nuevo escenario para la salud

Nuevo Escenario para la Salud

D R . C A R L O S J A V I E R R E G A Z Z O N I

La teoría de los paradigmas

  Joel Barker. “Paradigmas”: Conjunto de ideas que determinan una forma efectiva de resolver problemas.

t

Paradigma I Paradigma II

Nivel de prob

lemá2

ca so

cial

Problema en plenitud

Acción del paradigma

Agotamiento del paradigma

Problemática en Ascenso

• Nueva Problemática: – Longevidad saludable

– Enfermedades complejas y crónicas

– Costos y Financiamiento

– Inequidad

•  Falta de Paradigma Adecuado

LONGEVIDAD Nuevos Escenarios para la Salud 1

•  AUMENTO DE LA ESPERANZA DE VIDA

L o n g e v i d a d y S a l u d

o  Concentración de las defunciones en torno a la seni l idad

Esperanza de vida al nacer

00

10

20

30

40

50

60

70

80

90

años

Esperanza de vida al nacer, OECD, ambos sexos Australia Austria Belgium Czech Republic France Germany Hungary Japan Mexico Netherlands New Zealand Norway Poland Portugal Slovak Republic Sweden Switzerland Turkey United States

Duración de la Vida La expectativa de vida podría estar lejos de su límite •  La esperanza de vida

aumenta: �–  linealmente, 3 meses/año

desde hace 160 años.�•  Nadie demostró que la

edad de fallecimiento no aumente.�

Jim Oeppen and James W. Vaupel. Broken Limits to Life Expectancy. Science 2002;296:1029

65646362616059585756555453525150494847

46454443424140393837363534333231302928272625242322212019181716151413121110

9876

www.sciencemag.org SCIENCE VOL 296 10 MAY 2002 1029

Is life expectancy approaching its limit?Many—including individuals planningtheir retirement and officials responsi-

ble for health and social policy—believe itis. The evidence suggests otherwise.

Consider first an astonishing fact. Fe-male life expectancy in the record-holdingcountry has risen for 160 years at a steadypace of almost 3 months per year [Fig. 1

and suppl. table 1(1)]. In 1840 therecord was held bySwedish women,who lived on aver-

age a little more than 45 years. Among na-tions today, the longest expectation oflife—almost 85 years—is enjoyed byJapanese women. The four-decade increasein life expectancy in 16 decades is so ex-traordinarily linear [r2 = 0.992; also seesuppl. figs. 1 to 5 (1)] that it may be themost remarkable regularity of mass endeav-or ever observed. Record life expectancyhas also risen linearly for men (r2 = 0.980),albeit more slowly (slope = 0.222): the gapbetween female and male levels has grownfrom 2 to 6 years (suppl. fig. 2).

In addition to forewarning any loominglimit to the expectation of life, trends inbest-practice life expectancy provide infor-mation about the performance of coun-tries. The gap between the record and thenational level is a measure of how muchbetter a country might do at current statesof knowledge and demonstrated practice.Although rapid progress in catch-up peri-ods typically is followed by a slower rise,life-expectancy trajectories do not appearto be approaching a maximum (Fig. 2).

The linear climb of record life ex-pectancy suggests that reductions in mor-tality should not be seen as a disconnectedsequence of unrepeatable revolutions butrather as a regular stream of continuingprogress (2, 3). Mortality improvements re-sult from the intricate interplay of advances

in income, salubrity, nutrition, education,sanitation, and medicine, with the mixvarying over age, period, cohort, place, anddisease (4). Before 1950, most of the gainin life expectancy was due to large reduc-tions in death rates at younger ages. In thesecond half of the 20th century, improve-ments in survival after age 65 propelled therise in the length of people’s lives. ForJapanese females, remaining life expectan-cy at age 65 grew from 13 years in 1950 to22 years today, and the chance of survivingfrom 65 to 100 soared from less than 1 in1000 to 1 in 20 (1). The details are compli-cated but the resultant straight line of life-expectancy increase is simple.

World life expectancy more than dou-bled over the past two centuries, fromroughly 25 years to about 65 for men and70 for women (4). This transformation ofthe duration of life greatly enhanced thequantity and quality of people’s lives. Itfueled enormous increases in economic

output and in population size, including anexplosion in the number of the elderly (5,6). Although students of mortality eventu-ally recognized the reality of improve-ments in survival, they blindly clung to theancient notion that under favorable condi-tions the typical human has a characteris-tic life-span. As the expectation of liferose higher and higher, experts were un-able to imagine its rising much further.They envisioned various biological barri-ers and practical impediments. The notionof a fixed life-span evolved into a belief ina looming limit to life expectancy.

Ultimate Expectations of LifeIn 1928, Louis Dublin quantified this con-sensus (7). Using U.S. life tables as aguide, he estimated the lowest level towhich the death rate in each age groupcould possibly be reduced. His calcula-tions were made “in the light of presentknowledge and without intervention ofradical innovations or fantastic evolution-ary change in our physiological make-up,such as we have no reason to assume.” His“hypothetical table promised an ultimatefigure of 64.75 years” for the expectationof life both for males and for females. Atthe time, U.S. life expectancy was about57 years. Because Dublin did not have da-ta for New Zealand, he did not realize thathis ceiling had been pierced by women

S C I E N C E ’ S C O M P A S S P O L I C Y F O R U M

P O L I C Y F O R U M : D E M O G R A P H Y

Broken Limits toLife Expectancy

Jim Oeppen and James W.Vaupel*

1840 1860 1880 1900 1920 1940 1960 1980 2000 2020 204045

AustraliaIcelandJapanThe NetherlandsNew Zealand non-MaoriNorwaySwedenSwitzerland

50

55

60

65

70

75

80

85

90

95

UN

World Bank

Olshansky et al.

Fries, Olshansky et al.Coale Coale & Guo

World Bank, UN

Bourgeois-Pichat, UN

Bourgeois-PichatSiegel

UN

UN, Frejka

Dublin

Dublin

Dublin & Lotka

Year

Lif

e e

xp

ecta

ncy in

years

Fig. 1. Record female life expectancy from 1840 to the present [suppl. table 2 (1)]. The linear-re-gression trend is depicted by a bold black line (slope = 0.243) and the extrapolated trend by adashed gray line. The horizontal black lines show asserted ceilings on life expectancy, with a shortvertical line indicating the year of publication (suppl. table 1). The dashed red lines denote projec-tions of female life expectancy in Japan published by the United Nations in 1986, 1999, and 2001(1): It is encouraging that the U.N. altered its projection so radically between 1999 and 2001.

J. Oeppen is with the Cambridge Group for the His-tory of Population and Social Structure, CambridgeUniversity, Cambridge, CB2 3EN, UK. He is associatedwith, and J. W. Vaupel is at, the Max Planck Institutefor Demographic Research, Doberaner Strasse 114,D-18057 Rostock, Germany.

*To whom correspondence should be addressed. E-mail: [email protected]

Enhanced online atwww.sciencemag.org/cgi/content/full/296/5570/1029

on M

arc

h 9

, 2011

ww

w.s

cience

mag.o

rgD

ow

nlo

aded fro

m

Compresión de la Mortal idad

B A

C

Nº Defun

cion

es

Edad •  Avance de la edad media de mortalidad. •  Menor dispersión.

Mortalidad Humana

EDAD

Prob

abilidad de

Morir

Strehler BL, Mildvan AS. Science 1960; 132:14-‐21

Curvas de defunciones

0

4.000

8.000

12.000

16.000

20.000

Defunciones, ambos sexos, cada 100.000, año 2009. Elaboración propia en base a WHO

Argen2na

Japón

Angola

Curvas de defunciones

0

4000

8000

12000

16000

20000

Defuncione

s cad

a 100.000

Defunciones, ambos sexos, c/100.000, a parGr de los 35 años. Elaboración propia en base a WHO (Japón 2009)

Argen2na 2009

Japón

•  La Argen2na 2ene un exceso de muertes en jóvenes

•  LONGEVIDAD PROLONGADA


o  Disminución de la mortal idad a edades avanzadas

Retraso de la mortalidad •  Postérgase mortalidad

a edades avanzadas. •  X5 y X10

–  Edad a la que quedan 5 y 10 años de vida promedio.

