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The literature comparing private not-for-profit, for-profit, and government providers mostly relies on empirical evidence from high-income and established market economies. Studies from developing and transitional economies remain scarce, especially regarding patient case-mix and quality of care in public and private hospitals, even though countries such as China have expanded a mixed-ownership approach to service delivery. The purpose of this study is to compare the operations and performance of public and private hospitals in Guangdong Province, China, focusing on differences in patient case-mix and quality of care. We analyze survey data collected from 362 government-owned and private hospitals in Guangdong Province in 2005, combining mandatorily reported administrative data with a survey instrument designed for this study. We use univariate and multi-variate regression analyses to compare hospital characteristics and to identify factors associated with simple measures of structural quality and patient outcomes. Compared to private hospitals, government hospitals have a higher average value of total assets, more pieces of expensive medical equipment, more employees, and more physicians (controlling for hospital beds, urban location, insurance network, and university affiliation). Government and for-profit private hospitals do not statistically differ in total staffing, although for-profits have proportionally more support staff and fewer medical professionals. Mortality rates for non-government non-profit and for-profit hospitals do not statistically differ from those of government hospitals of similar size, accreditation level, and patient mix. In combination with other evidence on health service delivery in China, our results suggest that changes in ownership type alone are unlikely to dramatically improve or harm overall quality. System incentives need to be designed to reward desired hospital performance and protect vulnerable patients, regardless of hospital ownership type.
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RESEARC H ARTIC LE Open Access
Comparing public and private hospitals in China:
Evidence from Guangdong
Karen Eggleston
1
, Mingshan Lu
2*
, Congdong Li
3
, Jian Wang
4
, Zhe Yang
5
, Jing Zhang
6
, Hude Quan
7
Abstract
Background: The literature comparing private not-for-profit, for-profit, and government providers mostly relies on
empirical evidence from high-income and established market economies. Studies from developing and transitional
economies remain scarce, especially regarding patient case-mix and quality of care in public and private hospitals,
even though countries such as China have expanded a mixed-ownership approach to service delivery. The purpose
of this study is to compare the operations and performance of public and private hospitals in Guangdong
Province, China, focusing on differences in patient case-mix and quality of care.
Methods: We analyze survey data collected from 362 government-owned and private hospitals in Guangdong
Province in 2005, combining mandatorily reported administrative data with a survey instrument designed for this
study. We use univariate and multi-variate regression analyses to compare hospital characteristics and to identify
factors associated with simple measures of structural quality and patient outcomes.
Results: Compared to private hospitals, government hospitals have a higher average value of total assets, more
pieces of expensive medical equipment, more employees, and more physicians (controlling for hospital beds,
urban location, insurance network, and university affiliation). Government and for-profit private hospitals do not
statistically differ in total staffing, although for-profits have proportionally more support staff and fewer medical
professionals. Mortality rates for non-government non-profit and for-profit hospitals do not statistically differ from
those of government hospitals of similar size, accreditation level, and patient mix.
Conclusions: In combination with other evidence on health service delivery in China, our results suggest that
changes in ownership type alone are unlikely to dramatically improve or harm overall quality. System incentives
need to be designed to reward desired hospital performance and protect vulnerable patients, regardless of
hospital ownership type.
Background
The roles of the government and the private sector in
health service delivery are controversial, especially in
developing and transitional economies. Some authors
argue for a dominant if not exclusive government role
in health service delivery in developing countries [1];
others call for broad and expanding engagement with
the private sector [2]. The relatively extensive literature
comparing the performance of private not-for-profit,
for-profit, and government providers mostly relies on
empirical evidence from hospitals in high-income and
established market economies [3-6]. Evidence from
developing and transitional economies remains limited
[7,8].
In China, although private ownership is common for
outpatient services such as village clinics, private pre-
sence in inpatient delivery is limited and recent [9]. Poli-
cies begun in the late 1990s and reinforced by the 2009
health policy reforms [10]which for the first time pro-
minently called for expansion of private not-for-profit
investments in health service delivery in Chinaset the
stage for nationwide reform from almost universal gov-
ernment ownership of hospitals to more ownership
types, albeit with government ownership still in the lead.
Only a few studies have examined how public and pri-
vate providers compete in China. Most patients may
self-refer to a provider of their choice, and many aspects
of operations appear similar across ownership types. For
* Correspondence: lu@ucalgary.ca
2
Department of Economics, University of Calgary, Calgary, Alberta, Canada
Eggleston et al.BMC Health Services Research 2010, 10:76
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© 2010 Egglesto n et al; licensee BioMed Central Ltd. This is an Open Access article distributed und er the terms of the Creative
Commons Attri bution License (http://creativecommons.org /licenses/by/2.0), which permits unrestricted use, distribution, and
reproductio n in any medium, provided the original work is properly cited.
example, all Chinese clinics and hospitals employ physi-
cians on their staff. Evidence suggests that providers
respond to the incentives of the system in similar ways,
such as avoiding unprofitable (public health) services
and overproviding profitable high-tech diagnostic ser-
vices and drugs [11-13]. Some studies find that private
hospitals, seeking to attract patients in a public-domi-
nated system of insurance and delivery, often charge
prices that are lower than those of public hospitals,
attract lower- and middle-income patients, and achieve
higher patient satisfaction [13]. Similarly, Huang and
colleagues [14] find that non-government hospitals
charge prices that are generally lower than or equal to
those of government hospitals. However, evidence speci-
fictothequalityofcareprovidedinpublicversuspri-
vate hospitals remains limited.
