OhLife：抛弃你的日记本吧！来自Y Combinator的第三个项目 | 36氪
还记得在博客以及twitter等出现之前，你是怎么记录自己生活的点点滴滴的吗？没错，或许就是藏在你柜子或者抽屉里的那本日记本。时至今日，还是有很多人保持着记日记的’好习惯‘（记得小时候，妈妈和老师都是这么说的）。而今，由
投资的创业公司
想把你的这一习惯‘数字化’，并推动‘写日记好习惯’的普及。
要理解这个服务，实在是再简单不过了。注册以后，每晚你都会收到来自OhLife的邮件，正文里面只有一个问题：‘今天过得怎么样？’你只要直接回复这封邮件，写入你本该写在日记本上的内容，就一切OK了。这些数字化的日记将被保存在OhLife的服务器，并完全私人保密。
有很多人没有写日记的习惯，其中有一些不是不喜欢写，而是每次总是在事后几天才想起来要记录下来。一旦你使用了OhLife服务，相信你再也不会忘记记日记了……因为那封每晚八点寄来的邮件。邮件的样板可以参考下图：
或许你会怀疑，有人会用这个服务吗？没错，我也很怀疑，尤其是在美国这种特别在意个人隐私的国家。不过OhLife称，目前的注册用户中，有50%真的每天在Ohlife写日记。或许这跟美观的网站设计有关。
目前，OhLife打算一直采取免费模式，如果他们的服务真的吸引来很多客户，或许他们会尝试freemium模式。这个我也很纳闷，这么简单的服务，如何freemium？以后会添加功能？还是向remember the milk一样，通过销售手机客户端？
目前，OhLife面临着几大问题：
*由于网站内容涉及隐私，完全保密，因此该网站很难通过正常的网络推广方式吸引来很大的流量。
*简单的功能很容易被复制，除了创业公司可以轻松复制外，如果Google、腾讯这样的公司复制了这个模式，Ohlife的竞争力就很不明显了。
*简单的功能也很难实现freemium，因为没有什么高级功能可以单独列出来吸引客户付费。
不过也未必，或许OhLife会专注于做好这一件事，并且能把它做得出人意料之外的好，就如evernote一样。
OhLife 是目前YC的第三个项目——之前的两个分别是
虽然健在，但没有一个稳定的用户群了。
原创文章，作者：刘成城打开微信“扫一扫”，打开网页后点击屏幕右上角分享按钮职位精选无需注册，直接使用社交账号登录登录没有帐号？
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a.register__login-btn href="javascript:void(0)" 登录注册右键另存为下载到本地分享到微博Life Is Beautiful: How My First Semester of College Life Taught Me More Than Any Class Ever Could&|&Rajat Bhageria
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Life Is Beautiful: How My First Semester of College Life Taught Me More Than Any Class Ever Could
After high school, everybody seems to throw around the saying that college is going to be the best time of your life, and to take advantage of it.
In the weeks preceding your trip as you pack up, enormous emotions overtake you: you're leaving behind the life you've had for the last 17 years. You won't see your friends and family for months. The institution that had governed your life for the past 17 years is gone. You're free. Free to do what you want to when you want to. No one is going to look over your shoulder and make sure you sleep before 11pm. As you step onto campus, an instant sense of questioning overcomes you. This is going to be amazing! But I won't see home again for a long time. So many amazing people! But what if I don't like my roommate?
So many amazing opportunities! But what if it this isn't the right school for me? And yet, there is hope. There is optimism. Somehow or another, you know that these truly will be some of the best years of your life. Instantly your old life leaves you and a new chapter begins. The semester passes by like a breeze and afterwards, you reminisce. At this same time last year, you were writing college applications. Now you have finished your first semester and you look back in awe at some of the best months of your life. You ask yourself whether college was worth the big fat check your parents wrote at the very beginning... You think about all the amazing people you've met... You think about all you've learned...I can personally say with a lot of certainty that my classes were by no means worth 60k/year.
