Discussion Paper No. 547

COMPARISON BETWEEN
TREATMENT OUTCOMES IN ISCHAEMIC
HEART DISEASE USING SURVEYS
OF MEDICAL CARE IN JAPANRei Watanabe
andYasushi Ohkusa

July 2001

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan29 June, 2001

Comparison between Treatment Outcomes in
Ischaemic Heart Disease Using Surveys of Medical
Care in Japan ∗)Rei Watanabe
Graduate School of Economics, Kyoto University
andYasushi Ohkusa
Institute of Social and Economic Research, Osaka University

Correspondence:Yasushi Ohkusa, Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka Ibaraki Osaka, Japan
Tel:81-6-6879-8566

Fax:81-6-6878-2766e-mail:ohkusa@iser.osaka-u.ac.jp

Summary
Objective The treatments of ischaemic heart disease have drastically changed due to thedevelopments in coronary-anterial bypass graft operations and coronary intervention procedures. The treatment outcomes of these procedures are compared using observational
data.
Method The data used are microdata from Surveys of Medical Care (SMC) in Japan
from 1992 to 1998, which are claim data, not using a randomized controlled trial. Hence,
endogeneity in the choice of treatment should be important and thus both the instrumental
variables method and the nonparametric propensity score matching method are adopted
for estimation.
Result Although the instrumental variables method and nonparametric propensity scorematching method produce diﬀerent results in many cases, predominance of the latter overthe instrumental method is conﬁrmed. Although claimed with some reservations, patientstreated with precutaneous transluminal coronary angioplasty (PTCA) have signiﬁcantly
lower possibility of severe heart failure than those treated with coronary artery by-pass
grafting (CABG). The results of comparing invasive procedures with preserving treatment depend on the existence of constraints. In data for more acute situations, invasive
procedures have a signiﬁcantly higher possibility of severe heart failure than preserving
treatment.
Keywords: Ischaemic heart disease, Coronary intervention, Percutaneous transluminal
coronary angioplasty, Coronary aorta bypass grafting, Instrumental variables method, Nonparametric propensity score matching method

1Introduction

Heart disease is the second most common cause of death in Japan, and more than half these
deaths are caused by ischaemic heart disease. The risk factors include age, hyperlipidaemia,
smoking, hypertension, and the importance of ischaemic heart disease is naturally rising inJapan.
Before the 1970s, the main treatments for ischaemic heath disease were conservative,
such as rest and medication. In the 1970s, treatment changed drastically with the development of coronary artery bypass grafting (CABG) and coronary intervention using coronary
catheterization. These new treatments have decreased mortality from ischaemic heart disease, but they are invasive and expensive. Indications for these treatments have to beconsidered carefully. Recently, the practice of evidence-based medicine (EBM) has been
established, and many randomized controlled studies are being performed for collecting
evidence of the outcomes of each treatment.
Some clinical studies of ischaemic heart disease have results contradicting other studies.
For example, some show evidence that percutaneous transluminal coronary angioplasty(PTCA) has better outcomes than percutaneous transluminal coronary recanalization forpatients with acute myocardial infarction1) , but others provide opposite evidence2) . By integrating many clinical studies, the American Cardiology Association and American Heart
Association have produced guidelines for the management of patients with acute myocardial
infarction. In these guidelines, they divide various diagnostic procedures and treatments
into three classes, of which class 1 shows positive evidence for its use and eﬀect, class 2
shows conﬂicting evidence, and class 3 shows negative evidence. These guidelines propose
recommendations and help clinicians’ decision-making3) . Although these guidelines are
based on many clinical studies examining cost-eﬀectiveness as well as short-term outcomes
and long-term prognoses, such a randomized trial has some serious problems, such as costin both money and time, generalizing from a speciﬁc kind of patient to other patients, and1

ethical problems arising when patients might be randomized out of a treatment suspected
to be eﬀective4) . Instead of a randomized control study, one study uses observational
data from patients’ medical records and investigates the incremental eﬀects of intervention
therapy for acute myocardial infarction by using the distance between their houses andhospitals as valid instrumental variables5) .
Some researchers point out that the eﬀects of treatments with high ﬁxed and marginal
costs, such as operations and coronary interventions, are over-estimated. Treatments for
ischaemic heart disease include low technical innovations with low ﬁxed and marginal costs,
such as aspirin or β-blockers, as well as the use of advanced technical innovations. One
study concludes that both low and high technical interventions contributed signiﬁcantly
to the improvements of ischaemic heart disease6) , and another found that low technicalinnovations probably accounted for the bulk of the heart attack mortality decline between
1975 and 19957) .
Unfortunately, in Japan, no large randomized controlled studies have been carried out,
and no treatment guidelines based on many clinical studies have been established. Clinicians usually make decisions on diagnostic procedures and treatments with the aim of
improving in-hospital prognoses, and make decisions without consideration of costs. Needless to say, clinical decision-making must not only be grounded on evidence from publishedarticles, but also on each clinician’s experience and each patient’s conditions. Considering
the diﬀerences of outcomes of surgery or intervention therapy for ischaemic heart disease
among medical providers and the need for eﬃcient use of medical resources, large clinicalstudies are needed in Japan.
With this background, this paper examines outcomes of the treatment for ischaemic
heart disease using observational microdata of the SMC gathered from claim data.

