Transcript Topics in Health and Education Economics

Topics in Health Economics –
class 3
Matilde P. Machado
[email protected]
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
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Title: Estimating the Quality of Care in Hospitals
using Instrumental Variables
Typically the literature on Hospital Quality has used
hospital-specific output measures such as mortality
rates to compare hospital performance.
Potential Problem with these measures is that they
suffer from ENDOGENEITY which can bias
estimates in the wrong direction i.e. the best
hospitals may show worse mortality data simply
because they treat the most difficult patients.
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

In an equation such as:
N
yit   bi H i  uit where i  hospital, yit  mortality rate
i 1
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It could be that the best hospitals receive the most difficult cases
i.e. attract a sicker population of patients and have higher
mortality rates. Therefore bi has two components (quality and
severity)
Researchers try to solve this selection bias by including casemix variables, i.e. variables that capture the severity of the
patients received at each hospital (DRGs, main diagnosis,
secondary diagnosis, demographic variables (age, gender,
N
comorbidities))
yit  X it   bi H i  uit where i  hospital
i 1
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
N
yit  X it   bi H i  uit where i  hospital
i 1
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Still the problem remains if there is unobserved severity that
is not captured by X but is correlated with the H dummies, in
which case the best hospitals may look worse or not much
better than the rest of hospitals (u’s are not equally distributed
across the H’s, the H’s are endogenous):
To avoid this bias, Gowrisankaran and Town (1999):
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Suggest to use IV that are correlated with H dummies but not to these
unobservables.
This paper shows evidence of remaining selection even after
controlling for observed patient characteristics.
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

The authors use mortality due to pneumonia from Medicare patients’ data
(patients older than 65 years of age) because:
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Pneumonia is a common disease among this age group
It may lead to death, it is the 4th leading cause of death among the elderly. In
their sample in-hospital mortality due to pneumonia in 1989 was 17.9%.
The outcome depends in a great deal on the hospital’s procedures, therefore,
an important part of the variance in outcomes can be explained by differences
in hospital quality.
Note: It is important to notice that the paper can estimate only the quality of
hospitals in pneumonia treatment (which may or may not be correlated with
other aspects of hospital quality)
Other researchers have used pneumonia mortality as a benchmark for hospital
quality (Keeler et al. JAMA , 1990, and McGarvey and Harper, 1993).
When diagnosed with pneumonia the patient/doctor have time to choose the
“right” hospital, they do not need to hurry to the closest one (as with heart
attacks). This allows to test whether selection is important.
Medicare patients pay the same out-of-pocket expenditures at every hospital
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

The Model of mortality and hospital choice
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The Choice Equation: Once the patient becomes ill with pneumonia
he (his physician) chooses the hospital based on: the perceived quality
of treatment, the patient’s severity of illness, the cost to the patient,
and the distance to the hospital.
1 if patient i chooses hospital j
cij  
j  1,...J
0
otherwise

cij  f ( z ,  i , uij ) there are J equations, one for each hospital
z set of hospital characteristics (location, services, ownership)
 i set of individual characteristics, include location or distance
uij characteristics unobserved to the researchers, not to the patient/physician including severity
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

The mortality Equation: They model it as a hazard function that
gives the probability of a patient dying in a given day conditional on
being alive at the beginning of the day. The hazard depends on the
observed and unobserved severity of the patient’s illness, the number
of days since admission and the quality of treatment received at the
hospital chosen.
mit  ci b   xi   dt  sit   it
bi are
interpreted
as hospital
quality.
Parameters
of interest
Linear probability model
1 if the patient i dies on day t
mit  
0 otherwise
ci  (ci1 ,...., ciJ ) vector of dummy variables
xi  vector of patient characteristics that affect the patient's mortality
age, gender, disease stage, income, diagnosis, comorbidities
dt  dummy variables for days since admission
sit  unobserved severity
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

