Alinia et al. BMC Health Services Research
(2021) 21:763
https://doi.org/10.1186/s12913-021-06697-6
RESEARCH
Open Access
Physician induced demand for knee
replacement surgery in Iran
Cyrus Alinia1, Amirhossein Takian2,3,4*, Nasser Saravi5, Hasan Yusefzadeh1, Bakhtiar Piroozi6 and
Alireza Olyaeemanesh2,7
Abstract
Background: The structure of the Iranian health system has raised this hypothesis that a part of the Knee
Replacement Surgery (KRS) services are provided due to Physician-Induced Demand (PID).
Methods: This paper used an unbalanced individual panel data covering the steady-state 15,729 KRSs performed
by 995 surgeons provided by the Armed Forces Insurance Organization at the provincial level over the 60 months
(2014–2018). We use a generalized method of moment’s system (GMM-SYS) to obtain consistent and asymptotically
efficient estimates, which provide a vital instrument for our dynamic panel data.
Results: The outcomes show that with unequal increasing orthopedic surgeons to population ratio, both the
number and size of KRS services were increased significantly at a 1 % level. Given that the positive elasticity
obtained for the service size was significantly larger than the number of services, the findings give strong support
for the existence of PID in the Iran system for KRS care. Also, the raw and population-adjusted number of KRS, cost,
and the surgery per active physician increased significantly at the monthly province level.
Conclusions: This is the first time that the existence of PID in the Iranian health system is investigated using
approved econometric models. The findings indicate that the health system structure has been provided the
conditions for aggressive, costly, and high-risk services such as KRS to be exposed to PID.
Keywords: Knee replacement surgery, Physician induced demand, Iran, Panel data, GMM
Background
Knee osteoarthritis is a chronic and age-related condition associated with pain and disability, and around 10 %
of men and 13 % of women over the age of 60 suffer its
typical symptoms [1]. The disease imposes significant
physical limitations on the patients and causes a loss of
19 and 34 % of health-related quality of life, respectively,
in moderate and severe cases, on average [2]. The prevalence of knee osteoarthritis disease in rural and urban
* Correspondence: takian@tums.ac.ir
2
Health Equity Research Center (HERC), Tehran University of Medical
Sciences, Tehran, Iran
3
Department of Global Health and Public Policy, School of Public Health,
Tehran University of Medical Sciences, Tehran, Iran
Full list of author information is available at the end of the article
areas of Iran is 19.3 and 15.3 %, respectively [3, 4], which
is more prevalent among women [5].
Starting in the 1970 s, Knee Replacement Surgery
(KRS) is an effective and expensive approach for endstage knee arthritis [6]. The demand for this treatment
method is increasing substantially. The rate of personyear in the United States has more than seven times over
the past four decades [7]. Its population-adjusted rate in
Iran has doubled in the last five years. The mean age of
patients undergoing KRS in Iran is reported about 65
years [8–10], lower than in developed countries [6, 11].
Increasing population age and obesity are introduced
as the most important reasons for performing KRS [12].
However, given that both the factors do not change instantly at the population level but show significant
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Alinia et al. BMC Health Services Research
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(2021) 21:763
changes gradually over time, the increased demand for
this service over shorter periods could have other structural reasons; technology advances, increasing the level
of community health knowledge, increasing the level of
community income, changing lifestyles, and increasing
access to services due to the entry of more orthopedic
surgeons into the health market [13–15]. Due to the
considerable information asymmetry between the
surgeon and the patient, the surgeons simultaneously act
as suppliers (as service providers) and demanders (as
patient agents). This means that surgeons are entirely
free to prescribe the type of treatment, providing conditions for an imperfect agency problem. In such cases,
the decision to prescribe the service is influenced by the
economic motivations of the providers, and the surgeon
prioritizes her/his preferences over the patient [16].
