Randomized Controlled Trial of an Implementation Science Tool to Increase Cervical Cancer Screening in Mombasa, Kenya
Phase: NA
Conditions: Cervical Cancer
Sponsor: University of Washington

 
1 
Randomized controlled trial of an implementation science tool to increase cervical cancer 
screening in Mombasa, Kenya 
 
 
STUDY PROTOCOL 
 
VERSION 1.0 
 
1st February, 2021 
NCT: STUDY00004431 
 
 

 
2 
 
TABLE OF CONTENTS: 
 
 
 
INVESTIGATORS  
 
 
 
 
 
 
 
 
 
3 
COLLABORTING INSTITUTIONS 
 
 
 
 
 
 
 
4 
FUNDING AGENCY  
 
 
 
 
 
 
 
 
4 
A. SUMMARY  
 
 
 
 
 
 
 
 
 
5 
B. ACRONYMS AND ABBREVIATIONS USED 
 
 
 
 
 
6 
C. INTRODUCTION  
 
 
 
 
 
 
 
 
7 
D. LITERATURE REVIEW 
 
 
 
 
 
 
 
 
8 
E. METHODOLOGY  
 
 
 
 
 
 
 
 
11 
 
1. STUDY AREA 
 
 
 
 
 
 
 
 
11 
 
2. STUDY POPULATION 
 
 
 
 
 
 
 
11 
 
3. STUDY DESIGN 
 
 
 
 
 
 
 
 
12 
 
4. PROCEDURES 
 
 
 
 
 
 
 
 
12 
 
5. SAMPLE SIZE DETERMINATION  
 
 
 
 
 
15 
 
6. DATA MANAGEMENT 
 
 
 
 
 
 
 
16 
 
7. VARIABLES 
 
 
 
 
 
 
 
 
16 
 
8. DATA COLLECTION INSTRUMENTS 
 
 
 
 
 
17 
 
9. QUALITY ASSURANCE PROCEDURES  
 
 
 
 
17 
 
10. MATERIALS 
 
 
 
 
 
 
 
 
17 
 
11. STUDY STRENGTHS/LIMITATIONS 
 
 
 
 
 
18 
 
12. STUDY RESULTS DISSEMINATION PLAN 
 
 
 
 
20 
F. PERSONNEL 
 
 
 
 
 
 
 
 
 
20 
G. TIMELINE  
 
 
 
 
 
 
 
 
 
21 
H. REFERENCES 
 
 
 
 
 
 
 
 
 
21 
I. ETHICAL CONSIDERATIONS 
 
 
 
 
 
 
 
25 
 
1. INVOLVEMENT OF HUMANS SUBJECTS 
 
 
 
 
25 
 
2. RESEARCH MATERIAL OBTAINED FROM PARTICIPANTS 
 
 
25 
 
3. RECRUITMENT OF SUBJECTS AND CONSENT PROCEDURES 
 
26 
 
4. RISK TO HUMAN SUBJECTS 
 
 
 
 
 
 
27 
 
5. ADEQUACY OF PROTECTION AGAINST RISKS 
 
 
 
27 
 
6. POTENTIAL BENEFITS 
 
 
 
 
 
 
 
28 
 
7. IMPORTANT OF KNOWLEDGE TO BE GAINED 
 
 
 
28 
 
8. DATA SAFETY AND MONITORING PLAN 
 
 
 
 
29 
 
9. CLINICAL TRIAL REGISTRATION 
 
 
 
 
 
29 
J. BUDGET 
 
 
 
 
 
 
 
 
 
 
29 
K. APPENDICES 
 
 
 
 
 
 
 
 
 
29 
 
 
 

 
3 
INVESTIGATORS PI, then alphabetical 
 
McKenna Eastment, MD, MPH 
Acting Instructor 
University of Washington, Department of Medicine 
Email: mceast@uw.edu 
Addresses: Box 359909; 325 9th Ave; Seattle, WA USA 
Signature: 
 
 
 
R. Scott McClelland, MD, MPH 
Professor of Medicine, Epidemiology and Global Health 
University of Washington 
Email: mcclell@uw.edu 
Addresses: Box 359909; 325 9th Ave; Seattle, WA USA 
Signature: 
 
 
 
Walter Jaoko, MBChB, MSc, PhD 
Co-Investigator, University of Nairobi 
Associate Professor, Department of Medical Microbiology  
University of Nairobi  
Email: wjaoko@kaviuon.org  
Addresses: 
Signature:  
 
 
 
John Kinuthia, MBChB, MMed, MPH 
Co-Investigator, Kenyatta National Hospital  
Director of Research, Kenyatta National Hospital 
Email: kinuthia@uw.edu  
Addresses: Kenyatta National Hospital; PO Box 20723; Nairobi, Kenya 
Signature:  
 
 
 
 
Kishorchandra Mandaliya, MBChB, MRCPath 
Co-Investigator, Women’s Health Project, Mombasa 
Affiliate Professor, University of Washington and Director, Pathcare Laboratories 
Mombasa 
Email: kmandaliya@gmail.com 
Addresses: Ganjoni Clinic; Mnazi Mmoja Road; Mombasa, Kenya 
Signature:  
 
 
 
Faiza Nassir, MBChB, MMEd 
Co-Investigator, Mombasa County 
Obstetrician/Gynecologist 
Email: f_nassir@hotmail.com 
Addresses: 
Signature: 

 
4 
 
COLLABORATING INSTITUTIONS (PIs institution, then alphabetical) 
 
University of Washington 
Kenyatta National Hospital 
Mombasa County 
University of Nairobi 
 
 
FUNDING AGENCY 
 
US National Institutes of Health 
National Cancer Institute 
Grant Title: Randomized controlled trial of an implementation science tool to increase 
cervical cancer screening in Mombasa, Kenya 
Grant Number: 1K08CA228761-01A1 
07/06/2018-06/30/2022 
 
 

 
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A. SUMMARY 
 
Location: Family Planning Clinics in Mombasa County, Kenya 
 
Introduction: Cervical cancer is the most common cancer in women in sub-Saharan 
Africa, and the majority of cervical cancer mortality occur in low and middle income 
countries (LMICs). Many of the disparities between high and LMICs are attributed to 
differences in screening. Kenyan guidelines recommend screening with visual inspection 
methods followed by treatment of pre-cancerous lesions with cryotherapy and loop 
electrosurgical excision procedure (LEEP). Implementation of these are poor with only 
14% of Kenyan women ever having been screened for cervical cancer as of 2014. 
 
Methods: In Aim 1, we will describe the cervical cancer screening care cascade, from 
identification of female clients age 21-65 years old, through referral for follow-up of 
clients with positive or suspicious screens, in family planning (FP) clinics in Mombasa 
County. Following characterization of this cascade, we will conduct an analysis of 
correlates of failure to screen for cervical cancer in FP clients seen over a one-year 
period in Mombasa County. Aim 2 will test whether SAIA increases cervical cancer 
screening compared to usual procedures in a cluster randomized trial in 20 FP clinics in 
Mombasa County. Finally, in Aim 3, we will determine the cost and budget impact of 
using SAIA to increase cervical cancer screening in FP clinics in Mombasa County. 
 
Anticipated Results: The results of this study have the potential to improve cervical 
cancer screening, and inform policy in the Mombasa DOH for a fiscally responsible 
evidenced-based approach for cervical cancer screening. 
 
Anticipated Conclusion: The long-term goal is to decrease cervical cancer mortality and 
improve women’s health.  
 
