1 / 27

Overview

Overview. Introduction Part I: Replication of previous work with extended data Part II: Decomposition of total HCE Discussion and summary /1 Part III: Prognosis of future health care expenditure Discussion and summary /2. Introduction /1. Average HCE rises with age Age or

satya
Download Presentation

Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Overview • Introduction • Part I: Replication of previous work with extended data • Part II: Decomposition of total HCE • Discussion and summary /1 • Part III: Prognosis of future health care expenditure • Discussion and summary /2

  2. 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

  3. 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

  4. 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

  5. Part I Replication of previous work but with • more observation for the deceased • and slightly different specification

  6. Part I / Data Descriptive statistics Deceased 2000-2004 Survivors Observations 5,075 57,085 Variable Mean SE Mean SE Total HCE in 1999 (CHF) 11,567 14,071 2,795 5,277 Age 75.78 13.23 54.09 14.39 TTD in months 29 17 >60 0 Share of men (SEXM) 0.41 0.49 0.40 0.49

  7. 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

  8. 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

  9. Part II Analysis of HCE components of deceased and survivors in a given year

  10. Part II / Data Descriptive statistics/1 Variable Deceased Survivors Components of HCE (in CHF) Mean SE Mean SE Ambulatory care (AC) 1,395 2,725 918 1,416 Nursing home care (NHC) 3,291 8,034 90 1,326 Home care (HC) 460 2,299 24 427 Hospital inpatient care (HIP) 3,261 8,316 544 2,911 1,426 Hospital outpatient care (HOP) 871 4,170 282 Prescription drugs (Drugs) 1,750 3,240 660 1,507 Other services (OS) 539 1,272 279 738

  11. Part II / Data Descriptive statistics/2 Observed age profiles of HCE components b) survivors a) deceased

  12. Part II / Model 3-stage model: 1. Probit for Pr (LTC>0) (LTC > 0 = NHC > 0 v HC > 0) 2. Multivariate probit for Pr (HCEj > 0) 3. SUR estimation for HCEj | HCEj > 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.

  13. 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

  14. Part II / Age effects in non-LTC patients Expected outlays for acute HCE components for deceased and surviving men deceased survivors

  15. Part IIAge effects in LTC patients Conditional and expected outlays for nursing home care (NHC) and home care (HC) of deceased and surviving men E(HCE) HCE | HCE > 0 LTC > 0

  16. Part IIAge effects in LTC patients Expected outlays for acute HCE components for deceased and surviving men a) deceased b) survivors

  17. 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

  18. 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 drugssurvivors: 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`!

  19. 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

  20. 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

  21. Part IIITwo models 1) n-model: based on “naïve” regression 2) q-model: based on regressions including survival status

  22. Part IIIAge profile of HCE Predicted expenditure for men

  23. Part IIIHCE with constant medical technology

  24. Part IIIHCE with 1% medical progress

  25. Part IIIDiscussion • 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

  26. Summary • 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.

More Related