Notice that sit will affect the decision of which hospital to go to, more
severe patients may choose higher quality hospitals, i.e. sit may be part
of the error term uit ,which implies that the error terms in both equations
are correlated, implying that ci is correlated with the error term in the
mortality equation.
mit  ci b   xi   dt  sit   it
1442 443
error term
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Deaths after discharge are not observed.
Important!: Assume sit (residual health status after controlling for all
the Xs) and it are identically and independently distributed in the
population and orthogonal to the regressors xi and dt.
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
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Instruments:
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There are J-1 endogenous variables (c1,….cJ-1), we need at least as many
instruments. There is a reference hospital, example for J=2:
yi  b1c1i  b2c2i  X i  vi  b1 (1  c2i )  b2c2i  X i  vi  b1  (b2  b1 )c2i  X i  vi
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Distance of each patient to hospitals as IVs for ci. Comments:
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A good IV must be correlated with ci and not with sit or it.
A patient’s distance to hospital j is negatively correlated to the choice of that
hospital so should be a good candidate.
A patient distance to a given hospital should not be correlated with the error
term sit+it under the assumption of equal distribution of sit
However, more severe patients may be willing to travel larger distances to
be treated at a particular hospital. So distance of a given patient to the
chosen hospital is correlated with severity.
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
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Imagine same distribution of severity in town A as in B. For simplicity,
assume each town has a hospital in downtown i.e. symmetric distance of
patients to A and B (dA and dB is independent of severity distribution)
Most severe patients (shown in red) are in the same proportion in both towns.
A
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B
Hospital A is of higher quality – so all patients in region A go to A and all
severe patients (in red) in region B go to A. So the average distance of
patients that choose hospital A is higher than the average distance of patients
that chose B. So distance to chosen hospital is correlated with severity
(invalid instrument)
a simple code
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

But is the assumption that severity is equally distributed a plausible one?
Presumably more sick patients may prefer to leave closer to a good hospital.
If this is so then their instrument is not valid.
A
B
Note: One instance in which this is so, is nursing
homes which tend to locate near high quality
hospitals. For these reason they discard patients
admitted from nursing homes.
Patients in red are severe while
patients in green are less
severe. In this case, severe
patients locate close to the best
hospital, say A. So distance to
A would be negatively
correlated with severity, those
with less distance to A are the
most severe. The opposite for
B. Distance to A and B would
not be valid instruments
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
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Instruments (cont.):
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Use 2J instruments:
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dij ≡ distance of patient i to each j hospital
exp(-fdij) ≡ a non-linear function of distance.
The reason is that most likely in the choice equation the effect of
distance is non-linear so they rely on more instruments (any
function of distance should do) but they rather estimate f.
They estimate the following equation for each hospital
separately:
cij  1 j   2 j dij   3 j exp(f dij )  uij
maximum likelihood would do. T
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
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For f<1 the function exp(-fd) looks as follows:
distance
For each variable cij they obtain an estimate of fj. They set f equal to the average value
of 0.25
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
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They estimate the mortality equation with a linear probability
models. Linear probability models:
 There is heteroskedasticity, can be solved via GLS.
 Predicted values lie outside the [0,1] range, cannot be
solved.
 The coefficients have nice interpretations, i.e. bj is the
incremental probability of death on any day at hospital j.
Technical:
In a linear regression, F is linear then: E ( y)  b x
y  b x   where   1  b x,  b x
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

Then the variance of the error term is given by:
 b x   0 if y  0
  y  b x  
1  b x  1   0 if y  1
For   0, variance is:
var( )  E ( 2 )  ( 0 ) 2 Pr( y  0)  (1   0 ) 2 Pr( y  1) 
 ( 0 )2 (1  b x)  (1   0 ) 2 ( b x) 
 ( 0 )2 (1   0 )  (1   0 ) 2 ( 0 )   0 (1   0 )  b x(1  b x)
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Then if the error term is hitsit+it the variance of the error
term is var(hit)= hit(1-hit).
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
Two step procedure:
1.
2.
3.
IV regression (not controlling for heteroskedasticity)  consistent (but
not efficient) estimates.
Construct hˆit for each observation based on the IV estimates from step 1
Because the var-cov matrix is diagonal with elements hˆit 1 hˆit  premultiply the mortality equation by 1 hˆit 1 hˆit  .Notice they use a
slightly different formula to correct for (rare) cases where the estimated
residuals are negative or greater than 1:
1
max hˆit (1 hˆit ),0.01
apply IV again to the transformed variables. Estimates will be consistent and
asymptotically efficient (it is not necessary to transform the instruments).
Gautam Gowrisankaran and Robert
Town (JHE, 1999)

Endogeneity Test (Hausman,1978) on the c’s: Compares IVGLS and GLS. H0: No endogeneity (IV-GLS≈GLS). Need a
consistent and efficient estimator under the null and a
consistent estimator under the alternative.

