The Insurance Value of Medical Innovation
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Transcript of The Insurance Value of Medical Innovation
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
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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
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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
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1816 16
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$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