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e future of LONGEVITY Peter W Gallagher

g Dra revision . D , 


Abstract Life-expectancy has grown at an astonishing rate. It took twenty thousand centuries for life-expectancy to double. But it grew by as much again in just one century in Australia. Survival accelerated due to changes in the way we live; improved planning, sanitation, education and nutrition has allowed the majority of people to achieve at least the seventy-year lifespan that characterises our species. Longer, healthier lives in the future, however, will depend on changes to the way we age. ese advances are still only on the horizon of genetics and biology, but there are massive reserves of potential support for the work. Research will be buoyed by the enormous, mostly hidden, economic value of longevity, which is rising rapidly as incomes rise in the giant emerging economies. In the next two decades, billions of Chinese and Indians will join a middle-aged, middle-class that has a strong stake in securing longer lives free from the degenerative diseases of age and a personal interest in early progress. But their prosperity will arrive on the cusp of a demographic transition in those countries, due in part to earlier longevity gains. Will their perspective change if their economies slow? Tradeoffs abound in the biology, too. ere cannot be, a ‘longevity gene’; whatever favours longer life has some other purpose. Genetic manipulation of these complex cellular mechanisms seems to carry collateral risk. e only certainty is that, in the future, death will still rule; but possibly from a much greater distance.

©  Peter Gallagher. All rights reserved. Please contact author before citing any parts of this work at peter@petergallagher.com.au e printed form of this document uses page-referenced endnotes. In this version of Dra revision . , the endnotes have been anged to footnotes for ease of reference on-screen. e DOI and URL have been omied from the citations for brevity.


CONTENTS

Abstract

Contents

Living longer . e price of life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How to grow old and rich . . . . . . . . . . . . . . . . . . . . . . . . .

  

e origins of longevity . Evolutionary explanations . . . . . . . . . . . . . . . . . . . . . . . . . Ageing in the genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ree theories of longevity . . . . . . . . . . . . . . . . . . . . . . . .

   

e retreat of death . e epidemiologic transition . . . . . . . . . . . . . . . . . . . . . . . . Longevity beyond the Transition . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

   

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

 

Notes


. L 

 

LIVING LONGER

Life-expectancy — the years that a baby can expect to live — is a stubborn, buoyant, barometer of well-being. While eco-pessimists, medical scolds and fast-food antagonists bombard us with gloomy warnings about poisonous carbon and unhealthy habits, people everywhere are living much longer lives in beer health than their parents or grandparents. Global average lifeexpectancy jumped by almost a third over the past half-century from  years in  to  years in .¹ In Australia, life-expectancy grew by more than % over the course of the th century from  years (for males) in  – about where Ethiopia is today – to almost  years.² A baby girl born today in Australia can expect to live until , a baby boy to ; longer than infants in all but two other countries, Japan and Iceland. But we can be fairly certain both these children will live even longer; that they will likely see the dawn of the twenty-second century because our expected remaining years of life increase as we get older. Australian men, today, add almost a year to their expected survival aer age  and another . years aer they reach . ose who celebrate their th birthday this year have earned another -year extension of life-expectancy to almost  years, some  years more than seemed likely in the year of their birth.³ Australian women also enjoy an extended life-expectancy in their later years; -year-olds this year are likely to see their nd birthday. Table : Years of life-expectancy at birth, WHO regions

Country group Low income Lower middle income Upper middle income High income

1990

2000

2008

54 62 68 76

55 65 69 78

57 67 71 80

Increase 5.6% 8.1% 4.4% 5.3%

Data from WHO (2010)

Around the world, life-expectancy at birth has raced ahead of official forecasts. ⁴ Even in the poorest countries where poor public-health and nutrition threaten hundreds of millions, World Health Organization data show that life-expectancy is growing rapidly; by almost % ¹ ☞ World Bank. Data | e World Bank ² ☞ Australian Institute Of Health And Welfare. Australia’s Health . Canberra, . :      ³Based on the estimated life expectancy of Australian males born in - ☞ Australian Bureau of Statistics. Australian Historical Population Statistics . Canberra,  ⁴e projections of the U.N., for example, about the rate of growth of life-expectancy and about ‘ceilings’ on survival limits have proved illusory.☞ Jim Oeppen and James W Vaupel. “Broken Limits to Life Expectancy”. In: Science News  (Mar. ), p. 


to an average of  years in the last two decades: see Table  on page  (a longer extract from this data is contained in Table  on page ).⁵ e global gains in life-expectancy would have been greater still had it not been for the devastating impact of AIDS in Africa — at  years, the average life-expectancy of a South African child was five years fewer in  than it had been three decades earlier — and the tumultuous social disintegration in Russia in the s when life expectancy fell by five years in a four-year period.⁶ See Figure  on page  South Africa

Russian Federation

69 60

Life-expectancy (years)

Life-expectancy (years)

68 58

56

67

66 54

65 52

1980

1985

1990

Date

1995

2000

2005

1980

1985

1990

Date

1995

2000

2005

Figure : Total life expectancy, South Africa and Russia (U.N. Population Division)

Despite these regional reversals, there is no sign of a slowdown in the pace of global lifespan growth. On the contrary, the average lifespan of females in the ‘record holding’ country has been increasing at a steady rate of about  months per year for at least the past  years. In , the record was held by women in Sweden whose daughters had an expected lifespan of  years. e current record is held by Japan ( years). Over those  decades, the addition to lifespan in each year for both sexes has been so regular that the leading national averages can be charted – years vs maximum lifespan – as a nearly (r2 =.) straight line with no sign that the rise is starting to plateau.⁷ Longer lives are a sure sign of beer health. In , on average, people in rich and poor countries alike, could expect to live at least % of their life in good health (see Table  on page ).⁸ At birth, Australian men and women could expect to enjoy a health-span of  years or ⁵Table : “Mortality and Burden of Disease” ☞ World Health Organization. “Part II. Global health indicator tables”. In: World Health Statistics. Vol. . -. , pp. – ⁶ ☞ Francis C Notzon et al. “Causes of Declining Life Expectancy in Russia”. In: JAMA: e Journal of the American Medical Association . (Mar. ), pp. –. :  ⁷See the graphs in… ☞ Jim Oeppen and James W Vaupel. “Broken Limits to Life Expectancy”. In: Science News  (Mar. ), p.  ⁸e World Health Organization (WHO), estimates a healthy life expectancy (HALE) at birth by deducting a number of years from the raw life-expectancy number in any country to account for the expected average years of non-fatal’ health disability in that country. ey based the estimates on data on surveys in  countries of  causes of disability. In , WHO estimated (Table , Part II of Global Health


. L 

Asia

Australasia

1980

1980

Developed

Developing

World

1980

1980

1980

Median population age

35

30

25

20

Years 1950 - 2010

Figure : Median population age rises as lifespan grows, fertility falls (U.N. Population Division)

more than % of their (joint) average  years’ life-span. Globally, the health-span for both sexes in  was  years; greater than the life-span in -. In large low-income countries such as China and India the health-span in  –  and  years, respectively – was greater Indicators) that HALE-expectancy was between % and % of the full life-expectancy for countries in different income groups and regions. Africans who are expected to live shorter lives could expect to enjoy the smallest part of their life in good health.☞ World Health Organization. “Part II. Global health indicator tables”. In: World Health Statistics. Vol. . -. , pp. –

Table : Average proportion of life lived in good health, by WHO region

Low income Lower middle income Upper middle income High income

Life-expectancy

HALE-expectancy*

57 67 71 80

49 61 61 70

Data from WHO (2010). *“Healthy Life Expectancy”

Proportion 0.860 0.910 0.859 0.875


.. e price of life

than the life-span in -. ⁹ As lifespans grow, whole populations are ageing rapidly. According to U.N. data, the median age of the Australia/New Zealand population — the age in the middle of the range from youngest to oldest — has risen . years in the past sixty. Half of the population is now  years old, or older. In the high-income countries as a group, where the fertility and migration rates have been lower than in Australia, the median age of the population has advanced . years over the same period to almost  years (see Figure  on page ; the sharp dip in median ages in the decades before  is due to the “baby-boom”.). But the high ratio of the health-span to the life span means that we have much to look forward to. Our old age will be wealthier and more comfortable than that any of the ten-thousand generations before us¹⁰ because we we live in an age of unprecedented global prosperity. Residents of the currently- rich economies of the West and the billions who live in the emerging giant economies of the BRICs (Brazil, Russia, India, China) have experienced the most rapid rise in global prosperity ever seen, when measured by average dollar incomes. Aer lying flat for two-hundred centuries, the income-per-person indicators in dozens of economies turned toward the sky almost two centuries ago and have climbed like a rocket ever since: see Figure  on page  that charts the best estimates we have of income since the Middle Ages in different regions of the world.¹¹ Despite slower “take-o” in some regions, global average incomes per-person have more than doubled since ; that is, in less than one generation. Living standards have improved rapdily, too, in other ways. For example, global food supplies have increased from from , kilocalories per person/day in  to , in .¹² e world is beer educated and more literate: % of adults (over  years) are able to read and write, up from just % in . And nearly everyone is much beer connected: there were just . mobile phones per  persons in , now there are ..¹³

. e price of life In spite of the cost of living, it’s still popular L. J. Peter ( - ) U.S. educator, e Peter Principle ⁹Based on data from the WHO World Health Reports in  and  as well as on historical WHO data on life-expectancy ☞ World Resources Institute. EarthTrends | Environmental Information.  ¹⁰On the basis that H.Sapiens emerged in Africa about , years ago and that a human generation is approximately  years ☞. Homo sapiens | e Smithsonian Institution’s Human Origins Program ¹¹Based on Angus Maddison’s assessment that the period  -  was an era of “unparalleled” prosperity and that the period  -  was the next most prosperous:“From the year  to , world per capita income rose . per cent a year. From  to , it averaged . per cent, nearly  times as fast.” ☞ Angus Maddison. “Contours of the World Economy and the Art of Macro-measurement - (Ruggles Lecture, )”. In: Review Literature And Arts Of e Americas August (), pp. –  ¹²☞ Food and Agriculture Organization. FAOSTAT.  ¹³☞ World Bank. Data | e World Bank


. L 

e prospect of a longer, healthier, wealthier life sounds like the definition of material wellbeing. Yet, strangely, we tend to focus only on the money, ignoring the health-span gains. Had other human characteristics or capacities changed as fast as longevity, and to such a degree, the impact would have been impossible to ignore. Imagine, for example, that humans had grown taller, on average, by a third in fiy years. e average height of an Australian man would now be . meters or more than  inches. e physical consequences would have been obtrusive and unavoidable. Everything made to fit us from shoes to concert seats, automobiles, and even doorways and elevators would have been redesigned a dozen times by now. Perhaps there’s a sort of gestalt problem that makes us blind to rapid change in our longevity prospects. Most people experience lifespan, if at all, as the absence of an event they cannot anticipate: their death, or the death of someone they know. Another year of life is not normally something we can choose and hardly figures among our expectations. When we are young it is a distant question that holds lile interest and when we are old we are fatalistic and disinclined to speculate, unless by hedging out bets in a funeral fund. We see each year that slips past us as part of our personal destiny, not an instance of a much larger and more mysterious trend affecting the whole of our species. Only when we aach a dollar-value to longevity does it becomes evident that the gains over the past decade-and-a-half have lied standards-of-living to levels not dreamed of by any of the thousands of generations of humanity that preceded us. In order to measure this standard of living accurately, we must consider both income and lifespan as two parts of a compound good that, for convenience, we could call a wealthspan. It’s a compound good because, although years of healthy life and financial wealth are separately valuable, the value we place in each also compounds the value we receive from the other. What is more valuable than another year of life unless it is the where-with-all to enjoy it? Alternately, what joy can there be in wealth without time to enjoy it? Why would you want to “take it with you” to your grave where you can neither shop nor gloat? Private benefits such as lifespan — for example, living to see grand-children grow up — can’t be bought or sold so they are conventionally “priceless”. Fortunately, the basilisk gaze of economists can fix a price, even for goods that have no market, by estimating what a person would be willing to pay (or be paid) for an additional year of life. e economists assume, no doubt correctly, that any dollar value the consumer places on life is likely to reflect their expectation for acceptable quality as well as quantity. Because it would be difficult, not to say unethical, to find the price of a life by holding an auction, they derive the price indirectly; for example, from the salary premium that workers demand for taking on jobs with a known higher risk of death.¹⁴ Boom line? Although in principle a life is priceless, in practice one “statistical life year”, for an Australian was worth about $, in .¹⁵ ¹⁴Some straightforward calculations using data on risk-of-death premiums give a discounted presentdollar value to a ‘statistical life’ of about $US million ☞ David Meltzer. “Economic Approaches to Valuing Global Health Research”. In: Disease Control Priorities in Developing Countries, nd edition. Ed. by Dean T Jamison et al. nd ed. Washington DC: World Bank, . Chap. , pp. –. : --- ¹⁵ ☞ Access Economics. Exceptional Returns e Value of Investing in Health R & D in Australia II. Sydney, 


.. e price of life

Africa

Asia (ex Japan)

E Eur and Russia

Japan

Latin America

West

20000 15000 10000

GDP per capita in PPP dollars (1990)

5000

20000 15000 10000 5000

20000 15000 10000 5000

1150

1400

1650

1900

1150

1400

1650

1900

Years 1000 - 2009

Figure : e West and Japan saw “unparalleled” growth post- (Maddison op. cit. for data to . Additional data from the Groningen Total Economy Database )

Using a similar approach to valuing a “life-year”, U.S. economists find that the dollar value of cumulative longevity gains during the twentieth century in the USA were worth more than $. million per person to the population. Given the size of the United States population ( million in ), this is an enormous sum. If added to total national “income” on the date they occurred, the ᵗʰ century additions to longevity in the USA would have raised GDP in the United States by more than half, depending on the period.¹⁶ In effect, additions to longevity are like a gigantic “economic stimulus”. e reductions in mortality between  and  alone – a period of accelerated reductions in mortality from ¹⁶In the period  to  Murphy and Topel estimate that the gains were worth about the same as the total production of goods and services. In the period aer  the productivity of the U.S. economy rose rapidly, reducing the relative size of the healthspan gains. ☞ Kevin M. Murphy and Robert H. Topel. “e Value of Health and Longevity”. In: Journal of Political Economy . (Oct. ), pp. –. : -


