Post on 14-Jan-2016
Health Status Transitions
Monika Riedel, IHS Vienna June 28-29, 2007
WP III: transition probabilities WP III: transition probabilities (PSSRU)(PSSRU)
WP IV: macro-demographic WP IV: macro-demographic accounting (IHS)accounting (IHS)
IHS HealthEcon 2June 28-29, 2007
Interfaces between Workpackages
WP 3 Transition
probabilities between health states
WP 5 Healthy life
expectancies
WP 4 Macro-
demographic accounting
IHS HealthEcon 3June 28-29, 2007
Work package III
Transition probabilities(PSSRU)
IHS HealthEcon 4June 28-29, 2007
Goals
To estimate transition probabilities between different health states and use of residential care
For total population (all ages, by sex) For all EU-countries with available ECHP
data
IHS HealthEcon 5June 28-29, 2007
Approach ordered probit regression conditional on
starting health estimates the j’s and k’s such that the probability of transition from state ‘k’ to state ‘j’ is estimated by
(j - k) - (j-1 - k)
with unknown threshold values j , and unknown individual health value k
estimated separately by country and for ages above/below 65
Pooled across ECHP waves with EUROSTAT weights
IHS HealthEcon 6June 28-29, 2007
Availability of Results by Country:transitions between health states in household population (ECHP)
Transitions available from WPIII
SAH Hampering Condition Institutions Absorbing state
< 65 > 65 < 65 > 65 < 65 > 65 < 65 > 65
Denmark
Netherlands
Belgium
France
Ireland
Italy
Greece
Spain
Portugal
Austria
Finland
Germany
UK
Sweden
IHS HealthEcon 7June 28-29, 2007
Illustration of probit formulaeTransition probability estimators, People < 65, Italy
Initial health
1 2 3 4 Age (years)
Gender
Very good
0.637(0.067
)
2.209(0.078
)
3.020(0.103
)
3.461(0.172
)
0.009(0.001
)
0.077*(0.042
)
Good -0.327(0.046
)
1.871(0.050
)
3.164(0.064
)
3.826(0.096
)
0.015(0.001
)
0.140(0.025
)
Fair -1.124(0.090
)
0.410(0.084
)
2.151(0.096
)
3.185(0.117
)
0.015(0.002
)
0.072*(0.043
)
Bad/Very bad
-1.660(0.227
)
-0.657(0.209
)
0.665(0.207
)
2.672(0.232
)
0.017(0.004
)
-0.205(0.095
) * Not statistically significant (5% level)
IHS HealthEcon 8June 28-29, 2007
Illustration of est. transition probabilities Man aged 40, Italy
Health before
Health After
Very good
Good Fair Bad Dead
Very good
0.4677 0.4394 0.0842 0.0080 0.0007
Good 0.1401 0.6785 0.1705 0.0105 0.0005
Fair 0.0619 0.3962 0.5022 0.0389 0.0008
Bad 0.0225 0.1262 0.3060 0.5361 0.0092
(These are conditional probabilities, on not entering an institution)
IHS HealthEcon 9June 28-29, 2007
Work package IV
Macro-demographic accounting(IHS)
IHS HealthEcon 10June 28-29, 2007
Goals of WP IV To produce a macro-demographic picture
of health states and use of residential care
Of the population 65+ by sex For single years of age For all EU-countries
Due to data availability we had to select EU-countries with “best” data: Belgium, Germany, UK
IHS HealthEcon 11June 28-29, 2007
Approach: Reconcile micro-information on transitions from ECHP with demographic macro-data
In year t
In Household
In Residential Care
Totals
Good Health
Bad Health
In year t+1
In Household
Good Health
ECHP ECHP 0 ECHP
Bad Health
ECHP ECHP 0 ECHP
In Residential Care
Approx. derived
Census etc
Dead
Approx. derived
Approx. derived
Death Registration
Totals
ECHP ECHP Census etc
Household -> res. care:WP III
Res. Care -> death: pattern from Netherlands
IHS HealthEcon 12June 28-29, 2007
Data collection:Availability of data on residential care
o ... Limited data available
Residential care population Deaths in residential care
Availa
bility
5-year
ag
e
gro
ups
Sin
gle
yea
rs
By s
ex
Availa
bility
5-year
ag
e
gro
ups
Sin
gle
yea
rs
By s
ex
Denmark 1994-2003 none
Netherlands 1995-2001
(ex. 1997)
1998-2003
(ex. 2002)
Belgium 1996-2001 2001 o
France 1994, 1998 o o 1994-2002 o
Ireland 2002 none
Italy 1999-2001 o o 1999-2000 o o
Greece
Spain 1994-2001 none
Portugal
Austria 1991, 2001 none
Finland 1995-2003 1995-2003
Germany 1997-2003 none
UK 1994-2001 o o none
Sweden 1994-2001 o o 1994-2001 o o
IHS HealthEcon 13June 28-29, 2007
Choice of countries Countries with good data on residential care
including death in res. care (NL, FIN) lack transition probabilities from WP III for population 65+
Of countries with ECHP transitions, residential care population by sex and age only available for Belgium, Germany, UK
For those three countries, several years are available
IHS HealthEcon 14June 28-29, 2007
Applied algorithm• developed by Richard Stone in 1981* for
constructing socio-demographic matrices• tries to find a solution for a set of linear
equations Ax=b; x is the vector of transition probabilites; A and b describe a set of constraints to x
• needs startmatrix x0 and start variance V0
• is a least-squares-method; delivers BLUE x** and V***Stone R (1982): Working with what we have: How can existing data be used in the construction and analysis of socio-demographic matrices? Review of Income and Wealth, 28, 3, Cambridge.
