dinámica del gasto en salud

Gasto en Salud y Financiamiento

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

Comparación: Gasto annual en sa lud, 1980–2006 !

$0

$1.000

$2.000

$3.000

$4.000

$5.000

$6.000

$7.000 United States Germany Canada Netherlands France Australia United Kingdom

Gasto annual promedio en salud ($US PPP*)

Evolución del Gasto en Salud, EE.UU.

-‐2 -‐1 0 1 2 3 4 5 6 7

2000-‐2007 2007-‐2010 2010-‐2013 Crecim

iento an

ual promed

io

Tasa anual promedio de crecimiento, Gasto Nacional en Salud real, per cápita

Elaboración propia, en base a: White House, November 2013

Total!Cuidado Hospitalario!Servicios Profesionales!Medicamentos!Cuidados Institucionales!

Evo luc ión de l Gas to en Sa lud , EE.UU.

Tomado de

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QUÉ ES EL GASTO EN SALUD

Gasto en Salud

Gasto en Salud Recursos

Procesos

Resultados

Expresión Monetaria

Impacto Social •  Humano •  Económico

Gasto en Salud

Gasto> Componentes: •  Precios •  Uso (CanZdad)

CUÁNTO SE GASTA EN SALUD

Gasto en Salud

2006 2007 2008 2009 2010 2011 2012

GASTO CONSOLIDADO EN SALUD en millones de pesos 29.552 38.865 52.912 71.152 88.246 120.753 153.740

Atención pública de la salud 12.871 16.862 22.620 29.420 36.804 50.565 61.639

Obras sociales - Atención de la salud 12.885 16.723 22.727 31.385 38.681 53.160 69.665

INSSJyP - Atención de la salud 3.797 5.280 7.564 10.347 12.761 17.028 22.436

Gasto Consolidado* en Salud. Serie Anual 2006 / 2012.En millones de pesos.

Fuente: Sistema SIDIF y Secretaría de Política Económica, MECON * Gasto Consolidado es Gasto Público más Obras Sociales

Gasto Consolidado en Salud por Jurisdicción. Serie Anual 2006 / 2012. En millones de pesos

Fuente: Sistema SIDIF y Secretaría de Política Económica, MECON

2006 2007 2008 2009 2010 2011 2012

GASTO CONSOLIDADO EN SALUD en millones de pesos

29.552 38.865 52.912 71.152 88.246 120.753 153.740

Nacional 13.870 18.386 25.980 37.143 45.870 61.988 81.573

Provincial 13.612 17.874 23.516 30.013 36.862 51.471 63.715

Municipal 2.070 2.605 3.415 3.996 5.514 7.294 8.451

Gasto Consolidado en Salud apertura total . Serie Anual 2003 / 2012.En millones de pesos. A pesos corrientes

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

GASTO CONSOLIDADO EN SALUD Nacional + Provincial+ Municipal 15.980 18.874 23.717 29.552 38.865 52.912 71.152 88.246 120.753 153.740

NACIONAL 7.662 9.172 11.188 13.870 18.386 25.980 37.143 45.870 61.988 81.573 Atención pública de la salud 1.229 1.448 1.564 1.881 2.502 3.484 5.378 6.470 8.613 10.733 Obras sociales - Atención de la salud 4.237 5.243 6.641 8.193 10.604 14.932 21.418 26.639 36.347 48.405 INSSJyP - Atención de la salud 2.196 2.482 2.983 3.797 5.280 7.564 10.347 12.761 17.028 22.436

PROVINCIAL 7.304 8.429 10.903 13.612 17.874 23.516 30.013 36.862 51.471 63.715 Atención pública de la salud 4.733 5.503 7.143 8.920 11.755 15.721 20.046 24.821 34.658 42.455 Obras sociales - Atención de la salud 2.571 2.925 3.760 4.692 6.119 7.795 9.967 12.041 16.813 21.260 INSSJyP - Atención de la salud 0 0 0 0 0 0 0 0 0 0

MUNICIPAL 1.014 1.273 1.626 2.070 2.605 3.415 3.996 5.514 7.294 8.451 Atención pública de la salud 1.014 1.273 1.626 2.070 2.605 3.415 3.996 5.514 7.294 8.451 Obras sociales - Atención de la salud 0 0 0 0 0 0 0 0 0 0 INSSJyP - Atención de la salud 0 0 0 0 0 0 0 0 0 0

Fuente: Sistema SIDIF y Secretaría de Política Económica, MECON. Estimaciones propias.

Gasto Consolidado en Salud. Serie Anual 2006 / 2012.En millones de pesos


$ 29.552 $ 38.865

$ 52.912 $ 71.152

$ 88.246

$ 120.753

$ 153.740

0

20.000

40.000

60.000

80.000

100.000

120.000

140.000

160.000

180.000

2006 2007 2008 2009 2010 2011 2012

$

Año

Gasto consolidado nominal anual en Salud 2006 / 2012- en millones de pesos (Total)

Gasto Consolidado en Salud. Año 2012

$ 61.638,97

$ 69.665,12

$ 22.436,10

Distribución Gasto Conosolidado en Salud 2012 en millones de pesos

Atención pública de la salud

Obras sociales - Atención de la salud

INSSJyP - Atención de la salud


Gasto Consolidado en Salud por Jurisdicción. Año 2012.En millones de pesos

Fuente: Sistema SIDIF , Secretaría de Política Económica y Presupuesto Nacional. MECON. Estimaciones propias

$ 81.573 $ 63.715

$ 8.451

Distribución Gasto Conosolidado en Salud por jurisdicción. Año 2012 en millones de pesos

Nacional

Provincial

Municipal

Gasto Consolidado en Salud. Serie Anual Nominal 2006 / 2012 como % del PBI .En millones de pesos.

