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| ID | Type | Description | Link |
|---|---|---|---|
| 1K08CA228761 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Cancer Institute (NCI) | NIH |
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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. To address this implementation gap, this application proposes three aims. In Aim 1, the investigators 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, an analysis will be conducted 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, the investigators will determine the cost and budget impact of using SAIA to increase cervical cancer screening in FP clinics in Mombasa County. 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. The long-term goal is to decrease cervical cancer mortality and improve women's health.
Specific Aims 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) (1-4). The significant disparity between cervical cancer outcomes in the United States and LMICs is largely attributed to differences in screening (5). While approximately 89% of US women receive cervical cancer screening (7), less than 5% of women in LMICs have been screened (4). Barriers to screening in LMICs include challenges with infrastructure to support screening, competing health interests, lack of education, low health literacy, and poverty (2, 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. The investigators 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. The investigators 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 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).
The proposed aims will provide valuable training in key competencies in implementation research, with measurable and objective indicators of success in skills building and career development. The proposal leverages exceptional resources at the University of Washington (UW) and our longstanding and productive partnership with multiple institutions in Kenya (see letters from Mombasa County DOH, University of Nairobi, Kenyatta National Hospital) to facilitate Dr. Eastment's career advancement.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control | No Intervention | 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. | |
| Intervention with SAIA | Experimental | Clinics randomized to the intervention arm will be introduced to the five steps of SAIA by study staff. 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Systems Analysis and Improvement Approach | Other | 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 (42-44). |
| Measure | Description | Time Frame |
|---|---|---|
| Cervical Cancer Screening | Proportion of all FP clients aged 21-65 years who were screened for cervical cancer over the total number of eligible clients | Aggregated data across 18 months, individual participants were not followed over time. |
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For FP Clinics:
Inclusion Criteria:
Exclusion Criteria:
FP clinic managers and staff:
Inclusion Criteria:
Exclusion Criteria:
Cervical cancer screening will only be performed in women. However, our unit of randomization and intervention are whole family planning clinics which have both men and women.
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| Name | Affiliation | Role |
|---|---|---|
| McKenna C Eastment, MD | University of Washington | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Family planning clinics | Mombasa | Kenya |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38012647 | Derived | Eastment MC, Wanje G, Richardson BA, Mwaringa E, Patta S, Sherr K, Barnabas RV, Mandaliya K, Jaoko W, Mcclelland RS. Results of a cluster randomized trial testing the Systems Analysis and Improvement Approach to increase cervical cancer screening in family planning clinics in Mombasa County, Kenya. Implement Sci. 2023 Nov 27;18(1):66. doi: 10.1186/s13012-023-01322-y. |
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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.
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Family planning clinics were the unit of randomization and whole clinics were randomized to intervention arm or control arm. Individual participants were not enrolled. Outcomes about women were aggregated across each clinic and not all individual level data including age were collected as we were using paper registry records with identifiable information redacted.
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| ID | Title | Description |
|---|---|---|
| FG000 | Intervention With SAIA | Systems Analysis and Improvement Approach: 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 (42-44). |
| FG001 | Control Arm | Following usual procedures |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
No individual participants were enrolled in the trial.
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| ID | Title | Description |
|---|---|---|
| BG000 | Control | 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. |
| BG001 | Intervention With SAIA |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Customized |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Cervical Cancer Screening | Proportion of all FP clients aged 21-65 years who were screened for cervical cancer over the total number of eligible clients | These are aggregated participants in intervention and control clinics. Individual participants were not enrolled in the trial as this was a cluster randomized trial. Results are aggregated across all clinics and presented here but these are not RCT participants as they were not enrolled. | Posted | Number | individuals screened | Aggregated data across 18 months, individual participants were not followed over time. | Family planning clinics | Family planning clinics |
|
18 months
Collected as any adverse event related to SAIA at the clinic level, as we did not enroll individual participants into the trial. "Participants" here are the FP clinics.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Control--no Individual Participants Enrolled | 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. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. McKenna Eastment | University of Washington | 5742101120 | mceast@uw.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Feb 1, 2021 | May 20, 2024 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D002583 | Uterine Cervical Neoplasms |
| ID | Term |
|---|---|
| D014594 | Uterine Neoplasms |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
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Cluster randomized trial of family planning clinics
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|
Systems Analysis and Improvement Approach: 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 (42-44). |
| BG002 | Total | Total of all reporting groups |
| FP clinics |
|
| Sex/Gender, Customized |
| Race and Ethnicity Not Collected | Race and Ethnicity were not collected from any participant. |
| Region of Enrollment | participants |
|
| OG001 | Intervention With SAIA | Systems Analysis and Improvement Approach: 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 (42-44). |
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|
|
| 0 |
| 10 |
| 0 |
| 10 |
| 0 |
| 10 |
| EG001 | Intervention With SAIA--no Individual Participants Enrolled | Systems Analysis and Improvement Approach: 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 (42-44). | 0 | 10 | 0 | 10 | 0 | 10 |
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| D009369 |
| Neoplasms |
| D002577 | Uterine Cervical Diseases |
| D014591 | Uterine Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D000091662 | Genital Diseases |