Underuse, Overuse, Comparative
Advantage and Expertise in
Healthcare
Amitabh Chandra
Harvard and NBER
Douglas Staiger
Dartmouth and NBER
Highest
Performance
Lowest
Performance
Source: Chandra, Staiger and Skinner (IOM, 2010)
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Large variations in
utilization and outcomes
across hospitals
Variations found in other
countries and even within
hospitals
Variations in utilization not
consistently related to
patient outcomes –
overuse?
But higher utilization is also
associated with higher
returns to treatment –
expertise (TFP)?
Economists know about
heterogeneity from
comparative-advantage!
Underuse, Overuse, Expertise and Comparative
Advantage, as Explanations
Underuse: Marginal patient would benefit from
more treatment in low-use hospitals
Overuse: Marginal patient is harmed by
treatment in high-use hospitals
Expertise: Some hospitals have an absolute
advantage (higher TFP) from treatment
Comparative Advantage: Hospitals with greater
relative benefit from treatment optimally treat
more patients
Basic Setup
Expertise in Medical Management
Y    Xih  
• Outcome if Treated Medically
1
1
1
1
Y



X



• Outcome if Treated Intensively
ih
h
ih
ih
0
0

0

0
Y

Y
Y
T



X

Y
T


• Observed Outcome
ih
ih
ih ih
h
ih
ih ih
ih
• Expected Benefit from Treatment
0
ih
0
h
0
0
ih
Yih   h  Xih   ih , where  h   1h   h0 ,     1   0 , and ih  ih1  ih0
Expertise in Intensive Management
Difference in productivities represents hospital’s comparative
advantage in providing the treatment. Hospitals may have a
comparative advantage in providing the treatment because of
being good at the treatment or being bad at caring for patients
without the treatment.
Roy Model
B = Benefit from treatment
τh = Threshold that must be exceeded to receive treatment
αh is Comparative Advantage
τh=0 Higher
reflectstreatment
optimal care:
Benefit = Xβ + αh + e
for similar
Patients
receive
patients
cantreatment
be due to
Pr(Treatment=1) = Pr (Benefit > τh)
if positive
benefit advantage or
comparative
= Pr (Xβ + αh + e > τh)
lower threshold
= Pr (Xβ + (αh - τh) > -e)
= Pr (I > -e), where I = Xβ + (αh - τh)
But look at treatment effect on the treated (TT):
E(Benefit | Treatment=1)
= Xβ + αh + E(e | I > -e)
= I + τh + E(e | I > -e)
= g(I) + τh
Conditional on treatment
Higher benefit, conditional
propensity (I), differences
on treatment propensity
in TT due to threshold, not
means underuse; Lower
comparative advantage
6
benefit means overuse
Benefit for Patients Over the Treatment Threshold
Benefit
E(Benefit | Benefit > Threshold)
Benefit of
Treatment is
increasing in the
propensity to
receive it
0
Harm
Propensity to get Treatment
Threshold is set at zero:
perform treatment until
there is no more benefit
1
Increasing the Treatment Threshold
E(Benefit | Benefit > Threshold)
Benefit
Higher Benefit for
all patients
Positive Benefit for
least appropriate
0
Harm
Propensity to get Treatment
1
Distinguishing Underuse and Overuse
E(Benefit | Benefit > Threshold)
Benefit
High Treatment
Threshold
Low Treatment
Threshold
τhigh>0 (underuse)
0
Propensity to get Treatment
Harm
τlow<0 (overuse)
1
Comparative Advantage
Benefit
E(Benefit | Benefit > Threshold)
Low Comparative Advantage
High Comparative Advantage
0
Harm
Propensity to get Treatment
1
Greater comparative advantage in
treating Intensively, means patient
propensity to receive treatment is
higher
Predictions
1. Patients with higher propensity should receive higher
benefit (key for economic model)
2. Lower thresholds implies patients with same treatment
propensity get less benefit in high-use hospitals
3. Overuse implies that low-propensity patients receive
negative benefits, instead of zero benefits.
4. Hospitals with higher hurdles should treat fewer patients
and have higher benefits to treatment (conditional on I)
5. Comparative advantage increases patient propensity to
be treated, but patients with same propensity receive
same benefit in all hospitals.
Empirical Work
• Cooperative Cardiovascular Project (CCP)
– Chart data on ~140,000 Medicare beneficiaries (over 65)
who had heart-attacks (fresh AMIs)
• Examine reperfusion within 12 hours
– Excellent patient controls let us estimate treatment effect.
Can replicate RCT effect of reperfusion of 0.20 impact on
log-odds of survival
– Comparative Advantage, expertise, overuse & underuse
were relevant concerns for reperfusion at this time
Patient Controls for Reperfusion within 12 hours
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
Age, Race, Sex
previous revascularization (1=y)
hx old mi (1=y)
hx chf (1=y)
history of dementia
hx diabetes (1=y)
hx hypertension (1=y)
hx leukemia (1=y)
hx ef <= 40 (1=y)
hx metastatic ca (1=y)
hx non-metastatic ca (1=y)
hx pvd (1=y)
hx copd (1=y)
hx angina (ref=no)
hx angina missing (ref=no)
hx terminal illness (1=y)
current smoker
atrial fibrillation on admission
cpr on presentation
indicator mi = anterior
indicator mi = inferior
indicator mi = other
heart block on admission
chf on presentation
hypotensive on admission
hypotensive missing
27.
28.
29.
30.
31.
32.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
shock on presentation
peak ck missing
peak ck gt 1000
non-ambulatory (ref=independent)
ambulatory with assistance
ambulatory status missing
albumin low(ref>=3.0)
albumin missing(ref>=3.0)
bilirubin high(ref<1.2)
bilirubin missing(ref<1.2)
creat 1.5-<2.0(ref=<1.5)
creat >=2.0(ref=<1.5)
creat missing(ref=<1.5)
hematocrit low(ref=>30)
hematocrit missing(ref=>30)
ideal for CATH (ACC/AHA criteria)
14
Estimation…Step 1
We want to Estimate:
Pr(Reperfusion=1) = F(Xβ + θh), θh=(αh - τh)
Strategy:
1. Use Conditional Logit for hospital fixed-effects
2. But worry that Fixed Effects estimation is not smart given
number of hospitals with small n
3. Estimate θh & I= Xβ + θh using random effects logit (xtmelogit)
4. Produces MLE of coefficients (β), SD of the hospital RE and
posterior estimates of the hospital RE (θh).
5. Combine to form an estimate of index (I= Xβ + θh) for each
patient
15
Estimation…Step 2
Want to estimate outcomes:
Yih  Yih0 YihTih   h0  Xih 0 YihTih  ih0
Strategy:
• Empirical analog:
Pr  Survival ih  1  F Treatedihih  Xih 0   h0 , where ih   h  g I ih 
• For estimation, simplify: ih  0  1Iˆih  2ˆh