•  Argentina 2000: – X5=89 años – X10=79 años

95

90

85

80

75

70

65

1861 1900 1950 2000Year

SwedenUSAJapan

X5

X10

Age (

yr)

Swedes 100+Japanese 105+

1,800

1,600

1,400

1,200

1,000

800

600

400

200

01861 1875 1900 1925 1950 1975 2000

YearNu

mbe

r of fe

male

s age

d 100

+ or 1

05+

death and of species-specific maximum lifespans were refuted in two back-to-back articles49,67 in Science in 1992. Research on Danish twins born since 1870 found no evidence for an innate maximum lifespan shared by identical twins. Only about 25% of the variation in adult lifespans could be attributed to genetic variation among individuals41,42. This percentage seems to increase slightly with age, but even among the elderly, genetic variation still seems to have only a modest impact44. Its impact might, however, be more significant at the oldest ages43. As dis-cussed in the next section, the progress made in saving lives seems to have also improved health, even in advanced age, but this is still uncertain and is an important topic for research.

The quest to uncover major longevity genes in humans has had little success45. Two variants of the apolipoprotein E gene (APOE) have been shown in multiple studies to be risk factors that lower or raise, respec-tively, the chances of death at higher ages by a factor of roughly 1.1 or 1.2 relative to the baseline risk faced by people with the common vari-ant47,48. Although studies have found many genes that reputedly affect lifespans, none has an effect as big as the modest effect of APOE, and few have been replicated in multiple studies. All functioning genes in all species contribute directly or indirectly to fertility, survival or both; evolutionary theory and a few empirical studies suggest that variants that substantially increase longevity are probably rare under natural conditions because they reduce reproduction55,57–59,62,68. In the nematode Caenorhabditis elegans, hundreds of genes have been artificially altered

to lengthen lifespans50, some with very large effects. The discovery of the first of these genes, age-1, was a seminal advance that revolutionized our understanding of the genetics of ageing51. In humans, it seems likely that polymorphisms at hundreds and perhaps thousands of genetic loci each have a small role in increasing or decreasing the risk of death and debility in advanced age.

The evolutionary theory of ageing has been interpreted as implying that senescence is inevitable for all multicellular species6. A major contribution of researchers in the nascent field of biodemography has been to show that the theory should be expanded to permit greater variation in patterns of ageing, including so-called inverse senescence — the decline of mortality and the improvement of health over all or most of adult life60–62. Research in the laboratory and in the field confirms that, for some species and some periods of adult life, mortality can decline with age and that changes in diet and other environmental factors, as well as genetic changes, can greatly alter age trajectories of survival49!51,54,55,60,64.

Progress in delaying debilityIn comparison with death, health is difficult to measure and is often unreliably reported. Estimates of population health are usually based on data from surveys hindered by low participation, especially among the sick. Demographers and epidemiologists have begun to compile serv-iceable information about the postponement of senescence as captured by various indices of health28–36, but the picture is much less clear and more mixed, especially with regard to data on individuals over age 85 and on cognitive performance, than the cogent perspective provided by mortality statistics.

The prevalence of diseases and morbid disorders among the elderly has tended to increase over time28. Part of the rise can be attributed to earlier diagnosis of, for example, type 2 diabetes, hypertension and some cancers. The prevalence of heart disease and arthritis seems to have increased, and individuals are more often reported to have multiple disorders.

Figure 1 | The postponement of mortality.!Historical trends in X5 and X10, the ages at which remaining life expectancies are, respectively, five and ten years, for females in Sweden (1861–2008), the USA (1933–2006) and Japan (1947–2008). For Swedish women, since 1950 senescence as measured by X10 has been postponed by about eight years. For Japanese women, since 1950 X10 has risen by about 12 years. Note that for all three countries, the curve for X5 follows the same general trajectory as the curve for X10 but at a roughly constant gap of about a decade of age. This indicates that senescence, as captured by these two measures, is being postponed rather than lengthened. Both indicators show that progress in postponing senescence was slow for women in the USA between 1980 and 2000. The prospects are that more rapid progress can be expected in the future77,83: the rapid rise in X5 and X10 in recent years for US women may be a harbinger of this. (Data extended and updated from a graph in ref. 14 using information from the Human Mortality Database (http://www.mortality.org), from Statistics Sweden for Sweden 2008 and from the Japanese Ministry of Health for Japan 2008.)

Figure 2 | The emergence of the extremely old.!The numbers of females aged 100+ in Sweden from 1861 to 2008 and aged 105+ in Japan from 1947 to 2007. Very old people were rare until roughly half a century ago. Since then, the number of Swedish centenarians has risen rapidly, and since 1975 the number of Japanese women 105 or older has climbed almost vertically. (Data from the Kannisto–Thatcher Database on Old Age Mortality (http://www.demogr.mpg.de) supplemented with data from Statistics Sweden and the Japanese Ministry of Health.)

537

NATURE|Vol 464|25 March 2010 REVIEW!INSIGHT

!"#$!%&'()*+,-.'/01234'567+)88'''!"9!"#$!%&'()*+,-.'/01234'567+)88'''!"9 :9;";:<'''::=%>=:!:9;";:<'''::=%>=:!

© 20 Macmillan Publishers Limited. All rights reserved10

Vaupel JW. Biodemography of human ageing. Nature 2010;464:536-542

Probabilidad de Morir

•  La probabilidad de morir desacelera luego de los 80 años.

EDAD

Prob

abilidad de

Morir

tality schedules dramatically.

Data from about 10 billion individuals in

two strains of S. cerevisiae were used to

estimate mortality trajectories (Fig. 3F). Be-cause the yeast were kept under conditions

thought to preclude reproduction, death

rates were calculated from changes in the

size of the surviving cohort. Although they

need to be confirmed, the observed trajec-tories suggest that for enormous cohorts of

yeast, death rates may rise and fall and rise

again.

The trajectories in Fig. 3 differ greatly.

For instance, human mortality at advanced

ages rises to heights that preclude the lon-gevity outliers found in medflies (3, 16, 17).

Such differences demand expla-nation. But the trajectories also

share a key characteristic. For all species for

which large cohorts have been followed to

extinction (Fig. 3), mortality decelerates

and, for the biggest populations studied,

even declines at older ages. A few smaller

studies have found deceleration in addition-

80 90 100 110 120Age (years)

Dea

th ra

teHumans

0.1

1.0

0 2 4 6 8 10 12 14Age (years)

0.001

0.01

1.0

Dea

th ra

te

Automobiles

0 30 60 90 120Age (days)

0.01

0.1

1.0Yeast

0 10 20 30 40Age (days)

0.001

0.01

0.1

1.0

0.0

0.5

1.0

1.5Nematodes

0 20 40 60 80 100 120 140Age (days)

0.0

0.1

0.2

0.3Anastrepha and wasps

0 20 40 60 80 100 120 140Age (days)

0.00

0.05

0.10

0.15

0.20Medflies

0 30 60Age (days)

0.001

0.01

1.0Drosophila melanogaster

A B

E F G

C D

Fig. 3. Age trajectories of deathrates (48). (A) Death rates fromage 80 to 122 for human females.The red line is for an aggregationof 14 countries (Japan and 13Western European countries)with reliable data, over the periodfrom 1950 to 1990 for ages 80 to109 and to 1997 for ages 110and over (49). The last observa-tion is a death at age 122, butdata are so sparse at the highestages that the trajectory of mortal-ity is too erratic to plot. Althoughthe graph is based on massivedata, some 287 million person-years-at-risk, reliable data wereavailable on only 82 people whosurvived past age 110. The expo-nential (Gompertz) curve that best fits the data at ages 80 to 84 is shown inblack. The logistic curve that best fits the entire data set is shown in blue (16).A quadratic curve (that is, the logarithm of death rate as a quadratic functionof age) was fit to the data at ages 105 and higher; it is shown in green. (B)Death rates for a cohort of 1,203,646 medflies, Ceratitis capitata (17 ). Thered curve is for females and the blue curve for males. The prominent shoulderof mortality, marked with an arrow, is associated with the death of protein-deprived females attempting to produce eggs (51). Until day 30, daily deathrates are plotted; afterward, the death rates are averages for the 10-dayperiod centered on the age at which the value is plotted. The fluctuations atthe highest ages may be due to random noise; only 44 females and 18 malessurvived to day 100. (C) Death rates for three species of true fruit flies,Anastrepha serpentina in red (for a cohort of 341,314 flies), A. obliqua ingreen (for 297,087 flies), and A. ludens in light blue (for 851,100 flies), as wellas 27,542 parasitoid wasps, Diachasmimorpha longiacaudtis, shown by thethinner dark blue curve. As for medflies, daily death rates are plotted until day30; afterward, the death rates are for 10-day periods. (D) Death rates for agenetically homogeneous line of Drosophila melanogaster, from an experi-ment by A.A.K. and J.W.C. The thick red line is for a cohort of 6338 fliesreared under usual procedures in J.W.C.’s laboratory. The other lines are for17 smaller cohorts with a total of 7482 flies. To reduce heterogeneity, eggswere collected over a period of only 7 hours, first instar larvae over a period ofonly 3 hours, and enclosed flies over a period of only 3 hours. Each cohortwas maintained under conditions that were as standardized as feasible.