Fuente: Sistema SIDIF y Secretaría de Política Económica, MECON. INDEC. Estimaciones propias.

2006 2007 2008 2009 2010 2011 2012

PBI precios corrientes ($) 654.439 812.456

1.032.758 1.145.458

1.442.655

1.842.022

2.164.246

Total Gasto Consolidado en Salud 29.552 38.865 52.912 71.152 88.246 120.753 153.740 Gasto consolidado en Salud como % del PBI

4,5% 4,8% 5,1% 6,2% 6,1% 6,6% 7,1%

Aportes de salud, Argentina, por sector, 2012. F Tobar

QUIENES GASTAN Gasto en Salud

QUIENES GASTAN> LAS PERSONAS DE EDAD

Gasto en Salud

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 edades, EE.UU 1999

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

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 (percenFlo)

ParFcipación en el Gasto en Salud, según canFdad 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 Gasto

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

PercenFlo de Gasto

ParFcipación 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

•  El 10% de la población concentra 60% del gasto Personas: - M a y o r e s d e 4 5 a ñ o s d e e d a d - Q u e e s t á n m á s e n f e r m a s

Concentración del Gasto

Esperanza a los 75 años

9,5

10

10,5

11

11,5

12

Años de vida

promed

io a parFr de los 7

5 añ

os de ed

ad

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

QUIENES GASTAN> LOS MÁS POBRES

Gasto en Salud

Mul

tim

orbi

lidad

y

stat

us

Articles

www.thelancet.com Published online May 10, 2012 DOI:10.1016/S0140-6736(12)60240-2 3

ResultsWe analysed data from 1 751 841 patients (about a third of the Scottish population) from 314 Scottish medical practices. Table 1 shows the demographic characteristics of the study population, the proportion of those with multimorbidity, and the proportion with physical and mental health comorbidity. Men and women were equally represented, as were all deprivation deciles. 42·2% (95% CI 42·1–42·3) of the population had one or more chronic morbidities, 23·2% (23·1–23·2) had multimorbidity, and 8·3% (8·3–8·4) had physical and mental health comorbidity. Of people with at least one morbidity, 54·9% (54·8–55·0) had multimorbidity and 19·8% (19·8–19·9) had physical and mental health comorbidity. Most people with common chronic mor-bidities had at least two, and frequently more, other disorders (appendix).

The number of morbidities and the proportion of people with multimorbidity increased substantially with age (table 1). By age 50 years, half of the population had at least one morbidity, and by age 65 years most were multimorbid (fi gure 1). However, in absolute terms, more people with multimorbidity were younger than 65 years than 65 years and older (210 500 vs 194 966), although older people had more morbidities on average (table 1).

The crude prevalence of multimorbidity increased modestly with the deprivation of the area in which patients lived (19·5%, 95% CI 19·3–19·6, in the most affl uent areas vs 24·1%, 23·9–24·4, in the most deprived; diff erence 4·6%, 95% CI 4·3–4·9; table 1). However, this fi nding should be interpreted with caution because the population in more deprived areas was, on average, younger (median age 37 years [IQR 21–53] in the most deprived areas vs 42 years [IQR 22–58] in the most affl uent areas). People living in more deprived areas were more likely to be multimorbid than were those living in the most affl uent areas at all ages, apart from those aged 85 years and older (fi gure 2). Young and middle-aged adults living in the most deprived areas had rates of multimorbidity equivalent to those aged 10–15 years older in the most affl uent areas (fi gure 2 and appendix).

8·3% (95% CI 8·3–8·4) of all patients, and 36·0% (35·9–36·2) of people with multimorbidity, had both a physical and a mental health disorder. The prevalence of physical and mental health comorbidity was higher in women than in men, and was substantially higher in older people than in younger people (table 1). Although older people were much more likely to have physical–mental health comorbidity, the absolute numbers were greater in younger people (90 139 people <65 years vs 55 912 people ≥65 years). The crude socioeconomic gradient in physical–mental health comorbidity was greater than that for any multimorbidity, with a near doubling in prevalence in the most deprived versus the most affl uent areas (table 1; diff erence 5·1%, 95% CI 4·9–5·3). In the logistic regres-sion analysis with the presence of any mental health

disorder as the outcome (table 2), we noted a non-linear association with age, so we included an age-squared term in the model. The predicted probability of having a mental health disorder increased with age up until about age 60 years, and then decreased (data not shown). Men were less likely to have a mental health disorder than were women, and those in the most deprived decile were more than twice as likely to have a mental health disorder than were those in the most affl uent decile (adjusted OR 2·28, 95% CI 2·21–2·32). The presence of a mental health disorder was strongly associated with the number of physical disorders that an individual had—eg, people with fi ve or more disorders had an OR of 6·74 (95% CI

0 disorders1 disorder2 disorders3 disorders4 disorders5 disorders6 disorders7 disorders≥8 disorders

100

0–4 5–910–14

15–1920–2

425–2

930–3

435–3

940–4

445–4

950–5

455–5

960–6

465–6

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475–7

980–8

485+

Age group (years)

Pati

ents

(%)

90

80

70

60

50

40

30

20

10

0

Figure 1: Number of chronic disorders by age-group

90

80

70

60

50

40

30

20

10

3·0

4·08·0

12·0

16·821·2

26·8

36·8

45·4

54·2

64·1

70·6

76·579·4

80·6

82·9

76·6

69·1

58·3

46·5

34·8

9·813·4

18·3

26·8

7·96·34·8

0

0–4 5–910–14

15–1920–2

425–2

930–3

435–3

940–4

445–4

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455–5

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475–7

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4≥8

5

Age group (years)

Pati

ents

wit

h m

ulti

mor

bidi

dty

(%)

Socioeconomicstatus

10987654321

Figure 2: Prevalence of multimorbidity by age and socioeconomic status On socioeconomic status scale, 1=most affl uent and 10=most deprived.

