today. The life expectancies at birth listed below take account of infant mortality but not pre-natal mortality (miscarriage or abortion).

Humans by Era Average Lifespan at Birth
Upper Paleolithic 33 At age 15: 39 (to age 54)
Neolithic 20  
Bronze Age 18  
Bronze age, Swedenmarker 40-60  
Classical Greece 20-30  
Classical Rome 20-30  
Pre-Columbian North America 25-30  
Medieval Islamic Caliphate 35+ The average lifespans of the elite class were 59–84.3 years in the Middle East and 69–75 in Islamic Spainmarker.
Medieval Britain 20-30  
Early 20th Century 30-45  
Current world average 66.57 2009 est.

During the Industrial Revolution, the life expectancy of children increased dramatically. The percentage of children born in Londonmarker who died before the age of five 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, 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.

In order to assess the quality of these additional years of life, 'healthy life expectancies' have been calculated for the last 30 years. Since 2001, the World Health Organization publishes 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 of America uses similar indicators in the framework of their nationwide health promotion and disease prevention plan "Healthy People 2010". An increasing number of countries are using health expectancy indicators to monitor the health of their population.

Regional variations

[[Image:Life Expectancy 2008 Estimates CIA World Factbook.svg|thumb|400px|CIA World Factbook 2008 Estimates for Life Expectancy at birth (years).

There are great variations in life expectancy worldwide, mostly caused by differences in public health, medical care and diet from country to country. Much of the excess mortality (higher death rates) in poorer nations is due to war, starvation, and diseases (AIDS, Malaria, etc.). Over the past 200 years, countries with Black or African populations have generally not had the same improvements in mortality rates that have been enjoyed by populations of European origin. Even in countries with a majority of White people, such as USA, England, and France, Black people tend to have shorter life expectancies than their White counterparts (although often the statistics are not analysed by race). For example, in the U.S. White Americans are expected to live until age 78, but African Americans only until age 71.. Climate may also have an effect, and the way data is collected may also influence the figures. According to the U.S. Census Bureau, Andorramarker has the world's longest life expectancy of 83.5 years.

There are also significant differences in life expectancy between men and women in most countries, with women typically outliving men by around five years. Economic circumstances also affect life expectancy. For example, in the United Kingdom, life expectancy in the wealthiest areas is several years longer 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 Glasgowmarker the disparity is among the highest in the world with life expectancy for males in the heavily deprived Caltonmarker standing at 54 – 28 years less than in the affluent area of Lenziemarker, which is only eight kilometres away.

Life expectancy is also likely to be affected by exposure to high levels of highway air pollution or industrial air pollution. Thus occupation may also have a major effect on life expectancy. Well-educated professionals working in offices have a high life expectancy, while coal miners (and in prior generations, asbestos cutters) do not. Other factors affecting an individual's life expectancy are genetic disorders, obesity, access to health care, diet, exercise, tobacco smoking, drug use and excessive alcohol use.

Gender differences

Women tend to have a lower mortality rate at every age. In the womb, male fetuses have a higher mortality rate (babies are conceived at a ratio of about 124 males to 100 females, but the ratio of those surviving to birth is only 105 males to 100 females). Among the smallest premature 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.

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 those of men. The reasons for this are not entirely certain. Traditional arguments tend to favor socio-environmental factors: historically, men have generally consumed more tobacco, alcohol and drugs than females 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 more likely to die from injuries, whether unintentional (such as car accidents) or intentional (suicide, violence, war). 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, and coronary heart disease . These far outweigh the female mortality rate from breast cancer and cervical cancer etc.

However, such arguments are not entirely satisfactory and, even if the statistics are corrected for known socio-environmental effects on mortality, females still have longer life expectancy. Interestingly, the age of equalization (about 13) tends to be close to the age of menarche, suggesting a potential reproductive-equilibrium explanation.

Some argue that shorter male life expectancy is merely another manifestation of the general rule, seen in all mammal species, that larger individuals tend on average to have shorter lives.. This biological difference occurs because women have more resistance to infections and degenerative diseases .

However, many do not agree that there is a difference and there is reason to suspect that this varies over a period of time and that gender is not a significant correlator of living longer.

Influence of disabilities

In the western world, people with a serious mental illness die on average 25 years earlier than the rest of the population. In the 1990s the life expectancy of the seriously mentally ill was 10 to 15 years shorter, and now has grown to a 25 year average shorter life span.

There is no objective test for mental illness, yet studies show the evidence of the increasingly earlier death of those diagnosed mentally ill.