2

2

Data

The data are from Surveys of Medical Care (SMC), which are conducted every year in Juneby the Ministry of Health and Welfare. The surveys are stratiﬁed samples of approximately
200,000 claims data from the government-managed health insurance and national healthinsurance scheme, written by medical providers all over Japan. Although it is the mostcomprehensive survey of medical care in Japan, there are several problems with the SMC
for research.
First, the survey is only conducted in June and does not cover all patients. Because
there are not enough samples, the data are pooled for several years.
The second problem is the coding used for each disease and drug. With the disease classiﬁcation, ischaemic heart disease consists of one group, but this corresponds to I20–I25 in
ICD-10. This includes I20, Angina pectoris; I21, Acute myocardial infarction; I22, Subsequent myocardial infarction; I24, Other acute ischaemic heart diseases; and I25, Chronic
ischaemic heart disease. From the SMC, acute myocardial infarction cannot be distinguished from chronic ischaemic heart diseases such as stable angina. With the coding ofdrugs, the SMC shows only a four-digit classiﬁcation combining drug code and drug pricedetermined by the government. However, many generic drugs have the same prices, andthus it is impossible to isolate them.
The third problem is related to the ﬁrst one. The SMC uses a sample from claim data
during one month, and there is no information about previous months and the following
months, even if th sampled patients were in hospital dring the other months. From thePatient Survey of the Ministry of Health and Welfare, the duration of hospitalization of
the ischaemic heart disease patient is 31.3 days on average. However, many patients have
hospitalization across months.
The fourth problem is that the SMC is based on claim data, not on patients’ clinicalcards. In the clinical setting, physicians and other medical providers record everything3

on the patient’s clinical card. Using clinical cards, clerical workers make claim data forcharging fees to insurers once a month. In claim data, clinical procedures are recorded
comprehensively, not chronologically. In other words, there is no information about the dateof the speciﬁed procedures, even though the information about drugs, tests and treatments
performed are almost perfect. Furthermore, the SMC does not indicate death, discharge
or continuation of therapy.
Owing to these problems, the SMC is not suited for analysis of the long-term prognosis
and medical expenditure of hospitalization periods longer than one month. Hence, it isimportant to specify proxy variables that represent short-term outcomes and analyse theeﬀects of operations and coronary interventions for ischaemic heart disease. The compared
operations are CABG and coronary interventions such as PTCA. Additionally, preserving treatments with invasive procedures for the patients of ischaemic heart disease withemergent and intensive care are compared.

3

Comparisons between outcome of CABG withcoronary interventionsFirst, CABG and coronary interventions such as PTCA are compared. The samples are
the patients for which the diagnosis is ischaemic heart disease and coronary angiography
is performed. Ischaemic heart disease is caused by stenosis or occlusion of the coronary
artery supporting the heart muscle. Coronary angiography shows the pathological regionsof the coronary artery, wall motion of the heart and haemodynamics of the heart in detail,and is always done before CABG and PTCA. By sampling the patients with coronary
angiography from all the patients, samples in which patients’ condition are so stationarythat angiography is not needed, or so severe that invasive procedures such as angiography
are impossible, are excluded. Note that, as for the second problem mentioned above, manycases are chronic, not acute. To exclude chronic cases, the data are limited to patients with4

hospital stay less than one month, and with blood type tests in the corresponding month.
Since blood type tests are always done at the ﬁrst admission and not at readmission or
re-operation, this indicates the ﬁrst procedure in the hospital. By adding each constraint,
the sample can be limited to patients in which the period from admission to corresponding
treatment is short, and chronic cases can be excluded.
In this analysis, data include heterogeneous patients, and treatments are selected endogenously, not exogenously, as in a randomized control study. Corresponding to thisendogeneity, we provide an account in ’the model’ section.
Although PTCA corresponds to the usual balloon angioplasty (POBA) in the SMC, there
are new devices such as the directional coronary atherectomy (DCA) and the transluminal
extraction catheter (TEC). However, PTCA here is broadly deﬁned as usual balloon angioplasty and other various new devices, because the data cannot provide information to
distinguish them.
Finally, the adopted outcome measures are explained. The most important factor determining the short-term prognosis is pump function of the heart. Killip’s classiﬁcation8) is
the most classical, but Forrester’s classiﬁcation is broadly used in deciding treatment, in
relation to patients’ heart function9) . In this classiﬁcation, catecolamines are recommended
for treatment of severe heart failure. Among catecolamines, dopamine and dobutamine areused most widely for heart failure, and norepinephrine and epinephrine produce such astrong constriction of peripheral vessels that they are not used as the ﬁrst choice drug, but
used for cases with the most severe malfunction of the heart. In particular, epinephrine isusually used for cardiac arrest. Accordingly, using of norepinephrine or epinephrine indicates that a patient has severe heart failure. As norepinephrine and epinephrine have thesame code and price, there is no way to distinguish them.
From the fourth problem, the patients using these drugs include the ones before and after
treatment. Thus, the patients in which heart failure has been very severe before treatment
cannot be excluded, but uncompensated heart failure is one of the contra-indications for5coronary angiography10) . We assume that a few patients were using these drugs for advanced coronary angiography.
To sum up, the use of norepinephrine or epinephrine is a proxy variable for severe heart
failure, and we compare the eﬀect of the PTCA with CABG on severe heart failure.