As such, the question raised by health policymakers is
whether the KRS prescribed by the surgeons were based
on the patient’s needs or not? This question becomes
especially acute when the physician to population ratio
has increased over time and the physician’s share of patient
decreases and naturally reduces his/her income. Physician
Induced Demand (PID) is seen to compensate for lost income due to fewer patients. However, this solution is more
suitable for diagnostic and elective surgery services [17].
The existence of PID in the Iranian health system is
very likely for structural reasons. Physicians have a very
high degree of freedom of action in the Iranian health
system, and regulatory bodies do not effectively monitor
their performance. Reimbursement to the providers is in
the form of Fee-For-Service and dramatically increases
the motivation of providers to deliver more services.
Also, the physician-population ratio has increased significantly over the past decade [18].
Various types of health insurance cover more than
90 % of Iran’s population. Social Security Organization,
Health Insurance Organization, and Armed Forces Insurance, with the coverage rates of 42.87 %, 42.80 %, and
5.00 %, respectively, are the largest Iran’s health insurance
organizations. The first two insurances are committed to
providing services to Iranian workers, the underprivileged,
government employees, and rural residents, covering 90 %
of inpatient services and 70 % of outpatient services in
public health facilities. Also, the Armed Forces Insurance
is committed to providing outpatient and inpatient
services to the Armed Forces and their families, most of
whose services are free of charge in government and military centers and offset between 65 and 90 % of privatesector costs [18–20].
Using unbalanced panel data covering KRS services
provided by the Armed Forces Insurance Organization
at the provincial level for 60 months, this study has
designed econometric modeling to answer the above
question and fill this knowledge gap.
Methods
Dataset
We use microeconomic data for the monthly average
number of KRS activities by each orthopedic specialist
over 2014 to 2018, compiled from the Iranian Armed
Forces Insurance Organization at the provincial level.
The unbalanced individual panel data covers the steadystate 15,729 surgeries performed by 995 surgeons. These
suppliers satisfy this condition that began their activity
before 2014 and had not been retired during the study
time. Population data, including population size of
people older than 50 years and average monthly income
for each (province) and each (month) are extracted from
the census results for 2011 and 2016 years. To estimate
the population size of the middle years, the constant annual multiplier of 0.021 was used as the average annual
growth.
The economic theory of supply and demand explains
the presence of PID for KRS when an increased number
of the surgeons decreases the patient’s quota for each
The economic theory of supply and demand explains the
presence of PID for KRS when an increased number of
surgeons decreases the patient’s quota for each surgeon,
thereby lead to reducing the number of surgeries and
his/her income. Therefore, within the context of fixed
prices, the increase in supply has led to imbalance, and
in order to achieve equilibrium, doctors are trying to regain their level of income in two ways; first, increase the
number of surgeries, and second, increase the price of
each surgery by changing the type of surgery and making
it more expensive. Ultimately, an increase in supply leads
to an increase in demand [17, 21]. Part of this elevated
demand has been increased access to the services by increasing the household’s income, improving the level of
health insurance coverage, the emergence of new advanced technologies, raising the level of public health
awareness, or increasing the elderly population. However, according to the theory, part of this increase in demand could be due to PID. So, the induced KSR should
be measured as the increment in the activities that
would not have performed whiteout training and
employing more orthopedic surgeons. Therefore, in the
current study, the changes in the supply of KRS services
are measured by the density variable (d it ) of orthopedic
surgeons, which is equal to the ratio of surgeons to every
100,000 population over 50 years of age.
