Health Status Transitions Monika Riedel, IHS Vienna June 28-29, 2007

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

2. WP 3 Transition probabilities between health states WP 5 Healthy life expectancies Interfaces between Workpackages WP 4 • Macro-demographic accounting   IHS HealthEcon

3. Work package III Transition probabilities (PSSRU) 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 IHS HealthEcon

5. Approach • ordered probit regression conditional on starting healthestimates 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

6. Availability of Results by Country:transitions between health states in household population (ECHP) IHS HealthEcon

7. Illustration of probit formulaeTransition probability estimators, People < 65, Italy * Not statistically significant (5% level) IHS HealthEcon

8. Illustration of est. transition probabilitiesMan aged 40, Italy (These are conditional probabilities, on not entering an institution) IHS HealthEcon

9. Work package IV Macro-demographic accounting (IHS) 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 IHS HealthEcon

11. 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 Approach: Reconcile micro-information on transitions from ECHP with demographic macro-data Res. Care -> death: pattern from Netherlands Household -> res. care:WP III IHS HealthEcon

12. Data collection:Availability of data on residential care IHS HealthEcon o ... Limited data available

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 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 andb describe a set of constraints to x • needsstartmatrix x0and 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

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 IHS HealthEcon

16. Stone algorithm x0 ... observation table set up as vector, V0 ... start variance matrix,A ,b ... constraints IHS HealthEcon

17. Transition into residential care:Belgium male female 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 IHS HealthEcon

19. Healthy life expectancy in WP IV and WP V % ... share spent in ill health IHS HealthEcon

20. Healthy life expectancy in WP IV and WP V IHS HealthEcon

21. Healthy life expectancy – Belgium 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 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 IHS HealthEcon

24. Male (%) Male (%) Female (%) Female (%) Belgium Belgium 12.5 4.9 6.3 13.8 Germany Germany 12.4 9.4 13.1 9.0 UK UK 5.4 12.0 12.6 5.6 Necessary shifts of transition probabilities to achieve a 10% reduction in time spent in residential care Scenario 1: direct transitions to RCI Scenario 2: general health improvement 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 IHS HealthEcon