The aim of this study is to compare the operations
and performance of public and private hospitals in
Guangdong Province, China, focusing on differences in
patient case-mix and quality of care. Such evidence can
be valuable for understanding the role of the private
sector in health service delivery in developing and tran-
sitional economies and for informing policy in countries
that are contemplating how best to regulate mixed-own-
ership markets.
Methods
Sampling
We conducted a survey, approved and supported by the
Division of Health Information of the Department of
Health of Guangdong Province, to collect data on a
sample of government-owned and private hospitals in
Guangdong Province in 2004 and 2005. The data we
collected contained only hospital-level aggregate infor-
mation; there was no patient-level data. In addition, this
project involved no intervention. According to the Uni-
versity of Calgary IRB definition, this qualifies for not
human subjects researchand does not requires an ethi-
cal approval. Hospitals participated in this survey
granted consent to the Division of Health Information
of the Department of Health of Guangdong Province to
be included in this project.
Our sample was purposively constructed to oversample
theprivatesectorandcannotbeconsideredarandom
sample of hospitals in that region. Specifically, the initial
sampling design included all private for-profit hospitals
officially registered by 2002 in five citiesGuangzhou,
Zhongshan, Panyu, Jiangmen, and Dongguanas well as a
stratified random sample of not-for-profit hospitals in
these same localities, stratified according to the official
Chinese classification system for hospitals (jibie). The
sample design called for random selection of one hospital
out of five in the most selective category (level 3 first
rank: san ji jia deng); one hospital out of 10 among
lower-rank level-3 and level-2 categories; and one hospi-
tal out of 30 for the level-1 hospitals.
To take account of this sampling in the statistical ana-
lyses, we create indicator variables for hospital owner-
ship categories (private not-for-profit, private for-profit,
and government owned) and separate indicator variables
for hospital levels of accreditation (jibie levels 1, 2, and
3) to use as explanatory variables in the multi-variate
regression analyses (described further below). Since we
do not claim that this sample is representative of the
broader ownership mix and characteristics of hospitals
in Guangdong Province or China as a whole, we do not
weight the sample according to the stratified sampling
design;todosowebelievewouldprovideafalsesense
of accuracy, using what should be interpreted as a rich
but limited sample of public and private hospitals in one
region of China.
Data collection
The data set includes data mandatorily reported by hos-
pitals of all ownership types to the provincial health
bureau, as well as information from an original survey
instrument fielded in the fall of 2005 by enumerators
who interviewed an administrator and collected admin-
istrative data by hand from each hospital. Our analytic
sample includes 362 hospitals, both government and
private, for which we have relatively complete data
collected in 2005, reporting 2004 outcomes.
Measurement of hospital characteristics
We use affiliation with a university as a proxy for teach-
ing status. To designate a hospital as located in an urban
or rural area, we used secondary data from Guangdong
statistical yearbooks on the percentage of residents that
are designated agricultural in each county or district
[15,16]. Ninety-two hospitals, representing 25.4% of our
sample, are located in areas where more than 50% of the
population is agricultural; the remaining 270 are located
in predominantly urban areas. We code whether each
hospital is an appointed hospitalfor social insurance
beneficiaries (dingdian yiyuan) based on secondary data
from social insurance bureau documents.
Data analysis
We compare hospitals using both univariate analyses
and multi-variate regressions. Univariate analyses com-
pare median and mean values across hospital categories
(such as ownership type). Mean values are calculated as
the average of individual hospital rates. To study
whether differences among hospitals of different owner-
ship types are statistically significant, we run regressions
of the variable of interest on a categorical variable
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indicating ownership type (1, 2, 3) and report the p
value of the ownership category coefficient in Table 1.
We further study the association between hospital
ownership type and available measures of hospital struc-
tural and outcomes quality for the sub-sample of gen-
eral-acute hospitals using the following multi-variate
regression model:
YNFX  
  
12 .(1)
The dependent variable, Y, is a measure of each hospi-
tals quality. Quality measures used in this study include
structural and outcome measures and are all as continu-
ous variables. Structural measures include four continu-
ous variables: the value of a hospitalsassets;the
number of machines valued at over 10,000 RMB owned
by a given hospital (measured by ln(number of machines
valued over 10,000 RMB)); the total number of employ-
ees of the hospital (measured by ln(number of employ-
ees); and the number of physicians on a hospitalsstaff
(measured by ln(number of physicians)). The outcome
measurewasonecontinuous variable: the inpatient
mortality rate. The inpatient mortality rate is defined as
the total number of patients who die during inpatient
admission per 1,000 admissions to that hospital in that
year. Nand Fare dummy variables indicating the hospi-
tal ownership type: Nrepresents non-government non-
profit ownership, and Findicates private for-profit own-
ership. (The omitted group is government ownership). X
is a vector of hospital and market characteristics, such
as ln (beds), indicators for whether the hospital is
affiliated with a university or an appointed hospital for
social insurance, and the percentage of the population
that is agricultural in the hospitals county or district;
and ξistheerrorterm.Thecoefficientsb
1
and b
2
cap-
ture the effect on Yof non-government non-profit and
for-profit ownership, respectively, relative to government
ownership, controlling for other factors.
For some dependent and independent variables, loga-
rithm transformation is used to normalize variable dis-
tribution. We take the natural logarithm of the
inpatient mortality rate, for example, and use that con-
verted form as the dependent variable for the econo-
metric analysis to avoid an artificial bias in the
statistical results from the skewed distribution of mor-
tality rates. The importance of using such a transforma-
tion has been shown in previous analyses of hospital
performance [17]. Ordinary least squares (OLS) regres-
sion is used in all the multi-variate regressions because
all five dependent variables are continuous rather than
categorical or count variables. All analyses were per-
formed in Stata 9.0.
