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Life expectancy is the expected (in the statistical sense) number of years of life remaining at a given age.^[1] It is denoted by e_x, 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, excluding fractions of a year. The corresponding statistic including fractions of a year, the normal meaning of life expectancy, has a symbol with a small circle over the e.) In modern times, life expectancy has substantially changed on a yearly basis and cannot be used accurately for long-term predictions.^{[citation needed]}

The term that is known as life expectancy is most often used in the context of human populations, but is also used in plant or animal ecology;^[2] 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^[3] although the related term shelf life is used for consumer products and the terms "mean time to breakdown" (MTTB) and "mean time before failures" (MTBF) are used in engineering literature.

Interpretation of life expectancy[link]

General explanation: It is important to note that life expectancy is an average. In many cultures, particularly before modern medicine was widely available, the combination of high infant mortality and deaths in young adulthood from accidents, wars, and childbirth, significantly lowers the overall life expectancy. But for someone who survived past these early hazards, living into their sixties or seventies would not be uncommon. For example, a society with a life expectancy of 40 may have very few people dying at age 40, most will die before 30 or after 55.

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. Because of this sensitivity to infant mortality, simple life expectancy at age zero can be subject to gross misinterpretation, leading one to believe that a population with a low overall life expectancy 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, the life expectancy at age zero will be about 37 years, while 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 age 5 would be another 65 years. Aggregate population measures such as the proportion of the population in various age classes should also be used alongside individual-based measures like formal life expectancy when analyzing population structure and dynamics.

Human life expectancy patterns[link]

Humans live on average 31.88 years in Swaziland and 82.6 years in Japan, although Japan's recorded life expectancy may have been very slightly increased by counting many infant deaths as stillborn.^[4] An analysis published in 2011 in The Lancet attributes Japanese life expectancy to equal opportunities and public health as well as diet.^[5][6]

The oldest confirmed recorded age for any human is 122 years (see Jeanne Calment). 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.^[7]

Life expectancy variation over time[link]

This section may be confusing or unclear to readers. Please help clarify the section; suggestions may be found on the talk page. (September 2011)

The following information is derived from Encyclopædia 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.

Life expectancy at birth takes account of infant mortality but not pre-natal mortality.

In some cases life expectancy may increase with age as the individual survives the higher mortality rates associated with childhood. For instance, the table above listed life expectancy at birth in Medieval Britain at 30. A male member of the English aristocracy at the same period could expect to live, having survived until the age of 21^[17]:

• 1200-1300 A.D.: 43 years (to age 64)
• 1300-1400 A.D.: 34 years (to age 55) (due to the impact of the Black Death)
• 1400-1500 A.D.: 48 years (to age 69)
• 1500-1550 A.D.: 50 years (to age 71).

In general, the available data indicate that longer lifespans became more common recently in human evolution.^[9][22] This increased longevity is attributed by some writers to cultural adaptations rather than genetic evolution,^[23] although some research indicates that during the Neolithic Revolution natural selection favored increased longevity.^[10] Nevertheless, all researchers acknowledge the effect of cultural adaptations upon life expectancy.^[22]

During the early 1600s in England, life expectancy was only about 35 years, largely because two-thirds of all children died before the age of four.^[24] The average life expectancy in Colonial America was under 25 years in the Virginia colony,^[25] and in New England about 40% of children failed to reach adulthood.^[26] During the Industrial Revolution, the life expectancy of children increased dramatically.^[27] The percentage of children born in London who died before the age of five decreased from 74.5% in 1730-1749 to 31.8% in 1810-1829.^[28][29]

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.^[30]

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[link]

World map on life expectancy from WLE

+80   +77,5   +75   +72,5   +70   +67,5   +65

+60   +55   +50   +45   +40   - 40

Plot of life expectancy vs. GDP per capita in 2009.