 

Pr  Survival ih  1  F Treatedih0  Treatedih Iˆih 1  Treatedihˆh 2  Xih 0   h0
Treatment on Treated increasing
in propensity (λ1>0)
If hospitals
vary
CA, but
if hospitals
vary
in in
hurdle
but not
not in
in
their minimum
threshold
then
2=0
comparative
advantage,
then
λ2λ<0;
the treatment effect is smaller in
hospitals with a high propensity to
treat

Hospital Effects are Mean 0, so this
effect of Reperfusion at typical
hospital
Index Normed to
Zero: Effect for
average patient effect
Roy Model of Labor
at average hospital
Economics at work!
Since these specifications do not
condition on the propensity,
coefficient is biased in the positive
direction; not a strong test of
whether hospitals differ in their
minimum treatment threshold.
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Figure 2: Survival Benefit from Reperfusion,
According to Hospital Effect in Treatment Propensity
Low Propensity Patients
-.4
-.4
Effect of reperfusion on logodds of 30-day survival
-.3
-.2
-.1
0
.1
.2
.3
Effect of reperfusion on logodds of 30-day survival
-.3
-.2
-.1
0
.1
.2
.3
.4
.4
All Patients
-.6
-.4
-.2
0
.2
.4
.6
Hospital effect from propensity equation
-.6
-.4
-.2
0
.2
.4
.6
Hospital effect from propensity equation
Figure 3: Survival Benefit from Reperfusion According to Patient’s
Treatment Propensity. High & Low Treatment Rate Hospitals.
of Hospital Effects from Propensity Equation
of Hospital Effects from Propensity Equation
-.5
-.5
Effect of reperfusion on logodds of 30-day survival
-.25
0
.25
.5
.75
Hospitals in Highest Tercile
Effect of reperfusion on logodds of 30-day survival
-.25
0
.25
.5
.75
Hospitals in Lowest Tercile
-2
-1
0
Patient Treatment Propensity Index
1
2
-2
-1
0
Patient Treatment Propensity Index
1
Summary…Part I
1. Patients with higher propensity receive higher benefit.
 Providers triage based on benefit
2. Intensive hospitals have lower benefit
 not consistent with comparative advantage; expect
same benefit
 consistent with differences in minimum hurdle
3. Low-propensity patients are harmed in more
aggressive hospitals
 consistent with overuse
Jointly Estimate Treatment Propensity and Survival
Equation With Hospital-level Random Coefficients
Propensity Equation:
(1) Pr(Treatment=1) = F(Xβ + θh), θh=(αh - τh)
Survival Equation:
Also add hospital-level random
intercept in survival equation.
Pr(Survival=1) = F(reperf
λo +TFP
(reperf*I)
λ1 + reperf* τh + Xβ)
Captures
at medicine
split I = Xβ + θh into Xβ and θh to get:
(2) Pr(Survival=1) = F(reperf λ0 + (reperf*Xβ) λ1 + reperf* μh + Xβ + δh)
where μh = λ1 αh + (1- λ1) τh
Joint estimation
using
hierarchical
logit recovers joint distribution of expertise
Use
Xβ
estimate
from
prior
Hospital-level
and thresholdrandom
reperfusion
coefficients (joint
normal)logit
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Table 3: Survival Benefit from Reperfusion According to Patient’s
Treatment Propensity
Capture Treated on Treated
Variation across Hospitals
Most of the variation across
Some (but far from all) of the
variation in treatment rates across hospitals in the observed
treatment is the result of variation
hospitals (theta) is associated
in the treatment threshold rather
with variation in the treatment
Hospitals with better survival ratesthan
whencomparative
not using the
advantage.
threshold
treatment tend to set too low a treatment threshold and
overuse the treatment – perhaps hospitals that are
highly skilled at caring for patients without reperfusion
overestimate the benefits of treatment and therefore
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overuse reperfusion.
Summary
1. Large variation in hospital outcomes: SD=0.45 in
logodds
(some due to being worse at non-reperfusion)
2. Substantial variation in threshold: SD=0.33 in logodds
3. Positive correlation in hurdle & comparative advantage
 lower expertise hospitals also overuse
Closing Thoughts
• Powerful framework for analyzing variation in
treatment and outcomes across populations.
• Propensity score helps identify heterogeneous
treatment effects – key to finding overuse
• What factors drive differences across hospitals
in expertise & threshold?
• If treatment variation due to expertise, there is a
welfare loss from uniform treatment guidelines