Results
Comparing hospital characteristics
As shown in Table 1, 212 (59%) of the hospitals in our
sample are government-owned and 71 (20%) are private
for-profit hospitals. Private hospitals, particularly for-
profits, are less likely than their government counter-
parts to be general-acute hospitals and more likely to be
specialty hospitals.
Compared to private hospitals, government hospitals
are more concentrated in urban areas (p<0.164),gen-
eral acute care (p < .006), and tertiary services (level-3
jibie hospitals in the Chinese accreditation system)
(p< 0.0005). Government hospitals are also much lar-
ger, in terms of beds, asset value, staffing, and outpatient
and inpatient volume (p< 0.0005).
The average government hospital in our sample (G)
has 256 beds and employs 114 physicians, compared to
124 beds and 33 physicians in the average non-govern-
ment non-profit (N) and 67 beds and 24 physicians in
the average private for-profit (F).
Even among general-acute hospitals, non-government
hospitals are smaller: the median government hospital
has 150 beds and 204 employees, including 74 physi-
cians, whereas the median non-government non-profit
hospital has 71 beds and 80 employees, and the median
for-profit has 30 beds and 73.5 employees (data not
shown). Government-owned and private for-profit hos-
pitals have similar average numbers of employees per
bed (1.3 and 1.4); non-government non-profit hospitals
have fewer employees per bed, on average (120.1/124 =
0.97). Chinese hospitals hire about one pharmacist for
every four or five physicians, regardless of ownership
type.
Regarding level of accreditation, 57 hospitals are level-
1 or community health centers; 84 are level-2; 29 are
level-3, the highest level of tertiary care; and the remain-
ing half (53%) of the sample are not classified. Non-clas-
sification may indicate that a hospital is too small or
new to be prepared for accreditation review, did not
have the staffing or equipment to qualify, or is waiting
for results after applying for accreditation. Non-govern-
ment hospitals are the majority of those at lower levels
of accreditation or without a classification. The hospitals
accredited at the top tertiary level (level 3) are virtually
all government owned; only 1 (of 29) is a non-govern-
ment non-profit.
Information about patient case-mix is only available
for five broad categories of outpatient services. For out-
patient surgical visits, private for-profit hospitals serve a
higher proportion of patients than would be expected
given their small size. The reverse is true for outpatient
visits for traditional Chinese medicine, for which
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Table 1 Hospital characteristics*
Government
(N = 212)
Nongovernment nonprofit
(N = 79)
Private for-profit
(N = 71)
P-value
Rural (%) 27.8
(44.9)
24.0
(43.0)
19.7
(40.1)
0.1642
Acute-care (%) 83.0
(37.6)
62.0
(48.8)
71.8
(45.3)
0.0064
Jibie (Hospital accreditation level): 0.0000
Not classified 32.4 75.0 87.3
Level 1 21.9 5.3 9.9
Level 2 32.4 18.4 2.8
Level 3 (highest) 13.3 1.3 0.0
Average number of beds 256.3
(308.4)
124.0
(155.9)
66.8
(114.0)
0.0000
Value of total hospital assets (RMB) 15,935.6
(29,091.3)
2,208.3
(3,080.0)
2,717.3
(11,831.5)
0.0000
Number of machines valued over 10,000 RMB 310.0
(611.7)
44.7
(63.5)
41.3
(70.7)
0.0000
Staffing (averages):
Number of employees 355.1
(420.7)
120.1
(134.1)
89.0
(109.9)
0.0000
Number of physicians 114.1
(134.0)
32.8
(37.7)
24.0
(29.6)
0.0000
Number of nurses 115.8
(152.3)
32.9
(47.1)
24.8
(44.1)
0.0000
Number of pharmacists 23.6
(27.4)
7.3
(8.1)
4.8
(5.3)
0.0000
Patient volume (averages):
Inpatient admissions 7,138.1
(8,395.0)
1,326.5
(3,081.4)
1,060.6
(3,139.7)
0.0000
Total outpatient visits 3,72,521.2
(4,19,051.5)
60,006.18
(83,287.9)
48,010.68
(1,12,454.4)
0.0000
Internal medicine 91,391.4
(1,29,503.7)
18,990.6
(31,165.4)
12,534.8
(28,439.2)
0.0000
Surgery 28,471.6
(41,400.0)
5,623.1
(8,904.8)
8,365.1
(18,076.1)
0.0000
Ob/Gyn 42,820.8
(51,862.3)
6,446.8
(11,973.2)
8,423.5
(23,649.6)
0.0000
Pediatrics 31,387.0
(79,364.0)
3,051.6
(6,022.9)
4,107.3
(11,671.7)
0.0000
Traditional Chinese medicine (TCM) 38,047.7
(68,017.8)
5,998.2
(9,839.4)
2,399.8
(4,128.2)
0.0000
Emergency room (ER) visits 42,532.5
(48,046.8)
7,711.5
(14,863.1)
6,158.5
(17,050.1)
0.0000
Average occupancy rate (%) 70.4
(33.5)
39.0
(32.9)
39.9
(35.0)
0.0000
Average length of stay (days) 22.0
(159.0)
61.2
(212.7)
14.6
(56.6)
0.8449
Average inpatient mortality rate (per 1000 admissions) 18.0248
(29.8192)
37.9922
(113.1023)
9.8640
(33.9575)
0.8337
Note: The table reports variable means and standard deviations (in parentheses) for all variables except for jibie (hospital accreditation level), for which frequency
is reported. The p-value column refers to the p-value on the coefficient of an ownership categorical variable (1, 2, 3) in a regression with the row variable as the
dependent variable. A p-value of 0.0000 indicates that the p-value was less than 0.00005.
