14/11/2014

The Insurance Value of Medical Innovation

Darius Lakdawalla Anup Malani Julian ReifUSC and NBER University of Chicago University of Illinois

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Consider a standard coin toss gamble

Game 1 Game 2$0

$5

$10

$15

$20

$25

$30

$35

$40

$45

20

30

40 40

HeadsTailsPayoff

Risk matters when evaluating payoffs

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Valuing medical technology: Gleevec for treatment of chronic myeloid leukemia

Pre-gleevec Post-gleevec0

5

10

15

20

25

7.5

17.5

20.19 20.19

CML patientsAverage people

Life Ex-pectancy for 65 year-old

male (years)

Technology may produce “self-insurance” value

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Valuing medical technology: Highly Active Antiretroviral Therapy (HAART) for HIV

Pre-AZT Post-AZT Post-HAART0

2

4

6

8

10

12

14

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1816 16

5

$0

$8

$10AIDS Deaths per 100KAnnual Cost ($1000's)

Technology may produce “market insurance” value

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Risk-reduction value of technology raises several research questions

1. How can we define and measure the risk-reduction value of medical technology?

2. Under what conditions is it appropriate to abstract away from risk-reduction value?

3. How empirically significant is risk-reduction value over a range of real-world medical technologies?

4. What are the implications for how we value and pay for different types of medical technologies?

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Value of medical technology can be intuitively illustrated in a simple two-good model

• Imagine that utility depends on consumption and health for a (weakly) risk-averse consumer

– • There are two health states – “sick” and “well” – and the risk,

, of the “sick” state in which:

– Consumption endowments fall to – Health endowments fall to

• There exists some medical technology sold at price that raises health in the sick state to

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Intuition can be illustrated in a one-good model

𝑐+h𝑠 𝑐+h𝑤

𝐸 (𝑢0)

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Conventional “risk-free” value is the movement along the original expected utility chord

𝑐+h𝑠 𝑐+h𝑤𝑐+h𝑠+Δ−𝑝

𝐸 (𝑢0)

𝐸 (𝑢1)Traditional value

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Additional “self-insurance” value accounts for the movement up the risk-averse utility curve

𝑐+h𝑠 𝑐+h𝑤𝑐+h𝑠+Δ−𝑝

𝐸 (𝑢0)

𝐸 (𝑢2)Self-insurance value

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Additional “market insurance” value accounts for the incremental value of financially insuring the technology

𝑐+h𝑠 𝑐+h𝑤−𝜋𝑝𝑐+h𝑠+Δ−𝜋𝑝

𝐸 (𝑢0)

𝐸 (𝑢3) Market-insurance value

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Traditional valuations ignore the “insurance value”

𝑐+h𝑠 𝑐+h𝑤−𝜋𝑝𝑐+h𝑠+Δ−𝜋𝑝

𝐸 (𝑢0)

𝐸 (𝑢3)

Traditional value

Insurance value

Insurance value is the sum of self insurance and market insurance

𝐸 (𝑢1)

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Results from theoretical analysis

• Three separate components of value:

– Traditional Value of Technology• Corresponds to the “risk-free” value

– Self-Insurance Value of Technology (SIVT)– Market-Insurance Value of Technology (MIVT)

• Accounting for only the traditional valuation causes researcher to underestimate the total value of technology

• This underestimate is particularly bad for severe diseases with low quality of life, i.e., high “unmet need”

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Empirical framework is based on Cobb-Douglas utility

• Consider the two-good version of the model implied by:

• measures quality of life, and is consumption

• determines the MRS between consumption and health – i.e., the willingness to pay for health improvement

• determines the demand for insurance – the higher is , the greater the demand for insurance

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Parameterizing the utility function

• We pick a baseline value of equal to 0.3

• We calibrate using estimates of risk-aversion

– Baseline value of 3 (implies relative risk-aversion equal to 1.6)

• We set annual income equal to $50,000

• We obtain measures of price and quality of life from data on cost-effectiveness studies

– Uses the “QALY” framework– Quality of life ranges from 0 to 1

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“Cost-effectiveness” of technology drives variation in the risk-free and insurance values

Simulated estimates of RFVT, SIVT, and MIVT as a function of price. Total = RFVT + SIVT + MIVT. Parameters are = 0.3, =$50,000, = 1, = 0.7, and = 0.1.

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Value of health technology is right-skewed

Traditional value Self-insurance value Market-insurance value

Sigma (RRA) Median Mean Median Mean Median Mean

0.5 (0.85) $108 $564

1 (1) $108 $564

3 (1.6) $108 $564

5 (2.2) $108 $564

8 (3.1) $108 $564Notes: Sample is 1,188 interventions from CEAR. Estimates are weighted by the prevalence of disease.

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Value of health technology is right-skewed

Traditional value Self-insurance value Market-insurance value

Sigma (RRA) Median Mean Median Mean Median Mean

0.5 (0.85) $108 $564 ($9) ($114) ($1.60) ($5.01)

1 (1) $108 $564 $0.17 $3 $0.03 $5

3 (1.6) $108 $564 $51 $839 $10 $70

5 (2.2) $108 $564 $120 $1,928 $27 $184

8 (3.1) $108 $564 $243 $3,193 $55 $403 Notes: Sample is 1,188 interventions from CEAR. Estimates are weighted by the prevalence of disease.

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Insurance value dominated by self-insurance and comparable in magnitude to risk-free value

Traditional value Self-insurance value Market-insurance value

Sigma (RRA) Median Mean Median Mean Median Mean

0.5 (0.85) $108 $564 ($9) ($114) ($1.60) ($5.01)

1 (1) $108 $564 $0.17 $3 $0.03 $5

3 (1.6) $108 $564 $51 $839 $10 $70

5 (2.2) $108 $564 $120 $1,928 $27 $184

8 (3.1) $108 $564 $243 $3,193 $55 $403 Notes: Sample is 1,188 interventions from CEAR. Estimates are weighted by the prevalence of disease.

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Providing special reimbursement for treating diseases with high unmet need remains controversial

• “[The fund] not only undermines NICE, it undermines the entire concept of a rational and

evidence-based approach to the allocation of finite health-care resources.”

• “New cancer treatments clearly challenge the cost thresholds set by NICE”

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Self insurance value (SIV) is large for diseases with high unmet need

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Treating diseases with unmet needs – e.g., cancer – is much more valuable than previously recognized

HIV/AIDSGleevec

Alzheimer’s

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Policy implications of risk-reduction value

• Economic value of health increases may be larger than previously thought

– Greater expenditures on health-related research may be worthwhile

• Health technology assessment

– Risk-reduction value should be incorporated into value assessments

– Treatments for diseases with high unmet need are especially undervalued, perhaps by an order of magnitude

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Reexamining the role of medical innovation

• Policies to promote new health technology also function like insurance reform

– Financial markets may have played a secondary role in reducing society’s exposure to health risk

• The distributional implications of health technologies have been poorly understood

– The poor benefit disproportionately from health risk-reduction (McClellan and Skinner, 2006)

– Access to medical technology is a policy substitute for financial redistribution or means-tested health insurance