4Comparison between outcome of preserving procedures with invasive procedures

The sample used cases with emergent or intensive treatments, and we compared invasive
procedures with preserving treatments. To do this, the following procedure was adopted.
First, the sampled cases were reﬁned so that the hospitalization fee was for the emergency
ward or intensive care unit, so as to limit acute cases and chronic patients managed to justafter operations such as CABG. Moreover, to exclude chronic cases, hospitalization lessthan one month and blood type tests in this are required. The outcome measure for severe
heart failure is the same.

5

Estimation model

When using observational data, it is extremely important that selections of treatment are
determined not exogenously, as in a randomized controlled study, but endogenously. To
treat this problem of endogeneity, preceding investigations have used the distance between
patients’ houses and hospitals as an instrumental variable in evaluating the eﬀect of cardiac
catheterization from the 180,000 patients’ records of acute myocardial infarction in theUS11) .
On the other hand, the problem of endogeneity is often raised in econometrics. Although
the instrumental variables method is used to solve this, the nonparametric propensity scorematching method12),13) is widely employed and recognized for its advantages14) . It has now
begun to be used in the analysis of treatment eﬀects in the ﬁeld of health economics15) . In

6

this paper, both the nonparametric propensity score matching method and instrumental
variables methods are employed and compared for evaluation of treatment eﬀects.
The nonparametric propensity score matching method is explained as follows. Consider
patients with certain treatments or procedures i and j. Information on respective patientsis denoted by Xi and Xj , treatment outcome of respective patients by Yi and Yj . The ﬁrst
stage in estimation is probit estimation with regard to treatment selection. The estimatedparticipation probabilities of each types are denoted as P (Xi) and P (Xj ). Then, thetreatment eﬀect is evaluated as:
Y K[(P (Xi )−P (Xj ))/h]
j j}
i {Yi −j

K[(P (Xi )−P (Xj ))/h]N(1)

where K[·] denotes the kernel estimator, and the Epanechnikov, biweight, triangular, Gaussian, Rectangular procedures are used16) . h is set 0.9σP (Xj ) N −0.2 , which is regarded as the
optimal value, and σP (Xj ) denotes the standard deviation of P (Xj ). N denotes sample size.
This procedure is called kernel matching.
However some investigators recommend local linear matching rather than kernel matching with respect to eﬃciency17) . This method is explained as:
i {Yi −j

Yj {Kij
K (P (X )−P (Xk ))2 −Kij (P (Xi )−P (Xj )) k Kik (P (Xi )−P (Xj ))} k ik i

}
2
2
j

Kij

k

Kik (P (Xi )−P (Xk )) −(NkKik (P (Xi )−P (Xk ))(2)

where Kij = K[(P (Xi ) − P (Xj ))/h] and k denotes the summation of patients undertaking
a certain treatment. Statistics estimated using equation (1) or (2) are called genericallyaverage treatment eﬀects.
Further, the distributions of the average treatment eﬀects are not speciﬁed. Accordingly,
their conﬁdence intervals are calculated using bootstrapping. Concretely, replication isdone 1,000 times and bias correction is used16 .

7

5.1

Estimation model for comparison of PTCA and CABG

The explained variable is dichotomous, being 1 when either of two drugs is used and 0
when not used. Explanatory variables are patients’ characteristics such as age and sex,
providers’ information such as number of beds in the hospital, type of hospital (non-proﬁt,
proﬁt and public) and whether teaching hospital, and dichotomous treatment variables,
being 1 when PTCA is performed and 0 when CABG is performed.

5.2

Estimation model for comparison between invasive procedures and preserving procedures

Almost all of the estimation equations are the same as in the previous subsection except for
the treatment variables. The dichotomous variable is 1 when invasive procedures (PTCA
or CABG or coronary angiography) are performed, and 0 otherwise.