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government hospitals provide the overwhelming major-
ity of services.
The average occupancy rate is substantially higher at
government hospitals (70%) than at non-government
non-profits (39%) or for-profit hospitals (40%)
(p< 0.000). Average length of stay is very heterogeneous
(p< 0.845), with the highest level and standard devia-
tion among non-government non-profit (and specialty)
hospitals. Average reported mortality rates are highest
for non-government non-profit hospitals and lowest for
private for-profit hospitals, with government hospital
mortality rates falling in between. Among general-acute
hospitals, however, the average mortality rates for gov-
ernment and non-government non-profits do not statis-
tically differ (Table 2).
Over 80% of the government general-acute hospitals
are appointed hospitals for social insurance beneficiaries,
whereas the corresponding percentages for non-govern-
ment non-profits and for-profits are 45% and 40%,
respectively. Non-government hospitals are primarily
established and controlled by social organizations, col-
lectives, and firms. For-profit hospitals are much more
likely than non-profits to be controlled by individuals.
To focus on comparing hospitals of differing owner-
ship types that are otherwise quite similar, the remain-
der of the analyses restrict the sample to general-acute
hospitals only (i.e., they exclude specialty hospitals).
Comparing hospital case-mix and quality
Table 2 summarizes inpatient mortality rates (total
number of patients who died during inpatient admis-
sion, per 1,000 admissions in the year 2004) across var-
ious categories of general-acute hospitals. These
mortality rates reflect the combined effect of patient
case-mix and quality of care.
Median inpatient mortality rates are higher in larger
hospitals compared to smaller hospitals, insurance net-
work ("appointed) hospitals compared to non-network
hospitals, and university-affiliated hospitals compared to
non-teaching hospitals. Median inpatient mortality rates
increase with the official rank of the hospital, so that
the highest-accredited (level-3) hospitals have the high-
est inpatient mortality rates.
Multi-variate analysis of the correlates of hospital quality
Table 3 reports results of multi-variate regression analy-
sis of the correlates of hospital structural quality as
proxied by assets and staffing. In a regression exploring
the correlates of the value of a hospitalstotalassets
(measured with a log-transformed continuous variable),
a 10% increase in beds was correlated with a 10.7%
increase in assets. Controlling for this effect, govern-
ment hospitals had a higher total value of assets than
private hospitals. Being an appointed hospital in the
social insurance network was associated with more
Table 2 Inpatient mortality (number of deaths per 1,000 admissions) for general-acute hospitals (N= 307)
Inpatient mortality
central tendency
Inpatient mortality inter-quartile range
Sub-groups of general-acute hospitals N Median Mean 25th percentile 75th percentile
Ownership
Government 176 9.5032 16.9060 5.3402 16.4919
Non-government non-profit 49 7.3910 19.0579 <0.0001 20.9790
Private for-profit 51 <0.0001 10.0783 <0.0001 6.5200
Size
20100 beds 115 4.0912 16.3998 <0.0001 9.4737
>100 beds 161 10.5263 15.7597 5.8560 16.2728
Contracted hospital for social insurance beneficiaries
Not appointed 84 2.7911 9.7748 <0.0001 12.8986
Appointed (dingdian) hospital 192 8.6595 18.7615 4.5191 16.2873
Academic affiliation (proxy for teaching status)
Non-university hospital 263 6.9565 15.7932 2.5100 14.3541
University hospital 13 17.4364 20.7440 12.8205 28.4837
Urban/rural location
Urban 201 7.1259 18.6061 0.8300 17.0828
Rural 75 8.1411 9.1129 4.4577 13.0880
Jibie (Hospital accreditation level)
Not classified 121 5.7358 16.3540 <0.0001 12.5829
Level 1 (community health center) 54 4.7862 10.5825 2.9674 8.6754
Level 2 76 10.8236 16.8193 6.2358 15.3246
Level 3 (highest tertiary care) 25 21.1464 23.7896 15.3653 28.7565
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assets. Table 3 next reports OLS regressions of hospital
staffing, measured as continuous variables by taking the
natural logarithms of the total number of employees
(model 3) and of the number of physicians (model 4).
Non-government non-profit ownership was associated
with fewer total employees and fewer physicians, con-
trolling for beds and social insurance appointment sta-
tus (which were both associated with more personnel).
For-profit private and government hospitals did not sta-
tistically differ in total employment, although for-profits
had fewer medical professionals and more support staff.
Table 4 presents our OLS regression results for the
log transformation of inpatient mortality rates. Case-mix
controls include the number of emergency patients, the
percentage of outpatient visits for five categories of out-
patient service, the percentage of inpatient beds across
several different departments, and the official accredita-
tion level of the hospital. As for the other regressions,
we also control for possible confounding from differ-
ences in the total number of hospital beds, urban or
rural locations, inclusion in the social insurance net-
work, and university affiliation. We find that mortality
rates for non-government non-profit and for-profit hos-
pitals did not statistically differ from those of govern-
ment hospitals of similar size and patient mix. Factors
significantly associated with higher mortality rates
include being an appointed hospital for social insurance
beneficiaries, having larger percentages of outpatient vis-
its for internal medicine and traditional Chinese medi-
cine, and having a higher percentage of inpatient beds
in departments of internal medicine and tumors. Factors
significantly correlated with lower inpatient mortality
rates include having a higher percentage of beds devoted
to surgery, obstetrics, and gynecology.