 
 
 
 
 
 

 
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B. ACRONYMS AND ABBREVIATIONS USED 
AIDS: 
 
 
Acquired immune deficiency syndrome 
ART: 
 
 
Antiretroviral therapy 
BIA: 
 
 
Budget Impact Analysis 
CFAR: 
 
 
Center for AIDS Research 
CFIR: 
 
 
Consolidated Framework for Implementation Research 
CQI: 
 
 
Continuous quality improvement 
DOH:  
 
 
Department of Health 
DHIS: 
 
 
District Health Information System 
FP: 
 
 
Family planning 
HIV:  
 
 
Human immunodeficiency virus 
HPV: 
 
 
Human papillomavirus 
IDIs: 
 
 
In-depth interviews 
IS:  
 
 
Implementation science 
LEEP: 
 
 
Loop electrosurgical excision procedure 
LMIC: 
 
 
Low and middle-income countries 
MOH: 
 
 
Ministry of Health 
NIH: 
 
 
National Institutes of Health  
PMTCT: 
 
 
Prevention of mother-to-child transmission 
RCT: 
 
 
Randomized controlled trial 
SAIA: 
 
 
Systems analysis and improvement approach 
SSA:  
 
 
Sub-Saharan Africa 
UW: 
 
 
University of Washington 
VIA: 
 
 
Visual inspection with acetic acid 
 
 
 
 

 
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C. INTRODUCTION 
Eighty-seven percent of cervical cancer deaths worldwide occur in low and middle income 
countries (LMICs) and cervical cancer is the most common cancer in sub-Saharan Africa 
(SSA) (2-5). The significant disparity between cervical cancer outcomes in the United States 
and LMICs is largely attributed to differences in screening (6). While approximately 89% of US 
women receive cervical cancer screening (7), less than 5% of women in LMICs have been 
screened (5). Barriers to screening in LMICs include challenges with infrastructure to support 
screening, competing health interests, lack of education, low health literacy, and poverty (3, 
8-12).  
In addition to the general lack of cervical cancer screening, SSA carries the highest 
global burden of human immunodeficiency virus (HIV) infection.  Women account for 59% of 
all people living with HIV (13) and cervical cancer incidence is higher in women with HIV (14). 
With the advent of antiretroviral therapy (ART), women receiving HIV treatment have 
increased life expectancy approaching that of HIV-negative women (15). However, cervical 
cancer rates do not significantly decline despite ART and immune reconstitution (16), and 
invasive cervical cancer incidence remains high even with ART (17). The aging population of 
HIV-positive women will continue to face a large lifetime risk of cervical cancer (18).  
Because of the burden of both cervical cancer and HIV infection in SSA, improving 
implementation of cervical cancer screening and treatment of pre-cancerous lesions in this 
region is critical. Existing methods for cervical cancer screening include cytology, human 
papillomavirus testing (14), and visual inspection methods (19). Pairing screening with 
treatment of positive screens using cryotherapy or loop electrosurgical excision procedures 
(LEEP) could prevent progression to cervical cancer (20), and greatly reduce morbidity and 
mortality in women. To address this implementation gap, simple, scalable, and sustainable 
interventions are imperative to improve screening and treatment of pre-cancers. The Kenyan 
Ministry of Health (MOH) guidelines stress the need to strengthen capacity, streamline, and 
standardize screening, diagnosis, and treatment of cancer (21). To achieve this, our long-term 
partners in the Mombasa County Department of Health (DOH) are eager to increase rates of 
cervical cancer screening. We aim to test an implementation science methodology, Systems 
Analysis and Improvement Approach (SAIA), to address systems barriers to screening and 
provide quality improvement while relying on existing infrastructure to conduct screening. 
Rather than directly offering screening, this intervention aims to support systematic 
improvements in screening processes in facilities throughout the county. We propose a 
collaborative research project with Mombasa County to achieve the following specific aims:  
AIM 1:  To describe the cervical cancer screening care cascade, from identification of 
female clients age 21-65 years old, through referral for follow-up of clients with 
positive or suspicious screens, in family planning (FP) clinics in Mombasa 
County. Following characterization of this cascade, we will conduct an analysis 
of correlates of failure to screen for cervical cancer in FP clients seen over a 
one-year period. 
HYP 1: While many FP clinics are capable of providing cervical cancer screening, the 
majority of clients are not screened appropriately. Failure to screen for cervical 
cancer will be associated with both patient-level (e.g. age) and clinic-level (e.g. 
resources available) factors.  
AIM 2:  To test whether SAIA increases cervical cancer screening compared to usual 
procedures in a cluster randomized trial in 20 FP clinics in Mombasa County. 
HYP 2: Family planning clinics randomized to SAIA will have increased rates of cervical 
cancer screening by modifying bottlenecks in screening processes compared to 
clinics randomized to usual procedures. 
AIM 3: To determine the cost and budget impact of using SAIA to increase cervical 
cancer screening in FP clinics in Mombasa County. 
 
Expected Outcomes and Public Health Impact 
As one of the leading causes of cancer mortality in African women, immediate attention to 
increase rates of cervical cancer screening and treatment of pre-cancers is crucial. This 

 
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implementation tool holds potential for addressing gaps in cervical cancer prevention and 
lowering cancer morbidity and mortality. Use of the reproducible SAIA methodology could 
provide a template for broader rollout of cervical cancer screening throughout the country and 
region. Using the Consolidated Framework for Implementation Research (CFIR) to guide the 
evaluation of this intervention will provide insight about the potential generalizability of the 
intervention, and improve the likelihood of its successful implementation in diverse settings 
(22). 
 
D. LITERATURE REVIEW 
 
A.1. As a major contributor to mortality in African women, cervical cancer demands 
attention. Cervical cancer is the most common cancer in sub-Saharan Africa (SSA) and is 
potentially preventable with appropriate screening and treatment of pre-cancers (2-5). High 
HIV prevalence in Africa further increases the risk of cervical cancer in a substantial proportion 
of women (13, 14), underscoring the need for strengthened screening programs. Potential 
barriers to cervical cancer screening include challenges with infrastructure to support 
screening, competing health interests, lack of education, low health literacy, and poverty (3, 
8-12). The Kenyan Ministry of Health (MOH) stresses the need to strengthen capacity, 
streamline, and standardize screening, detection, diagnosis, and treatment of cancers (21). 
Screening is recommended for women aged 21-65 years. In HIV-negative women, the current 
guidelines recommend cervical cancer screening using visual inspection with acetic acid (VIA) 
every 5 years. Screening by VIA in HIV-positive women is recommended every 6 months for 
the first year, and annually thereafter. Human papillomavirus (HPV) testing and cytology are 
other screening methods used less frequently in Kenya. At a national level, the MOH has 
tasked family planning (FP) clinics with implementing cervical cancer screening, and has 
included specific items to document screening in FP registers (23). In 2014 (this remains the 
most recent country survey), 44% of women in Mombasa County used contraceptives and 
60% of these obtained products from public FP clinics (24), illustrating that FP clinics are a 
good venue to reach a substantial proportion of women. Our partners in the Mombasa County 
DOH are eager to collaborate in applying implementation science to improve screening rates 
in their FP clinics (see C.1.D).  
 
A.2. Cervical cancer screening and treatment of pre-cancers has been studied in low 
and middle income countries (LMICs), but implementation remains a challenge, and 
strategies to improve the process are under-studied. While VIA is a cost-effective and 
validated method in LMICs (25), data on cervical cancer screening in Africa are limited (19, 
26). The WHO supports the ‘screen and treat’ method for cervical cancer screening and 
treatment of pre-cancerous lesions with cryotherapy and loop electrosurgical excision 
procedure (LEEP) (20). These services are available in Mombasa County, but linkage to 
services is challenging. In the most recent survey in 2014, only 14% of women in Kenya had 
ever been screened for cervical cancer (24). Data on follow-up of positive screens in Africa 
are sparse, though a single visit screen and treat approach likely results in fewer women lost 
to follow-up than referral-based linkage to care (27-30). Studies in Malawi and South Africa 
highlight challenges of linking women with a positive or ‘suspected’ VIA to appropriate 
treatment with cryotherapy or LEEP (31, 32). In Kenya, research suggests that cryotherapy 
was the least expensive option and resulted in the highest life expectancy, though VIA 
was most cost effective if HPV testing took more than 1 visit (33). In a recent study in 
Kenya, investigators found that of 1180 women surveyed, 16.4% had been screened for 
cervical cancer (34). Of those women who had never been screened, the majority 
(67.9%) were aware of the need to screen for cervical cancer. Women with more 
education and wealth and residing in urban areas were more likely to be screened 
compared to women with less education and wealth, and residing in more rural 
settings. There is an implementation gap between the evidence for cervical cancer screening 
efficacy in reducing population rates of cervical cancer and the effective scale-up in many 

 
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LMICs (35).  We aim to address this gap by testing the effectiveness of a standardized 
implementation approach to improve this process. 
 
A.3. Utilizing the Systems Analysis and Improvement Approach (SAIA) is a 
methodology that addresses the current cervical cancer screening and referral system 
within the existing healthcare framework. Implementation science (IS) research utilizes 
strategies to adapt, implement, and optimize evidenced-based interventions to improve health 
outcomes (36). SAIA is based on systems engineering to improve implementation. This tool 
analyzes the existing care cascade, then maps the work flow process, designs and 
implements a workflow adaptation, assesses the impact, and repeats this cycle for continued 
iterative system and process improvement (1). This approach works within the existing health 
system to address barriers and bottlenecks in the cervical cancer screening cascade (1), 
without simply performing the screening as an external study team. The SAIA methodology 
has the benefit of being a generalizable intervention that can be adapted to many different 
disease models and settings, contributing to the longevity and sustainability of this 
intervention.   
 