But I can also say that the act of simply being in college taught me more than any class ever has. College's real power is its humbling experience. When I first arrived, I -- like you -- was just one out of 2,500 students. Nobody really knew each other. I was literally nobody. After a semester, I purposely or accidentally, superficially or deeply, met a few hundred new people. But at times I just couldn't help but think. Just like I'm looking back today, there was someone else like me last year looking back. And someone the year before that. And before that. This has been going on for the past three hundred years just at my university. I'm just one of the hundreds of thousands who has been in the same shoes. Just like I'm giddy about the past few months, there was someone in 1780 who was contemplating his past few months.
And this same situation has been going on at thousands of colleges around the world for thousands of years. You see, the institution of "college" is just so old and widespread that I'm just one of the hundreds of millions who has gone through it. Every one of those millions stayed up some days till four in morning working.
Every one of those millions was homesick for a few days. And every one of those millions felt alone for sometime. So if I'm just one of a hundred million to have gone through it, what is my role? Who am I? How will I make my mark on society? Every college freshman goes though some variation of this existential crisis. Even though all the long nights working on problem sets and hard days, the crisis lasts. And yet it's not a crisis. It's a gift: it teaches you your purpose in life.
This is the real power of college: it forces you to find yourself and ask yourself what you want to accomplish not only in the next four years but also in life. You see the point of education is not to fill our minds with useless facts, many of which we will never use again -- as classes force us to do -- but rather to inspire us to love learning, and more importantly to help us find what we'll do with our time on our pale blue dot. And that's where the people you meet come into play. Every fellow student, every professor, every single frat boy and sorority girl, every party lover, every hated friend and loved friend, every acquaintance, and every single awe-inspiring leader who you met in your first semester changes you. Why? They're going through the same self-finding process that you are. They're asking themselves about the purpose of their lives. It's apparent now that college isn't the best time of your life because of your classes, or because of your activities, or even because of
but rather it's because college groups together a few thousand people trying to find themselves, each of whom inspires you. It really doesn't matter "where you go"; the experience will fundamentally be the same.Now don't get me wrong: college isn't as rosy as I've painted it. Bad stuff happens. During my first semester, someone at my school committed suicide. One of my friends failed his classes. And yet another one is taking the next semester off because of psychological problems. But no matter all these problems, for some reason there is still hope. Everything is going to be all right. See, college puts you into this trance of safety and happiness -- this trance that you'll go home every night, you'll work hard, you'll have fun, you'll graduate, and everything will work out at the end. Very quickly, you realize that whatever happens, our great world will just keep spinning. You may fail a course. You may lose a best friend. You may find your dream job. Whatever happens, college helps you realize that life is beautiful and that even though you may be one in a hundred million, you have a chance to leave your mark. You leave each semester rosy cheeked and optimistic about the future. In my classes, I learned about data structures in Java, how to find net present value, and how to generate hype for a product. In college life I met hundreds of people who inspired me to find myself. Now I'll ask you: which is more important?------
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is a freshman student at the University of Pennsylvania (Penn) and the author of
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Thanks for your report!From Wikipedia, the free encyclopedia
This article is about the measure of remaining life.
For the Dean Koontz novel, see .
Life expectancy at birth by region,
Life expectancy is a statistical measure of how long a person or organism may live, based on the year of their birth, their current age and other demographic factors including gender. At a given age, life expectancy is the average number of years that is likely to be lived by a group of individuals (of age x) exposed to the same mortality conditions until they die. The most commonly used measure of life expectancy is life expectancy at age zero, that is, at birth (LEB), which can be defined in two ways: while cohort LEB is the mean length of life of an actual birth cohort (all individuals born a given year) and can be computed only for cohorts that were born many decades ago, so that all their members died, period LEB is the mean length of life of a hypothetical cohort assumed to be exposed since birth until death of all their members to the mortality rates observed at a given year.
