SEU Health & Medical Public Healthcare Facilities in KSA Discussion

Live Session
Module 3
HCM515
Health Law and Ethics
Instructor Name
Module 3 Learning Outcomes
1. Describe the legal structure of healthcare
organizations.
2. Analyze different types of healthcare organizations
and structures from a legal and ethical perspective.
3. Analyze the impact of regulatory, legal, political, and
ethical environments on hospital/healthcare
governance.
HCM515: Health Law and Ethics
What is a Health System?
HCM515: Health Law and Ethics
Healthcare Structures in Saudi Arabia
HCM515: Health Law and Ethics
A deeper look at healthcare structures
• Limited Liability Company
• Joint Stock Company
• Corporation
• Partnership
HCM515: Health Law and Ethics
The Kingdom of Saudi Arabia
HCM515: Health Law and Ethics
The 2019 Budget
• The healthcare sector holds the third largest share
• Healthcare is 15.6% of the budget
• Increase of 8% from last year.
HCM515: Health Law and Ethics
National Transformation Program
• Initiated to implement Vision 2030
• Identified key targets
• Emphasis on the private healthcare market
• Looking at public-private partnerships to disburse financing of
projects.
HCM515: Health Law and Ethics
NTP Targets
What does KSA need to do to meet NTP targets and Saudi
Vision 2030 goals?
HCM515: Health Law and Ethics
Types of Hospital Public-Private Partnerships
PPP Category
Regulatory Mechanism
Definition
Services
Contract
A private entity contracts with a public facility to
operate and deliver healthcare that is funded by
government.
Infrastructure
Joint venture
A private entity designs, builds, and finances
hospital facilities. The financing generally has a
term, but it can be years after the facility has
been built. Note: Public employees continue to
provide the services.
Combination
Joint Venture
A private entity designs, builds and finances the
hospital facility, and also has healthcare workers
that provide services.
(Source: Hellowell, 2019)
HCM515: Health Law and Ethics
Public-Private Partnership Advantages
Some of the advantages of public-private partnerships include:
• Increased collaboration
• Access to greater contracting options
• Standard practices across multiple countries
• Greater view of relevant data sources and portals for future work
• Pre-competitive collaboration
(National Academies of Sciences Engineering Medicine, 2016)
HCM515: Health Law and Ethics
Public-Private Partnerships Issues
• Outcomes are dependent on a well-drafted contract
• Specific processes for performance measurement must be
implemented
• Penalties for failure to meet performance goals
• Generation of short-term capital that causes overinvestment in PPPs
HCM515: Health Law and Ethics
Public-Private Partnership Draft Law in Saudi
Arabia
• Article 12: Government Financial Support for PPP Projects
• Article 13: Rights of The Private Party
• Article 28: Duration of the PPP Contract
• Article 49: The Law of Real Estate Ownership and Investment by NonSaudis
• Article 50: The Labor Law
• Article 56: Ownership of Healthcare Companies
HCM515: Health Law and Ethics
Effect of Governance Structures and Internal
Regulation in the Kingdom of Saudi Arabia
• Distinguishes nonprofit companies from charitable associations and
institutions.
• Nonprofit companies will help reinforce the corporate social
responsibility initiatives of private companies.
• Now that you have a basic understanding of public versus private,
and nonprofit and for-profit structures in healthcare, how can these
entities work together?
HCM515: Health Law and Ethics
Module 3 Assignment Requirements
• Assignment: Case Study – New Hospital
Proposal
o Describe and assess each international entity and
rules it must follow.
o Compare and contrast the three types of facilities,
including legal and ethical aspects for each.
o Argue your recommendation for your chosen
structure that would best serve the needs of your
community.
HCM515: Health Law and Ethics
References
• Colliers. (2018). Kingdom of Saudi Arabia Healthcare Overview 2018. Retrieved from
https://www.colliers.com/-/media/files/emea/uae/case-studies/2018-overview/ksahealthcare-overview-thepulse-8th-edition.pdf?la=en-gb
• Global Health Exhibition. (2019). 2019 Saudi Arabia healthcare industry overview: Towards
the healthcare goals of Saudi Vision 2030. Retrieved from
https://www.globalhealthsaudi.com/content/dam/Informa/globalhealthsaudi/downloads/
GHE19-KSA-HEALTHCARE-INDUSTRY-OVERVIEW.pdf
• Hellowell, M. (2019). Are public-private partnerships the future of healthcare delivery in
sub-Sharan Africa? Lessons from Lesotho. BMJ Global Health, 4(2). Retrieved from
https://gh.bmj.com/content/4/2/e001217
• Jones Day. (2015). Saudi Arabia: New Companies Law 2015 approved. Retrieved from
http://www.jonesday.com/saudi-arabia-new-companies-law-2015-approved-11-20-2015/
• Mahayni, Z. (2016). Saudi Arabia: The draft Saudi Arabian Non-Profit Companies Law.
Retrieved from
http://www.mondaq.com/saudiarabia/x/488884/Corporate+Governance/The+Draft+Saudi
+Arabian+NonProfit+Companies+Law
• National Academies of Sciences Engineering Medicine (2016, July 11). Public-Private
Partnerships as Driving Forces for Innovative Treatments and Research Policies. Retrieved
from
http://nationalacademies.org/hmd/Activities/PublicHealth/StrategiestoImproveCardiacArr
estSurvival/2016-JUL-11/Day%201-Videos/Session%201%20Videos/11-Hughes-Video.aspx
HCM515: Health Law and Ethics
Questions
Take advantage of this opportunity
to seek further clarification.
HCM515: Health Law and Ethics
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HCM515: Health Law and Ethics
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INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
2021, VOL. 14, NO. 4, 1303–1310
https://doi.org/10.1080/20479700.2020.1757875
Application of discrete event simulation for performance evaluation in private
healthcare: The case of a radiology department
Mwafak Shakoor a, Mohamed Raﬁk Qureshi
Moayyad Al-Nasrae
b
, Wisam Abu Jadayil
c
, Nasser Jaber
d
and
a
Mechanical Engineering Department, American University of Madaba, Madaba, Jordan; bIndustrial Engineering Department, King Khalid
University, Abha, Saudi Arabia; cIndustrial Engineering and Engineering Management, Abu Dhabi University, Abu Dhabi, UAE; dDepartment
of Mechanical and Industrial Engineering, American University of Ras Kaimah, Ras Kaimah, UAE; eDepartment of Civil and Infrastructure
Engineering, American University of Ras Kaimah, Ras Kaimah, UAE
ABSTRACT
ARTICLE HISTORY
Radiology departments face tremendous challenges to meet speciﬁc service requirements and
reach an acceptable level of patients’ satisfaction. This requires dedicated management
attention and eﬀective decision-making process. It is of utmost importance for the
management to evaluate the eﬀectiveness of the applied strategies in comparison with the
proposed strategies for the performance evaluation of resources. The main goal of this study
is to evaluate the eﬀectiveness of the applied and proposed management strategies for
managing resources in a radiology department of private healthcare facilities. Data were
collected regarding the service time and arrival rates. A model is constructed on the Arena
simulation software to investigate diﬀerent strategies of management in a private healthcare
facility. The study evidenced that the newly applied strategy is more eﬀective compared to
the old one, but there is room for improvement in the services provided to treat all patients
on the same day upon arrival with no rescheduled patients. The study demonstrated that
the proposed strategy will enable the healthcare facility to treat all patients on the same day
upon arrival with no rescheduled patients. In addition to that, the study revealed that the
management of private healthcare facilities in Saudi Arabia is more eﬃcient and eﬀective
compared to the management of the public healthcare facilities. The study also
demonstrated how the Arena simulation software can be utilized to evaluate the
eﬀectiveness of the management decision-making process.
Received 10 May 2019
Accepted 31 March 2020
Introduction
Most of the healthcare facilities use radiology as a key
diagnostic medical aid and medical tool which uses
medical scanning capabilities to diagnose and cure diseases within the human body. It is also used as a followup treatment of patients and foretell the consequences
of treatments. Patients suﬀering from a variety of diseases or being subjected to minor or major accidents
need to be investigated through one or more of the
radiology imaging machines. One of the powerful scanning machines with imagining capabilities that provide
precise and detailed images, which facilitate the
doctor’s work in diagnosing diseases, is the magnetic
resonance imaging (MRI) machine. Nowadays the
radiology machines are extensively used in healthcare
facilities. The waiting times in assessing this service
pose lots of challenges to patients. Moreover, the
delay in obtaining such services in a timely manner
causes a signiﬁcant delay in medical treatment. Consequently, the performance of the radiology department
is of great signiﬁcance due to the high rate of demand.
This also can be looked at as a patient’s health issue. To
avoid patient overcrowding at the radiology divisions
CONTACT Mwafak Shakoor
mmshakoor@yahoo.com
© 2020 Informa UK Limited, trading as Taylor & Francis Group
KEYWORDS
Discrete event simulation;
healthcare services; magnetic
resonance imaging (MRI);
radiology services
eﬀectiveness; Saudi Arabia;
strategic decision-making
and to improve the customer service at the radiology
department, the healthcare facilities management is
encouraged to evaluate and investigate the performance of these departments. The purpose is to improve
customer satisfaction and to increase the opportunity
of building trust between the customer and the healthcare system. Since the patients’ arrival time and the
patients’ processing time are random, the performance
evaluation can be best achieved by utilizing simulation
software as an assessment tool to build and simulate
the arrival time and the service time of the patients.