A.4. Budget impact analysis (BIA) of SAIA in collaboration with the DOH aims to 
improve screening by determining the affordability of this tool. With limited budgets for 
health care programs in many LMICs, budget impact analyses provide valuable information to 
guide policy changes that are fiscally responsible and have high potential health impacts. If 
the budget impact analysis shows a modest impact on the County budget, this argues for 
further expansion of this tool to other clinics, and potentially to other counties across Kenya.   
 
B. INNOVATION 
B.1. First application of SAIA in the field of cervical cancer; one of a few IS approaches 
applied to cervical cancer. Community engagement (37), education (38, 39), combined 
cancer screening programs (40), and media interventions (41) have been used in SSA to 
improve cervical cancer screening. SAIA is an innovative tool for the healthcare setting, and 
has previously been trialed successfully in other settings for different healthcare indications. 
This application of SAIA in a randomized controlled trial is an essential step forward for the 
field of cervical cancer screening and addresses the need for integrating screening into 
existing healthcare systems (11). Successful completion of this research will demonstrate 
improved performance at a local level, while providing more generalizable data about the utility 
of SAIA to guide scale-up nationally and in other countries facing similar challenges with 
cervical cancer screening. 
 
B.2. A BIA of SAIA has the potential to move the field forward. Understanding the budget 
impact on the DOH can support broader roll-out of this program in other counties in Kenya 
and other LMICs facing similar challenges to screening. BIAs move beyond cost-effectiveness 
to highlight how this approach will affect the budget from the perspective of the payer in the 
health care system. This BIA is an important and necessary step to scale-up this intervention. 
 
C. APPROACH. C.1.Preliminary Studies 
C.1.A. Leader in IS. The UW Department of Global Health is a leader in IS research, including 
85 projects in 41 countries, involving 119 faculty (42). Implementation science spans 
departments and programs including the Health Alliance International, International Training 
and Education Center for Health, and the Kenya Research and Training Center. 
Implementation research is also supported by the Center for AIDS Research (CFAR) 
Implementation Science Scientific Working Group, and an Implementation Science Core is 
proposed in our 2017 competing renewal (priority score 19). This institutional experience and 
leadership will benefit the successful completion of this research and Dr. Eastment’s training.  
 

 
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C.1.B. Significant experience with SAIA. This research team has significant experience with 
SAIA, which was originally adapted for healthcare by Dr. Sherr (co-mentor) as an iterative 
process effective in improving HIV testing and linkage to care in Prevention of Mother-to-Child 
Transmission of HIV (PMTCT) services in 
Mozambique, Kenya, and Cote d’Ivoire (PMTCT 
SAIA Trial) (1, 43). Dr. Sherr is involved in 3 
funded ongoing studies adapting SAIA to new 
disease models, and will provide Dr. Eastment 
with exceptional mentoring in this field. SAIA has 
five steps. The first step uses an Excel-based 
tool to quantify drop-offs, or people who did not 
progress, in each step of a process (Figure 1). 
This tool also allows the user to see the 
downstream effect when improving one step in 
the cascade, and holding the other steps 
constant. Step 2 involves process flow mapping 
with clinic staff to identify modifiable bottlenecks 
in the process. Step 3 develops and implements 
a workflow modification to address a bottleneck 
identified 
in 
step 
2 
(continuous 
quality 
improvement [CQI] step). Step 4 assesses 
impact of the modification and recalculates the 
cascade analysis in step 1 (CQI step). Step 5 
repeats the cycle for CQI. SAIA draws from 
systems engineering in the Toyota Production 
Systems and from research in LMICs. Studies in 
quality improvement in LMICs highlight that CQI 
processes led to more sustainable, effective, 
and appropriate interventions (44-46).  
 
C.1.C. 
Consolidated 
Framework 
for 
Implementation Research (CFIR) and SAIA: CFIR is an evidenced-based collection of 
constructs that aim to synthesize multiple implementation theories to explain what works, 
where it works, and why it works (22). There are five domains in CFIR that describe all aspects 
of the implementation realm including intervention characteristics, outer setting, inner setting, 
characteristics of individuals, and process. Each domain includes constructs that are 
associated with successful implementation (Figure 2).  For example, in the PMTCT SAIA trial, 
five CFIR constructs differed between high and low performing clinics: networks and 
communication in inner setting, available resources in outer setting, external change agents, 
executing and reflecting, and evaluating in implementation process (47). Furthermore, 
qualitative interviews identified flow mapping and CQI as particularly important for process 
improvement in all study settings (47).  We will use CFIR to evaluate the SAIA intervention to 
improve cervical cancer screening. 
 
 
Figure 1. Example of the cascade analysis using 
SAIA for HIV testing in PMTCT (1).  
PMTCT:  Preventing mother to child transmission; ANC: 
antenatal clinic; cART: combination antiretroviral 
therapy; PPO: prophylaxis 
 
Figure 2. Consolidated framework for implementation research domains and constructs (21). 

 
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C.1.D. Adaptations of SAIA methods in ongoing trial, led by Dr. Eastment.  Adaptations 
of SAIA methods are currently being used to address linkage of HIV services in the ongoing 
trial “RCT of an implementation science tool to integrate HIV testing into family planning 
services” (K24 HD088229-01; PI, McClelland; FP HIV SAIA Trial) with Dr. McClelland (primary 
mentor) and Dr. Sherr (co-mentor). To meet the national guideline that all women should be 
tested for HIV when accessing FP services (48), a data entry template for recording of HIV 
counseling and testing was integrated into the Kenyan FP registers in 2008. Our UW group is 
addressing this guideline in the FP HIV SAIA trial that compares SAIA to usual procedures to 
improve HIV testing at FP clinics in Mombasa County. A preliminary performance review of 
60 FP clinics in Mombasa County collected data from FP registers and clinic manager 
interviews. Of 60 clinics, a total of 4389 new FP clients were seen in a 3-month period (mean 
of 73 new clients per clinic (standard deviation [SD] 81)). Only 23 clinics reported any HIV 
testing, with a total of 420 new FP clients screened in a 3-month period. Three of these clinics 
performed the majority of screening (302 screens, 72%). As a Senior Fellow in Infectious 
Diseases, Dr. Eastment has led the implementation for this project. She prepared the 
applications for ethical and humans subjects approval in the US and Kenya, developed data 
collection tools, trained study staff, created the database, launched the study, and coordinated 
planning, logistics, and dissemination of findings with Mombasa County. This study has 
provided Dr. Eastment with valuable experience in implementing SAIA, and demonstrates the 
great potential for additional implementation research in Mombasa. 
 
C.1.E. Preliminary data on cervical cancer screening in Mombasa County from the FP 
HIV SAIA trial show low screening and incomplete documentation.  Dr. Eastment has 
had discussions with key collaborators in Mombasa County including the Director of Health 
(see letter), noting that the majority of cervical cancer screening is conducted in either FP 
clinics or HIV clinics. The County’s policy is that screening should be documented in the FP 
register. Cervical cancer screening is reported to the County for all clinics that receive County 
FP commodities. These data are compiled into the District Health Information System (DHIS). 
The quality of different data sources has not been formally evaluated. Preliminary results from 
the cervical cancer screening review linked to the FP HIV SAIA trial underscore the importance 
of the project proposed in the present application. The 60 clinics analyzed saw a mean of 204 
FP clients per clinic (SD 172) during a 3-month period (a total of 12,236 clients over the three-
month period of our data review). Of the 60 clinics, 4 (7%) did not record cervical cancer 
screening in the FP register, but reported recording it elsewhere. Because we did not review 
these alternative records in this initial survey, these clinics are excluded from subsequent 
analyses. Of the 56 remaining clinics, 7 (13%) recorded any cervical cancer screening. Only 
120 women (1%) were screened. These preliminary data suggest few FP clinics perform 
cervical cancer screening, and that most women accessing FP services are probably not being 
screened for cervical cancer. Of the clinics offering screening, not all are documenting this 
activity in the FP register. Moving forward, data collected in outside logs could be entered into 
FP registers or abstracted separately. These preliminary data provide valuable information for 
this K08 proposal, which will comprehensively document screening practices in Mombasa 
County using all the available documentation sources. We will explore why screening 
prevalence and appropriate documentation is so low, and address this implementation gap 
with clinic-specific interventions using SAIA. 
 
E. METHODOLOGY 
 
1. STUDY AREA 
 
This study will be based at Family Planning clinics in Mombasa County, Kenya. In 2016, 
there were estimated 132 FP clinics in Mombasa County from which we will select our 
sample. Family planning clinics will be public facilities and private facilities with 
County-supplied FP products. These FP clinics will be located across Mombasa 
County in all 6 sub-counties (Changamwee, Jombvuu, Mvita, Likoni, Kisauni, Nyali). 

 
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Included FP clinics may only provide FP care, or may be part of a larger facility with 
other types of care (e.g. ANC, pediatrics, CCC, etc).  
 