National LEB figures reported by statistical national agencies and international organizations are indeed estimates of period LEB. In the Bronze and Iron Age LEB was 26 the 2010 world LEB was 67.2. For recent years in
LEB is about 49 years while in Japan is about 83 years. The combination of high infant mortality and deaths in young adulthood from accidents, epidemics, plagues, wars, and childbirth, particularly before modern medicine was widely available, significantly lowers LEB. But for those who survive early hazards, a life expectancy of sixty or seventy would not be uncommon. For example, a society with a LEB of 40 may have few people dying at age 40: most will die before 30 years of age or very few after 55. In countries with high
rates, LEB is highly sensitive to the rate of death in the first few years of life. Because of this sensitivity to infant mortality, LEB can be subjected to gross misinterpretation, leading one to believe that a population with a low LEB will necessarily have a small proportion of older people. For example, in a hypothetical
in which half the population dies before the age of five, but everybody else dies at exactly 70 years old, LEB will be about 36 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 groups, should also be used alongside individual-based measures like formal life expectancy when analyzing population structure and dynamics.
Mathematically, life expectancy is the mean number of years of life remaining at a given age, assuming constant mortality rates. It is denoted by , which means the
number of subsequent years of life for someone now aged , according to a particular
experience.
and life expectancy are not synonyms. Life expectancy is defined statistically as the average number of years remaining for an individual or a group of people at a given age. Longevity refers to the characteristics of the relatively long life span of some members of a population. Moreover, because life expectancy is an average, a particular person may well die many years before or many years after their "expected" survival. The term "" has a quite different meaning and is more related to longevity.
Life expectancy is also used
(also known as ). The term life expectancy may also be used in the context of manufactured objects, although the related term
is used for consumer products and the terms "mean time to breakdown" (MTTB) and "" (MTBF) are used in engineering.
Human beings are expected to live on average 49.42 years in
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. An analysis published in 2011 in
attributes Japanese life expectancy to
as well as diet.
The oldest confirmed recorded age for any human is 122 years (see ). This is referred to as the "", which is the upper boundary of life, the maximum number of years any human is known to have lived.
The following information is derived from , 1961 and other sources, some with a questionable accuracy. Unless otherwise stated, it represents estimates of the life expectancies of the
as a whole. In many instances, life expectancy varied considerably according to class and gender.
Life expectancy at birth takes account of , but not pre-natal mortality.
Life expectancy at birth
Life expectancy at older age
Based on the data from recent hunter-gatherer populations, it is estimated that at age 15, life expectancy was an additional 39 years (total age 54).
At age 10, life expectancy was 35 to 37 years.
Average lifespan of scholars was 59–84.3 years in the
and 69–75 in .
At age 21, life expectancy was an additional 43 years (total age 64). 
2010 world average
Life expectancy increases with age as the individual survives the higher mortality rates associated with childhood. For instance, the table above listed the life expectancy at birth in Medieval Britain at 30. Having survived until the age of 21, a male member of the English aristocracy in this period could expect to live:
: to age 64
: to age 45 (due to the impact of the )
: to age 69
: to age 71
In general, the available data indicate that longer lifespans became more common recently in human evolution. This increased longevity is attributed by some writers to cultural adaptations rather than genetic evolution, although some research indicates that during the
natural selection favored increased longevity. Nevertheless, all researchers acknowledge the effect of cultural adaptations upon life expectancy.
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. The life expectancy was under 25 years in the , and in seventeenth-century New England, about 40 per cent died before reaching adulthood. During the , the life expectancy of children increased dramatically. The percentage of children born in London who died before the age of five decreased from 74.5% in
to 31.8% in .
measures are credited with much of the recent increase in life expectancy. During the 20th century, despite a brief drop due to the
starting around that time the average lifespan in the United States increased by more than 30 years, of which 25 years can be attributed to advances in public health.
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 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,
publishes annual statistics called
(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 "". An increasing number of countries are using health expectancy indicators to monitor the health of their population.
Further information:
Further information:
  &80
  75.5-79
  74-78
  72.5-75
  70-72.5
  67.5-70
  65-67.5
  60-65
  55-60
  50-55
  45-50
  40-45
  &40
Plot of life expectancy vs.
in 2009. This particular phenomenon is known as the .
Graphs of life expectancy at birth for some sub-Saharan countries showing the fall in the 1990s primarily due to the .