The magnetic resonance imaging (MRI) machines
are used as a scanning tool in many diﬀerent healthcare
providers. In both the public and private health care
facilities, there is a high demand rate for the use of
the MRI scanning. This high rate of demand and the
overcrowding of patients in the queue at the imaging
facilities are common problems facing the health care
facility management all over the world. This critical
problem needs serious attention and direct involvement by the healthcare management to serve all of
the patients with a reasonable and healthy waiting
time. This eventually results in improving the quality
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M. SHAKOOR ET AL.
of the provided service and also increases the customers’ satisfaction. Ignoring the patients’ overcrowding
and making them wait too long in the queue at the
radiology departments aggravates the problem and
sacriﬁces the patient’s valuable lives. Also, it signiﬁcantly reduces the customers’ satisfaction and jeopardizes losing the patient as a customer. In summary,
improving the radiology department performance
reduces patient waiting time, increases the throughput
and improves patient satisfaction, which is one of the
major objectives of any healthcare facility management. Any proposed improvement in the radiology
departments of any speciﬁc healthcare organization
has to be supported with proofs and quantitative evidence. Modeling of the existing condition and simulating the patients’ arrival time and service time are
considered eﬀective decision support tools. These
tools are utilized in solving many healthcare issues in
many countries all over the world. Discrete event simulation (DES) is considered an eﬃcient analyzing tool
that has been widely applied in healthcare organizations. It has proved its considerable success in assessing the ineﬃciency of many healthcare systems. This
tool is also used in examining the relationship between
the variables aﬀecting the healthcare systems, in
addition to probing the eﬀect of the probable changes
as well as assessing proposed alternatives for improvement [1].
Hospitals around the world face phases of strategic
renewal to change into ﬂexible organizations that can
oﬀer higher quality services at lower costs. Enhancing
the performance and competence of healthcare services
taking into consideration cost constraints and quality
concerns are the prime challenge for management in
healthcare facilities [2, 3]. Eﬀective managerial involvement and intervention in taking the right decisions
improve the patients’ waiting time and decrease the
number of patients waiting in the queue of the healthcare facility. This aﬀects the performance and quality of
the provided services in the healthcare facilities. Therefore, healthcare organizational studies concentrated on
the participation of associational solutions to enhanced
performance [4].
This study investigates the eﬃciency of the decisionmaking strategy employed at the MRI radiology division in one of the largest private healthcare providers
in Saudi Arabia.
The interview with the hospital management
revealed that the old management strategy for the
MRI radiology department in the hospital was to schedule the department to open for six hours a day,
speciﬁcally from 9:00 am to 12:00 pm and 4:00 pm to
7:00 pm. That increased the number of patients waiting
for imaging services, increased the patients waiting
time, increased patients’ complain and reduced the
patients’ satisfaction. Also, the old strategy at the hospital reduced the overall revenue. To provide the same
day imaging service to a larger number of patients, the
hospital management adopted another diﬀerent strategy. This strategy reduced the patients’ waiting times
and enhanced customers’ satisfaction. The hospital
management decided to schedule the MRI imaging
department for two shifts successively, with no breaks
from 9:00 am till 10:00 pm over 6 days per week. The
ﬁrst shift is from 9:00 am till 5:00 pm while the second
shift is scheduled from 5:00 pm till 10:00 pm.
The study group constructed a discrete event simulation model using Arena simulation software to investigate the eﬀect of the new applied decision-making
strategy and compare its results with a proposed management strategy. The proposed management strategy
takes into consideration the eﬀect of adding three
break periods during the two working shifts and
extending the working time of the second shift by 1
hour on the patients’ waiting time and the chances of
the department to scan all the patients at the same day.
Literature review
The discrete event simulation (DES) is deﬁned as a
technique that imitates the processes of real or
suggested systems as it changes over time through
the construction of models on a computer. The event
variables in a discrete event simulation evolve only at
speciﬁc discontinuous and particular points in time
[5–7]. DES found its origin in the industrial engineering and operations research ﬁelds to facilitate the
analysis and improve the productivity of commercial
systems. Afterward, DES became a popular and eﬀective tool in the analysis and decision-making operations that facilitate and enhance management tasks.
Based on that, healthcare managers adopted discrete
event simulation in the analysis of diﬀerent suggested
alternatives for the improvement of the performance
of healthcare facilities. Healthcare managers applied
DES for improving the patients’ ﬂow in the organization, planning for the required optimum resources,
increasing customer loyalty and satisfaction, and optimizing the facility cost and proﬁt.
DES has been widely used in healthcare applications
and its use has been increased over the last 40 years [8].
The application of DES was mainly in the analysis of
healthcare systems to reduce waiting times, increase
productivity and improving the delivered services of
healthcare organizations. Discrete event simulation
has been applied in diﬀerent healthcare facilities such
as hospitals [9–12], emergency departments [13–18],
special units [19–21], intensive care units [22–24], surgical procedures [25–28], outpatient clinics [29–33],
radiology departments [34, 35], and facilities allocated
in the healthcare supply chain [36, 37].
Modeling and simulation have a cutting edge of permitting designers to construct models on computers
for the facilities under investigation to assist the
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
managers in the decision-making process. The
designers applied simulation in the analysis of a large
number of service provider facilities such as medical
management to explore various proposed strategies
and scenarios in a complex environment and to select
the optimum strategy. The simulation process may
yield in vain if speciﬁc procedures are not followed
by the designer. Common steps for eﬀective simulation
through DES study were proposed by diﬀerent
researchers [5, 6]. The suggested standard steps include
the formulation of the problem, overall study schedule,
design conceptualization, data collection, translation of
model, model veriﬁcation and validation, design of an
experiment, production run and analysis, documentation, and application. The key steps of a successful
simulation study include the formulation of the problem, the collection of data, and model translation.
Therefore, it is of utmost importance to conduct modeling studies with great caution and care. Failing to do
so may result in the loss of precious lives. The designer
may follow the basic tutorial suggested by Mahachek
[38] for running a discrete event simulation study in
a healthcare setup.
Methodology
This study is conducted at an MRI division of the radiology department in a private, proﬁt-based hospital
and managed by the private sector in Saudi Arabia.
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Arena simulation software is employed for developing an eﬀective simulation model to investigate three
diﬀerent strategies including (1) investigating the existing situation with a new applied strategy for working
over two continuous shifts during 6 days per week
period, (2) investigating the old strategy for working
with only one shift, and (3) investigating the current
situation with a proposed strategy for working over
two continuous shifts during 6 days per week with
two break periods for the ﬁrst shift and one break
period during the second shift to accommodate the
personnel needs of the administrative staﬀ and technicians in the MRI radiology room. Two break periods
of 15 min each in the ﬁrst shift and one break of 20 min
in the second shift will be investigated in the proposed
strategy. It is found that the patients who arrive at the
MRI department enter a single queue due to a fact that
only one MRI imaging machine is in service at the
department. First-in, ﬁrst-served (FIFS) policy is normally used at the present facility, with some exception
in case of emergency. The emergency status is given to
patients based on a case by case evaluation.
More accurate output results can be achieved by
running the simulation model for a longer time [40].
This can create more trust in the outcome results of
the model. It is recommended to run the simulation
model as large as possible for ensuring at least 1000
incidents [40]. Hence, the designed model was
remained executing for 312 days with 100 replications
and the output results of the model were averaged by
Arena modeling package.
Simulation model
Simulation is deemed to be one of the most widely used
tools in research as an aid technique in the decisionmaking process. The concept is based on probing and
analyzing diﬀerent processes of a highly complicated
environment in which a high variability is inherent in
the customers’ arrival time and processing rate [39].
A modeling design is developed for the MRI room to
understand the behavior of the radiology system and
examine the eﬀectiveness of diﬀerent strategies and
scenarios applied in the radiology room. The purpose
is to increase the probability of the same day scanning
for all patients upon their arrivals and to improve the
decision-making process in such a complex environment. Also, the purpose is to reduce the number of
balking and no shows for the patients who are scheduled for scanning service on the following days. The
simulation model investigates the possibility of
improving the patients’ ﬂow in the department to
reduce the number of customers balking to zero
which means that all of the patients can be scanned
on the same day. Achieving this goal will improve the
patients’ satisfaction, increase the number of returned
patients to the hospital and consequently increase the
hospital revenue.
Data collection
Building up an eﬀective and rigorous simulation model
is one of the most critical steps that lead to meaningful
results. Also collecting relevant and most needed data
to construct an eﬀective model is the most signiﬁcant
and diﬃcult step [6]. This data controls the precision
of model outcomes leading to conclusions of high
value. Hence, the data plays an important role that
can produce well-designed outcomes.
The data in this study was collected by three members, who worked under the supervision of administrative staﬀ, as well as an imaging technician at an MRI
testing room. The administrative staﬀ participated in
recording the arrival time of the patients at the MRI
room. The technician participated in recording the
starting time and the ending time of the imaging process of the patient under consideration. The role of
the members concentrated on the data collection process by recording the arrival time of the patients, the
start time of the imaging process, the ﬁnish time of
the imaging process, and the departure time out of
the MRI room. The data related to the number of
patients arrival time at the MRI testing room has
been recovered from the hospital records. This data
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M. SHAKOOR ET AL.
Figure 1. Patients processing time at the MRI room.
covers 5 months. The data related to the processing
time has been collected by the study personnel over a
month. Figure 1 represents a sample of the patient’s
processing time in the MRI room. There is a randomness in the patient’s processing times as illustrated by
Figure 1.
