A typology of power in implementation: Building on the exploration, preparation, implementation, sustainment (EPIS) framework to advance mental health and HIV health equity

Background Persistent inequities in HIV health are due, in part, to barriers to successful HIV-related mental health intervention implementation with marginalized groups. Implementation Science (IS) has begun to examine how the field can promote health equity. Lacking is a clear method to analyze how power is generated and distributed through practical implementation processes and how this power can dismantle and/or reproduce health inequity through intervention implementation. The aims of this paper are to (1) propose a typology of power generated through implementation processes, (2) apply this power typology to expand on the Exploration, Preparation, Implementation, Sustainment (EPIS) framework to advance HIV and mental health equity and (3) articulate questions to guide the explicit examination and distribution of power throughout implementation. Methods This paper draws on the work of an Intermediary Purveyor organization implementing trauma-informed care and harm reduction organizational change with HIV service organizations. The expanded framework was developed through analyzing implementation coaching field notes, grant reporting, and evaluation documents, training feedback, partner evaluation interviews, and existing implementation literature. Results The authors identify three types of power working through implementation; (1) discursive power is enacted through defining health-related problems to be targeted by intervention implementation, as well as through health narratives that emerge through implementation; (2) epistemic power influences whose knowledge is valued in decision-making and is recreated through knowledge generation; and (3) material power is created through resource distribution and patterns of access to health resources and acquisition of health benefits provided by the intervention. Decisions across all phases and related to all factors of EPIS influence how these forms of power striate through intervention implementation and ultimately affect health equity outcomes. Conclusions The authors conclude with a set of concrete questions for researchers and practitioners to interrogate power throughout the implementation process. Plain language summary Over the past few years, Implementation Science researchers have committed increased attention to the ways in which the field can more effectively address health inequity. Lacking is a clear method to analyze how implementation processes themselves generate power that has the potential to contribute to health inequity. In this paper, the authors describe and define three types of power that are created and distributed through intervention implementation; discursive power, epistemic power, and material power. The authors then explain how these forms of power shape factors and phases of implementation, using the well-known EPIS (exploration, preparation, implementation, sustainment) framework. The authors draw from their experience working with and Intermediary Purveyor supporting HIV service organizations implementing trauma-informed care and harm reduction organizational change projects. This paper concludes with a set of critical questions that can be used by researchers and practitioners as a concrete tool to analyze the role of power in intervention implementation processes.

Health Inequity and Institutional Ethnography: Mapping the Problem of Policy Change

Health equity (HE) is a central concern across multiple disciplines and sectors, including nursing. However, the proliferation of the term has not resulted in corresponding policymaking that leads to a clear reduction of health inequities. The goal of this paper is to use institutional ethnographic methods to map the social organization of HE policy discourses in Canada, a process that serves to reproduce existing relations of power that stymie substantive change in policy aimed at reducing health inequity. In nursing, institutional ethnography (IE) is described as a method of inquiry for taking sides in order to expose socially organized practices of power. Starting from the standpoints of HE policy advocates we explain the methods of IE, focusing on a stepwise description of theoretical and practical applications in the area of policymaking. Results are discussed in the context of three thematic areas: 1) bounding HE talk within biomedical imperialism, 2) situating racialization and marginalization as a subaltern space in HE discourses, and 3) activating HE texts as ruling relations. We conclude with key points about our insights into the methodological and theoretical potential of critical policy research using IE to analyze the social organization of power in HE policy narratives. This paper contributes to critical nursing discourse in the area of HE, demonstrating how IE can be applied to disrupt socially organized neoliberal and colonialist narratives that recycle and redeploy oppressive policymaking practices within and beyond nursing.

Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism

The use of artificial intelligence (AI) and machine learning (ML) in clinical care offers great promise to improve patient health outcomes and reduce health inequity across patient populations. However, inherent biases in these applications, and the subsequent potential risk of harm can limit current use. Multi-modal workflows designed to minimize these limitations in the development, implementation, and evaluation of ML systems in real-world settings are needed to improve efficacy while reducing bias and the risk of potential harms. Comprehensive consideration of rapidly evolving AI technologies and the inherent risks of bias, the expanding volume and nature of data sources, and the evolving regulatory landscapes, can contribute meaningfully to the development of AI-enhanced clinical decision making and the reduction in health inequity.