. L 

heart and artery disease – had an economic value to the  U.S. population of about $. trillion every year; about / of annual GDP at the turn of the ˢᵗ century¹⁷ at’s more than four times the size of the economic “stimulus” package approved by the U.S. Congress in February, , to kickstart the U.S. economy aer the collapse of financial markets.¹⁸ Except that the economic stimulus of longer lives takes place every year with no political wrangling or log-rolling and accrues directly to individuals in the form of an addition to their wealthspan. But counting the benefits as a one-shot welfare boost barely begins to tell the whole story. An investment such as an improvement in health treatments is valued by the sum over all the benefits that it returns today and all those it is expected to return in future. e sum of the -year (-) U.S. longevity gains in  was about $ trillion split between persons then alive (/ of the benefit) and future generations. at is, the discounted increment to the stock of national wealth due to longevity gains was roughly ten-times the measured output of the U.S. economy in that same year. Admiedly, these are the gross gains; expenditures on health consumed about % of these gains over the same thirty-year period leaving a net gain of about $ trillion. But even the net gain is more than six times the annual value of the entire U.S. economy in  as measured by the standard GDP.¹⁹ e estimated addition to Australians’ wealthspan from longevity gains is proportional to those in the United States. Access Economics puts the value of Australian health gains up to  at more than $ billion ( dollars).²⁰ Based on their report the present gross value of the stream health-span benefits to Australians between  and  seems to be more than $A trillion or about -times our  GDP. But wait! ere’s more! ese estimates — astronomical though they are — are certainly too conservative because they use today’s value of a statistical life-year to denominate the value. e extension of the health-span will be still more valuable in the future than it is today — even when measured in discounted future dollars — for two reasons. First, the value of a life-year rises as incomes rise because people are “willing to pay” more when they’re richer. Second, economic modelling confirms the intuitive observation that contributions to health-span are complementary. Delaying the onset of Alzheimer’s’ disease, for example, does not necessarily extend the number of years of life but it adds to the quality of life. People are ‘willing to pay’ more for an extra year of life — gained, say, through improvements in the treatment of cancer — if, in that extra year, Alzheimer’s disease is a reduced threat.²¹ e most impressive gains in the value of the health-span are occurring, not in high-income countries such as the USA or Australia, but in the BRIC economies that are, for the present, poor. e added-value in the giant, low-income countries such as China, India and Indonesia are world-changing. e size of their populations and their much greater longevity ‘headroom’ (they start from a lower base) lead to galactic valuations of national health-span improvements. For example, the value of a statistical life-year in India has been assessed () as US$, to US$,. Given India’s gains in life expectancy of about  years since  ¹⁷☞ibid. ¹⁸e “American Recovery and Reinvestment Act of ” authorised measures worth $ billion ¹⁹☞Murphy and Topel, op. cit. ²⁰ ☞ Access Economics. Exceptional Returns e Value of Investing in Health R & D in Australia II. Sydney,  ²¹☞Murphy and Topel, op. cit.




.. e price of life

and a population in  of about  billion persons, the gains in life expectancy in India alone over the period are worth about US$ trillion or about four times the gains in the United States in last three decades of th century.²² Of course the different timeframe for the estimate —  years in India vs  years in the USA— accounts for some of the much larger gains in India. But the more important reasons for the difference are the larger gains in life-expectancy in India —  years in India,  years in the USA — and India’s four-times larger population. Table : Human Development Index scores, selected countries ( - )

Country

1870

1913

1950

1995

2007

Chart

UK USA West Germany Brazil China India Japan Russia Lesotho Mozambique

0.496 0.467 0.397 NA NA NA 0.160 NA NA NA

0.730 0.733 0.632 0.159 NA 0.055 0.381 0.252 NA NA

0.844 0.866 0.787 0.371 0.159 0.160 0.607 0.651 0.191 0.112

0.932 0.943 0.925 0.809 0.650 0.451 0.940 0.769 0.469 0.281

0.947 0.956 0.947 0.813 0.772 0.612 0.960 0.817 0.514 0.412

. . . . . . . . . .

Data from Cras, 1999 op. cit. & U.N. Population Division

Official data acknowledges this growth in the wealthspan, although the United Nations, which thrives on gloomy prognoses, tends to so-pedal its significance. e “capped” indicators of the U.N.’s Human Development Index (HDI) combine income, education and lifespan factors using a formula that purports to describe something like “the escape from poverty” rather than the full extent of human development which would also reveal the heights of prosperity. By these estimates (table  on page ) the standard of living in most rich countries has doubled over the past  years, while it has grown five-times in Brazil and China and eleven-times in India.²³ Even in the poor economies of Africa, the standard of living as measured by the HDI has grown four times in just sixty years! ²⁴ In these countries, where financial incomes are growing rapidly, we need to be careful about our estimate of the value of a life-year. e ‘willingness to pay’ increases in proportion to ²² ☞ David Meltzer. “Economic Approaches to Valuing Global Health Research”. In: Disease Control Priorities in Developing Countries, nd edition. Ed. by Dean T Jamison et al. nd ed. Washington DC: World Bank, . Chap. , pp. –. : --- ²³e data up to  are from a backwards re-construction by Nicholas Cras ☞ N Cras. “Economic growth in the twentieth century”. In: Oxford Review of Economic Policy . (Dec. ), pp. –. :  ²⁴Unfortunately, the U.N. “adjusts” the data used to calculate the HDI to downplay the contribution to human development of higher income or lifespan beyond  years! Also, the current income celing for the HDI is $, (PPP basis) — about the average per capita income of Australians at the turn of the ᵗʰ century. For a discussion of the impact of the adjustments, see See the centenary edition of the Australian Treasury’s Economic Roundup ☞ Australian Treasury. “Global poverty and inequality in the th century: turning the corner?” Canberra, 

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increases in income in every economy; because richer people will pay more for an additional year of life. How much more will they pay? In poor countries, the value of that extra year has been shown to rise about half as fast as income rises (or a lile bit faster). So a doubling of national income leads to a rise of % - % in the value of a year of life.²⁵ In China, for example, growth in real per capita income from  to  was about  percent per year. Economists estimate that this income growth translates into increases of about  per cent per year in the value of a year of life. So, even allowing for money inflation, the value a Beijing resident places on one more year of life is actually % of the value that she placed on living through the current year. e observed growth in Chinese life expectancy at birth from  to  years between  and  represented an increase of about  percent per year in lifespan. e value of longevity in China over that period, therefore, grew by as much as  percent annually (% increase in value + % increase in life-expectancy) for two or three decades. e rate of increase in the average Chinese lifespan is likely to slow — it may already be slowing (see Figure  on page ) — as China ‘catches up’ with the record-holding countries, but because the main driver of China’s valuation of health outcomes is income growth rather than health improvements as such, the slowdown in the rate of longevity growth will probably not slow China’s “willingness to pay” for still greater longevity while its income growth remains strong. India

China 73

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60

58

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69

68

67 56

66 1980

1985

1990

Date

1995

2000

2005

1980

1985

1990

Date

1995

2000

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Figure : Total life expectancy, India and China, by year (U.N. Population Division)

ere are fears that China’s (and India’s) productivity — and therefore income — growth may be dampened by the ageing of a longer-lived population toward the middle of the ˢᵗ century. In other words, the rate of growth in these countries’ wealthspan may slow on the income side even as the longevity component of wealth continues, perhaps slowly, to rise. is maers a great deal to all of us; the wealth of such a large number of people inevitably spillsover to the rest of the world if only because the enjoyment of (income) wealth is, by definition, ²⁵ ☞ David Meltzer. “Economic Approaches to Valuing Global Health Research”. In: Disease Control Priorities in Developing Countries, nd edition. Ed. by Dean T Jamison et al. nd ed. Washington DC: World Bank, . Chap. , pp. –. : ---




.. How to grow old and rich

expenditure that allows the wealth to “leak” to others. An increasing valuation of lifespan in these countries while their incomes rise might also lead to global longevity spill-overs via additional publicly supported research on medical advances. In other words, the rest of the world holds a couple of stakes in the continuing demand for longevity, and the capacity to pay for it, in the large emerging, but ageing, Asian economies. We’ll come back to these prospects in Chapter .

. How to grow old and ri Considering the simultaneous growth in money income and in lifespan, it is tempting to suppose that a higher income buys a longer life. Certainly, the data shows that people in lowerincome countries have lower life-expectancy. Also, life-expectancy seems to be correlated with differences in wealth within countries: the poorest people in wealthy countries have higher mortality at any age than their rich compatriots. It seems that growth in income and lifespan must be related, too. For example, poor countries’ incomes are rising faster than rich countries incomes in part because even small increments make a big proportional change on a low base. At the same time, life-expectancy is growing more rapidly in poor countries than it is in rich countries. Between  and , Low middle income countries saw the greatest increase in life-expectancy: .% to  years. For High income countries the increase over the corresponding period was .% to  years ²⁶ (see Table  on page ) It’s a surprise, therefore, to find that despite these apparent correlations, growing rich does not “cause” longevity. Demographers discovered forty years ago that the story is more complex than this; that the relationship of lifespan and wealth is too weak to to explain changes in one by changes in the other.²⁷ When lifespan and income for a range of countries in any year are ploed on a chart, they cluster about a steep curve from low-income/short-lives to highincome/long-lives. But when we chart the changes in the combination of income and lifespan for these countries over time we find that the plots for most poor countries do not track along the steep curve. Instead their income/lifespan co-ordinates jump out ahead of the curve. In a few cases, they fall behind the curve. e clustering of income and lifespan coordinates around the initial curve is not a predictive correlation.²⁸ A recent comprehensive review of the evidence of the reasons for differing rates of mortality among countries by U.S. economists confirms this longstanding finding: ²⁶ ☞ World Health Organization. “Part II. Global health indicator tables”. In: World Health Statistics. Vol. . -. , pp. – ²⁷is initially surprising result was first demonstrated by Preston in  ☞ Samuel H Preston. “e changing relation between mortality and level of economic development. Population Studies, Vol. , No. , July .” In: International journal of epidemiology . (June ), pp. –. : - ²⁸Just such an experiment was used by Samuel Preston in his groundbreaking  paper. See Figure  in ibid. An excellent graphic illustration of this variable relationship between income growth and life-expectancy can be found in the animated charts at hp://www.gapminder.org/world. Try playing through the timeline from the start of the ᵗʰ century and observe the growing spread of the observations aer about .

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. L 

“…e cross-country data show almost no relationship between changes in life expectancy and economic growth over -, -, or -year periods between  and .”²⁹ For instance, almost all of China’s remarkable achievements in cuing infant mortality occurred before the economy took-off in the late s; India’s strong economic growth following the economic reforms of the s was accompanied by a slowdown in infant mortality improvements. e U.N. life-expectancy data also support this conclusion. As we saw in Figure India

China 73

72 62

Life-expectancy (years)

Life-expectancy (years)

71

60

58

70

69

68

67 56

66 500

1000

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GNI per capita (PPP basis)

2500

1000

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Figure : Total life expectancy, India and China, by Gross National Income (U.N. Population

Division)  on page , there has been a remarkably steady year-on-year growth in longevity in both China and India over the past thirty years. But when we look at the change in longevity by income (Figure  on page )we see a different story. It appears that the very fast rate of income growth in the past decade-or-so has outstripped the growth in life expectancy at birth so that the slope of the curve flaens-out quickly near the top. Relative poverty — like relative wealth — is an unreliable indicator of relative lifespan. Consider a child born in  in Shanghai. She could expect to live to  while a child born in San Francisco could expect just five years more ( years) although the Californian is likely to be almost -times wealthier than her Chinese cousin. Relatively wealthy African developing countries (such as Equatorial Guinea, South Africa) are near the boom of the longevity ranks so their place in the world-wide ranks of a lifespan-adjusted system of national accounts would slip. But poor developing countries in Asia — Vietnam, for example — are in the middle- or upper-ranks of the longevity table; they would rise up the ranks of a global wealthspan table. It is no surprise that war and disease shorten lives. Iranian, Pakistani and Uzbek children, for example, can expect to live at least twenty years longer than their cousins in war-wracked Afghanistan, just across the border. Other differences in the national averages have more ²⁹ ☞ David Cutler, Angus Deaton, and Adriana Lleras-Muney. “e Determinants of Mortality”. In: Journal of Economic Perspectives . (June ), pp. –. : -




.. How to grow old and rich

complex explanations involving different population sizes, income inequality, urbanization, disease control, access to recent drugs and public health facilities. e incidence of HIV and access to modern drugs seem to explain why, for example, a newborn child in Namibia can expect to live for  years, yet just across the border in South Africa, the on-average-muchwealthier children of a more-advanced state can expect only  years of life. It is very surprising, however, to learn that income growth can also shorten lifespan. In the United States, the world’s richest economy throughout the ᵗʰ century, periods of increasing economic growth has been consistently associated with small increases in mortality while periods of falling growth (rising unemployment) are linked to falling mortality. Over the period  to  — and over twenty-year spans covering the same period — a one percent increase in economic growth measured by GDP was associated with a .% increase in the annual rate of age-adjusted mortality from major cardiovascular and renal diseases, cancer, traffic injuries, flu and pneumonia, and liver cirrhosis.³⁰ Only the rising rates of suicide to be an “anticlyclical” cause of death, rising when the economy slumps. e most plausible explanation for this bizarre correlation is not that economic growth kills people but that both economic growth and mortality are associated with third factors that insult health. In a country where heart aacks among workers peak on Mondays, it seems likely that the pace of work and the amount of work time might kill.³¹ e health hazards of good times include rising levels of alcohol and tobacco consumption, increased levels of overweight and obesity, reduced exercise and hours of sleep, and reduced opportunity for social interactions. It looks like income growth, in the absence of knowledge, planning and the right policies, can even shorten life expectancy . is makes intuitive sense. For example, growing wealth is almost always accompanied by greater urbanisation of the population. Cities are incubators of the intense exchange of goods, services and ideas that make wealth. Urbanisation can also be good for population health: cities oen provide beer sanitation, water supply, nutrition and higher employment than the countryside. But not necessarily, as ᵗʰ century Londoners (below) and ˢᵗ century Romans (see page ) could tell us: in the absence of the right policies, cities can also be incubators of disease. Policies to improve nutrition, public infrastructure, maternal education and access to medical advances cut infant and childhood mortality which accounted for most of the improvement in life-expectancy up to the end of the ᵗʰ century. In , for example, in a slum not far from London’s Regent Street, John Snow demonstrated the importance of public health measures by proving that public water pumps contaminated with human waste were the source of a cholera epidemic. e importance of maternal education has been demonstrated many times, including by United States’ national mortality data from the late ᵗʰ century that shows white infants of mothers with less than twelve years of education have a mortality rate ( per ,) that is twice as high as that of white infants of mothers with a college degree. ³⁰e association has been known, although controversial, since the s. ere is, however, continuing support in the data as revealed in ☞ José a Tapia Granados. “Increasing mortality during the expansions of the US economy, -.” In: International journal of epidemiology . (Dec. ), pp. –. : - ³¹See references in ☞ Christopher J Ruhm. “ARE RECESSIONS GOOD FOR YOUR HEALTH ?” In: arterly Journal of Economics May ()




