Reflection Paper on these two chapters
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Chapter14.pptxCHAPTER 14 HEALTH TECHNOLOGY ASSESSMENT
Bhattacharya, Hyde and Tu – Health Economics
Intro
Health technology assessment (HTA) is comprised of two parts:
Cost effectiveness analysis (the science of comparing the costs and benefits of different medical treatments)
Cost-benefit analysis (the process of choosing an optimal treatment by creating a tradeoff between money and health)
HTA may sound dry and technical but it generates enormous controversy because it involves placing an explicit value on human life.
Bhattacharya, Hyde and Tu – Health Economics
Ch 14 | Health technology assessment
COST EFFECTIVENESS ANALYSIS
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Cost effectiveness analysis
Definition: the process of measuring the costs and health benefits of various medical treatments, procedures, and therapies.
Cost effectiveness analysis (CEA) is the less
controversial part of HTA, because it is concerned
with measuring costs and benefits, not balancing
them against each other.
Bhattacharya, Hyde and Tu – Health Economics
Cost effectiveness analysis
Often multiple treatments, with varying costs, can be used to treat a given disease.
In such cases
How do insurance companies decide which treatments, if any, to provide coverage for?
How do patients decide between an expensive and highly effective treatment and a low-cost treatment that is less effective?
Bhattacharya, Hyde and Tu – Health Economics
Cost effectiveness analysis
If one treatment is both cheaper and more effective than a second treatment, then the second treatment is said to be dominated by the first.
It is never optimal to use a dominated treatment, because there is always a more effective and cheaper alternative available.
Bhattacharya, Hyde and Tu – Health Economics
Cost effectiveness analysis
If neither treatment is dominant, one treatment must be both more expensive and more effective.
In such cases, cost-effectiveness analysis is used to help people decide whether the extra expenditure is worth it.
Bhattacharya, Hyde and Tu – Health Economics
Incremental cost-effectiveness ratio (ICER)
Consider two treatments for the same disease: A and B. A is both more expensive and more effective than B, so neither treatment dominates the other.
The ICER of using A over B is:
Bhattacharya, Hyde and Tu – Health Economics
Lead poisoning example
Which treatment strategy is superior?
Bhattacharya, Hyde and Tu – Health Economics
Lead poisoning example
This ICER provides a price for avoiding a reading disability.
In some sense, people can avoid a reading disability for an average price of $7,241.
Note that the ICER does not make a determination about whether this is worth it or not, it is just an empirical fact about costs.
Bhattacharya, Hyde and Tu – Health Economics
The average cost-effectiveness ratio (ACER)
Q: So why not just look at the various treatments’ ACERs and pick the one with the lowest cost per additional year of life?
A: ACERs typically will not reveal all the potentially cost-effective drugs.
Bhattacharya, Hyde and Tu – Health Economics
Ch 14 | Health technology assessment
THE COST EFFECTIVENESS FRONTIER
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Cost-effectiveness frontier (CEF)
Definition: a subset of treatment strategies for a condition that are not dominated by any other treatment. Any treatment on the CEF is said to be potentially cost-effective.
The CEF simplifies comparisons between treatments by allowing analysts to rule out dominated drugs (which should never be used), and focus only on options that potentially cost-effective.
Bhattacharya, Hyde and Tu – Health Economics
EX: Consider possible treatment options for the disease “bhtitis”: A, B, C, …, I
Cost-effectiveness frontier (CEF)
Bhattacharya, Hyde and Tu – Health Economics
Cost-effectiveness frontier (CEF)
Connect non-dominated options to form CEF
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The slope of the CEF between two points is equal to the inverse of the ICER between the two.
Cost-effectiveness frontier (CEF)
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Ch 14 | Health technology assessment
MEASURING COSTS
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Measuring costs
In order to calculate an ICER, we need to measure the costs of each treatment.
Whether a treatment is found to be cost- effective depends upon the perspective taken, because treatment costs and benefits differ for each party.
The social planner perspective: all costs count.
The patient perspective: only costs directly borne by patients count.
Bhattacharya, Hyde and Tu – Health Economics
Which costs count?
Suppose a complete course of a new lung cancer treatment costs $1,000. Is this the only cost to consider?
What if…
the treatment must be administered in a distant location, or for extended periods of time?
the treatment is uncomfortable—or has unwanted side effects?
the treatment will lead to adverse health effects in the future?
Bhattacharya, Hyde and Tu – Health Economics
Which costs count?
How should future costs be counted? If lung-cancer patients are cured but then go on to have costly heart attacks, should those costs count against the treatment?
There is active debate about which kinds of future costs should be included.
Bhattacharya, Hyde and Tu – Health Economics
Ch 14 | Health technology assessment
MEASURING EFFECTIVENESS
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How is “effectiveness” measured?
One common measure of effectiveness is increased life expectancy.
But how do we account for other health benefits that affect quality of life (e.g. increased mobility and freedom from pain)?