Multi-variate analysis of other hospital characteristics
For completeness we mention additional statistical ana-
lyses that are not the primary focus of this article. Lar-
ger size, being an appointed hospital, and government
control are all associated with higher volumes of both
inpatient admissions and outpatient visits. Private own-
ership (regardless of profit status) is correlated with
fewer outpatient visits overall for most sub-categories of
visits. However, for outpatient surgical visits, for-profit
hospital volume is not statistically different from that of
government hospitals. Government control and
appointed status are associated with more patients per
doctor, more patients per nurse, and higher occupancy
rates. Higher cooperative medical system coverage rates
are associated with higher occupancy rates, suggesting
Table 3 Correlates of structural quality (hospital assets and staffing), general-acute hospitals
Model 1 Model 2 Model 3 Model 4
Dependent variable: Assets (ln (value of assets in
10,000 RMB))
Number of machines valued
over 10,000 RMB
(ln (#machines valued over
10,000 RMB))
Total
employees
(ln
(#employees))
Physicians
(ln
(#physicians))
Explanatory variables:
Non-government non-profit (indicator
variable)
-0.974
(6.69)**
-0.662
(3.87)**
-0.392
(4.20)**
-0.629
(5.70)**
Private for-profit (indicator variable) -0.433
(2.62)**
0.024
(0.12)
-0.147
(1.37)
-0.329
(2.60)**
ln (beds) 1.073 0.886 0.729 0.657
(19.19)** (13.57)** (20.09)** (15.34)**
Contract with social insurance ("appointed
indicator variable)
0.513
(4.02)**
0.401
(2.67)**
0.245
(2.96)**
0.296
(3.03)**
University hospital (indicator variable) 0.1
(0.37)
0.409
(1.33)
0.148
(0.85)
0.259
(1.26)
Rural hospital (indicator variable) 0.002 0.189 0.016 -0.054
(0.01) (0.84) (0.13) (0.37)
Constant 3.022 -0.042 1.61 0.825
(10.25)** (0.12) (8.42)** (3.66)**
Observations 286 275 288 288
R-squared 0.74 0.58 0.73 0.66
Note: Absolute value of tstatistics in parentheses
* significant at 5%; ** significant at 1%
Each column presents a separate ordinary least squares (OLS) regression, with the dependent variable as listed in the column heading and the explanatory
variables as listed in the first column; each cell of the table reports the estimated regression coefficient, with the absolute value of the associated t statistic in
parentheses below the estimated coefficient. We take the natural logarithm of each dependent variable e.g., ln (value of assets in 10,000 RMB) and use that
converted form for the econometric analysis to avoid an artificial bias in the statistical results from the skewed distribution of the continuous dependent
variables.
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Table 4 Correlates of outcome quality with limited control for case-mix, general-acute hospitals
Inpatient mortality rate
Model 1 Model 2
Non-government non-profit (indicator variable) 0.003
(0.48)
-0.005
(0.9)
Private for-profit hospital (indicator variable) -0.012 -0.002
(1.92) (0.34)
ln (beds) 0.003 -0.001
(1.3) (0.34)
ln (emergency patients) -0.005 -0.001
(3.67)** (1.2)
Contract with social insurance ("appointedindicator variable) 0.012
(2.50)*
0.014
(3.33)**
University hospital (indicator variable) -0.006 -0.012
(0.64) (1.31)
Rural hospital (indicator variable) -0.004 -0.002
(0.49) (0.33)
Internal medicine visits as % of op visits 0.02
(2.03)*
Surgery visits as % of op visits -0.028
(1.55)
Ob/gyn visits as % of op visits 0.011
(0.42)
Pediatrics visits as % of op visits 0.041
(1.33)
TCM visits as % of op visits 0.044
(3.58)**
Internal medicine ip beds as % of beds 0.054
(5.36)**
Surgery ip beds as % of beds -0.027
(2.40)*
Pediatrics ip beds as % of beds -0.05
(1.4)
Obgyn ip beds as % of beds -0.045
(2.49)*
Psychiatry dept ip beds as % of beds -0.013
(0.34)
Infectious disease dept ip beds as % of beds -0.041
(0.83)
Tumors ip beds as % of beds 0.292
(4.11)**
TCM ip beds as % of beds -0.007
(0.92)
Hospital accreditation level 2 (indicator variable) 0.006
(1.04)
Hospital accreditation level 3 (highest; indicator variable) 0.002
(0.21)
Not classified into a level (indicator variable) 0.004
(0.79)
Constant 0.044 0.019
(3.54)** (1.24)
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that expanding insurance coverage to Chinasrural
majority increases utilization of inpatient resources. All
results and their associated statistical tests are available
in the working paper [18].
Discussion
In this study, we examined the overall operations of
over 360 public and private hospitals in Guangdong
Province in southern China. As one of the wealthier
provinces with vibrant non-state firms in other sectors
of its economy, Guangdong makes an interesting case
study and may exemplify the path that other parts of
China might take in the future.
We find that government-owned hospitals continue to
dominate the highest levels of tertiary hospital care and
remain the primary provider of outpatient traditional
Chinese medicine services. Private hospitals are more
likely to be specialty hospitals, and private for-profit hos-
pitals aggressively cater to the surgical outpatient market,
attracting a clientele in that area disproportionate to
their size. Among general-acute hospitals, private hospi-
tals appear to be scaled-down versions of their govern-
ment-owned counterparts along most dimensions, from
staffing and equipment to patient volumes and outcomes.
Consistent with previous literature [19,20], we find
that hospitals have responded to the incentives implicit
in Chinas fee-for-service payment system. For example,
across all ownership forms, hospitals hire about one
pharmacist for every four or five physicians (Table 1).