Death rates were smoothed by use of a locally weighted procedure with awindow of 8 days (52). (E) Death rates, determined from survival data frompopulation samples, for genetically homogeneous lines of nematodeworms, Caenorhabditis elegans, raised under experimental conditionssimilar to (53) but with density controlled (21). Age trajectories for thewild-type worm are shown as a solid red line (on a logarithmic scale givento the left) and as a dashed red line (on an arithmetic scale given to theright); the experiment included about 550,000 worms. Trajectories for theage-1 mutant are shown as a solid blue line (on the logarithmic scale) andas a dashed blue line (on the arithmetic scale), from an experiment withabout 100,000 worms. (F) Death rates for about 10 billion yeast in twohaploid strains: D27310b, which is a wild-type strain, shown in red; andEG103 (DBY746), which is a highly studied laboratory strain, shown in blue(34). Surviving population size was estimated daily from samples of knownvolume containing about 200 viable individuals. Death rates were calcu-lated from the estimated population sizes and then smoothed by use of a20-day window for the EG103 strain and a 25-day window for theD27310b strain. Because the standard errors of the death-rate estimatesare about one-tenth of the estimates, the pattern of rise, fall, and rise ishighly statistically significant. (G) Death rates for automobiles in the UnitedStates, estimated from annual automobile registration data. An automobile“dies” if it is not re-registered (26, 54). The blue and dashed blue lines arefor Chevrolets from the 1970 and 1980 model years; the red and dashedred lines are for Toyotas from the same years.

www.sciencemag.org ! SCIENCE ! VOL. 280 ! 8 MAY 1998 857

on

Augu

st 1

1, 2

011

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

tality schedules dramatically.

Data from about 10 billion individuals in

two strains of S. cerevisiae were used to

estimate mortality trajectories (Fig. 3F). Be-cause the yeast were kept under conditions

thought to preclude reproduction, death

rates were calculated from changes in the

size of the surviving cohort. Although they

need to be confirmed, the observed trajec-tories suggest that for enormous cohorts of

yeast, death rates may rise and fall and rise

again.

The trajectories in Fig. 3 differ greatly.

For instance, human mortality at advanced

ages rises to heights that preclude the lon-gevity outliers found in medflies (3, 16, 17).

Such differences demand expla-nation. But the trajectories also

share a key characteristic. For all species for

which large cohorts have been followed to

extinction (Fig. 3), mortality decelerates

and, for the biggest populations studied,

even declines at older ages. A few smaller

studies have found deceleration in addition-

80 90 100 110 120Age (years)

Dea

th ra

te

Humans

0.1

1.0

0 2 4 6 8 10 12 14Age (years)

0.001

0.01

1.0

Dea

th ra

te

Automobiles

0 30 60 90 120Age (days)

0.01

0.1

1.0Yeast

0 10 20 30 40Age (days)

0.001

0.01

0.1

1.0

0.0

0.5

1.0

1.5Nematodes

0 20 40 60 80 100 120 140Age (days)

0.0

0.1

0.2

0.3Anastrepha and wasps

0 20 40 60 80 100 120 140Age (days)

0.00

0.05

0.10

0.15

0.20Medflies

0 30 60Age (days)

0.001

0.01

1.0Drosophila melanogaster

A B

E F G

C D

Fig. 3. Age trajectories of deathrates (48). (A) Death rates fromage 80 to 122 for human females.The red line is for an aggregationof 14 countries (Japan and 13Western European countries)with reliable data, over the periodfrom 1950 to 1990 for ages 80 to109 and to 1997 for ages 110and over (49). The last observa-tion is a death at age 122, butdata are so sparse at the highestages that the trajectory of mortal-ity is too erratic to plot. Althoughthe graph is based on massivedata, some 287 million person-years-at-risk, reliable data wereavailable on only 82 people whosurvived past age 110. The expo-nential (Gompertz) curve that best fits the data at ages 80 to 84 is shown inblack. The logistic curve that best fits the entire data set is shown in blue (16).A quadratic curve (that is, the logarithm of death rate as a quadratic functionof age) was fit to the data at ages 105 and higher; it is shown in green. (B)Death rates for a cohort of 1,203,646 medflies, Ceratitis capitata (17 ). Thered curve is for females and the blue curve for males. The prominent shoulderof mortality, marked with an arrow, is associated with the death of protein-deprived females attempting to produce eggs (51). Until day 30, daily deathrates are plotted; afterward, the death rates are averages for the 10-dayperiod centered on the age at which the value is plotted. The fluctuations atthe highest ages may be due to random noise; only 44 females and 18 malessurvived to day 100. (C) Death rates for three species of true fruit flies,Anastrepha serpentina in red (for a cohort of 341,314 flies), A. obliqua ingreen (for 297,087 flies), and A. ludens in light blue (for 851,100 flies), as wellas 27,542 parasitoid wasps, Diachasmimorpha longiacaudtis, shown by thethinner dark blue curve. As for medflies, daily death rates are plotted until day30; afterward, the death rates are for 10-day periods. (D) Death rates for agenetically homogeneous line of Drosophila melanogaster, from an experi-ment by A.A.K. and J.W.C. The thick red line is for a cohort of 6338 fliesreared under usual procedures in J.W.C.’s laboratory. The other lines are for17 smaller cohorts with a total of 7482 flies. To reduce heterogeneity, eggswere collected over a period of only 7 hours, first instar larvae over a period ofonly 3 hours, and enclosed flies over a period of only 3 hours. Each cohortwas maintained under conditions that were as standardized as feasible.

Death rates were smoothed by use of a locally weighted procedure with awindow of 8 days (52). (E) Death rates, determined from survival data frompopulation samples, for genetically homogeneous lines of nematodeworms, Caenorhabditis elegans, raised under experimental conditionssimilar to (53) but with density controlled (21). Age trajectories for thewild-type worm are shown as a solid red line (on a logarithmic scale givento the left) and as a dashed red line (on an arithmetic scale given to theright); the experiment included about 550,000 worms. Trajectories for theage-1 mutant are shown as a solid blue line (on the logarithmic scale) andas a dashed blue line (on the arithmetic scale), from an experiment withabout 100,000 worms. (F) Death rates for about 10 billion yeast in twohaploid strains: D27310b, which is a wild-type strain, shown in red; andEG103 (DBY746), which is a highly studied laboratory strain, shown in blue(34). Surviving population size was estimated daily from samples of knownvolume containing about 200 viable individuals. Death rates were calcu-lated from the estimated population sizes and then smoothed by use of a20-day window for the EG103 strain and a 25-day window for theD27310b strain. Because the standard errors of the death-rate estimatesare about one-tenth of the estimates, the pattern of rise, fall, and rise ishighly statistically significant. (G) Death rates for automobiles in the UnitedStates, estimated from annual automobile registration data. An automobile“dies” if it is not re-registered (26, 54). The blue and dashed blue lines arefor Chevrolets from the 1970 and 1980 model years; the red and dashedred lines are for Toyotas from the same years.

www.sciencemag.org ! SCIENCE ! VOL. 280 ! 8 MAY 1998 857

on

Augu

st 1

1, 2

011

ww

w.s

cien

cem

ag.o

rgD

ownl

oade

d fro

m

Vaupel JW, et al. Science 1998;280:855-‐860

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

Probabilidad anual de morir por rango etario. ArgenGna, hombres, 2009, 2000, y 1990 Elaboración propia en base a WHO