. L 

Medical advances are sometimes given credit for achieving mortality “breakthroughs”, for example in cuing deaths from infectious diseases in the first decades of the ᵗʰ century and cardiovascular disease in the s and s. It is unclear, however, whether these discoveries account for more than a small fraction of historical improvements in mortality. First, the fall in infant mortality that was so significant in raising life-expectancy during the century did not depend primarily on new medicines, and; second, it is not evident what would have happened without the new drugs so it is difficult to strike a fair baseline for comparison. ere is no question that many modern drugs have been very effective but this does not mean that they were responsible for achieving progress in the treatment of the diseases that they targeted. We will return to this question in Chapter  (see page ) where we’ll see some evidence that public health information, education and controls played an important, possibly predominant, role in greatly reducing the incidence of diseases that were the major causes of death early in the century.³² e colossal implicit value of lifespan evokes only mild interest from planners, however, as they plot future policies: they are much more concerned about the smaller, but identifiable, costs. ey are aware that rapidly rising average longevity is likely to increase demand for extended social support for the ageing and changes in labor laws and pension regulations to promote new forms of participation in the workforce. It may lead to changes in priorities for infrastructure supply, including distributed information infrastructure and new approaches to transport or urban planning that facilitate continuing social participation for the aged who (as we’ll see in the Chapter ree) are, contrary to the stereotype, surprisingly independent. With few exceptions, politicians either don’t see the elephant, or they ignore it because there is no pressure on them to do anything. Unlike the costs of ageing, the benefits of longevity are invisible and have no exchange value; but because the sum of private valuations may be almost as great as everything else produced by the goods and services sectors of our economies, it is very likely to give rise to a demand for higher levels of expenditure on “upstream” public goods that favour longer-life. Aer all, we typically want more of what we value most. In the past two centuries, most of the increase in life-expectancy came from cuts to infant and mid-life mortality where expenditure on public goods such as water and waste infrastructure, hospitals and health education were the means best adapted to the most important causes of mortality. In the future, however, gains to life-expectancy are likely to depend more on medical advances to combat disease or directly to lengthen survival via e.g. genetic manipulation. In one way or another, whether through input subsidies to research (including education) or subsidies for treatment of patients, these are likely to become much higher priorities for public expenditure, especially as understanding of the processes of ageing advances and public interest awakens.

³²ere is an extended discussion in ☞Cutler, Deaton, and Lleras-Muney, op. cit.




.. How to grow old and rich

T G M C “…it is our expectation of life and not our experience of it that determines our conduct and character. Consequently, the very vulgar proposition that you can not change human nature is valid only on the assumption that you can not change the duration of human life. If you can change that, you can change political conduct.” G. B. Shaw, “Short Stories, Scraps and Shavings”, Works Vol. 

e biggest increases in this demand for new public expenditure on an increasingly valuable longevity will be felt in domains of interest to the middle-aged, not the already-aged. We’ve already seen hints (on page ) of why this may be so when we noted that your expected age of death is postponed as you age, so that your life-expectancy at birth is likely to be only the earliest average age of death of your birth-year cohort. e most important factor predicting survival to  years is survival to  years. A discounted lifetime-income hypothesis suggests that the years of middle-age are where the private valuation of longevity is highest, both because this is when personal wealth is at a life-time peak and because personal interest in survival — to see the grandchildren, for example — is likely to be highest, too. So the most interested and aggressive constituency for increased public policy aention to longevity is likely to be the most politically challenging: skeptical, confident, middle-class, middle-aged voters. Why will the middle-class voters be influential? Because they are sufficiently wealthy to place a high value on the marginal life-year and because their numbers, and share of the vote, are growing rapidly. By  there is likely to be at least . billion people or just over  percent of the projected global population whose estimated income (based on purchasing power) will put them in the middle-class as defined by World Bank forecasters: somewhere between average incomes in Brazil and Italy in the year . e largest single middle-class will be found in China;  % of the world-wide total. Half of the total  million new entrants to the global middle-class between  and  will be Chinese. India will add another %. In total, developing country nationals will account for % of the coming global middle-class by . e new entrants to the middle-class will likely re-shape politics in some countries. Although at present they represent only a small proportion of their home country populations, they will account for significant proportions of the voting population by  and are likely to be much more influential. In China, for example, the modal income-earner will be a member of the global middle-class by . e new middle-class wealth will also re-shape global demand and tastes in many ways, but their demand for services is likely to stand out. Services such as education and health care that respond very strongly to rising income and that, by deepening human capital and improving productivity, also contribute strongly to the performance of




. L 

national economies.³³ Synergy between the private value in longer lifespan and the shared public benefit in economic development has the potential to be a powerful force for growth in the emerging economies. Increases in national income are the most common measure of economic development. But economic development is also likely to lead to an improvement in health outcomes by improving access to up-to-date technology, for example, and by improving health infrastructure, housing, pollution control, education and nutrition. We already saw that increased income, on its own, does not guarantee reductions in mortality (see page ). But the addition of lifespan gains to the overall measure of welfare allows us to describe a virtuous cycle that links lifespan and economic growth. Because wealthier people place a higher value on an additional year of life, they value each increment in health-span that arises from underlying economic development more than the last, at least over any currently feasible life-span. is is in contrast to the value of money where each incremental dollar is slightly less valuable than the last. Now, as an economy grows, the average level of wealth rises and individuals’ valuation of an additional year of lifespan rises in proportion, as does the value they place on further economic development that leads to beer health/lifespan outcomes. So economic development creates incentives, via the impact on lifespan, to support even stronger programs of economic development that, in turn, add to the average value of a greater lifespan. Notice, however, that public policies are an essential “bridge” in this virtuous cycle. Completing the cycle depends on individuals expressing their private value of a longer lifespan in a way that influences public policies that promote a longer lifespan. For example, policies to orient public investment towards medical research. Will this happen? Will the world’s largest middle-classes in China and India drive the demand for longevity-enhancing, publicly supported, research? It’s an important question not only for giant emerging economies such as China and India but also for the rest of the world that would expect to benefit substantially from the “spillovers” from expanded Chinese or Indian medical research. But why don’t we hear from the lobby already in rich countries? It’s a puzzle. e choruses demanding government action on purely speculative benefits such as avoiding distant catastrophic climate change are much larger and louder than any concerned with closer and more personal benefits such as a long, healthy life. We can speculate that there are two reasons for this. e first is a familiar problem usually called “market failure”; no-one can win for themselves even a tiny fraction (beyond their own) of the huge implicit value of additional longevity and no one will spend more than they expect to gain directly on lobbying for it. If you spend more than you expect to gain from your own additional years, you’ll be doing someone else a favour (they’ll spend less). But market-failure is chiefly a barrier to transparent collaboration not, ultimately, to action. We could easily imagine an instant and global lobby for government spending on longevity enhancement if the means of lengthening life were discovered and it were feasible for governments to do something about it. at’s the second problem and, probably, the main reason for the present silence of the voters. ere are no sure recipes for a longer lifespan, yet; nothing to spur hopes or bring out the lobbies. But there are many hints. ³³e data and projections on the global middle-class from ☞ Maurizio Bussolo et al. “Global growth and distribution: are China and India reshaping the world?” In: Southern Engines of Global Growth November (), p. 

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 

THE ORIGINS OF LONGEVITY

“And the life of man, solitary, poore, nasty, brutish, and short” omas Hobbes, Leviathan

Hobbes was not alone in believing that in the absence of civilising influences of education and government primitive human lives were short. Historians believed, as recently as the s, that few pre-modern humans lived beyond forty and that only exceptional individuals survived into their fiies. Estimates derived from Roman memorial inscriptions, for example, suggested that the ancients had a life expectancy at birth of  to  years and an average age-at-death (for those surviving to age ) of between  in Rome itself and  for those living in the African Province.³⁴ But Rome was was exceptional in more ways than one. e world’s first — and for centuries threaer, only — mega-city was lethal for both rich and poor. Nutrition was terrible. Grain was subsidised, but other foods were expensive; most calories came from gruel rather than bread. Infectious and epidemic diseases such as malaria, typhoid, tuberculosis, gastroenteritis and the plague were common, especially in summer months. Few dwellings were connected to the city’s renowned public sanitation; hygiene in the public baths was doubtful; housing was crowded and frequently gerry-built; refuse disposal and even burial grounds were overwhelmed.³⁵ Romans cremated outside the city walls the bodies of many of the fiy thousand who died each year. But they oen dumped the bodies of the poor, along with household trash, into open pits where they were picked over by vermin, vultures and strays. e historian Suetonius claims, only in passing, that one morning a stray dog dumped a human hand at the feet of the future emperor Vespasian (an omen of his power).³⁶ ³⁴Hopkins demonstrates, however, that these inscriptional data are very unreliable and, when compared with the distribution of variance in standard U.N. life-tables, implausible. ☞ Keith Hopkins. “On e Probable Age Structure of the Roman Population”. In: Population Studies . (), pp. – ³⁵Life in Rome in mediaeval times must have been just as science fiction writers have imagined living in say, New York, centuries aer a nuclear winter. “Rome…had a population of about  million inhabitants. at is vastly more than was to be normal in medieval or early-modern times. In  A.D., only four European cities had more than , inhabitants. Between them, those four cities had only , inhabitants. e city of Rome was and remained unique in European pre-industrial history. It was by far the biggest city in the world, and remained so until the growth of the big Chinese cities of the Sung dynasty in the eleventh to thirteenth century A.D. e first European city to have  million inhabitants again was London, in the early nineteenth century.” ☞ W. M. Jongman. “Rome: e Political Economy of a World-Empire”. In: e Medieval History Journal . (Apr. ), pp. –. : - ³⁶☞Suetonius, Vespasian .




. T   

P You might guess that archeological evidence should tell us how long the ancients lived. But skeletal remains from classical and pre-historic times are sparse and difficult to interpret for evidence of longevity. e most obvious source of bias is that the bones of old individuals tend to dissolve more rapidly than those of young individuals: the remaining bones are likely, if only for this reason, to suggest high mortality. ere are also statistical problems; it is difficult reliably to estimate a mean population mortality based on estimates of mean mortality in the sample bone assemblies. One widely-cited recent study seemed to show a large jump in human longevity coinciding with the invention of agriculture more than ten thousand years ago. e researchers aempted to estimate longevity by measuring the ratio of “old” (> years) to young adults represented in a collection of hundreds of small collections of fossilised remains of pre-human, Neanderthal and modern human individuals from the late Palaeolithic using molar eruption in the jawbones and wear on the dental enamel as indicators of age at death. ey found that longevity increased throughout this period of almost  million years culminating in a rapid acceleration of lifespan — a five-times increase in the proportion of old individuals represented in the burials — in the period of transition between Neanderthals and modern humans.³⁷ Critics have pointed out, however, that the ratio of old-adults to young-adults cannot be a measure of longevity because it is a nearly constant ratio across different homininae species (monkeys, chimpanzees and man) that, in reality, have very different longevity. Longevity and age at maturity tend to be closely correlated; late-maturing species such as large mammals or tortoises also tend to live longer. So the ratio of immature to surviving mature individuals tends to be about the same in species with different longevity. Variations in the ratio that the archeologists detected in the paleo- and neolithic skeletal remains appear to be nothing more than a reminder of the incompleteness of archeological records. ³⁸ It is possible, however, to estimate the longevity of ancient humans without relying on archeology by using contemporary proxies. Detailed, ᵗʰ century anthropological records exist for several isolated, hunter-gatherers and foraging-horticultural groups that, at the time they were studied, had no exposure to modern medicine, foods, hygiene or standards of living. Although most these tiny groups have since disappeared or aached themselves to the periphery of modern societies, life-tables constructed from anthropologist’s data allow us to make inferences about the characteristic lifespan of the human species under pre-historic conditions. e mortality records show that the characteristic lifespan of these populations was surprisingly long; about seventy years, other things being equal. Life-expectancy at birth varied between  and  years but mortality fe;; sharply aer infancy and through childhood, aer which ³⁷ ☞ Rachel Caspari and Sang-Hee Lee. “Older age becomes common late in human evolution.” In: Proceedings of the National Academy of Sciences of the United States of America . (July ), pp. – . : - ³⁸e ratio of young-to-old adults in modern populations of monkeys, chimpanzees and man is close to : in each case. But longevity, measured as the modal age of adult death, is very different for each of these three species; about  in macaque monkeys ,  in chimpanzees and  in man (USA) ☞ K Hawkes and J F O’Connell. “How old is human longevity?” In: Journal of human evolution . (Nov. ), –; discussion –. : -




risk of death in the next year remained essentially constant to about the age of  years. Aer that, mortality rose steadily in statistically predictable way, doubling roughly every - years, a trend that is identical to mortality in modern populations.³⁹ A prolonged, post-reproductive adulthood lasted until the seventh decade when senescence set in rapidly, followed by death. Survival of women beyond reproductive age was common: those reaching age  could expect to live about another two decades. Apart from differences in the risk of violent death among different primitive populations, these mortality characteristics are remarkably constant across groups widely dispersed around the world in very different environments from the Hazda of Tanzania and the Ache of Paraguay — both hunter-gatherer groups — to the Yamomamo indians of the Amazon and the Gainj of Papua New-Guinea — both forager-horticltural tribes — to the Warao of the Venezuelan swamps and Aborigines of the Australian Northern Territory (data from the late s) who are, or were, ‘acculturated’ hunter-gatherers.⁴⁰ Of course, a “characteristic” lifespan of seven decades even in primitive conditions does not strongly determine actual lifespan. ere have been times and places when most people appear to have had much shorter lives, such as in Rome during classical times or Europe during the famine and plague decades of the fourteenth and fieenth centuries in England when life expectancy at age  was  to  years among landowners and as low as  years in some monasteries.⁴¹ By the early ᵗʰ century, the modal age-at-death in six rich countries (England and Wales, France, Italy, USA, Japan Sweden) was already in the eighth decade ( -  years of age) and in the s reached the ninth decade ( -  years of age).⁴² Given that the global average life-expectancy at birth these days is  years, most adults can expect to live well beyond their seventh decade. It is striking, however, that the characteristic life history of these pre-modern populations was marked different to that of even our closest homininae relatives, the chimpanzees, who experience higher mortality and lower survival rates than humans at all adult ages. Even in captivity, where the chance of a young adult chimpanzee living to age  is seven times greater than in the wild, only  percent reach this age; less than half the proportion of pre-modern humans that do so. At age , the expected lifespan of a chimpanzee in a zoo is less than  years, about a third of the expectation for pre-modern humans. Darwin showed us that differentiation between species has an evolutionary explanation. So ³⁹See the discussion of the Gompertz survival curve below ⁴⁰e data on characteristic lifespans of pre-modern populations is from a review article by ☞ Michael Gurven and Hillard Kaplan. Longevity Among Hunter- Gatherers: A Cross-Cultural Examination. June  ⁴¹Mortality in medieval England is of interest because it is a time when the population actually fell. But records are few and faulty. Some estimates are based on mathematical models of incomplete landholding records. Monastic records are relatively full and reliable, but relate to a special — in some ways privileged — population. Nonetheless the evidence of appalling health and early death is striking. ☞ JOHN HATCHER, A. J. PIPER, and DAVID STONE. “Monastic mortality: Durham Priory, –”. In: e Economic History Review . (Nov. ), pp. –. : - ☞M. a. Jonker. “Estimation of life expectancy in the Middle Ages”. In: Journal of the Royal Statistical Society: Series A (Statistics in Society) . (Feb. ), pp. –. : - ⁴² ☞ Vladimir Canudas-Romo. “e modal age at death and the shiing mortality hypothesis”. In: Demographic Resear  (July ), pp. –. : -

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. T   

some essential mutation must have occurred in our branch of the evolutionary tree to bring about such a big difference in a fundamental characteristic such as longevity between chimpanzee and man. But why?