2.  STUDY POPULATION 
 
There will be three different groups of subjects in this research proposal:  
1. Individual FP clients’ data will be abstracted from FP registers. However, there will be no 
contact with individual FP clients by study staff. 
2. Family planning clinics as a unit to be randomized. Study staff will work with clinic staff for 
both data abstraction and during the intervention if randomized to this arm of the trial. In 
addition, micro-costing and time-and-motion studies will be conducted in FP clinics for the 
SAIA budget impact analysis.  
3. Family planning clinic managers and staff.  
 
Inclusion Criteria: 
Family planning clinics  
 
Sampling plan: A selection of 20 FP clinics will be selected to be randomized to SAIA versus 
usual procedures. The FP clinics will receive County-supplied FP products, which generally 
means they are reporting their activities to the County, including cervical cancer screening. 
Clinics will be spread out throughout the 5 sub-Counties in Mombasa. 
Eligibility: All FP clinics that receive County-supplied FP products will be eligible to 
participate. 
 
FP clinic managers and staff 
Sampling plan: A selection of FP clinic managers and staff will be approached to answer 
questions about their respective FP clinics in a semi-structured interview. To understand the 
implementation of cervical cancer screening in the CFIR framework, in-depth interviews will 
also be conducted with a random sampling of FP clinic managers. 
Eligibility: Any FP clinic manager that is 18 years and older is eligible to be interviewed. These 
clinic managers can be male or female.  
 
FP client data abstraction: Family planning client data will be abstracted from included FP 
clinics for female FP clients who access FP clinics during our data collection windows. We 
anticipate including all female FP clients. 
 
Exclusion Criteria 
Family planning clinics  
Any clinic that is expected to be closed during part or all of the SAIA intervention period will 
be excluded. Any FP clinic that was previously included in the FP HIV SAIA trial will be 
excluded. 
FP clinic managers and staff 
No exclusion criteria 
FP client data abstraction: No exclusion criteria. 
 
3. STUDY DESIGN 
This is a cluster-randomized controlled trial with FP clinics as the unit of randomization. 
 
4. PROCEDURES 
C.2.A. AIM 1: To describe the cervical cancer screening care cascade, from 
identification of female clients age 21-65 years old, through referral for follow-up of 
clients with positive or suspicious screens, in FP clinics in Mombasa County. Following 
characterization of this cascade, we will conduct an analysis of correlates of failure to 
screen for cervical cancer in FP clients seen over a one-year period. 

 
13 
Study design:  This 
aim 
will 
be 
accomplished 
by 
abstracting data from 
FP 
registers 
and 
interviewing 
clinic 
managers. Given the 
results 
of 
the 
preliminary 
data 
presented 
in 
C.1.E, 
any 
other 
logs 
for 
cervical 
cancer 
screening will also be 
examined to get the 
most 
complete 
and 
accurate 
representation of current screening practices. Brief structured interviews with clinic managers 
will be conducted to understand screening and referral practices in individual FP clinics. Data 
from FP registers will be merged with clinic-level data from FP clinic manager structured 
interviews to explore correlates of screening. 
 
Procedures: Cervical cancer screening care cascade: Quantifying the screening care 
cascade from FP client identification through referrals of positive or suspicious screens will 
follow a series of steps, illustrated in Figure 4. First, all FP clients 21-65 years of age and 
recorded at least once during a 3-month period will be identified as a ‘cohort’ for evaluation 
over the next 12 months of register data. This step is illustrated taking place retrospectively 
during the first three months in the figure. Any FP client outside this age range will not be 
counted as screening is not recommended. The cohort will be followed, using periodic data 
abstraction from the register over the next 12 months. The goal will be to identify whether or 
not cervical cancer screening was completed within 12 months. Among women with 
completed screens, we will also determine how many were positive (lesions meeting criteria 
for dysplasia, or other positive test) or suspicious, and how many of these women were 
referred for care. To supplement these data, any clinic-based cervical cancer screening logs 
will also be examined for the same time-period as the FP register abstraction. To understand 
if FP register documentation is capturing screening, this cascade will be compared with the 
County DHIS reports, which provide individual clinic level data. The level of correlation 
between these two sources for total FP clients and total screens will be calculated. While we 
anticipate using FP registers in Aim 2, we may modify this approach, based on quality and 
correlation of DHIS and FP register data.  
 
Correlates of screening: Data will be abstracted from the FP registers for individual level 
information (e.g. number of HIV-positive clients, number of FP commodities dispensed). Brief 
in-person structured interviews with clinic managers (e.g. staff to patient ratio, manager 
experience, commodities/resources available in clinic, size of population served, 
urban/periurban/rural, public vs. public/private partnership) will collect clinic-level 
characteristics from all included clinics and together, these sources will be used to explore 
individual- and clinic-level correlates of screening that are expected to be generalizable to all 
FP clinics in Mombasa County. 
 
Figure 3. Planned cervical cancer screening care cascade using FP register 
and other log abstractions 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Months in the FP register 
Data abstraction of 
FP clients 
13 
14 
15 
Abstraction following FP clients from first 3 months through a full 12 months 
Figure 4. Family planning register abstraction schema.  

 
14 
 
Data management: Data abstracted from FP registers, other clinic logs, and structured 
interview data from clinic managers will be aggregated in a REDCap database (49).  
 
C.2.B. AIM 2: To test whether SAIA increases cervical cancer screening compared to 
usual procedures in a cluster randomized trial in 20 FP clinics in Mombasa County. 
 
Procedures: Quantitative data: Initial information from intervention and control clinics will be 
obtained from clinic managers. Details about clinic characteristics such as size of the 
population served and what other services are provided will be collected. Staff characteristics 
will be collected including the number and type of staff, level of education, and experience of 
the clinic managers. Cervical cancer screening data will be collected from FP registers 
including screening method (VIA, visual inspection with Lugol’s iodine [VILI], HPV with VILI, 
HPV alone, Pap smear), clients’ HIV status, cervical cancer screening results (positive, 
negative, suspected), and information on referrals of positive or suspicious screens. These 
data will be collected at the beginning of the trial during a baseline period before any study 
procedures are completed, and then periodically every 3 months for all clinics.  
Qualitative data: Qualitative IDIs will be conducted with clinic managers to understand cervical 
cancer screening practices and barriers. Study staff will explain the risks and benefits of 
participating in the IDIs and the procedures in place to safeguard their opinions. Clinic 
managers will then be asked to provide written informed consent to participate in an IDI. Types 
of questions study staff would ask include: “Describe what you know about cervical cancer 
screening.”; “In your clinic, what do you think are barriers to screening more women for cervical 
cancer?”’ “What are some ways that cervical cancer screening could be improved in your 
clinic?”. These IDIs will be completed both prior to the trial and during the trial. Questions in 
the IDIs will be adapted from readily-available CFIR questions to understand where, when, 
and why the intervention works (50). 
 
Randomization: Restricted randomization will be used to randomize FP clinics into intervention 
and control arms based on clinic size and location in Mombasa County(51). If facilities differ 
dramatically in cervical cancer screening rates at baseline, we will also stratify the 
randomization according to this factor. 
 
Testing SAIA in intervention clinics: Clinics randomized to the intervention arm will be 
introduced to the five steps of SAIA by study staff (see C.1.B and C.1.D). The cascade analysis 
will be performed within the FP clinic to identify drop-offs in cervical cancer screening and 
referrals, using an Excel-based tool adapted from previous SAIA trials. Flow mapping 
performed by clinic and study staff will describe the cervical cancer screening process 
including who the client interacts with, timing of these interactions, any cervical cancer 
screening performed, and any referrals made. Initial drafts will be reviewed together with clinic 
and study stuff to ensure adequate and complete representations of processes. Study staff 
will work with clinic staff to identify bottlenecks in the process and potential solutions to 
improve flow. Proposed solutions will be implemented, and the process will be examined again 
to determine the effect of the implemented changes. The cycle will be repeated approximately 
every 6-8 weeks during the RCT. Data similar to baseline quantitative data, including numbers 
of FP clients, number of FP clinic visits, and proportion of FP clients who are screened for 
cervical cancer, will be abstracted to monitor change in screening over the study period.  
Control clinics: Clinics randomized to the control arm will continue usual procedures. Periodic 
evaluation of cervical cancer screening rates will be examined every 3 months using FP 
register data.  
 
C.2.C: AIM 3: To determine the cost and budget impact of using SAIA to increase 
cervical cancer screening in FP clinics in Mombasa County. 
 

