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Multi-level dissemination strategies are needed to increase equitable access to effective treatment for high-risk outpatients with COVID-19, particularly among patients from disproportionately affected communities. Yet assessing population-level impact of such strategies can be challenging.
Methods:
In collaboration with key contributors in Colorado, we conducted a retrospective cohort study to evaluate a multi-level dissemination strategy for neutralizing monoclonal antibody (mAb) treatment. Real-world data included county-level, de-identified output from a statewide mAb referral registry linked with publicly available epidemiological data. Outcomes included weekly number of mAb referrals, unique referring clinicians, and COVID-19 hospitalization rates. We assessed weekly changes in outcomes after dissemination strategies launched in July 2021.
Results:
Overall, mAb referrals increased from a weekly average of 3.0 to 15.5, with an increase of 1.3 to 42.1 additional referrals per county in each post-period week (p < .05). Number of referring clinicians increased from a weekly average of 2.2 to 9.7, with an additional 1.5 to 22.2 unique referring clinicians observed per county per week beginning 5 weeks post-launch (p < .001). Larger effects were observed in communities specifically prioritized by the dissemination strategies. There were no observed differences in COVID-19 hospitalization rates between counties with and without mAb treatment sites.
Conclusion:
Real-world data can be used to estimate population impact of multi-level dissemination strategies. The launch of these strategies corresponded with increases in mAb referrals, but no apparent population-level effects on hospitalization outcomes. Strengths of this analytic approach include pragmatism and efficiency, whereas limitations include inability to control for other contemporaneous trends.
Research on complex behavior change interventions has largely focused on intervention development and testing their effects in feasibility trials, pilot studies, and randomized controlled trials. However, a significant gap exists in translating behavior interventions informed by theory into real-world practice. This chapter describes how engaging stakeholders can improve the likelihood that effective behavior change interventions are put into practice. The chapter begins with an overview of implementation science and normalization process theory – which outlines how effective interventions are routinely implemented. The roles of stakeholders as research partners and research participants are differentiated using research in health contexts. For example, the process of stakeholder involvement is illustrated using digital health interventions for people with long-term physical health conditions with reference to UK Medical Research Council guidelines on complex interventions. The examples illustrate (1) how stakeholder support in the co-design of complex interventions can improve their utility, usability, accessibility, and acceptability and (2) how stakeholder perspectives elicited using mixed methods during the feasibility and pilot phases of intervention development can help inform subsequent stages of intervention development. Finally, the evaluation and implementation phase is explored, using a case study to illustrate the need to engage with additional stakeholders to translate effective interventions into routine practice.
This chapter provides an overview of implementation science approaches relevant for behavior change interventions, including those developed to (1) identify and explain implementation processes (e.g., the Ottawa model for research use); (2) identify implementation determinants, including barriers and facilitators (e.g., the consolidated framework for implementation research); and (3) select implementation outcomes and conduct the evaluation of implementation processes (e.g., the RE-AIM model). Implementation approaches may be further divided into implementation theories, evidence-based frameworks, and taxonomies used to identify and manage key aspects of implementation that contribute to determining the efficacy of behavior change interventions delivered in “real-world” contexts. The chapter concludes with an example of a translational research model providing a set of overarching descriptions of the processes linking basic science findings with the evidence-based applications.
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