. Evolutionary explanations at longevity evolves is both obvious and deeply puzzling . e first axiom of evolutionary theory is that all life on earth shares a biological heritage. Somewhere in the tangy slime of a warm Cambrian backwater lived single-cell creatures that are the genetic ancestors of both worms and humans. at is why we share relatively complex cell chemistry and biological mechanisms with even the humblest forms of life. But humans live  times longer than worms. Somehow, tweaks in the biology we share with creatures as diverse as insects, mice and tortoises has produced lifespans ranging from days to centuries. Evolution is the only theory successfully to explain this diversity. It’s far from obvious, however, why evolution would confer a lengthy period of ageing on a species. According to the theory the characteristics of a species are the heritage of individuals who were best adapted to environmental conditions such as climate, competition for resources, emergence of predators or a decline in prey and so forth. e fiest prosper and have more descendants. But ageing leads to increasing unfitness and death (it has been called a sexuallytransmied terminal disease).⁴³ Where’s the evolutionary advantage in a woman living a third or more of her life aer menopause? Strictly speaking, a human population could survive with very brief life-spans. If about a quarter of human babies died – as young apes do in their natural environment – and every year aer that about - percent of survivors died, life-expectancy at birth would be only about  years. But even in such a morbid environment a sufficient number of females would survive, to about  years, to sustain population numbers.⁴⁴ In other words, a brief life for humans — much briefer than our characteristic seventy years — would meet the same test of evolutionary success that the great apes have passed. Characteristics such as longevity that emerge aer reproduction is finished in the third or fourth decade of a human lifespan have no selective effect. To explain longevity in evolutionary terms we have to identify some reason for evolution to select for longevity other than by selecting for longevity itself. One line of speculation proposes a cultural basis for the evolution of longevity; that a long adulthood and survival into old age increases evolutionary fitness because it allows time for the transmission between generations of the complex physical, technical and cultural skills necessary to produce the difficult-to-acquire, specialised goods and services that humans consume.⁴⁵ e Grandmother hypothesis, first put forward in the s, suggests that genes favouring survival to late adulthood have a ‘fitness’ edge because women whose childbearing years are behind them help to feed and educate surviving children, leaving ⁴³Aributed to the dyspeptic Scots “anti-psychiatrist” A. D. Laing ⁴⁴e data on bare survival is taken from ☞ Robin Holliday. Aging: e Paradox Of Life. Dordrecht: Springer Netherlands, , pp. –. : ---- ⁴⁵e best case for cultural evolution depends on the credibility of some sophisticated statistical modelling combined with archeology. See the discussion in ☞ Kevin N Laland, John Odling-Smee, and Sean Myles. “How culture shaped the human genome: bringing genetics and the human sciences together.” In: Nature reviews. Genetics . (Feb. ), pp. –. : -

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.. Ageing in the genes

mothers to invest their energy in having (more) babies. e grandmother’s genes for longer life, passed onto larger numbers of offspring who would also be more productive as adolescents in food collection — and thus beer able to help the family-group survive — thanks to her tutelage, warranted the evolutionary investment in longer-life.⁴⁶ It’s a plausible story that receives some support from the life-table data on pre-modern populations mentioned above. e observations show that, aer the age of about , the proportion of a woman’s descendants who are pre-adolescent (<  years) falls rapidly. Her nurturing and teaching role is at an end. From this point on, lile evolutionary benefit can be extracted from the costly maintenance of health and function and senescence accelerates. But it’s a potential explanation that offers no proof. It is not clear from the archeological evidence where the cultural causality lies; whether “grandmother genes” favoured longer survival or longer survival (for whatever reason) favoured the appearance of grandmothers. Also, the idea that prolonged adulthood favours the education of offspring who are fier to survive applies equally to mothers, whose genetic investment in offspring is twice that of grandmothers. In fact, A generalised form of the “grandmother” hypothesis can be applied to both sexes.⁴⁷ e skills required to produce the complex physical and cultural goods that distinguish human lives from that of other homininae require a large brain and lengthy development. is extended learning phase, during which productivity is low — childhood and adolescence — is compensated by higher productivity during adulthood. Evolution selects adolescents of both sexes who go on to have longer and hence richer-because-more-productive lives and whose wealth is expressed, among other ways, in larger families that carry their longevity genes. e idea that along with the upright stance, large brains and speech, evolution selected for a life long enough to allows humans to take advantage of the “embodied capital” they acquire during a long adolescence is appealing. But it lacks detail. It does not bring us any closer to identifying the target of evolutionary selection that is not — and cannot be — simply a “longevity gene.” To find a candidate for that mysterious agent, we have to turn to cell biology.

. Ageing in the genes “If you live to be one hundred, you’ve got it made. Very few people die past that age” George Burns, US actor & comedian ( - ) e ᵗʰ century German biologist August Weismann speculated that “death takes place because a worn-out tissue cannot forever renew itself, and because a capacity for increase by means of cell division is not ever-lasting but finite”. Weismann’s idea was discounted for many ⁴⁶Plausible support from current anthropologic studies of hunter-gatherer societies were collected in ☞ J.F. O’Connell, K Hawkes, and N G Blurton Jones. “Grandmothering and the evolution of Homo erectus”. In: Journal of Human Evolution () ⁴⁷See ☞ Michael Gurven and Hillard Kaplan. Longevity Among Hunter- Gatherers: A Cross-Cultural Examination. June 

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. T   

decades aerwards, apparently contradicted by a faulty experiment that seemed to show that cells could continue to divide and multiply indefinitely if supplied with warmth and nutrition.⁴⁸ But modern biology once more considers his description ageing at the cellular level to be correct…as far as it goes. Weismann recognised that there is a difference in the mortality of germ cells (sperm, ova) and somatic cells (everything else) in the body. Each of us carries at birth our personal genetic code preserved in female egg cells or (the precursors o) male sperm cells. ese cells are able, from the moment of conception to reproduce vigorously by cell division, transferring our genetic information with extraordinary fidelity to our children, who in turn carry that transcription to the next generation. It’s possible to imagine the nuclear “payload” in these cells enjoying a kind of immortality, reproducing by cloning and mixing its distinctive genetic information with other genetic lines, but never ageing and never dying out.⁴⁹ Somatic cell lines do, however, grow old and die. eir evolutionary purpose is only to ensure the organism survives to and, as we have seen, beyond reproduction in order to give the genes in the germ cells the best chance of “immortality”. Cells reproduce by cell-division during the life of the individual as the structures of the body develop, fight infection, repair themselves and deal with the insults of the environment. But eventually their reproductive rate falls; aer no more than  to  divisions, the individual cells of the muscles, connective tissue, bone, organs and skin are unable to divide further. e “Hayflick Limit” , foreseen by Weismann and named for the biologist Leonard Hayflick who confirmed it experimentally in , has had a powerful influence on the imagination of researchers, philosophers, science journalists, and the religious enthusiasts of various ’life-extension’ faiths. It seems to import death into the very base of life’s pyramid by seing a short and certain term for every building block of our bodies. But that image, it turns out, is misleading. Death at the Hayflick Limit is an unusual sacrifice cells make to the survival of the organism. In one of Nature’s sharp ironies, somatic cell ageing and death results from the way our chromosomes carry out their role in creating new life. When it is time for the cell to reproduce by division into two ‘daughter’ cells, enzymes in the nucleus of the cell start to prise apart each of the twisted double-strands of DNA that make up our chromosomes, working at many points along the ribbon simultaneously to speed up the duplication.⁵⁰ In the gaps of the unravelling DNA ribbon, an enzyme called DNA polymerase travels along each of the unwound strands — designated the “leading” and “lagging” strands — using the paern of DNA bases as a template to create a new strand. Each base in the original strand is matched with its chemical partner to create a new strand that is the chemical mirror of the template strand in the same sense as ⁴⁸Alexis Carrel’s experiment with cells taken from chicken hearts was probably contaminated by the reintroduction of new cells, intentionally or not, with the culture nutrient. It was never reproduced. Carrel, who was awarded a Nobel prize for inventing new techniques of vascular surgery, was a vigorous publicist who co-authored a book with the aviator Charles Lindburgh on the culture of organs outside the body and a book on eugenics that advocated, among other punishments, the euthanising of criminals and the criminally insane ☞ J W Shay and W E Wright. “Hayflick, his limit, and cellular ageing.” In: Nature reviews. Molecular cell biology . (Oct. ), pp. –. : - ⁴⁹Of course, unfertilized eggs do age and die. It is only the genetic information in the fertilzed egg that could be considered “immortal” ⁵⁰Otherwise it would take days, not seconds to reproduce all . billion base-pairs in the human genome, even at speeds of  base-pairs a second




.. Ageing in the genes

your le hand is a mirror of your right. e “mirror” arrangement allows the two strands in the double-helix to intertwine just as your fingers do when you clasp hands. e duplication procedure is astonishingly fast and accurate but there is a awkward physical complication that arises from the mirroring of the strands. A DNA strand comprises millions of short fragments whose “handedness” is defined by the different chemical characteristics of the DNA backbone at the “upstream” and “downstream” ends of each fragment. e fragments arranged end to end along the curly backbone all point in the le-handed direction on one strand forming the double-helix, and point in the right-handed direction on the other. But paern duplication works in only in the “forwards” direction from upstream to downstream. On the lagging strand, the duplicator enzyme has to move in the mirror-forward direction across each fragment: that is, backwards. e enzymes that control duplication work around this complication but there’s a price to pay: the work-around doesn’t work when the duplicator arrives at the last fragment on the strand. Oops! A truncated DNA strand would be a bad thing. A strand that lost genetic instructions at every duplication would soon cause havoc in the cells leading, possibly, to the malfunction or death of the organism. e solution is a loop of disposable DNA ‘boilerplate’ stuck on the end of each strand. is piece, called a telomere (“end bit” in Greek), carries no genetic instructions but DNA polymerase happily reproduces all of it except the last lile piece during every division of the cell. In other words, telomeres are sacrificial offerings; they are unrolled and effectively “clipped” at every duplication event when the reverse-signal fails to mark the very last piece of one DNA strand. e loss of piece of telomere does not affect the genetic information carried by the DNA and, in most cells, the “clipped” telomere pieces are never replaced. Consequently, the telomere gets shorter and shorter with each duplication of the strand. Experiments in the mid-s demonstrated that, unless the telomere is extended, the cells in laboratory cultures eventually stop reproducing and die. In other words, they reach the Hayfli limit. Telomeres are a countdown-clock of mortality for somatic cells.⁵¹ eir role is at least an analogue for the ageing of the organism and possibly a proximate mechanism. If a cell’s telomeres become arbitrarily short — there is still not much clarity about the relevant length except that it’s variable — a series of events collectively known as “apoptosis” (more Greek, meaning “decline”) leads to the death of the cell. Under the control of a cascade of specialised enzymes, the cell starts to shrink, its DNA and nuclear protein is degraded and it breaks up into small pieces, signalling the body’s immune system as it does so. Specialised, adaptable ⁵¹e impact of telomere shortening is discussed by Shay and Wright, op. cit., Holliday, op. cit., and by Predrag Ljubuncic and Abraham Z Reznick. “e evolutionary theories of aging revisited–a minireview.” In: Gerontology . (Jan. ), pp. –. : - e number of papers on telomeres and ageing is huge. Geraldine Aubert and Peter M Lansdorp. “Telomeres and Aging”. In: Physiological Reviews (), pp. – and Karen Anne Mather et al. “Is Telomere Length a Biomarker of Aging? A Review.” In: e journals of gerontology. Series A, Biological sciences and medical sciences (Oct. ), pp. – . : -X are valuable review articles as is Elizabeth Blackburn’s article (Elizabeth H Blackburn, Carol W Greider, and Jack W Szostak. “Telomeres and telomerase: the path from maize, Tetrahymena and yeast to human cancer and aging.” In: Nature medicine . (Oct. ), pp. –. : -)with her Nobel co-laurates on telomerase. e actual identification (hypothesized) of telomeres dates to work in the s by Hermann Muller (Nobel prize ) and Barbara McClintock (Nobel prize ), long before the discovery of DNA,s double-helix