6

Estimation result for comparison between PTCA
and CABG

Table 1 shows the average of each sample, and Tables 2–5 indicate the estimation results from the whole sample. Tables 6–9 show results from data of less than one month
hospitalization. As there were insuﬃcient observations for the sample with blood type
test, we omitted that estimation. Each sample produced results of the probit estimation,
regressing outcome on treatment (PTCA or CABG) and other variables (Tables 2, 6),
probit estimation for treatment selection (Tables 3, 7), probit estimation for outcome using estimated probability of treatment selection as instrumental variables (Tables 4, 8),
and average treatment eﬀect constructed by the nonparametric propensity score matching
method using estimated probability of PTCA choice as P .
In simple probit estimation without consideration of endogeneity (Table 2), age and forproﬁt hospitals are signiﬁcantly positive, and PTCA, age-squared and teaching hospitalare signiﬁcantly negative. The probability of severe heart failure increases until about 75
8years of age, and then decreases. For-proﬁt hospitals have higher probability of severe heart
failure for their patients than public hospitals, and teaching hospitals have a lower probability than other hospitals. Most importantly, PTCA has a signiﬁcantly lower probabilityof severe heart failure than CABG, by an amount of 35 percentage points. This meansthat PTCA reduces the probability of severe heart failure to 15% if it supposed to be aprobability of severity of 50%
However, the choice of treatment should be an endogenous variable determined by various properties of patients and hospitals. In our data, we do not have detailed informationof these properties, and thus this information is consigned to the error term, and all of theestimators have biases due to correlation between the error term and treatment selection.
To examine such bias, Table 4 shows estimation results from the instrumental variables
method. Comparing Table 4 and the result of simple estimation (Table 2), positive significance in for-proﬁt hospitals and negative signiﬁcance in PTCA and teaching hospital do
not change. However, age, signiﬁcant in Table 2, is not signiﬁcant in Table 4; conversely,
the number of beds, not signiﬁcant in Table 2, is negatively signiﬁcant in Table 4. PTCA
has a more signiﬁcantly lower probability of severe heart failure than CABG by 45 percentage points, and the direction of bias from the endogeneity seems to be zero. This result is
conﬁrmed also in Table 5 using the nonparametric propensity score matching method, andis robust from the deﬁnition of the kernel. Since the advantage of PTCA compared withCABG is 26% compared with 33% in this table , it is a little weaker than the instrumental
variables results.
Table 6 indicates the result of simple estimation with less than one month hospitalization.
This shows that age and for-proﬁt hospitals are signiﬁcantly positive and age-squared issigniﬁcantly negative. These are almost the same as for the whole data set. PTCA isnegative but not signiﬁcant. In other words, PTCA has a lower probability of severe
heart failure, but the diﬀerence is not signiﬁcant. The advantage is only eight percentage
points, and much lower than for the whole data set. Estimation using the instrumental
9variables method (Table 8) has almost the same result as in Table 6, and PTCA is negative
but not signiﬁcant. In the nonparametric propensity score matching method, the signs of
the coeﬃcients depend on the deﬁnition of the kernel. PTCA has a lower probabilityof severe heart failure than CABG for two kernels and no signiﬁcant diﬀerence for theothers. In the former cases, PTCA reduces the probability of severe heart failure by 9–31
percentage points, and this diﬀerence is smaller than for the whole data set. Note that somereservations are needed in interpretation because of the instability of the results. This may
due to be poorness-of-ﬁt in estimation in treatment selection caused by a small number ofobservations.
To sum up, the advantage of PTCA over CABG cannot be denied, but with reservations. Even in the case of lack of ﬁt in the treatment selection without a signiﬁcantrelationship, the nonparametric propensity score matching method results are signiﬁcantin some cases compared with the instrumental variables method approach. This may imply
the methodological superiority of nonparametric propensity score matching method overthe instrumental variables method.

7

Estimation results of a comparison between invasive procedures and preserving procedures

Next, we compare invasive procedures and preserving procedures for patients with intensive or emergency care. Estimations are performed on the whole data set and restricteddata sets (with less than one month hospitalization or blood type test). Tables 11–14 indicate the estimation results for the whole sample, Tables 15–18 the results for less than
one month hospitalization , and Tables 19–22 the results for blood type test. Each sample
produces estimation results of probit estimation of outcome on treatment selection (invasive procedures or preserving procedures) and other variables (Tables 11, 15, 19), probit
estimation for treatment selection (Tables 12, 16, 19), probit estimation of outcome on

10

estimated probability of treatment selection as instrumental variables (Tables 13, 17, 21),and average treatment eﬀects constructed by the nonparametric propensity score matching
method using the estimated probability of invasive procedures as the choice of P (Tables
14, 18, 22).
In the simple probit estimation without consideration of endogeneity (Table 11) andwith the instrumental variable method (Table 13), there are no signiﬁcant variables. Even
though both are insigniﬁcant, invasive procedures increase the probability of severe heart
failure in the simple probit estimation, and decrease it with the instrumental variables
method. On the other hand, the nonparametric propensity score matching method does
not show any signiﬁcant eﬀect except with one kernel. In this kernel, invasive procedures
increase the probability of severe heart failure signiﬁcantly by about seven percentage points
compared with preserving procedures.
Table 15 indicates the results of simple estimation using the data for less than one month
hospitalization. The number of beds and for-proﬁt hospitals are signiﬁcantly negative.
Invasive procedures is negative but not signiﬁcant; that is, invasive procedures have a lower
probability of severe heart failure, but this diﬀerence is not signiﬁcant. The estimation
results using the instrumental variables method (Table 16) have no signiﬁcant coeﬃcients,
but invasive procedures have a signiﬁcantly lower probability of severe heart failure thanpreserving procedures. For the nonparametric propensity score matching method, Table
17 shows that the signs of the coeﬃcients are mixed across the kernels. Invasive procedures
have a lower probability of severe heart failure than preserving procedures in two cases,
but no signiﬁcant diﬀerence in three cases. In the former cases, the diﬀerences are 7–9
percentage points, and are inconsistent with the case for the whole data set.
Table 18 indicates the result of a simple probit estimation using the data with blood type
test, and this shows that non-proﬁt hospital is signiﬁcantly negative. Invasive procedures
have a lower probability of severe heart failure, but this is not signiﬁcant. The instrumental
variables method results in Table 20 show that female, teaching hospital and invasive11