Graphs of life expectancy at birth for some sub-Saharan countries showing the fall in the 1990s primarily due to the AIDS pandemic.^[31]

There are great variations in life expectancy between different parts of the world, mostly caused by differences in public health, medical care and diet. Much of the excess mortality (higher death rates) in poorer nations is due to war, starvation, and diseases (AIDS, Malaria, etc.). The impact of AIDS is particularly notable on life expectancy in many African countries; According to the UN^[32] the life expectancy at birth for 2010–2015 (if HIV/AIDS did not exist) would have been:

• 70.7 years instead of 31.6 in Botswana
• 69.9 years instead of 41.5 in South Africa
• 70.5 years instead of 31.8 in Zimbabwe.

As a result, over the past 200 years, countries with African populations have generally not had the same improvements in mortality rates that have been enjoyed by populations of Asian, Latin American or European origin. Notably, even in countries with a majority of European people, such as the United States, Britain, or Ireland, African people still tend to have shorter life expectancies than their European counterparts. For example, in the United States, Euro-Americans are expected to live until age 78.2, but African Americans only until age 73.6.^[33]

In contrast, Asian-Americans live the longest of all ethnic groups in the United States, with a life expectancy of 87 years, almost ten years longer than Euro-Americans.,^[34] which falls in line with the longer life expectancy among East Asian countries globally compared to European, Latin American or African countries. Surprisingly, Latino-Americans are second place among ethnic groups on life expectancy, living on average two years longer than Euro-Americans and seven years longer than African-Americans, with a life expectancy of 80.6 years.,^[35] in contrast to the on average lower life expectancy of most Latin American countries. These variations among ethnic groups may be ascribed to differing economic circumstances of the groups, and in the United States, notably differing access to health care. It may also be ascribed to different cultural patterns of eating or diet that may cross international lines and explain the variation within ethnic groups in a multiethnic society such as the United States.

Climate may also have an effect, and the way data is collected may also influence the figures.

Economic circumstances may 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.^[36] In Glasgow the disparity is among the highest in the world with life expectancy for males in the heavily deprived Calton standing at 54 – 28 years less than in the affluent area of Lenzie, which is only eight kilometres away.^[37][38]

The relationship between economic circumstance and life expectancy is not clear-cut, however. 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.^[39] 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.^{[citation needed]} This is one way that occupation can have a major effect on life expectancy. Coal miners (and in prior generations, asbestos cutters) often have shorter than average life expectancies. 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.

Sex differences[link]

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.^[40][41]

Women tend to have a lower mortality rate at every age. In the womb, 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).^[42] 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. 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.^[43]

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 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.^[44] Men are also more likely to die from injuries, whether unintentional (such as car accidents) or intentional (suicide, violence, war).^[44] A 2005 study found that the level of patriarchy predicts men's mortality rates. High levels of patriarchy were associated with high levels of male mortality; low levels of patriarchy were correlated with low mortality levels. The researchers argue that while patriarchy grants men certain privileges over women, it also promotes gender stereotypes which harm men.^[45] 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.^[7] These far outweigh the female mortality rate from breast cancer and cervical cancer etc.

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.^[46][47] This biological difference occurs because women have more resistance to infections and degenerative diseases.^[7]

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. 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, the males have higher mortality than the females. Evidence suggests that the sex mortality differential in humans is due to both biological/genetic and environmental/behavioral risk and protective factors.^[48]

Centenarians[link]

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.^[49] 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.^[50]

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).^[51]

Mentally ill[link]

Adults with serious mental illness (SMI) die about 25 years earlier, on average at age 51 versus 76 for Americans generally, primarily due to cardiovascular disease.^[52][53]

Evolution and aging rate[link]

Various species of plants and animals, including humans, have different lifespans. There is an evolutionary theory of aging, and general consensus in the academic community of evolutionary theorists; however the theory doesn't work well in practice, and there are many unexplained exceptions. 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 aging - which often translates to good cellular repair. This is theorized to be true because if predation or accidental deaths prevent most individuals from living to an old age, then there will be less natural selection to increase intrinsic life span.^[54] The finding was supported in a classic study of opossums by Austad,^[55] however the opposite relationship was found in an equally prominent study of guppies by Reznick.^[56][57]

