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3/12/2009Decision and Cost-Effectiveness Analysis
Eran Bendavid
When Rationality Falters: Limitations and Extensions of
Decision Analysis
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Experiment Part 1
3/12/2009Decision and Cost-Effectiveness Analysis
Assume that the United States is preparing for the outbreak of an unusual Icelandic disease, which is expected to kill 600 people in the absence of intervention.
Two alternative programs to combat the disease have been proposed. Assume that the exact estimates of the programs are as follows:
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Experiment 1
3/12/2009Decision and Cost-Effectiveness Analysis
If program A is adopted, 200 people will be saved.
If program B is adopted, there is 1/3 chance that 600 people will be saved and 2/3 probability that no people will be saved.
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Experiment 1 – Switch Groups
3/12/2009Decision and Cost-Effectiveness Analysis
If program A is adopted, 400 people will die.
If program B is adopted, there is a 1/3 chance that nobody will die, and 2/3 chance that 600 people will die.
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Experiment 2 – Everyone
3/12/2009Decision and Cost-Effectiveness Analysis
Imagine yourself $3000 richer than you are right now. You have to choose between (a) a sure gain of $1000, (b) a 50% chance of gaining $2000 and 50% of gaining nothing.
Imagine yourself $5000 richer than you are right now. You have to choose between (a) a sure loss of $1000, (b) a 50% chance of losing nothing and 50% chance of losing $2000.
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Cartoon
3/12/2009Decision and Cost-Effectiveness Analysis
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Three Topics
3/12/2009Decision and Cost-Effectiveness Analysis
FramesEquityEconomic epidemiology
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Normative Problem Formulation
3/12/2009Decision and Cost-Effectiveness Analysis
Classical decision theory axioms Ordering of preference Transitivity of preference Quantification of
judgment Comparison of
alternatives Substitution
Cost benefit rationale
“Risky prospects arecharacterized by their possible outcomes and by the probabilities of these outcomes.
The same option, however, can be framed or described
in different ways.” -- Tversky & Kahneman, 1981
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Formulation Effects
3/12/2009Decision and Cost-Effectiveness Analysis
Positive formulation Keep the status quo Risk averse
Negative formulation Gamble to achieve a better result Risk seeking
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Mental Accounting
3/12/2009Decision and Cost-Effectiveness Analysis
You set off to buy an iPod shuffle at what you believe to be the cheapest store in your neighborhood. When you arrive, you discover that the price of the Shuffle is $75, a price you believe is consistent with low estimates of the retail price.
A friend walks into the store and tells you a store 10 minutes away sells Shuffles for $55.
Do you go to the other store?Now suppose you are buying a MacBook Pro for
$1960, and a friend tells you it sells for $1940 in a store 10 minutes away. Do you go?
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Different Frames
3/12/2009Decision and Cost-Effectiveness Analysis
Real versus Hypothetical Experiment 1: What do you think? Experiment 2: Framing with hypothetical payoffs Experiment 3: Framing with real payoffs
Framing the choice to the civil jury can greatly affect the award
Framing the choice to the criminal jury Can help decide guilt or innocence Can affect the sentencing of the guilty
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Framing Effects in Medical Decision-Making: Treatments
3/12/2009Decision and Cost-Effectiveness Analysis
When framed positively (i.e. survival vs. mortality): Respondents 1.5 x more likely to choose surgery
over other treatments (i.e. radiotherapy) Respondents demonstrated increased preference
for invasive/toxic treatments No framing effect noted in hypothetical vs.
real life treatment decisionsMedicine use intention higher when results
presented as RRR vs. ARR or NNT
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RRR, ARR, and NNT
3/12/2009Decision and Cost-Effectiveness Analysis
RRR = Relative Risk ReductionARR = Absolute Risk ReductionNNT = Numbers Needed to Treat Dead AliveMeds 404 921CABG 350 974
Risk of death (from having CABG) = 350/1324 = 0.264Relative risk of death = 0.264/0.305 = 0.87 = 87%RRR = Amt of risk of death is reduced by surgery: 100% - 87%
= 13% ARR = Absolute amt of risk surgery reduces death: 30.5% -
25.4% = 4.1% NNT = # pts needing surgery to prevent 1 death: 1/ARR = 24
Source: http://www.ebm.worcestervts.co.uk/trial_results.htm
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Conclusions on Frames
3/12/2009Decision and Cost-Effectiveness Analysis
Humans are inconsistent. Framing is effective Framing can be manipulated to achieve desired outcomes Awareness of framing effects can make you a better
decision maker
Crucial in understanding discrepancies and inconsistencies in individual preferences.
