Life expectancy for any given age may be defined as the average number of additional years of life persons of that age may expect to live, under the condition that the schedule of age-specific mortality rates prevails. Life expectancy is most frequently calculated and reported for persons at birth, that is, at age 0, but, as is shown in this entry, may be calculated for persons of any age. Information on the life expectancy for persons of a given age indicates how many years of life, on average, persons may expect to live if, during their lifetimes, they are subjected to the prevailing agespecific probabilities of dying.
Life expectancy is calculated via the life table, a mathematical table that presents the mortality experiences of a population. The life table dates to John Graunt (1620-1674). The life table starts with a population (a radix) of 100,000 at age 0; from each age to the next, the population is decremented according to age-specific mortality probabilities, until all members have died; the mortality schedule is fixed and does not change over the life of the population.
Table 1 is an abridged life table for U.S. females in the year of 2005, developed by the World Health Organization. Given the very different agespecific death rates and, therefore, age-specific probabilities of dying for females and males, life tables are usually calculated separately for the two groups. Eight columns are shown in the table.
Column 1 refers to the age intervals of each group. The entries refer to the range of years between two birthdays. For example, the age group 5-9 refers to the 5-year interval between the 5th and the 10th birthdays.
Column 2 reports for each age group the agespecific death rates, designated as nMx. These are the only empirical data needed to build a life table.
Column 3 reports for each age group the probabilities of dying, designated as nqx. This is the most basic column of the life table; the probabilities are derived from the death rates in column 2. The column 3 data are the probabilities that persons who are alive at the beginning of an age interval will die during that age interval, before they reach the start of the next age interval.
Column 4 presents data on the number of people alive at the beginning of the age interval, designated as lx. This column of data is calculated by subtracting the ndx value (column 5) from the lx value in the age interval immediately preceding the one being calculated.
Column 5 shows the number of people who die during a particular age interval, designated as ndx, and is determined by multiplying lx by nqx.
Column 6 reports for each age interval the total number of years lived by all persons who enter that age interval while in the age interval. The nLx values are roughly given by the formula, nLx = (1x - 1/2 ndx (n).
Column 7 reports the total number of years lived by the population in that age interval and in all subsequent age intervals, and is designated as Tx. To determine the values of Tx for each age interval, one sums the nLx from the oldest age backward.
Column 8 presents the average number of years of life remaining at the beginning of the age interval, designated as ex, and calculated by dividing column 7 by column 4. These are the life expectancy data.
Usually life expectancy for any given age, ex, is larger than ex + n, since ex + n refers to additional life to be lived after the years x + n have already been lived. However, Nathan Keyfitz and Wilhelm Flieger have found that in many countries (particularly developing countries and some developed countries), it turns out that e1 is sometimes larger than e0; this was the case in the United States as recently as the 1970s, when for females e0 was 74.8 and e1 was 75.2; life expectancy data for females in Mexico for the same year were 63.6 for e0 and 66.9 for e1. These patterns indicate a relatively higher infant mortality rate; they imply that if an infant is strong enough to survive the first year of life, then the average remaining lifetime on his or her first birthday is greater by more years than it was at the time of birth.
Walter Scheidel reports that life expectancy at birth in ancient populations was in the range of 20 years to 30 years. In contrast, data from the Population Reference Bureau indicate that in 2007 the life expectancy at birth in the world was around 66 for males and 70 for females.
A major explanation for changes over time in mortality has its origins in demographic transition theory, which proposes four stages of mortality and fertility decline that occur in the process of societal modernization. The first stage is the preindustrialization era, with high birth and death rates along with stable growth. With the onset of industrialization and modernization, the society transitions to lower death rates, especially lower infant and maternal mortality, but maintains higher birthrates, so that rapid population growth is the result. The third stage is one of decreasing population growth due to lower birth and death rates, which lead then to the final stage of low and stable population growth.
Life expectancy changes may also be explained in terms of epidemiological transition theory. This theory focuses on the society-wide decline of infectious disease and the rise of chronic degenerative causes of death. According to epidemiological transition theory, as postulated by Abdel R. Omran, there are three stages. The first is the age of pestilence and famine in which the primary causes of mortality are influenza, pneumonia, smallpox, tuberculosis, and other related diseases, with high infant and childhood mortality and life expectancy averaging between 20 and 40 years. The second is the age of receding pandemics in which there is a decline in mortality due to improved sanitation, increases in standards of living and public health, resulting in a steady increase in life expectancy to around 30 to 50 years. The third stage is known as the era of degenerative and man-made diseases (heart disease, cancer, and stroke), in which mortality declines are due to medical advances in the prevention and treatment of infectious diseases. Richard G. Rogers and Robert Hackenberg have identified a fourth “hybristic stage,” in which mortality is heavily influenced by individual behavior or lifestyle choices, and deaths are due to social pathologies such as accidents, alcoholism, suicide and homicide, as well as lifestyle issues such as smoking and diet.
