1. Population ageing and health care expenditure: a school of ‘red herrings’? * Stefan Felder University of Magdeburg, ISMHE LTC-conference Mannheim October 20-21, 2005

2. ISMHE Universität Magdeburg Mannheim 2 Overview 1.Introduction 2.Part I: Replication of previous work with extended data 3.Part II: Decomposition of total HCE 4.Discussion and summary /1 5.Part III: Prognosis of future health care expenditure 6.Discussion and summary /2

3. ISMHE Universität Magdeburg Mannheim 3 Introduction /1 Average HCE rises with age –Age or –Time to death (TTD)main driver of health care costs? Difficult to separate age effect from proximity to death –Strong positive relationship between both –Econometric problems with Heckit-approach –In particular when analyzing HCE towards the end of life

4. ISMHE Universität Magdeburg Mannheim 4 Introduction /2 Previous work by Zweifel, Felder & Co. –Red herring paper (HE, 1999): small sample of deceased persons, Analysis of HCE towards the end of life. –Geneva Papers on Risk and Insurance: Issues and Practice, 2004: Analysis of HCE of deceased and survivors in a given year (1999) Maximum value of TTD of 42 months (3.5 years) –Result: TTD is main driver of HCE but not age

5. ISMHE Universität Magdeburg Mannheim 5 Introduction /3 This paper Part I –Analysis of total HCE of deceased and survivors in a given year (1999) –Maximum value of time to death of 60 months Part II –Analysis of HCE components of deceased and survivors in a given year –Maximum value of TTD of 60 months –Is there ‘a school of red herrings’? Part III –Prognosis of future HCE with model from part I

6. ISMHE Universität Magdeburg Mannheim 6 Part I Replication of previous work but with –more observation for the deceased –and slightly different specification

7. ISMHE Universität Magdeburg Mannheim 7 Part I / Data 0.490.400.490.41Share of men (SEXM) SEMeanSEMeanVariable 0>601729 TTD in months 14.3954.0913.2375.78Age 5,2772,79514,07111,567Total HCE in 1999 (CHF) 57,0855,075Observations SurvivorsDeceased 2000-2004 Descriptive statistics

8. ISMHE Universität Magdeburg Mannheim 8 Part I / Model 2-stage model:1.Probit for Pr (HCE>0) 2. OLS estimation for HCE | HCE > 0 same regressors for the first and second stage r.h.s variables: TTD, Age, Age^2, Age^3, SEXM, SEXM * Age, Death, Death * Age plus variables describing region, choice of deductible and suppl. insurance

9. ISMHE Universität Magdeburg Mannheim 9 Part I / Results Exp. HCE of surviving and deceased men as a function of age No age effect for deceased men Pos. age effect for survived men only between 55 and 70 Results confirmed by bootstrap strong TTD effect

10. ISMHE Universität Magdeburg Mannheim 10 Part II Analysis of HCE components of deceased and survivors in a given year

11. ISMHE Universität Magdeburg Mannheim 11 Part II / Data 1,426 6603,2401,750Prescription drugs (Drugs) SEMeanSEMean Variable 2,9115448,3163,261Hospital inpatient care (HIP) 427242,299460Home care (HC) 1,326908,0343,291Nursing home care (NHC) Survivors Deceased Descriptive statistics/1 Ambulatory care (AC)1,3952,7251,416918 1,507 2824,170871 Hospital outpatient care (HOP) 279539 Other services (OS) 7381,272 Components of HCE (in CHF)

12. ISMHE Universität Magdeburg Mannheim 12 Part II / Data Descriptive statistics/2 Observed age profiles of HCE components a) deceased b) survivors

13. ISMHE Universität Magdeburg Mannheim 13 Part II / Model 3-stage model:1.Probit for Pr (LTC>0) (LTC > 0 = NHC > 0 v HC > 0) 2.Multivariate probit for Pr (HCE j > 0) 3. SUR estimation for HCE j | HCE j > 0 j = AC, Drug, HOP, HIP, NHC, HC, OS Second and third stage for LTC and non-LTC users separately r.h.s variables: TTD, Age, Age^2, Age^3, SEXM, SEXM * Age, Death, Death * Age + variables describing region, deductible and suppl. ins.

