Projections of Health and Long Term Care public expendituresJoaquim Oliveira Martins (OECD and PSL,...

Projections of Health and Long Term Care public expenditures

VI International Congress Long-Term care and Quality of Life

Madrid, 23-24 May 2017

Joaquim Oliveira Martins (OECD and PSL, University Paris-Dauphine)

Characteristics and trends of health

spending

3 Source: OECD Health Statistics 2016

Wide dispersion of health expenditure across OECD & BRICs

0

2

4

6

8

10

12

14

16

18

0

2

4

6

8

10

12

14

16

18

IDN

IND

TUR

CH

NLV

AM

EX

RU

SB

RA

ES

TP

OL

LTU

SV

KH

UN

LUX

CO

LK

OR

ISR

CZE

CH

LG

RC

SV

NIS

LZA

FE

UP

RT

OE

CD

ES

PIT

AA

US

CR

IIR

LN

ZL FIN

GB

RN

OR

CA

NA

UT

BE

LD

NK

NLD

FRA

SW

ED

EU

JPN

CH

EU

SA

2015¹ 2010

Average (2015)

As a % of GDP

OECD Health expenditure is mainly allocated to individual health services (above 60% on average)

Health Expenditure by function, 2014 (or nearest year)

Source: OECD Health Statistics 2016, OECD

26 2718

2835 33

26

41

2734

27 2731 28 28 30

22

3329 28

1928 29 29 28 29

23 2230

2129 28

48 46

5240

31 3238

22

3628

34 34 30 33 33 3037

2631 31

3930 29 29 29 28

34 3426

3524 24

2 85 5

6 9 92

18 15 1924

2014

6 10 12

23

1218

14

4

18

28 2622

1525 27

20 15

1421 23 20 22

31

1517 13

10 15

1930 23

30

20

11

3520

15 2033

20

1112

20

1422

16 12

4 511

5 5 6 5 5 5 6 6 5 4 6 4 7 10 8 7 6 9 9 8 5 4 4 510 7 7 6 9

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Inpatient care* Outpatient care** Long-term care Medical goods Collective services

The public sector is the main source of Health financing in most OECD countries (above 70% on average)

Expenditure on health by type of financing, 2014 (or nearest year)


74

7 8

84

12

83

8

52

5 4

7279

4

2111

31

76

10

62

36

93

69 65 69 68

9

65

19 16

2

60

28

10

24 26

11

78 76 72 74

29

76 76

8

75

5666

45

66

13

37

6268

25

58

1

4746

58

31

4628 23

14 13 13 14 13 1611 17

1218

13 157

18 18 18 22 23 1920 22

13 1425

15 2028 28 27

2433

39 3537

41

11

1 2 2 15 6 5 4

144 5

13 6 4

15 13

5

13 93 5 7

117

14 6 5

34

0

10

20

30

40

50

60

70

80

90

100

Government schemes Compulsory health insurance Out-of-pocket Voluntary health insurance Other%

Public Health spending has displayed a long-term tendency to increase in % of GDP

6 Source: OECD Health database (2016).

4.5

5

5.5

6

6.5

7

Public Health and Long-term care spending(% of GDP)

OECD unweighted average

7

Growth in Health spending is picking-up

Source: OECD Health Statistics 2016

0

1

2

3

4

5

6

7

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Real health spending growth(Constant 2011 PPPs)

0

1

2

3

4

5

6

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Real health spending per capita growth(Constant 2011 PPPs)

8 Source: OECD Health Statistics 2016, Eurostat Database

EU average, 2015-14

Pharmaceutical and prevention spending have been the main areas for cuts in EU countries

3,3

3,8

5,2

1,4

5,1

1,9

0,9 1,2

2,3

-1,1

-1,9

0,8

-3

-2

-1

0

1

2

3

4

5

6

Inpatient care Outpatient care Long-term care Pharmaceuticals Prevention Administration

2005-09 2009-14Annual growth rate in real terms (%)

Drivers of health spending

Main health expenditure drivers

Health care expenditure

Demography (I)

Income (II)

Residual (III)

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It is not ageing per se that will create

expenditure pressures

Only an income elasticity of 1.8 could explain most of the

expenditure growth in the OECD

(I) The share of population aged over 65 and 80 countries will increase sharply between 2010-50

Source: OECD Historical Population Data and Projections Database, 2015

3X 2X

Cross section of OECD countries Sources: EC + National sources

Spending p.c. in group [i]

normalised by GDP p.c.

12

010

2030

% o

f GD

P pe

r cap

ita

2 7 12 17 22 27 32 37 42 47 52 57 62 67 72 77 82 87 92 97age (middle of 5-years age brackets)

(II) Health care expenditures per capita increase by age groups, but not because of ageing per se

(II) Health income elasticity is roughly unitary

Source: Getzen (2000) and authors’ compilation. 13

Papers ElasticityIndividuals (Micro)Newhouse and Phelps (1976) <1Manning et al. (1987) ≈0Regions (Intermediate)Feldstein (1971) 0.5Backer (1997) 0.8Nations (Macro)Newhouse (1977) 1.3Fogel (1999) 1.6

Taking into account cointegration Baltagi and Moscone (2010) <1Bech et al . (2011) ≈1Dreger and Reimers (2005) ≈1Freeman (2003) ≈0.8Narayan et. al (2011) <1

Using Instrumental VariablesAcemoglu et al. (2009) 0.7

Holly et al (2011)0.75-0.95

(In the fixed effect model and much smaller in the dynamic one)

This paper 0.5 - 1.0(Depending on the specification)

(III) What is the size of the unexplained expenditure residual?