2009

2000

1990

Probabi l idad de morir, desacelera

Expectativa a los 65

100 102 104 106 108 110 112 114 116 118 120 122

2009 2000 1990

Varia

ción

porcentua

l

ExpectaGva de vida a los 65-‐69 años de edad, ambos sexos, variación porcentual, tres períodos Elaboración propia en base a WHO

Argen2na

Brasil

Canadá

Japón

Centenarios

•  Cohortes y edad a la cual el 50% estará vivo

Canadá 102

Canadá 103

Canadá 103

Japón 104

Japón 105

Japón 106

=Año de nacimiento de la cohorte

Christensen K. Ageing popula2ons: the challenges ahead Lancet 2009; 374: 1196–1208

•  ESPERANZA DE VIDA FINAL


o  La esperanza de vida aumenta por mayor longevidad

Esperanza de vida y Senectud

0 a 14 años

15 a 49 50 a 64

65 a 79

>80 años

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1850-‐1900 1900-‐25 1925-‐50 1950-‐75 1975-‐90 1990-‐2007

ParGcipa

ción

en la gan

ancia total de

espe

ranza de

vida

ParGcipación de los grupos etarios en el incremento de la esperanza de vida máxima para mujeres, 1850-‐2007

Elaboración propia en base a Christensen K et al, Lancet 2009;374:1196-‐1208

•  ENVEJECIMIENTO L o n g e v i d a d y S a l u d

o  Longevidad y menor fecundidad l levan a l envejecimiento poblacional

Edad Media, Evolución

0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

40,00

45,00

50,00

Edad Media Popula2on Division of the Department of Economic and Social Affairs of the United Na2ons Secretariat, World Popula2on Prospects: The 2008 Revision,

hlp://esa.un.org/unpp

Argen2na

Bolivia

Brazil

Chile

Colombia

South America

Europe

Fecundidad en descenso

0

1

2

3

4

5

6

7

8

1869 1895 1914 1947 1960 1970 1980 1991 2001

Hijos/vida

férGl fem

enina

ArgenGna Tasa de Fecundidad (hijos/mujer) Elaboración propia en base a INDEC

Población Mayor: Argentina

10,5% 11,9% 13,6% 15,6% 19%

0

5.000.000

10.000.000

15.000.000

20.000.000

25.000.000

30.000.000

35.000.000

40.000.000

45.000.000

50.000.000

55.000.000

2010 2020 2030 2040 2050

Años

Población total

Población 65 años y más

•  POSTERGACIÓN DE LA DISCAPACIDAD


o  Compresión de la morbilidad

Compresión de la Morbil idad Enfermedad postergable

Sano

Enf. Precoz

Postergada

Longevidad postergable

Fries JF. Aging, natural death, and the compression of morbidity. N Engl J Med 1980; 303:130-135

A B

0

20

40

60

80

100

Sobrevivientes

(%)

Edad

A B

Envejecimiento, Riesgo, y Discapacidad

•  1.741 alumni Univ •  Edad ≈43 años •  77% varones

Nivel inicial de discapacidad Health Assesment Ques.onaire

Reevaluados

1962 1986

Vita AJ, Terry RB, Hubert HB, Fries JF. Aging, health risks, and cumula2ve disability. N Engl J Med 1998; 338:1035-‐1041

1994

Nivel inicial de Riesgo •  BMI •  Tabaquismo •  Ejercicio

•  Discapacidad Anual •  Muerte

AGING, HEALTH RISKS, AND CUMULATIVE DISABILITY

Volume 338 Number 15 ! 1039

high-risk groups differed by a factor of approximate-ly two, and the differences were statistically signifi-cant. Disability was postponed by more than fiveyears in the low-risk group as compared with thehigh-risk group. Among the subjects who died,both cumulative disability and disability in the oneor two years before death were much lower in thelow- and moderate-risk groups than in the high-riskgroup. Similarly, among the survivors, cumulativedisability was much lower in the low-risk group thanin the other two groups.

Caveats apply to the results. The study populationhad a high educational level, was relatively homoge-neous in terms of age and socioeconomic status, andwas almost entirely white. Over three fourths of thesubjects were men, but separate analyses of men andwomen had similar results. In addition, since thestudy end points were determined on the basis of re-sponses to a questionnaire, there is the possibility ofbias. However, the health-assessment questionnaireused to determine disability has been repeatedly val-idated.31-35 In a study of runners and controls, for ex-ample, we found no differences between the twogroups in reliability or in correlations with spousalestimates of disability.21 However, for the subjects inour study, the time of greatest disability (after theage of 85 years) is still in the future. As the studycontinues, it will be possible to assess the effects ofchanges in specific risk factors such as cessation ofsmoking.

Eleven percent of the subjects were lost to follow-up and their status (dead or alive) was not known,which is another potential source of bias. These sub-jects were slightly older and more disabled and had

more hospitalizations in 1986 than the subjects whowere followed, and it is possible that many of themdied or were institutionalized. If more low-risk per-sons than moderate- or high-risk persons were lostto follow-up, the imbalance could produce a bias.However, the proportions of persons lost to follow-up were similar in the low-, moderate-, and high-riskgroups and in the groups of patients with and with-out disability in 1986.

This study documents a strong association be-tween the level of health risk and subsequent disabil-ity but does not prove causality. It is possible thatother, unmeasured variables are correlated both withrisk-factor scores and with cumulative disability. How-ever, with age, education, and race essentially heldconstant in our study, and with prior studies indicat-ing that smoking, obesity, and level of exercise areindependently related to disability, it is difficult tothink of additional causal variables.

Initial disability might have been a confoundingvariable, since it is strongly associated with cumula-tive disability and since health habits might havebeen modified in response to early disability. In thegroup of subjects without initial disability, the re-sults were less robust. It is more likely, however, thatthe early disability was the result of a high health riskbefore the study began. We examined this issue bothby performing a separate analysis of the subjectswithout initial disability and by assigning the sub-jects to risk groups at an average age of 43 years,when disability should have been minimal. In bothinstances, the results were consistent with those inthe overall study population.

Our data base contains serial data on disability,

Figure 2. Disability Index According to Age at the Time of the Last Survey and Health Risk in 1986.Average disability increased with age in all three risk groups, but the progression to a given level ofdisability was postponed by approximately seven years in the low-risk group as compared with thehigh-risk group. The horizontal line indicates a disability index of 0.1, which corresponds to minimaldisability.

0.0063 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78

0.30

0.05

0.10

0.15

0.20

0.25

Age (yr)

High risk

Moderate risk

Low risk

Dis

ab

ilit

y In

dex

The New England Journal of Medicine Downloaded from nejm.org by Carlos Javier Regazzoni on February 16, 2011. For personal use only. No other uses without permission.

Copyright © 1998 Massachusetts Medical Society. All rights reserved.

Dis

abil

idad

A

cum

ula

da

Vita AJ, Terry RB, Hubert HB, Fries JF. Aging, health risks, and cumula2ve disability. N engl J med 1998; 338:1035-‐1041

Envejecimiento, Riesgo, y Discapacidad

•  Hay predictores de discapacidad (riesgo) – Tabaquismo – Sedentarismo – BMI

•  A menor riesgo, la discapacidad se post-pone

•  A mayor discapacidad, peor progresión

Vita AJ, Terry RB, Hubert HB, Fries JF. Aging, health risks, and cumula2ve disability. N engl J med 1998; 338:1035-‐1041

Evolución del paciente mayor

0 10 20 30 40 50 60 70 80 90

Sano 75 Ins2tución 75

Sano 85 Ins2tución 85 Pr

obab

ilida

d (%

) de

cam

biar de estado

el año

siguiente

Probabilidad de cambio de estado en pac. Mayores Elaboración propia en base a Lubitz J et al, 2003

Sano

Ins2tucionalizado

Fallecido

Lubitz J, Cai L, Kramarow E, Lentzner H. Health, life expectancy, and health care spending in the elderly. N Engl J Med 2003; 349:1048-‐55

Llegar a los 100

– No todos los centenarios contraen una enfermedad crónica asociada a la edad en el mismo momento de su vida.