Variables
Following Delattre and Dormont [22], to estimating the
potential PID for KRS, we require measuring the activities of the surgeons in the two following ways:
nit :the number of performed KRS by each physician at
province i at month t. This variable can show changes
Alinia et al. BMC Health Services Research
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in the number of surgeries over time, but has four
main drawbacks; First, it is not able to identify the
access effect for cases that had an unmet need for any
reason, including the inability to purchase the services,
lack of geographical access to the surgeon, lack of
knowledge about the existence of treatment and the
like. Second, it does not show the content of the
service. As mentioned earlier, the physician can
manipulate the overall cost of the service by changing
the type and quality of service, but nit variable cannot
measure it. Third, it does not depict the initiating
effect. If the patient asked the surgery, this request
should not be considered a PID, even if the operation
was unnecessary. Fourth, it does not consider the
practice style of physicians. Different surgeons do not
necessarily make the same decision for a particular
patient regarding whether or not to perform the
procedure and service type, so the procedure alone
should not be measured as induced demand. However,
the nit variable is a crucial factor in measuring PID in
this study.
sit : the size of performed KRS by each physician at
province i at month t. This variable is obtained by
multiplying the number of operations performed by the
cost of each operation, which is paid by the patient and
the health insurance; sit ¼ nit f it . A significant increase
in the mean of this variable over time clearly indicates
a change in the type of operation, a rise in the price of
the material used, or a combination of both. Given the
sit can simultaneously measure the surgeries’ amount
and content, it can eliminate all four weaknesses of the
previous variable. Even if the service would be patientinitiated, the decision about the intensity of the service
is made by the surgeon. So, increasing the mean cost of
each KRS over time cannot be due to increased levels
of access to the service, and also the relative value of
the cost of each surgery eliminates the constant effects
of the providers’ behavior, such as physicians’
preferences and practice style.
The variables nit and sit represent the demand for the
service, and their changes indicate the demand shock. In
a supply-demand framework, the study of changes in
each of these variables in response to changes in service
supply defined by the ratio of surgeons to the elderly
population, d it , with controlling other influential factors,
can show the existence of induced demand. Increasing
the d it causes a positive shift in the total supply curve
and exits from the initial equilibrium point. Due to the
fixed prices, the new supply and demand curves intersect
at the disequilibrium price. According to the logic of
Delattre and Dormont [22] and Sorensen and grytten
[23], if physicians do not induce the KRS, supply rationing will arise, the patient quota of each physician will
decrease, and therefore the microeconomic elasticity of
nit will be negative. On the other hand, if surgeons increase the size of the service (sit ), in response to the supply rationing, they can partially compensate for their lost
income. Given that no managed care exists in Iran, surgeons can freely increase the sit . Therefore, the sufficient
conditions for the existence of PID for KRS are; the elasticity of sit will be positive and greater than the elasticity
of nit .
The models
This section presents a dynamic panel data (DPD) model
for investigating the induced demand in KRS in Iran.
The specification is in accordance with the model performed by Delattre and Dormont [22], which relates to
the investigation on the existence of PID for French
physicians. This model makes it possible to an induced
demand estimation applying the elasticity of the KRS in
response to changing physician density. Besides, DPD
allows us to consider both random and permanent
unobserved heterogeneities related to the characteristics
of the supplier and demanders.
This is especially important when unobserved factors
are correlated with independent variables, which can induce bias in estimation. The most important constant
and permanent feature of physicians in determining
service delivery is their practice style that our model can
consider. The presence of lagged dependent variables in
the model puts our regression inside the context of
dynamic panel models. All variables are transformed to
logarithm forms so that coefficients may be interpreted
as elasticity.
To test the PID, we need three separate econometric
models that are exactly the same, but their dependent
variables include nit , S it , and qit :
logðnit Þ ¼ α logðdit Þ þ γ logðincit Þ þ Z0t½1;k θ½k;1
þ δ þ vi þ "it
t ¼ m4
2014; …; m3
ð1Þ
2019; i ¼ 1; 2; …; 22
The constants ni denotes for those fixed and inherent
characteristics of the patients in each province that are
not obviously considered in the model, including gender,
age, disease severity, level of insurance coverage, level of
earning, and selecting the physicians based on their
reputation. Given that d it is an aggregate variable because
it shows the intensity of competition between orthopedic
surgeons to perform KRS at the provincial level, presence of a random term xd in the perturbation is recommended. The number and type of KRSs also depends on
time-varying determinants such as the development and
Alinia et al. BMC Health Services Research
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supply of new technologies, economic growth, changes
in demographic characteristics, and lifestyle changes that
affect all surgeons alike. The sum of these factors is
0
denoted by Zt½1;k . Also, in the one-step estimator, the
error term eit is assumed to be i.i.d. (0, d2 ) across provinces and time. In the two-step estimator, the residuals
of the first step are applied to consistently estimate the
variance-covariance matrix of the perturbation eit , relaxing the homoscedasticity assumption.