Average annual growth rate 1995-2009 of health expenditures per capita (in %)

14 With an income elasticity of 0.8

Health spending Age effect Income effect Residual

Memo item : Residual with

unitary income elasticity

Selected countries:

Austria 3.3 0.4 1.3 1.5 1.2Denmark 3.7 0.2 0.8 2.7 2.5Finland 4.1 0.6 2.0 1.5 1.1France 1.6 0.5 0.9 0.3 0.0Germany 1.7 0.6 0.8 0.2 0.0Italy 3.1 0.6 0.4 2.1 2.0Japan 2.7 1.2 0.8 0.7 0.5Korea 11.0 1.1 3.1 6.5 5.7Netherlands 5.2 0.5 1.4 3.3 2.9Portugal 4.6 0.6 1.5 2.4 2.0Spain 3.4 0.5 1.5 1.4 1.0Switzerland 2.9 0.4 0.9 1.6 1.4United Kingdom 4.6 0.2 1.5 2.8 2.5United States 3.6 0.3 1.1 2.3 2.0

OECD total average 4.3 0.5 1.8 2.0 1.5BRIICS average 6.2 0.5 3.2 2.5 1.7Total average 4.6 0.5 2.0 2.0 1.5

Unbundling the expenditure residual

Residual (III)

a) Relative prices

b) Technology

c) Institutions and policies

If price elasticity is below 1 then price increases also

increase expenditure 15

There are efficiency gains that could slow down the expenditure residual

Average length of stay in hospital, 2000 and 2013 (or nearest year)

1. Data refer to average length of stay for curative (acute) care (resulting in an under-estimation).


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18% 2006-2010

Cost pressure, 2060

Cost containment, 2060

Projections of Public Health + Long-term care expenditures (in % of GDP)

Cost pressure scenario: healthy ageing, income elasticity=0.8, residual=1.7% per year Cost containment scenario: healthy ageing, income elasticity=0.8, residual phasing out over the projection period Convergence mechanism based on differences across countries in health shares to GDP in the base year compared with OECD average Source: de la Maisonneuve and Oliveira Martins (2013)

The age structure of Health expenditures will significantly change

Expenditure shares below and above 65

0

10

20

30

40

50

60

70

2010 2030 2060

People aged below 65People aged over 65

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NB: Non-demographic effects are assumed to be homothetic across ages, so they do not change the age structure of spending

Unbundling the expenditure residual

Residual (III)

a) Relative prices

b) Technology

c) Institutions and policies

19

Recent work investigates (1) the relationship between policy and institutional factors and healthcare expenditures and (2) how much policy/institutions can explain of cross-country dispersion in expenditures

Policy and

Institutional determinants

of Health spending

The information concerning the set of different policies and institutions used in this

paper was derived from official questionnaires sent to governments by the OECD. This qualitative information

(269 variables) was transformed into quantitative indicators, ranging from 0-6.

This set of indicators for policies and institutions was subsequently limited to 20

(see Paris et al., 2010).

20

Characteristics of health systems in OECD countries

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Policy and institutions indicators Category Institutional

aspect Variable name Short definition and interpretation Effect on health spending

Expected Estimated Linear model

Estimated Non-Linear model

Supply-side Provider payment

Physician payment Incentives for higher volume in physician payment mechanisms (primary care, outpatient and inpatient specialists): predominant mechanism(s) from salary, capitation, FFS (higher score = stronger incentive to generate volume)

Positive Negative Negative


Hospital payment Incentives for higher volume in hospital payment mechanisms: line-item or prospective global budgets, per case/DRG, per procedure/diem, retrospective funding, and their combinations (higher score = stronger incentive to generate volume)

Positive No effect No effect


Incentives for quality Incentives for health care quality (patient outcomes and satisfaction): guidelines/protocol adherence incentives (including financial) and sanctions for physicians and/or specialists and/or hospitals (higher score = stronger incentives)

Ambiguous Positive Positive

Supply-side Provider competition

Choice among providers Degree of patient choice of physician, specialist and hospital (higher score = more choice)

Negative No effect No effect

Supply-side Insurer competition

User choice of insurer Single or multiple insurers; degree of patient choice of insurer for basic coverage and their market shares (higher score = more choice)

Ambiguous No effect Positive

Supply-side Insurer competition

Lever Existence of levers for competition in insurance markets: whether insurers have some control on benefit package, level of coverage and premia, and whether they can selectively contract with providers (including pharmaceutical companies); existence of risk-equalisation/risk-adjustment schemes; availability of consumer information on premia/coverage (higher score = more levers for competition)