One prominent and very popular theory attributes aging to a tight budget for food energy called caloric restriction.^[58] Caloric restriction observed in many animals (most notably mice and rats), shows a near doubling of life span due to a very limited calorific intake. Support for this theory has been bolstered by several new studies linking lower basal metabolic rate to increased life expectancy.^[59][60][61] This is the key to why animals like Giant Tortoises can live so long.^[62] Studies of humans with 100+ year life spans have shown a link to decreased thyroid activity, resulting in their lowered metabolic rate.^[63]

In a broad survey of zoo animals, no relationship was found between the fertility of the animal and its life span.^[64]

Calculating life expectancies[link]

The starting point for calculating life expectancies is the age-specific death rates of the population members. In the past, a very simple model of age-specific mortality used the Gompertz function, although these days more sophisticated methods are used.^[65]

In cases where the amount of data is relatively small, the most common methods are to fit a mathematical formula, such as an extension of the Gompertz function, to the data, or to look at an established mortality table previously derived for a larger population and make a simple adjustment to it (e.g. 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 (e.g. by cubic splines) to iron out any apparently random statistical fluctuations from one year of age to the next.

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.^[66] 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.^[3]

The age-specific death rates are calculated separately for separate groups of data which are believed to have different mortality rates (e.g. 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 Failed to parse (Missing texvc executable; please see math/README to configure.): \,_np_x\!

and the probability of dying during age x (i.e. between ages x and x+1) is denoted Failed to parse (Missing texvc executable; please see math/README to configure.): 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%. Note that this is a probability rather than a mortality rate.

The life expectancy at age x, denoted Failed to parse (Missing texvc executable; please see math/README to configure.): \,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).

Failed to parse (Missing texvc executable; please see math/README to configure.): 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 denoted by Failed to parse (Missing texvc executable; please see math/README to configure.): \,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 (or equivalently to the maximum lifespan, sometimes called 'omega'). For an extinct or completed 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. These estimates are called period cohort life expectancies.

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 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, 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^[67]).

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.^[7]

Life expectancy forecasting[link]

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 systems, because the cash flow in these systems depends on the number of recipients still living (along with the rate of return on the investments or the tax rate in PAYGO systems). With longer life expectancies, these systems see increased cash outflow; if these systems underestimate increases in life-expectancies, they won't be prepared for the large payments that will inevitably occur as humans live longer and longer.

Life expectancy forecasting usually is based on two different approaches:

• Forecasting the life expectancy directly, generally using ARIMA or other time series extrapolation procedures: This approach has the advantage of simplicity, but it cannot account for changes in mortality at specific ages, and the forecasted 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 R, SAS, Matlab, or SPSS.

• Forecasting age specific death rates and computing the life expectancy from the results with life table methods: This approach 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. This approach also yields a set of age specific rates that may be used to derive other measures, like survival curves or life expectancies at different ages. The most important approach within this group is the Lee-Carter model,^[68] 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 for this approach include Professor Rob J. Hyndman's R package and UC Berkeley's LCFIT system.

Policy uses of life expectancy[link]

This section requires expansion.

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.^[69]

Life expectancy is also used in describing the physical quality of life of an area.

Life expectancies are also used when determining the value of a life settlement, a life insurance policy sold for a cash asset.

Life expectancy vs. life span[link]

Life expectancy is often confused with life span to the point that they are nearly synonyms; when people hear 'life expectancy was 35 years' they often interpret this as meaning that people of that time or place had short life spans.^[70] 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 forever."

This ignores the fact that the life expectancy generally quoted is the at birth number which is an average that includes all the babies that die before their first year of life as well as people that die from disease and war. The genetics of humans and rate of aging were no different in preindustrial societies than today, but people frequently died young because of untreatable diseases, accidents, and malnutrition. Many women did not survive childbirth, and when a person did reach old age they were likely to succumb quickly to health problems.

It can be argued that it is better to compare life expectancies of the period after adulthood to get a better handle on life span.^[71] Even during childhood life expectancy can take a huge jump as seen in the Roman Life Expectancy table at the University of Texas where at birth the life expectancy was 25 but at the age of 5 it jumped to 48. 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.