. T   

scavenger cells (macrophages) that range through tissue as part of the front-line defence from infection, surround and ’swallow’ the cell pieces breaking them down into their constituent parts and recycling them. It sounds like a dismal process but, in fact, it is an essential defence against cancer. If human telomeres become too short to safeguard accurate transcription of DNA during cell division or to prevent chromosomes becoming jumbled by random connections between un-capped ends of the chromosomes, it becomes safer to terminate the cell rather than risk uncontrolled or unpredictable growth that could lead to cancer. So how is it that germ cells that carry half the DNA complement of the organism are able to reproduce without limit and thus achieve immortality? ey too have sacrificial telomeres at the ends of their chromosomes, but they are richly endowed with an enzyme that re-creates telomeres.Telomerase, or“ribonucleoprotein enzyme terminal transferase” to give its full title, is present in many cells but in most somatic cells there doesn’t seem to be enough of it to be effective. In germ cells, however, and in cells like foetal tissue, bone marrow stem cells, the testes, skin epidermis, and parts of the intestine that are worn away by passing food, levels of telomerase are high enough to keep the telomeres replenished. Telomerase works in these cells by adding strings of telomeres to the end of the DNA strand and so reseing the mortalityclock. Just as well; without infinitely divisible germ cells or regularly replenished skin and gut-lining none of us would be here at all. A  ’   e confirmation in the s of the crucial role of telomeres in cell reproduction and ageing naturally prompted research interest in the possible manipulation of these processes as a means of extending cell life and possibly slowing the ageing process in organisms. For instance, artificial introduction of telomerase has allowed researchers to enhance the reproductive vigour of human cell lines in culture, such as skin-cells for transplants.⁵² Although it has been shown, experimentally, that artificially extending telomeres delays ageing in mice, the cancer risk is so high that only mice specially bred to resist cancer last long enough to show results.⁵³ Researchers have shown, too, that the artificial activation of telomerase can expose the organism to an “aack of the clones”; that is, cells, especially stem-cells, that continue to reproduce (“clone” themselves) indefinitely because their telomeres are indefinitely extended. Were this permitted, bodies would be weaker for having most or all of some cells in crucial places such as the gut or kidney or the blood derived from a single individual, with all of that individual’s possible weaknesses. e eventual ageing and death of cells means variety in cell-lines, reducing the impact of possible “rogues” or “regressions.” e gradual, progressive, shortening of the telomeres offers a plausible analogue, at least, of ageing at the cellular level in the form of progressive loss of reproductive vigour followed by cell death. But it is not clear in practice that it explains ageing. For one thing, many cells never reach the Hayfli limit that marks telomere exhaustion. Instead, they succumb to the wear and tear of keeping the organism healthy and functional. Just breathing, for example, generates highly-reactive oxygen free radicals that are short-lived but able to damage DNA, proteins ⁵²Discussed in ☞Aubert () op. cit. ⁵³See, for example, ☞ Antonia Tomás-Loba et al. “Telomerase reverse transcriptase delays aging in cancer-resistant mice.” In: Cell . (Nov. ), pp. –. : -




.. Ageing in the genes

and the fay (lipid) molecules that provide both important structures and energy stores in the cell. Obviously, oxygen is essential for the release of energy in the body. So cells deploy sophisticated physical and chemical defences to the dangerous free-radical intruders, at the price of a big investment of effort and energy. Fighting infection and dealing with the damaging by-products of inflammation also knocks cells around. Managing the immune system and its occasional ‘friendly fire’ errors; capturing and disposing of introduced toxins; healing wounds and maintaining physical systems such as temperature, fluids and blood pressure on an even keel (homeostasis); each of these is a vital function but each depletes cellular resources. In short, even if it were possible to ignore the telomeric clock without risking the development of tumours, cells would still face the challenges of daily life. Telomeres continue to command a central role in the reductionist explanation of ageing. Researchers have recently discovered that many organs — including slowly-renewed organs such as the brain and the pancreas that were once thought to stop growing aer childhood — depend on the action of adult stem-cells for tissue renewal and repair. Like the embryonic stem-cells that direct the development of the foetus before birth, these adult stem-cells are the ancestors of entire new generations of the diverse, specialised cells that cooperate to ensure some complex bodily function such as the liver. e adult stem-cells continue to do this throughout the life of the organism, but their capacity progressively deteriorates as the organism ages. Individual stem-cells grow and reproduce using the telomere-mediated DNA duplication mechanisms described above. It now appears that they suffer the same risk of “ageing” and heritable DNA degeneration as other somatic cells, as well as the wear and tear of changes in their immediate environment, leading to apoptosis under the control of the cellular cancer-control mechanisms. Researchers have shown that this ageing of the adult stem-cells slows their capacity to generate new specialised cells in key organs of the body and, they speculate, may further explain the ageing of the entire organism.⁵⁴ But it remains difficult to incorporate telomeres and telomerase in the picture of ageing at the macroscopic level. But at the macroscopic level, the evidence of a link between telomere-length and ageing in humans remains equivocal. So far, population studies of telomere length are mostly crosssections. at is, they study a group of individuals of different ages at the time of the research rather than follow a group of individuals as they age. ese studies measure telomeres in a variety of different cell types and many have not been designed to exclude factors that could confound the association between telomere length and age such as genetic inheritance, physical condition (other than age), diet or disease status.⁵⁵ Several studies show that human telomeres shorten with age in many tissues, but there is not a simple relationship between telomere length and age; for example, the most rapid shortening seems to occur in young adults. A high-quality prospective ten-year cohort-study of a multi-racial group of adults aged - years, reported in , showed no statistical correlation between the telomere length and ⁵⁴is research is reported in Norman E Sharpless and Ronald a DePinho. “How stem cells age and why this makes us grow old.” In: Nature reviews. Molecular cell biology . (Sept. ), pp. –. : - ⁵⁵ is is the principal conclusion of a  review of  papers reporting studies of the telomereageing link in more than  persons were reviewed in ☞ Karen Anne Mather et al. “Is Telomere Length a Biomarker of Aging? A Review.” In: e journals of gerontology. Series A, Biological sciences and medical sciences (Oct. ), pp. –. : -X




. T   

overall survival or between telomere length and specific causes of death such as cancer, kidney disease, infection etc. ere was, however, a correlation between telomere length and selfreported health and (observed) years of healthy life, which suggests that telomere length may be a biomarker for healthy ageing.⁵⁶ Intensive studies of the telomere-ageing link in small animals with shorter lifespans have also been inconclusive. For example, researchers studying the relationship between telomere length and lifespan in mice — including special breeds that resist the cancer-risks of telomere erosion — have found no clear relationship. ⁵⁷

. ree theories of longevity e discovery and elucidation, over several decades, of the action of telomeres has gradually introduced a new approach to the biology of ageing that continues, but also subtly subverts, the view aributed earlier to August Weismann (see page ) that growing old is a process of haphazard deterioration in the tissues of the body.⁵⁸ “Deterioration” is clearly an element in ageing but maybe a consequence rather than a cause. “Haphazard” looks less and less plausible. e more recent approach implicitly, at least, aributes ageing not primarily to progressive depletion of cell resources such as telomeres or to chance wear and tear but rather, to purposeful, gene-controlled processes at the cellular level that result, ultimately, from the regulation of genes that code for proteins crucial to growth, metabolism of nutrients and cellular protection from environmental stress.⁵⁹ In animal models for human cell biology, such as worms and mice, there are so far a dozen or so genes whose actions are associated with longevity.⁶⁰ Among the most studied are genes that code for molecules involved in signalling the required activity level of other genes, and genes that code for enzymes known as transcription factors that directly determine the expres⁵⁶ ☞ Omer T Njajou et al. “Association between telomere length, specific causes of death, and years of healthy life in health, aging, and body composition, a population-based cohort study.” In: e journals of gerontology. Series A, Biological sciences and medical sciences . (Aug. ), pp. –. : -X ⁵⁷e recent evidence on telomere length and lifespan in mice and humans is reviewed in ☞ Norman E Sharpless and Ronald a DePinho. “How stem cells age and why this makes us grow old.” In: Nature reviews. Molecular cell biology . (Sept. ), pp. –. : - ⁵⁸Robin Holliday, a distinguished geneticist in the s and s who subscribed to the view that ‘normal’ ageing was the consequence of a long, inevitable deterioration at the cellular level probably represents the latest expression of this view. “One of the main causes of ageing is the inability of the organism to replace cells in vital organs, such as the heart or brain. Individual cells die either through the accumulation of genetic damage in their genes and chromosomes, or through the inability to get rid of defective proteins, or by breaking down such proteins to harmful smaller fragments.” (Holliday, op. cit., page ). ⁵⁹An excellent survey of the more recent evidence that ageing is due not to haphazard deterioration but rather, to purposeful processes under the control of genes that code for well-studied metabolic signalling pathways is contained in ☞ Cynthia J Kenyon. “e genetics of ageing.” In: Nature . (Mar. ), pp. –. : - ⁶⁰See Table  in ☞Kaare Christensen, omas E Johnson, and James W Vaupel. “e quest for genetic determinants of human longevity: challenges and insights.” In: Nature reviews. Genetics . (June ), pp. –. : - that is, already, slightly out of date and the detailed review in ☞Kenyon, op. cit.




.. ree theories of longevity

sion of other genes that code for a hormone such as insulin. Manipulating genes that have a genetic-regulatory function or, sometimes, blocking the receptors in the cell that respond to their regulatory signals, can change the level of activity or even the “on/o” status of a gene acquired from both parents or an allele of the gene acquired from only one parent. e ultimate targets for regulation include the production of insulin and insulin-like growth factors; the action of a molecule known as rapamycin (Sirolimus in the pharmacopeia) that surpresses the activation of the immune system; a protein known as AMP kinase that wields high-level control over cell uptake of glucose and the activity of the mitoondria that are the source of most of the cell’s chemical energy, and; sirtuin molecules — thought to be affected by the popular but unproven “life extension” drug reservatrol — that can silence the signals cells normally send as their telomeres erode, precipitating apoptosis. Many of these regulatory-molecule genes are found — or have analogues — in the human genome where they might, or might not, have the same functions that they have in worms or mice; the research is far from complete. So far, only one or two genes have been repeatedly associated with longevity or frailty in humans: the p gene whose products mediate the apoptosis of telomerase-depleted cells and also have a broader role in tumour control and fitness⁶¹ and APOE, a gene whose different alleles code for different degrees of control of cholesterols circulating in the blood and are thus associated with small differences in the risk of arteriosclerosis and Alzheimer’s disease.⁶² As ever, when the data is incomplete or continues to puzzle, conceptual frameworks — sometimes a theory, sometimes looser than a theory — shape the direction of research by influencing what researchers expect to find. Although a “genetic blueprint” for longevity, if it exists, will be found by cell biologists, the design must have an evolutionary overlay that conforms to the observation that evolution evolves. So it’s reasonable to ask if evolutionary theory could shed some light on the design that cell biologists are aempting to find. As we have already seen, the challenge for evolutionary theorists of longevity is to explain how a deterministic system (evolution) could favour the prosperity of species on the basis of a characteristic (longevity) that it does not target. e bale between different approaches to resolving this dilemma has raged since Weismann’s day, resolving itself gradually to two ideas that aempt to explain both prolonged post-reproductive ageing and the late-life slowdown in mortality (see below on page ) that has been known for a century or more but has become more evident in the past four or five decades.⁶³ One theory (‘Mutation accumulation’) that aracted the support of eminent biologists in the mid ᵗʰ century suggests that longevity is a sort of evolutionary accident. It proposes that genetic mutations that have late-life impacts are simply not “selected-out” by the fitness-test of reproductive success. Because evolution has no interest in genetic factors that come into play only late in life aer the typical age of reproduction, these mutations might be either harmful ⁶¹See ☞ Geraldine Aubert and Peter M Lansdorp. “Telomeres and Aging”. In: Physiological Reviews (), pp. – ⁶²One of the four forms of APOE seems to lead to a small reduction in annual mortality (-%) over the variant most commonly found. A third variant of APOE is associated with a % increase in annual mortality. Details in ☞Christensen, Johnson, and Vaupel, op. cit. ⁶³is discussion summarizes ☞ Predrag Ljubuncic and Abraham Z Reznick. “e evolutionary theories of aging revisited–a mini-review.” In: Gerontology . (Jan. ), pp. –. : -




. T   

or beneficial in older adults; for example, a genetic disposition to a cancer of old-age such as prostate cancer or a disposition to dementia, or a mutation that protects from either of these conditions. Since they were already present in the germ cells of the individuals manifesting these diseases later in life, the mutant genes are passed on to descendants, accumulating in the gene pool and ignored by the evolutionary “sieve”. e genetic endowment of an individual will reflect the pool of these heritable mutations that have accumulated in his or her ancestors, leading to potentially wide variations in the ageing and lifespan of individuals. A refinement of the theory suggests that if harmful genetic dispositions come into play at two different times in post-reproductive years then old-age will be longer for some than for others. “Genetic disposition” is not a strict recipe; some individuals will escape the first round disposing them to mortality (e.g. cancer) and enjoy a longer healthy life, only to be faced with the consequences of the second round later in life aer many of their birth-year cohort have succumbed. A more recent design for the evolutionary “overlay” on the genetic blueprint — antagonistic pleiotropy — explains ageing as no accident but, rather, the price we pay for genes that evolution has selected to ensure robust health through the reproductive years. “Pleiotropy” refers to a single gene that codes for more than one inherited trait; a common mechanism in genetics. For example, approximately % of cats with white fur and blue eyes are deaf because, as it turns out, the same pleiotropic gene affects both eye colour and hearing (white cats with one blue eye one yellow eye have a tendency to deafness in the ear on the blue-eyed side).⁶⁴ It was first suggested in the s that a gene that encoded for a trait beneficial to survival through reproduction might also code for a trait that would be deleterious later in life. is is subtly different from the idea of mutation accumulation; in that case, the genes that code for deleterious (or beneficial) effects later in life are accidentally conserved by evolution because it ignores post-reproductive adaptations. In the case of the “pay later” gene the same gene that has the late-life effects is deliberately conserved by evolution for its advantageous effects during the individual’s earlier life. e p gene mentioned earlier has a pleiotropic action. It codes for the enzymes that stop cells with damaged DNA from reproducing and brings about the cell’s death, which is good for early life and reproduction because the stress of wear-and-tear on cells — let alone the clipping of telomeres — could cause cancer in individuals without this protection before they produced offspring. But the p gene also codes for enzymes that suppress the division of stem cells that allow the body to continue renewing and replacing essential tissues later in life. As we saw, this can help defend against an “aack of the clones”; but it is now also implicated in the gradual ageing of the organism.⁶⁵ In animal models and in worms there are several well-established longevity mutations — a roundworm’s life can be prolonged eightfold by a suitable combination of mutation and starvation — most of which are pleiotropic. ey code for increased resistance to the physical consequences of stress, or improved innate immunity or the ability to metabolise toxins and are associated with increased robustness in later life.⁶⁶ ⁶⁴is example is from ☞ Ingrid Lobo. Pleiotropy : one gene can affect multiple traits.  ⁶⁵Kenyon, op. cit. cites IGF--receptor inhibition as another pleiotropic mutation that is common in centenarians (page. ) ⁶⁶An excellent summary of research on pleiotropic longevity genes is ☞ Kaare Christensen, omas E Johnson, and James W Vaupel. “e quest for genetic determinants of human longevity: challenges and insights.” In: Nature reviews. Genetics . (June ), pp. –. : -