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Implications for Cost-Effectiveness Analysis
3/12/2009Decision and Cost-Effectiveness Analysis
Important when considering perspective for analysis.
Preferences are dependent on framing and point of reference. Individual preferences vs. community preferences Preferences at time of illness or during recovery Availability of alternative treatments
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Three Topics
3/12/2009Decision and Cost-Effectiveness Analysis
FramesEquityEconomic epidemiology
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Equity
3/12/2009Decision and Cost-Effectiveness Analysis
Efficiency and Equity Both important for health care resource allocation
decisions Few guidelines for measuring or incorporating
equity Equity ~ Values
How can equity concerns be incorporated in cost-effectiveness analyses?
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What is equity?
3/12/2009Decision and Cost-Effectiveness Analysis
An equal and fair distributionAre treatments fairly allocated? Or
Are benefits fairly distributed?Canadian Common Drug Review Pharmacoeconomic
Review Template: “What equity assumptions were made in the analysis?”
No guidance on how to assess
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Vertical Equity
3/12/2009Decision and Cost-Effectiveness Analysis
Principle of vertical equity = allocation linked to “need”
Greater care is given to people with greater health needs
Sicker patients first priority for funding Goal is to create equity in eventual health status
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Neglecting Vertical Equity
3/12/2009Decision and Cost-Effectiveness Analysis
Implies all health outcomes are valued equally
Regardless of to whom they accrue
Conversely, paying attention to equity: Could make some relatively inefficient technologies more
attractive If benefits groups with greater claim to treatment Or could make efficient options less attractive
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NICE (UK) Decisions, 1999 to 2002
3/12/2009Decision and Cost-Effectiveness Analysis
Cost per QALY, £
Accepted Restricted Rejected
<20,000 14 3 1
20,000–30,000 0 4 0
>30,000 1 4 3
Sculpher, M.The use of quality-adjusted life-years in cost-effectiveness studies.Allergy 61 (5), 527-530.
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3/12/2009Decision and Cost-Effectiveness Analysis
Vertical equity may be controversialIf your definition of “need” is different than mineAssume we accept vertical equityWhat characterizes equity?How should it measured?
Controversy
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3/12/2009Decision and Cost-Effectiveness Analysis
The Incremental Cost-Effectiveness Ratio Comparing treatments A and B:
The cost of obtaining one extra unit of health effect
Cost-effectiveness analysis A measure of efficiency
Tradeoff between made explicit between scarce resources potential changes in health
AB
AB
effecteffect
costcostICER
Review of Efficiency
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3/5/2009MS&E 292 - Health Policy Modeling
QALYs as a Measure of Health
Quality Adjusted Life Year
Life expectancy = 10 yearsQuality adjusted LE = 6.45 QALYs
0.5 0.5 0.5
0.750.750.8 0.8 0.8 0.8
0.25
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 2 3 4 5 6 7 8 9 10Years
Quality of LifeWeight
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Are All QALYs Gains Equivalent?
3/12/2009Decision and Cost-Effectiveness Analysis
4 QALYs
0
5
10
15
20
25
0 0.2 0.4 0.6 0.8 1
Quality of Life
Life
Exp
ect
an
cy
B
A
CD
E
1 QALY
7 QALYs
A’
B ′
E ′
C ′D ′
Each associated with a gain of 3 QALYs!
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Steps in Applying Equity to CEA
3/12/2009Decision and Cost-Effectiveness Analysis
1.Define groups which should receive priority to advance equity
2.Derive equity weights3.Determine how equity weights can be applied to
results of cost-effectiveness analyses (CEA) 4.Apply equity weighting to CEA results as a form of
sensitivity analysis
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Some Possible Equity Factors
3/12/2009Decision and Cost-Effectiveness Analysis
Baseline life expectancy
Baseline quality of life
Improvement in or final life expectancy
Improvement in or final quality of life
Duration of health benefits
Direction of health benefits
Distribution of benefits (number of people)
Health care endowment (prior expenditure)
Age
Personal behaviours
Relation to others
Social status
Lifetime health
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Steps in Applying Equity to CEA
3/12/2009Decision and Cost-Effectiveness Analysis
1.Define groups which should receive priority to advance equity
2.Derive equity weights3.Determine how equity weights can be applied to
results of cost-effectiveness analyses (CEA) 4.Apply equity weighting to CEA results as a form of
sensitivity analysis
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3/12/2009Decision and Cost-Effectiveness Analysis
Attribute Levels
Baseline quality of life (0 to 100) 306085
Gain in quality of life (0 to 100) 05
15
Baseline life expectancy (years) 210
Gain in life expectancy (years) 12
10
Number of beneficiaries 10010,000
1,000,000
Pre-program financial resources allocated to treat the condition ($)
05,000
50,000
Age (years) 154575
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Survey to Understand Equity
3/5/2009MS&E 292 - Health Policy Modeling
Pilot in elected officials, municipal and provincial public clerks.