The data that has been retrieved from the hospital
internal system indicates that 42% of the patients registered and scanned on the same day. The rest, which
constitutes 58%, is scheduled for scanning in the following days with a maximum waiting period of 5
days. The recovered data out of the hospital records
showed that 31% of the rescheduled patients did not
show up. These patients, most likely, received services
by other private hospitals in the same vicinity. This
reduces the potential and much-needed revenue for
the hospital.
The new applied strategy showed that only 12% of
the patients are rescheduled for the following day. A
substantial reduction that mounts up to 46% of the
patients. The study team also retrieved the patients’
waiting time out of the previous data in the radiology
department. The patients waiting time in the imaging
room was calculated as the elapsed time of each patient
starting from the time of the scheduled service at the
imaging room to the beginning time of receiving service. The data exhibited strong evidence demonstrating
that the new applied strategy reduced the waiting
period at the MRI room and enhanced the quality of
service provided.
It has been observed during the data collection process that only one MRI imaging machine is utilized in
the radiology department. Also, the study team conducted a direct interview with randomly selected
patients at the radiology department regarding the
quality of the provided service at the department.
This task is given to the study team in addition to the
collection of other related data. Furthermore, the
study team conducted a direct interview of the onduty radiology technicians at the MRI testing room
during the two working shifts to explore the types of
problems facing the technicians at that department.
Eﬀective and appropriate statistical distributions for
the inter-arrival and service rates have been selected
Figure 2. Sample of patients’ arrival pattern at MRI room per
day.
using a commercial software package. The gathered
empirical data from the radiology department were
entered and the software automatically generated the
graphical and statistical reports and recommending
the best-ﬁtting distributions. The collected data from
the radiology department showed that the patients’
processing time is according to the triangular probability distribution function while the arrival rate of
the patients in the MRI room is according to the exponential probability distribution function. Figure 2 represents a sample of the number of patients arrived at
the radiology room per day and Figure 3 explains the
probability statistical distribution that ﬁts the patients’
arrival rate at the MRI radiology room as generated by
the commercial software.
Model building
A number of assumptions were made in the model
building process:
1. The system at the MRI radiology department runs
over 6 days per week; Saturday till Thursday.
2. The system runs continuously over two-shifts
period; 9:00 am till 10:00 pm.
3. Patients’ delay for the walking distance from the
waiting room to the imaging room was added to
the imaging processing time.
4. The time elapsed in undress and dress activity was
added in the scanning processing time.
Model veriﬁcation
The simulation model is a simpliﬁed representation of
a real system under study. Therefore, it is seldom that
the model would be a true representation and capture
all of the characteristics of the real system due to variations between the system behavior and the statistical
distributions that ﬁts the arrival rate and processing
times and in the variations between staﬀ qualities and
capabilities. Therefore; it is rare that the models will
always foretell accurately the performance of the
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
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Figure 3. Probability statistical distribution that ﬁts the arrival rate at radiology room.
system [41]. Consequently; the main goal of model
builders is to build a correct model.
Model veriﬁcation is deﬁned as the process of determining that conceptual description and speciﬁcations
were implemented correctly and accurately during
building the model [42] to construct a robust, accurate
and credible model. In this study, the basic dynamic
animation provided by Arena simulation software is
enabled for the correct determination of model construction. Therefore, observing the model animation
simpliﬁed the veriﬁcation process of model logical
operations in the radiology room which assured the
existence of all scanning resources and processes in
the model and conﬁrmed the duplication of the real
system.
Simulation results and discussion
The interview conducted by the study team revealed
several complaints by the MRI radiology staﬀ and technicians working at the hospital about the lack of breaks
during the working shift. The staﬀ and the technicians
justiﬁed the need for the break as a personal need for
time out. The other complaints were directed toward
being an overworked and energy-draining job. Also,
the machine in service is relatively small and hard to
work with compared with bigger and more advanced
machines available at the public hospitals.
The hospital administration used to run the radiology department with an old strategy. The administration noticed that this strategy is not eﬀective in
managing the MRI scanning room. Therefore, the
administration replaced the old strategy with a current
one based on working for two shifts during the day for
6 days per week.
The simulation output results of the designed model
on Arena simulation software showed that the average
waiting time and average total time in the system of the
current situation are 56 and 84 min respectively if the
old strategy is kept in place for managing the MRI testing room as shown in Table 1. These results indicated
that the current situation will be even worse by managing the department with the old strategy. The patients’
waiting time will get longer and it will become inconvenient. Also, the appointment scheduling time will
be longer and more patients will be rescheduled for
the following days as provided in Table 2. At the
same time, it is expected that more of these rescheduled
patients will not return for service, but rather will seek
service somewhere else in the nearby private healthcare
facilities. This means more potential revenue will be
lost. In addition to that, the simulation output results
revealed that running the MRI scanning room under
the new applied current strategy will improve the performance of the system. The performance measures
provided in Table 1 demonstrated that the average
waiting time and the average total time in the system
dropped to 41 and 66 min respectively compared
with the old strategy. These results proved that there
is an improvement in the average waiting time and
the average total time in the system by 26.8% and
21.4% respectively. In addition to that, the results presented in Table 2 demonstrated that there is an
improvement in the appointment scheduling process
through the application of the current strategy.
Furthermore, the simulation model output results
related to the performance measures presented that
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M. SHAKOOR ET AL.
Table 1. The simulation model output results of investigated strategies for system performance related to the MRI scanning room.
Strategy
Current strategy
Old strategy
Proposed strategy
Break (min)
Minimum
waiting time
(min)
Average
waiting time
(min)
Maximum
waiting time
(min)
Average total
time in the system
(min)
0
MRI room is idle for 240 min (12:00–4:00 pm)
Day shift: 2 breaks @ 15 min Night shift: one break @ 20 min
0
0
0
41
56
37
58
97
45
66
84
57
Table 2. System performance related to the appointment scheduling process of the investigated strategies.
Strategy
Current strategy
Old strategy
Proposed
strategy
Break (min)
0
MRI room is idle for 240 min (12:00–4:00 pm)
Day shift: 2 breaks @ 15 min Night shift: one break @
20 min
running the MRI radiology room under the proposed
strategy will not increase the patients’ waiting time as
demonstrated in Table 1. The performance measures
provided in Table 1 and Table 2 demonstrated that
running the MRI room under the proposed strategy
will lead to an extra reduction in the average waiting
time and average total time in the system compared
with the current applied strategy.
The study team observed during the data collection
period that there are several management problems
related to setting up appointments and scheduling
patients for the imaging process. The quality of service
can be substantially improved by providing positive
changes in the scanning scheduling process. It is also
recommended to replace the MRI scanning machine
with a state-of-the-art machine capable of scanning
the arriving patients in real-time to reduce waiting
time to be minimal.
Limitations
Even though there is a proven capability of the simulation and modeling studies to provide rigorous results
and insights into the healthcare systems, there are several limitations that aﬀect the quality of the outcomes.
The limitations encountered in this study can be summarized as follows: the quality of the collected data
which aﬀects the simulation results, and the lack of
statistical distribution that can ﬁt accurately the arrival
time and the processing rate with minimal signiﬁcant
error.
Conclusion
Healthcare services are expected to be state-of-theart, sustainable, competitive and available for 24 ×
7 × 365. MRI application is used in invasive and
non-invasive medical treatments frequently. It is
being considered to be vital in accumulating diagnostics information that is not provided in other imaging
techniques. MRI plays a vital role in examining the
accidental injury in the neck, spinal cord, human
Minimum waiting time
(min)
Average waiting time
(h)
Maximum waiting time
(days)
0
0
0
2.48
103
0.3
1
7
0
limbs, etc. It is also used in examining the disorder
in the musculoskeletal system resulting due to incorrect body-posture and intensive use of the electronic
gadgets violating ergonomic design. The leaping
changes in lifestyle have become the root cause of
many musculoskeletal disorders (MSD) and diseases
thus need MRI services. Thus non-availability or
overloading of the radiology department will hinder
diagnostic procedure, speedy recovery of patients
suﬀering from MSD and other diseases. The service
eﬀectiveness of the radiology department is of great
signiﬁcance.
The simulation model outcome results related to the
performance measures revealed that the new applied
strategy is eﬀective compared to the old one, but still,
there is room for improvement in the services provided. The model output results showed that there
are a number of the patients scheduled for scanning
in the following days under the application of the current applied strategy. The simulation output results of
the proposed strategy evidenced that the new applied
strategy is not the best strategy for running the MRI
radiology room. The results indicated that the proposed strategy for managing the MRI scanning room
is more eﬀective than the current applied strategy
and it will enable the management to treat all patients
on the same day upon arrival with no rescheduled
patients. Hence the study recommends providing the
suggested break periods to satisfy the staﬀ and technicians’ personal needs.
The study concluded that adding several short
breaks to satisfy the personal needs of the employee
within the long working shifts through the application
of the proposed strategy will not harm the waiting time.
Adding such breaks during the working shift will have
a positive impact on the overall performance of the
employees.
Replacing the existing MRI machine with a state-ofthe-art machine improves the imaging scheduling process at the MRI room and enables the radiology department in the hospital to scan all the patients on the same
day of their arrival. The proposed strategy will improve
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
service quality, customer satisfaction and hospital
revenue.