procedures are signiﬁcantly negative, and thus invasive procedures decrease the probabilityof severe heart failure by 20 percentage points, in comparison with preserving procedures.
In contrast, for the nonparametric propensity score matching method results shown in Table
21, invasive procedures have a higher probability of severe heart failure than preserving
procedures for all kernels, and the diﬀerence is 9–16 percentage points.
The results of comparison between invasive procedures and preserving procedures are
summarized as follows. With the instrumental variables method, there is no signiﬁcantdiﬀerence between the two procedures for the whole sample and the less than one month
hospitalization sample, but predominance of invasive procedures over preserving proceduresin the sample with blood type test is indicated. For the nonparametric propensity scorematching method, results show a lack of robustness for the whole sample and less than
one month hospitalization sample, and we found superiority of preserving procedures overinvasive procedures in the blood type test sample regardless of kernel. The predominanceof these two procedures is unstable across sample deﬁnition, and thus estimation interpretations should be made carefully. The results of the instrumental variables method and thenonparametric propensity score matching method are inconsistent, and thus the usefulnessof the instrumental variable method seems to be limited.

8

Concluding remarks

Using microdata of the SMC based on observational patient data, this paper compares theoutcomes of patients with ischaemic heart disease between PTCA and CABG, or of patientswith intensive or emergent care between invasive procedures and preserving procedures.
Taking endogeneity of treatment selection into consideration, the instrumental variables
and nonparametric propensity score matching methods are employed for estimation. We
found that there is no case in which both methods are consistent regarding signiﬁcance. Of
ﬁve sample sets (two from section 6 and three from section 7), one sample set (the sample set

12with blood type tests in comparison of invasive procedures with preserving procedures) has
opposite results between the two methods: the nonparametric propensity score matching
method results show that invasive procedures have a signiﬁcantly higher probability ofsevere heart failure than preserving procedures, but the instrumental variables method
shows the opposite result.
However, the nonparametric propensity score matching method seems to be more ﬂexible
than the instrumental variables method in general. Moreover, the theoretical advantagesof the nonparametric propensity score matching method over the instrumental variable
method are well known. If we believe the results of the nonparametric propensity scorematching method, PTCA has a signiﬁcantly lower probability of severe heart failure than
CABG, with some reservations, and invasive procedures have a signiﬁcantly higher probability of severe heart failure than preserving procedures in the blood type test sample.
These results contain two important reservations. Firstly, there are several problems
with the SMC as described in Section 2. These data take samples from clinical records in
hospitals all over Japan, and report all medical procedures. This kind of data is uniquecompared with other countries. However, there is only a little information about diseases or
patients’ conditions. Thus, clinical outcome has to be inferred from corresponding clinicalprocedures. This procedure depends heavily on some assumptions, in cases where the orderof the clinical procedures is unknown.
The second problem is endogeneity of treatment selection. Two estimation methods are
employed, but the results are not robust between the methods. This seems to be caused
by insuﬃcient information about the clinical course, pathological lesion, detailed heart
function and so forth, and we cannot control these factors, which are closely related totreatment selection. A more organized and complicated nonparametric propensity scorematching method should overcome this informational limitation in the future.

13

Acknowledgement

This paper is part of a set of research results of the Research Group of Innovations ofInstitutions in Aging Society (director: Professor Toshiaki Tachibanaki, Kyoto University)
funded by the Millennium Project of the Economic Planning Agency. The authors would
like to thank members of the research group for active discussion and Ms. Tomoko Nagase
for supporting the research. Naturally, mistakes in this paper belong not to the researchgroup but to the authors.

14

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16

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17

Table 1: Averages
Allsamples

Severe Heart Failure.354067
PTCA.6220096
Age
64.09569
Female1.232057
Number of Beds（Log） 6.366292
Non-proﬁt Hospital.1028708
For-proﬁt Hospitals.2416268
Teaching Hospital.2105263

Less than
one month
hospitalization
sample.2876254.6822742
64.08027
1.200669
6.276663.1036789.2073579.1672241

Note: The number of samples is 223 in all samples, and 152 in samples less than One
Month Hospitalization.