Participants recruited from waiting rooms at major Toronto downtown teaching hospital.
Asked to imagine they were voting in a referendum between 2 programs.
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An Example
3/12/2009Decision and Cost-Effectiveness Analysis
AttributesScenario
A B
Baseline QOL 30 30
Gain in QOL 0 15
Baseline LE 10 10
Gain in LE 10 2
N 10,000 100
Prior Allocation 50,000 5,000
Age 75 15
Number Selecting (%) 79 (29) 191 (71)
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Solve the problem of equity?
3/12/2009Decision and Cost-Effectiveness Analysis
Personal circumstances made such decision making challenging.
Several disliked the conceptual basis of the study, Fairness factors “aren’t measurable”
Trade-offs between attributes too complex
Individual or group values should dominate over centralized decision making
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More Comments
3/12/2009Decision and Cost-Effectiveness Analysis
7: interesting and thought-provoking14: challenging (3 both interesting and
challenging)5 wanted “equal” option to indicate that they
considered some scenarios equivalentSome felt that any rationing was objectionable
“Everyone should have a chance to be treated. It is up to the patient and his doctor to decide whether it is worthwhile”.
Choices are best left in the hands of God
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Significant factors in equity…
3/12/2009Decision and Cost-Effectiveness Analysis
Consistent with prioritization for those with poorer health
Less prior resource allocation viewed as having priority
Equal priority two groups alike except: 1st had a quality of life that was 50 points worse 2nd had an expected 10 year increase in life
expectancy
Equal priority two groups alike except: 1st 10 years younger 2nd had received about $13,000 less in prior
resources
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Some Factors Not Significant
3/12/2009Decision and Cost-Effectiveness Analysis
Number of people expected to benefit
Potential improvement in quality of life
Could have important implications for resource allocation models
Distributional aspects (“how many benefit?”) may be less important than the characteristics of individuals (“who benefits?”)
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Steps in Applying Equity to CEA
3/12/2009Decision and Cost-Effectiveness Analysis
1.Define groups which should receive priority to advance equity
2.Derive equity weights3.Determine how equity weights can be applied to
results of cost-effectiveness analyses (CEA) 4.Apply equity weighting to CEA results as a form of
sensitivity analysis
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Equity-Weighted QALYs
3/12/2009Decision and Cost-Effectiveness Analysis
Vertical equity Implies society values some health gains
more than othersFor example
A QALY gain a sick person more valuable than a QALY gain for a well person
Cancer drug vs. lifestyle drugOne often proposed solution is to adjust
QALYsQALYs transformed into “eQALYs”
= equity-weighted QALYs
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An alternative to focusing on QALYs
3/12/2009Decision and Cost-Effectiveness Analysis
Rather than focusing on health outcomeFocus on resource allocation decisionReframe vertical equity as “more willing to pay
for some health outcomes than others”i.e., a higher (or lower) willingness to pay
threshold
Advantage: Policy implications more transparent Accommodated by current CEA methods
Disadvantage Measurement more difficult “Knee-jerk” rejections more common?