The constant effects of ni and xd are eliminated by differencing the first order and our specification is optimized as follows:
nit ¼ αd it þ γin cit þ λnit
1
þ ct þ "it
ð2Þ
In the resulted specification, the variables of the model
represent the first difference of the corresponding logarithm forms and eit is outcome of first-order difference
0
of error term eit . Besides, the Zt½1;k is reduced to fixed
time effects ct . Also, to tackle with the endogeneity and
addressing the unobserved heterogeneity, we added a
lagged dependent variable as an instrumental variable
and we specified a dynamic panel data model. The same
logic was applied on the other two models:
sit ¼ αd it þ γin cit þ λsit
f it ¼ αd it þ γin cit þ λf it
1
1
þ ct þ "it
ð3Þ
þ ct þ "it
ð4Þ
Empirical specification and estimation
OLS-pool model is the most straightforward and most
standard approach to estimate the coefficient of a panel
data model. Nevertheless, this method produces sharply
biased estimates if there is province heterogeneity [24].
Based on Arellano and Bover [25] and Blundell and
Bond [26] recommendation, the generalized method of
moments system (GMM-SYS) is the best alternative approach that is consistent, asymptotically efficient, and
provide a strong instrument for dynamic panel data
which has more than three time-series observations.
Because, GMM-SYS model uses the lagged dependent
variable as an instrument from more than two lag series.
Also, lags of dependent variable will highly correlated
with lagged dependent variable but uncorrelated with
the composite disturbance, if eit is a white noise.
To test the validity of GMM assumptions, we used the
m1 and m2 tests to check the conditions of first and
second-order of serial correlation of the estimated residuals, respectively, and applied Sargan test, which checks
the validity of the instruments used. Also, the Hansen
test is used to test for the overall effectiveness of all the
instrumental variables, and the Wald Chi-Squared test is
used to check a possible heteroskedasticity of residuals if
we obtain a regression model with fixed effects. All statistical analyzes were performed using STATA version 13
(Stata Crop LP, College Station, TX, USA).
The Research Ethics Committee approved this study
protocol at the health equity research center, Tehran
University of Medical Sciences and National Institute for
Medical Research Development (No. NIMAD.REC.1397.290)
and was found to comply with ethical standards.
Results
The monthly-province level statistics of physician’s
steady-state activities in KRS from 2014 to 2018 are presented in Table 1, which includes variables in level and
growth rate; the number of surgeries, cost of each operation, population-adjusted number of the surgery, and
the number of the surgery per active surgeon. It should
be noted that by standardizing the operating costs in
terms of the growth rate approved by the government,
the effect of increasing the annual tariff is eliminated.
The mean (median) monthly number of KRS in each
province in 2014 was 11.03 (3), which increased by
64.91 % (67.00) to 18.19 % (5) in 2018. However, the
population and physician-adjusted mean growths in
these five years were 94.74 (71.43 %) and 35.25 (44.63),
respectively. All the variables are positively skewed, as
their mean is significantly larger than the median value.