.. ree theories of longevity

Pleiotropy looks like a good candidate for a framework, consistent with evolution, that can explain the selection of genes associated with longevity. But it does not offer much insight into the actions and interactions of the genes concerned. Furthermore, and antagonism in pleiotropic genes — conserving genes that are beneficial early in life and that accelerate ageing later in life — suggests that there may trade-offs involved in manipulating genes that code for longevity⁶⁷ Many long-lived mutants are slow-growing, with reduced fertility; they are not likely to have a high evolutionary “fitness” in a challenging environment. Even in the wild among small mammals and reptiles such as tortoises, there seem to be direct trade-offs between higher fertility and rapid development on one hand, and increased stress resistance and longer lifespan on the other. Animal studies have shown, too, that there is an important element of “luck” in lifespan. Genetically identical individuals that are grown in a common environment do not have the same lifespan, which may help to explain why observations of large populations of animals and humans suggest the heritability of lifespan is, at best, moderate: about % according to studies of elderly Danish twins.⁶⁸ It is unlikely that any single gene will be found that specifically codes for longevity, or even a gene that unambiguously codes for longevity. We have already seen that evolution has no interest in selecting for such a gene and that a pleiotropic gene with longevity benefits is likely to be conserved for some other crucial metabolic role. If there were a “longevity gene” we would expect to see at least some families with very long lifespans in every generation. But this is not what we observe. Certainly, there is evidence that long lifespan occurs in clusters of siblings. For example, in a survey of  brothers and sisters of centenarians born in  in the United States, researchers found that having a centenarian sibling meant you would be likely not only to live - more years than others in your birth-year but also to enjoy beer health throughout life. e mortality rates of centenarian siblings proved to be roughly half the rate of people of similar age throughout their lives.⁶⁹ It takes a long time to complete the longitudinal experiments using reliable, modern demographic data sets, that would be needed to establish the exact heritability of longevity. Humans are “characteristically” long-lived and there are many factors that confound the inference of heritability, so large datasets over several generations are required. e available data, however, hints that strong heritability may depend on having parents with an exceptional lifespan, well beyond the “characteristic” span of seventy years or so. For example, a study that used the careful records of five centuries of German nobility contained in the Gotha Almanac showed that daughters are more than twice as likely to inherit the lifespan of a long-lived father ( years or more) than the lifespan of a father who died young.⁷⁰ Also, a recent very large co⁶⁷Not all genetic manipulation need involve trade-offs. Kenyon, op. cit. is optimistic on this score, pointing out that some mutations that slow ageing in animal models also slow age-related disease (p. ) ⁶⁸See the references in ☞Christensen, Johnson, and Vaupel, op. cit. ⁶⁹Reported in ☞ omas T Perls et al. “Life-long sustained mortality advantage of siblings of centenarians.” In: Proceedings of the National Academy of Sciences of the United States of America . (June ), pp. –. : - ⁷⁰e actual results of the regression show an additional . years of life for the daughter with every additional year of the father’s life. ese results reflect the “upper bounds” of potential heritability because the results do not differentiate between genetic and environmental influences ☞ Leonid A Gavrilov




. T   

hort studies of twins born between  and  in the Nordic countries find that the genetic influences on lifespan appear only aer  years or so and that a propensity to robustness at advanced age is approximately % heritable.⁷¹ 1.0

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Figure : Survival curve for male Adventist and all male Californians, - (Redrawn from: Fraser () Op. Cit.)

e uncertain heritability of longevity may be explained by the variability of genetic expression and the relationship between different genetic endowments. Plausibly, each individual could have a variety of different genes that code for resistance to disease or for robustness at the cellular level to the wear and tear of stress and inflammation. Some individuals might have an exceptional “set” of such endowments. For instance, a paper published in mid- reporting on a “genome-wide” study of almost  U.S. centenarians by researchers associated with the New England Centenarian Study. ey claimed to find as many as  small genetic mutations that appeared to be associated with longer life in some way. How well people aged depended on their individual genetic signature; in other words, how many of the  lifelengthening variants they had, and which ones. Based on these signatures, the researchers claim, most centenarians could be grouped into one of  clusters, some correlating with reduced prevalence of age-related diseases (e.g., stroke, diabetes, dementia), others with delayed age of onset of disease.⁷² and Natalia S Gavrilova. “Biodemographic Study of Familial Determinants of Human Longevity”. In: Population (English Edition) . (), pp. – ⁷¹Reported in ☞Christensen () op.cit. ⁷²e study has been criticised for torturing the statistics to argue for the significance of so many




.. ree theories of longevity

Further obscuring the genetic “blueprint” for longevity it the apparent importance of variable environmental factors. e metabolic environment, in particular, seems to maer a great deal. ere is persuasive evidence that Seventh Day Adventists, for example, have a lifestyle that disposes to longer lives. A study that followed , Adventist men and women over a twelve-year period showed that their lifestyle choices of vegetarianism, exercise, eating nuts, maintaining a mean Body Mass Index of  ( for women) and not smoking resulted in expected ages at death that were  -  years greater than other Californians in .⁷³ But such a large group of Adventists are unlikely, on average, to be genetically different to any other large sample of Californians; so it’s unlikely that these individuals’ genes alone are responsible for their longer lives. H   Biology — whether cellular or evolutionary — is not the only way to map longevity. e social map of longevity has also seen some striking changes (described in Chapter ) that may affect the priority, location and resources governments allocate to future aempts to understand and manipulate our biological destinies. Just as the interpretation of the genetic blueprint for longevity may be assisted by considering its evolutionary overlay, so an understanding of the statistical data that contours the social map may benefit from an overlay of systems theory. Reliability eory aempts to predict the robustness of complex systems — ranging from jet engines to accounting soware to the logistics of warfare — given their architecture.⁷⁴ One of its tenets is that systems deteriorate — fail more oen — with age if these systems rely on a store of redundant numbers of exhaustible but irreplaceable elements. Systems that have only one copy of an irreplaceable element will fail as soon as that element fails: they do not experience “ageing”. But if a system comprises many copies of an essential part, as we have trillions of cells that are either duplicates or capable of duplicating, then failure is progressive. Ageing, in other words, is a direct consequence of systems redundancy. It doesn’t maer to the outcome whether or not the redundant irreplaceable elements also age, as individual cells do. Even if the irreplaceable elements have a constant, non-ageing, failure rate like the super-reliable parts of a jet-engine (which are replaced on a schedule, before they fail) any initial redundancy in the supply of irreplaceable parts will eventually disappear. When that happens, any ageing in the system is over. e next failure of an irreplaceable element means the failure of the entire system. Now, there is an astronomical but finite ( trillion, estimated) number of cells in the human body, most of them redundant and everyone of them more or less unreliable. Most are replaceable, but each somatic cell can reproduce only a limited number of times and most never get to that limit. While it can still reproduce, redundancy is maintained; single-nucleotide mutations. is is not an uncommon criticism of genome-wide analyses: it casts doubt on the reliability of the estimates. ☞ Paola Sebastiani et al. “Genetic Signatures of Exceptional Longevity in Humans.” In: Science (New York, N.Y.) July (July ). : - ⁷³e mean benefit from each of these lifestyle choices was cumulative. e incremental benefit in years of each discipline, assuming the preceding disciplines were adopted, were : vegeterianism (.), exercise (.), eating nuts (.), maintaining BMI (.), never-having-smoked (.). ☞ G.E. Fraser and D.J. Shavlik. “Ten Years of Life”. In: Arives of internal medicine  (), pp. – ⁷⁴ ☞ L A Gavrilov and N S Gavrilova. “e reliability theory of aging and longevity.” In: Journal of theoretical biology . (Dec. ), pp. –. : -




. T   

the system ages as, for example, ageing stem-cells spawn new generations, but at a slower rate. Over time, however, owing to wear and tear, heritable DNA damage and possibly to the culling functions of the cancer-control mechanisms, cellular redundancy disappears. Ageing ends at that point and the whole system becomes vulnerable to the next failure of an essential element. Any theory is only as good as its predictive record. Reliability theory has a singular strength in its application to ageing; that it accounts for two phenomena that have been recognized since actuarial life-tables were first constructed in the early ᵗʰ Century. e first is that, over large ranges of the life-tables, the number of deaths increases with age but at an exponential (or “geometric”) rate, not at a linear (“additive”) rate. In other words, as the population ages, the rate of death steadily accelerates. e second observation drawn from life-tables is that at some advanced age, about  years or more, this exponential growth in the death rate levels off. e rate of death in the oldest old slows down to a sort of plateau. e rate does not fall — people still die within a few years — but it stops geing faster.




 

THE RETREAT OF DEATH

We know not our own hour, but death strikes all in our birth-year with geometric regularity. As we age, the risk that we will die in the next year grows, very slowly at first but then at a faster and faster rate each year. Benjamin Gompertz, a self-taught London mathematician who was one of the first employees of the Alliance (later Sun Alliance, now RSA) Insurance Company discovered the “law” that governs the population rate of mortality in . ⁷⁵ When ploed as the relationship between the proportion of survivors from those born in any year against the age of the survivors, his exponential expression describes a characteristic curve (see Figure  on page  for an example). A reliable, if slightly chilling prediction derived from Gomberg is that, over the course of your adult life, the chance you will die in the next year doubles roughly every eight years of your life. As you trace the curve for your birth-year your annual risk of death first reaches just one percent in middle age — about the age of  for those Australians born in  — but grows very rapidly thereaer so that by the time you reach the your late ’s those born in your birth-year are dropping off the perch at a prey brisk rate. One in ten Australians who celebrate their ᵗʰbirthday in  won’t celebrate their ˢᵗ.⁷⁶ en, just when it seems least likely, death retreats a lile. When the remaining members of the birth cohort reach their late seventies the acceleration of death rate starts to slow and the proportion of deaths among surviving cohort members starts to fall. By the age of eighty the acceleration stops and the curve begins to flaen out. By the time the survivors have reached their late nineties, the survival curve is no longer exponential but almost linear:⁷⁷ life expectancy continues to fall every year but at a much slower rate. Figure  on page  illustrates this dramatic change using projections for Australians born . e annual fall in life-expectancy of people in their late-’s will be about one third of the annual fall for people in their early-’s.⁷⁸ Why does the rate of death start to slow at advanced ages? e theory is still up for grabs. e most intuitive proposal that happens to be consistent with Reliability eory, is that each of us has an inherited or acquired propensity to frailty at advanced years — rather than a specific lifespan — that potentially sets us apart from others in our birth year. ⁷⁹ is ⁷⁵A useful description is contained in ☞ Michael L Miller and Felicitie C Bell. Life Tables for the United States Social Security Area -.  ⁷⁶Data from ☞. Human Mortality Database ⁷⁷ ☞ omas T Perls. “e oldest old”. In: Scientific American January (Nov. ), pp. –. : - ⁷⁸e flaening of the “tail” in the mortality curve is becoming more marked over time as the modal age of death advances. e decline in the old-age mortality risk is more marked for later birth cohorts. For Australians born in , for example, the annual mortality risk in their late s will be about % of their risk in their late eighties versus % for the  cohort. ☞ Human Mortality Database ⁷⁹For an overview of the research on the difference between lifespan heritability and the heritability




. T   

Percent of birth cohort surviving

100

75

50

1900 1950 2000 2050 2100

25

0

1

21

41

61

81

101

Age

Figure : ‘Gompertz-ian’ survival curves for U.S. Birth-date cohorts  -  (source: United States Social Security Administration)

heterogeneity means that some individuals will survive longer than others born in the same year and a small number mu longer than their cohort. e laer, will survive to a point where the rate of physical ageing in the population that has been accelerating since middle–age while less-robust individuals die, plateaus. Now the most robust individuals remain; they continue to age, in accordance with the predictions of Reliability eory, but at a slower rate. Ageing almost stops. e population is now dominated by these slowly-ageing individuals and the population survival curve starts to flaen, but every survivor at this age dies within a few years. Death’s last-minute hesitation holds an absorbing interest for those likely to experience it (before the close of this essay we will return to ask whether they are, or are not, fortunate in that prospect). But the twitch in the tail of Gompertz’ survival curve is not, in fact, the sign of death’s retreat that maers most for most people. e more significant retreat is revealed by a movement of the curve, not a movement along the curve. Consider the changes in the Gompertzian survival curves for different age cohorts in the USA ((Figure ) from ,  and . e change in the location and shape of these curves has an ugly name: the “rectangularisation” of survival. Which rectangle? e area under the curve. is is the domain of life, bounded by the perimeter of survival. rough the century the perimeter — in the USA but also in all other rich countries and in many middle- and low-income countries — has moved of frailty see ☞ Kaare Christensen, omas E Johnson, and James W Vaupel. “e quest for genetic determinants of human longevity: challenges and insights.” In: Nature reviews. Genetics . (June ), pp. –. : -




.. e epidemiologic transition

10 9 8 Expected years of life remaining

Fall in LE 80 to 85 = 2.73 years 7 6 5 4

Fall in LE 95 to100 = 0.95 years

3 2 1 0

Source: International Mortality Database 80

82

84

86

88

90

92

94

96

98

100 102 104 106 108 110+

Age

Figure : Expected remaining years of life for oldest-old (Australia,  birth corhort)

right and changed shape in two dimensions. First it has all but lost it’s initial slope, streaming out almost horizontally from nearly the top le corner thanks to the near elimination of infant mortality. Second, the slope maintains a largely horizontal direction for many decades with only a slight decline toward later and later middle age when it begins to drop ever more steeply as the precipice grows later and higher. e effect is to “compress” most mortality into a relatively short period when, inevitably, the curve touches the boom, flaening out as it does so. But casual inspection shows that, over the course of the ᵗʰ century, the area inside the curve has almost doubled and is continuing to expand. “Rectangularization” means, more life.⁸⁰

. e epidemiologic transition Demography is dominated by data and grand narratives. Possibly the best documented of these is the story of “epidemiologic transition” which accounts for modernization as a journey from ⁸⁰e concept of rectangularization was first employed by demographic modellers in the s who were interested in describing the rising modal age of death in rich countries and the compression of mortality around the modal age ☞ Vladimir Canudas-Romo. “e modal age at death and the shiing mortality hypothesis”. In: Demographic Resear  (July ), pp. –. : -




. T   

a society that is young, short-lived and experiences high levels of births and deaths to an older, longer-lived society that experiences low levels of both. is profound change affects the size and age of the population, generational and family structures, standards of living, personal horizions and even ethical values.⁸¹ For the world as a whole the transition is already more than half complete. According to one calculation, if we date the start of transition to  and assume it will be complete by , the population of the world will have grown almost ten times over the three centuries. ere will be  times as many elderly at the end of the transition as there were at the start, but only  times as many children. e years of life-expectancy at birth, which have already more than doubled, will have tripled, while births per woman will have dropped from six to two.⁸² Yet three centuries is the blink of an eye in the perspective of our history. e original doubling of life expectancy at birth from about  years for primitive humans to  years on average in  took twenty thousand centuries. It took only fiy years to grow by a further third to  years in . ⁸³ Table : Age-specific contributions to the increase in record life expectancy of women from  to , percent of total