Shakoor [34] explored the eﬀectiveness of the management system in one of the public and governmental
healthcare providers in the southern region of Saudi
Arabia and demonstrated that the management system
is not eﬀective. The author recommended an urgent
intervention by the hospital management to deploy
managerial policies to improve machine utilization
and reduce the long waiting time in the department.
This study is also conducted in the same city and the
same region but is limited to a private healthcare provider. The major ﬁnding of this study shows that the
management is more eﬀective in the private sectors
compared to the public sectors in Saudi Arabia because
managers in private sectors are subjected to a performance evaluation process that may aﬀect their job security. On the other hand, the managers in the public and
governmental sectors in Saudi Arabia enjoy better job
security. The managers at the public and governmental
sectors, in general, are not subjected to the same scrutiny as their counterparts in the private sectors. Therefore poor performance may not lead to job
termination.
Finally, the present work has demonstrated the
simulation study to investigate and understand the
eﬀectiveness of health care management systems in a
complex environment such as a radiology department
in private and public hospitals. The study demonstrated how simulation software can be helpful to better understand the system under consideration.
Moreover, the simulation software may be utilized to
evaluate service eﬀectiveness and strategic decisionmaking.
Implications for healthcare management
practice
This paper demonstrates the use of discrete event
simulation in strategic planning to investigate prevailing and proposed strategies for enhanced performance.
The illustrated application of simulation and modeling
in the radiology service center assists decision-makers
(DMs) in evaluating the performance of various
suggested strategies. It helps DMs to identify the optimum strategy based on scientiﬁc evidence rather than a
personal opinion without disturbing the system. The
derived results indicate that simulation can successfully
identify the optimum strategy among the set of available strategies without disturbing the prevailing system
at minimal or no cost. On improving the system
eﬃciency, resources can be utilized in an optimum
manner. Moreover, the patient will be more satisﬁed
when the waiting time in the system is reduced. The
proposed methodology may be extended to other
areas where value-added services or improved
eﬃciency are essential.
1309
Disclosure statement
No potential conﬂict of interest was reported by the
author(s).
ORCID
Mwafak Shakoor http://orcid.org/0000-0001-8353-7249
http://orcid.org/0000-0002Mohamed Raﬁk Qureshi
9508-8724
http://orcid.org/0000-0002-8858Wisam Abu Jadayil
6442
Nasser Jaber http://orcid.org/0000-0002-3738-3717
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Dennis et al. International Journal for Equity in Health
https://doi.org/10.1186/s12939-020-1150-8
(2020) 19:35
RESEARCH
Open Access
Examining user fee reductions in public
primary healthcare facilities in Kenya,
1997–2012: effects on the use and content
of antenatal care
Mardieh L. Dennis1* , Lenka Benova1,2, Catherine Goodman3, Edwine Barasa4,5, Timothy Abuya6 and
Oona M. R. Campbell1
Abstract
Background: In 2004, The Kenyan government removed user fees in public dispensaries and health centers and
replaced them with registration charges of 10 and 20 Kenyan shillings (2004 $US 0.13 and $0.25), respectively. This
was termed the 10/20 policy. We examined the effect of this policy on the coverage, timing, source, and content of
antenatal care (ANC), and the equity in these outcomes.
Methods: Data from the 2003, 2008/9 and 2014 Kenya Demographic and Health Surveys were pooled to
investigate women’s ANC care-seeking. We conducted an interrupted time series analysis to assess the impact of
the 10/20 policy on the levels of and trends in coverage for 4+ ANC contacts among all women; early ANC
initiation and use of public facility-based care among 1+ ANC users; and use of public primary care facilities and
receipt of good content, or quality, of ANC among users of public facilities. All analyses were conducted at the
population level and separately for women with higher and lower household wealth.
Results: The policy had positive effects on use of 4+ ANC among both better-off and worse-off women. Among
users of 1+ ANC, the 10/20 policy had positive effects on early ANC initiation at the population-level and among
better-off women, but not among the worse-off. The policy was associated with reduced use of public facilitybased ANC among better-off women. Among worse-off users of public facility-based ANC, the 10/20 policy was
associated with reduced use of primary care facilities and increased content of ANC.
Conclusions: This study highlights mixed findings on the impact of the 10/20 policy on ANC service-seeking and
content of care. Given the reduced use of public facilities among the better-off and of primary care facilities among
the worse-off, this research also brings into question the mechanisms through which the policy achieved any
benefits and whether reducing user fees is sufficient for equitably increasing healthcare access.
Keywords: Universal healthcare coverage, User fees, Antenatal care, Maternal health, Kenya
* Correspondence: mardieh.dennis@lshtm.ac.uk
1
Faculty of Epidemiology & Population Health, London School of Hygiene &
Tropical Medicine, London, UK
Full list of author information is available at the end of the article
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Page 2 of 13
Background
In the decades since the widespread African independence movements of the mid-1900s, countries in subSaharan Africa have struggled to develop economically
sustainable healthcare financing models that ensure universal coverage of essential health services. Faced with
budgetary constraints and external pressures to both independently finance local healthcare systems and reduce
government spending, many African countries introduced user fees in public sector health facilities in the
late 1980s [1, 2]. Proponents of user fees argued that
these charges would improve efficiency and the quality
of health services by generating revenue to help cover
costs for general operations and the supply and maintenance of health commodities and infrastructure [2].
Others argued that user fees were important for discouraging unwarranted use of care and ensuring that people
attach value to health services [3].
In reality, as user fees were being introduced widely
across African countries from the late 1980s to 1990s,
emerging evidence during that same period raised
doubts as to whether the expected benefits of user fees
were always achieved in practice. For example, in settings such as Burkina Faso, the Gambia, Ghana, Kenya,
Lesotho, Mozambique, Niger, Swaziland, Zaire, Zambia,
and Zimbabwe, the introduction or increase of user fees
was immediately followed by reduced care-seeking in
public sector health facilities [4–13]. Also, contrary to
expectations, available evidence at that time suggested
that unwarranted health service use comprised a small
proportion of the cases contributing to reduced service
volumes [11]. Research from Kenya, Lesotho, and
Swaziland further suggested that introducing or increasing fees in public facilities sometimes shifted patients
away from the public sector and into the private sector,
rather than decreasing overall demand [5, 6, 8]. Studies
on health service cost recovery from several countries in
Africa revealed that while user fees did generate revenue,
often this was low and insufficient for making impactful
investments in quality improvement [4, 6, 11, 14, 15].
Further, evidence from countries such as Ghana, Kenya,
and Zimbabwe suggested that inefficient management of
this revenue also inhibited user fees from translating into
large improvements in quality of care [4, 10, 14].
Kenya, similarly to these other African countries, has
struggled to develop a health financing system that sustainably and equitably increases access to good quality
care while ensuring that its citizens have financial risk
protection from the hardship that may result from outof-pocket healthcare payments. Kenya’s public health
system is organized into six levels ranging from
community-based care (level 1) to tertiary hospitals
(level 6) [16, 17]. Level 1 consists of health promotion
and awareness-raising activities at the community level;
levels 2–3 include primary health care facilities, including dispensaries and health centers; and levels 4–6 include county and national referral hospitals [16, 17].
Since introducing user charges in 1989 for the first
time after independence, Kenya has implemented a
series of user fee removals, re-introductions, and reductions, sometimes targeting specific levels of care (Fig. 1)
[18, 19]. Although these user fees were introduced in
conjunction with a waiver system for fee exemptions
based on ability to pay, there were concerns about the
negative impact of the user fees on access to health services among the poor. This led to fees being suspended
in 1990 and subsequently re-introduced in phases between 1991 and 1992, with a stronger focus on ensuring
that the user fee policy and fee waiver system were implemented properly [12, 18]. In 2003, the Kenyan government developed an economic recovery strategy that
declared that investing in a healthy population, and in
particular the poor, was a necessity for accelerating economic growth [20]. Within this context, Kenya’s Minister of Health in 2004 declared that user fees were to be
eliminated in public primary healthcare facilities (health
centers and dispensaries), effective 1 July 2004, and instead replaced with nominal registration charges of 10
Kenyan shillings (KSh) in dispensaries and KSh20 in
health centers (2004 US$0.13 and 0.25). Under this 10/
20 policy, certain groups and services were exempted
from any payment, including the poor, children below 5
years, and those seeking treatment for malaria and tuberculosis [21]. While multiple reports indicate that
pregnant women seeking antenatal care (ANC) were also
intended to be exempted from any payment under the
10/20 policy, this may not have been implemented consistently in practice [22–24]. In 2007, the government
also announced that women seeking facility-based childbirth care would be exempt from paying the 10/20 registration fees [21]. Most recently, in 2013, the Kenyan
government removed user fees for all services provided
in public health centers and dispensaries, and introduced
free maternity services in public facilities at all levels
from primary to tertiary [25], policies which both stand
to this day.
While a few studies have examined the short-term impact of the 10/20 policy, there is little evidence of the
long-term effects of the policy leading up to Kenya’s
2013 health sector financing reforms. An evaluation conducted shortly after the 10/20 policy was introduced in
2004 suggested that public health centers and dispensaries experienced a sharp increase in patient volumes in
the months immediately following the policy change [21,
22]. The rate of increase in patient numbers eventually
declined, but the patient volumes remained higher than
those seen before the policy [21, 22]. A more recent
study of the long-term population-level effects of the 10/
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Page 3 of 13
Fig. 1 Timeline of public health facility user fee reforms in Kenya
20 policy on women’s source of childbirth care by Obare
and colleagues found that the 2004 policy did not increase the proportion of women delivering in public sector facilities or the change in public facility deliveries
over time; instead the policy was associated with an immediate increase in the proportion of poor women who
delivered outside of a health facility [26]. Further, the
study found that after the removal of 10/20 registration
fees for childbirth care in public health centers and dispensaries in 2007, there was an immediate increase in
the use of public facility-based childbirth care and decrease in non-facility births among the wealthiest
women, but no change in childbirth service-seeking
among the poorest women.