H  ˆIV  ˆGLS
1 K
^
^
 ˆ

2
ˆ
ˆ
ˆ
var(

)

var(

)

IV
GLs   IV   GLS ~  J 1




K K
Full vector of estimates
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
K 1
J-1 number of
endogeneous
variables
Overidentification test (Bowden and Turkington, 1984), the
idea is that the Z and the h’s should not be correlated:
O  sˆ2  Z hˆ 
 Z Z 
1
 Z hˆ  ~  J21 where J+1 is the number of overidentifying restrictions
1cols ( Z ) cols ( Z )cols ( Z ) cols ( Z )1
(2J instruments for J-1 endogeneous variables)
S2 is the estimated variance of the residuals in the second stage.
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
Data Selection
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They keep patients ≥65 years old that entered into a hospital in one of 4 of
the California counties with a diagnosis of pneumonia (they specify
carefully their selection based on the diagnosis)
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They eliminate all patients that were transferred from nursing homes and
other medical facilities since these facilities hold the most severe cases
and are usually close to the highest quality hospital so that the assumption
that sit is equally distributed in the population would be violated.
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Remove small hospitals from the sample because the FE for these
hospitals would be very imprecise.
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Drop patients where no matching with income data is possible due to
“unknown race” for example.
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Final data has 178,972 observations
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
Results:
1.
Their main result is that the Hausman test rejects (at 1%) the null
hypothesis of exogeneity. So c’s are endogenous, i.e. there is selection.
2.
Moreover they find that the spread of IV coefficients of hospital quality is
larger than the spread of the GLS coefficients. Because the severity is
positively correlated with quality (i.e. negatively correlated with the beta’s
since lower betas means high quality) this is evidence of this correlation.
Why? For the low true betas (high quality hospitals) the non-iv estimate is
higher because these hospitals receive more serious cases i.e. it
underestimates the true quality, and the opposite for the low quality
hospitals. Therefore the non-iv estimates of beta have less variance than
the unbiased estimates.
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
Estimated
betas
Unbiased
estimates 45º
line
GLS estimates Low
quality
High
quality
GLS estimates
True betas
Take beta=0 as the
benchmark. So beta
below 0 is better than
the benchmark
hospital. The more
negative the better
(less mortality).
- - GLS estimates
Underestimates
the quality here
Overestimates the
quality here
Gautam Gowrisankaran and Robert
Town (JHE, 1999)
Results (cont.):
3.
They also find that the correlation between the GLS and IV was negligible which
implies that the GLS completely misses the correct hospital ranking.
4.
The standard deviations of the IV estimates are high which implies that the quality
of most hospitals is not statistically significantly different from the quality of the
average hospital.
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What explains hospital quality? Why are some hospitals better than others:
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The authors cannot include in their mortality regression hospital characteristics (invariant with time) because these are colinear with hospital dummies.
What is possible is once we obtain FE estimates, to regress them against a bunch of
hospital characteristics to see what characteristics better explain the differences in
quality. {type of organization (public, not-for-profit, for-profit), teaching hospital
dummy, dummy for operating long-term-facility, geriatric care unit, size (usually
measured as number of beds) interacted with hospital type, average length of stay for
medicare pneumonia patients and occupancy rates.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
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What are report cards? Report cards constitute a public
disclosure of information on the performance of physicians or
hospitals.
Interesting web-page (report cards for several states):
http://www.consumerhealthratings.com/index.php?action=showSubCats&cat_id=301
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Example for NY 2004 (hospitals and surgeons) for coronary
artery bypass graft (CABG):
http://www.health.state.ny.us/statistics/diseases/cardiovascular/docs/pci_2005-2007.pdf
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Net effects of report cards are unclear:
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positive because they decrease asymmetries of information and may
give incentives to increase quality
Possibly negative: give physicians and hospitals incentives to decline
the most difficult cases in order to avoid worsening their mortality score.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”