Barne` K, et al. Epidemiology of mulZmorbidity and implicaZons for health care, research, and medical educaZon: a cross-‐secZonal study. Lancet, May10, 2012 DOI:10.1016/S0140-‐6736(12)60240-‐2

QUIENES GASTAN> LA SALUD Y LOS MÉDICOS

Gasto en Salud

Efectos de los cuidados sobre la salud

•  3.A. Densidad de Médicos

•  3.B. Expansión de la Cobertura

•  3.C. Mayor Complejidad

3.a. Densidad de médicos

•  La mayor densidad de médicos se asocia a una menor mortalidad materna e infantil, independientemente de otras variables.

Sudhir Anand, Till Bärnighausen. Human resources and health outcomes: cross-‐country econometric study. Lancet 2004; 364: 1603–09

Articles

ResultsTable 1 shows the mean and SD of every dependent andindependent variable in natural units (non-log form). Themean values of the variables in the subsample of83 countries were similar to those in the 117-countrysample, with the exception of gross national income perperson (table 1).

Tables 2 and 3 present the results of the regressionequations. All coefficients had the expected signs in termsof the direction of the relation between the independentand dependent variables. The explained variation, orcoefficient of determination R2, in all equations was 79%or more, and the F tests decisively rejected the hypothesisof joint non-significance of the independent variables. Theindependent variables varied in both size (elasticity) andlevel of significance or p value.

Human resources for health in aggregate termssignificantly accounted for the three health outcomemeasures: maternal, infant, and under-five mortalityrates. Doctors, nurses, and midwives togethersignificantly lower these three mortality rates aftercontrolling for other variables used to account for thesehealth outcomes.

DiscussionOur findings are consistent across all model specificationsused. Thus, investment in human resources can beexpected to contribute significantly to the achievement ofthe MDGs—in addition to and independently of policiesthat bring about income growth, poverty reduction, andexpansion of female education.

As we expected, the human resources for healthelasticity of the maternal mortality rate is higher than thatof the infant and the under-five mortality rate. The effectof human resources for health is greater in reducingmaternal mortality than either infant or child mortalitybecause qualified medical personnel are able to address alarger proportion of conditions that put mothers atimmediate risk of death compared with infants orchildren. The higher human resources for health elasticityof under-five mortality than of infant mortality might bethe result of similar considerations: infants may face fewermedical conditions that put them at risk of death thanchildren between 1 and 4 years of age, because infantsmay be relatively better protected by breastfeeding andother behaviours of mothers.

In view of these broad findings for our aggregatemeasure of human resources for health, we proceeded toinvestigate the effect of specific types of health workers,and disaggregated the human resources for healthvariable into what we judged to be fairly homogeneouscategories. Thus, instead of aggregate human resourcesfor health, doctors and the combined category of nursesand midwives were entered separately in a parallel set ofregressions.

As was the case for human resources in aggregate,doctor density was important in accounting for all threehealth outcomes. Thus, we reject any notion of doctoranomaly or invisibility, as indicated in some earlierstudies.6–8 Our estimated elasticity of doctor density rangedfrom –0·174 to –0·386 (table 3). Further, the coefficient ofnurse density was significant (p=0·0443) when maternal

www.thelancet.com Vol 364 October 30, 2004 1607

Regressions without income poverty Regressions with income poverty

Dependent variables Maternal mortality Infant mortality Under-five mortality Maternal mortality Infant mortality Under-five mortality

Independent variablesConstant 13·596 10·362 9·234 10·302 9·009 7·598

(13·999) (16·264) (13·996) (8·390) (9·573) (7·741)<0·0001 <0·0001 <0·0001 <0·0001 <0·0001 <0·0001

Gross national income per person –0·776 –0·647 –0·660 –0·403 –0·500 –0·488(–7·326) (–9·307) (–9·174) (–2·959) (–4·784) (–4·484)<0·0001 <0·0001 <0·0001 0·0041 <0·0001 <0·0001

Income poverty .. .. .. 0·158 0·103 0·129(1·925) (1·633) (1·972)0·0580 0·1065 0·0522

Female adult literacy –0·292 –0·245 –0·256 –0·309 –0·272 –0·281(–1·351) (–1·726) (–1·742) (–1·471) (–1·689) (–1·670)

0·1793 0·0872 0·0843 0·1454 0·0952 0·0990Doctor density –0·325 –0·183 –0·225 –0·386 –0·174 –0·216

(–4·450) (–3·822) (–4·534) (–5·230) (–3·079) (–3·657)<0·0001 0·0002 <0·0001 <0·0001 0·0029 0·0005

Nurse density –0·162 –0·062 –0·047 –0·102 –0·044 –0·024(–2·034) (–1·186) (–0·874) (–1·250) (–0·702) (–0·364)

0·0443 0·2380 0·3838 0·2150 0·4848 0·7170n 117 117 117 83 83 83R2 0·808 0·827 0·835 0·823 0·799 0·808F 117·628 133·807 141·218 71·695 61·331 64·855p <0·0001 <0·0001 <0·0001 <0·0001 <0·0001 <0·0001

All dependent and independent variables were transformed into natural logarithms for the regressions. The numbers in the cells are b (regression coefficient), tb (t value of b), and p value.