As the government of Kenya continues to develop
their health financing mechanisms for maternal health,
it is critical to understand the long-term effects of past
reforms and identify strategies for ensuring that current
and future financing policies have lasting impact. Given
the strong link between ANC and subsequent use of
intrapartum and postpartum maternal health services
[27–34], it is important to investigate the relationship
between the implementation of the 10/20 policy and
women’s experiences during pregnancy, and whether
this may help explain why the policy did not increase
coverage of facility deliveries, particularly among the
poor. Additionally, studying ANC allows us to examine
the effect of the policy on multiple dimensions of service
use beyond coverage, including number and timing of
visits, type of provider, and content of care. The objective of this paper is therefore to examine if the introduction of the 2004 10/20 policy was associated with any
changes in ANC care-seeking practices and quality of
care, as measured by the content of ANC. Specifically,
this study assesses if the removal of user fees and introduction of the 10/20 registration charge policy was associated with increases in frequency of ANC visits, early
ANC initiation, and use of public sector ANC services.
As the 10/20 policy specifically targeted public primary
care facilities, we also examined whether there was a
shift from secondary and tertiary healthcare facilities
(hospitals) towards lower-level facilities among users of
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Page 4 of 13
public sector care. Additionally, we investigated whether
any such evidence of increased use of ANC services was
accompanied by reduced content of care, resulting from
higher demand on public health services. Lastly, as the
policy was intended to ensure that the most vulnerable
could access essential care, we explored whether any observed changes in service-seeking and content of care associated with the 10/20 policy were equitable between
better-off and worse-off women.
categorized ANC users into two categories: any public
sector facility-based ANC and no public sector facilitybased ANC. We considered facilities owned by the government to be public and all other facilities, including
for-profit, non-profit, and faith-based, to be private. As
women could report receiving ANC from more than one
location, we considered any public sector facility-based
ANC to include (a) women who received ANC exclusively from a public health facility and (b) women who
received care both in a public health facility as well as in
a private facility or at home/other location. We categorized women who received care exclusively in a private
facility and/or exclusively at home or another location as
having received no public sector facility-based ANC.
Among users of public sector facility-based ANC, we
investigated whether there were any changes in facility
level and content of care. With regard to level of care,
we examined the distribution of women who sought care
in public primary care facilities (dispensaries or health
centers) versus public secondary and tertiary facilities
(hospitals). In terms of content of care, we examined six
components of ANC routinely assessed in the DHS
questionnaires: (1) blood pressure measured; (2) urine
sample taken; (3) blood sample taken; (4) received tetanus injection; (5) given iron supplements; and (6) told
about pregnancy complications, at least once during
pregnancy [39]. We considered women who reported receiving all six of these components to have received
good content of care. Although the 10/20 policy specifically targeted public primary care facilities, we were unable to examine the impact of the 10/20 policy on the
content of care received by the subset of women who
accessed care in public primary care facilities due to
small sample sizes in some of the study periods (Additional file 1). We therefore examined content of care
among all users of public facility-based ANC.
In addition to estimating the effects of the 10/20 policy
on the key study outcomes, we conducted stratified analyses to examine whether any observed effects were
equitable between women of different socioeconomic
groups. We defined women’s socioeconomic status using
wealth quintiles based on the household asset indices
derived from the DHS household questionnaire [37]. For
each of the ANC outcomes, we ran the analyses separately among women from the top two (40%) household
wealth quintiles (better-off) and among women in the
bottom three (60%) quintiles (worse-off). We included
tables with the results stratified by urban and rural residence in Additional file 2.
Methods
Data and study population
We used the 2003, 2008/9 and 2014 Kenya Demographic
and Health Survey (DHS) woman’s questionnaire datasets
for this analysis. We excluded earlier surveys (1998, 1993,
1989), as they did not collect information on one or more
of the study’s key outcomes of interest. The 2003 and
2008/9 datasets sampled a total of 8561 and 9057 households, respectively [35, 36]. The 2014 dataset sampled a
total of 36,430 households; of these, one in every two
households was randomly selected to complete a long version of the woman’s questionnaire, and the other half were
administered a shorter woman’s questionnaire [37]. As the
shorter questionnaire did not ask questions related to the
source or content of ANC, we limited our analysis of the
2014 dataset to the 17,409 households in which women
completed the full questionnaire.
All women aged 15–49 years in the included households
were selected for participation in the surveys. Among the
31,380 eligible women interviewed across the three surveys, all 15,230 women who reported having their most
recent live birth with an estimated date of conception before July 2012 were included in this analysis. We used
women’s reports on their most recent live birth rather
than all live births, as the included surveys only asked
questions on ANC for women’s most recent births.
Study outcomes
We examined one indicator of ANC coverage among all
women in the analysis sample: 4+ ANC, defined as the
proportion of women reporting four or more ANC contacts. We did not examine use of 1+ ANC, as this indicator remained above 90% throughout the study period
[179,310,311]. We examined the proportion of women
receiving 4+ ANC because at the start of the pregnancies
included in this analysis (2012 and earlier), the World
Health Organization (WHO) was still recommending
that women should make a minimum of four ANC visits
during pregnancy, though they subsequently increased
to a minimum of eight visits [38].
Among users of 1+ ANC, we examined timing of
ANC initiation and source of care. We defined early
ANC as ANC users who had their first visit during the
first 3 months of their pregnancy. For source of care, we
Statistical analysis
We conducted an interrupted time series analysis using
segmented linear regression models to assess the impact
of the introduction of the 10/20 policy in 2004 on the
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Page 5 of 13
study outcomes. As this study aimed to examine
whether the 10/20 policy influenced timing of ANC initiation, measured from the start of pregnancy, and subsequent use of ANC, we categorized each woman’s
outcomes into a half-year period according to her estimated time of conception. To set up the data for analysis, we appended the three DHS datasets and estimated
outcomes for each half-year from July 1997 to December
2012. Each half-year estimate was weighted to account
for the multi-stage cluster sampling design of the DHS.
We assumed each birth had a gestational age of 38
weeks, based on a weighted median of the most recent estimates of the distribution of full term and preterm birth
in sub-Saharan Africa [40, 41]. Additional file 3 contains
our calculations for the weighted median gestational age.
To approximate time of conception, we subtracted 38
weeks from the date of each woman’s most recent birth.
Based on these calculations, approximately 2% of women
included in the sample could potentially access ANC services both before and after the 10/20 policy was introduced, as their pregnancies spanned the half-year periods
immediately before and after the policy change. Our analysis categorized women according to when their pregnancy began; thus, this 2% sub-sample was treated as if
they received care before the policy change.
For each model, we tested for evidence of the impact
of the 2004 10/20 policy introduction on the study outcomes. As there are too few data points after the introduction of the free maternity services policy in June
2013 to examine its impact, our analysis excludes births
that were conceived in the half-years beginning July
2012 and later. We tested the data for autocorrelation
using the Cumby-Huizinga test and identified evidence
of serial autocorrelation in even number lags [42]. We
assumed that this was due to seasonality, with observations from one half-year (e.g. January to June of year X)
correlated with observations from two half-years prior
(e.g. January to June of year X-1). We corrected for this
using the Newey estimator with a lag of two [42]. For
the purposes of this analysis, we considered the period
from July 1997 until just before the policy change on 1
July 2004 to be “pre-policy,” (14 half-year periods) and
the period from just after 1 July 2004 through December
2012 to be “post-policy” (17 half-year periods). As the
estimates for each half-year period were derived from
survey data and have different sample sizes and levels of
uncertainty, we weighted our time series analysis by the
inverse of the variance for the estimates at each half-year
period. This means that time points with greater uncertainty around the estimate contributed less to model,
while time points with lower uncertainty contributed
more to the models. Additional file 1 contains a table
listing the sample size for each study population by halfyear. All analyses were conducted in Stata SE version 15.
For each outcome, we reported two measures of the
impact of the 10/20 policy: the immediate change in
level and the immediate change in slope. The immediate
change in level estimates the amount by which the percent of the study population reporting a particular outcome changed immediately after the 10/20 policy was
introduced. The immediate change in slope estimates
the amount by which the change over time in the outcome sped up (accelerated) or slowed down (decelerated) immediately after the 10/20 policy was introduced.
In addition to these measures of the impact of the 10/
20 policy, we also reported on three general estimates of
the level and changes over time in the outcomes: the
pre-policy starting level, the pre-policy half-yearly trend,
and the post-policy half-yearly trend. The pre-policy
starting level is a model-based estimate of the percentage
of the study population reporting the outcome of interest during the first half-year period in the analysis. As
this is a model-based estimate rather than a direct estimate, it was possible for the results to return a point estimate or confidence interval below 0 % or above 100%.
In such cases, we truncated the estimates and confidence
intervals to between zero to 100% to exclude impossible
values. The pre-policy half-yearly trend estimates the
average change over time in the level of the outcome between each six-month period from the first half-year in
the analysis until the period immediately before the 10/
20 policy change. Similarly, the post-policy half-yearly
trend estimates the average change over time in the level
of the outcome between each six-month period after the
10/20 policy. Both of these measures refer to the general
trends over time, rather than the effect of the 10/20 policy on these trends.