The Quality-Adjusted Life Years (QALY) approach combines quality of life and life expectancy into a single index.
Bhattacharya, Hyde and Tu – Health Economics
QALYs
In a QALY calculation, each year of life receives a quality weight q between 0 and 1 that reflects the quality of that life-year.
A year lived in perfect health has a quality of weight of q = 1.
Maybe a year with chronic cough and insomnia is only worth q = 0.5, or a year confined to a wheelchair is only worth q = 0.25.
Who has the right to make this judgment? We will return to this question
Bhattacharya, Hyde and Tu – Health Economics
QALEs
Calculating QALYs requires estimating three pieces of information:
the probability Pt of surviving to each year t
the quality of life qt for each year
a time-discount rate (usually between 3% and 5%)
A person’s quality-adjusted life expectancy (QALE) is the number of additional years he expects to live, weighted by the discounted quality of his life in each of those years (i.e. the sum of his QALYs).
Bhattacharya, Hyde and Tu – Health Economics
Survey methods: quality weights
Visual analogue scale (VAS) asks respondents to rate health outcomes between 0 (worst) and 100 (best)
Pros: simple to administer and easy for respondents to understand
Cons: does not require respondents to think about tradeoffs between different health states. Thus, results may not reflect the intensity of respondents’ preferences.
Bhattacharya, Hyde and Tu – Health Economics
Survey methods: quality weights
Standard Gamble (SG): For health condition H, respondents choose between having H with certainty or a gamble with probability p of full health and probability (1- p) of death. The point of indifference between these two options is used as the quality weight q of health condition H.
Pros: reflects intensity of preferences better than VAS.
Cons: this approach may be affected by risk aversion, and people often respond in counterintuitive ways to such uncertain gambles.
Bhattacharya, Hyde and Tu – Health Economics
Pros: Reflects intensity of preferences better than VAS.
Cons: may be biased if Ŧ* is a function of age
Survey methods: quality weights
Time trade-off (TTO): respondents choose between 1) living for t years with a health state H before dying, and 2) living for a shorter amount of time Ŧ in full health before dying. The quality weight q of health state H is the ratio Ŧ*/t (with Ŧ* representing the point of indifference between the two options).
Bhattacharya, Hyde and Tu – Health Economics
Whose opinion matters in QALY surveys?
Healthy survey respondents may be unequipped to imagine the quality of life in health states they have not experienced.
Expert panels are unlikely to ably represent patients’ preferences.
People who have lived with a condition for decades tend to understate the suffering that healthy people would feel if they suddenly developed a condition (e.g. blindness).
Bhattacharya, Hyde and Tu – Health Economics
Ch 14 | Health technology assessment
COST-BENEFIT ANALYSIS: PICKING THE OPTIMAL TREATMENT
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Cost-benefit analysis (CBA)
Definition: Cost-benefit analysis (CBA) is the process of choosing an optimal treatment among all potentially cost-effective ones, given a certain monetary value for each unit of health effect.
This optimal treatment is then termed cost-effective for a person or agency with that valuation.
Bhattacharya, Hyde and Tu – Health Economics
When we place a monetary value on each QALY, we implicitly create a set of indifference curves that can be plotted with the CEF (why?)
Example: Let us assume that a person values each QALY at $100,000. As a result, his indifference curves slope such that he is indifferent between one additional QALY and $100,000.
We will revisit the crucial question of how to value life-years accurately.
Cost-benefit analysis (CBA)
Bhattacharya, Hyde and Tu – Health Economics
Plot the indifference curves
Find the tangency point
With these indifference curves, the cost-effective treatment is Drug C.
Cost-benefit analysis (CBA)
Bhattacharya, Hyde and Tu – Health Economics
The impact of insurance
Consider the same situation, but now with an insurance package that covers 90% of drug costs.
This insurance coverage lowers a patient’s out- of-pocket price per QALY. With insurance:
Drug C now costs 10% of $160,000 = $16,000 for 3 QALYs
Drug H now costs 10% of $360, 000 = $36,000 for 3.4 QALYs.
Bhattacharya, Hyde and Tu – Health Economics
Moral hazard and HTA
With insurance, the person chooses Drug H, whereas without insurance, he would have opted for the cheaper Drug C.
Bhattacharya, Hyde and Tu – Health Economics
Moral hazard and HTA
The additional costs of Drug H are shared by every one in the patient’s insurance pool.
Thus, insurance encourages technology overuse and the development of inefficient innovations.
Given the problem of moral hazard, many insurance companies use rationing, in order to limit such technology overuse.
Bhattacharya, Hyde and Tu – Health Economics
Rationing
Definition: any method for allocating a scarce resource other than prices.
Example: Insurance companies or national health systems may decline to pay for certain treatments that are not cost-effective.
In the case of our CEF, the insurance company might decline to cover the expensive Drug H.
As the insurance company is only willing to cover 90% of expenses for Drugs A and C, patients would likely respond by choosing Drug C.