0-14 years 15-49 years 50-64 years 65-79 years >80 years

1850-1900

1900–1925

1925–1950

1950–1975

1975-1990

1990-2007

62.13 29.09 5.34 3.17 0.27

54.75 31.55 9.32 4.44 -0.06

30.99 37.64 18.67 12.72 -0.03

29.72 17.70 16.27 28.24 8.07

11.20 6.47 24.29 40.57 17.47

5.93 4.67 10.67 37.22 41.51

Reproduced from Christensen, Doblhammer et al, 2009

e engine of epidemiologic transition is growth in life-expectancy; the sum of improvements in survival at the two ends of a human lifespan that are, broadly, consecutive beginning with the fall in infant mortality followed by the decline in mortality at older ages. You can see this consecutive change occurring in Table  on page . It shows the percent contribution to the longest female lifespans of improvements in survival at different ages. Over the -year period, the largest contributions to life expectancy move from the youngest age group to the oldest. In the last half of the ᵗʰ century (first column), two-thirds of the improvements in the life-expectancy (at birth) of the record-holding country (Norway) occurred in the youngest age group (- years) due to improvements in infant survival. In the first four decades of the ᵗʰ century, the record was held by New Zealand; there, infant survival still accounted for ⁸¹e term first appears in a  paper that has been reproduced as ☞ Abdel R Omran. “e epidemilogic transition: a theory of the epidemiology of population change”. In: World Health . (), pp. – A good exploration of the the transition “theory” and a discussion of its predictive weakness that should, probably, disqualify it as a theory is found in ☞ D Kirk. “Demographic transition theory.” In: Population studies . (Nov. ), pp. –. : - ⁸²Estimates from ☞ Ronald Lee. “e Demographic Transition: ree Centuries of Fundamental Change”. In: Journal of Economic Perspectives . (Dec. ), pp. –. : - ⁸³ ☞ World Bank. Data | e World Bank




.. e epidemiologic transition

between one-third and half the improvements but adult survival also improved. By the end of the century, almost half the improvements (in Japan) were due to the longer survival of the aged. T    So much for the narrative of transition. e plot is less straightforward and derives mainly from the experience of high-income countries that have progressed furthest through the transition. From the end of the ᵗʰ century until about  in high-income countries infant deaths fell from more than one tenth of newborns to to fewer than  deaths per  births; maternal deaths and deaths from childhood illnesses were cut to very low levels. Some of these improvements in survival were undoubtedly due to medical advances. Vaccination agains smallpox (from the s) and diptheria (), the triumph of the ‘germ’ theory of disease and the adoption of aseptic surgery and midwifery were crucial advances. But, as British medical researcher Phillip D’Arcy Hart observed, as late as the s medical students were taught that only one disease had ever been cured medically; scurvy, in the s.⁸⁴ e contribution of medicine may have been less significant than ᵗʰ century public health innovations such as the use of public landfills, sewerage systems and water treatment plants that helped to contain and then to eliminate recurrent outbreaks of communicable diseases such as water-borne cholera. Higher female educational achievement and, specifically, public programs of maternal education and support also contributed strongly to improved infant and child survival, as we saw in the first Chapter (page ). In the rich countries that have completed transition, the second leg of death’s retreat began in the ᵗʰ century with the fall in adult and, especially, middle-age mortality. Here, medical treatments made a more important contribution to increased life expectancy. e effectiveness of medical treatments improved decisively in the first decades of the century with the discovery of the sulfa drugs that reduced mortality and morbidity due to infectious diseases including plague, influenzas, pneumonia, tetanus, tuberculosis and syphilis. en, aer , ‘broad spectrum’ antibiotics appeared that that could cure these diseases. But the contribution of medicine is still a subject of controversy. It is very difficult to disentangle the different contributions of medicine and other health care using aggregate data because it’s not at all clear how to estimate a “counterfactual” — the case where everything else was the same as the real world, but the medical treatment was not available – that can be used for comparison. Even if this were possible, crucial data on the actual impact of medicines that treat life-threatening conditions is not available. It has been estimated, for example, that common asthma drugs work in only  percent of patients and that migraine drugs help only about half the people who take them. Drugs for Alzheimer’s disease are estimated to offer benefit to about  percent of patients and cancer drugs may, at best, work  percent of the time.⁸⁵ In the absence of such data, the dramatic fall in male cardiovascular deaths in the last third of the century century has been credited to medical advances on the basis of the potential for reductions in mortality reported in clinical trials. ⁸⁴ ☞ E M Tansey, Philip Montagu, and D Arcy Hart. Records.  ⁸⁵ ☞ By omas Goetz. e Wonder Drug Myth. 




. T   

In the absence of data, pharmaceutical lobbies oen rely on economic modelling of the impact of medical treatments to boost the case for government support. One prominent pharmaceutical economist claims that % of the increase in global life-expectancy over the period - can be aributed to medical advances.⁸⁶ Close examination of these claims for the value of drugs and devices in extending life-expectancy, however, reveals fragile assumptions and technical flaws that tend to inflate the apparent impacts of medical innovation.⁸⁷ en consider the contribution of streptomycin, discovered in the United States in the late s and shown in a famous U.K. clinical trial be a “breakthrough” cure for tuberculosis. e drug has subsequently acquired the reputation of having stopped the disease in its tracks. Although one of the top five killers in , tuberculosis had been all but eradicated ( per million deaths) in the USA by . Yet the data shows that between  and  in the USA, before the discovery of the antibiotic, tuberculosis deaths were already falling rapidly — five-fold in Massachuses, for example, over that time — thanks to public health and sanitation measures. Even today, in developing countries, the testing for tuberculosis and isolation or observation of the positive cases undoubtedly saves more lives than the drugs.⁸⁸ Outside the rich countries of the West, escape from the scourge of infectious disease is far from complete. In  these diseases were still the biggest health burdens in poor countries (% of ‘years of life lost’) and for half of the burden in middle-income countries. In  there were  million cases of malaria reported around the world; all but one million of them in poor countries.⁸⁹ Globally, progress against life-threatening diseases has been mixed according to WHO statistics: the numbers of deaths from heart and artery disease have fallen rapidly since the s in many high and middle income countries. e global average adult mortality rate — that is, the numbers of deaths per  people aged between  and  — fell from  to  in the years -. at’s a % improvement. In high income countries the decline in adult mortality over that time was greater than %. Once again, the story varied markedly between regions. In Africa, adult mortality increased over the full period by almost %. In the European ⁸⁶Lichtenberg asserts that his model constructed from pharmaceutical industry data on drug interventions and WHO global “Burden of Disease” data shows that the average increase in global life expectancy due to the launch of new drugs alone is about one week each year at a cost of about $US, which is well below any valuation of a statistical life-year ☞ Frank R Lichtenberg. “e impact of new drug launches on longevity: evidence from longitudinal, disease-level data from  countries, -.” In: International journal of health care finance and economics . (Mar. ), pp. –. : - ⁸⁷Economists use economic models to demonstrate that medical innovation contributes to longer lifeexpectancy. But the models have been criticised for asserting implausibly high productivity on the part of pharmaceutical companies, for having large forecast errors, for being vulnerable to small changes in their assumptions and, for failing adequately to disentangle pharmaceutical innovation from other contributing factors including lifestyle improvements that may contribute to health outcomes ☞ Paul Grootendorst, Emmanuelle Piérard, and Minsup Shim. “Life-expectancy gains from pharmaceutical drugs: a critical appraisal of the literature.” In: Expert review of pharmacoeconomics & outcomes resear . (Aug. ), pp. –. : - ⁸⁸See ☞ George Rust et al. “Triangulating on success: innovation, public health, medical care, and cause-specific US mortality rates over a half century (-).” In: American journal of public health  Suppl (Apr. ), S–. : - ⁸⁹ ☞ World Health Organization. Burden of disease : DALYs. 




.. e epidemiologic transition

region, the re-integration of the Eastern European countries aer  was apparently responsible for a disappointing % improvement in adult mortality. Adult mortality fell by % in the Americas and it fell by an astonishing % in the Western Pacific (including Australia) in these  years. ⁹⁰ Significantly, the decline in adult mortality accelerated, in all regions but Africa, between the s and the ’s. F   Falling mortality — at least the fall in infant and childhood — is closely linked to a second fundamental demographic change that spread throughout the ᵗʰ century and continues to shape the prospects for individuals and for whole populations around the world: a widespread fall in total fertility. at is, a fall in the average number of children for each woman. When mothers, babies and children survive the risks of birth, infection and childhood disease more reliably, the number of births needed to produce a given family size is smaller. e question, thus, becomes one about desired family sizes. Why do parents want fewer children? e reasons for the fall in fertility rates are complex and projections tend to be fragile.⁹¹ As countries grow richer, accumulate more capital, achieve higher levels of education and require higher educational aainments for employment, the opportunity cost for women of pausing their career, or education for a career to have babies, rises. In wealthier economies, the most desirable lifestyle goods demand more time, independence and mobility on the part of both sexes. en, too, the cost of raising a child goes up owing to, among other things, the length of time needed to meet educational standards, while the value of large families as a social security measure declines. For all of these reasons, and for others, fertility falls as an economy develops and grows richer. e record prosperity of the ᵗʰ century defused the much-hyped “population bomb” of the s; like other fabled weapons of mass-destruction, it was a myth fed on fear and a partial account of the facts.⁹² Using the United Nations’ Human Development Index (HDI) cited in Chapter one, demographers have described a strong, linear correlation between falling fertility rates and higher HDI levels over a thirty year period to . At the top of the Index, an HDI of . corresponds, roughly, to  years of life expectancy at birth, a GDP per capita of $, U.S. in year- purchasing power, and a high level of aainment in literacy and primary, secondary and tertiary enrolments. In , every % increment in the HDI of a country that had ⁹⁰ ☞ World Health Organization. “Part II. Global health indicator tables”. In: World Health Statistics. Vol. . -. , pp. – ⁹¹e discussion here is based on Lee () op. cit. and on data showing a small but widespread recovery in fertility in rich economies in ☞ Mikko Myrskylä, Hans-Peter Kohler, and Francesco C Billari. “Advances in development reverse fertility declines.” In: Nature . (Aug. ), pp. –. : . Curiously, although he discusses the epidemiological roots of demographic transition, Lee does not cite Omran op. cit, who is now generally credited with inventing the notion of an epidemic transition ⁹²Paul ehrlich even today insists his  best-seller warning that population would shortly overwhelm the capacity of the earth to sustain the necessary food production was far too optimistic in some respects because he neglected to take account of the adverse climate-change potential of growth. His collaborator in the luddite “IPAT” theory of disastrous growth, John Holdren, has been promoted to wave a gloomwand conjuring climate catastrophe as President Obama’s science advisor. Clearly, pessimism pays ☞ Paul R Ehrlich. e Population Bomb. New York: Bantam Books, 




. T   

not yet reached the . level was associated with cut in its fertility rate by about %. Today, more than half of the global population lives in regions with a fertility rate below the level (. children per woman) needed for this generation to replace itself in the next. ⁹³ On the world’s stage, population is the sum of entrances and exits. A fall in fertility, therefore, even matched with rapidly falling levels of mortality (rising life-expectancy), slows the rate at which population grows. e rate fell from  percent every five years in the early s to less than  percent every five years in the early s. According to United Nations projections, global population numbers are likely to ‘top out’ at about  billion during this century.⁹⁴ Extrapolating the trend of falling fertility remains risky, however. ere is some evidence that in the richest countries fertility rates are rebounding slightly, perhaps because of “baby bonuses” (unlikely) or because of a change in tastes or because technology has made later pregnancies safer or perhaps because a rise in incomes has reduced the opportunity cost of expensive child-rearing. On average, in , when countries at the top of the HDI ranks improved their economic performance by just %, fertility jumped by % (on a very low base). Not all rich countries experienced this rebound, however. Six of the twenty-six countries in this group (Austria, Australia, Canada, Japan, Korea, Swizerland) saw a continuing fall in fertility. Whatever the reason for the rebound, it has not restored fertility to replacement levels in these countries.⁹⁵ F    As you might guess, the interaction of economic growth and total fertility works in both directions; just as rising prosperity has brought secular changes in fertility, so falling fertility has important impacts on growth. e interaction is set to change the shape of the world economy yet again in the second half of this century. What’s at stake is the welfare, and perhaps the social stability, of the most populous nations on earth and, because of their size and importance to the world economy, the welfare of everyone else, too. e changes have been characterised as a transition from a period of demographic “bonus” to a demographic “onus.” A “demographic bonus” arises when infant mortality falls faster than the total fertility rate; there is an increase in the population at young ages. is was a typical situation in many emerging economies in the ᵗʰ century. Within a few years many more people enter the workforce, ensuring a large supply of labor. At this point, typically, the total fertility rate starts to fall due in part to the rising incomes of the new labor force. e economy enters a phase when the dependency ratio (taxpayers to non-taxpayers) falls; the size of the productive-age population increases in relation to the size of the population as a whole. Since the tax burden on the working-age population (involuntary saving) is small, consumption levels are likely to be high. e economy booms. In time, however, those born during the period of rapidly falling infant mortality grow old. By now, the total fertility rate has fallen. As the generation born during the fall in total fertility reaches working age they face a rising dependency ratio because the decline in young dependents (lower fertility equals fewer children) is more than offset by the increase old dependents. ⁹³Data from ☞Myrskyla () op. cit. ⁹⁴☞ United Nations. WORLD POPULATION TO . New York,  ⁹⁵Data from ☞Myrskyla () op. cit.