14. ISMHE Universität Magdeburg Mannheim 14 Part II Prevalence of LTC Probability of LTC > 0 of surviving and deceased men Strong positive age effect Small negative TTD effect But TTD is important

15. ISMHE Universität Magdeburg Mannheim 15 Part II / Age effects in non-LTC patients Expected outlays for acute HCE components for deceased and surviving men deceasedsurvivors

16. ISMHE Universität Magdeburg Mannheim 16 Part II Age effects in LTC patients Conditional and expected outlays for nursing home care (NHC) and home care (HC) of deceased and surviving men HCE | HCE > 0  LTC > 0 E(HCE)

17. ISMHE Universität Magdeburg Mannheim 17 Part II Age effects in LTC patients Expected outlays for acute HCE components for deceased and surviving men a) deceased b) survivors

18. ISMHE Universität Magdeburg Mannheim 18 Discussion and summary /1 Methodology Decomposition of HCE in its components Multivariate probit and SUR estimation to account for correlation between components Empirical results Non-LTC patients: –Decreasing age profile for all HCE components among the deceased –Outlays for ambulatory care, drugs and inpatient care among survivors rise with age

19. ISMHE Universität Magdeburg Mannheim 19 Discussion and summary /2... Empirical results LTC patients –Pos. age gradient for nursing home and home care - for deceased as well as for survivors (due to a rising prevalence) –Outlays for acute HCE: deceased: small pos. age effect for ambulatory care and drugs survivors: small pos. age effect for all components of acute HCE Conclusion Most components of HCE are driven not by age but by TTD Exception: outlays for LTC patients There is a school of ´red herrings`!

20. ISMHE Universität Magdeburg Mannheim 20 Part III Forcast of future HCE for Switzerland 2000-2060 using –Model from part I –Age specific survival rates –Population forecasts of the Swiss Statistical Office

21. ISMHE Universität Magdeburg Mannheim 21 Part III Competing hypotheses: a) Status-quo hypothesis: age-specific per-capita expenditures depend only on medical technology. b) Expansion-of-morbidity hypothesis: prolonging life means prolonging morbidity and increasing costs c) Time-to-death hypothesis: health care expenditures are determined by proximity to death. “Compression of morbidity” effect: sickness gets compressed in a shorter and shorter period

22. ISMHE Universität Magdeburg Mannheim 22 Part III Two models 1) n-model: based on “naïve” regression 2) q-model: based on regressions including survival status

23. ISMHE Universität Magdeburg Mannheim 23 Part III Age profile of HCE Predicted expenditure for men

24. ISMHE Universität Magdeburg Mannheim 24 Part III HCE with constant medical technology n-modelq-modelError of n- model in % Year Absolute2000=100Absolute2000=100 20002,1551002,1551000 20052,182101.232,175100.9224.85 20102,217102.852,205102.3218.54 20202,301106.762,276105.6117.12 20302,388110.802,348108.9617.04 20402,451113.722,400111.3717.13 20502,471114.652,414112.0018.14 20602,451113.742,381110.5023.58

25. ISMHE Universität Magdeburg Mannheim 25 Part III HCE with 1% medical progress n-modelq-modelError of n-model in % Year Absolute2000=100Absolute2000=100 20002,1551002,1551000 20052,293106.392,286106.075.02 20102,448113.612,436113.024.28 20202,808130.272,777128.864.67 20303,219149.343,165146.865.03 20403,649169.313,574165.815.05 20504,064188.563,970184.194.94 20604,453206.634,326200.745.52

26. ISMHE Universität Magdeburg Mannheim 26 Part III Discussion Small demographic effect in the forecast of future health care expenditure (index in 2060 = 114) The cost-of-dying effect reduces the forecast of future expenditure (index in 2060 = 110) With growth factor “technology change” of 1% per annum the increase of per-capita expenditure is stronger (index in 2060 = 207, resp. 201) => cost-of-dying effect is small in comparison to the effect of technology changes

27. ISMHE Universität Magdeburg Mannheim 27 Summary 1.Purely demographic growth of per-capita HCE is not really dramatic. 2.The (strong) time-to-death hypothesis, which claims that ageing as such will have no positive effect on HCE, is not confirmed. 3.Explicit distinction between expenditures of survivors and those of decedents reduces the growth forecast only by one-fourth. 4.Accounting for costs in the last years of life leads to a downward correction of the demographic impact on HCE, as compared to a calculation on the basis of crude age-specific HCE. 5.Impact of medical progress on HCE is much greater than the error in the forecast of ageing.