Bhattacharya, Hyde and Tu – Health Economics
Rationing
While moving from α to β leads to worse health and fewer QALYs, the money saved on health care costs can now be spent elsewhere.
Bhattacharya, Hyde and Tu – Health Economics
Ch 14 | Health technology assessment
VALUING LIFE
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Estimating the value of life
The CEF helps us find the best treatments for a given budget, but it does not indicate how much we should spend to obtain more life years or QALYs.
In other words, how much is a QALY worth?
To answer these questions, value of life estimates rely primarily on three sources:
labor market choices, product purchase decisions, and government policies.
Bhattacharya, Hyde and Tu – Health Economics
Estimating the value of life
You may think life cannot be valued economically or has an infinite value. Consider the following example:
There is a suitcase across a busy street with a million dollars in it.
If you cross the busy street to get the suitcase, there is a 1% chance you will be struck by a bus and killed.
Do you risk it?
If you answer yes, your life cannot be worth more than $100 million to you ($1 million divided by 0.01).
Bhattacharya, Hyde and Tu – Health Economics
Using the labor market to reveal VSL
In order to attract workers to more hazardous jobs, high-risk employers offer additional wages (“risk premiums”), which supplement the wages workers would earn in comparable, but lower-risk jobs.
If researchers know both the risk premium for a job and the difference in risks, then they can estimate how much a worker values his life.
Example: A worker who would take a job with a 1% higher fatal injury risk for $50,000 more in wages has a VSL of $50,000 ÷ 0.01 = $5 million.
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Using purchase decisions to reveal VSL
Example: Jenkins et al. (2001) used price data for children’s bike helmets to estimate their VSLs.
The decision to wear a helmet indicates a judgment that the risk reduction of head trauma from bike accidents is worth the cost of buying helmets.
Researchers used the prices of helmets to estimate a lower bound for the value of risk reduction and use that to calculate a lower bound for the VSL of helmet-wearers.
Other purchasing decisions: smoke detectors, safe cars vs unsafe cars.
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Using government policies to reveal VSL
Example: In 1972, a U.S. law guaranteed kidney dialysis to all patients under 65 with end-stage renal disease for free.
Kidney dialysis costs approximately $50,000 per QALY.
The passage of this amendment suggests that a QALY is worth at least $50,000 to American taxpayers.
Bhattacharya, Hyde and Tu – Health Economics
Conclusion
Insurers can neither cover every single new technology, nor refuse to cover all new procedures
Instead, most insurers are selective about which procedures to cover
HTA is a tool that many insurers and national health systems use to make these coverage decisions
In the policy lectures ahead, we will hear a lot more about HTA
Bhattacharya, Hyde and Tu – Health Economics
Chapter 14: Cost-Effectiveness Analysis | 387
In the case of health insurance, though, customers do not make socially optimal decisions be- cause prices are distorted. Moral hazard justifies cost-effectiveness analysis, which in turn neces- sitates research into quality weights and life valuation. Without such research, companies and policymakers could never figure out which treatments are worth covering from a social planner’s perspective.
Although rationing is understandably contentious, it might nevertheless be optimal for insur- ance customers. Rationing can drastically reduce overall health costs and allows for lower pre- miums (or a lower tax burden in the case of public insurance). Recall Figure 14.8, which depicts the tradeoff between moral hazard and risk. The contract at ↵ is full insurance and induces the most moral hazard; this is the case where an insurance company is willing to cover any treatment, including ones like Drug H that do not produce much health benefit. The contract at � is partial in- surance with rationing, perhaps a contract that does not cover cost-ineffective treatments like Drug H but still covers treatments like Drug C.
Figure 14.8: Rationing and moral hazard.
fullness
health care expenditures
full
↵ (no rationing)
� (rationing)
The indifference curves in this figure suggest that customers are actually happy to live with rationing in insurance. Rationing reduces the fullness of their insurance because there are some moments – when they catch bhtitis, for instance – when rationing will prevent them from enjoying extra years of life they could have had with Drug H. But rationing also saves customers from having to pay for everyone else’s ineffective drugs. Sometimes, those savings are enough to make rationing appealing. Moving from ↵ to � leads to worse health and fewer QALYs, but the money saved on health care costs can be spent on other things that are more valuable.
In order to actually decide whether this decrease in health expenditures is worth it, we need some measure of the value of life. Figure 14.6 depicted indifference curves that implied that people valued each quality-adjusted life year at $100,000. If this figure underestimates the true value of life, socially-optimal treatments will not be covered. If this figure is an overestimate, then there will be substantial harm from moral hazard.
14.6 Valuing Life How much is your life worth to you right now? Perhaps you think it is infinite or cannot be valued economically. No amount of money could convince you give up your life right now. Similarly, you would never take even the slightest risk like interacting with a sick person or driving a car. If your valuation of life is truly infinite, you would make some odd decisions. For example, suppose an