.. e epidemiologic transition

Japan

40

1950

1990

Hong Kong

1965

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Singapore

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Thailand

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China

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South Korea

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Malaysia

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India

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Figure : Estimated phase of “demographic bonus” in selected Asian economies; the period when the dependency ratio is falling. Redrawn from Komine ()

is is quite a different situation than their parents faced during their working career. e working-age population as a share of the total population falls; the “demographic onus” now places larger demands on current incomes and savings, formerly available for capital investment are drawn down to support the ageing population. Economic growth slows; so it is feared. e transition from “bonus” to “onus” is clearly visible in Japan where the cycle is already complete. For Japan, the years from  through  were a typical “bonus” period. Economic growth, supported by growth in the labor force was rapid and savings rates among the large working-age proportion of the population were high. Because the numbers of aged was small relative to the numbers of workers, Japan was able to afford a generous, unfunded “pay-as-yougo” pension system in which benefits to the aged, for example, were funded directly from the pension contributions of workers. Aer , however, Japan entered a typical demographic onus phase (see Figure  on page ; the end of the demographic “bonus” period for each economy corresponds to the start of the “onus”. Economic growth was lackluster, the labor force began to shrink, and savings rates declined. e burden of pensions and health care on the current generation of workers has risen sharply and several reforms have cut benefits causing many to be anxious about their future.⁹⁶ A rapid decline in mortality in later years speeds up the ageing of a population whose total fertility rate is falling, making the transition from “bonus” to “onus” more acute and difficult to manage. We can gauge the ageing of a population as the proportion of the post-working-age ⁹⁶e description of the transition from bonus to onus and the data on East Asian economies is taken from ☞ Takao Komine and Shigesaburo Kabe. “Long-term Forecast of the Demographic Transition in Japan and Asia”. In: Asian Economic Policy Review . (June ), pp. –. : 




. T   

population that survives from “old” (+) to “aged” (+). Europe and the USA in the ᵗʰ and ᵗʰ centuries were fortunate to experience a gradual rise in the “onus”. e population aged at a moderate pace taking - years for the proportion of “aged” in the population to double. e burden of the “onus” period crept up as survival rates improved and was absorbed over two or more generations. In Asian economies, however, where the “onus” period commenced only in  for the Japan — the first country to make the transition — survival rates for the old and even the aged were already high. Consequently the speed of transition is likely to be much greater, taking possibly - years and placing a much greater burden on the workforce and national savings over a period of one generation or less. On one estimate, by , more than half of all the “aged” (+) in the world will live in Asia.⁹⁷ is will impose a big burden on Asian economies but it will also strongly affect the rest of the world. In Beijing, especially, but also in Hanoi the biggest single preoccupation of economic managers is already whether the speed of the transition through the current “bonus” phase will find the economy in good shape to weather the “onus” phase ahead. China’s total fertility rate has been below the replacement rate of . since -. e labor force has continued to rise but the rate of increase is now near-zero, according to Japanese researchers. Sometime in the next five years, China’s “bonus” period will be over. If the population ages quickly, as expected, China will be in danger of becoming old before it becomes rich enough to pay the bills for health and social security during the “onus” period. e coming demographic transition in China (followed soon by Singapore, ailand, Korea and Vietnam) will swing the whole world economy onto another heading. e remarkably high current savings rate in China is very likely to fall as the smaller working population stretches its resources to meet the burdens of a rapidly ageing population. is will shrink a globally important source of capital that has fed Western investment growth over the past decade and a half. Chinese domestic consumption will take a larger share of output, shrinking export surpluses that are the counterpart of outward investment flows. e rest of the world will see less competitive pressure on manufactured product prices as Chinese exports shrink, cuing consumer surplus around the world. In Australia and other wealthy countries the economic slowdown of the “onus” period is likely to be affordable because people will be wealthier in, say,  than they are today.⁹⁸ Governments have begun aempts to moderate the impact of the slowdown with “baby bonuses”� and measures to encourage higher workforce participation rates such as paid maternity leave and postponed dates for pension-eligibility. Some governments have also created wealth funds — such as the Future Fund in Australia — to capture savings from the recent economic boom against the slowdown and higher costs of the coming “onus” era. is story of bonus turning into onus seems to contain a paradox at its heart. Why would economies, as they grow richer, increase their spending on health care, improved public infrastructure, beer education and nutrition only to promote longer lives that add to the depend⁹⁷Estimates from ☞Komine () op. cit. ⁹⁸is was the conclusion of the  Productivity Commission enquiry. Australia’s circumstances are different from other economies, in part because past productivity has been high, some Australian governments have made provisions against future social security liabilities (the Future Fund) and compulsory superannuation is likely to reduce the dependency burden☞ Australian Productivity Commission. Economic Implications of an Ageing Australia. 




.. Longevity beyond the Transition

ency burden when fertility falls, as it seems inevitably to do in wealthier countries? Wouldn’t it be more rational to trim health care budgets — since health is the budget item most directly responsible for survival and apparently least related to capital growth — to acheive survival through the productive years without promoting “wasteful” longevity? Shouldn’t budget planners be optimising inputs here to ensure that people live just long enough to pay their taxes and then shuffle-off before they become a burden on the next generations? If the “bonus to onus” story is taken at face value, then: yes. But just as we saw the gigantic value of longevity lies mostly hidden below the surface of growth accounting, so health spending has been somewhat arbitrarily treated in aempts to understand trends in economic growth. Typically, economists’ models of the pathway to economic growth treat the level of health care spending as a “given”, not determined by demand in the model. Recently, researchers in Hong Kong aempted to account for growth with health care demand as a variable to be determined by interactions in the model between health care, life expectancy and economic growth, based on observed behaviour. In this model, health care like other investments adds to the stock of (human) capital. e probability of survival of individuals to the second period depends on the stock of health capital. is results in two opposing effects on economic growth: on the one hand spending on health diverts capital from other productive uses in the economy but it also increases life-expectancy which encourages capital formation. e intuition is that people who look forward to a long healthspan save accordingly; for example into their pension funds or building an enduring business. e research shows that economic output, per-capita income and welfare are higher in model economies where health care is treated as a variable than in models where health care and life-expectancy are ignored (or “given”) and that income and welfare increase as health care spending and life-expectancy increase.⁹⁹ In short, a more fine-grained account of the relationship between economic growth and longevity suggests we may not need to worry too much about the “onus.” e positive impact on per capita income arising from higher savings by individuals who anticipate a long healthspan could be large enough to outweigh the negative impact for growth and welfare of a higher dependency ratio.

. Longevity beyond the Transition Dual reductions in early and late mortality have lengthened life expectancy at birth, which is nothing more than the expected median (mean = “average”) age of death for any birth-year cohort. Because infant mortality dropped quickly in the ᵗʰ and ᵗʰ centuries, life expectancy also grew rapidly, as we saw in Table  on page . Improvement in later-life mortality also made a contribution to the advancing mean, but the overall rate at which life expectancy is growing has slowed as the potential gains from further “rectangularization” of the survival curve have become smaller and smaller.¹⁰⁰ e projected survival curves in Figure  (page ) for  and ⁹⁹ ☞ Michael C. M. Leung and Yong Wang. “ENDOGENOUS HEALTH CARE, LIFE EXPECTANCY AND ECONOMIC GROWTH”. In: Pacific Economic Review . (Feb. ), pp. –. : X ¹⁰⁰See Table A and the assessment of the U.N. forecasts by S. Jay Olshansky (p. ) in ☞ United Nations. WORLD POPULATION TO . New York, 




. T   

 birth cohorts, for instance, show less and less change from earlier years. e contribution to life-expectancy from improvements in infant mortality is almost exhausted in the survival curves of currently living cohorts in the leading “transition” countries. e “compression” of old-age mortality due, for example, to the beer health status of the middle-aged will contribute some further extension. But future improvements in life-expectancy will be due mostly to an increase in the modal age of death: the age at which the largest numbers of that birth-cohort die.¹⁰¹ Absent surprises such as medical breakthroughs, longevity will have a different dynamic in the future. It seems implausible that that the dramatic, “linear” growth in record-holding lifeexpectancy of the past century and a half could continue. But neither is there any warrant in the data on old-age survival to doubt that the rightward shi in the survival curve will continue. In fact, the remarkable robustness of the most aged citizens of the “transition” leaders hints at a sunny upland further off. T O O Demographers increasingly talk of four “stages” of maturity: adulthood, middle-age, old-age and the oldest-old. e boundaries are indistinct. ere is obviously an increasing degree of frailty in the passage from middle-age to old-age and beyond. But much less than you might imagine. A longitudinal study, published in , of the entire Danish  birth cohort managed to assess more than % of those still surviving in  —  individuals — four times over the subsequent seven years, until the survivors had reached  years of age. Remarkably, a nearly constant  - % of the respondents at each checkup remained independent, maintained their grip strength, cognitive scores, and freedom from symptoms of depression. Aer examining the missing records due to non-response and deaths, the researchers showed that high proportion of robust elderly was due to to the earlier death of frail and disabled members of the cohort. Consistent with the predictions of Reliability eory, the characteristics of the cohort remained nearly unchanged because the ageing of the strongest individuals had slowed.¹⁰² e oldest-old in developed countries includes a surprisingly large group of “supercentenarians” : people older than  years. e International Database on Longevity (Max Planck Institute), recorded  individuals, most no longer living, in about a dozen countries as at the end of October .¹⁰³ About half of all individuals die within one year aer becoming ¹⁰¹See ☞ Vladimir Canudas-Romo. “e modal age at death and the shiing mortality hypothesis”. In: Demographic Resear  (July ), pp. –. : - ¹⁰²Reported in ☞ Kaare Christensen et al. “Exceptional longevity does not result in excessive levels of disability.” In: Proceedings of the National Academy of Sciences of the United States of America . (Sept. ), pp. –. : - ¹⁰³As of , there were approximately , centenarians and  aested supercentenarians in Australia . e analysis of the supercentenarians in the IDL database is contained in ☞ Jua Gampe. “Human mortality beyond age ”. In: Supercentenarians. Ed. by Heiner Maier et al. Demographic Research Monographs Idl. Berlin, Heidelberg: Springer Berlin Heidelberg, . : ----. Data on Australian centenarians and supercentenarians comes from ☞ John McCormack. “Being very old in a young country: Centenarians and supercentenarians in Australia”. In: Supercentenarians. Ed. by Heiner Maier et al. Demographic Research Monographs. Berlin, Heidelberg: Springer Berlin Heidelberg, ,




.. Conclusion

a supercentenarian, and about three-quarters die within two years aer their th birthday. e annual risk of death never reaches . in Australia or other high-income countries.¹⁰⁴ e record survival is held by a French woman, Jeanne Calment who died at age  in . Amazingly, the robust-survivor rule holds true for supercentenarians as it does for the “youngsters” in the Danish  cohort study. A  survey of  United States supercentenarians aged  -  showed that % remained independent or required minimal assistance.¹⁰⁵

. Conclusion ere’s some comfort in the idea that, even in the future, everyone will die. Immortality, even in robust health, would be existence without meaning. Edna St Vincent Millay made a joke of it: “It’s not true that life is one damn thing aer another; it is one damn thing over and over.” But there’s a nightmare version, too: Bill Murray’s “Groundhog day” without escape.¹⁰⁶ Still, the appetite for longer healthy life is undimmed. e economic research on health spending and longevity shows that health is a “normal” good that people demand more of as their income increases. As the welfare rocket described in Chapter  continues its climb, health care demand rises strongly too, with likely positive feedback into capital formation, greater welfare and demand for a longevity. How far can this continue? It’s anybody’s guess. Longevity records in post-transition countries are already astonishing and were not foreseen. For now, there is no warrant to say the record-breaking will slow. On the contrary, the longevity records since the mid-ᵗʰ century suggest that the record-holders are harbingers of the future. e regular, reliable, recording of nonagenarians and centenarians that began in the ᵗʰ century and super-centenarians in the ᵗʰ century suggests someone will break the current age record of  years in the ˢᵗ century if only because there is every reason to expect health to improve with beer understanding of the role of nutrition, disease — including management of chronic age-related disease — and “lifestyle” factors that contribute to survival. It is likely, too, that some current drugs such as aspirin might still have much to offer.¹⁰⁷ Improvements in the quality of life have accompanied improvements in survival so that the - and -year- olds of today have the same mortality pp. –. : ---- ¹⁰⁴Data from ☞. Human Mortality Database ¹⁰⁵ ☞ Emily A Schoenhofen et al. “Characteristics of  supercentenarians.” In: Journal of the American Geriatrics Society . (Aug. ), pp. –. : - ¹⁰⁶An infinite capacity for forgeing would help. Jorge Luis Borges begins his mysterious short story e Immortal with a quote from Francis Bacon’s essay Of the Vicissicitude of ings: “Salomon saith, ere is no new thing upon the earth. So that as Plato had an imagination, that all knowledge was but remembrance; so Salomon giveth his sentence, that all novelty is but oblivion.” A precis of Borges’ story and a psychoanalytic history is included in ☞ Catalina Bronstein. “Borges, immortality and the circular ruins.” In: e International journal of psyo-analysis .Pt  (June ), pp. –. : - ¹⁰⁷Reports from large, long-duration longitudinal studies in the USA, UK and Sweden of low-dose aspirin use are being analysed for hypothesized beneficial effects on cancer, dementia and heart disease with some signs of positive results. e potential beneficial effects of low-dose aspirin specifically for the elderly and aged is the subject of a substantial longitudinal trial begun in  ☞] see: hp://www.aspree.org




. T   

risk as the - and -year-olds in . e frail aged of yesterday are today’s more robust elderly. e intriguing evidence that ageing is at least in part a purposeful biological process under the control of (probably) pleiotropic genes and not merely a haphazard deterioration of the body points to a future role for intervention in the prevention of degenerative disease and ageing. How successful will such intervention be? Researchers in the field are optimistic, but that’s a sentiment, not a projection.¹⁰⁸ It seems very likely, however, that interventions that result in further extensions of survival will be aimed at one or more of the major “degenerative” diseases such as dementia or atherosclerotic disease or cancer rather than at the delay, or prolongation, of ageing. It may be cause for hope that the horizon for personal survival is now more blurred than it has ever been. Despite the refusal of demography, biology and evolutionary theory to identify an age that humans are unlikely to exceed, there is no known upper limit to human lifespan or healthspan. No more than an upper limit to welfare.

¹⁰⁸See, for example ☞ Cynthia J Kenyon. “e genetics of ageing.” In: Nature . (Mar. ), pp. –. : -




.. Conclusion

Table : Life-expectancy, selected countries ( - )

Country

Rank*













Chart

Afghanistan Australia Bangladesh Brazil Cambodia China Congo Cuba Egypt Eritrea Guinea India Indonesia Japan Kenya Lesotho Malawi Mozambique New Zealand Nigeria Russian Fed. South Africa Sudan Tanzania Timor-Leste United States Vietnam Zambia Zimbabwe

                            

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Sourced from U.N. Population Division,  *Rank in  among these  countries




N

NOTES

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Profile for Peter Gallagher

The future of Longevity  

Life-expectancy has grown at an astonishing rate. It took twenty thousand centuries for life-expectancy to double. But it grew by as much ag...

The future of Longevity  

Life-expectancy has grown at an astonishing rate. It took twenty thousand centuries for life-expectancy to double. But it grew by as much ag...

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