Table 3: Multiple regression equations with doctors and nurses as separate independent variables

Articles

IntroductionHuman resources for health are clearly a prerequisite forhealth care, with most medical interventions needing theservices of doctors, nurses, or other types of healthworker.1,2 In turn, health care is one of the determinants ofpopulation health, with other determinants includingsocioeconomic, environmental, and behavioural factors.These two relations generate a link between humanresources and population health, even if the link might beweakened by the presence of non-health-care factors.Here, we test the extent to which human resources affectpopulation health outcomes.

The population health outcomes that we focus on arethe standard measures of maternal, infant, and under-fivemortality. All three have been incorporated as indicators ofthe United Nations Millennium Development Goals(MDGs), and various exercises are underway by nationalgovernments, international agencies, and others toinvestigate how the mortality rate reduction targets can beachieved by the year 2015.3 The results of this study willhelp to assess the role of human resources for health inachieving the health MDGs, including tradeoffs with otherfactors.

The few cross-sectional studies that have studied theeffect of health workers on health outcomes have reacheddiffering conclusions. To our knowledge, there are fivecross-country studies that use either doctor density ordoctor and nurse densities as independent variables to

account for mortality outcomes. Robinson and Wharrad4,5

found that a high density of doctors has a beneficial effecton maternal, infant, and under-five mortality. By contrast,Cochrane and colleagues6 showed doctor density had anadverse effect on infant and perinatal mortality (they call ita doctor anomaly), but no effect on maternal mortality.Conversely, Kim and Moody7 recorded no significantassociation between doctor density and infant mortality,and Hertz and co-workers8 did not note an associationbetween doctor density and either infant or maternalmortality. Three of these five studies also investigated thelink between nurse density and health outcomes, and allrecorded a nurse invisibility—in other words, noassociation between nurse density and maternal mortality,infant or under-five mortality, and infant mortality.4,5,7

All five studies have relevant shortcomings, which stemfrom the methods, variables, and procedures they use.They all used national income per person as anindependent variable, but they all measured nationalincome in US$ at market exchange rates rather than ininternational dollars at purchasing power parity (PPP)rates. This method will exaggerate the real income gapbetween richer and poorer countries and lead to a biasedestimate of the income coefficient. None of the studiesincluded absolute poverty as an explanatory variable,which has been shown to have an effect on healthoutcomes independent of average income per person.9

Furthermore, all five studies used stepwise regression to

Lancet 2004; 364: 1603–09

See Comment page 1558

University of Oxford,Department of Economics,Oxford, UK (Prof S Anand DPhil);Harvard University, GlobalEquity Initiative, Cambridge,MA, USA (Prof S Anand); andHarvard School of PublicHealth, Department ofPopulation and InternationalHealth, Boston, MA, USA (T Bärnighausen MD)

Correspondence to: Prof Sudhir Anand, St Catherine’sCollege, Oxford OX1 3UJ, [email protected]

www.thelancet.com Vol 364 October 30, 2004 1603

Human resources and health outcomes: cross-countryeconometric studySudhir Anand, Till Bärnighausen

SummaryBackground Only a few studies have investigated the link between human resources for health and health outcomes, andthey arrive at different conclusions. We tested the strength and significance of density of human resources for healthwith improved methods and a new WHO dataset.

Methods We did cross-country multiple regression analyses with maternal mortality rate, infant mortality rate, andunder-five mortality rate as dependent variables. Aggregate density of human resources for health was an independentvariable in one set of regressions; doctor and nurse densities separately were used in another set. We controlled for theeffects of income, female adult literacy, and absolute income poverty.

Findings Density of human resources for health is significant in accounting for maternal mortality rate, infant mortalityrate, and under-five mortality rate (with elasticities ranging from –0·474 to –0·212, all p values !0·0036). Theelasticities of the three mortality rates with respect to doctor density ranged from –0·386 to –0·174 (all p values!0·0029). Nurse density was not associated except in the maternal mortality rate regression without income poverty(p=0·0443).

Interpretation In addition to other determinants, the density of human resources for health is important in accountingfor the variation in rates of maternal mortality, infant mortality, and under-five mortality across countries. The effect ofthis density in reducing maternal mortality is greater than in reducing child mortality, possibly because qualifiedmedical personnel can better address the illnesses that put mothers at risk. Investment in human resources for healthmust be considered as part of a strategy to achieve the Millennium Development Goals of improving maternal healthand reducing child mortality.

Mortalidad materna, e InfanFl : GDP/Cápita; Densidad de Médicos

3.b. Expansión de la cobertura

•  El aumento de la cobertura en salud, se asocia a reducciones de la mortalidad de la población, y a un incremento de la accesibilidad.

Medicare y medicaid

•  Hay estados que vienen expandiendo el Medicaid desde el año 2000.

•  Esa política: ¿cambió la mortalidad?

E l C a s o M e d i c a i d

Sommers BD, Baicker K, Epstein AM. Mortality and access to care among adults ager State Medicaid expansions. N Engl J Med, July 25, 2012

Diseño

•  -Mortalidad adultos ⁄

•  -Percepción de salud ⁄

•  -Accesibilidad ⁄


5 años 5 años

Expansión del Medicaid: •  Jóvenes 19 – 64 años •  Sin hijos •  Ingresos <100% línea de

pobreza

•  Arizona •  Maine •  New York

•  N Hampshire •  Pennsylvania •  Nevada

Resultados

•  Luego de la expansión del Medicaid:

1. Reducción de la mortalidad. •  Reducción relativa del 6,1% de la mortalidad (Estados con

expansión del Medicaid versus controles). •  Reducción de la mortalidad luego de la expansión del

Medicaid de 25,4 muertes/100.000.

2. Aumento de la accesibilidad. 3. Mejoría de la autopercepción de

salud.