42%

45%

13%

Enfermedad <80

Enfermedad >80

No Enfermedad

Sobrevivientes

Retrasados

Escapados

Terry, D.F. et al. Cardiovascular advantages among the offspring of centenarians. J. Gerontol. A Biol. Sci. Med. Sci. 2003; 58, M425–M431

•  MEDICINA Y BIENESTAR L o n g e v i d a d y S a l u d

o  La longevidad saludable depende de la medicina y el bienestar general

Medicamentos y Longevidad

0,12

0,30 0,45

0,56

0,62

0,70

0,79

0,0

0,6

1,2

1,8

2,4

1988 1990 1992 1994 1996 1998 2000

Año

s d

e vi

da

gan

ado

s

Efecto de los medicamentos sobre la longevidad

Longevidad ganada con medicación

Resto de longevidad ganada

0

20

40

60

80

100

120

140

160

180

Varia

ción

porcentua

l respe

cto de

1993

Variación porcentual de PBI y Mortalidad InfanGl, 1993=base 100 Elaboración propia sobre datos de INDEC

PBI Mortalidad Infan2l

Salud y Economía: Argentina

•  CONCLUSIÓN L o n g e v i d a d y S a l u d

o  Más bienestar y más medicina

COMPLEJIDAD Nuevos Escenarios para la Salud 2

Cambio de Patología

0"1000"2000"3000"4000"5000"6000"7000"8000"9000"

10000"

2008" 2015" 2030"

Mue

rtes

en

.000

s/añ

o!

Causas de Muerte por grupos!América Latina, WHO!

Traumaticas"

Enfermedades no comunicables"

Enfermedades comunicables, condiciones maternas y neonatales y nutricionales"

Causas de Mortalidad, 2030

of demographic change are labelled ‘‘population growth’’ and‘‘population ageing’’ in Figure 7. The total projected changein numbers of deaths between 2002 and 2030 is the sum of thepopulation growth, population ageing, and epidemiologicalchange components.

In almost all cases, demographic and epidemiologicalfactors are operating in opposing directions in determiningmortality in 2030. The major exception is HIV/AIDS, wheredemographic and epidemiological change are acting in thesame direction to increase total HIV/AIDS deaths to 6.5million deaths in 2030 under the baseline scenario. Demo-graphic change dominates, as the majority of HIV/AIDSdeaths are in sub-Saharan Africa, where population growth is

highest and where HIV/AIDS incidence rates are assumed toremain largely constant under the baseline scenario.For Group I conditions other than HIV/AIDS for which

substantial declines in mortality rates are projected, the effectof these declines will be attenuated in most regions bydemographic change leading to an increase in the childpopulation most at risk for these conditions. Populationgrowth and population ageing act in opposite directions forGroup I mortality excluding HIV/AIDS in low-incomecountries, but not in other income groups. If future fertilityrates are higher than projected, then the higher childpopulation numbers will further offset the projected reduc-tions in death rates for Group I conditions.

Figure 5. Projections of Global Deaths (Millions) for Selected Causes, for Three Scenarios: Baseline, Optimistic, and Pessimistic, 2002–2030

doi: 10.1371/journal.pmed.0030442.g005

PLoS Medicine | www.plosmedicine.org November 2006 | Volume 3 | Issue 11 | e4422020

Projections of Global Mortality

Factores de Riesgo en la Argent ia

0,0

5,0

10,0

15,0

20,0

25,0

30,0

35,0

40,0

45,0

Prevalencia (%) de Detección de HTA, DLP, DBT "Programa de Vigilancia de la Salud y Control de Enfermedades" VIGI+A e INDEC,

Encuesta Nacional de Factores de Riesgo 2005.

Hipertensión arterial Hipercolesterolemia Diabetes

Tabaquismo

0 10 20 30 40 50

Santa Cruz Tierra del Fuego

La Pampa Chubut

Neuquén San Luís

Catamarca Tucumán La Rioja

Río Negro Salta

San Juan Mendoza Córdoba

Total del país Buenos Aires

Corrientes San2ago del Estero

Entre Ríos Chaco

Ciudad de Buenos Aires Jujuy

Misiones Santa Fe Formosa

(%) Mayores de 18 años que fuman actualmente

Prevalencia de Tabaquismo Elaboración propia según: VIGI+A e INDEC, ENFR 2005

COSTOS Nuevos Escenarios para la Salud 3

•  AUMENTO INCESANTE C o s t o y S a l u d

o  El gasto en salud tiende a aumentar

Gasto y Eficiencia

Argen2na 2008 Brasil 2008

Chile 2008

Base, año 2000

Hungría 2008

100

110

120

130

140

150

160

170

55 60 65 70 75 80 85 90 95 100

Gasto en

salud/cápita $-‐PPP

Mortalidad en <5 años

Gasto en Salud y Mortalidad<5 años; 100=año 2000 -‐Gasto en salud, PPP-‐U$/capita, total, y Mortalidad en <5 años-‐ WHO

NO H AY N I N G U N A R A Z Ó N PA R A D E F I N I R A R B I T R A R I A M E N T E U N N I V E L D E G A S TO E N S A L U D. S I E S O B L I G ATO R I O P R E T E N D E R O B T E N E R M AY O R VA L O R P O R D I C H O G A S TO.

Gasto en salud

Fuchs VR. Health care expenditure reexamined. Ann Intern Med 2005; 143: 76-‐8

Gasto en Salud •  Efectos del Aumento del Gasto en Salud

– Sobre las cuentas públicas •  Quita fondos a otras áreas

– Sobre la economía real •  Aumenta los costos de bolsillo en un área que altera la dinámica económica

–  No sigue leyes de mercado »  Asimetría de información »  Es imprescindible »  El decisor (médico) incen2vado por un sector más que otro

–  Afecta a trabajador y empleador

Orszag PR. How health care can save or sink America. Foreign Affairs 2011; July/August Fuchs VR. Health care expenditure reexamined. Ann Intern Med 2005; 143: 76-8

•  HAY SUBTRATAMIENTO C o s t o y S a l u d

o  El subt ra tamiento se asoc ia a un costo de opor tun idad desaprovechado

Cal idad de Atención en Adul tos

•  6.712 personas •  Adultos •  12 ciudades USA •  Contacto tel. •  Acceso a

Historias clínicas

30 Condiciones seleccionadas agudas

y crónicas

439 indicadores de calidad de atención

Tratamientos y medidas preven2vas 1998 2000

PARA CADA CONDICIÓN: •  Medición de tratamiento

recibido •  Comparación con tratamiento

recomendado

RAND RESEARCH AREAS

THE ARTS

CHILD POLICY

CIVIL JUSTICE

EDUCATION

ENERGY AND ENVIRONMENT

HEALTH AND HEALTH CARE

INTERNATIONAL AFFAIRS

NATIONAL SECURITY

POPULATION AND AGING

PUBLIC SAFETY

SCIENCE AND TECHNOLOGY

SUBSTANCE ABUSE

TERRORISM ANDHOMELAND SECURITY

TRANSPORTATION ANDINFRASTRUCTURE

WORKFORCE AND WORKPLACE

The Health Insurance ExperimentA Classic RAND Study Speaks to the Current Health Care Reform Debate

After decades of evolution and experiment, the U.S. health care system has yet to solve a funda-mental challenge: delivering quality

health care to all Americans at an a! ordable price. In the coming years, new solutions will be explored and older ideas revisited. One idea that has returned to prominence is cost sharing, which involves shifting a greater share of health care expense and responsibil-ity onto consumers. Recent public discussion of cost sharing has often cited a landmark RAND study: the Health Insurance Experi-ment (HIE). Although it was completed over two decades ago, in 1982, the HIE remains the only long-term, experimental study of cost sharing and its e! ect on service use, quality of care, and health. " e purpose of this research brief is to summarize the HIE’s main fi ndings and clarify its relevance for today’s debate. Our goal is not to conclude that cost sharing is good or bad but to illuminate its e! ects so that policymakers can use the information to make sound decisions.

Learning from Experiment: Conducting the HIE In the early 1970s, fi nancing and the impact of cost sharing took center stage in the national health care debate. At the time, the debate focused on free, universal health care and whether the benefi ts would justify the costs. To inform this debate, an interdisciplinary team of RAND researchers designed and car-ried out the HIE, one of the largest and most comprehensive social science experiments ever performed in the United States.

" e HIE posed three basic questions: • How does cost sharing or membership in

an HMO a! ect use of health services com-pared to free care?