What may be the problems with report cards:
1.
2.
3.
Mortality rates must be adjusted for patients’ risk factors (“risk adjustment”)
otherwise providers treating more serious cases would fare worse.
But analysts can only adjust for characteristics in databases, providers
always have better information  capacity of selecting patients and casemix unable of capturing it.
Even if risk adjustment was correct there may still be incentives to select
patients, why? Suppose the matrix of mortality by hospital quality and
patients’ risks is the following:
Patients’s severity
Risks
Quality
hospitals
A (High)
B (Low)
D
High
5
10
5
Low
1
2
1
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Mortality
10
Regression for the
Low quality
hospitals
5
Regression for the
high quality
hospital
2
1
0
1
Patients’ risk ={0,1}
If we could observe hospital quality we would estimate two different regressions. Key: The
difference in outcomes (between High and Low quality hospitals) is higher for high risk patients!
Low quality providers look more similar to High quality for less sick patients.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”

i.e. if we observe the quality of the hospital, we could run two regressions:
M H  cH   H R   H for High Quality Hospitals where R= 0,1 for patients' risk
M L  cL   L R   L for Low Quality Hospitals
we observe that ˆ H 

5 1
10  2
 4  ˆ L 
8
1 0
1 0
If we do not observe the quality of the hospital and we run a single
regression
M  c R 
4  ˆ  8
and the exact value of c and ˆ depends on the mixture of patients.
If both hospitals have the same proportion of H and L risk patients
then ˆ  6 but if the high quality hospital has a higher proportion
of high risks than ˆ  6 and closer to  H , because the identification
of this parameter comes from more observations of the high quality hospital
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Mortality
Regression without selection
(equal proportion of high and
low risks in both hospitals);
c=1.5; =6
10
Regression with selection
(higher proportion of high
risks at the best hospital)
c=1.8; =4.2
5
2
1
1
Patients’ risk ={0,1}
With selection the constant (i.e. the mortality of the low risk patients, R=0) is
identified by the low quality hospital and slope (the mortality of the high risk
patients, R=1) by the high quality hospital. The residuals from both the low
quality and the high quality hospital are now smaller and therefore the adjusted
mortalities more similar among the two type of hospital.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
But what does that imply? Suppose the high quality hospital has a higher
proportion of high risks then  = 4.2, and the adjusted mortalities:
H
L
Average Predicted Performance
Mortality Mortality (residuals)
A (H)
80%
20%
4.2
5.16
-0.96
B (L)
20%
80%
3.6
2.64**
0.96
|Diff. with patient Selection|
0.6
2.52
1.92
A (H)
3
4.5*
-1.5 (below the
50%
50%
mean)
B (L)
50%
|Diff. without patient
Selection|
*0.5×1.5+0.5×(1.5+6)
** 0.8*1.8+0.2*(1.8+4.2)
50%
6
4.5
1.5
3
0
3
The Low
quality
fares better
when it has
a lower
proportion
of high risk
patients
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
4.
Finally, even if risk adjustment was correct (on average) but noisy and if
providers were risk averse then again the providers may shun away high
risks patients if the variance of outcomes is higher for the high risks.
Variance for the
low risks
EULow risk
EUHigh risk
Variance for the high
risks
Risk Adjusted
Mortality
mortality
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”

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Mandatory CABG (coronary artery bypass graft) report cards were introduced
in the beginning of the 90’s in NY and Pennsylvania.
They use Medicare data from 1987- 1994 and estimate the short-run effects of
the introduction of report cards in New York and Pennsylvania.
In particular they are interested in the effects of report cards on:
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The matching of patients to providers – may improve if sicker patients have more
to gain from being treated at higher quality hospitals and report cards gives correct
information about high quality hospitals.
The incidence and quantity of CABG surgeries – The incidence of surgeries may
have shifted from sicker to healthier patients and the quantity of surgeries may have
gone up or down. If sicker patients have more to gain from the surgeries then social
welfare may decrease.
The incidence and quantity of substitute treatments – substitute treatments may
have increased for sicker patients, however if hospitals avoid patients altogether
then we would observe a decrease in these as well.
Health care expenditures and patients health outcomes -
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”