3.c. Efecto de la complej idad

•  La mayor complejidad hospitalaria se asocia a reducciones de la mortalidad.

Volumen hospi ta lar io y morta l idad !

N Engl J Med 2010;362:1110-8

La complejidad médica salva vidas, es más costosa, y agrega calidad

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 prevenZvas

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 aff 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 eff ect on service use, quality of care, and health. Th 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 eff 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.

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

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

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

• What are the consequences for health?

Th 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. Th 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 y efecZvamente recibido por los pacientes.

EE.UU., 12 áreas metropolitanas, 2003. RAND, The First NaZonal 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:




Calidad de Atención

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

100%

Proporción del tratamiento teóricamente recomendado, efecFvamente recibido por los pacientes.

EE.UU., 12 áreas metropolitanas, 2003. RAND, The First NaZonal 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:




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

QUIENES GASTAN> LOS MEDICAMENTOS Y TECNOLOGÍA

Gasto en Salud

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

Tecno log ía Tecnología y Salud

Nacim

15 años

45 años 65 años

0

20.000

40.000

60.000

80.000

100.000

120.000

140.000

160.000

180.000

1960 1970 1980 1990 2000

Cos

to p

or a

ño d

e vi

da g

anad

o (U

$S)

Cutler DM, Rosen AB, Vijan S. N Engl J Med 2006 G = Q . P

Q

P

•  Demanda de salud •  Tecnología/

Metodología

•  Mercado/Regulac. •  Tecnología/

Metodología

QUIENES PAGAN> EL ESTADO

Gasto en Salud

Gobierno y gasto en salud PERSPECTIVE

n engl j med 363;23 nejm.org december 2, 20102182

turers of drugs, devices, and equipment, as well as physicians and hospitals — prefer higher expenditures to lower ones. But isn’t that true in every country? The difficult question is why the special interests have more in-fluence over health policy in the United States than they do else-where. The answer probably lies in part in the structure of the U.S. political system, including the role of primary elections, long and ex-pensive election campaigns, the separation of powers, the numer-ous congressional committees and subcommittees with overlapping authority, and the need for super-majorities in the Senate in order to pass meaningful legislation. But the quirks of the political system can’t be the whole answer. If the U.S. public wanted a differ-ent outcome, over time they could move policy in that direction.

It should be noted that the higher expenditures in the Unit-ed States do confer some bene-fits. There is less likelihood of having to wait for a diagnostic or therapeutic procedure or to travel far to obtain it. Also, the amenities in hospitals, clinics, and physicians’ offices are usu-ally superior to those in other

countries that have a per capita income close to that of the Unit-ed States. It would be of interest to determine how these benefits are distributed and how they are valued by people at different in-come levels.

A second large difference be-tween health care in the United States and in countries with na-tional health insurance is the more important role of redistri-bution in the latter countries. Such redistribution is evident in the greater equality of access to care and in the sharing of costs through taxes on income or pay-roll, value-added tax or sales tax, or other forms of taxation that are either proportional or pro-gressive with respect to income. Of course, all insurance is redis-tributive after the fact. The large amount of care utilized by a small proportion of policy holders is paid from the premiums of oth-ers who use little care. The im-portant distinction is that under a national health insurance sys-tem, the redistribution occurs be-fore the event, since it is clear that some individuals will pay much less tax than the value of their insurance and some will pay much more.

Since redistribution plays a greater role in the health care systems of other countries than it does in the United States, there is an implication that a more egalitarian ethos holds sway in Europe, Canada, Australia, and New Zealand. From de Tocqueville to the present, many observers have commented on the stronger role of individualism in the Unit-ed States than elsewhere, but there is no consensus regarding its ex-planation. Possible contributors to the phenomenon include the heterogeneity of the population, the revolutionary origins of the country with its dedication to “life, liberty, and the pursuit of happiness,” and the absence of many centuries of a common lan-guage, history, and culture. In speculating about the possible rise of despotism in a democracy, de Tocqueville painted a grim pic-ture of individualism taken to the extreme. He wrote, “Each . . . living apart, was a stranger to all the rest — his children and private friends constitute to him the whole of mankind; as for the rest of his fellow citizens, he is close to them, but he sees them not; he exists but in him-self and for himself alone.”3

The lower spending and the greater redistribution in countries that have national health insur-ance are not independent phe-nomena. If spending in these countries were at U.S. levels, the taxation required to accomplish their redistribution goals would probably wreck the economy. Giv-en the social or political desire to redistribute health care resources, constraints on spending become a necessity. These constraints take various forms, such as controls over the number and specialty mix of physicians, limits on facilities

Government Payment for Health Care

So

urc

e o

f F

un

ds

(%)

90

40

50

10

60

20

30

0

70

80

1960 1970 1980 1990 2000 2007

Private

Government

Source of Funds for Personal Health Care Expenditures in the United States, 1960–2007.

The New England Journal of Medicine Downloaded from nejm.org by Carlos Javier Regazzoni on July 28, 2012. For personal use only. No other uses without permission.

Copyright © 2010 Massachusetts Medical Society. All rights reserved.

VR Fuchs. Government Payment for Health Care — Causes and Consequences. N Engl J Med 2010; 363: 2181-‐83

Gobierno y gasto en salud

VR Fuchs. Government Payment for Health Care — Causes and Consequences. N Engl J Med 2010; 363: 2181-‐83

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

100%

1960 1970 1980 1990 2000 2007

ParFcipación en el gasto personal en salud, según fuente de financiamiento, EE.UU.