Hospitals in China earn 40 to 50 percent of their reven-
ues from drug sales [21], and the 2009 reform plan
explicitly calls for reducing this reliance on pharmaceu-
tical sales revenue.
Does the quality of care differ across ownership types?
Qualityofhealthcareisfundamentally multi-dimen-
sional and has long been difficult to measure. Our data
only include measures of quality based on structural fea-
tures (such as equipment and staffing) and some patient
outcomes such as inpatient mortality rates, which are
known to have limitations [22-25].
For structural quality, we find that compared with pri-
vate general-acute hospitals (and controlling for hospital
beds, urban location, insurance network, and university
affiliation), government hospitals own more total assets
and more pieces of expensive medical equipment and
hire more employees, including physicians. Other factors
associated with higher structural quality include having
more beds and being an appointed hospital in the social
insurance network. Government and for-profit private
hospitals do not statistically differ in total staffing,
although for-profits have proportionally more support
staff and fewer medical professionals.
In univariate analysis, median inpatient mortality rates
increase with the official rank (jibie) of the hospital.
Since the level-3 hospitals are the most sophisticated
and the largest tertiary hospitals, while level-1 and level-
2 hospitals are of more modest scope and clinical repu-
tation, that mortality rates increase with accreditation
level clearly indicates that mortality rates are a measure
of illness severity and patient case-mix as much as qual-
ity of care.
In our multi-variate analysis, case-mix controls include
the number of emergency patients, the percentage of out-
patient visits for five categories of outpatient services, the
percentage of inpatient beds across several different
departments, and the official accreditation level of the
hospital. These hospital-level indicators remain imperfect
because they reflect hospital management decisions as
well as case-mix, and they do not capture differences in
severity of disease within service categories. Nevertheless,
these controls help to disentangle the effect of ownership
type from that associated with serving different patient
clienteles. For example, it seems perfectly plausible that
hospitals that treat cancer patients have higher mortality
rates than those that specialize in childbirth and/or rou-
tine surgeries, as our results indicate (Table 4).
With these admittedly limited controls for case-mix,
inpatient mortality rates at non-government non-profit
and for-profit hospitals do not statistically differ from
government hospitals of similar size and patient mix.
Thus we conclude that there is no evidence in our sam-
ple that non-government hospitals are of systematically
better or worse quality than Chinese government
hospitals.
Table 4: Correlates of outcome quality with limited control for case-mix, general-acute hospitals (Continued)
Observations 265 265
R-squared 0.12 0.44
Note: Absolute value of t statistics in parentheses
All dependent variables are analyzed in ln (.) form
op = outpatient; ip = inpatient
* significant at 5%; ** significant at 1%
Model 1 and Model 2 are separate OLS regressions, with the explanatory variables as listed in the first column; each cell of the table reports the estimated
regression coefficient, with the absolute value of the associated t statistic in parentheses below the estimated coefficient. A blank cell for Model 1 indicates the
row variables (e.g., Internal medicine visits as % of op visits) that were not included in the Model 1 regression. We take the natural logarithm of the dependent
variable (e.g., ln (inpatient mortality rate)) and use that converted form for the econometric analysis to avoid an artificial bias in the statistical results from the
skewed distribution of mortality rates.
Eggleston et al.BMC Health Services Research 2010, 10:76
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These results contribute to the literature on whether
public and private hospitals in developing and transi-
tional economies differ in quality of care. Although eco-
nomic theories provide a valuable conceptual framework
for how ownership affects quality, they do not provide a
single prediction (see [5] for a review). For example, one
prominent theory of ownership [26] predicts that private
providers will invest more in cost control but may be of
either higher or lower quality than their government-
owned counterparts.
How quality differs with ownership is therefore an
empirical question. But the empirical evidence from
high-income and established market economies is mixed
[4,5], and the literature on private providers in develop-
ing and transitional economies [7,8,28-30] rarely studies
quality of care. As Mills and colleagues [8] point out, pol-
icymakers frequently have concerns about the uneven
quality of private outpatient providers. The scope of the
private sector in inpatient care services is often more lim-
ited. Palmer and Mills [31] highlight the importance of
contractual relationships. A recent systematic review [32]
finds a positive impact of private providers on the quality
of primary or public health services for the poor. Loeven-
sohn and Harding [2] argue that contracting for health
service delivery from non-government organizations can
improve quality. Yet the success stories they emphasize
all focus on primary care, not inpatient services.
One of the few studies that examine public and pri-
vate delivery of hospital services [33] finds that contract-
ing out management of three rural district hospitals to
private for-profit management in South Africa was asso-
ciated with higher nursing quality but lower structural
quality and no difference in maternal and perinatal mor-
tality rates compared to three paired public hospitals.
Our study provides a larger sample size for comparing
quality in public and private hospitals from the largest
developing and transitional economy.
In China government-owned hospitals continue to
dominate the commanding heightsof inpatient health
service delivery, in contrast to the mixed ownership
markets of some developing and many higher-income
countries, where government hospitals play a more
dominant role in safety net and specialty services and in
rural areas. Although government hospitals in our
Guangdong sample have higher measures of structural
quality along most dimensions compared to non-gov-
ernment hospitals, multi-variate analyses suggest that
outcome quality does not systematically differ by owner-
ship type after taking account of the different case-mix
of patients seeking care in different hospitals.
Study Limitations
Although we analyze one of the most detailed data sets
available to date on government and non-government
hospitals in China, data limitations are numerous. The
analysis and conclusions must be interpreted with cau-
tion in light of the deliberate oversampling of urban and
periurban areas and private for-profit hospitals, and the
quasi-random sample design. The data cannot be con-
sidered representative of Guangdong Province or the
country as a whole.