We also displayed the outcome measures graphically
in Additional file 4. In the graphs, the x-axis represents
half-year periods. For example, “h1” represents the first
half of the year (January–June) and “h2” represents the
second half of the year (July–December). The lines represent the predicted trend over time in coverage of the
outcome variable. The circles represent the estimated
coverage during a given half-year. The size of each circle
is proportional to the inverse of the variance for the estimated coverage during that half-year period.
Results
Number of ANC visits (4+ ANC)
In contrast to the consistently high percentage of
women making at least one ANC visit during pregnancy,
only 62.3% of women made the recommended minimum
of four ANC visits during pregnancy at the beginning of
the study period (Table 1). The results show that before
the introduction of the 10/20 policy, the proportion of
pregnant women who made 4+ ANC contacts decreased
by approximately 1.2 percentage points every 6 months
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Page 6 of 13
Table 1 Use of 4+ ANC among most recent births
4+ ANC (All women)
4+ ANC (Worse-off women)
p-value
Estimate [95% CI]
Estimate [95% CI]
p-value
51.6% [47.0,56.3%]
4+ ANC (Better-off women)
Estimate [95% CI]
p-value
Pre-policy starting level
62.3% [57.4,67.1%]
Pre-policy half-yearly trend
−1.2% [− 2.2,-0.3%]
0.009
− 0.8% [− 1.5,-0.1%]
0.033
− 2.0% [−3.2,-0.9%]
0.001
Immediate change in level
+ 0.3% [− 11.8,12.3%]
0.965
−5.8% [− 18.9,7.3%]
0.372
+ 10.4% [0.0, 20.7%]
0.051
Immediate change in slope
+ 2.4% [1.1,3.6%]
0.001
+ 2.0% [0.9,3.1%]
0.001
+ 2.9% [1.5,4.2%]
< 0.001 Post-policy half-yearly trend + 1.1% [0.4,1.8%] 0.003 + 1.2% [0.4,2.1%] 0.006 + 0.8% [0.2,1.4%] 0.010 (p = 0.009). After the 10/20 policy was introduced, the trend in use of 4+ ANC accelerated by 2.4 percentage points per half-year (p = 0.001); however, there was no immediate change in the proportion of women who made at least four ANC visits. Use of 4+ ANC increased by 1.1 percentage points per half-year (p = 0.003) after the 10/20 policy was introduced. At the start of the study period, an estimated 51.6% of worse-off women and 78.9% of better-off women made a minimum of four ANC visits. Before the 10/20 policy was introduced, use of 4+ ANC significantly decreased over time among both worse-off and better-off women. Although the proportion of better-off women making 4+ ANC contacts may have increased by 10.4 percentage points immediately after the 10/20 policy was introduced (p = 0.051), there was no evidence of an immediate impact on the level of 4+ ANC use among worse-off women. The 10/20 policy was associated with 2.0 (p = 0.001) and 2.9 (p < 0.001) percentage points per halfyear accelerations of the trends in 4+ ANC use among worse-off and better-off women, respectively. Thus, after the 10/20 policy was introduced, use of 4+ ANC increased by 1.2 percentage points per half-year (p = 0.006) among worse-off women and 0.8 percentage points per half-year (p = 0.010) among better-off women. Timing of ANC initiation among users of 1+ ANC At the start of the study period, only 14.0% of 1+ ANC users reported making their first ANC visit within the first 3 months of their pregnancy (early ANC initiation) (Table 2). Prior to the introduction of the 10/20 policy, early ANC initiation remained constant over time. While 78.9% [72.1,85.8%] there was no immediate change in the percentage of women who started ANC early after the policy was introduced, the trend in early ANC initiation accelerated by 1.0 percentage points per half-year (p = 0.008) after the policy change. After the introduction of the 10/20 policy, the proportion of 1+ ANC users who initiated ANC early increased by 0.7 percentage points every 6 months (p < 0.001). At the start of the study period, 20.4% of better-off ANC users started ANC within the first 3 months of pregnancy, while coverage of early ANC initiation was 4.5% among worse-off ANC users. Prior to the policy change, early ANC initiation increased by 0.7 percentage points per half-year among worse-off ANC users (p = 0.047) and decreased by 1.0 percentage point per half year (p = 0.049) among better-off ANC users. Among better-off ANC users, the trend in early ANC accelerated by 2.0 percentage points per half-year (p = 0.002) immediately after the 10/20 policy was introduced. There was no immediate change in the level of or trend in early initiation among worse-off ANC users. In both groups, early ANC initiation gradually increased over time during the years after the 10/20 policy was introduced. Source of care among users of 1+ ANC An estimated 66.0% of 1+ ANC users received care from a public sector health facility at the start of the study period in 1997 (Table 3). Use of public health facility-based ANC increased by 1.0 percentage points every 6 months before the 10/20 policy was introduced (p = 0.044); however, the policy was not associated with any immediate change in the percentage of Table 2 Early ANC initiation among users of 1+ ANC Early ANC (All women) Estimate [95% CI] Early ANC (Worse-off women) p-value Estimate [95% CI] Early ANC (Better-off women) p-value 4.5% [0.0,9.5%] Estimate [95% CI] p-value Pre-policy starting level 14.0% [10.2,17.9%] 20.4% [15.1,25.6%] Pre-policy half-yearly trend −0.3% [− 0.9,0.3%] 0.355 + 0.7% [0.0,1.3%] 0.047 −1.0% [− 2.0,0.0%] 0.049 Immediate change in level + 2.6% [− 2.5,7.6%] 0.303 −4.5% [− 11.4,2.4%] 0.191 + 9.3% [− 0.4,18.9%] 0.059 Immediate change in slope + 1.0% [0.3,1.7%] 0.008 −0.2% [− 0.9,0.5%] 0.609 + 2.0% [0.8,3.3%] 0.002 Post-policy half-yearly trend + 0.7% [0.5,0.9%] < 0.001 + 0.5% [0.2,0.8%] 0.006 + 1.0% [0.5,1.5%] < 0.001 Dennis et al. International Journal for Equity in Health (2020) 19:35 Page 7 of 13 Table 3 Use of ANC from a public sector health facility among users of 1+ ANC Any public facility (All women) Estimate [95% CI] Pre-policy starting level p-value 66.0% [59.6,72.4%] Any public facility (Worse-off women) Estimate [95% CI] p-value 69.2% [59.2,79.3%] Any public facility (Better-off women) Estimate [95% CI] p-value 62.7% [58.2,67.2%] Pre-policy half-yearly trend + 1.0% [0.0,2.0%] 0.044 + 0.7% [−0.8,2.1%] 0.358 + 1.5% [0.7,2.2%] < 0.001 Immediate change in level + 3.0% [− 6.1,12.1%] 0.499 + 8.7% [−4.0,21.3%] 0.171 −5.0% [− 13.2,3.3%] 0.226 Immediate change in slope −1.0% [−2.0,0.0%] 0.042 −0.4% [− 1.9,1.0%] 0.541 −1.7% [− 2.6,-0.9%] < 0.001 Post-policy half-yearly trend 0.0% [− 0.2,0.2%] 0.976 + 0.2% [0.0,0.4%] 0.041 − 0.3% [− 0.7,0.1%] 0.187 1+ ANC users who sought care from a public facility. The results indicate that the 10/20 policy did not appear to accelerate the previously increasing trend in use of public sector health facilities; instead, they suggest that the policy decelerated the trend in use of public health facilities by 1.0 percentage points per half-year (p = 0.042). After the 10/20 policy was introduced, use of public facility-based ANC remained constant over time. At the start of the study period, approximately 69.2% of worse-off women and 62.7% of better-off women received their ANC from a public sector health facility. Before the 10/20 policy was introduced, use of public facility-based ANC increased by 1.5 percentage points per half-year among better-off ANC users (p < 0.001), but remained constant over time among the worse-off. While the policy had no impact on the level of public facility-based ANC use among either group nor on the trend in use of public ANC services among the worseoff, the results suggest that the change over time in use of public facilities among better-off ANC users decelerated by 1.7 percentage points per half-year immediately after the policy change (p < 0.001). In the years after the 10/20 was introduced, use of public facility-based ANC increased by 0.2 percentage points per half-year (p = 0.041) among the worse-off. Use of primary care facilities among users of public facility ANC Approximately 64.5% of all public facility ANC users received care from a primary care facility (dispensary or health center) at the beginning of the study period (Table 4). Use of primary care facilities remained constant over time both before and after the 10/20 policy was introduced, and the policy did not have any measurable impact on the use of primary care facilities among public facility-based ANC users. An estimated 65.9 and 63.0% of worse-off and betteroff public facility-based ANC users sought care from a primary care facility at the start of the study period, respectively. Before the 10/20 policy was introduced, use of primary care facilities increased by 1.2 percentage points every 6 months (p = 0.010) among worse-off women and remained constant over time among betteroff women. The share of worse-off public facility users who sought care from a primary care facility decreased by 9.5 percentage points (p = 0.023) immediately after the 10/20 policy was introduced and use of primary care facilities decelerated by 1.3 percentage points per halfyear (p = 0.013). Among the better-off, on the other hand, the 10/20 policy was not associated with any immediate effects the level of or change over time in primary care facility use. During the period after the 10/20 policy was introduced, use of primary care facilities remained constant over time among both worse-off and better-off public facility users. Content of care among users of public facility-based ANC Only 9.4% of public health facility-based ANC users reported receiving all six routinely measured ANC components (good content of care), at the beginning of the study period in 1997 (Table 5). The results suggest that the percentage of public facility-based ANC users who received good content of ANC remained constant over time before the 10/20 policy was introduced, and the policy did not have any immediate effect on the level of coverage or change over time in receipt of good content of care. The proportion