Talks from the Swiss Health Economics Workshop 2013

1. Health Care Financing in Aging Societies
First Swiss Health Economics Workshop
Lucerne, September 13, 2013
Viktor von Wyl

2. Clash of Generations ?
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl2

3. Background: Analysis of premium burden
for young adults
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl3

4. Who pays for whom?
Age group beneficiary
0 - 18 19 - 25 26 - 35 36 - 45 46 - 55 56 - 65 66+
Agegroup
payer
0 - 18 1% - - - - - -
19 - 25 1% 50% - - - - -
26 - 35 18% 1% 88% 1% - -
36 - 45 49% 6% 5% 91% 2% - -
46 - 55 28% 32% 2% 6% 93% 2% -
56 - 65 3% 11% 4% 1% 5% 93% 1%
66+ - 1% 1% - - 5% 99%
Example (green shading): 32% of all premiums owed by 19-25 year olds are billed to
insured aged 46 – 55.
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl4

5. Transfers btw. young and old
Young adults
(19 – 25 years)
Risk adjustment transfers
to adults:
1’392 Mio.
Own health care
expenditures:
617 Mio.
Adults
(26+ years)
Premium surcharge to
finance rebates to young
adults: 226 Mio.
Premium subsidies
(tax financed):
263 Mio.
Premium payments by
parents: 675 Mio.
CHF 1’176
Mio.
CHF 1’164 Mio.
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl5
Source: Statistik der obligatorischen Krankenversicherung 2010, BAG 2012
Premium rebate to young
adults: -226 Mio.
Total of premiums
1’763 Mio.
Costs for insurers
2’009 Mio.

6. Cost growth and demographic change:
Swiss data
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl6
Source: Swiss Risk Adjustment Statistics 1996-2011
Health care
expenditures
% Cost increase Monthly RA into
(<0) or from (>0)
the fund
% RA increase
(CHF per month) (base year 1996) (base year 1996)
Year
Prop. of
young
adults
Young
adults
Adults
Young
adults
Adults
Young
adults
Adults
Young
adults
Adults
1996 11.1% 61 159 100% 100% -87 11 100% 100%
2011 10.3% 80 283 132% 178% -182 21 209% 191%

7. The role of population aging in rising
premium burden for young adults
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl7

8. How does demography affect risk
adjustment?
Young Old Number
insured
Per capita
health care
expenditures
(HCE)
Young Old
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl8
Today Future

9. Risk adjustment calculation for young adults
Young Old Number
insured
Per capita
health care
expenditures
(HCE)
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl9
‫ܥ‬̅
‫ܥ‬̅஺
‫ܥ‬̅௒
=‫ܥ‬̅௒
−‫݌‬௒
‫ܥ‬̅௒
−(1 − ‫݌‬௒
)‫ܥ‬̅஺
ܽ௒ = ‫ܥ‬̅௒ − ‫ܥ‬̅
‫݌‬௒
(1 − ‫݌‬௒
)

10. ∆࡭
Change parameters
Young Old Number
insured
Per capita
health care
expenditures
(HCE)
Young Old
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl10
∆ࢅ
ࢊࢅ

11. Model construction
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for empirical Health Economics Beck / von Wyl11
ܽ଴
௒
=࡯ഥࢅ
−࢖ࢅ
࡯ഥࢅ
−(૚ − ࢖ࢅ
)࡯ഥ࡭
ܽ௧
௒
=࡯ഥࢅ
(૚ ൅ ∆ࢅ
)࢚
−࢖ࢅ(૚ ൅ ࢊࢅ)࢚࡯ഥࢅ(૚ ൅ ∆ࢅ)࢚
−ሾ૚ − ࢖ࢅ(૚ ൅ ࢊࢅ)࢚ሿ࡯ഥ࡭(૚ ൅ ∆࡭)࢚
Next, we construct the difference equation of ܽ௧
௒
- ܽଵ
௒
ܽଵ
௒
− ܽ଴
௒
=(૚ − ࢖ࢅ)(࡯ഥࢅ∆ࢅ − ࡯ഥ࡭∆࡭)+ࢊࢅ࢖ࢅሾ࡯ഥ࡭ ૚ ൅ ∆࡭ − ࡯ഥࢅ ૚ ൅ ∆ࢅ ሿ
demography-
independent
demography-
dependent
If t=1 then, after some rearrangement of terms, we obtain

12. Plugging in real data
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl12
ܽଵ
௒
− ܽ଴
௒
=(૚ − ࢖ࢅ)(࡯ഥࢅ∆ࢅ − ࡯ഥ࡭∆࡭)+ࢊࢅ࢖ࢅሾ࡯ഥ࡭ ૚ ൅ ∆࡭ − ࡯ഥࢅ ૚ ൅ ∆ࢅ ሿ
demography-
independent
<0
demography-
dependent
>0
For 2011 (t = 15):
࡯ࡴࡲ ૢ૞ = ࡯ࡴࡲ ૢ૜ + ࡯ࡴࡲ ૛
(98%)(98%)(98%)(98%) ((((2222%) %) %) %)
∆௒= 1.9% p. a.
∆஺
= 3.9% p. a.
݀௒ = 0.6% p. a.

13. Is there a way to stabilize risk adjustment
payments for young adults?
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl13

14. Capping RA payments for young adults
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl14
ܽ଴
௒
(1 ൅ ∆௒)௧=ܽ௧
௒
− ‫ݔ‬
General idea: we shorten risk adjustment transfers by an amount x
x is obtained by solving the following equation
(condition: let risk adjustment payments only rise by (1 ൅ ∆௒)௧
‫݌=ݔ‬௒‫ܥ‬̅௒ (1 ൅ ∆௒)௧ሾ1 − (1 ൅ ݀௒)௧ሿ − ‫ܥ‬̅஺ሾ 1 ൅ ∆௒ ௧ −
1 ൅ ∆஺ ௧ሿ+‫݌‬௒‫ܥ‬̅஺ሾ 1 ൅ ∆஺ ௧ 1 ൅ ݀௒ ௧ − 1 ൅ ∆௒ ௧ሿ
Solution

15. The stabilization method is compatible with
additional (constant) RA reductions
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl15
࣋ܽ଴
௒
(1 ൅ ∆௒)௧=࣋ܽ௧
௒
− ࣋‫ݔ‬
If ߩ is an undefined factor <1 to reduce risk adjustment payments
for young adults, it can be shown that the following equation holds
We now project RA-transfers into the future
-without any changes (status quo)
-with stabilization
-with stabilization & additional reduction

16. Impact of modification on RA payments
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for empirical Health Economics Beck / von Wyl16
Age 26+
Age 19-25
Current RA
Modified RA*
Current RA
Modified RA*
*growth rate linked to HCE inflation rate in young adults (i.e. 2% instead of 4%)
Modified RA* &
additional reduction
Modified RA* &
additional reduction

17. Conclusion
(1) Young adults aged 19-25 years are among the most affected by
rising RA transfers in aging societies.
(2) Refusal to pay health insurance premiums more prevalent
among young insured (not shown). Acts of protest?
(3) Rather than demographic change, different health utilization
patterns of young and old is the main driver for expansion of
risk adjustment transfers.
(4) A reduction of RA payments made by young adults may be
appropriate, which could eliminate inefficiencies (e.g. premium
subsidies).
________________________________________________________________________________________________________
for empirical Health Economics Beck / von Wyl17

18. Thanks for your attention! Questions?
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for empirical Health Economics Beck / von Wyl18