See also[link]

Increasing life expectancy[link]

• Strategies for Engineered Negligible Senescence (SENS)
• John Sperling
• Life extension
• Longevity
• Rejuvenation
• Public health
• Infant mortality

References[link]

Further reading[link]

• Leonid A. Gavrilov & Natalia S. Gavrilova (1991), The Biology of Life Span: A Quantitative Approach. New York: Harwood Academic Publisher, ISBN 3-7186-4983-7

External links[link]

Wikimedia Commons has media related to: Life expectancy

• Charts for all countries
• Rank Order - 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 Statistics.
• 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.

Hans Rosling
Rosling at the 2012 Time 100 gala
Born	(1948-07-27) July 27, 1948 (age 63)
Institutions	Karolinska Institute
Alma mater	Uppsala University, St. John's Medical College
Known for	Gapminder Foundation and Trendalyzer

Hans Rosling (born 27 July 1948^[1] in Uppsala, Sweden) is a Swedish medical doctor, academic, statistician and public speaker. He is Professor of International Health at Karolinska Institute^[2] and co-founder and chairman of the Gapminder Foundation, which developed the Trendalyzer software system.

Study and career[link]

From 1967 to 1974 Rosling studied statistics and medicine at Uppsala University, and in 1972 he studied public health at St. John's Medical College, Bangalore. He became a licenced physician in 1976 and from 1979 to 1981 he served as District Medical Officer in Nacala in northern Mozambique.

On 21 August 1981, Rosling discovered an outbreak of konzo, a paralytic disease,^[3][4] and the investigations that followed earned him a Ph.D. degree at Uppsala University in 1986. He spent two decades studying outbreaks of this disease in remote rural areas across Africa and supervised more than ten Ph.D. students^{[citation needed]}. Outbreaks occur among hunger-stricken rural populations in Africa where a diet dominated by insufficiently processed cassava results in simultaneous malnutrition and high dietary cyanide intake.^[4]

Rosling's research has also focused on other links between economic development, agriculture, poverty and health^[5] in Africa, Asia and Latin America. He has been health adviser to WHO, UNICEF and several aid agencies. In 1993 he was one of the initiators of Médecins Sans Frontières in Sweden. At Karolinska Institutet he was head of the Division of International Health (IHCAR) from 2001 to 2007. As chairman of Karolinska International Research and Training Committee (1998–2004) he started health research collaborations with universities in Asia, Africa, the Middle East and Latin America. He started new courses on Global Health and co-authored a textbook on Global Health that promotes a fact-based world view.

Rosling presented the television documentary The Joy of Stats, which was broadcast in the United Kingdom by BBC Four in December 2010.^[6]

Gapminder[link]

Rosling co-founded the Gapminder Foundation together with his son Ola Rosling and daughter-in-law Anna Rosling Rönnlund. Gapminder developed the Trendalyzer software that converts international statistics into moving, interactive graphics. His lectures using Gapminder graphics to visualise world development have won awards.^[7] The interactive animations are freely available from the Foundation's website. On 16 March 2007 Google acquired the Trendalyzer software with the intention to scale it up and make it freely available for public statistics. In 2008 Google made available a Motion Chart Google Gadget and in 2009 the Public Data Explorer.^[8]

Rosling is also a sword swallower, as demonstrated in the final moments of his second talk at the TED conference.^[9] In 2009 he was listed as one of 100 leading global thinkers by Foreign Policy Magazine,^[10] and in 2011 as one of 100 most creative people in business by the Fast Company Magazine.^[11] In 2011 he was elected member of the Swedish Academy of Engineering Sciences.

Awards[link]

Hans Rosling at the Swedish pavilion of Expo 2010 in Shanghai

• 2007 – Statistician of the Year Award from the Swedish Association for Statistics
• 2007 – Jubilee Prize from the Swedish Medical Society
• 2007 – Knowledge Prize from the National Encyclopaedia of Sweden
• 2008 – Speaker of the Year from the Swedish Event Academy
• 2008 – The Big Debate Award from Dagens Medicine
• 2009 – Georg and Greta Borgstrom Award from the Swedish Royal Academy of Agriculture and Forestry
• 2010 – Illis Quorum, Highest Award conferred by the Government of Sweden
• 2010 – The Grand Prize from the Royal Institute of Technology, Stockholm, Sweden
• 2010 – Gold Medal from Royal Swedish Academy of Engineering Sciences
• 2010 – World Technology Award in Design together with Ola Rosling & Anna Rosling Rönnlund
• 2010 – The Gannon Award for the Continued Pursuit of Human Advancement (US)^[12]
• 2011 - Grierson 2011 Award for the Best British Science Documentary, presenter in "The Joy of Stats"
• 2012 - Time 100 most influential people list