of public facility-based ANC Table 4 Use of primary care facilities among users of any public facility-based ANC Primary care facility (All women) Estimate [95% CI] Pre-policy starting level p-value 64.5% [59.2,69.8%] Primary care facility (Worse-off women) Estimate [95% CI] p-value 65.9% [59.0,72.9%] Primary care facility (Better-off women) Estimate [95% CI] p-value 63.0% [57.3,68.8%] Pre-policy half-yearly trend + 0.4% [− 0.5,1.4%] 0.356 + 1.2% [0.3,2.2%] 0.010 −0.7% [− 1.6,0.1%] 0.095 Immediate change in level − 4.7% [− 14.6, 5.2%] 0.335 −9.5% [− 17.6,-1.4%] 0.023 0.3% [− 9.5,10.2%] 0.945 Immediate change in slope − 0.7% [− 1.7,0.4%] 0.193 −1.3% [− 2.3,-0.3%] 0.013 + 0.3% [− 0.9,1.4%] 0.653 Post-policy half-yearly trend −0.2% [− 0.7,0.2%] 0.247 0.0% [− 0.4,0.3%] 0.803 −0.5% [− 1.1,0.2%] 0.164 Dennis et al. International Journal for Equity in Health (2020) 19:35 Page 8 of 13 Table 5 Received good content of care among users of public facility-based ANC Received all 6 routine ANC components (All women) Estimate [95% CI] Pre-policy starting level Received all 6 routine ANC components (Worse-off women) p-value 9.4% [4.7,14.2%] Estimate [95% CI] p-value 9.0% [4.6,13.3%] Received all 6 routine ANC components (Better-off women) Estimate [95% CI] p-value 9.9% [3.9,15.9%] Pre-policy half-yearly trend + 0.4% [−0.6,1.4%] 0.402 + 0.1% [− 0.6,0.8%] 0.740 + 0.9% [− 0.5,2.3%] 0.204 Immediate change in level + 4.9% [− 4.9,14.6%] 0.313 + 3.6% [− 4.1,11.3%] 0.344 + 6.5% [− 6.9,19.9%] 0.329 Immediate change in slope + 1.1% [− 0.2,2.3%] 0.087 + 1.2% [0.3,2.2%] 0.014 + 0.7% [− 0.9,2.4%] 0.356 Post-policy half-yearly trend + 1.5% [1.0,2.0%] < 0.001 + 1.3% [0.8,1.9%] < 0.001 + 1.6% [1.1,2.2%] < 0.001 users who received good content of care increased by 1.5 percentage points per half-year (p < 0.001) in the years after the 10/20 policy was introduced. In Additional file 5, we included tables with estimates of the proportion of women who received each of the six components as well as all six components combined, stratified by source of care and number of ANC contacts. At the start of the study period, only 9.0 and 9.9% of worse-off and better-off public facility ANC users reported receiving good content of care, respectively. The proportion of women receiving good content of care remained constant over time prior to the policy change among both groups, and there was no immediate change in the proportion of women who received good content of care in either group. After the 10/20 policy was introduced, the rate of change in coverage of good content of ANC accelerated by 1.2 percentage points per half-year (p = 0.014) among worse-off public facility-based ANC users only. The proportion of women who received good content of care increased over time among both groups after the 10/20 policy was introduced. Table 6 contains a summary of the impact of the 10/ 20 policy on all of the ANC outcomes examined among all women and stratified by wealth group. Additional file 2 includes similar tables illustrating greater positive impacts of the 10/20 policy among women living in urban areas compared to those in rural areas. Table 6 Summary of the effects of the 10/20 policy on ANC Immediate change in level Immediate change in slope All women none increased Worse-off women none increased Better-off women none increased All women none increased Worse-off women none none Better-off women none increased All women none decreased Worse-off women none none Better-off women none decreased (1) 4+ ANC (most recent births) (2) Early ANC (users of 1+ ANC) (3) Public facility-based ANC (users of 1+ ANC) (4) Primary care (users of any public facility-based care) All women none none Worse-off women decreased decreased Better-off women none none (5) Received good content of ANC (users of any public facility-based care) All women none none Worse-off women none increased Better-off women none none increased: increasing effect or trend, p < 0.05 decreased: decreasing effect or trend, p < 0.05 none: no effect, p > 0.05
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Discussion
Summary of findings
Our study shows that over the past two decades, content
of ANC has been universally low and there have been
historical wealth-based disparities in the frequency and
timing of ANC. The 10/20 policy was associated with
the acceleration of the changes over time in use of 4+
ANC and early ANC initiation. The evidence suggests
that the 10/20 policy was not associated with
population-level increases in use of public facility-based
ANC among ANC users nor on use of primary care facilities and content of care among users of public facilities. When disaggregated by wealth groups, the findings
further suggest that the 10/20 policy may have been
more beneficial to better-off women compared to poorer
women.
Understanding the causal mechanisms driving the 10/20
policy’s impact on ANC
Examining the findings stratified by wealth group raises
important questions with regard to the causal mechanisms by which the 10/20 policy might have impacted
the coverage, timing, frequency, and source of antenatal
care. We hypothesized that reducing the cost of accessing ANC might lead to earlier ANC initiation, higher
coverage of ANC, and increased number of ANC visits.
Additionally, we expected that any increases in 4+ ANC
coverage would be accompanied by increases in the proportion of ANC users who sought care from the public
sector and the proportion of public facility-based ANC
users who sought care at a primary care facility. Finally,
we hypothesized that increased patient volumes in public primary care facilities as a result of the 10/20 policy
might contribute to reduced content of care in the public sector.
Instead, we found that while the 10/20 policy had no
impact on the timing of ANC initiation among worse-off
women, the proportion of worse-off ANC users who
made four or more ANC contacts began to increase at a
faster rate immediately after the 10/20 policy was introduced. This suggests that for worse-off women, the policy was unable to immediately change practices around
the timing of the first ANC visit among users, but successfully increased the proportion of women who made
four or more ANC visits. We also found that while the
policy did not increase the proportion of worse-off
women using public sector care, it did accelerate improvements in receipt of good content of care among
worse-off users of public facility-based ANC. As the policy change was associated with a shift towards greater
use of public hospitals among worse-off users of public
facility-based care, these findings suggest that the observed improvements in content of ANC among worseoff women may have been due to a combination of
Page 9 of 13
decreased use of public sector primary care facilities and
increased number of ANC visits. Among better-off
women, the 10/20 policy was associated with improvements in the timing, and number of visits. However, in
contrast with our hypotheses, these improvements were
also accompanied by decreased use of public sector facilities and no change in the use of primary care or content
of care among users of public facility-based ANC.
A critical look into the design, implementation, and
context of the 10/20 policy provides helpful insights for
understanding why the policy may not have had the expected effect on a primary care service such as ANC.
For instance, the 10/20 policy aimed to improve the financial accessibility of primary care but did not include
any interventions to address other barriers that influence
whether a woman accesses one or more ANC visits during her pregnancy. Although indirect financial costs,
such as paying for transportation to and from health facilities, can serve as a significant barrier to care, the 10/
20 policy only addressed direct costs for ANC in public
primary care facilities. A study on catastrophic health
spending in Kenya found that transportation costs account for nearly one quarter of households’ total out-ofpocket spending on health, and that the burden of transportation costs relative to total spending was highest
among the poor [43]. This suggests that the high costs
of transportation may have significantly influenced the
impact of the 10/20 policy on ANC service use. In terms
of non-financial barriers, a qualitative study on women’s
beliefs and practices around ANC in Kenya revealed that
while raising money for out-of-pocket fees sometimes
required women to postpone their first ANC visit, factors related to women’s knowledge, beliefs, and traditions appeared to be more influential contributors to
delayed ANC initiation [44]. Additionally, findings from
two quantitative studies on determinants of ANC timing
in Kenya also suggest that barriers including distance,
knowledge, and customs might also inhibit early ANC
initiation, as evidenced by the impact of factors such as
living in a community with access to a community
health worker, being from certain ethnic groups, parity,
and being married on the timing of women’s first ANC
visits [45, 46]. The fact that only better-off women experienced immediate increases in early ANC initiation after
the introduction of the 10/20 policy therefore supports
findings from other research suggesting that sometimes
the impacts of user fee exemptions are inequitable because the poor tend to be disproportionally affected by
indirect financial and non-financial barriers to healthcare
[47].
With regard to source of care, there are many possible
reasons why the policy did not lead to an increased use
of public primary care facilities for ANC among the
worse-off. For instance, although ANC services were
Dennis et al. International Journal for Equity in Health
(2020) 19:35
Page 10 of 13
intended to be available at the lowest levels of care, the
2004 Kenya Service Provision Assessment (KSPA) reported that only 77% of dispensaries offered ANC, compared to 86% of health centers and 84% of hospitals [16].