Even information on ownership or location can be sub-
ject to error. For example, we use the administrative local-
ity of a hospitals address to classify hospitals as urban or
rural, but this measure is far from synonymous with a hos-
pitals true market. Another example is ownership categor-
ization. The category non-government non-profitshould
be interpreted with caution; these hospitals represent a
heterogeneous mixture of non-governmental organizations
and providers that may be controlled by a government
agency but operated as a semi-autonomous unit.
We analyze imperfect proxies for hospital structure
and outcome quality. The available information does
not include patient-level data, hospital-level measures of
patientsinsured or uninsured status, disease severity, or
number of admissions by disease category (although we
do know how outpatient visits are distributed across five
broad areas of service and whether the hospital is
appointed within the social insurance network). Hospi-
tals with a more severe case-mix will have higher mor-
tality rates, even if they are providing exemplary quality
of care. More detailed analysis, using data at the patient
level before and after a plausibly exogenous policy
change, would be of value to determine the causal effect
of ownership on quality, rather than the correlations
observed in cross-sectional data.
Conclusion
Comparing hospital operations across multiple dimen-
sions for over 360 public and private hospitals in Guang-
dong Province, we find that private hospitals are
generally smaller and attract a less severe case-mix, in
part because they are newer market entrants and less
likely to be in the network of providers contracted by
social health insurance programs. Mortality rates are
higher in larger, more highly accredited, and govern-
ment-owned hospitals that treat a more severe case-mix
of patients. In descriptive multi-variate regression ana-
lyses, controlling for hospital characteristics and available
measures of case-mix, mortality rates do not statistically
differ between government and private hospitals.
In combination with other evidence on health service
delivery in China [10-14,20], we conclude that owner-
ship reforms alone are unlikely to dramatically
improve or harm quality. System incentives need to be
designed to reward desired hospital performance and
protect vulnerable patients, regardless of hospital own-
ership type.
Eggleston et al.BMC Health Services Research 2010, 10:76
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The future of mixed-ownership delivery in China
remains uncertain. The plan for reform of Chinashealth
system announced in April 2009 [10] calls for continued
dominance by public providers over most service delivery,
while simultaneously calling for increased non-govern-
ment investment in both financing and delivery. As
reforms unfold, the challenge will be to harness the poten-
tial innovation, efficiency, and responsiveness of private
providers, while enhancing capacity to regulate and moni-
tor in order to assure equitable access and avoid unhealthy
market segmentation. Chinas response to this challenge in
the next few years will shape the equity and efficiency of
the health-care system for decades to come.
Acknowledgements
The authors gratefully acknowledge financial support from the World Bank
to collect the 2004 data; the valuable comments of Magnus Lindelow and
Adam Wagstaff on an earlier project report; and the excellent research
assistance of Huiyu Huang and Bing Li. Lu also thanks the Institute of Health
Economics in Alberta for financial support. All remaining errors are our own.
Author details
1
Shorenstein Asia-Pacific Research Center, Stanford University, Stanford, CA,
USA.
2
Department of Economics, University of Calgary, Calgary, Alberta,
Canada.
3
Jinan University Management School, Guangzhou, PR China.
4
Center for Health Management and Policy, Shandong University, Shandong,
PR China.
5
Guangdong Bureau of Health Statistics Center, Guangzhou, PR
China.
6
Department of Economics, University of Maryland, USA.
7
Department
of Community Health Sciences and Centre for Health and Policy Studies,
University of Calgary, Calgary, Alberta, Canada.
Authorscontributions
KE participated in the study design, survey instrument construction, data
collection and analysis, and wrote the first draft of the manuscript. ML
contributed to the study design, statistical analysis, and interpretation, as
well as writing of the manuscript. CL, JW, and ZY contributed to the survey
and data collection. JZ contributed to the data management and analysis.
HQ contributed to the interpretation and writing of the manuscript. All
authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 2 June 2009 Accepted: 23 March 2010
Published: 23 March 2010
References
1. Crowley P: Private health care in developing countries: strong public
provision is only hope for health care in developing countries. BMJ 2001,
323(7311):463-464.
2. Loevinsohn B, Harding A: Buying results? Contracting for health service
delivery in developing countries. Lancet 2005, 366(9486):676-681.
3. Devereaux PJ, Peter TL, Choi CL, Weaver B, Schünemann HJ, Haines T,
Lavis JN, Grant BJB, Haslam DRS, Bhandari M, Sullivan T, Cook DT, Walter SJ,
Meade M, Khan H, Bhatnagar N, Guyatt GH: A systematic review and
meta-analysis of studies comparing mortality rates of private for-profit
and private not-for-profit hospitals. CMAJ 2002, 166(11):1399-1406.
4. Shen Y, Eggleston K, Lau J, Schmid C: Hospital ownership and financial
performance: what explains the different findings in the empirical
literature. Inquiry 2007, 44(1):41-68.
5. Eggleston K, Shen Y, Lau J, Schmid CH, Chan J: Hospital ownership and
quality of care: what explains the different results. Health Economics
2007, 17:1345-1362.
6. Mache S, Vitzthum K, Nienhaus A, Klapp BF, Groneberg DA: Physicians
working conditions and job satisfaction: does ownership in Germany
make a difference? BMC Health Services Research 2009, 9:148.
7. Hanson K, Berman P: Private health can provision in developing
countries: a preliminary analysis of levels and composition. Health Policy
and Planning 1998, 13(3):195-211.
