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Grant Details

Grant Number: 1U01CA209861-01A1 Interpret this number
Primary Investigator: James, Aimee
Organization: Washington University
Project Title: Reducing Rural Colon Cancer Disparities Through Multi-Level Intervention on Follow-Up After Abnormal Screening Tests
Fiscal Year: 2017


ABSTRACT Colorectal cancer (CRC) is a leading cause of cancer death in the US and many rural areas experience disparately high CRC mortality rates. In rural Southern Illinois, in particular, CRC mortality is persistently high despite state-wide and nation-wide declines. Routine screening can reduce mortality, but is most impactful when individuals who test positive receive a timely and complete diagnostic follow-up. Our long-term objective is to eliminate the CRC disparities in rural Southern Illinois. For many rural and safety-net health systems, including in Southern Illinois, fecal testing (FOBT) remains a common first-line strategy for CRC screening, and many do not receive timely and complete diagnostic follow-up. Our objective in the current application is to collaborate with a rural health system to implement and evaluate a multi-level intervention (patient, provider- staff, clinic) to improve follow-up of positive FOBTs. Based on the Consolidated Framework for Implementation Research and using a Stepped Wedge Research Design, our study will address both effectiveness and implementation outcomes. Our Specific Aims are: (Aim 1) Assess pre-implementation conditions to inform strategies that will maximize the likelihood of successful implementation of the multi-level intervention; (Aim 2) Evaluate the impact of a multi-level intervention addressing patient, provider-staff, and clinic-level factors to improve follow-up after positive FOBT; and (Aim 3) Evaluate moderation and mediation of multi-level intervention effects using mixed methods. To achieve this, we will conduct a pre-implementation evaluation of each clinic (n=23) and randomly assign sites to one of three clusters to receive the multi-level intervention in staggered intervals. We will estimate the intervention effect both within and between clusters using data collected by the healthcare organization including report of a positive FOBT result to the referring provider and the patient, initiation of colonoscopy after positive FOBT, and completion of colonoscopy after positive FOBT. Secondary outcomes are documentation of screening/surveillance interval after colonoscopy and time to colonoscopy after positive FOBT. Quantitative methods will be used to assess clinic-level mediators and moderators and patient-level moderators of the intervention effect. Qualitative methods will explore other potential moderators or mediators at the patient-, provider-staff-, clinic-levels that generate explanations of how and why the intervention worked for some but not others. The Stepped Wedge approach is particularly fitting for our research purposes because practical, logistical and financial reasons make it impossible to implement the intervention in half of all clusters simultaneously. Our innovative multi-level approach has been guided by implementation theory and developed in close partnership with a rural health system. The co-construction of this study and proposed intervention with rural healthcare stakeholders enhances the potential for significant and sustainable change. Twenty-percent of the US population live in rural areas, and there is a critical need for real-world interventions that can function in these predominantly underserved regions of the United States.


An overview of optimal designs under a given budget in cluster randomized trials with a binary outcome.
Authors: Liu J. , Liu L. , James A.S. , Colditz G.A. .
Source: Statistical methods in medical research, 2023 Jul; 32(7), p. 1420-1441.
EPub date: 2023-06-07.
PMID: 37284817
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Distance and Transportation Barriers to Colorectal Cancer Screening in a Rural Community.
Authors: Lee K.M.N. , Hunleth J. , Rolf L. , Maki J. , Lewis-Thames M. , Oestmann K. , James A.S. .
Source: Journal of primary care & community health, 2023 Jan-Dec; 14, p. 21501319221147126.
PMID: 36594346
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Sample size calculation in three-level cluster randomized trials using generalized estimating equation models.
Authors: Liu J. , Colditz G.A. .
Source: Statistics in medicine, 2020-10-30; 39(24), p. 3347-3372.
EPub date: 2020-07-28.
PMID: 32720717
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