Factors Influencing Implementation of a Colorectal Cancer Screening Improvement Program in Community Health Centers: an Applied Use of Configurational Comparative Methods

Abstract

BACKGROUND: Evidence-based programs such as mailed fecal immunochemical test (FIT) outreach can only affect health outcomes if they can be successfully implemented. However, attempts to implement programs are often limited by organizational-level factors. OBJECTIVES: As part of the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) pragmatic trial, we evaluated how organizational factors impacted the extent to which health centers implemented a mailed FIT outreach program. DESIGN: Eight health centers participated in STOP CRC.The intervention consisted of customized electronic health record tools and clinical staff training to facilitate mailing of an introduction letter, FIT kit, and reminder letter. Health centers had flexibility in how they delivered the program. MAIN MEASURES: We categorized the health centers' level of implementation based on the proportion of eligible patients who were mailed a FIT kit, and applied configurational comparative methods to identify combinations of relevant organizational-level and program-level factors that distinguished among high, medium, and low implementing health centers. The factors were categorized according to the Consolidated Framework for Implementation Research model. KEY RESULTS: FIT tests were mailed to 21.0-81.7% of eligible participants at each health center. We identified a two-factor solution that distinguished among levels of implementation with 100% consistency and 100% coverage. The factors were having a centralized implementation team (inner setting) and mailing the introduction letter in advance of the FIT kit (intervention characteristics). Health centers with high levels of implementation had the joint presence of both factors. In health centers with medium levels of implementation, only one factor was present. Health centers with low levels of implementation had neither factor present. CONCLUSIONS: Full implementation of the STOP CRC intervention relied on a centralized implementation team with dedicated staffing time, and the advance mailing of an introduction letter. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01742065 Registered

What’s the “secret sauce”? How implementation variation affects the success of colorectal cancer screening outreach

“…To achieve data reduction, we used a configurational method to identify candidate factors, described in detail in prior studies [ 32 – 34 ]. To summarize, we used the “minimally sufficient conditions” function within the R package “cna” to look across all 17 cases and all 8 factors at once.…”

Facility-Level Conditions Leading to Higher Reach: A Configurational Analysis of National VA Weight Management Programming

“…program distinguishing 11 higher-performing facilities from 11 lower-performing facilities [15]. More recently, a 2019 Cochrane Review prominently used con gurational comparative methods to identify conditions aligned with successful implementation of school-based interventions for asthma selfmanagement [16]; a new comprehensive 2020 handbook on implementation science dedicated a complete chapter to CCMs [17]; and researchers have published numerous articles featuring CCMs in prominent health services journals in 2020, including BMC Health Services Research [18][19][20][21][22].…”

“…Next, we proceeded with data reduction by adopting a con gurational approach to factor selection that has been described previously. [18,20,21] Brie y, we began by using the "minimally su cient conditions" (i.e., msc) function within the R package "cna" [24] to look across all candidate factors for all 100 facilities to identify con gurations of conditions with strong connections to the outcome condition (i.e., high reach). We considered all one-, two-, and three-condition con gurations of conditions instantiated within our dataset that met the designated thresholds for consistency and coverage.…”

Coincidence Analysis: A New Method for Causal Inference in Implementation Science

“…Investigators applying CNA can conduct analyses across entire datasets to identify speci c combinations of components and conditions that consistently lead to outcomes, and can be applied to large-n as well as small-n studies. Peer-reviewed, implementation-related work speci cally involving CNA has started to emerge in implementation science, including podium presentations at major implementation conferences (15,16); methods workshops dedicated speci cally to CNA (17); published protocols (18); and full-length articles in established journals (19)(20)(21)(22)(23).…”