Further, the 2004 KSPA found that among facilities offering ANC, availability of the resources and infrastructure necessary for quality ANC was low, particularly in
health centers and dispensaries [16]. In addition to this
lower availability of quality ANC services in public primary care facilities, distrust related to the lack of clarity
around the conditions of the policy; facilities’ failure to
comply with the policy’s recommended fees; and concerns about the policy’s impact on quality of care may
have also acted as deterrents. A qualitative study examining perceptions of the 10/20 policy among community
members and health workers found that both the general public and health workers were confused about
which aspects of care were covered under the policy and
which services and groups were eligible for fee exemptions [21]. The study also found that some health providers and community members believed that the 10/20
policy reduced the cost of seeking care at the expense of
quality of care, particularly in terms of drug availability
[21]. Additionally, two nationally representative surveys
of health facilities in Kenya found that 6 to 8 years after
the 10/20 policy was introduced, health facility staff reported routinely overcharging for ANC in both health
centers and dispensaries [23, 24]. An assessment conducted in 2012, for instance, found that public health
centers and dispensaries reported charging KSh 58 and
KSh 46 per ANC visit, respectively, while hospitals reported charging similar fees of KSh 55 per visit [24]. Finally, although the 10/20 policy purportedly reduced
user fees in public primary care facilities, by many accounts, services were already being provided for free in
some public dispensaries prior to the policy change [8,
11, 12, 21]. Thus, in some areas, rather than decreasing
fees at the dispensary-level, the 10/20 policy potentially
introduced official fees that previously did not exist.
The decreased use of public sector care among betteroff ANC users after the 10/20 policy could be due to the
comparative costs of seeking care in public versus private facilities after the policy change. A nationally representative survey of the fees charged by health facilities
years after the 10/20 policy was introduced revealed that
the cost of ANC was comparable between public and
private facilities at the dispensary level [24]. Although
the study also found that the fees for ANC in hospitals
and health centers were higher in the private sector than
in the public sector, the difference in pricing may not
have been a sufficient barrier to stop better-off women
from switching to private sector care [21, 24].
With regard to receipt of good content of ANC, the observed improvement in content of ANC among worse-off
women may also be related to changes in the global guidelines on ANC around the same time that the 10/20 policy
was introduced. From 1996 to 1998, the WHO conducted
a multi-country randomized control trial of a new fourvisit model of ANC delivery. Later, in 2002, the WHO
published guidelines on the focused, or four-visit, ANC
model and which interventions should be provided during
each visit [48]. Simultaneously in 2001, this model was
piloted in two out of Kenya’s then 72 districts and later
scaled up to 19 additional districts in 2002 [49]. Although
there were no national standards or guidelines for implementing focused ANC in Kenya at the time of the 10/20
policy change [49], it is plausible that as these guidelines
were being piloted in select districts, there was a more
general emphasis on improving the content of ANC
throughout the country.
Comparing effects of 10/20 policy on coverage of ANC vs.
delivery care
Despite evidence that women’s experiences during ANC
can influence care seeking for childbirth [27–34], most
studies on the effects of user fees on maternal health
service coverage have looked exclusively at delivery care.
Our study demonstrates the value of examining the influence of health financing strategies on a broader range
of maternal health outcomes and comparing findings
across service types and sub-populations. The findings
suggest that there were important differences and similarities between the impact of the 10/20 policy on coverage of antenatal care versus delivery care. In a recent
paper using Kenya DHS data to examine the impact of
the 2004 10/20 policy on coverage and source of delivery
care, Obare et al. found that the proportion of women
who delivered outside of a health facility immediately increased at the population level and among poor women
(defined as the bottom two wealth quintiles), but had no
immediate effect on home-based delivery care among
wealthy women (defined as the top two wealth quintiles)
[26]. Further, the study found no immediate effect of the
2004 10/20 policy on use of public facility-based delivery
care; instead, the observed reduction in facility-based
care was due to decreased use of private facilities and increased home-based births among the poor [26]. While
Obare and colleagues’ findings suggest that the 2004 10/
20 policy change was associated with decreased coverage
of institutional deliveries, particularly among the poor,
our findings suggest that the policy change was associated with increased coverage of 4+ ANC, particularly
among the better-off. Thus, although the 10/20 policy’s
impact on antenatal and delivery care coverage may have
differed, both studies suggest that the policy contributed
to better improvements in service coverage for women
with higher socioeconomic status compared to those
with lower socioeconomic status. These findings are
Dennis et al. International Journal for Equity in Health
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Page 11 of 13
consistent with other studies reporting that fee exemption policies may not always reduce inequities in access
to care, particularly if non-financial barriers are not sufficiently addressed [47, 50–52].
There are a few plausible explanations for why the impact of the 10/20 policy change in 2004 might have differed between ANC and delivery care. For example, the
impact of the policy might be related to the nature of
the service. While ANC is an outpatient, largely preventative and promotive service, facility-based childbirth
care is an inpatient service requiring a skilled provider.
As a result, the proportion of health centers and dispensaries that offered delivery care in the early months
after the policy change was substantially smaller than
the proportion that offered ANC [16]. Due to these differences in service availability, the potential for the 10/
20 policy to facilitate a population-level increase in use
of facility-based delivery care was lower than for facilitybased ANC. Secondly, it is likely that facilities’ inconsistent compliance with the policy impacted ANC and delivery care differently. Qualitative research conducted after
the 10/20 policy was introduced suggests that health facilities often did not adhere to the policy’s recommended
charges, and health care users were charged additional
fees for certain drugs, laboratory tests, and services [21,
23, 24]. Health centers providing any inpatient services,
in particular, reported that the 10/20 registration fees
did not provide adequate cost recovery, which contributed to their noncompliance with the policy [21, 24].
Additionally, a nationally representative survey of Kenyan health facilities conducted in 2010 found that facility
in-charges reported higher levels of overcharging for delivery services compared to ANC [23]. This study was
conducted 6 years after the 10/20 policy was introduced
and the findings may therefore be related to the duration
of time passed since the policy change. However, given
the comparatively higher costs for providing delivery
care, it is conceivable that this practice of greater overcharging for delivery care was also prevalent during the
time immediately after the policy change.
be pregnant both before and after its implementation.
Such cases, though relatively few (approximately 2% of
the study sample), could potentially have contributed to
a crossover effect, whereby the impact of the policy on
ANC may have been underestimated due to women who
were categorized as conceiving before the policy change
having access to its benefits. Measurement of the policy
impact may have also been affected by small sample
sizes in certain periods (Additional file 1); however, we
adjusted for this by weighting each half-year observation
by the precision of the outcome’s estimate for that
period. Additionally, because the 10/20 policy was implemented at the national level, it was not possible to
compare the time trends in a comparable control group
that was not exposed to the policy change. Finally, although we used the content of antenatal care as a proxy
for quality of care, this is not a comprehensive measure
of quality of care, as it only measured a relatively small
number of ANC components and did not assess more
systems-level aspects of service quality or aspects related
to respectful care.
Limitations
This study has some limitations. First, the data are subject to recall bias, as the DHS asks women to provide details about the antenatal care that they received for
pregnancies that occurred up to 5 years prior to the
interview date. Secondly, this analysis relies on categorizing women’s pregnancies by their estimated dates of
conception. As it is difficult to accurately estimate the
duration of a woman’s pregnancy using information on
her child’s birthdate alone, our assumptions may have
resulted in the misclassification of some births into the
wrong half-year period. There was also potential for
women who conceived just before the policy change to
Conclusions
This study showed that the user fee reductions under the
10/20 policy in Kenya were associated with increased
coverage and frequency of antenatal care. However, these
improvements were not achieved through greater use of
the public primary care facilities targeted under the policy,
but instead through greater use of higher-level public facilities among the worse-off and private facilities among
the better-off, leaving unanswered questions about the
mechanisms through which the policy change may have
affected service use patterns. Findings like these highlight
the need to conduct qualitative research alongside the
introduction of new health financing policies to better
understand how they work in practice and the reasons for
certain health seeking practices. This study also revealed
that improvements in the timing and frequency of ANC
were inequitable between better-off and worse-off women.
On one hand, these findings imply that the policy may
have increased out-of-pocket expenditures for the poor by
pushing worse-off ANC users towards higher-level publicsector care for services that could be provided for lower
costs in primary care facilities that complied with the 10/
20 policy. On the other hand, the findings indicate that
the policy may have stimulated more effective market segmentation by pushing the better-off towards the private
sector and potentially increasing the public-sector resources available to those with lower ability to pay. Taken
together, these findings contribute to the evidence that reducing user fees alone is not sufficient for equitably increasing access to primary healthcare services such as
antenatal care. To ensure the success of the national health
financing strategy that is currently being finalized in Kenya,
Dennis et al. International Journal for Equity in Health
(2020) 19:35
policymakers must therefore develop strategies for concurrently addressing the key financial and non-financial barriers to recommended service-seeking practices.
Page 12 of 13
4.
5.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12939-020-1150-8.
6.
Additional file 1. Sample sizes for time series analysis.
Additional file 2. Results from time series analysis stratified by
residence.
7.
Additional file 3. Mean gestational age calculations.
Additional file 4. Graphs of trends in ANC frequency, timing, source of
care, and content of care.
8.
Additional file 5. Receipt of individual ANC components.
9.
10.
Acknowledgements
We thank the Measure Demographic and Health Survey program for making
the DHS data available as well as the women who participated in the
surveys. We also thank Schadrac Agbla and Matteo Quartagno for their
statistical advice.
11.
12.
Authors’…

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