IHS HealthEcon 15June 28-29, 2007
Start matrix Create start matrix:
Using headcounts derived from ECHP Transitions from WP III Known data from our data collection
Create Variance matrix: Identity matrix The inverse of the transitions The inverse of the transitions, squared
IHS HealthEcon 16June 28-29, 2007
Stone algorithm
x0 ... observation table set up as vector,V0 ... start variance matrix,A ,b ... constraints
b'AAV'AVxA'AAV'AVI*x1000100
01000* AV'AAVAVVV
IHS HealthEcon 17June 28-29, 2007
Transition into residential care:Belgium
male female
stone_bel55_var_Male
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Age
Tran
sitio
n pr
obab
ility
Very good -Institution
Good -Institution
Fair - Institution
Bad - Institution
WP IV
stone_bel55_var_Female
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
Age
Tran
sitio
n pr
obab
ility
Very good -Institution
Good -Institution
Fair - Institution
Bad - Institution
WP IV
IHS HealthEcon 18June 28-29, 2007
Conclusions from the Stone results
Stone provides reasonable results only if data are smoothed – we got better results with „variable Stone“
Many results differ not too much from WP III results, however, given the different estimation techniques they cannot be as smooth as in WP III
Deviations from WP III are largest for oldest people, where residential care and death are most important –remember WP III: conditional estimation on staying out of residential care
Country differences persist Results for Germany seem more problematic than
those for UK or Belgium
IHS HealthEcon 19June 28-29, 2007
Healthy life expectancy in WP IV and WP V
Age
LE HLE % LE HLE % LE HLE % LE HLE % LE HLE % LE HLE %
65 21.4 11.3 47.4 14.8 8.5 42.4 14.3 8.8 38.7 21.5 11.3 47.3 18.7 10.3 45.1 17.9 9.6 46.3
80 12.7 5.9 53.6 6.2 3.4 44.7 12.7 5.9 53.8 7.2 3.5 51.2
65 16.8 4.5 73.3 15.7 4.3 72.9 14.3 4.8 66.4 16.4 4.9 69.9 19.5 5.0 74.2 15.3 4.7 69.3
80 9.4 2.0 79.2 5.7 1.8 67.8 9.2 2.3 75.5 5.1 1.3 73.9
65 16.4 9.6 41.2 15.4 9.2 40.4 14.7 9.7 33.9 14.9 9.2 38.0 19.4 11.5 40.9 16.6 10.4 37.5
80 9.7 4.8 50.4 6.1 3.8 37.9 8.8 4.7 47.2 6.7 3.7 45.4
Belg
umG
erm
any
UK
WP IVWP V - unadjusted WP V - adjusted
Men Women
WP V - unadjusted WP V - adjusted WP IV
% ... share spent in ill health
IHS HealthEcon 20June 28-29, 2007
Healthy life expectancy in WP IV and WP V
Belgium - Comparision of HLE in WP IV and WP V (unadjusted)
02468
10121416
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
Age
Yea
rs
Men - HLE WP IV
Men - HLE WP V
Women - HLE WP IV
Women - HLE WP V
IHS HealthEcon 21June 28-29, 2007
Healthy life expectancy – Belgium
Belgium - Life expectancy and healthy life expectancy
0
5
10
15
20
25
Age
Ye
ars
Men LE
Men HLE
Women LE
Women HLE
IHS HealthEcon 22June 28-29, 2007
Summary: HLE Life Expectancies (at age 65) tend to be lower
than those derived from WP V-unadjusted results (consider: different maximum life expectancy: WP V 100 years, WP IV 90 years due to lack of observations)
Healthy life expectancy for women is mostly lower, that for men mostly higher than the respective numbers derived from WP V-unadjusted results
... But keep in mind we have only data for three countries, which makes any conclusions rather preliminary
IHS HealthEcon 23June 28-29, 2007
Policy scenarios to reduce time in residential care
Two approaches: reduce the transition probability of
directly entering residential care general improvement of health by
increasing an individual’s chance of transition to more favourable health states
IHS HealthEcon 24June 28-29, 2007
Necessary shifts of transition probabilities to achieve a 10% reduction in time spent in residential care
Male (%)
Female (%)
Belgium 12.5 13.8
Germany 12.4 13.1
UK 12.0 12.6
Male (%)
Female (%)
Belgium 4.9 6.3
Germany 9.4 9.0
UK 5.4 5.6
Scenario 1: direct transitions to RCI
Scenario 2: general health improvement
IHS HealthEcon 25June 28-29, 2007
Thank you for your attention!
Monika Riedel+43-1-59991-126riedel@ihs.ac.at
Alexander Schnabl+43-1-59991-211schnabl@ihs.ac.at
Institut für Höhere StudienStumpergasse 56, A-1210 Viennahttp://www.ihs.ac.at