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Limitations of eQALYs
3/12/2009Decision and Cost-Effectiveness Analysis
QALYs already controversialConstruct is artificial, somewhat foreignMeasurement issuesAlready conflate survival, quality of lifePutting equity in might confuse more than it
illuminatesAnd exacerbate concerns about subjectivity, valuesi.e. eQALY components:
Survival Objective Quality of life (preference) Subjective Equity weight Subjective and
value-laden
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The Net Benefit Approach
3/12/2009Decision and Cost-Effectiveness Analysis
Consider an ICERΔ C / Δ EDecision favorable if:
ICER < society’s willingness to pay for an extra unit of E (λ) Δ C / Δ E < λ
Define Net Monetary Benefit (NMB) asNMB = λ∙ Δ E- Δ CDecision favorable if NMB>0
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Equity-weighted NMB
3/12/2009Decision and Cost-Effectiveness Analysis
Assume an interventionWe want to assign an equity weight to the health
effectCall the equity weighting function f(∙)Equity-weighted health effect is f(Δ E,q)Where q is a vector of equity factors
So equity-weighted NMB is
NMB = λ∙ f(Δ E,q)- Δ C
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Steps in Applying Equity to CEA
3/12/2009Decision and Cost-Effectiveness Analysis
1.Define groups which should receive priority to advance equity
2.Derive equity weights3.Determine how equity weights can be applied to
results of cost-effectiveness analyses (CEA) 4.Apply equity weighting to CEA results as a form of
sensitivity analysis
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Conclusions
3/12/2009Decision and Cost-Effectiveness Analysis
Equity weighting the willingness to pay threshold is algebraically equivalent to equity adjusting QALYs
A form of sensitivity analysis, offers transparency, reproducibility
Focus on methods to estimate the relative attractiveness of allocating to different groups
Doesn’t obviate need for determining societal willingness to pay threshold
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Equity Considerations
3/12/2009Decision and Cost-Effectiveness Analysis
Fairness in process Accountability for reasonableness Fairness in outcomes
A decision that is: Transparent Principled Defensible
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Three Topics
3/12/2009Decision and Cost-Effectiveness Analysis
FramesEquityEconomic epidemiology
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Traditional View of Epidemics
3/12/2009Decision and Cost-Effectiveness Analysis
How is an epidemic started?
Index case The first case to start an epidemic Not necessarily the first case of the disease Epidemic is an interaction of the disease, the host, and the
susceptible population
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Finding the Index Case
3/12/2009Decision and Cost-Effectiveness Analysis
• Detective work to find “Patient Zero”• Gaëtan Dugas
• the French-Canadian gay flight attendant reputed to introduce HIV to the US.
• Made famous by the book “And the Band Played On”• The research used for that study was later repudiated• Introduction probably through Haiti rather than Africa
• Typhoid Mary• The SARS outbreak
• On Feb 21, 2003, a 65-year-old medical doctor from Guangdong checks into the 9th floor of the Metropole hotel in Hong Kong
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The importance of a susceptible population and hosts
3/12/2009Decision and Cost-Effectiveness Analysis
156 closecontactsof HCW
and patients
Index case from
Guangdong
Index case from
Guangdong
Hospital 2Hong Kong
4 HCW +2
Hospital 2Hong Kong
4 HCW +2
Hospital 3Hong Kong
3 HCW
Hospital 3Hong Kong
3 HCW
Hospital 1Hong Kong
99 HCW
Hospital 1Hong Kong
99 HCW
Canada12 HCW +
4
Canada12 HCW +
4
Hotel MHong Kong
IrelandIreland
USAUSA
New YorkNew York
Singapore34 HCW +
37
Singapore34 HCW +
37
Viet Nam37 HCW +
?
Viet Nam37 HCW +
?
BangkokHCW
BangkokHCW
4 otherHong Konghospitals28 HCW
4 otherHong Konghospitals28 HCW
Hospital 4Hong KongHospital 4
Hong Kong
B
I
K
F G
ED
CJ
H
A
GermanyHCW +
2
GermanyHCW +
2
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SARS: 8,445 probable cases, 790 deaths
3/12/2009Decision and Cost-Effectiveness Analysis
China (5328)
Singapore (206)
Hong Kong (1755)
Viet Nam (63)
Europe:10 countries (38)
Brazil (3)
South Africa (1)
Canada (238)
USA (70)
Colombia (1)
Kuwait (1)
South Africa (1)
Mongolia (9)
India (3)
Australia (5)
New Zealand (1)
Mongolia (9)
Russian Fed. (1)
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3/12/2009Decision and Cost-Effectiveness Analysis
Epidemic Models
S-I-R Models
Susceptible
Infected Removed
• Have no immunity• Never had the
disease• Have not
been immunized
Recovered and immune
Dead
POPULATION
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S-I-R Models – simple setup
3/12/2009Decision and Cost-Effectiveness Analysis
dS/dt=-S dI/dt=S-I dR/dt=-I
N = S + I + R
Susceptible
Infected Removed
POPULATION
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S-I-R Models – more realistic
3/12/2009Decision and Cost-Effectiveness Analysis
dS/dt=N-S-S dI/dt=S-I-I dR/dt=-I-R
N = S + I + R
Susceptible
Infected Removed
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3/12/2009Decision and Cost-Effectiveness Analysis
1. The prevalence of infection in the population2. How frequently a susceptible individual comes in
contact with an infected individual3. The probability of transmission from infected to
uninfected per contact
What determines infection ()?