 
15 
Activity Based Micro-Costing Methods: Costs associated 
with implementing SAIA will be estimated using micro-costing 
methods to estimate costs for SAIA start-up, personnel 
required, 
transportation 
to 
and 
from 
study 
clinics, 
communication costs, consumable materials, and any 
overhead costs. These cost data will be abstracted from the 
study budget, clinic expense reports, published labor costs, 
and interviews with the staff. Data on costs of the SAIA 
intervention will be collected at both the beginning of the 
intervention to estimate start-up costs and during the 
intervention to estimate on-going implementation costs. Costs 
will be characterized as fixed costs or variable costs that could 
change over time. We will not include private identifiable 
information about interviewees. 
 
Time-and-Motion 
Studies: 
Time-and-motion 
studies 
estimate the time involved in each step of SAIA. These will be 
estimated over a 2-week period at each intervention site. 
Multiple SAIA visits will be averaged to give the best estimate 
of time needed for the intervention. Any time that is for 
research purposes only, like consent processes, will be 
subtracted from the total time of the intervention. Multiple 
providers and clients will be observed to provide a good estimate of the time from clinic entry 
to clinic exit for a cervical cancer screening. We will not collect private identifiable information 
about individuals that we will be observing and we will not interact with FP clients or clinic staff 
while we are doing these observations. The micro-costing and time-and-motion studies will be 
used to calculate the average cost of a new cervical cancer screen. 
 
Budget Impact Analysis: The budget impact analysis will include direct program costs from 
the perspective of the DOH, that are estimated from the micro-costing methods illustrated 
above. Both the ‘top-down’ approach using routine expense reports and the ‘bottom-up’ 
approach using micro-costing will be used to refine our analyses. Standard practices for 
economic evaluations in health (52) and guidelines from the Panel of Cost-Effectiveness in 
Health and Medicine (53) will be used where possible to produce results that are accurate and 
generalizable. A standard 3% discounting rate will be used, with sensitivity analyses that use 
no discounting and 5% rate. Further sensitivity analyses will be conducted to produce best 
estimates under varying scenarios.  
 
 
Figure 5. Study Design.  

 
16 
5.  SAMPLE SIZE DETERMINATION 
Aim 1: In 2016, there were an estimated 140 FP clinics in Mombasa County. We anticipate 
abstracting data from roughly 75% (N=105) of these FP clinics for this aim.  The clinics 
would be randomly selected from across Mombasa County, stratified by clinic size, including 
both high-volume and low-volume facilities. This cascade would aim to provide more 
information from a larger selection of clinics than the preliminary review in C.1.E. For the 
correlates of screening analysis, we estimate needing a total sample of 1672 FP clients, with 
49 (3%) women screened (Table 1). While we saw 1% of clients screened in our preliminary 
data, we anticipate that a higher proportion of women would be screened if we incorporate 
other registers being used by clinics. We anticipate being able to reach this number of 
clients in this study based on data from our preliminary study (with over 12,000 clients seen 
in 60 clinics in a 3-month period).  
 
 
 
 
 
 
 
 
Aim 2: The FP HIV SAIA Trial preliminary results suggest that very little cervical cancer 
screening is performed or being documented in FP registers. Of these available data, the 
proportion of FP clients screened was 0.6%. Because this screening prevalence may not be 
representative, we have used a more liberal estimation of 8% prevalence of screening based 
on the 2014 DHIS estimates (the most recent resource available). FP clinics had an average 
of 183 clients in a 3-month period. Most clients follow-up every 3 months so this is a 
conservative estimate of total clients in a 12-month period. We will assume that following the 
intervention, 20% of women will be screened for cervical cancer. To calculate a sample size 
for a cluster RCT, the cluster size variability (k) must be estimated with literature often citing 
k£0.25 and rarely exceeding 0.5 (54). We have estimated a k of 0.5 to be conservative, 
which translates to an intra-class correlation coefficient of approximately 0.05. Cluster size 
variability of FP clinics was calculated as 0.74 using data in 1.C.E. With an a=0.05, 10 
clusters per arm will provide 90% power to detect a risk difference of 12% from the 
intervention. 
6. DATA MANAGEMENT 
 
Aim 1:  
Cervical cancer screening care cascade: The care cascade will examine the counts and 
proportions aggregated from the selected FP clinics as outlined in Figure 3. This cascade will 
account for appropriate exits from the cascade (e.g. outside screening age range, or screened 
negative) and losses in the cascade (e.g. not screened, or positive screen not referred for 
follow-up). Our research group has experience working with this type of complex care cascade 
in the setting of isoniazid prophylactic therapy for tuberculosis prevention, where there are 
multiple steps at which individuals can exit appropriately, be lost, or move on to the next step 
(55). We aim to model a cervical cancer screening care cascade in a similar manner. 
Table 1. Sample sizes for failure to screen correlates for individual and clinic-level characteristics. 
 
Correlate present 
in women not 
screened 
Correlate present 
in women 
screened 
Absolute 
risk 
difference 
Total 
sample 
size* 
Individual-level characteristics 
 
 
 
 
     HIV-positive 
60% 
20% 
0.4 
376 
 
60% 
40% 
0.2 
1672 
     Younger age 
80% 
50% 
0.3 
581 
 
60% 
30% 
0.3 
717 
Clinic-level characteristics 
 
 
 
 
     Facility level 3-4 
20% 
60% 
0.4 
325 
 
40% 
60% 
0.2 
1672 
     High staff:patient ratio  
10% 
50% 
0.4 
237 
 
20% 
60% 
0.4 
325 
*This is the sample size required to have 80% power at an alpha 0.05 with varying proportions of women with 
different characteristics between women not screened and women screened. A ratio of screened to not 
screened of 1:33 was assumed. 

 
17 
Correlates of screening: Cervical cancer screening will be modeled as a binary outcome. 
Logistic regression will be used to examine correlates of screening including age, HIV status 
of clients, and specific clinic characteristics (detailed in Procedures). Analyses will be adjusted 
for any clustering by clinic. 
 
Aim 2: The primary outcome is the proportion of all FP clients aged 21-65 years who were 
screened for cervical cancer over the total number of eligible clients. These outcomes will be 
assessed every 3 months in the 12-month study period. We are requesting an extension of 
the trial for an additional 6 months.  Women who are not FP clients who are referred to a 
clinic solely for screening will be excluded as they could inflate the proportion of women 
screened. These women will be identified because they did not receive FP products or HIV 
testing and were only entered in the FP register because of screening. Generalized 
estimating equations with a binary link, clustered by clinic, will compare proportions 
screened in intervention versus control clinics longitudinally to account for correlation across 
time and between clinics. Interviews will be audio-recorded, transcribed, translated, coded 
for major themes, and analyzed using Dedoose software (56). 
 
7.  VARIABLES – DEPENDENT, INDEPENDENT, CONFOUNDERS 
 
Cervical cancer screening care cascade: The care cascade will examine the counts and 
proportions aggregated from the selected FP clinics. The variables to be abstracted from the 
FP register includes whether new or return FP client, HIV status, whether cervical cancer 
screening was conducted, what method of screening was used, what the results were, and if 
any referrals were made for this screening. This cascade will account for appropriate exits 
from the cascade (e.g. outside screening age range, or screened negative) and losses in the 
cascade (e.g. not screened, or positive screen not referred for follow-up).  
Correlates of screening: Brief structured interviews with clinic managers as well as FP register 
data abstraction will collect information on for example, size of the FP clinic, staff, training of 
staff, age of FP clients, HIV status of clients, availability of FP commodities, level of experience 
of FP clinic manager, availability of cervical cancer screening commodities, referral practices, 
and any perceived barriers or facilitators of cervical cancer screening.  
Cluster randomized trial: The primary outcome is the proportion of all FP clients aged 21-65 
years who were screened for cervical cancer over the total number of eligible clients. These 
outcomes will be assessed every 3 months in the 12-month study period. We are requesting 
an additional 6 months for a total RCT study duration of 18 months.  In-depth interviews will 
collect information about implementation of SAIA, barriers and facilitators to screening, and 
also using consolidated framework for implementation research (CFIR) constructs and 
domains.  
Aim 3: Interviews with clinic managers and clinic observations along with data regarding time 
and expenses to implement SAIA will be collected. This will include general salary ranges for 
study staff and clinic staff, commodities needed, time for different clinic procedures, for 
example.  
 
8. DATA COLLECTION INSTRUMENTS 
 
For Aim 1, we will be abstracting quantitative data from the family planning registers. We will 
also collect basic information including, for example, clinic location, clinic environment (rural, 
peri-urban, urban), primary population served, number of women seen per day and number 
of new women to the clinic, for example. 
 
In-depth interviews (IDIs) for the qualitative component of Aim 2 will be performed by trained 
study staff. This will follow a topic guide, and we anticipate asking open-ended questions 
about individual level, facility level and higher-level facilitators and barriers to cervical cancer 
screening of women presenting for family planning services. Examples of the most sensitive 
types of questions we would ask include: 

 
18 
• 
What patient characteristics informed your decision of whether to offer cervical 
cancer screening or not?  
• 
Describe how time limitations, financial limitations or staff conflicts/concerns affected 
your decision to offer cervical cancer screening.   
 