There are great variations in life expectancy between different parts of the world, mostly caused by differences in , medical care, and diet. The impact of
on life expectancy is particularly notable in many African countries. According to projections made by the United Nations (UN) in 2002, the life expectancy at birth for
did not exist) would have been:
70.7 years instead of 31.6 in
69.9 years instead of 41.5 in South Africa
70.5 years instead of 31.8 in
The UN's predictions were too pessimistic. Actual life expectancy in Botswana declined from 65 in 1990 to 49 in 2000 before increasing to 66 in 2011. In South Africa, life expectancy was 63 in 1990, 57 in 2000, and 58 in 2011. And in Zimbabwe, life expectancy was 60 in 1990, 43 in 2000, and 54 in 2011.
During the last 200 years, African countries have generally not had the same improvements in mortality rates that have been enjoyed by countries in Asia, Latin America, and Europe.[]
In the United States, African-American people have shorter life expectancies than their European-American counterparts. For example, white Americans born in 2010 are expected to live until age 78.9, but black Americans only until age 75.1. This 3.8-year gap, however, is the lowest it has been since at least 1975. The greatest difference was 7.1 years in 1993. In contrast, Asian-American women live the longest of all ethnic groups in the United States, with a life expectancy of 85.8 years. The life expectancy of Hispanic Americans is 81.2 years.
Cities also experience a wide range of life expectancy based on neighborhood breakdowns. This is largely due to economic clustering and poverty conditions that tend to associate based on geographic location. Multi-generational poverty found in struggling neighborhoods also contributes. In United States cities such as , 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 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 , the disparity is amongst the highest in the : life expectancy for males in the heavily deprived
area stands at 54, which is 28 years less than in the affluent area of , which is only 8 km away.
A 2013 study found a pronounced relationship between
and life expectancy. However, a study by José A. Tapia Granados and Ana Diez Roux at the
found that life expectancy actually increased during the , and during recessions and depressions in general. The authors suggest that when people are working extra hard during good economic times, they undergo more , exposure to , and likelihood of injury among other longevity-limiting factors.
Life expectancy is also likely to be affected by exposure to high levels of
or industrial . 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, drug use, , excessive alcohol consumption, obesity, access to health care, diet and exercise,.
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.
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). 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. Also, data from the UK shows the gap in life expectancy between men and women decreasing in later life. This may be attributable to the effects of infant mortality and young adult death rates.
In the past,
were higher than for males at the same age. This is no longer the case, and female human life expectancy is considerably higher than that of males. The reasons for this are not entirely certain. Traditional arguments tend to favor socio-environmental factors: historically, men have generally consumed more ,
than women in most societies, and are more likely to die from many associated diseases such as ,
and . Men are also more likely to die from injuries, whether unintentional (such as
or ) or intentional (). Men are also more likely to die from most of the leading causes of death (some already stated above) than women. Some of these in the United States include: cancer of the respiratory system, motor vehicle accidents, suicide, cirrhosis of the liver, emphysema, prostate cancer, and coronary heart disease. These far outweigh the female mortality rate from breast cancer and cervical cancer.
Some argue that shorter male life expectancy is merely another manifestation of the general rule, seen in all mammal species, that larger (size) individuals (within a species) tend, on average, to have shorter lives. This biological difference occurs because women have more resistance to infections and degenerative diseases.
In her extensive review of the existing literature, Kalben concluded that the fact that women live longer than men was observed at least as far back as 1750 and that, with relatively equal treatment, today males in all parts of the world experience greater mortality than females. Of 72 selected causes of death, only 6 yielded greater female than male age-adjusted death rates in 1998 in the United States. With the exception of birds, for almost all of the animal species studied, males have higher mortality than females. Evidence suggests that the sex mortality differential in people is due to both biological/genetic and environmental/behavioral risk and protective factors.
There is a recent suggestion that
mutations that shorten lifespan continue to be expressed in males (but less so in females) because mitochondria are inherited only through the mother. By contrast,
weeds out mitochondria that re 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.
In developed countries, the number of
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).
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
centenarians per million inhabitants).