18

Table 2: Estimation Results by Simple Probit (All Samples)

Age
Age2
FemalePTCA
Number of Beds （Log）
Non-proﬁt Hospital
For-proﬁt Hospital
Teaching Hospital
Constant

Estimates.5966369-.0042646.1824614-1.271928-.0839713-.6163245.9385328-1.023907-19.25944

z-value
2.278-2.0890.402-2.941-0.217-1.1452.336-1.915-2.218P-value
0.023
0.037
0.688
0.003
0.828
0.252
0.019
0.055
0.027

Marginal Eﬀect.17839615-.00127514.07255956-.3625063-.02510766-.2185034.34456916-.31989167

Note: The log likelihood is -118.22913, and quasi R2 is 0.2338. The null hypothesis thatall variables except with constant are 0 is rejected under 10% signiﬁcant level.

19

Table 3: Estimation Results of Treatment Selection (All Samples)
EstimatesAge-.0469925
2
Age.0003431
Female-.0164387
Number of Beds（Log） -.6521113
Non-proﬁt Hospital-.0280082
For-proﬁt Hospital.8226628
Teaching Hospital-.5034769
Constant
6.16708

z-value-0.231
0.211-0.034-1.565-0.0522.079-1.0200.849

P-value
0.818
0.833
0.973
0.118
0.959
0.038
0.308
0.396

Marginal Eﬀect-.0146637.00010706-.00554354-.20348711-.00947649.19909877-.18805565

Note: The of likelihood is -122.61888, and quasi R2 is 0.1112. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

20

Table 4: Estimation Results for Outcome using Instrumental
Variable Method (All Samples)
Estimate
Age.3720292
Age-.0026644
Female.0387424
PTCA Selecting Probability(Estimates） -9.112694-1.335078
Number of Beds（Log）
Non-proﬁt Hospital-.324814
For-proﬁt Hospitals
2.679349
Teaching Hospital-2.238602
Constant
1.680618

z-value
1.415-1.3070.083-2.037-1.778-0.6652.639-2.381
0.144

P-value
0.157
0.191
0.934
0.042
0.075
0.506
0.008
0.017
0.886

Marginal Eﬀect.12358848-.0008851.01539951-.45850835-.44351456-.12454906.53648192-.44893838

Note: The log likelihood is -130.34474, and quasi R2 is 0.1553. The null hypothesis thatall variables except with constant are 0 is rejected under 10% signiﬁcant level.

21

Table 5: Estimation Results for Outcome using Nonparametric
Propensity Score Method (All Samples)
KernelEpanechnikov
Biweight
Triangular
Gaussian
Rectangular

Average-.3262025-.6030771-.3283879-.2558149-.2674383

95%CI Upper-.4336698-1.73575-.4264668-.3311637-.3735231

22

95%CI Lower-.2349667-.197184-.2277659-.1775348-.1592936

Table 6: Estimation Results without accounting for Endogeneity(Less than One Month Hospitalization Samples)
Estimates
Age
1.5662492
Age-.0112827
Female-.3810702
PTCA-.3374903
Number of Beds（Log） -.6504081
Non-proﬁt Hospital-.7992492
For-proﬁt Hospital.9374392
Teaching Hospital-.4971548
Constant-49.41318

z-value
2.189-2.165-0.805-0.611-1.131-1.2902.005-0.689-1.971

P-value
0.029
0.030
0.421
0.541
0.258
0.197
0.045
0.491
0.049

Marginal Eﬀect.41259756-.00297222-.08747231-.07921911-.17133729-.14610727.33579038-.10735859

Note: The log likelihood is 699.40835, and quasi R2 is 0.2884. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

23

Table 7： Estimation Results of Treatment Selection（Less than
One Month Hospitalization Samples ）Estimates
Age.3392646
2-.0028532
Age
Female.1156836
Number of Beds（Log） -.1495782
Non-proﬁt Hospital-.1138539
For-proﬁt Hospital
1.403338
Teaching Hospital-.6261293
Constant-8.272516

z-value
1.237-1.3230.199-0.264-0.1892.767-0.872-0.822

P-value
0.216
0.186
0.842
0.792
0.850
0.006
0.383
0.411

Marginal Eﬀect.08025054-.0006749.02496931-.03538163-.02772146.13964116-.18888053

Note:The log likelihood is 64.809806, and quasi R2 is 0.1416. The null hypothesis that all
variables except with constant are 0 is not rejected under 1024

Table 8： Estimation Results of Outcome using Instrumental
Variable Method（Less than One Month Hospitalization
Samples）
Estimates
Age
2.096679
2-.0155398
Age
Female-.212923
PTCA Choosing P-value（Estimates） -5.3015-.6767463
Number of Beds（Log）
Non-proﬁt Hospital-.878099
For-proﬁt Hospital
2.382303
Teaching Hospital-1.279259
Constant-61.99018

z-value
2.581-2.553-0.423-1.313-1.137-1.452
1.872-1.257-2.338P-value
0.010
0.011
0.673
0.189
0.256
0.147
0.061
0.209
0.019

Marginal Eﬀect.53839867-.0039904-.05045125-.18025783-.17377923-.14372993.74867916-.16612725

Note:The log likelihood is 68.383353, and quasi R2 is 0.3043. The null hypothesis that all
variables except with constant are 0 is not rejected under 10% signiﬁcant level.