• How does cost sharing or membership in an HMO a! ect appropriateness and quality of care received?

• What are the consequences for health?

" e HIE was a large-scale, randomized experiment conducted between 1971 and 1982. For the study, RAND recruited 2,750 families encompassing more than 7,700 indi-viduals, all of whom were under the age of 65. " ey were chosen from six sites across the

This product is part of the RAND Corporation research brief series. RAND research

briefs present policy-oriented summaries of individual

published, peer-reviewed documents or of a body of

published work.

Corporate Headquarters 1776 Main Street

P.O. Box 2138 Santa Monica, California

90407-2138 TEL 310.393.0411

FAX 310.393.4818

© RAND 2006

www.rand.org

Key fi ndings:

• In a large-scale, multiyear experiment, participants who paid for a share of their health care used fewer health services than a comparison group given free care.

• Cost sharing reduced the use of both highly effective and less effective services in roughly equal proportions. Cost sharing did not signifi cantly affect the quality of care received by participants.

• Cost sharing in general had no adverse effects on participant health, but there were exceptions: free care led to improve-ments in hypertension, dental health, vision, and selected serious symptoms. These improvements were concentrated among the sickest and poorest patients.

McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr EA. The Quality of Health Care Delivered to Adults in the United States. N Engl J Med 2003;348:2635-‐45.

Calidad de Atención

45,1 45,1 46,5 43,9

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

General Prevención Agudo Crónico Tipo de tratamiento

Proporción del tratamiento teóricamente recomendado, efecGvamente recibido por los pacientes. EE.UU., 12 áreas

metropolitanas, 2003 Elab propia s/RAND, The First Na2onal Report Card on Quality of Health Care in America

No recibido Recibido

RAND RESEARCH AREAS

THE ARTS

CHILD POLICY

CIVIL JUSTICE

EDUCATION




NATIONAL SECURITY


PUBLIC SAFETY


SUBSTANCE ABUSE
















published work.



90407-2138 TEL 310.393.0411

FAX 310.393.4818

© RAND 2006

www.rand.org

Key fi ndings:




Calidad de Atención

35 41 42 42 45 50 55 55 60

90

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

Proporción del tratamiento teóricamente recomendado, efecGvamente recibido por los pacientes. EE.UU., 12 áreas

metropolitanas, 2003 Elab propia s/RAND, The First Na2onal Report Card on Quality of Health Care in America

No recivido Recivido

RAND RESEARCH AREAS

THE ARTS

CHILD POLICY

CIVIL JUSTICE

EDUCATION




NATIONAL SECURITY


PUBLIC SAFETY


SUBSTANCE ABUSE
















published work.



90407-2138 TEL 310.393.0411

FAX 310.393.4818

© RAND 2006

www.rand.org

Key fi ndings:




Mamografía

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Mamograha en los úlGmos dos años según provincia. Localidades de 5.000 y más habitantes. Total del país. Noviembre de 2009. Se toma como población de referencia a mujeres de 40 años y más que se realizaron por lo

menos una mamograxa en los úl2mos 2 añ

Mamograxa No

Mamograxa Sí

Oportunidad de Tratar HTA

Si: 24,6%

Tratamiento

Si: 38,5%

No: 61,5%

No: 75,4%

•  DETERMINANTES C o s t o y S a l u d

o  El gasto en salud cambia con la demografía

C o n t r i b u c i ó n r e l a t i v a d e d i f e r e n t e s s e r v i c i o s d e s a l u d a l c r e c i m i e n t o t o t a l d e l

g a s t o , U S A 1 9 9 6 - 2 0 1 7

Otros 17.8% Other Personal

Health Care 12.1%

Home Health Care 1.8%

Nursing Home Care 4.4%

Medicamentos 14.3%

Médicos 21.0%

Hospitales 28.6%

Notes: Percentages may not total 100% due to rounding. Other Personal Health Care includes, for example, dental and other professional health services, durable medical equipment, etc. Other Health Spending includes, for example, administra2on and net cost of private health insurance, public health ac2vity, research, and structures and equipment, etc. Source: Kaiser Family Founda2on calcula2ons using NHE data from Centers for Medicare and Medicaid Services, Office of the Actuary, Na2onal Health Sta2s2cs Group, at hlp://www.cms.hhs.gov/Na2onalHealthExpendData/ (see Historical; Na2onal Health Expenditures by type of service and source of funds, CY 1960-‐2006; file nhe2006.zip).

Causas de Gasto Total

0

10

20

30

40

50 109 U$S

Gasto Total, 10 primeras causas, Adultos, US 2008 Center for Financing, Access, and Cost Trends, AHRQ, Household Component of

the Medical Expenditure Panel Survey, 2008

Mujeres Hombres

GASTO RELATIVO EN SALUD Y EDAD

0

1

2

3

4

5

6

0-‐5 6-‐14 15-‐24 25-‐34 35-‐44 45-‐54 55-‐64 65-‐74 75+

Gasto relativo

Gasto relativo per cápita en salud, por grupo etario, EE.UU 1999

Edad 35-44 años=1 Meara E, White C, Cutler DM, 2003

Causas de la demanda

0

1

2

3

4

5

6

Varia

ción

anu

al (%

)

Modificación de la acGvidad anual, según drivers demográfico y otros

Elaboración propia en base a Dash P, Llewellyn C, Richardson B. Developing a regional health system strategy. McKinsey Quarterly 2011

Otros Demografía

Causas de Gasto, >65 años

0

10

20

30

40

50

Enf. Cardíaca Cáncer Osteoartri2s Hipertensión Trauma Asoc

109 U$S

Gasto Total, Primeras causas, Mayores, US 2008 Center for Financing, Access, and Cost Trends, AHRQ, Household Component

of the Medical Expenditure Panel Survey, 2008

Gasto en Medicamentos

22,5

15,1 12,3

8,7 8,4

0

5

10

15

20

25

DBT y DLP Analgésicos, Anticonvulsivos, Antiparkinson

Cardiovascular Gastrointestinal Psicotrópicos (%) d

el to

tal prescrip

to ambu

latorio

Drogas más prescriptas, Ambulatorio, Adultos, US 2008 Center for Financing, Access, and Cost Trends, AHRQ, Household and Pharmacy

Components of the Medical Expenditure Panel Survey, 2008

Top 5 33%

Gasto Ambulatorio

Drogas más vendidas

0 20 40 60 80 100

Oncología Diabetes

Respiratorias Colesterol

Angiotensina Autoinmunes

Anti HIV Antipsicóticos

Antiagregantes Anti-ulcerosos Antidepresivos

Anti-epilépticos Esclerosis Múltiple

Osteoporosis Analgésicos

ADHD Eritropoyesis

Alzheimer Antivirales Glaucoma

U$S miles de millones

Clases terapéuGcas de mayor facturación, mundo, proyección 2015 Elaboración propia en base a: IMS. The Global Use of Medicines: Outlook

Through 2015. Report by the IMS Ins2tute for Healthcare Informa2cs

41% Resto

Proporción del mercado global, 2015=U$S1012!

Top Ten año 2014 FARMA USO DROGA LAB U$S X109

Avasta2n Cáncer Bevacizumab Roche 8,9

Humira Artri2s Adalimumab Abol 8,5

Enbrel Artri2s Etanercept Pfizer 8

Crestor Colesterol Rozuvasta2na AstraZeneca 7,7

Remicade Artri2s Infliximab Merck 7,6

Rituxan Cáncer Rituximab Roche 7,4

Lantus Diabetes Insulina Glargina Sanofi-‐Aven2s 7,1

Advair Asma/EPOC Flu2casona-‐Sameterol GSK 6,8

Hercep2n Cáncer Trastuzumab Roche 6,4

Novolog Diabetes Insulina-‐Aspartato Novo Nordisk 5,7

TOTAL 74,1

Total Global Drug Sales 1.000 (*)

FACTBOX-‐World's top-‐selling drugs in 2014 vs 2010. Thomson-‐Reuters (*) Global drug sales to top $1 trillion in 2014: IMS. Thomson Reuters

22,7%

50,2%

65,5% 74,4%

80,6%

96,5%

3,5% 0%

20%

40%

60%

80%

100%

Top 1% Top 5% Top 10% Top 15% Top 20% Top 50% Bolom 50% Po

rcen

taje del gasto to

tal en salud

Porcentaje de la población rankeada según nivel de gasto

Note: Dollar amounts in parentheses are the annual expenses per person in each percen2le. Popula2on is the civilian nonins2tu2onalized popula2on, including those without any health care spending. Health care spending is total payments from all sources (including direct payments from individuals, private insurance, Medicare, Medicaid, and miscellaneous other sources) to hospitals, physicians, other providers (including dental care), and pharmacies; health insurance premiums are not included. Source: Kaiser Family Founda2on calcula2ons using data from U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality, Medical Expenditure Panel Survey (MEPS), 2005.