Population Reference Bureau data indicate that in 2007 life expectation at birth in the world was 66 for males and 70 for females. In more-developed countries it was 73 and 80, and in less-developed countries (excluding China), 62 and 65. The highest life expectation at birth was in Japan (79 for males and 86 for females); the lowest was in Botswana (35 for males, 33 for females) and Swaziland (33 for males, 34 for females).
In general, the higher the country's level of economic development is, the higher its life expectation is for both males and females. The primary causes of death in more-developed countries are heart disease and cancer, whereas in less-developed countries infant mortality and HIV are the leading causes of death. For example, Population Reference Bureau data for 2007 indicate that the infant mortality rate in Botswana was 56, in Lesotho 91, and in Swaziland 73, compared to less than 3 in Japan and Sweden. The percentage of people living with HIV between the ages of 15 and 49 in these countries is among the highest in the world. In 2007, the percentage in Botswana was 24.1, in Lesotho 23.2, and in Swaziland 25.9, whereas in Japan it was less than 0.1%.
In more-developed countries, life expectancy at birth for females is approximately 5 to 7 years more than for males. This difference is attributed largely to biological and hormonal advantages that function as protective factors in women's health. However, this benefit is not always found for women in less-developed countries. For example, life expectancy in 2007 for males in Japan was 79 and 86 for females; in the United States it was 75 and 80, and in Western Europe, 77 and 83. In lessdeveloped countries like Afghanistan, life expectancy is equal for both sexes: 42 years.
In Botswana, life expectancy for women is in fact lower than for males (35 for males, 33 for females). Women in less-developed countries tend to have lower education and social status than males; these social disadvantages tend to obstruct the natural biological advantage that women have over men in life expectancy.
There are also life expectancy differences among racial and minority populations. In 2003, the life expectancy advantage for white females over black females in the United States was 4.4 years, and the advantage for white males over black males was 6.3 years. The racial differential in mortality in the United States has been studied and analyzed by medical and social scientists for many decades, but the differences have remained. A major reason for the racial differential is attributed to the socioeconomic consequences of lifelong poverty and experiences of racial discrimination, which limit the potential quantity and quality of health care available.
Of particular interest in any analysis of majority-minority group differences in life expectancy is the consistent finding by Richard Rogers and his colleagues that Mexican Americans in the United States have an expectancy similar to, and sometimes higher than, Anglos (i.e., white non-Hispanics). Thus despite the fact that Mexican Americans and African Americans have higher rates of poverty and unemployment than do Anglos, and have also experienced more discrimination from the majority, Mexican Americans compared to Anglos are not disadvantaged with regard to life expectancy and other measures of longevity, but African Americans are. Several hypotheses have been offered to account for the Hispanic paradox and may be subsumed into three groups: data artifacts, migration effects, and cultural effects.
High levels of life expectation have been reached in this new century by many of the countries of the developed world. At issue is the likelihood that mortality rates will continue to fall, resulting in even higher levels of life expectation than those already attained. There are two positions: One argues for a limit and the other against.
An upper limit to human life expectancy is noted by James Fries, who predicted in 1980 that humans have a maximum potential life expectancy averaging about 85 years. Jay Olshansky and Bruce Carnes agree, noting also that all living organisms are subjected to a biological warranty period. If it is possible for humans to live to 100, people beyond age 80 should not be showing functional decline. But the data show substantial decline by age 80. Thus they contend that human life expectancy in the United States is not likely to exceed 90 years at any time in this century.
The major proponents on the other side, proclaiming the possibility of future and continued mortality declines, are James Carey and James Vaupel. They note that every time a maximum life expectancy number is published, it is soon surpassed. They also note that death rates in human and many nonhuman populations do not continue to increase with increasing age, but there is a slowing or deceleration of mortality at the oldest ages.
Many of the developing countries still have high rates of infant mortality and general mortality. Infectious diseases remain a dominant cause of death in many of these countries. Modern medical and public health techniques will surely bring about further reductions in mortality from these causes, leading to declines in mortality in many countries.
Degenerative diseases are the major causes of death in the developed world. It is expected there will be future improvements in the treatment of these diseases in the next decades. However, only a basic breakthrough in the area of the physiological process of aging will increase substantially the length of time people in the developed world will live. Even the total elimination of a specific degenerative disease would not greatly increase life expectancy because, as Conrad Taeuber pointed out many years ago, if a cure is found for one degenerative disease, this will provide the opportunity for death to occur from another.
Demographic Transition Model
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