25

Table 9: Outcome Estimates using Nonparametric Propensity
Score Method (Less than One Month Hospitalization Samples)

Epanechnikov
Biweight
Triangular
Gaussian
Rectangular

Average-.0170832-.0926619-.0733864-.3057324.0194312

95% CI Upper-.1503817-.1833897-.1926208-.4355384-.1149288

26

95% CI Lower.1186205-.0032472.0522043-.1637794.1604738

Table 10: Averages
All
Samples

Severe Heart Failure.2
Invasive Procedures.4864198
Age
66.58148
Female1.398765
Number of Beds（Log） 6.255634
Non-proﬁt Hospital.0567901
For-proﬁt Hospital.3
Teaching Hospital.2802469

Less thanOne
Month
Hospitalization
Samples.1795717.4629325
65.24547
1.357496
6.283452.0560132.3212521.2784185

Blood
Type Test
Samples.2071006.4319527
65.88462
1.45858
6.227614.0710059.2899408.2781065

Note: The number of samples is 447 in all samples, 345 in Less than One Month Hospitalization

Samples and 193 in Blood Type Test Samples.

27

Table 11：Estimation Results of Simple Probit（All Samples）
Estimates
Age.07414792
Age-.0005246
Female-.2150448
Invasive Procedures.0131575
Number of Beds（Log） -.1300969
Non-proﬁt Hospital.6017939
For-proﬁt Hospital-.371838
Teaching Hospital.0640221
Constant-2.005997

z-value
1.068-1.004-0.7670.046-0.8171.352-1.1660.185-0.801

P-value
0.285
0.315
0.443
0.963
0.414
0.176
0.243
0.854
0.423

Marginal Eﬀect.02259805-.00015989-.06286574.00418386-.03964964.2205717-.10180657.02070098

Note:The log likelihood is 241.63305, and quasi R2 is 0.0597. The null hypothesis that all
variables except with constant are 0 is not rejected under 10% signiﬁcant level.

28

Table 12：Estimation Results of Treatment Selection（AllSamples）
Estimates
Age.04468872-.0003181
Age
Female-.3432881
Number of Beds（Log） .2528266
Non-proﬁt Hospital-.9171804
For-proﬁt Hospital-.7173538
Teaching Hospital-.2130057
Constant-2.148546

z-value
0.761-0.712-1.2801.739-2.177-2.446-0.660-0.973P-value
0.447
0.476
0.201
0.082
0.030
0.014
0.510
0.331

Marginal Eﬀect.01593775-.00011343-.13144744.09016785-.30588178-.2534792-.08306005

Note:The log likelihood is 279.65664, and quasi R2 is 0.0947. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

29

Table 13： Estimation Results of Outcome using Instrumental
Variable Method（All Samples）
Estimates
Age.5793714
2-.0041362
Age
Female-4.087484
Invasive Procedures Choosing P-value（Estimates） -31.31108
Number of Beds（Log）
2.542564
Non-proﬁt Hospital-10.13291
For-proﬁt Hospital-8.998187
Teaching Hospital-2.039323
Constant-9.269772

z-value
1.275-1.274-1.202-1.1611.070-1.088-1.220-1.032-1.226

P-value
0.202
0.203
0.229
0.246
0.285
0.277
0.222
0.302
0.220

Note:The log likelihood is 236.679, and quasi R2 is 0.0790. The null hypothesis that all
variables except with constant are 0 is not rejected under 10% signiﬁcant level.

30

Marginal E.17255549-.00123189-.23439953-.23440028.75725776-.23440028-.23440028-.23154326

Table 14： Estimation Results of Outcome using Nonparametric
Propensity Score Method（All Samples）

Epanechnikov
Biweight
Triangular
Gaussian
Rectangular

Average.0690913.0019177.0623893.0057966.0239519

95% CI Upper.010771-.0558639-.0016824-.0521428-.0337745

31

95% CI Lower.1381654.0623594.120463.0693814.0846671

Table 15：Estimation Results of Simple Probit（Less than One
Month Hospitalization Samples ）Estimates
Age.0401907
2-.000204
Age
Female-.1112446
Invasive Procedures-.2025692
Number of Beds（Log） -.4093291
Non-proﬁt Hospital.3665715
For-proﬁt Hospital-.8165761
Teaching Hospital.444591
Constant.4466926

z-value
0.487-0.314-0.343-0.615-1.6470.761-2.0230.828
0.143

P-value
0.627
0.754
0.732
0.539
0.100
0.447
0.043
0.408
0.886

Marginal Eﬀect.01056798-.00005364-.02923743-.05100845-.10763148.11612355-.14845607.14421667

