This document summarizes a study on how endogenous cost-effectiveness analysis impacts health care technology adoption. It finds that when prices are set based on cost-effectiveness thresholds, rather than costs alone, it can lead technologies with higher costs but more demand to be adopted over those with lower costs. This is because demand allows prices and markups to exceed costs. The study uses data from the National Institute for Health and Clinical Excellence in the UK from 1999-2005 to show some evidence of reversals in adoption decisions compared to what cost-effectiveness alone would predict. It concludes more research is needed to fully understand the impact of endogenous cost-effectiveness on technology adoption decisions.

1. Endogenous CEA in
Health Care Technology
Adoption
(NBER WP #15032)
Anupam Jena
Harvard University
Tomas J. Philipson
University of Chicago
Leonard Davis Institute
December 4, 2009

2. Motivation
 New technology is a driving force behind
growth in health care spending
 How do we value new technologies?
 “Cost-Effectiveness” (CE): “Bang-for-the-Buck”
 CE Analysis largest subfield of health economics?
 Research Question: Efficiency implications of
adopting new technologies based on CE?

3. Cost-Effectiveness in Practice
 European Union
 “Fourth hurdle” Prior to 1993, few countries had agencies responsible
for economic assessments of new medical products
 Now, majority do (Drummond, 1991; OECD, 2001; Cookson et al.,
2003)
 United Kingdom
 Threshold for adopting new technologies by NICE appears to be ~
$60,000 per QALY (Raftery et al., 2001)
 Australia
 First country to require pharmacoeconomic assessments of all new
drugs submitted for national coverage
 By 2001, only 2 of 26 new submissions were accepted whose cost per
QALY exceeded $57,000 (Bethan et al., 2001)

4. Cost-effectiveness and the probability of treatment
adoption, NICE 1999-2005
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
< 10,000 £ 10,000 - 20,000 £ 20,000 - 30,000 £ 30,000 - 40,000 £ 40,000 - 50,000 £ > 50,000 £
Cost-effectiveness (£ per QALY/LYG)
Probabilityofacceptance

5. Preview of Punch Lines
 Exogenous CE uses resource COSTS
 Determines economic efficiency or gains from trade
 Endogenous CE uses PRICES
 Mark-ups above costs affected by:
 Patient & Doctor Demand + Adoption Rules (!)
 Bang for the Buck? “Buck” depends on Demand
 Endogenous CE reverses exogenous CE
 When mark-up differences reverse cost differences (Devices vs Drugs?)
 How to Test for Reversals
 Data from NICE 1999 - 2005

6. Exogenous and Endogenous CE
 p = price of medical product (drug, device, service)
 q = quality or “effectiveness” of product (QALY)
 c = cost of producing product
 Exogenous CE : c/q
 Endogenous CE : p/q
 However: Endogenous prices are affected by the
reimbursement rule used!
 Example: Fixed thresholds cause firms to price up to
threshold regardless of costs

7. Mark-ups and Reversals
 Prices marked up above costs
 p = m*c
 For two technologies, reversals occur whenever
 Treatment 1 is more cost effective exogenously:
 c1/q1 < c2/q2
 Treatment 2 is more cost effective endogenously:
 p1/q1 > p2/q2
 Mark-ups offset exogenous cost-effectiveness
 m1/m2 > [c2/q2]/[c1/q1]
 Example: NICE pricing p/q=T  m=T/[c/q]

8. Profits and Technology Adoption
 Demand: y(p,q )
 Profits conditional on approval
 π(p) = [p-c(q)]y(p,q)
 A(p) = Probability of technology approval falls in
price
 Example: CE ratios lowers adoption A(p/q)
 Expected Profits=Probability of Approval*Profits
 A(p)*π(p)

9. Mark-up Determination
 Mark-ups depend on demand
 In standard monopoly pricing models, markups falls
with the elasticity of demand E
 Lerner condition p = m*c where m = 1/[1+E]
 Here, markups depend on two demand sides
 Price sensitivity of adoption rule: A(p)
 Price sensitivity of ex-post demand: y(p,q)
 Both demand sides affect mark-up
 P = m(Demand,Approval)*c
 If CEA is used by governments for adoption, then this
determines endogenous CE!

10. Optimal Pricing
- Nonzero rejection
- Reduced price due to technology adoption
0
1
A(p)
p
A(p)π(p)
π(p)
Adoption Probability Profits

11. Optimal price balances
gains in profits with increased rejection:
A’π + A π’=0
π’/π h
π’/π h
Price, p

12. Class Dummies and Reversals
 Cannot directly identify reversals without
information on prices, costs, and quality
 Test for reversals of a “Procedure”
 Adoption not solely driven by endogenous CE
 Low Goodness of Fit consistent with political factors
affecting adoption
 Class heterogeneity induces reversals
 Class Dummies to test for reversals

13. Reversals in cost-effectiveness &
Class heterogeneity in adoption
Price
Costs and Exogenous CE
Low Adoption
Class p(c)
High Adoption
Class p(c)
cL cH
pL
pH
pM

14. Empirical Analysis – Data from NICE
 Since 1999, NICE issued 141 guidances
 Our data includes 86 guidances involving 145
treatments
 30 percent recommended unconditionally
 32 percent w/ minor restrictions
 22 percent w/ major restrictions
 76 of these treatments have explicit CE data
 12/76 of these treatments flat out rejected

15. Estimated unconditional acceptance (A) and hazard (h) as a
function of CE levels (p/q), NICE 1999 - 2005
0
0.1
0.2
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0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70
Endogenous cost-effectiveness, p/q (£ per QALY)
Acceptanceprobability,Hazard
Acceptance
probability
Hazard

16. Number of treatments submitted and accepted by
disease class (k) and endogenous cost-effectiveness
(p/q), NICE 1999-2005
Endogenous Cost-effectiveness (1,000£/QALY)
Disease Class < 10 10 - 20 20 - 30 30 - 40 40 - 50 > 50
Arthritis 0/0 5/5 0/0 2/2 0/0 0/1
Cancer 6/6 8/8 3/4 5/5 2/3 0/0
Heart 6/6 1/1 4/4 0/0 0/0 0/0
Infectious 2/2 0/0 2/2 0/3 1/1 ¼
Mental 0/1 4/4 0/0 1/2 0/0 0/1
Prevention 1/1 1/1 2/2 0/0 0/0 0/0
Other 2/2 1/1 1/1 1/1 1/1 1/1
Source: NICE published treatment guidances, 1999 – 2005. Each cell reports the number of accepted treatments/submitted
treatments for a given disease class and endogenous cost-effectiveness range.

17. Impact of endogenous cost-effectiveness and disease class on probability of treatment acceptance
Variable
Mean cost-effectiveness (1,000£/QALY) -0.009*
(0.002)
Cancer -0.034
(0.098)
Heart -0.031
(0.122)
Infectious -0.322*
(0.120)
Mental health -0.310*
(0.132)
Prevention -0.008
(0.171)
Constant 1.154
(0.096)
R2
0.38
F-test of equality of disease indicators p = 0.03
Source: NICE published treatment guidances, 1999 – 2005. Table presents coefficients of a linear probability model of the impact
of cost-effectiveness and disease class (excluded class: diabetes) on the probability of treatment adoption by NICE. Standard
errors are in parentheses. * Significant at p < 0.05.

18. Limitations & Future Issues
 Sample Reversals vs Procedure Reversals
 Difficult as markups unobservable
 Endogenous Effectiveness as opposed to Costs
 Learning by doing rises with lower price (devices)
 Transparency
 Measured by goodness if fit of criteria explaining
adoption
 Endogenous Comparative Effectiveness