Selected publications[link]

• Thorson, A.; Ragnarsson, A.; Rosling, H.; Ekström, A. (2010). "Male circumcision reduces HIV transmission. The risk of transmission from woman to man is halved". Lakartidningen 107 (46): 2881–2883. PMID 21197783.  edit
• Hanson, S.; Thorson, A.; Rosling, H.; Örtendahl, C.; Hanson, C.; Killewo, J.; Ekström, A. M. (2009). Husereau, Don. ed. "Estimating the Capacity for ART Provision in Tanzania with the Use of Data on Staff Productivity and Patient Losses". PLoS ONE 4 (4): e5294. DOI:10.1371/journal.pone.0005294. PMC 2667213. PMID 19381270. //www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2667213.  edit
• Von Schreeb, J.; Riddez, L.; Samnegård, H.; Rosling, H. (2008). "Foreign field hospitals in the recent sudden-onset disasters in Iran, Haiti, Indonesia, and Pakistan". Prehospital and disaster medicine : the official journal of the National Association of EMS Physicians and the World Association for Emergency and Disaster Medicine in association with the Acute Care Foundation 23 (2): 144–151; discussion 151–3. PMID 18557294.  edit
• Schell, C. O.; Reilly, M.; Rosling, H.; Peterson, S.; Ekström, A. M. (2007). "Socioeconomic determinants of infant mortality: A worldwide study of 152 low-, middle-, and high-income countries". Scandinavian Journal of Public Health 35 (3): 288–297. DOI:10.1080/14034940600979171. PMID 17530551.  edit
• Von Schreeb, J.; Rosling, H.; Garfield, R. (2007). "Mortality in Iraq". The Lancet 369 (9556): 101. DOI:10.1016/S0140-6736(07)60058-0. PMID 17223462.  edit
• Elrayah, H.; Eltom, M.; Bedri, A.; Belal, A.; Rosling, H.; Ostenson, C. (2005). "Economic burden on families of childhood type 1 diabetes in urban Sudan". Diabetes Research and Clinical Practice 70 (2): 159–165. DOI:10.1016/j.diabres.2005.03.034. PMID 15919129.  edit
• Thanh, H. T. T.; Jiang, G. X.; Van, T. N.; Minh, D. P. T.; Rosling, H.; Wasserman, D. (2005). "Attempted suicide in Hanoi, Vietnam". Social Psychiatry and Psychiatric Epidemiology 40 (1): 64–71. DOI:10.1007/s00127-005-0849-6. PMID 15624077.  edit
• Rosling, H. (2004). "New map of world health is needed. North and South is changed to healthy and ill and West to rich and poor". Lakartidningen 101 (3): 198–201. PMID 14763091.  edit

References[link]

Wikiquote has a collection of quotations related to: Hans Rosling

External links[link]

• Hans Rosling on Twitter
• Hans Rosling shows the best stats you've ever seen at the 2006 TED Conference in Monterey, CA highlighting novel ways of presenting global statistics.
• Hans Rosling's new insights on poverty, from TED2007, featuring a surprise ending. (video)
• Video of Hans Rosling speech at the OECD World Forum in Istanbul
• Hans Rosling's blog
• Ola Rosling giving a Gapminder presentation at Google (video)
• Motion Chart Google Gadget
• Hans Rosling: Global health expert; data visionary profile on TED.com

Dan Buettner
Buettner in October 2010
Born	1960 St. Paul, Minnesota
Occupation	explorer, educator

Dan Buettner (born 1960 in St. Paul, Minnesota) is an American explorer, educator, author, public speaker and co-producer of an Emmy Award-winning documentary who also holds three world records for endurance bicycling.^[1][2][3] He is the founder of the online Quest Network, Inc., which provides opportunities for students to interactively engage with explorers on expedition.^[3] In February 2007, an expedition led by Buettner to Nicoya, Costa Rica uncovered the longest-lived people known in the Western Hemisphere.^[4][5]

Biography[link]

Early life[link]

Raised by an adventurous father, Buettner's appreciation of travel was encouraged by a series of international trips he won as a teenager selling newspapers.^[1] His brothers Steve and Nick also followed in their father's footsteps.