9. QUALITY ASSURANCE PROCEDURES 
 
For quantitative data, data will be manually line-listed where the original images are 
compared to the soft copy data by a two-person team, with one reading data from the image 
documented and the other verifying the hard copy to ensure that registry abstraction match 
the soft-copy database. Incorrect data entries are revised based on re-evaluation of the 
digital image of the register. If necessary, we will go back to the original register (source 
document) to look at the data again and clarify response that could not be read clearly in the 
image. A record of incorrect entries is maintained. Following our data management SOP for 
the Women’s Health Project in Mombasa, we will aim to have a data entry key-in error rate 
of less than 2 per 1000 entries, and monitor this periodically.  
 
For the in-depth interviews (IDIs), a modified member check will be conducted by presenting 
the final key findings to a subgroup of clinic administrators and providers to assess 
completeness, salience, and relevance of our identified themes. 
10. MATERIALS – EQUIPMENT AND SUPPLIERS 
 
We anticipate performing in-depth interviews and group activities such as sequential process 
flow mapping with facility staff and managers either at their facilities (where feasible and 
desirable) or at our research clinic. Our research clinic is located at Ganjoni Health Center, 
and has multiple private interview rooms, a boardroom that accommodates 20, and a 
classroom that accommodates 40. The clinic has a DS-2 Olympus Digital Voice recorder 
and microphone suitable for both individual and group recording.  
We have a central server, LAN, and fifteen computers in Mombasa (3 lab, 12 clinic/office). 
These are used for data entry, cleaning, analysis, administration, and communications. Two 
printers are located at the laboratory and three are located at the clinic.  
We have 5 offices at Ganjoni clinic, which house the research, clinical, and administrative 
staff. A fully equipped and furnished boardroom (capacity=20) and classroom (capacity=40) 
with LAN, internet connection, large screen monitor, white board, blackboard, are available 
at the clinic for training and teaching activities.  
There is one large office available at the laboratory at Coast Hospital, which is on the 
opposite side of town from Ganjoni Health Center. Depending on the location of activities 
with FP clinics in different areas, the study team may use the laboratory office for work or 
meetings when convenient.  
We have 2 vehicles in Mombasa for project use, including staff and participant transport, 
logistical support, and supplies. 
11. STUDY STRENGTHS/LIMITATIONS 
 
Aim 1 Limitations:  
Family planning register use: There are limitations in using program data from FP registers. 
These registers can have missing information or illegible entries. Also, some FP clinics may 
be using prior versions of FP registers that lack key information about cervical cancer 
screening. However, use of routinely collected register data is a main objective of IS and has 

 
19 
the advantage of scalability as new data sources are not required. This aims to keep 
programmatic costs low, and increase feasibility and scalability. To address issues with FP 
registers, any clinic-based logs will supplement data in FP registers. 
 
Poor correlation between FP register data and DHIS reports: After discussions with members 
of the Mombasa County DOH, cervical cancer screening should be documented in both FP 
registers and DHIS reports.  Results of this comparison will be presented to our County 
collaborators to find a solution for appropriate documentation. The County may decide to 
provide further guidance in register documentation to all FP clinics, or the County may decide 
that it is acceptable to use DHIS reports only for cervical cancer screening documentation. 
The current state of documentation for screening will guide the outcome assessment in Aim 2 
(see C.2.B). 
Screening outside FP clinics: The register abstraction process may not capture whether 
women have been recently screened. These women would appear to need screening and 
would be counted as a loss in the cascade. According to the DOH, the most common clinics 
for cervical screening are FP clinics and HIV clinics, but it is unclear where HIV-positive and 
HIV-negative women are actually accessing screening. The 2014 estimates (the most recent 
survey) of screening on the coast of Kenya suggest only 8% of women have ever been 
screened, which is higher than the 3-month screening prevalence of 0.6% in our preliminary 
data (C.1.E). Given this estimate, the possibility that a woman had been screened recently is 
expected to be quite low. Consequently, the vast majority of women require screening. Aim 1 
will be used to explore how many clinics are performing any screening, the proportion of 
women screened, and if screening differs by HIV status. We are focusing on FP clinics, as 
women attending these clinics are generally expected to be sexually active, at risk for HPV 
infection, and consequently, cervical cancer. Some HIV-positive women could access 
screening in HIV clinics. We anticipate quantifying the care cascade separately by client HIV 
status to better illustrate the landscape of screening practices. Future work could also examine 
cervical cancer screening in HIV clinics.  
 
Aim 2 Limitations:  
New versus all FP clients: If prevalence of screening is found to be very low in Aim 1 (as we 
expect), the RCT will aim to improve screening for both new and existing FP clients. However, 
if Aim 1 screening prevalence is higher than expected (we anticipate a cut-off of around 20% 
screening prevalence), then including existing clients may lead to significant misclassification. 
In this scenario, Aim 2 will focus on new FP clients only. We anticipate having sufficient power 
for this alternative approach. Preliminary data suggest an average of 60 new FP clients per 
clinic in a 3-month period (C.1.E), which we would anticipate would continue throughout a 12-
month period.  Ten clinics per arm in the RCT with only 60 new clients per clinic would provide 
at least 80% power to detect a 12% difference between intervention and control arms. 
 
Identifying women who should be screened: While it is a goal of IS studies to work within 
existing systems, the use of routinely collected FP register data to assess the RCT outcome 
has its challenges. It may be difficult to assess if a woman had been recently screened. This 
potential limitation may vary in women based on their HIV-status. Because screening for HIV-
positive women is recommended annually, HIV-positive women who should be screened may 
be identified by collecting FP register data over a 12-month period. On the other hand, HIV-
positive women may be accessing screening at HIV clinics and not FP clinics. In contrast to 
HIV-positive women, HIV-negative women should be screened every 5 years. As a result, 
data abstraction over a 12-month period may incorrectly identify women needing screening 
when they had recently been screened. The intervention then may appear to be more effective 
in HIV-positive women only because of the data abstraction period or alternatively, miss 
screening in HIV-positive women if it is occurring outside of FP clinics. We will use Aim 1 to 
understand screening practices for HIV-positive and HIV-negative women. We believe it will 
be reasonable to assume that the majority of women have not been screened recently, should 
be screened, and then counted in the outcome assessment. If our data in Aim 1 does not 

 
20 
support this assumption, we will adjust our approach depending on what we discover. For 
example, we may focus on HIV-negative women in the primary analyses as they would be 
less likely than HIV-positive women to have accessed screening elsewhere, or only include 
new FP clients as outlined in the first limitation. Alternately, we may find in Aim 1 that HIV-
positive women are screened at HIV clinics and not FP clinics. To address differences in 
screening practices by HIV status, we may stratify results based on HIV status, or exclude 
HIV-positive women from our primary analysis. The cluster RCT design provides robust power 
with the same clinic numbers and varying clients in each clinic. Using our conservative 
estimates of clients per clinic, we will have a sufficient sample size to stratify by HIV status or 
exclude HIV-positive women from the analysis.  
 
Current screening practices vary between FP clinics: It is possible that randomization would 
select a disproportionate number of clinics in either intervention or control arms that already 
perform screening. This may bias results. Aim 1 results will be used to decide whether to 
stratify randomization based on past screening rates.   
 
SAIA interventions not readily identifiable or feasible: Clinic staff may not identify solutions to 
address bottlenecks. In discussions with Dr. Sherr (based on his experience with both CQI 
and the development of SAIA), this can be addressed by highly trained, experienced study 
staff to guide clinic staff through SAIA. Additionally, other quality improvement tools like root 
cause analysis and fishbone diagrams can be helpful (57).  It is also possible that solutions 
proposed by staff members may not be feasible. Study staff will help staff to identify solutions 
that are more feasible in the given context and budget.  
 
Focus on cervical cancer screening: This study will focus on increasing screening and will not 
address the linkage to treatment for positive screens. While we recognize that referral and 
treatment are also important, these steps would add complexity to this small study, and we 
likely would not have sufficient power. In addition, we felt that focusing on screening provided 
a useful metric for evaluating whether SAIA is potentially effective for addressing cervical 
cancer prevention. To insure adequate care during the study, we will partner with our 
Mombasa County collaborators to work programmatically to ensure that women with positive 
screens are linked to treatment. The extension of SAIA to improve linkage to treatment is a 
potential aim of a future study. 
 