Mental illnesses include schizophrenia, bipolar disorder and major depression. Three out of five mentally ill die from mostly preventable physical diseases. Diseases such as Heart/Cardiovascular disease, Diabetes, Dyslipidaemia, Respiratory ailments, Pneumonia, Influenza.

Stress also decreases life expectancy. The side effects of stress are: pain of any kind, heart disease, digestive problems, sleep problems, depression, obesity, autoimmune diseases, skin conditions, etc., all of which contribute to mental disorders, faster ageing, and other physical diseases.



The number of centenarians is increasing at 7% per year. Japan has the highest ratio of centenarians. In Okinawa, there are 34.7 centenarians for every 100,000 inhabitants .

In the United States, the number of centenarians grew from 15,000 in 1980 to 77,000 in 2000.

Evolution and aging rate

It is interesting to consider why the 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 whilst avoiding accidents, disease, predation, etc., are likely to have genes that code for slow ageing - which often translates to good cellular repair.

This is so because if a random genetic trait found in the organism increases its survivability (at least up to the time when it reproduces) it is more likely to pass on its genes to the next generation. Thus, a member of the population with genes that lend to increased survivability will tend to reproduce more and have more successors. This gene which increases survivability will thus be increasingly spread throughout the species, increasing the survivability of the species as a whole.

Conversely a change to the environment that means that organisms die younger from a common disease or a new threat from a predator will mean that organisms that have genes that code for putting more energy into reproduction than repair will do better.

For example, better-defended animals such as small birds, that can fly away from danger, live for a decade or more, whereas mice, which cannot, die of old age in a year or two. Tortoises and turtles are very well defended and can live for over 100 years. A classic study comparing opossums on a protected island with unprotected opossums also supports this theory.

But there are also counterexamples, suggesting that there is more to the story. Guppies in predator-free habitats evolve shorter lifespans than nearby populations of guppies where predators exact a large toll. A broad survey of mammals indicates many more exceptions. The theory of evolution of aging may be in flux.

Natural selection tends to favor short-term survival traits. Human-technology-driven artificial selection, however, now appears to have prioritized long-term survival traits, having previously improved short-term survival rates through global food-chain dominance.

Calculating life expectancies

The starting point for calculating life expectancies is the age-specific death rates of the population members. A very simple model of age-specific mortality uses the Gompertz function, although these days more sophisticated methods are used.

In cases where the amount of data is relatively small, the most common methods are to fit the data to a mathematical formula, such as an extension of the Gompertz function, or to look at an established mortality table previously derived for a larger population and make a simple adjustment to it (eg multiply by a constant factor) to fit the data.

With a large amount of data, one looks at the mortality rates actually experienced at each age, and applies smoothing (eg by cubic splines) to iron out any apparently random statistical fluctuations from one year of age to the next.

While the data required is 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, such as in aircraft methods such as 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 which are believed to have different mortality rates (eg males and females, and perhaps smokers and non-smokers if data is 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 x to age x+n is denoted \,_np_x\! and the probability of dying during age x (i.e. between ages x and x+1) is denoted q_x\!. For example, if 10% of a group of people alive at their 90th birthday die before their 91st birthday, then the age-specific death probability at age 90 would be 10%.

The life expectancy at age x, denoted \,e_x\!, is then calculated by adding up the probabilities to survive to every age. This is the expected number of complete years lived (one may think of it as the number of birthdays they celebrate).

e_x =\sum_{t=1}^{\infty}\,_tp_x = \sum_{t=0}^{\infty}t \,_tp_x q_{x+t}

Because age is rounded down to the last birthday, on average people live half a year beyond their final birthday, so half a year is added to the life expectancy to calculate the full life expectancy. (This is \,e_x\! with a circle over the e.)

Life expectancy is by definition an arithmetic mean. It can also be calculated by integrating the survival curve from ages 0 to positive infinity (the maximum lifespan, sometimes called 'omega'). For an extinct cohort (all people born in year 1850, for example), of course, it can simply be calculated by averaging the ages at death. For cohorts with some survivors it is estimated by using mortality experience in recent years.

It is important to note that this 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 are not generally appropriate for calculating how long any given individual of a particular age is expected to live. But they are a useful 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, thus assuming that age-specific death rates will continue to decrease over the years, as they have done in the past. This is often done by simply extrapolating past trends; however some models do exist to account for the evolution of mortality (e.g., the Lee-Carter model).

As discussed above, on an individual basis, there are a number of factors that have been shown to 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.

Life Expectancy Index

The Life Expectancy Index is a statistical measure used to determine the average lifespan of the population of a certain nation or area. 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 a factor in finding the physical quality of life of an area.

See also

Increasing life expectancy


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