The cost of each KRS, depending on the type of
Table 1 The monthly-province level population characteristics; 2014-2018 (N=15729)
Years
2014
Statistics
Mean (SE)
N
11.03 (2.59)
2015
2016
2017
2018
Growth rate
Mean
12.48 (2.66)
14.83 (2.71)
16.27 (3.05)
18.19 (3.19)
64.91%
Pn
1759.54 (75.02)
1793.20 (68.39)
1810.49 (77.04)
1884.47 (61.36)
1940.19 (55.51)
10.27%
Pp
0.19 (0.02)
0.23 (0.02)
0.26 (0.02)
0.32 (0.02)
0.37 (0.02)
94.74%
Pph
1.39 (0.05)
1.44 (0.05)
1.53 (0.05)
1.65 (0.06)
1.88 (0.06)
35.25%
N number of KRS at monthly-province level, Pn cost of each surgery, Pp number of the surgery per 100,000 population, Pph number of the surgery per
active physician
Alinia et al. BMC Health Services Research
(2021) 21:763
operation (total versus partial knee replacement), the
surgical approach (traditional surgery/minimally invasive
surgery [quadriceps-sparing or lateral approach]), and
the type of implant used, can vary greatly, which is confirmed by statistics depicted by Pn variable in Table 1.
Table 2 presents the results of a panel-data unit root
estimation based on the Augmented Dickey-Fuller Fisher
test. The outcomes indicate that the non-stationary null
hypothesis is rejected at the 1 % significance level for all
variables. So, it can be said that all variables are integrated of the first order, which justifies the use of the
GMM-SYS estimator.
The findings of OLS-pool and two-step GMM-SYS regression models are reported in Table 3. The first two
columns show the results of the standard OLS-pool
regression, and the second two columns present the
results of two steps of GMM-SYS regression. The
dependent variables in columns (1) and (4) are the average number, in columns (2) and (5) are the volume, and
in columns (3) and (6) are the weighted values of the
services. The results of OLS-pool regressions provide
evidence that all variables, except for the factor in columns (1) and (3), have the expected sign and are highly
statistically significant at the 1 % level.
The alternative regression models, the Two-step
GMM-SYS approach, which is applied to avoid endogeneity biases and omitted variables and provides a
short-run demand elasticity, confirm the previous outcomes except for factors in all models. The findings reveal that the physician density and lagged dependent
factors significantly affect demand for KRS in Iran.
Unexpectedly, the results suggest that the patients’
income level is not an essential feature in demand for
the surgery.
The F-test results indicate that the GMM-SYS model
is significant at the 1 % level and the Wald test does not
show the presence of heteroscedasticity, and its Chisquare test statistic is significant at the 1 % or lower level
in various model specifications. Also, the Hansen test
does not reveal any problem about over-identification
restrictions and confirms the validity of variables as
Table 2 Panel-data unit root test (Fisher type based on
Augmented Dickey-Fuller)
Variables
Inverse chi-squared P
Inverse normal Z
nit − 1
665.66**
-22.50**
sit − 1
520.99**
-19.22**
fit − 1
**
395.89
-15.85**
nit
645.13**
-22.20**
inc
**
**
124.21
-9.47**
Indicate that the non-stationary null hypothesis is rejected at the 1%
significance level
Page 5 of 8
instruments in the two-step GMM-SYS model. The
Sargan test does not detect any correlation between the
used instruments and the residuals. Finally, The M2 test
confirms the absence of a second-order serial correlation
of the residuals in the GMM-based regression.
Discussion
Knee replacement surgery is a standard and very effective
treatment option for managing severe end-stage knee pain
resulting from osteoarthritis, post-traumatic arthritis, and
inflammatory arthritis [1]. However, a significant proportion of postoperative patients report persistent knee pain,
poor knee function, and patient dissatisfaction [1, 27, 28].
Over the past four decades, there have been numerous innovations in total knee replacement design and implantation techniques, prosthesis diversity, and even alternative
treatment scenarios with lower cost or lower revision risk
[29]. In situations where the surgeons act as a patient’s
perfect agent, the choice between these options is based
on provider discretion and patient preference. Otherwise,
service providers choose the type of treatment scenario
based on non-therapeutic factors, such as financial incentives [30]. In this study, we tested the possibility of PID for
KRS in the Iranian health system by two separate econometrics models.
