Evolución  de  la  salud  de  los  

argentinos;  nuevo  escenario

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

Tendencias 1.  Longevidad

2. Estilo de vida

3.  Tecnología

4.  Inequidad

Longevidad

Longevidad

Sobrevida:

a) Mayor

b) Saludable

Aumento  de  Sobrevida

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  

Aumento  de  Sobrevida

• Menor Asar

• Mejor Senilidad

¿Por  qué?

Asar  y  Mortalidad

EDAD

Prob

abilida

d  de

 Morir

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

Dis t r i buc ión  de  mor t a l idad

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

20.000

Muertes/100.000,  Año  2009

Argentina

Japón

Angola

Asar

Sobrevida  a  los  75  años

9,5"

10"

10,5"

11"

11,5"

12"

Año

s de

vid

a pr

omed

io a

par

tir d

e lo

s 75

año

s de

eda

d!

EE.UU. Expectativa de vida a los 75 años!CDC. Health, United States 2009 Web Update"

Aumento  de  Sobrevida

Causas •  Menor mortalidad infantil

•  Menor mortalidad del adulto o  Infecciones

o  Accidentes

o  Enfermedad vascular

o  Cáncer

Reserva  funcional  orgánica  y  Atrición

EDAD

Reserva  Fu

nciona

l

N

N+

N-­‐‑

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

Vitalidad

Probabilidad  de  Morir

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

EDAD

Prob

abilida

d  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

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1, 2

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cien

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ag.o

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

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

Centenarios

Cohortes y edad a la cual el 50% estará vivo

102 Canadá

103 Canadá

103 Canadá

104 Japón

105 Japón

106 Japón

=Año  de  nacimiento  de  la  cohorte

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

Tendencias:  1.  Longevidad

Argentina

Esperanza  de  vida

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Año 2009

Argentina

Canadá

Japón

Angola

Expectativa  a  los  65

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

2009 2000 1990

Variación  porcentual

Ambos sexos, variación porcentual

Argentina

Brasil

Japón

Mortalidad  a  edad  avanzada

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

0,16

1990 2000 2009

Mortalidad  anual  c/100.000

Mortalidad (c/100.000) a los 85-89 años.

Argentina

Japón

Curvas  de  defunciones

0

4000

8000

12000

16000

20000

Defunciones  cada  100.000

Defunciones, ambos sexos, c/100.000, >35 años

Argentina 2009

Japón

La  Argentina  tiene  un  exceso  de  muertes  en  jóvenes

Estilo  de  vida

Riesgo:  enfermar  o  morir

Global Burden of Disease

Factores  de  Riesgo  en  la  Argentia

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

Hipertensión  arterial Hipercolesterolemia Diabetes

Sedentarismo

Tecnología

Tecnología a) Más tecnología

b) Más efectiva

c) Más costosa

a) Más tecnología

b) Más efectiva

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 Longevidad ganada con medicación

Resto de longevidad ganada

Contribución  relativa  de  diferentes  servicios  de  salud  al  crecimiento  total  del  

gasto,  USA  1996-­‐‑2017

Otros 17.8%

Otros cuidados de salud 12.1%

Domiciliarios 1.8%

Geriátricos 4.4%

Fármacos 14.3%

Médicos 21.0%

Hospitales 28.6%

c) Más costosa

Tendencias:  2.  Tecnología

Argentina

Tecnología

Japón 34%

USA 21%

Chin 10%

UK 7%

Ger 6%

Fr 6%

Resto 16%

Origen  de  Publicaciones  Científicas,  2004-­‐‑2008

Inequidad

Inequidad

a)  Ingreso

b) Recursos de Salud

c)  En mortalidad

Ingreso

1% 13%

20% 1,3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Población Ingreso Finance  &  Development  September  2011

Recursos    en  salud

18%

89%

82%

11%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Población Gasto en Salud

Subdesarrollados

Desarrollados

G  Schieber,  Health  Affairs  1999  

Ingreso  y  Mortalidad

0

0,5

1

1,5

2

2,5

>$70  mil $50-­‐‑70  mil $30-­‐‑50  mil $20-­‐‑30 $<15  mil

Od

ds

Ratio

, pa

ra m

uert

e

Ingreso Anual

PROBABILIDAD  RELATIVA  ANUAL  DE  MORIR/  INGRESO  ANUAL McDonough et al. American Journal of Public Health 1997

Mortalidad  y  Economía �

DZA

ARG

AUS

AUT

BGD

BRB

BEL

BENBOL

BRA

BFA

BDI

CMR

CAN

CPV

TCD

CHL

CHN

COL

COG

CRI

CIV

DNK

DOM

ECU

EGY

SLV

GNQETH

FINFRA GAB

GMB

GHA

GRC

GTM

GIN

GNB

HND

HKG

ISL IND

IDN

IRN

IRL

ISRITA

JAM

JPN

KEN

KOR

LSO

LUX

MDG

MWI

MYS

MLI

MUS

MEX

MAR

MOZ

NPL

NLD

NZL

NICNER

NGA

NOR PAK

PAN

PRY

PER

PHL

PRTROM

RWASEN

SYC

ZAF

ESP

LKASWE

CHE

TZA

THA

TGO

TTOTUR

UGA

GBR

USA

URY

VENZMB

ZWE

!2

02

46

An

nu

al G

row

th o

f p

er

Ca

pita

In

com

e 1

96

0!

20

00

.1 .2 .3 .4 .5 .6Adult mortality, male, age 15!60 (WB)

Figure 2 ! Growth 1960!2000 and Adult Mortality

Peter Lorentzen, John McMillan, Romain Wacziarg!. Death and development. Center for Global Business and the Economy at the Stanford Graduate School of Business. July 2007

Tendencias:  3.  Inequidad

Argentina

Ingreso  medio  total/familia  2º   t r imes t r e   2 010 ,   EPH

1 2 3 4 5 6 7 8 9 10

Ing

reso

to

tal f

am

ilia

r/m

es

($)

Decilos  de  hogares  (cada  uno  contiene  10%  de  la  población)

Recursos/Cápita/Año  (2010)

0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

$/Cápita/año

Estratificación

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"

Mortalidad  en  Gran  Buenos  Aires

0

10

20

30

40

50

60

70

80

90

100 (%)

Porcentaje acumulado de muertes por grupo etario Elaboración  propia,  Municipio  de  GBA,  año  2009

50% de las muertes: antes de 65 años

41%  81%  

1900 2000

US.  Sobrevivientes  a  los  65  años  de  

edad  

Mamografía

No: 76,4%

52,5% 47,6% 27,7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Sin instrucción Primario completo

Secundario completo

Universitario completo

Alguna mamografía en 2 años Mujeres, de 40 a 65 años. ENFR 2005

Mamografía  e  Ingreso

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

Mujeres,  de  40  a  65  años,  alguna  mamografía  en  2  años.  Elaboración propia, sobre ENFR 2005

Autopercepción

0%

20%

40%

60%

80%

100%

Ingreso  total  del  hogar/$/mes,  y  Autopercepción  de  Salud Elaboración  propia,  en  base  a  ENFR2005,  MSN

Mala

Regular

Buena

Muy  buena Excelente

  55%

Mujeres : Secundario Incompleto

30%

Varones: Informal /Desempleado

26,9 40,5 34,8

Total 0  a  13 14  a  22  

Edad  (años)

Pobreza/edad. 2006, EPH, INDEC

Pobre