8. Mills A, Brugha R, Hanson K, McPake B: What can be done about the
private health sector in low-income countries? Bulletin of the World Health
Organization 2002, 80(4):325-330.
9. Jing M, Lu M, Quan H: From a national, centrally planned health system
to a system based on the market: lessons from China. Health Affairs 2008,
27(4):937-948.
10. The standing conference of State Council of China adopted Guidelines
for Furthering the Reform of Health-care System in principle. [http://
www.moh.gov.cn/publicfiles/business/htmlfiles/mohbgt/s3582/200901/
38889.html].
11. Meng Q, Liu X, Shi J: Comparing the services and quality of private and
public clinics in rural China. Health Policy and Planning 2000,
15(4):349-356.
12. Lim MK, Yang H, Zhang T, Feng W, Zhou Z: Public perceptions of private
health care in socialist China. Health Affairs 2004, 23(6):222-234.
13. Liu Y, Berman P, Yip W, Liang H, Meng Q, Qu J, Li Z: Health care in China:
The role of non-government providers. Health Policy 2006, 77:212-220.
14. Huang C, Zhai Z, Sun B, Chen S, Liang H: Guangdongsheng Minying
Yiyuan Fazhan Cunzai Wenti Fenxi (Analysis of problems with the
development of non-governmental hospitals in Guangdong Province).
Chinese Health Economics 2006, 25(8):31-33.
15. Guangdong Yearbook Press: Guangdong Yearbook Guangzhou 2004.
16. The Bureau of Health of Guangdong Province: Guangdong Bureau of Health
Guangdong weisheng tongji xinxi jianben Guangzhou 2004.
17. Shen Y, Eggleston K, Lau J, Schmid CH: Hospital ownership and financial
performance: what explains the different findings in the empirical
literature? Inquiry 2007, 44(1):41-68.
18. Eggleston K, Lu M, Li C, Wang J, Yang Z, Zhang J: Comparing public and
private hospitals in China: evidence from Guangdong. Stanford University
Shorenstein Asia-Pacific Research Center Asia Health Policy Program working
paper #7 2009 [http://asiahealthpolicy.stanford.edu/publications/list/0/0/1/].
19. Hu S, Tang S, Liu Y, Zhao Y, Escobar ML, de Ferranti D: Reform of how
health care is paid for in China: challenges and opportunities. Lancet
2008, 372:1846-1853.
20. Eggleston K, Ling L, Qingyue M, Lindelow M, Wagstaff A: Health service
delivery in China: a literature review. Health Economics 2008, 17:149-165.
21. Sun Q, Santoro MA, Meng Q, Liu C, Eggleston K: Pharmaceutical policy in
China. Health Affairs 2008, 27(4):1042-1050.
22. Dubois RW, Rogers WH, Moxley JH, Draper D, Brook RH: Hospital inpatient
mortality: is it a predictor of quality? N Engl J Med 1987,
317(26):1674-1680.
23. Sivak ED, Rogers MAM: Assessing quality of care using in-hospital
mortality. Chest 1999, 115(3):613.
24. Thomas JW, Hofer TP: Accuracy of risk-adjusted mortality rate as a
measure of hospital quality of care. Med Care 1999, 37(1):83-92.
25. Thomas JW, Holloway JJ, Guire KE: Validating risk-adjusted mortality as an
indicator for quality of care. Inquiry 1993, 30(1):6-22.
26. Hart O, Shleifer A, Vishny RV: The proper scope of government: theory
and an application to prisons. Quarterly Journal of Economics 1997,
112(4):1127-1161.
27. Barber SL, Bertozzi SM, Gertler PJ: Variations in prenatal care quality for
the rural poor in Mexico. Health Affairs 2007, 26(3):W310-23.
28. Brugha R, Zwi A: Improving the quality of private sector delivery of
public health services: challenges and strategies. Health Policy Plan 1998,
13(2):107-120.
29. Montagu D: Franchising of health services in low-income countries.
Health Policy Plan 2002, 17(2):121-130.
30. Tangcharoensathien V, Bennett S, Khongswatt S, Supacutikul A, Mills A:
Patient satisfaction in Bangkok: the impact of hospital ownership and
patient payment status. International Journal for Quality in Health Care
1999, 11(4):309-317.
31. Palmer N, Mills A: Classical versus relational approaches to understanding
controls on a contract with independent GPs in South Africa. Health
Econ 2003, 12(12):1005-1020.
32. Patouillard E, Goodman CA, Hanson KG, Mills AJ: Can working with the
private for-profit sector improve utilization of quality health services by
the poor? A systematic review of the literature. Int J Equity Health 2007,
6(1):17.
Eggleston et al.BMC Health Services Research 2010, 10:76
http://www.biomedcentral.com/1472-6963/10/76
Page 10 of 11
33. Broomberg J, Masobe P, Mills A: To purchase or to provide? The relative
efficiency of contracting out versus direct public provision of hospital
services in South Africa. Private Health Providers in Developing Countries:
Serving the Public Interest? London, Zed Books 1997.
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This study makes a review on literature of the healthcare quality of public and private hospital to figure out the trend of the study on impact from public and private ownership on service quality and conduct the direction for future study. From individual level, organization level, industry level, national service system level, a review on the studies is made from different perspective to figure out the study on the service quality from a multilevel view. Study on the relationship between service quality and ownership mostly focused on organization, industry, and system level. Studies from the behaviour of physician and patient are fewer. Ownership effect studies were carried out mostly in universal coverage of healthcare service. More studies on the human behaviour related to the service quality and hospital ownership to make complementation to the literature, in order to make better policy-making decision support and individual behavior intervention.KeywordsOwnershipService qualityMultilevelHospitalService system
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