As part of the 13th AcademyHealth Annual Conference on the Science of Dissemination and Implementation, our team led an online workshop on equity and implementation science.
At the workshop, we talked about frameworks. We consider three types of implementation science frameworks: 1) determinant (what are barriers?), 2) process (how will we implement?), and 3) evaluation (did it work?).
There are adaptations to all three types of frameworks to focus on equity, and some have been more broadly tested and applied than others. Examples of implementation science frameworks focusing on equity include:
- Adaptation of Proctor’s measurement framework
- Extension of RE-AIM to focus on equity and sustainability
There were many questions that arose in the Zoom “chat” and breakout rooms during the workshop. Let’s review some of the common ones.
What is an important assumption(s) before using these implementation science frameworks to focus on disparities?
Assumption 1: There is a disparity in access to, receipt of, quality of, or outcomes from some innovation (or healthcare intervention). If you do not know this, you need to look for existing data on this in your setting, find existing data of this that approximates your setting (e.g., receipt of an intervention in your county or state even if it’s not your hospital), or collect data showcasing a healthcare disparity. This is important because it makes more precise who needs to benefit more and in what way. The figure 1, in this paper by Chinman, M. and colleagues can be helpful.
Assumption 2: Your research has some focus on understanding implementation, reach, or adoption of an intervention.
Assumption 3: You do not need to know why the disparity in implementation exists yet. Using implementation determinant frameworks with an equity lens and focus on social determinants of health can infuse your assessment to ask what the barriers are to equitable implementation and, more broadly, to health equity.
An important question is also: what are the (explicit and non-explicit) assumptions that we have in our implementation science frameworks? Assumptions have tremendous implications for our own examining of health equity. For example, how was the evidence for the evidence-based intervention generated? Who gets to decide what counts as evidence-based? Does the evidence reflect experiences of the populations or settings experiencing inequities and does it address structural factors (e.g. discrimination, racism) that impact health and health equity? There is a lot of work outside of our field that is useful regarding multi-level influences on inequities and specifically, how racism leads to inequities.
What is the rationale for adapting implementation models to include health equity lens (when these models were originally designed with a blind eye to equity) versus adapting existing health equity models to implementation science?
It depends on the focus of the work and social position of the researcher or practitioner – if they come from a health equity or disparities background and are more comfortable and experienced with that work and want to move into implementation, they might adapt a health equity model to D&I science. And vice versa. There are many excellent frameworks from health disparities research where we can go in more depth with respect to specific determinants, specific health problems, or specific populations. On the other side, healthy equity and disparity frameworks do not consider implementation and dissemination adequately. You could combine frameworks as well, depending where on the research continuum the work falls.
It is important for implementation scientists to partner with equity and disparities researchers earlier in the research continuum so the bidirectional learning from each other’s frameworks can be meaningfully integrated into the work.
Should we be explicitly measuring and looking for inequities in our work and after interventions? These inequities often are already in place and maintained due to structural and systemic factors that exist outside of our interventions.
Yes! Implementation evaluation frameworks can guide that measurement. As you look for barriers to equitable implementation, also emphasize assets (e.g., strengths practitioners bring that can be used to address equity in implementation efforts such as knowledge, networks, etc.) and capacity-building. In some cases, you may be tracking inequities that arise in the context of your studies to inform future efforts and potentially unpack ‘why.’ Inequities may also arise across different phases of an effort (e.g., planning, active implementation), and you may make adaptations (in recruitment, in intervention, in strategies) to address these inequities. As you do this work, however, be careful how you measure determinants and interpret their association with inequities to avoid victim blaming.
How should we assess distrust and address it?
It is important to make distinctions between distrust, mistrust, and trust. We can conceptualize distrust and mistrust as adaptive in response to historical and personal experiences of racism. The goal of our interventions should not necessarily be to reduce mistrust. The focus should be on understanding existing sources of trust and working with those trusted channels and messengers, and identify ways to increase trustworthiness of implementers and health care stewards.
What are other good thought pieces on integrating health equity/disparities work with implementation science?
- Implementation Research Methodologies for Achieving Scientific Equity and Health Equity
- Promoting Health Equity through De-implementation research
- Implementation Science to Address Health Disparities During the Coronavirus Pandemic
- GRAIDS: A framework for closing the gap in the availability of health promotion programs and interventions for people with disabilities.
- Upcoming: Shelton RC, Adsul P, Oh A. Recommendations for Addressing Structural Racism in Implementation Science: A call to the field. In Press, Ethnicity & Disease. Special Issue on Structural Racism and Health.
How can we continue the conversation?
Prajakta (Padsul@salud.unm.edu) Twitter: @PrajaktaAdsul
Leopoldo (email@example.com) Twitter: @lcabassa
Rachel (firstname.lastname@example.org) Twitter: @DrRachelShelton
Eva (Eva.Woodward2@va.gov) Twitter: @evawoodwardphd
Ana (email@example.com) Twitter: @BaumannAna