Privado

Gobierno

0 2 4 6 8 10 12 14 16 18

United States Netherlands (2)

France Germany Denmark

Canada Switzerland

Austria Belgium (1)

New Zealand Portugal (2008)

Sweden United Kingdom

Iceland Greece (2007)

Norway Ireland OECD Spain

Italy Slovenia

Finland Slovak Republic Australia (2008)

Japan (2008) Chile

Czech Republic Israel

Hungary Poland Estonia

Korea Luxembourg (2008)

Mexico Turkey (2008)

GASTO TOTAL EN SALUD, COMO PORCENTAJE DEL PBI, 2009. OECD

Public Private

EE.UU. posee: -‐Menor parZcipación pública en el gasto en salud. -‐Pero mayor gasto total en salud

GASTO EN SALUD Y ECONOMÍA

Gasto en Salud

Gasto en Salud

•  PBI=C+G+I+(X-M)=DA=Y – El gasto en salud es una fracción del PBI – Pero todo país gasta todo su PBI – Considerar: “valor para la sociedad”

•  Eficiencia del gasto, en relación al “valor agregado”

•  Costo laboral – Pero “salud”, es parte de los costos de

salario •  Relacionar con “producZvidad”

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) incenZvado 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

Part icularidades del Gasto en Salud

1. Rol en las cuentas públicas –  Un peso gastado en salud no tiene efectos

fiscales diferentes de cualquier otro gasto público

–  Pero… •  Como es “esencial”, obliga al gasto: ¿Cómo gastar? •  El aumento del gasto en salud, se financia con

impuestos, con efectos en la economía •  Tragedia de los comunes

Fuchs VR. Health care is different-That’s why expenditure matters. JAMA 2010; 303: 1859-1860

Part icularidades del Gasto en Salud

2. Incertidumbre •  Riesgo

– Frecuencia de eventos – Eventos más costosos –  Población:

•  Más enferma •  Más demandante de servicios

•  Incertidumbre – Concentración del riesgo – Criterio longitudinal –  Predictores de eventos: Predictores de gasto

Fuchs VR. Health care is different-That’s why expenditure matters. JAMA 2010; 303: 1859-1860

Gasto en salud y PBI potencial PERSPECTIVE

n engl j med nejm.org2

ministering government health insurance programs; the net cost of private insurance; and spend-ing on public health, noncom-mercial research, and structures and equipment).

Excess growth decreased from more than 3% during 2003 to less than 1% starting in July 2005 and continuing, for the most part, until near the end of the reces-sion in June 2009. Excess growth exceeded 1% during the post- recession period, until May 2011, when it again dropped below 1%, going negative during the latter part of that year. If we use 1% as a threshold to denote moderation in excess health care spending, these data show that July 2005 marked the onset of moderation. Although the level of excess spend-ing was above 1% for a few months in 2006, that was the year in which Medicare Part D prescription-drug coverage began and pre-scription-drug spending was a major driver of excess spending. Without Part D spending, excess growth would have been 1% or less throughout the pre-reces-sion period starting in July 2005.

“Non–personal health care” contributed greatly to increased excess spending in 2003 and to reduced excess spending in 2008 and 2009. Although each com-ponent of this category repre-sents a relatively small share of overall spending, some of these components are quite volatile and therefore capable of notice-ably affecting excess-growth es-timates. The most important factor in 2003 was the net cost of private insurance (roughly the difference between premium rev-enues and payments to health care providers), which rose sharply. In 2008 and 2009, that net cost dropped sharply, which, com-bined with reduced spending on structures and equipment, drove down overall excess spending.

In a further analysis (see Fig. 2), we eliminated the volatility asso-ciated with these factors by fo-cusing strictly on personal health care spending. We grouped the data into four periods according to the timing of the recession and our conclusion that spending moderation began in July 2005. From January 2003 through June

2005, all categories grew faster than potential GDP, and excess growth in health care spending averaged 1.9%. From July 2005 through November 2007, excess growth averaged 0.5%, with spending on physician services actually growing more slowly than potential GDP. During the recession, there was 0.4% excess growth, with prescription-drug spending growing more slowly than potential GDP. In the post-recession period, excess growth has averaged 0.9%, but it acceler-ated initially, driven primarily by hospital spending, and then sharp-ly declined, bringing current levels close to zero. Spending for physi-cian services has grown more slowly than potential GDP since mid-2010, contributing to the slowdown.

Our analysis shows that cost moderation predated the recession by about 2.5 years, so the bend in the curve cannot be attributed solely to the economy. In fact, there was lower excess spending before the recession than after it (though this pattern emerges only when the low economy-wide in-

When the Cost Curve Bent

Total NHE

Other personal healthcare

Non–personal healthcare

Prescription drugs

Hospital care

Physician and clinicalservices

RecessionNet cost of privateinsurance

Medicare Part Dprescription-drug

coverage

Net cost of privateinsurance and spending

on structuresand equipment

Sp

end

ing

Gro

wth

in

Exc

ess

of

Po

ten

tial

GD

P (

%) 4

−2

−1

0

1

2

3

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Figure 1. Growth of National Health Expenditure (NHE) in Excess of Potential Gross Domestic Product (GDP), with Component Effects.The net cost of private insurance (a major contributor to increased excess spending in 2003 and reduced excess spending in 2008 and 2009) is premium revenues minus health care payments, whereas spending on structures and equipment represents investments in health care delivery systems. Medicare Part D, implemented in 2006, introduced prescription-drug coverage for the first time to Medicare beneficiaries and was a major cause of increased excess spending. Spending estimates are from Altarum Health Sector Economic Indicators. Estimates of potential GDP are from the Congressional Budget Office. Growth rates for each month are computed relative to the same month a year earlier, smoothed by means of a 3-month moving average.

The New England Journal of Medicine Downloaded from nejm.org by Carlos Javier Regazzoni on August 8, 2012. For personal use only. No other uses without permission.

