1. Health Status Transitions Monika Riedel, IHS Vienna June 28-29, 2007 WP III: transition probabilities (PSSRU) WP IV: macro-demographic accounting (IHS)

2. June 28-29, 2007 IHS HealthEcon 2 Interfaces between Workpackages WP 3 Transition probabilities between health states WP 5 Healthy life expectancies WP 4 Macro- demographic accounting 

3. June 28-29, 2007 IHS HealthEcon 3 Work package III Transition probabilities (PSSRU)

4. June 28-29, 2007 IHS HealthEcon 4 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

5. June 28-29, 2007 IHS HealthEcon 5 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

6. June 28-29, 2007 IHS HealthEcon 6 Availability of Results by Country: transitions between health states in household population (ECHP)

7. June 28-29, 2007 IHS HealthEcon 7 Illustration of probit formulae Transition probability estimators, People < 65, Italy Initial health 11 22 33 44 Age (years) Gender Very good0.637 (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)

8. June 28-29, 2007 IHS HealthEcon 8 Illustration of est. transition probabilities Man aged 40, Italy Health before Health After Very good GoodFairBadDead Very good 0.46770.43940.08420.00800.0007 Good0.14010.67850.17050.01050.0005 Fair0.06190.39620.50220.03890.0008 Bad0.02250.12620.30600.53610.0092 (These are conditional probabilities, on not entering an institution)

9. June 28-29, 2007 IHS HealthEcon 9 Work package IV Macro-demographic accounting (IHS)

10. June 28-29, 2007 IHS HealthEcon 10 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

11. June 28-29, 2007 IHS HealthEcon 11 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 0 Bad Health ECHP 0 In Residential Care Approx. derived Census etc Dead Approx. derived Death Registration Totals ECHP Census etc Household -> res. care: WP III Res. Care -> death: pattern from Netherlands

12. June 28-29, 2007 IHS HealthEcon 12 Data collection: Availability of data on residential care o... Limited data available

13. June 28-29, 2007 IHS HealthEcon 13 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

14. June 28-29, 2007 IHS HealthEcon 14 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 x 0 and start variance V 0 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.

15. June 28-29, 2007 IHS HealthEcon 15 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

16. June 28-29, 2007 IHS HealthEcon 16 Stone algorithm x 0... observation table set up as vector, V 0... start variance matrix, A,b... constraints

17. June 28-29, 2007 IHS HealthEcon 17 Transition into residential care: Belgium male female

18. June 28-29, 2007 IHS HealthEcon 18 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

19. June 28-29, 2007 IHS HealthEcon 19 Healthy life expectancy in WP IV and WP V %... share spent in ill health

20. June 28-29, 2007 IHS HealthEcon 20 Healthy life expectancy in WP IV and WP V

21. June 28-29, 2007 IHS HealthEcon 21 Healthy life expectancy – Belgium

22. June 28-29, 2007 IHS HealthEcon 22 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

23. June 28-29, 2007 IHS HealthEcon 23 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

24. June 28-29, 2007 IHS HealthEcon 24 Necessary shifts of transition probabilities to achieve a 10% reduction in time spent in residential care Male (%) Female (%) Belgium12.513.8 Germany12.413.1 UK12.012.6 Male (%) Female (%) Belgium4.96.3 Germany9.49.0 UK5.45.6 Scenario 1: direct transitions to RCI Scenario 2: general health improvement

25. June 28-29, 2007 IHS HealthEcon 25 Thank you for your attention! Monika Riedel +43-1-59991-126 riedel@ihs.ac.at Alexander Schnabl +43-1-59991-211 schnabl@ihs.ac.at Institut für Höhere Studien Stumpergasse 56, A-1210 Vienna http://www.ihs.ac.at