After graduating from the College of St. Thomas in 1984, Buettner took a year to explore Spain before taking a job with National Public Radio in Washington, D.C. recruiting celebrity participation in a fund-raising croquet tournament with journalist George Plimpton.^[1]

Expeditions[link]

The experience taught him a lot about the fundraising value of publicity, so he set off to achieve several world records, cycling thousands of miles on several continents.^[1] The first trip, called "Americastrek," covered 15,500 miles from Alaska to Argentina and was completed in 1987; the second, "Sovietrek," in 1990 covered 12,888 miles; the third, inspired by 1992's Los Angeles riots, was "Africatrek," covering 11,885 miles through Africa.^[6] Buettner has completed these treks in spite of suffering from Ménière's disease.

In February 1995, he and a team including Mayanist Julie Acuff, photographer Douglas Mason, and his two brothers, Steve and Nick, embarked on a three-month, 3,244-mile mountain bicycling expedition known as MayaQuest. They travelled through Mexico's Yucatan Peninsula, Belize, Guatemala, and Honduras to gain insights into the causes of the collapse of the Maya civilization around A.D. 900. Carrying laptop computers and a minisatellite dish on loan from Rockwell, these adventurers were able to link up with 40,000 schools involved in the project via Prodigy, as well as other Internet users. The team logged on twice a week, uploaded their findings, fielded questions from students and teachers, and received input from more than one million online participants.

Buettner first utilized the internet in publicizing his adventures on the last of these trips, as he and his companions posted on Mosaic the details.^[7] The last of these trips also became the subject of an Emmy-winning PBS documentary co-produced by Buettner and a book he authored, Afratrek: A Journey by Bicycle through Africa, which won the Young Reader Award of Scientific American.^[6]

Activities in Support of Classroom Education[link]

In 1993, Buettner first began transmitting videos and stories from his expeditions into classrooms and in 1995 founded "Earthtreks, Inc." to support that effort.^[6] He sold the company to textbook publisher Classroom Connect in 1997, but continued to lead expeditions until 2004.

His Africatrek and MayaQuest were adapted into educational computer games by MECC in the late 90s.

Blue zone research[link]

During his bicycling trips, Buettner became interested in demographics and longevity and began his research into "blue zones,"^[8] his term for the regions on Earth with the longest life expectancy, disability-free life expectancy or concentration of persons over 100,^[1] He began investigating these "blue zones" with physicians and demographers, focusing his initial efforts in Sardinia, Italy; Okinawa, Japan;, Monterrey, Nuevo Leon and Loma Linda, California.^[8]

Buettner reported his interest in "blue zones" in his cover story for National Geographic Magazine's November 2005 edition, "Secrets of Longevity."^[1][9] When Costa Rican demographer Dr. Luis Rosero-Bixby revealed in 2005 that Costa Ricans who survive to the age of 60 "have the longest life expectancy of anyone in the world," National Geographic funded an expedition led by Buettner to investigate.^[5] Buettner's expedition confirmed Rosero-Bixby's findings and discovered further that in the specific region of Nicoya, in the northwestern part of Costa Rica, residents were longer-lived even than other Costa Ricans, making it the "blue zone" of the Americas.^[5]

In April 2008, Buettner released a book on his findings, The Blue Zone: Lessons for Living Longer From the People Who've Lived the Longest, through National Geographic Books.

Quest Network, Inc[link]

Following his association with Earthtreks, Buettner founded a new company, Quest Network, Inc., to carry on his educational goals, beginning with his 2005 "blue zone" exploration of Okinawa, Japan.^[7] His next Quest Network project covered his return visit to Nicoya in early 2007.^[10] Quest Network's interactive classroom experiences allow students worldwide to vote on upcoming activities of the expedition team.^[1]

References[link]