Copyright © 2012 Massachusetts Medical Society. All rights reserved.

Roehrig C, Turner A, Hughes-‐Cromwick P, Miller G. When the Cost Curve Bent — Pre-‐Recession ModeraZon in Health Care Spending. N Engl J Med, August 8, 2012

Dinero público a la salud

Producción

$

Impuestos

Gobierno

Gasto Salud

Gasto en

Salud

Gasto Púb

lico

Precio

Uso (CanFdad)

Gasto Público -‐Cuanto mayor la parZcipación del Estado en el gasto en salud, mayor necesidad de contención de costos

Gasto en sa lud y dé f i c i t soberano

Costo de salud y economía

0 10 20 30 40 50 60 70 80 90 100

x x + 1

Costo en

Salud

P e r í o d o

¿Quién asume el aumento? •  EL SALARIO DEL TRABAJADOR?

o  Mayor cuota? o  Co-‐pagos?

•  EL INGRESO DEL EMPRESARIO? o  Mayor cuota patronal?

•  EL ESTADO? o  Más impuestos?

Eithoven AC, Fuchs VR. Employment-‐based health insurance: past, present, and future. Health Affairs 2006; 25:1538-‐1547


0 10 20 30 40 50 60 70 80 90

100

x x + 1

Cos

to e

n Sa

lud

Período

¿Quién asume el aumento? •  EL SALARIO DEL TRABAJADOR?

o  Mayor cuota o  Co-‐pagos



0 10 20 30 40 50 60 70 80 90

100

x x + 1

Cos

to e

n Sa

lud

Período

¿Quién asume el aumento? •  EL INGRESO DEL EMPRESARIO?

o  Mayor cuota patronal



0 10 20 30 40 50 60 70 80 90

100

x x + 1

Cos

to e

n Sa

lud

Período

¿Quién asume el aumento? •  EL ESTADO?

o  Más impuestos


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 Í 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

PAGO DE LA SALUD DE TODOS>NECESIDAD Y POSIBILIDAD

Gasto en Salud

“ … l a d i s p o n i b i l i d a d d e c u i d a d o s m é d i c o s v a r í a i n v e r s a m e n t e c o n l a n e c e s i d a d d e l o s m i s m o s e n l a p o b l a c i ó n , h e c h o q u e s e m a g n i fi c a e n o p e r a n d o f u e r z a s d e m e rc a d o … ”

Ley del cuidado inverso

Hart JT. The inverse care law. Lancet 1971; i:405-412

Accesibilid

ad

Necesidad

Ley del cuidado inverso en salud

Mul

tim

orbi

lidad

y

stat

us

Articles

www.thelancet.com Published online May 10, 2012 DOI:10.1016/S0140-6736(12)60240-2 3

ResultsWe analysed data from 1 751 841 patients (about a third of the Scottish population) from 314 Scottish medical practices. Table 1 shows the demographic characteristics of the study population, the proportion of those with multimorbidity, and the proportion with physical and mental health comorbidity. Men and women were equally represented, as were all deprivation deciles. 42·2% (95% CI 42·1–42·3) of the population had one or more chronic morbidities, 23·2% (23·1–23·2) had multimorbidity, and 8·3% (8·3–8·4) had physical and mental health comorbidity. Of people with at least one morbidity, 54·9% (54·8–55·0) had multimorbidity and 19·8% (19·8–19·9) had physical and mental health comorbidity. Most people with common chronic mor-bidities had at least two, and frequently more, other disorders (appendix).

The number of morbidities and the proportion of people with multimorbidity increased substantially with age (table 1). By age 50 years, half of the population had at least one morbidity, and by age 65 years most were multimorbid (fi gure 1). However, in absolute terms, more people with multimorbidity were younger than 65 years than 65 years and older (210 500 vs 194 966), although older people had more morbidities on average (table 1).

The crude prevalence of multimorbidity increased modestly with the deprivation of the area in which patients lived (19·5%, 95% CI 19·3–19·6, in the most affl uent areas vs 24·1%, 23·9–24·4, in the most deprived; diff erence 4·6%, 95% CI 4·3–4·9; table 1). However, this fi nding should be interpreted with caution because the population in more deprived areas was, on average, younger (median age 37 years [IQR 21–53] in the most deprived areas vs 42 years [IQR 22–58] in the most affl uent areas). People living in more deprived areas were more likely to be multimorbid than were those living in the most affl uent areas at all ages, apart from those aged 85 years and older (fi gure 2). Young and middle-aged adults living in the most deprived areas had rates of multimorbidity equivalent to those aged 10–15 years older in the most affl uent areas (fi gure 2 and appendix).

8·3% (95% CI 8·3–8·4) of all patients, and 36·0% (35·9–36·2) of people with multimorbidity, had both a physical and a mental health disorder. The prevalence of physical and mental health comorbidity was higher in women than in men, and was substantially higher in older people than in younger people (table 1). Although older people were much more likely to have physical–mental health comorbidity, the absolute numbers were greater in younger people (90 139 people <65 years vs 55 912 people ≥65 years). The crude socioeconomic gradient in physical–mental health comorbidity was greater than that for any multimorbidity, with a near doubling in prevalence in the most deprived versus the most affl uent areas (table 1; diff erence 5·1%, 95% CI 4·9–5·3). In the logistic regres-sion analysis with the presence of any mental health

disorder as the outcome (table 2), we noted a non-linear association with age, so we included an age-squared term in the model. The predicted probability of having a mental health disorder increased with age up until about age 60 years, and then decreased (data not shown). Men were less likely to have a mental health disorder than were women, and those in the most deprived decile were more than twice as likely to have a mental health disorder than were those in the most affl uent decile (adjusted OR 2·28, 95% CI 2·21–2·32). The presence of a mental health disorder was strongly associated with the number of physical disorders that an individual had—eg, people with fi ve or more disorders had an OR of 6·74 (95% CI