Susceptible
Infected
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What determines the rate of “removal” ()?
3/12/2009Decision and Cost-Effectiveness Analysis
1. The duration of infection2. The disease’s case-fatality rate (of the people who
get infected, how many die)
Infected Removed
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What does an epidemic look like?
3/12/2009Decision and Cost-Effectiveness Analysis
0
200
400
600
800
1000
1200
0 0.1 0.2 0.3 0.4 0.5
numbe
r
time
St
It
Rt
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R0
3/12/2009Decision and Cost-Effectiveness Analysis
Called the basic reproduction numberThe average number of secondary cases a typical
infectious individual will cause in a completely susceptible population
Time = 0 Time = 1; R0=2
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R0
3/12/2009Decision and Cost-Effectiveness Analysis
R0>1
R0<1
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Examples of R0
3/12/2009Decision and Cost-Effectiveness Analysis
Disease Transmission R0
Measles Airborne 12-18
Pertussis Airborne droplet 12-17
Diphtheria Saliva 6-7
Smallpox Social contact 6-7
Polio Fecal-oral route 5-7
Rubella Airborne droplet 5-7
Mumps Airborne droplet 4-7
HIV/AIDS Sexual contact 2-5
SARS Airborne droplet 2-5Influenza (1918 pandemic strain) Airborne droplet 2-3
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How does an epidemic propagate?
3/12/2009Decision and Cost-Effectiveness Analysis
R0 only describes the initial epidemic dynamics, and will tell you whether an epidemic is likely to take hold in a population
To find out what will happen to the epidemic, need to look at the effective reproductive rate
Re(t) = R0 * S(t)
It is the number of new infections by a typical individual at a particular time
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Other reproduction numbers
3/12/2009Decision and Cost-Effectiveness Analysis
Eventually, insufficient susceptibles to maintain chains of transmission
When each infectious person infects less than 1 other (on average), epidemic dies out
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Historical example
3/12/2009Decision and Cost-Effectiveness Analysis
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Implications
3/12/2009Decision and Cost-Effectiveness Analysis
What is the effective reproductive rate for an endemic disease? Re(t)=1
Implications for preventing epidemics: Re(t)=1=R0*S(t)
As long as S(t) stays below 1/R0, the epidemic cannot propagate in a population!
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1/R0 is the susceptibility threshold
3/12/2009Decision and Cost-Effectiveness Analysis
If R0 is 2, then keeping 50% of the population protected will prevent an epidemic
If R0 is 10, then keeping 10% of the population susceptible (90% protected) will prevent an epidemic
What are the implications for vaccines? Measles (R0 is 17)?
Seasonal influenza (R0 is 2)?
This is the elimination criteria for epidemics
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Vaccine coverage and disease
3/12/2009Decision and Cost-Effectiveness Analysis
1-1/Ro
rubella measles
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Traditional view of epidemics
3/12/2009Decision and Cost-Effectiveness Analysis
Preventive efforts → reduction in disease transmission Reduced contact (abstinence) Reduced chance of transmission per contact (gloves) Reduced disease transmission → reduced prevalence Reduced prevalence → further
reduction in transmission
Risk of infection is directly proportional to prevalence. Therefore controlling prevalence is highest priority.
Preventive efforts are a public health imperative
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Behavioral view of epidemics
3/12/2009Decision and Cost-Effectiveness Analysis
Economists’ view: prevention and prevalence affect each other High prevalence disease → people increase personal
preventive measures
Low prevalence disease → people increase risky behaviors
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Economic Epidemiology
3/12/2009Decision and Cost-Effectiveness Analysis
Mathematical conceptualization of the interplay between economics, human behavior and disease to improve our understanding of the emergence, persistence and spread of infectious agents optimal strategies and policies to control their spread
Prevalence response elasticity Hazard rate into infection of susceptibles is a decreasing
function of prevalence (opposite of epidemiological model predictions)
Application to HIV Application to Measles
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Empirical evidence?
3/12/2009Decision and Cost-Effectiveness Analysis
Geoffard and Philipson, Int. Econ. Rev., 1996
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Implications of behavioral view
3/12/2009Decision and Cost-Effectiveness Analysis
Any highly prevalent epidemic is self-limiting because of preventive measures taken by individuals
Any campaign to eradicate a disease will run into trouble when after prevalence decreases HIV in USA
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3/12/2009Decision and Cost-Effectiveness Analysis