Health Status Transitions Monika Riedel, IHS Vienna June 28-29, 2007 WP III: transition...

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


Work package III

Transition probabilities(PSSRU)


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


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


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


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)


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)


Work package IV

Macro-demographic accounting(IHS)


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


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


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


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


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.


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


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


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


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


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


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


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


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


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


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


Thank you for your attention!

Monika [email protected]

Alexander [email protected]

Institut für Höhere StudienStumpergasse 56, A-1210 Viennahttp://www.ihs.ac.at

Health Status Transitions Monika Riedel, IHS Vienna June 28-29, 2007 WP III: transition...

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