Endogenous CEA in Health Care Technology

Adoption(NBER WP #15032)

Anupam Jena Harvard University

Tomas J. Philipson University of Chicago

Leonard Davis InstituteDecember 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

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< 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)

Pro

babi

lity

of

acce

ptan

ce

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

1A(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

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0 10 20 30 40 50 60 70

Endogenous cost-effectiveness, p/q (£ per QALY)

Acc

epta

nce

prob

abil

ity,

Haz

ard

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