12. STUDY RESULTS DISSEMINATION PLAN 
Results of this study will be published so that the research community and public will have 
access to it. All publications and presentations of material and data related to this study will 
include acknowledgement of NIH grant support and a disclaimer following NIH guidelines. In 
accordance with NIH Public Access Policy, all publications will be submitted to PubMed 
Central.  
Data from this study will be available upon request after publication of the main study 
manuscripts. A standard approach will be followed for data sharing. Researchers requesting 
access to data will need to first submit a request in writing describing their qualifications, 
local IRB approval for the planned analyses, statistical analysis plans, and plans to secure 
the confidentiality and safety of the data. They will be required to agree, in writing, that they 
will not share the data with others, will use it only for the research purpose(s) delineated, 
and will return or destroy the data upon completion. All data will be de-identified. Approval 
from the Kenyatta National Hospital-University of Nairobi Ethics and Research Committee 
(KNH-UON ERC) will also be required to have access to any data.  
We will not develop any unique research resources, inventions, or patents as a result 
of this work.  
 
F. PERSONNEL 

 
21 
 
McKenna Eastment, MD, MPH, principal investigator (PI): Dr. Eastment is an acting instructor 
in the Department of Medicine at the University of Washington. She has worked previously 
with her mentor, Dr. McClelland, in leading implementation of his grant in family planning (FP) 
clinics in Mombasa County. This project is testing an implementation science tool to improve 
HIV testing in FP clinics. She will provide study oversight and act as the main implementer of 
the study.   
 
R. Scott McClelland, MD, MPH, mentor to principal investigator: Dr. McClelland is Professor 
of Medicine, Epidemiology and Global Health at the University of Washington. He is also a 
Visiting Lecturer, Department of Medical Microbiology, University of Nairobi. Dr. McClelland is 
the site leader for the Mombasa Cohort, a long-term open cohort study of female sex workers 
at risk for HIV and STIs. He has studies heterosexual transmission of HIV-1 in Kenya since 
1998. Dr. McClelland will serve as the mentor for Dr. Eastment for this study. He will provide 
mentorship and study oversight.  
 
Walter Jaoko, MBChB, MSc, PhD, Co-investigator, University of Nairobi: Dr. Jaoko is an 
Associate Professor in the Department of Medical Microbiology at the University of Nairobi. 
He has been a collaborator in Dr. McClelland’s research in Mombasa for over 10 years, and 
is the UON PI for Mombasa Field Site. Dr. Jaoko is also a Director of the Kenya AIDS Vaccine 
Initiative. In this project, he will liase with the KNH ERC, will work closely with Dr. Eastment 
and the site staff in Mombasa to implement the research, and facilitate communication 
between UON and Mombasa County.  
John Kinuthia, Site Principal Investigator, Kenyatta National Hospital Discordant Couples 
Center: Dr. Kinuthia is a Kenyan Obstetrician and Gynecologist who has worked with 
investigators at the University of Washington including Dr. McClelland for the past 10 years. 
For this project, Dr. Kinuthia will contribute to the development, implementation, analysis, and 
interpretation of the research.  Additionally, Dr. Kinuthia will provide content expertise in 
cervical cancer screening methods.  
 
Kishorchandra Mandaliya, MBChB, Laboratory Director, Mombasa: Dr. Mandaliya is a 
Pathologist at Pathcare Laboratories in Mombasa, and Director of our Research Laboratory 
in Mombasa. He has conducted research with Dr. McClelland and the UW group for over 20 
years.  In this project he will contribute to supervision of staff and trainees. Dr. Mandaliya will 
also provide content expertise in cervical cancer screening methods.  
 
Faiza Nassir, MBChB, MMed is an Obstetrician and Gynecologist employed by Mombasa 
County. The County Chief Officer of Health has identified her as the County Representative 
who will work closely on implementation and diseemination of the study findings. Dr. Nassir 
has prior experience in revising the family planning register in Mombasa as part of the 
APHIA II effort to expand HIV testing. She provided input on the development of this grant 
proposal, and will continue to serve as a co-investigator and advisor during implementation. 
She will act as a liaison with the County. 
 
 
G. TIMELINE 
 
Table 2: Timeline for activities during the K08 funding period 
 
 
Year 1 
Year 2 
Year 3 Year 4 
Aims 1, 2, 3 
Protocol and instrument development 
 
 
 
 
   
 
        
IRB application &  In-country ethical 
approval 
 
 
 
 
   
 
        
Staff hiring and training 
 
 
 
 
   
 
        
Aim 1 data collection 
 
 
 
 
   
 
        

 
22 
 
Year 1 is expected to be July 2018-June 2019 
Year 2 is expected to be July 2019-June 2020 
Year 3 is expected to be July 2020-June 2021 
Year 4 is expected to be July 2021-June 2020 
 
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Data analysis 
 
 
 
 
   
 
        
Manuscript preparation** 
 
 
 
 
   
 
        
Manuscript submission** 
 
 
 
 
   
 
        

 
23 
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27 
I. ETHICAL CONSIDERATIONS 
1. 
Involvement of Human Subjects 
There will be three different groups of subjects in this research proposal:  
1. Individual FP clients’ data will be abstracted from FP registers. However, there will be no 
contact with individual FP clients by study staff. We are seeking a waiver of written informed 
consent for this population.  
2. Family planning clinics as a unit to be randomized. Study staff will work with clinic staff for 
both data abstraction and during the intervention if randomized to this arm of the trial. In 
addition, micro-costing and time-and-motion studies will be conducted in FP clinics for the 
SAIA budget impact analysis.  We will ask for assent of clinic managers to participate in the 
randomized trial.  
3. Family planning clinic managers and staff.  We will verbally consent clinic managers/staff 
for brief semi-structured interviews. We will seek written informed consent for in-depth 
interviews with clinic managers/staff.  
 
All the proposed research activities involving human subjects will occur in Mombasa County, 
Kenya. This is in collaboration with Mombasa County Department of Health, and will provide 
real-time input and sharing of results. Given the location of this study in Mombasa County, we 
expect that all participants will be of African descent. The FP clinic staff and managers will be 
both men and women. All FP clients eligible for cervical cancer screening are expected to be 
women aged 21-65 years. 
 
This study will be conducted in collaboration with the Mombasa County Department of 
Health (DOH). Dissemination of results will be implemented in collaboration with 
representatives from the Mombasa County Department of Health. 
2. Research Material Obtained from Participants 
Family planning registers: Routinely collected data will be abstracted from FP registers about 
basic demographics, HIV status, cervical cancer screening and referrals of FP clients.  
 
Family planning clinic managers and staff: Family planning clinic managers and staff will be 
approached to participate in brief semi-structured interviews and in-depth interviews in clinics 
chosen for the RCT. Managers will be asked to provide verbal assent for semi structure 
interviews. Clinic staff participating in IDIs will be asked to sign written informed consent. 
These interviews will be recorded and detailed field notes will be collected by the interviewer.  
 
Data Management: Abstracted FP register data will be uploaded to a REDCap database. 
Again, there will be no personal identifiable data collected or uploaded. Interview data will be 
transcribed and translated. These data will be stored on a password protected encrypted 
computer. Data that have not been decrypted appear as blank files. Interview recordings will 
be destroyed five years after completion of the research. Informed consent documents will be 
stored in a locked cabinet and separate from both recordings and transcripts from interviews. 
 
3. Recruitment of Subjects and Consent Procedures 
Recruitment of Subjects: 
Individual FP clinics will be approached after the sub-county Ministries of Health are aware 
of the study. A letter of introduction from the Mombasa County Department of Health will 
accompany study staff. Clinic managers will assent to the preliminary clinic review and then 
if selected to be randomized into the study.  
 
Clinic managers and clinic staff will be approached by study staff to discuss participating in in-
depth interviews. Study staff will identify clinic managers and staff by visiting the clinic and 
asking for the clinic ‘in-charges’. Staff will ask the clinic managers when would be an 
appropriate time to answer brief questions for Aim 1 and Aim 3 brief structured interviews. Aim 

 
28 
2 in-depth interviews will be scheduled at a time and place convenient to the clinic members. 
Individual health workers will be recruited confidentially at a convenient time in their work day. 
They will be reassured that they are free to decline to participate. 
 
Consent procedures:  
Family planning clients: As only FP register data will be used, individual FP clients will not be 
participating in this study. We will use a unique code number to track women over the year in 
the FP register for outcome data. Client names and contact information will be covered during 
image capture and for storage. Personal data will be de-identified upon entry into a database 
as aggregated data. Digital records will include a unique code without personal identifying 
information.  
 
Family planning clinics: Using previously successful procedures with FP clinics during the FP 
HIV SAIA trial, FP clinic managers will be approached. Study staff will explain the study and 
ask that FP clinic manager provide assent to participate in the study.  
 