A Multisectoral Approach to Advance Health Equity in Northern Arizona: County-level Leaders’ Perspectives on Health Equity

Background Multisectoral and public-private partnerships are critical in building the necessary infrastructure, policy, and political will to ameliorate health inequity. By focusing on health equity, researchers, practitioners, and decision-makers make explicit the systematic, avoidable, unfair, and unjust differences in health status across population groups sustained over time and generations, beyond the control of individuals. Health equity requires a collective process in shaping the health and wellbeing of the communities in which we live, learn, work, play, move, and grow. Methods Data are drawn from the Southwest Health Equity Research Collaborative Regional Health Equity Survey (RHES). RHES is a community-informed, cross-sectional online survey comprised of 31 quantitative and 17 qualitative questions. Generated to elicit an interdisciplinary body of knowledge and guide future multisectoral action for improving community health and well-being, the RHES targeted leaders representing five large rural northern Arizona counties and 13 distinct sectors. To explore, multisectoral leaders’ knowledge, attitudes, and actions to address the social, environmental, and economic conditions that produce and sustain health inequity were analyzed using a priori coding scheme and emergent coding with thematic analysis. Results Although leaders were provided the definition and asked to describe the root causes of inequities, the majority of leaders described social determinants of health (SDoH). When leaders described root causes of health inequity, they articulated systemic factors affecting their communities and described discrimination and unequal allocation of power and resources. Most leaders described the SDoH of their communities by discussing compounding factors of poverty, transportation, and housing among others, that together exacerbate inequity. Leaders also identified specific strategies to address SDoH and advance health equity in their communities, ranging from providing direct services, to activating partnerships across organizations and sectors in advocacy for policy change. Conclusion Our findings indicate that community leaders in the northern Arizona region acknowledge the importance of multisectoral partnerships and collaborations in improving health equity for the populations that they serve. However, a common understanding of health equity remains to be widely established, which is essential for conducting effective multisectoral work with the goal of advancing health equity.

The Impact of Racial and Ethnic Health Disparities in Diabetes Management on Clinical Outcomes: A Reinforcement Learning Analysis of Health Inequity Among Youth and Young Adults in the SEARCH for Diabetes in Youth Study

<a><b>Objective: </b></a>To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario whereby non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. <p><b>Research Design and Methods:</b> Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). <a>In the White versus non-White subgroups, propensity scores model estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use.</a> An analysis based on policy evaluation technique in reinforcement learning estimated the effect of each treatment regimen on hemoglobin A1c (HbA1c) and diabetes complications for non-White YYA. </p> <p><b>Results: </b>The study included n=978 YYA. The sample was 47.5% female and77.5% non-Hispanic White, with mean age 12.8±2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference=0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95%CI: -0.45%, -0.21%) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining approximately 35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (p<0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations.</p> <p><b>Conclusions: </b>Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race/ethnicity-based health inequity.</p>

The Impact of Racial and Ethnic Health Disparities in Diabetes Management on Clinical Outcomes: A Reinforcement Learning Analysis of Health Inequity Among Youth and Young Adults in the SEARCH for Diabetes in Youth Study

<a><b>Objective: </b></a>To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario whereby non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. <p><b>Research Design and Methods:</b> Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). <a>In the White versus non-White subgroups, propensity scores model estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use.</a> An analysis based on policy evaluation technique in reinforcement learning estimated the effect of each treatment regimen on hemoglobin A1c (HbA1c) and diabetes complications for non-White YYA. </p> <p><b>Results: </b>The study included n=978 YYA. The sample was 47.5% female and77.5% non-Hispanic White, with mean age 12.8±2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference=0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95%CI: -0.45%, -0.21%) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining approximately 35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (p<0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations.</p> <p><b>Conclusions: </b>Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race/ethnicity-based health inequity.</p>