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)
Source: OECD Health Statistics 2016, OECD
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).
Source: OECD Health Statistics 2015, OECD
17
0
2
4
6
8
10
12
14
16
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
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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
21
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
Supply-side Provider payment
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
Supply-side Provider payment
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)
Negative Negative Negative
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)
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)
Negative Negative Negative
Supply-side Provider price 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)
Negative Negative Negative
Supply-side Provider price 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)
Negative No effect No effect
Supply-side Provider price regulation
Regulation of prices charged to third-party
Regulation of prices/fees paid to providers by third-party payers
Negative No effect No effect
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)
Negative No effect No effect
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)
Negative No effect No effect
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)
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ktititititi ufPQdrcdepbyaH ,,,,,, )log()log()log()log( +++⋅+⋅+⋅+⋅+= ∑δα
[ ] tittitititik
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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
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)
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
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)
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)
31
0
2
4
6
8
10
12
14
16
18
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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