Both analytical models obtained the elasticity of number
of KRS variable (˙nit ) as positive and less than one. This
value was 0.71 and 0.94 in the OLS, and GMM-SYS approaches, respectively. With the increase in the surgeons’
density, the number of performed surgeries has also increased significantly at a 1 % level. This finding indicates
demand rationing so that the average number of performed KRS by each surgeon has increased over time.
The service size variable (sit ) elasticity in both static
and dynamic equations was obtained as 0.79 and 1.01,
respectively. These values were higher than the elasticity
obtained for the nit variable. These positive differences
are confirmed by specification models (3) and (6) for the
services relative value factor in (Table 3). Generally, the
outcomes show that with increasing orthopedic surgeons
to population ratio, both the number and size of KRS
services were increased significantly at 1 % level. The
positive elasticity associated with the service number
variable may be due to the availability effect, but the increase in the service size certainly cannot be related to
this issue.
Therefore, the researchers conclude that even if we attribute the increased number of surgeries performed per
surgeon to the availability effect, we still have a PID for
the KRS in Iran, at least as much as the elasticity obtained for the relative value of the service (f it ). If we
accept the GMM-SYS model as an appropriate approach, the PID is about 6 %.
Alinia et al. BMC Health Services Research
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Table 3 Estimates of OLS-Pool and Two-Step Difference GMM models
Independent
Variables
OLS-Pool
˙yit ¼ ˙nit
(1)
˙yit ¼ ˙sit
(2)
˙yit ¼ ˙f it
(3)
˙yit ¼ ˙nit
(4)
˙yit ¼ ˙sit
(5)
˙yit ¼ ˙f it
(6)
˙dit )SE(
0.71a (0.03)
0.79a (0.03)
0.07 (0.01)
0.94a (0.05)
1.01aa (0.04)
0.06a (0.02)
0.63a (0.02)
0.63a (0.02)
0.68a (0.03)
-0.04a (0.05)
0.07a (0.03)
0.11a (0.05)
0.15 (0.08)
0.09a (0.07)
-0.04 (0.02)
-1.25 (1.88)
0.80 (0.33)
0.54 (0.35)
˙yit
1
)SE(
˙inc )SE(
a
Two-Step Difference GMM
a
a
a
a
Constant
7.96 (1.51)
13.06 (1.69)
4.35 (0.85)
36.18 (32.56)
11.85 (10.96)
0.84a (5.94)
N. observation
823
823
823
823
823
823
R-squared
0.826
0.805
0.473
F-test (p-value)
0.000
0.000
0.036
Wald test
601.34
599.96
8.55
Hansen test
0.361
0.284
0.413
Sargan test
0.277
0.175
0.339
M2 test
0.671
0.589
0.712
a
Indicate that the coefficients are significant at the 1% level
The observed positive association between physician
to population ratio and the number of the KRS service
per physician is contrary to that of Delattre and Dormont [22] which studied the behavior of general and
specialist physicians in France and that of Redisch et al.
[31] that analysis the PID among Unites States general
physicians. However, they presented the same final results; the existence of a significant PID. Carlsen and
Grytten [32], Sorensen and Grytten [23], Grytten and
Sorensen [33] in similar studies did not show an established PID among Norwegian primary care physicians.
These differences may be attributed to the difference in
the health system structure, the type of payment system,
the existence of a fixed or flexible reimbursement fee,
and the type of services studied. In those health systems
with managed care, apply flexible fees and use the FeeFor-Service method to reimburse the providers, creating
a PID is much higher.
Iranian health insurances cover up to 90 % of the inpatient services costs, such as KRS in the public sector,
but are highly inefficient in the private sector. Therefore,
the financial burden of this PID will be on both the
buyers and the recipients of the service. It should be
noted that the value of 6 % is the minimum PID for KRS
and may be much higher, which this study is not able to
show its actual value. It seems that the main reasons for
the existence of PID for KRS service in Iran are the lack
of a managed care system, not using the clinical guidelines, lack of adequate supervision of surgeons, the existence of Fee-For-Service payment system, and severe
information asymmetry between the health insurances
and the service providers. Therefore, to reduce the
sizeable economic burden of PID on the health system,
each of the above reasons should be considered and
modified by the health policymakers. Besides, we
emphasize the need for new strategies to treat earlystage osteoarthritis, ultimately reducing the demand for
surgery.
