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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
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?
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)
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
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
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
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]
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)
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!
Optimal Pricing
- Nonzero rejection
- Reduced price due to technology adoption
0
1
A(p)
p
A(p)π(p)
π(p)
Adoption Probability Profits
Optimal price balances
gains in profits with increased rejection:
A’π + A π’=0
π’/π h
π’/π h
Price, p
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
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
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
Estimated unconditional acceptance (A) and hazard (h) as a
function of CE levels (p/q), NICE 1999 - 2005
0
0.1
0.2
0.3
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
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.
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.
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

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Tomas Philipson

  • 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 0.3 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