Methodology: they use a differences-in-differences approach where they
compare the trends before and after the report cards for the NY and Penn
states with the trends for control states (where there was no introduction of
report cards).
Findings:
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Report cards improve the matching of patients to hospitals +
CABG surgery increased and changed the incidence from sicker to healthier
patients ± ?
Higher health costs –
Deterioration of health outcomes, specially amongst the most ill patients –
Conclusion: report cards were welfare-reducing
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”

Identifying assumptions (in order to be able to conduct the differences-indifferences approach):
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Assumption 1: The adoption of report cards must be exogenous i.e. uncorrelated
with state level unobservables in trends in costs, health outcomes, and
treatments.
Assumption 2: AMI patients (acute myocardial infarction) patients are a
relevant at-risk population for CABG surgery and its composition does not
change by the introduction of report cards (means that people do not have more
or less hearth attacks because of the introduction of report cards).
In December 1990 NY released report cards on raw and risk-adjusted
hospitals’ mortality on patients receiving CABG surgery
In 1992 NY released surgeon specific mortality
November 1992, Pennsylvania published hospital+surgeon report cards on
risk adjusted CABG mortality.
NY and Penn are treatment states, the rest are control states
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
1st: Show evidence that report cards did not alter the distribution of AMI patients
(that was assumption 2). Note that SEVERITY is measured by the
expenditures in the year previous to admission.
Nationwide increase in intensity of treatment for people
with heart problems, not different at treatment states,
consistent with assumption 2.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
2nd: Trends in the people that undergo CABG surgery look very different
Smaller expenditures of people undergoing CABG surgery in
treatment states consistent with a shift towards healthier
patients in these states.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”

Empirical Model

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
They study cohorts of AMI patients – all those that suffered at least one
heart attack, they may or may not go through CABG.
They study cohorts of CABG patients - who may or may not have had
an AMI.
The idea here is that (at least in the short run) the CABG report cards
did NOT affect the AMI population (this is an emergency event) but in
contrast may affect the composition of those receiving CABG because it
is an elective procedure.
They estimate two models


Hospital is the unit of analysis
Patient as the unit of analysis
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”

Empirical Model – in the hospital-level analysis, the effects of
report cards are measured by comparing the evolution of
treatment states with the evolution of control states.


Matching – check whether report cards led to greater homogeneity (in
health status) of patients within the hospital.
Incidence – check whether the health status of patients going through
CABG changed due to report cards. If the health status has changed
there may be two explanations:


1. report cards had to do with it
2. the underlying health of the population has changed – to dismiss this
possibility they do the same exercise with AMI patients (which by
assumption are not affected in the short run by report cards).
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Hospital Level analysis :
ln(hlst )  A s  Bt  g Zlst  p L st  q N st  elst
l  index for hospital
s  index for state
t  index for time = 1987,...1994
p is the difference-in-difference estimate
coefficient of the effect of report cards on
the severity of CABG patients. If p<0
then report cards caused a shift in
incidence of the surgeries to an average
healthier patient.
hlst  mean of the illness severity before treatment of Medicare CABG's patients
(they use hospital expenditures and days in the hospital in the year before admission)
A s  state fixed effects
Bt  8 annual dummies 1987,...1994
Zlst  hospital characteristics (rural location, size, ownership categories, teaching status)
L st  =1 if the hospital is in NY in or after 1991 or in Penn in or after 1993, 0 otherwise
N st  number of hospital (its square and cube) in state s in year t (competition)
each observation is weighted by the number of CABG patients addmitted.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
A negative p could be caused by an effect of report cards on a better health status
of all patients (in risk of CABG surgery). To discard this explanation they
examine the effects of report cards on AMI patients (there should be no
effect since these patients are not subject to selection). Re-estimate the model
using the mean severity of AMI patients as the dependent variable. Compare
the estimated p’s of both models.
Also, instead of using mean severity as dependent variable they use the coefficient
of variation (s.d./ mean) before treatment within each hospital. Improved
matching of patients would cause the coefficient of variation to decline in
NY and Penn relative to other states, again a negative p.
Finally, with improved sorting we should observe more ill patients to be at higher
quality hospitals. Since quality is not observed they use teaching status as a
quality proxy and re-estimate the model with an interaction term
ZTEACHlst×Lst if the coefficient is positive it means report cards lead more sick
patients to be treated at teaching hospitals.
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
3rd: hospital level regressions
Sample with selection
Decline in illness severity => shift in incidence
towards healthier patients
Sample without selection
Difficult to interpret because
the increase in cv is also due
to decrease mean severity
Better
matching
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
3rd: hospital level regressions (cont.) they also find that the more serious cases go
more to the teaching hospitals after the report cards.
Constant severity at
teaching hospitals
Increase severity of patients at teaching hospitals:
evidence of better matching
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Patient Level Analysis – similar regressions:
To test for a quantity effect of report cards on CABG surgery:
Ckst  A s  Bt  g Zkst  p L st  ekst
(2)
k  index for patient
s  index for state
t  index for time (1987,...1994)
Ckst  1 if patient k has CABG surgery within 1 year after admission for AMI (heart attack)
Zkst
0 otherwise
 vector of patient characteristics (rural residency, gender, race, age, and interactions
between gender, age and race
L st  1 if patient k's residence is in NY in or after 1991 or Penn state in or after 1993,
0 otherwise
A positive p means that report cards increase the probability of an AMI patient
having a CABG surgery. To test for the effect on substitute treatments (PCTA and
cath) replace C by these treatments and re-estimate (2).
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Patient Level Analysis (cont.):
To assess the effects of report cards on sick versus healthier patients they include
an interaction term:
Ckst  A s  Bt  g Zkst  p L st  qwkst  rLst wkst  ekst
(3)
wkst  measures patient k's illness severity
if r  0 then report cards altered the incidence of CABG
if r<0 healthier patients have higher probability of CABG
if r>0 it is the sicker that have now higher probability
where C is the probability of CABG for example
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Patient level regressions: 1) increase in CABG quantity; 2) that increase was based
on healthier patients; 3) decrease in PCTA; 4) delay in treatment
(1)
(2)
(3)
The second part of table 4 has PTCA within one day of admission and cath
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Patient Level Analysis (cont.):
For health outcomes and health expenditures follow similar procedure.
Okst  1 if patient k from state s, at time t experienced an adverse health effect
(e.g. heart failure), 0 otherwise
ln ykst  total hospital expenditures of patient k in the year after admission with AMI
reestimate (2) if p  0 then report cards increase the incidence of adverse outcomes
and hospital expenditures.
If report cards increase the incidence of adverse health outcomes and increase
costs  decrease welfare
If report cards decrease the incidence of adverse health outcomes and decrease
costs  increase welfare
Otherwise they can compute the cost-effectiveness of report cards
(1)
(2)
(1) Increase in costs because now the average patient is more likely to undergo CABG; Also more costs to the
severely ill although these were less likely to undergo treatment, which means they became even sicker and
therefore eventually demanded more expensive treatment. Other results show health status declined with RC,
specially among the sicker (2).
Dranove et al. (JPE, 2003) – “Is more
information better, the effects of report cards”
Patient level regressions (cont):
1. increase in expenditures for all patients (surprisingly since the more severe
were now less likely to receive CABG and less likely to receive PTCA now,
the problem is that they will have more complications and that is costly too)
2. worse health outcomes one year after admission for sicker patients episodes
of AMI and heart failure and slightly better outcomes for healthier patients
(due to higher CABG procedures).
Overall they conclude that the welfare was reduced.
 They claim that report cards should focus on the population at risk, say the
AMI population and not only report information on those that actually
received the procedure
 They claim that sorting i.e. the apparent better matching of patients to
providers is positive
 The short run analysis cannot say anything about the long run impact on
providers’ quality