Applying FRAME-IS to characterize provider-led adaptations to a cervical cancer prevention intervention in Kenya.

backgroundImplementation strategies that are contextually refined are essential for optimizing the delivery of evidence-based interventions (EBI) to prevent cervical cancer in low-resource settings. This paper reports the application of the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies (FRAME-IS) to capture and disseminate strategy adaptations made to a single-visit, screen-and-treat approach with thermal ablation (SV-SAT + TA) strategy aimed at establishing sustainable cervical cancer prevention services in Kenya.

methodsA FRAME-IS-based tracking spreadsheet was developed to document bundled site-specific implementation strategy adaptations and across 10 facilities. Data was collected during technical assistants' (TAs) site visits, phone calls, and monthly meetings with health providers between March 2023 and September 2024. Sources of adaptation included tracking spreadsheets, TAs narrative reports, and field notes from direct observations during the implementation phase. Descriptive statistics summarized site characteristics and adaptation trends. The exact Poisson test compared adaptation rates by facility level (medium vs large) and period (early vs late).

resultsA total of 28 adaptations were identified. Most adaptations (70%, n = 20) occurred in the early phase. Over half were planned (57%, n = 16). We made modifications to module two (What was modified). Educational adaptations were most common (57%, n = 16), primarily targeting providers delivering screening and treatment services. Resources-related adaptations accounted for 21% (n = 6). Additionally, 43% (n = 12) of the adaptations aimed to increase adoption by expanding the number of clinicians offering the SV-SAT + TA. Nearly half (46%, n = 13) targeted the organization level. Over six months, larger facilities had 2.67 adaptations per facility, compared to 2.85 in medium level facilities (rate ratio = 0.93 (95% CI = 0.39-2.08, p = 0.89), indicating no statistically significant difference in adaptation rates by facility levels. However, adaptation rates significantly declined, from 2.0 per facility in the early phase to 0.80 in the late phase (rate ratio = 2.50, 95% CI: 1.12-6.02, p = 0.02), suggesting a reduction in adaptations over time.

conclusionEducation and resource-related adaptations were critical to improving SV-SAT + TA implementation. Future research should focus on evaluating the impact of these adaptations on implementation and clinical outcomes, refining the FRAME-IS framework, and supporting the establishment of an adaptome to guide scalable strategies in similar settings.

trial registrationNCT05472311.