Negative No effect Negative

Supply-side Workforce supply

legislation

Regulation of physician supply

Existence of quotas for medical students, specialties and location; policies for shortage/redistribution (higher score = stronger regulation)

Ambiguous Positive No effect

Supply-side Hospital supply legislation

Regulation of capital investment

Regulation of hospitals (opening, bed supply, services, high-cost equipment): quotas, authorisation at local and/or central level (higher score = stronger regulation)

Negative Negative Negative

Supply-side Provider price regulation

Regulation of price for physician services

Regulation of prices/fees for physician services: degree of flexibility for charges (higher score = less flexibility, stronger regulation)


Category Institutional aspect Variable name Short definition and interpretation

Effect on health spending Expected Estimated

Linear model Estimated Non-Linear model

Supply-side Workforce supply

legislation

Regulation of physician supply

Existence of quotas for medical students, specialties and location; policies for shortage/redistribution (higher score = stronger regulation)

Ambiguous Positive No effect

Supply-side Hospital supply legislation

Regulation of capital investment

Regulation of hospitals (opening, bed supply, services, high-cost equipment): quotas, authorisation at local and/or central level (higher score = stronger regulation)



Regulation of price for physician services

Regulation of prices/fees for physician services: degree of flexibility for charges (higher score = less flexibility, stronger regulation)



Regulation of price for hospital services

Regulation of prices for hospital services: degree of flexibility for setting charges (higher score = less flexibility, stronger regulation)



Regulation of pharmaceutical price

Regulation of pharmaceutical prices: degree of flexibility that companies have to set their prices (higher score = less flexibility, stronger regulation)



Regulation of prices charged to third-party

Regulation of prices/fees paid to providers by third-party payers


Supply-side Budget caps Stringency of budget constraint

Expenditure targets or strict health budget and their allocation levels; consequences of budget constraint, including waiting times and compensation from providers to NHS/SHI (higher score = stronger presence and effects of budgets)


Supply-side Budget caps Control of volume Monitoring, regulations and controls on volumes of care: activity volume, monitoring of guideline adherence, drugs advertising to consumers, physician payment reduced according to exceeded volume targets (higher score = stronger controls)

Negative Positive Positive

Demand-side Gatekeeping Gatekeeping Requirement/incentives to register with primary care physician and/or referral to secondary care (higher score = more stringent gatekeeping)


Demand-side Cost-sharing Depth of basic insurance Basic primary services coverage with or without copays for 10 care functions (higher score = wider scope and more depth of coverage)

Ambiguous Positive Positive

Policy and institutions indicators (ct’d)

tititititititi ufeQdrcdepbyaH ,,,,,, )log()log()log()log( +++⋅+⋅+⋅+⋅+=α

titk

ki

ktititititi ufPQdrcdepbyaH ,,,,,, )log()log()log()log( +++⋅+⋅+⋅+⋅+= ∑δα

[ ] tittitititik

ki

kti ufQdrcdepbyaPH ,,,,,, )log()log()log()1()log( ++⋅+⋅+⋅+⋅⋅++= ∑δα

Model 1: traditional determinants of spending (income, age, prices and technology/quality), time and country-specific effects – FE estimation

Econometric specifications

Model 2: country-specific effects replaced by time-invariant policy and institutional variables (k = 20) – pooled OLS estimation

Model 3: non-linearities through interactions between the vector of policy and institutions and all other explanatory variables – non-linear LS estimation

Baseline results:

Public health

spending per capita

Similar results for: Total health spending Indicators added one-by-one

Institutions explain well and expenditure residuals across countries

Note: Residuals after age, income, relative prices and technology have been taken into account.

Source: Maisonneuve, Moreno-Serra, Murtin and O. Martins (2016), Health Economics

Drivers of long-term care spending

Dependency increases dramatically with Age > 75

Source: EC AWG Nb: For the projections an average curve was computed

020

4060

80%

of a

ge g

roup

s po

pula

tion


LTC costs/dependent are not related to Age

Assumption used in the projections: average constant cost per age by country

050

100

150

200

% o

f GD

P p

er c

apita


Source: EC AWG

Long-term care expenditure

Demographic drivers

(nb of dependents)

Life expectancy

at birth

Health care expenditure

Non-demographic drivers

Income Weak Productivity

Informal care supply

Labour force participation 50-64

30

Very different drivers for Long-term care

Income (elasticity=1)

Cost disease is driven by the growth rate of

aggregate labour productivity (elasticity=1)

Projected LTC expenditure have a lower impact than Public Health

(as a % of GDP in 2060)

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OECD Germany Austria Switzerland

LTCHealth care

Mean 2006-2010

CC

CP

Mean 2006-2010

Mean 2006-2010 Mean

2006-2010

CC

CC CC

CP CP

CP

CP = Cost pressure scenario: healthy ageing, income elasticity=0.8, residual=1.7% per year CC = Cost containment scenario: healthy ageing, income elasticity=0.8, residual phasing out over the projection period Convergence mechanism based on differences across countries in health shares to GDP in the base year compared with OECD average

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Projections of Health and Long Term Care public expendituresJoaquim Oliveira Martins (OECD and PSL,...

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