Evolución de la salud de los argentinos
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Transcript of Evolución de la salud de los argentinos
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
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1, 2
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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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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