Concentración del gasto en salud, USA 2005

Concentración del Gasto

18,7 �

44 �

59,5 �

81,9 �

0 10 20 30 40 50 60 70 80 90

100 0 � Top 1%� Top 5%� Top 10%� Top 25%� Top 50%� 100 �

Porcen

taje del Gasto Total en Salud

Porcentaje de la población según nivel de gasto (percenGlo)

ParGcipación en el Gasto en Salud, según canGdad de población. US, población, 2005-‐2006; MEPS (Cohen, Rohde, 2009)

18,7 �

44 �59,5 �

81,9 �95,7 �

Top 1%� Top 5%� Top 10%� Top 25%� Top 50%�

Predictores de Riesgo

25,3

36,6

13,2

45,1

35,1 26,8

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Población General�

Top 5%� Top 6-10%� Top 11-25%�Porcen

taje de po

blación según grup

o etario

P e r c enG lo de Ga s to

ParGcipación en el Gasto en Salud, según Edad. US, población, 2005-‐2006; MEPS (Cohen, Rohde, 2009)

65 y más

45-‐64

30-‐44

18-‐29

0-‐17

•  GASTO O INVERSIÓN C o s t o y S a l u d

o  El gasto en salud posee réditos sociales

Gasto en Salud y Mortal idad

Aumentar el gasto en salud 1% del PBI, baja la mortalidad infantil 0,6%

15,5 16

16,5 17

17,5 18

18,5 19

19,5 20

20,5

7 8 9 10

Mor

talid

ad In

fant

il (<

1año

/1.0

00 n

v)

Gasto en Salud (% del PBI)

Simulación: gasto en salud y mortalidad infantil Elaboración propia, en base a INDEC y Censo 2001

IMF Working Paper. Fiscal Affairs Department. Social Spending, Human Capital, and Growth in Developing Countries:Implica2ons for Achieving the MDGs. By Emanuele Baldacci, Benedict Clements, Sanjeev Gupta, and Qiang Cui. November 2004

Gasto en Salud y Riqueza !

Luxemburgo

EE.UU.

R² = 0,56879

0

1000

2000

3000

4000

5000

6000

7000

8000

0 20000 40000 60000 80000 100000

Gasto en

Salud

/año

/cáp

ita, $PP

P

PBI per cápita, $PPP

GDP PER CAPITA Y GASTO PER CAPITA EN SALUD, OECD 2007

OECD Economic Data 2009, OECD

•  EN LÍNEA CON DINÁMICA SOCIO-DEMOGRÁFICA

C o s t o y S a l u d

o  El gasto se concentra en lo más frecuente: añosos y cardiovascular

Relación crítica Salud

Gasto Longevidad


Heal th , L i fe Expectancy, and Heal th Care Spending among the Elder ly


N=16.964, Medicare >69 años

1992 – 1998: 3/año: Medicare Current beneficiary Survey

Nagi score

IADL

ADL

Ins2tucionalizado

Muerto

Limitaciones: •  Nagi +1 •  IADL+1 •  ADL+1 •  Instit.+ •  Muerto+

Esperanza de vida y estado funcional

n engl j med

349;11

www.nejm.org september

11

,

2003

The

new england journal

of

medicine

1052

Because our estimates of life expectancy andcumulative expenditures are complex functions ofthe transition probabilities, we used the bootstrapmethod to estimate standard errors.

27

We sampledrespondents from 67 primary sampling-unit groups.Within each group, we sampled Medicare benefici-aries with replacement with size equal to one lessthan the original group size. We then estimated thetransition probabilities of this bootstrap samplewith multivariate hazard models, as describedabove, and computed average life expectancy andexpenditures on the basis of simulations of 25,000persons at the age of 70. We performed this setof calculations 1000 times. Standard errors werecomputed from these 1000 estimates. Comparisonsbetween groups were performed with the use oftwo-sample t-tests. All reported differences are sig-nificant at the level of P!0.05 for a two-sided test.The relative standard errors for the functional stateor self-reported state of health in the figures wereless than 10 percent, except that in the figuresshowing life expectancy and expenditures in rela-tion to functional state, the relative standard errorsfor years lived and expenditures incurred in nonin-stitutional states for persons institutionalized atage 70 were about 25 percent.

At 70 years of age, 28 percent of the study popula-tion had no functional limitations, 40 percent hadonly Nagi limitations, 12 percent had at least onelimitation in an instrumental activity of daily liv-ing but no limitations in activities of daily living, 18percent had a limitation in an activity of daily liv-ing, and 2 percent were institutionalized (data notshown). At age 70, total life expectancy was 13.2years, of which 52 percent were active years (i.e., al-most 7 years with either no limitations or only Nagilimitations) (Table 2). Total expenditures for med-ical care from age 70 to death were about $140,700.The average expenditures per year increased withworsening health status, from about $4,600 for per-sons reporting no limitations to about $45,400 forinstitutionalized persons. The expected expendi-tures for men were lower than those for women.Men actually had higher expenditures per year in ev-ery health state but had lower total expendituresbecause of a shorter life expectancy and also feweryears in the health states that incurred the greatestexpenditures. Blacks had both a lower overall lifeexpectancy and a lower active life expectancy thanwhites, but had similar levels of expenditures.

results

Figure 1. Life Expectancy at 70 Years of Age According to Functional State at the Age of 70.

The shading in the bars indicates the expected number of years lived in vari-ous functional states. For example, a person with no limitations at the age of 70 is estimated to live an additional 14.3 years, on average. Of those 14.3 years, 0.7 will be spent in an institution, 4.9 with a limitation in at least one in-strumental activity of daily living (IADL) or activity of daily living (ADL), and 8.7 (61 percent of total life expectancy) with no limitation or only Nagi limita-tions. Instrumental activities of daily living, activities of daily living, and Nagi limitations are described in the Methods section.

Tota

l Life

Exp

ecta

ncy

(yr

)

Functional State at 70 Years of Age

16

14

12

10

8

6

4

2

0

No lim

itatio

n

Nagi li

mita

tion

IADL l

imita

tion

ADL lim

itatio

n

Insti

tutio

naliz

ed

No limitation or Nagi only

IADL or ADLlimitation

Institutionalized

Figure 2. Life Expectancy at 70 Years of Age According to Self-Reported Health at the Age of 70.

The shading in the bars indicates the expected number of years lived in vari-ous states of health. For example, a person who reports excellent health at the age of 70 is estimated to live an additional 13.8 years, on average. Of those 13.8 years, 2.7 will be lived in fair or poor health, 3.7 in good health, and 7.3 (53 percent of total life expectancy) in very good or excellent health.

Tota

l Life

Exp

ecta

ncy

(yr

)

Self-Reported Health at 70 Years of Age

16

14

12

10

8

6

4

2

0

Exce

llent

Very

good

Good

Fair

Poor

Excellent or very good

Good

Fair or poor

The New England Journal of Medicine Downloaded from nejm.org by Carlos Javier Regazzoni on August 18, 2011. For personal use only. No other uses without permission.


n engl j med

349;11


11

,

2003

The

new england journal

of

medicine

1052

Because our estimates of life expectancy andcumulative expenditures are complex functions ofthe transition probabilities, we used the bootstrapmethod to estimate standard errors.