The study data indicates unequal access to orthopedic
surgeons across the provinces. On average, there are
1.16 (SD: 1.08) surgeons with a median of 0.98 (IQR:
0.80) per 100,000 population. This is while the lowest
and highest values of this statistic belong to Lorestan
(6.11) and North Khorasan (0.33) provinces, respectively,
which have a difference of 18.52 times. Reducing this
unequal distribution to provide better access for less privileged provinces can be very effective and should be
considered by health policymakers.
Conclusions
This is the first time that the existence of PID in the
Iranian health system is investigated using approved
econometric models. Although the increase in the ratio of orthopedic surgeons to the population increased
patients’ access to KRS services, the econometrics
results show the mean of service size had increased
to a higher degree over time. This finding strongly
supports the existence of PID for aggressive, costly,
and high-risk services such as KRS. In other words, a
significant part of the increased demand for KSR
services was for PID.
Due to the high coverage of KRD service costs by the
Armed Forces Insurance in both the private and public
sectors, the financial burden of this induced demand is
imposed on the insurer. Therefore, health policymakers
can minimize this induced demand by setting stricter
criteria for KRS licensing and requiring physicians to adhere to the relevant clinical guideline.
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Abbreviations
PID: Physician-Induced Demand; GMM-SYS: Generalized method of
moment’s system; KRS: The Knee Replacement Surgery; DPD: Dynamic panel
data; SE: Standard Error; IQR: Interquartile Range
Acknowledgements
We thank all the staff from Armed Forces Insurance Organization for
assistance during data collection.
Authors' contributions
CA, AT, AO conceived the idea of the study, CA and AT contributed to the
study design, CA performed the statistical analysis, CA, HY, BP, and NS took
part in the interpretation of the results and CA, AT, AO, HY, NS, and BP
critically revised manuscript drafts. All authors read and approved the final
version of the manuscript.
5.
6.
7.
8.
9.
10.
Funding
This research was supported by the Deputy of research and technology of
Urmia University of Medical Sciences Grant Number IR.UMSU.REC.1398.022.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not
publicly available for confidentiality reasons since individual privacy could be
compromised but are available from the corresponding author on
reasonable request.
11.
12.
13.
Declarations
14.
Ethics approval and consent to participate
This study protocol was approved by Research Ethics Committee at the
health equity research center, Tehran University of Medical Sciences and
National Institute for Medical Research Development (No. NIMA
D.REC.1397.290) and was found to comply with ethical standards. This study
was accordance with the 1964 Helsinki declaration and its later amendments
or comparable ethical standards.
15.
Consent for publication
Not applicable.
18.
Competing interests
The authors declare that they have no competing interests.
19.
16.
17.
20.
Author details
1
Department of Health Economics and Management, School of Public
Health, Urmia University of Medical Sciences, Urmia, Iran. 2Health Equity
Research Center (HERC), Tehran University of Medical Sciences, Tehran, Iran.
3
Department of Global Health and Public Policy, School of Public Health,
Tehran University of Medical Sciences, Tehran, Iran. 4Department of Health
Management and Economics, School of Public Health, Tehran University of
Medical Sciences, Tehran, Iran. 5Health Insurance Research Center, Armed
Forces Medical Service Insurance Organization (AFMSIO), Tehran, Iran. 6Social
Determinants of Health Research Center, Research Institute for Health
Development, Kurdistan University of Medical Sciences, Sanandaj, Iran.
7
Department of Health Economics, National Institute for Health Research
(NIHR), Tehran University of Medical Sciences, Tehran, Iran.
21.
22.
23.
24.
25.
26.
Received: 8 May 2021 Accepted: 25 June 2021
27.
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