Family planning clinic managers and staff: Semi-structured interviews and IDIs with health 
workers will be conducted in confidential settings either within FP clinics, their associated 
facilities, or at our research facilities, depending on the space available on-site at the different 
clinics. Individual health workers will be recruited confidentially at a convenient time in their 
work day. They will be reassured that they are free to decline to participate. Clinic managers 
participating in semi-structured interviews will be asked to provide verbal assent as these 
interviews are anticipated to be brief. Health workers who agree to participate in IDIs will be 
asked to provide written informed consent. The consent forms will be approved in advance by 
the Kenyatta National Hospital-University of Nairobi Ethics and Research Committee (KNH-
UoN ERC) and the University of Washington Human Subjects Research Committee (UW 
HSRC). Informed consent procedures will be completed prior to initiating the interview. The 
consent form will include the possibility of negative consequences regarding employment if 
staff members are identified as providing negative information about the workplace.  
 
This consent process includes the following procedures: 
-Ensure that the consent process is taking place in a private area.  
-Use the informed consent checklist as a guide throughout the consent process and enter 
the time at the beginning of the sheet.  
-Give the consent form for the client (FP clinic staff member) to read in their language of 
choice (English or Kiswahili) and allow enough time for the staff member to read the consent 
form.   
-After the potential participant reads the consent/has it read to her, review the key points. 
-Ask the potential participant if they have any questions and discuss any questions or 
concerns.   
-Clarify any misunderstanding and determine eligibility.   
-Once eligibility is determined the counselor can proceed with the consent process.   
-Sign the informed consent checklist and retain if the staff member enrolls   
-The potential participant should sign and date first and must write her/his own name. The 
person obtaining consent signs last.   
Potential participants must demonstrate their understanding of key concepts about the 
informed consent before signing. Specific questions used to assess understanding may 
include items such as:  
 
1.  Please describe what your role will be in this research study? 
 
2.  Is participation in these interviews voluntary? 

 
29 
 
3.  If you do not participate, will this reflect negatively in your work evaluations or job 
status?  
The consent form will include the possibility of negative consequences regarding 
employment if staff members are identified as providing negative information about the 
workplace. We will also explain how we plan to keep these data confidential. 
 
4. Risk to Human Subjects 
No adverse effects would be expected to result directly from the semi-structured interviews 
or observation of clinic procedures. However, there is potential for negative consequences 
regarding employment for staff if they are identified as providing negative information about 
the workplace. This could take the form of harassment, transfer, lack of promotion, 
dismissal, etc. Staff in smaller facilities might be particularly at risk for inadvertent disclosure 
if the content of their interviews identified them as being in a particular position. Several 
steps will be taken to mitigate this risk, as detailed in the section, “Adequacy of Protection 
against Risks”.  
For patients accessing care through FP clinics, the risks associated with these services will 
not be different from the risks associated with these interventions in the absence of the 
research. Data will be abstracted from routinely collected data in FP registers. While this is 
personal identifiable data in the FP registers, a unique code will be used to track women 
over time in the register. 
5. Adequacy of Protection Against Risks 
Family planning clients: For patients accessing care through FP clinics, the risks associated 
with these services will not be different from the risks associated with these interventions in 
the absence of the research. Data will be abstracted from routinely collected data in FP 
registers. While this is personal identifiable data in the FP registers, a unique code will be 
used to track women over time in the register. Personal names and contact information will 
be covered and only the unique code will be used. Images captured will have the unique 
code but no other personal identifying information. Data entered into the database will be 
aggregated. 
 
FP clinic managers and staff: Brief semi-structured interviews will ask specific questions and 
record these answers on data collection forms. We do not anticipate that these questions 
would be sensitive. As more sensitive information is collected for IDIs, these procedures are 
slightly different. Health worker names will not be recorded in any location except on their 
informed consent form. Completed informed consent documents will be stored in a locked 
cabinet within a locked data room at our research facilities in Mombasa. No other personal 
identifying information will be collected. To further protect facility staff from negative 
repercussions if they report workplace problems, several additional steps will be taken as 
needed:  
• 
Staff will be specifically informed of potential risks during the written informed consent 
process, and will be assured that they may decline to participate   
• 
We will not inform supervisors of staff members decision to participate or decline 
participation in the interviews   
• 
Data from multiple staff members may be aggregated into a single report for the 
purposes of discussion, development of best practices, quality improvement steps, etc.  
• 
Participants will be asked, at the end of each interview, if they have concerns about 
the use of these data within the framework of the protections noted above. If so, these 
concerns will be taken into account in making decisions about whether/how to utilize 
these data.   
• 
On the informed consent document, we will provide telephone numbers for contacting 
both the research team and the ethics committee if an employee feels that they are 

 
30 
being treated unfairly as a result of participation in the research. If such an event 
occurs, we will work with our partners in the Mombasa County DOH to take steps to 
correct the problem and add protections against recurrence.   
 
Health worker IDIs will be recorded, and detailed field notes will be collected by the interviewer. 
Interview data will be stored on a password-protected encrypted computer. Data that have not 
been decrypted appear as blank files. Interview recordings will be destroyed at the completion 
of the research.  
 
6. Potential Benefits 
Benefits to all Subjects: 
The initial cervical cancer screening care cascade will be presented to Mombasa County. This 
presentation will inform the County of the current screening activities that may highlight areas 
for continued important. In addition, comparing the FP registers with DHIS report will provide 
the County with information about the current state of register documentation. If data quality 
is not consistent and there is not enough correlation between registers and DHIS reports, it 
might serve as an impetus for further guidance on appropriate documentation to all FP clinics.  
 
All clinics that could be randomized will have a performance evaluation of cervical cancer 
screening procedures completed. This evaluation alone is useful quality assurance 
information for the clinic. Clinics could then act independently to make changes to procedures 
to increase cervical cancer screening.   
 
Benefits to Subjects in the Intervention Arm of the Trial: 
Clinics in the intervention arm will have the benefit of in-depth review of procedures and then 
SAIA interventions that aims to increase cervical cancer screening.  This will benefit individual 
clinic attendees who may be due for screening or have never been screened for cervical 
cancer. Attempts will also be made to make sure that if women are screened by their 
FP clinics and have an abnormal screen that they are linked with follow-up care.  
 
The increase in cervical cancer screening and improvement of mechanisms to continue 
to screen women using SAIA will continue to benefit women and their reproductive 
health.  
 
7. Importance of knowledge to be gained: 
If successful, this research will provide tools that will assist health care workers and managers 
to understand and improve systems for integrating cervical cancer screening into FP services. 
The initial cervical cancer care cascade is valuable information for the County to understand 
the current state of screening and the quality of cervical cancer screening documentation. The 
SAIA intervention will be conducted in collaboration with local health authorities, and could be 
scaled up throughout Kenya and in other high burden African countries.  Further, the budget 
impact analysis of SAIA will provide useful information for both the local and national Ministries 
of Health to use in scaling up cervical cancer screening. The risks to participants are minimal, 
while the benefits to individual FP clinic clients and the broader population of HIV-positive and 
HIV-negative women are substantial.  
 
8. Data safety and monitoring plan 
The Principle Investigator will take responsibility for data safety and monitoring as part of the 
oversight of this study. Information collected from FP clients and FP clinic managers will all be 
de-identified upon storage. Because we will not be enrolling individual participants for the 
RCT, and individual participant data entered into our dataset will not contain personally 
identifiable information, we believe this trial does not require a DSMB. This is in conversation 
with NIH NCI Program Officer. However, if data safety issues arise, these would be reported 
promptly to both the Kenyatta National Hospital-University of Nairobi Ethics and Research 

 
31 
Committee and the University of Washington Human Subjects Research Committee. 
 
9. Clinical trial registration 
This trial has been registered with clinicaltrials.gov 
 
 
J. BUDGET 
 
Category 
Anticipated 
Amount in 
USD (Year 1) 
Anticipated 
Amount in 
USD (Year 
2) 
Anticipated 
Amount in 
USD (Year 3) 
Anticipated 
Amount in 
USD (Year 
4) 
Materials and 
Supplies 
$6679.00 
$6679.00 
$6479.00 
$6479.00 
Consultant services  
$1500.00 
$1500.00 
$1500.00 
$1500.00 
Communications  
$1000.00 
$1000.00 
$1000.00 
$1000.00 
Transportation/travel $2000.00 
$2000.00 
$2000.00 
$2000.00 
One computer 
$2000.00 
 
 
 
UW-Kenya employees 
and service fee 
$29,821.00 
$29,821.00 
$29,821.00 
$29,821.00 
Total costs:  
$43,000.00 
$41,000.00 
$40,800.00 
$40,800.00 
 
 
K. APPENDICES 
Informed consent documents 
Letter from Dr. Patta 
Curriculum vitae attached for principal investigators and co-investigators