27

We sampledrespondents from 67 primary sampling-unit groups.Within each group, we sampled Medicare benefici-aries with replacement with size equal to one lessthan the original group size. We then estimated thetransition probabilities of this bootstrap samplewith multivariate hazard models, as describedabove, and computed average life expectancy andexpenditures on the basis of simulations of 25,000persons at the age of 70. We performed this setof calculations 1000 times. Standard errors werecomputed from these 1000 estimates. Comparisonsbetween groups were performed with the use oftwo-sample t-tests. All reported differences are sig-nificant at the level of P!0.05 for a two-sided test.The relative standard errors for the functional stateor self-reported state of health in the figures wereless than 10 percent, except that in the figuresshowing life expectancy and expenditures in rela-tion to functional state, the relative standard errorsfor years lived and expenditures incurred in nonin-stitutional states for persons institutionalized atage 70 were about 25 percent.

At 70 years of age, 28 percent of the study popula-tion had no functional limitations, 40 percent hadonly Nagi limitations, 12 percent had at least onelimitation in an instrumental activity of daily liv-ing but no limitations in activities of daily living, 18percent had a limitation in an activity of daily liv-ing, and 2 percent were institutionalized (data notshown). At age 70, total life expectancy was 13.2years, of which 52 percent were active years (i.e., al-most 7 years with either no limitations or only Nagilimitations) (Table 2). Total expenditures for med-ical care from age 70 to death were about $140,700.The average expenditures per year increased withworsening health status, from about $4,600 for per-sons reporting no limitations to about $45,400 forinstitutionalized persons. The expected expendi-tures for men were lower than those for women.Men actually had higher expenditures per year in ev-ery health state but had lower total expendituresbecause of a shorter life expectancy and also feweryears in the health states that incurred the greatestexpenditures. Blacks had both a lower overall lifeexpectancy and a lower active life expectancy thanwhites, but had similar levels of expenditures.

results

Figure 1. Life Expectancy at 70 Years of Age According to Functional State at the Age of 70.

The shading in the bars indicates the expected number of years lived in vari-ous functional states. For example, a person with no limitations at the age of 70 is estimated to live an additional 14.3 years, on average. Of those 14.3 years, 0.7 will be spent in an institution, 4.9 with a limitation in at least one in-strumental activity of daily living (IADL) or activity of daily living (ADL), and 8.7 (61 percent of total life expectancy) with no limitation or only Nagi limita-tions. Instrumental activities of daily living, activities of daily living, and Nagi limitations are described in the Methods section.

To

tal L

ife

Exp

ecta

ncy

(yr

)


16

14

12

10

8

6

4

2

0

No lim

itatio

n

Nagi li

mita

tion

IADL l

imita

tion

ADL lim

itatio

n

Insti

tutio

naliz

ed



Institutionalized

Figure 2. Life Expectancy at 70 Years of Age According to Self-Reported Health at the Age of 70.

The shading in the bars indicates the expected number of years lived in vari-ous states of health. For example, a person who reports excellent health at the age of 70 is estimated to live an additional 13.8 years, on average. Of those 13.8 years, 2.7 will be lived in fair or poor health, 3.7 in good health, and 7.3 (53 percent of total life expectancy) in very good or excellent health.

To

tal L

ife

Exp

ecta

ncy

(yr

)

Self-Reported Health at 70 Years of Age

16

14

12

10

8

6

4

2

0

Exce

llent

Very

good

Good

Fair

Poor

Excellent or very good

Good

Fair or poor




Estado funcional y Gasto

n engl j med

349;11


11, 2003

health, life expectancy, and health care spending

1053

Expenditures incurred while a person had limi-tations in activities of daily living or was in an insti-tution accounted for a large part of total costs from70 years of age until death. For example, a personat age 70 could expect to live 34 percent of remain-ing life (4.5 years) with limitations in activities ofdaily living or in an institution but to incur 63 per-cent of medical expenditures (about $88,200) inthese health states (Table 2).

estimates of life expectancy and health care expenditures according to health status

Persons in better health at 70 years of age had alonger life expectancy than those in worse health(Fig. 1). Persons with no limitations had the long-est life expectancy, and institutionalized persons theshortest. Persons with better health were also ex-pected to be active for a longer period. For example,the 28 percent of persons 70 years of age who hadno limitations could expect to be active for 61 per-cent of their remaining years. In contrast, the 18percent of persons 70 years of age who had a limi-tation in an activity of daily living could expect to beactive for only 35 percent of their remaining 11.6years.

Persons who were living in the community atage 70, regardless of their state of health, could ex-pect to spend about 0.7 year in an institution. Per-sons in better health at age 70 might be expected tospend less time in an institution than persons withfunctional limitations, but persons in good healthlive longer, and longevity is associated with lack ofsocial support (e.g., widowhood) and frailty, andthus with a high risk of institutionalization. How-ever, in our study the annual risk of institutionaliza-tion was lower for those in better health at 70 yearsof age; they lived longer, but the expected time spentin an institution was the same as for persons in poor-er health.

The same pattern of longer life for persons inbetter health was found when we used self-report-ed health status as a measure of health (Fig. 2).Those who reported excellent health at 70 years ofage had a life expectancy of 13.8 years, with most ofthat time spent in excellent or very good health.Those who reported poor health had a life expect-ancy of 9.3 years, with most of that time spent infair or poor health.

Persons without functional limitations at 70years of age who lived longer did not incur higherhealth care expenditures (Fig. 3). Health care ex-

penditures for persons 70 years of age or older whowere living in the community at 70 years of agevaried little according to initial health status. Per-sons without functional limitations incurred an es-timated $136,000 in medical expenses from age70 until death, as compared with an estimated$145,000 for persons with a limitation in at leastone activity of daily living. Only those who were ini-tially in an institution had much higher expendi-tures, which were the consequence of high nursinghome costs. When we categorized persons only ac-cording to functional status, with no separate cate-gory for those institutionalized, and defined func-tional status as both having difficulty and receivinghelp with instrumental activities of daily living oractivities of daily living, those in better functionalstates had greater longevity, but there was little vari-ation in expected expenditures (data not shown).Similarly, health care expenditures from the age of70 years and onward varied little according to theinitial self-reported health state, despite differenc-es in longevity (Fig. 4).

Figure 3. Expected Expenditures for Health Care from 70 Years of Age until Death According to Functional State at the Age of 70.

Expenditures are in 1998 dollars. The shading in the bars indicates estimated health care expenditures for persons in various functional states. For example, a person with no limitation at the age of 70 is estimated to have cumulative health care expenditures of about $136,000 from the age of 70 until death. Of this amount, about $32,000 will be spent while the person is institutionalized, about $60,000 for care while the person has a limitation in at least one instru-mental activity of daily living (IADL) or activity of daily living (ADL), and about $44,000 (32 percent of total expenditures) for care in the absence of limita-tions or with only Nagi limitations. Instrumental activities of daily living, activ-ities of daily living, and Nagi limitations are described in the Methods section.

Hea

lth C

are

Expe

nditu

res

($)


250,000

200,000

150,000

100,000

50,000

0

No lim

itatio

n

Nagi li

mita

tion

IADL l

imita

tion

ADL lim

itatio

n

Insti

tutio

naliz

ed



Institutionalized



•  La limitación funcional a los 70 predijo: – Menor expectativa

de vida –  Igual gasto

acumulado en salud •  Institucionalizados

a los 70, gasto mucho mayor


INEQUIDAD Nuevos Escenarios para la Salud 4

Inequidad

B"

C"

D"

A"Prom"

5"10"15"20"25"30"35"40"45"50"55"60"65"70"

Def

unci

ones

en

<1 a

ño/1

.000

nv!

Mejoraron: Jujuy, E Ríos, R Negro, S del Estero, Chubut, S Cruz, S Fe"Empeoraron: La Pampa, S Juán"

Adelantadas: Mendoza, Neuquén, Bs As, CABA, T del Fuego"Resagadas: Chaco, Salta, Misiones, La Rioja, Corrientes, Tucumán, Catamarca, Formosa, San Luís"Promedio País"

ESCENARIO Nuevos Escenarios para la Salud 5

¿Qué paradigma? •  ¿CÓMO DAR RESPUESTA A ESTA

NUEVA PROBLEMÁTICA? – Longevidad creciente – Enfermedades complejas y senescencia – Tecnologías cada vez más efectivas – Costos crecientes y concentrados – Inequidad (necesidad de repartir mejor)

Replanteo del Problema

G = Q . P

Casos = D . π

D = i

Tx= i

π= Demo , Acceso

Q = Casos . Tx

Q = Resultados

Eficiencia

nuevo escenario para la salud

Health & Medicine