0 disorders1 disorder2 disorders3 disorders4 disorders5 disorders6 disorders7 disorders≥8 disorders

100

0–4 5–910–14

15–1920–2

425–2

930–3

435–3

940–4

445–4

950–5

455–5

960–6

465–6

970–7

475–7

980–8

485+

Age group (years)

Pati

ents

(%)

90

80

70

60

50

40

30

20

10

0

Figure 1: Number of chronic disorders by age-group

90

80

70

60

50

40

30

20

10

3·0

4·08·0

12·0

16·821·2

26·8

36·8

45·4

54·2

64·1

70·6

76·579·4

80·6

82·9

76·6

69·1

58·3

46·5

34·8

9·813·4

18·3

26·8

7·96·34·8

0

0–4 5–910–14

15–1920–2

425–2

930–3

435–3

940–4

445–4

950–5

455–5

960–6

465–6

970–7

475–7

980–8

4≥8

5

Age group (years)

Pati

ents

wit

h m

ulti

mor

bidi

dty

(%)

Socioeconomicstatus

10987654321

Figure 2: Prevalence of multimorbidity by age and socioeconomic status On socioeconomic status scale, 1=most affl uent and 10=most deprived.

Barne` K, et al. Epidemiology of mulZmorbidity and implicaZons for health care, research, and medical educaZon: a cross-‐secZonal study. Lancet, May10, 2012 DOI:10.1016/S0140-‐6736(12)60240-‐2

Mortal idad infanti l>Inequidad

@RegaCarlos

6,9 7,4 8,3 8,9

8,9

8,9 9,4

9,5

9,7

9,8

9,9 10,3

10,8

10,9

11,3

11,4

11,6

11,9

12

12,9

13,3

13,7

14,1

14,4 17,3

Neu

quén

T

del F

uego

C

ABA

Cat

amar

ca

La P

ampa

Sa

nta

Cru

z Rí

o N

egro

Men

doza

C

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ba

Chu

but

San

Juan

Sa

nta

Fe

Entre

Río

s Sa

n Lu

is

Mis

ione

s Bu

enos

Aire

s S

del E

ster

o

24 p

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os G

BA

Juju

y Sa

lta

Cha

co

La R

ioja

Tucu

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C

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ntes

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a

Mortalidad Infantil, año 2012. Defunciones <1año/1.000nv Elaboración propia en base a MSN, Anuario 2014

Salud>Inequidad

75

71

69

CABA Pcia Bs As Chaco

Esperanza de vida al nacer (años) Varones

Periodo 2008-2010. INDEC, erie Analisis Demografico no 37.

@RegaCarlos

5 años de diferencia

POBREZA E INEQUIDAD !

0 10 20 30 40 50 60 70 80 90

100

Parti

cipac

ión

(%) d

el 10

% m

ás ri

co e

n la

rique

za

tota

l del

país

Décadas

Participación porcentual del 10% más rico sobre la riqueza total del país

Francia

UK

USA

Suecia

Fuente: Elaboración propia en base a Thomas Piketty. The Capital in the 21st Century. Harvard University Press - March 2014 http://piketty.pse.ens.fr/capital21c

Inequidad en Perspectiva

@RegaCarlos

25%

30%

35%

40%

45%

50%

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Parti

cipac

ión

del 1

0% m

ás ri

co e

n el

ingre

so to

tal

Sources and series: see piketty.pse.ens.fr/capital21c.

Desigualdad en el ingreso: Europa vs. EE.UU, 1900-2010

U.S.

Europe

Inequidad y Salario Mínimo

@RegaCarlos

$0,00 $1,20 $2,40 $3,60 $4,80 $6,00 $7,20 $8,40 $9,60

$10,80 $12,00

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Salar

io m

ínim

o/ho

ra d

e tra

bajo

Sources and series: see piketty.pse.ens.fr/capital21c.

Salario mínimo, United States, 1950-2013

Minimum wage in 2013 dollars Minimum wage in current dollars

,

Inequidad y Capital

@RegaCarlos

0%

1%

2%

3%

4%

5%

6%

Tasa

anu

al de

reto

rno

(cap

ital) o

de

crec

imien

to (G

DP)

Sources and series : see piketty.pse.ens.fr/capital21c

Retorno al capital luego de impuestos, Vs. Crecimiento del producto mundial, desde la antigüedad hasta 2100

Pure rate of return to capital (after tax and capital losses)

Growth rate of world output g

¿QUIÉN VA A PAGAR? Gasto en salud

GASTO EN SALUD Y VALOR

Gasto en Salud

ArgenZna 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 salud, PPP-‐U$/capita, total, y Mortalidad en <5 años-‐ WHO

Gasto en Salus>Eficiencia

Costo-eficiencia Ø Gasto = Cantidad x Precio

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B

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0 20 40 60 80 100

Nivel de Gasto

Nivel de salud

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Nivel de Gasto

Nivel de salud

Más de lo mismo, o mejor rendimiento

Optimización del gasto •  Mayor eficiencia o menor precio

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Gasto

salud

Menor precio Más eficiencia

Optimización del gasto

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salud

PRECIO

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EFICIENCIA

$ Menor precio $ Más resultados

La Tragedia de lo Común

Garret Harding. The tragedy of the commons. Science 1968; 162: 1243 Farik Fadul. The Tragedy of the Commons Revisited. NEJM, August 26th, 2009;

dinámica del gasto en salud

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