Note:The log likelihood is 162.79032, and quasi R2 is 0.1112. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

32

Table 16：Estimation Results of Treatment Selection（Less than
One Month Hospitalization Samples）
Estimates
Age.0829551
2-.0006732
Age
Female-.6441801
Number of Beds（Log） .2396488
Non-proﬁt Hospital-.6660112
For-proﬁt Hospital-.6470829
Teaching Hospital-.2074055
Constant-2.836181

z-value
0.922-0.941-2.1171.211-1.471-1.855-0.494-0.892

P-value
0.357
0.347
0.034
0.226
0.141
0.064
0.621
0.372

Marginal Eﬀect.02864868-.00023249-.21512405.08276313-.22088191-.21589624-.07747569

Note:The log likelihood is 209.61745, and quasi R2 is 0.1045. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

33

Table 17： Estimation Results of Outcome using Instrumental
Variable Method （Less than One Month Hospitalization
Samples）
Estimates
Age.1928973
2-.0014347
Age
Female-1.57529
Invasive Procedures Choosing P-value（Estimates） -6.635654
Number of Beds（Log）.1059676
Non-proﬁt Hospital-1.190768
For-proﬁt Hospital-2.403003
Teaching Hospital.0349378
Constant-1.528419

z-value
0.809-0.753-0.734-0.7040.140-0.511-1.0350.045-0.357

P-value
0.418
0.451
0.463
0.482
0.889
0.610
0.301
0.964
0.721

Note:The log likelihood is 162.84529, and quasi Rα is 0.1109. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

34

Marginal E.05067594-.0003769-.18584387-.19315464.0278387-.17331625-.19261563.00972145

Table 18：Estimation Results of Outcome using Nonparametric
Propensity Score Method（Less than One Month Hospitalization
Samples）

Epanechnikov
Biweight
Triangular
Gaussian
Rectangular

Average-.0644408-.0884281-.0596314-.072562-.044513

95% CI Upper-.1277267-.1482805-.1262428-.1380501-.1040212

35

95% CI Lower.0080246-.0256893.0085508-.0053486.0212697

Table 19：Estimation Results of Simple Probit（Blood Type Test
Samples）
Estimates
Age.0249642-.0001039
Age
Female-.5475136
Invasive Procedures-.2815678
Number of Beds（Log） -.2903051
Non-proﬁt Hospital
1.404206
For-proﬁt Hospital-.5313012
Teaching Hospital.3107833
Constant.6818123z-value
0.260-0.145-1.116-0.647-1.1382.377-0.822
0.562
0.177

P-value
0.795
0.885
0.264
0.517
0.255
0.017
0.411
0.574
0.859

Marginal Eﬀect.00637125-.00002651-.12213132-.07170522-.074091.51501181-.11951618.10007992

Note:The log likelihood is 88.747851, and quasi R2 is 0.1716. The null hypothesis that all
variables except with constant are 0 is not rejected under 10% signiﬁcant level.

36

Table 20：Estimation Results of Treatment Selection（Blood
Type Test Samples）
Estimates
Age.2601578
2-.0020923
Age
Female-1.248489
Number of Beds（Log） .4424047
Non-proﬁt Hospital-.5958351
For-proﬁt Hospital.2633366
Teaching Hospital-1.568982
Constant-8.14102

z-value
1.838-1.865-2.8821.421-0.9770.432-2.367-1.652

P-value
0.066
0.062
0.004
0.155
0.329
0.666
0.018
0.098

Marginal Eﬀect.08028642-.0006457-.36477547.136529-.21450428.10475259-.40426955

Note:The log likelihood is 105.12992, and quasi R2 is 0.2140. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

37

Table 21: Estimation Results of Outcome using Instrumental
Variable Method (Blood Type Test Samples)

Age
Age2
FemaleInvasive Procedures Selecting Probability(Estimate)
Number of Beds(Log)Non-proﬁt Hospital
For-proﬁt Hospital
Teaching Hospital
Constant

Estimates.3454181-.0026482-2.437649-6.041505.0093336.1712881-.4380939-1.611602-4.382809

z-value
1.669-1.636-2.882-2.7520.041
0.238-0.668-2.509-0.859P-value
0.095
0.102
0.004
0.006
0.967
0.812
0.504
0.012
0.391

Note: The log likelihood is 79.3828, and quasi R2 is 0.2590. The null hypothesis that all
variables except with constant are 0 is rejected under 10% signiﬁcant level.

38

Marginal.07855998-.0006022-.1922997-.1927740.00212279.05030541-.0969659-.1861923

Table 22： Estimation Results of Outcome using Nonparametric
Propensity Score Method（Blood Type Test Samples）

Epanechnikov
Biweight
Triangular
Gaussian
Rectangular

Average.1223203.0874121.1621314.0944667.1441248

95% CI Upper.0416913.0143986.0793257.0232313.0517966

39

95% CI Lower.214348.1618662.2544301.1762032.2363143