Life expectancy in the seriously mentally ill is much shorter than the general population
The seriously mentally ill have a 10 to 25 year reduction in life expectancy. This reduction in life is mostly due to preventable diseases.
Children who have no physical illness , but are diagnosed with a mental illness and are given medicine have a 50% increased chance of developing .
Main article:
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 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. The 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 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
to increased life expectancy. This is the key to why animals like
can live so long. Studies of humans with 100+ year life spans have shown a link to decreased thyroid activity, resulting in their lowered metabolic rate.
In a broad survey of zoo animals, no relationship was found between the fertility of the animal and its life span.
This section may be too
for most readers to understand. Please help
this section to , without removing the technical details. The
may contain suggestions. (October 2014)
This section needs additional citations for . Please help
by . Unsourced material may be challenged and removed. (October 2014)
A survival tree to explain the calculations of life-expectancy. Red numbers indicate chance of survival at a specific age, and blue ones indicate age-specific death rates.
The starting point for calculating life expectancy is the
of the population members. If a large amount of data is available, the age-specific death rates can be simply taken as the mortality rates actually experienced at each age (i.e. the number of deaths divided by the number of years "exposed to risk" in each data cell). However it is customary to apply smoothing to iron out as far 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 , although these days more sophisticated methods are used.
The most common methods used for this purpose nowadays are:
to fit a mathematical formula, such as an extension of the Gompertz function, to the data,
for relatively small amounts of data, to look at an established
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 ).
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 , is also computed using similar methods. In the case of long-lived components, such as those used in critical applications, e.g. in aircraft, methods like
are used to model the life expectancy of a component.
The age-specific death rates are calculated separately for separate groups of data that are believed to have different mortality rates (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 , from which one can calculate the probability of surviving to each age. In , the probability of surviving from 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, then the age-specific death probability at age 90 would be 10%. Note that this is a probability rather than a mortality rate.
The expected future lifetime of a life age
in whole years (the curtate expected lifetime of (x)) is denoted by the symbol . It is the conditional expected future lifetime (in whole years), assuming survival to age . If
denotes the curtate future lifetime at , then : Substituting
in the sum and simplifying, we get the equivalent formula: If we make the assumption that, on average, people live a half year in the year of death, then the complete expectation of future lifetime at age
Life expectancy is by definition an . 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
(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 usually done in the past. This is often done by simply extra however, some models do exist to account for the evolution of mortality (e.g. the ).
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.
Forecasting life expectancy and mortality forms an important subdivision of . Future trends in life expectancy have huge implications for old-age support programs like
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
systems). With longer life expectancies, these systems see in 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
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
results. Analyses and forecasts using this approach can be done with any common statistical/ mathematical software package, like , , , , , or .
Forecasting age specific
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 , which uses the
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 's
Life expectancy is one of the factors in measuring the
(HDI) of each nation, along with adult literacy, education, and standard of living.
Life expectancy is also used in describing the
of an area or, for an individual, when determining the value of a life settlement, a life insurance policy 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 . For the top 21 industrialised countries, counting each person equally, life expectancy is lower in more unequal countries (r = -.907). There is a similar relationship among states in the US (r = -.620).
Life expectancy differs from . Life expectancy is an average, computed over all people including those who die shortly after birth, those who die in early adulthood in childbirth or in wars, and those who live unimpeded until old age, whereas lifespan is an individual-specific concept and maximum lifespan is an upper bound rather than an average.
It can be argued that it is better to compare life expectancies of the period after childhood to get a better handle on life span. Life expectancy can change dramatically after childhood, as is demonstrated by the
where at birth the life expectancy was 21 but by the age of 5 it jumped to 42. Studies like
similarly show a dramatic increase in life expectancy once adulthood was reached.
In standard actuarial notation, ex refers to the expected future lifetime of (x) in whole years, while ex with a circle above the e denotes the complete expected future lifetime of (x), including the fraction.
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from the CIA's World Factbook.
from the USA Centers for Disease Controls and Prevention, National Center for Health Statistics.
from the University of Texas.
Animal lifespans:
from Tesarta Online (Internet Archive);
from Dr Bob's All Creatures Site.
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