Translating economic evaluations into financing strategies for implementing evidence-based practices

Dopp, Alex R.; Kerns, Suzanne E. U.; Panattoni, Laura; Ringel, Jeanne S.; Eisenberg, Daniel; Powell, Byron J.; Low, Roger K.; Raghavan, Ramesh

doi:10.1186/s13012-021-01137-9

Cited by 15 publications

(18 citation statements)

References 67 publications

(79 reference statements)

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“…One major area of current focus with implementation science that is often missing detail is the costs of implementation [74][75][76][77]. Recent publications by the "Economics and Cost" action group of the Consortium for Cancer Implementation Science have set out definitions, guidance, methods, and best practices for understanding the costs of implementation and conducting economic evaluations in implementation science [36,74,[77][78][79][80] and complement ongoing work in the field [6,8,12,14,61,75,76,[81][82][83][84].…”

Section: Improve Reporting and Transparency Of Cost Datamentioning

confidence: 99%

Using decision analysis to support implementation planning in research and practice

Smith

Knocke

Lich

2022

Implement Sci Commun

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Background The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis. Main text When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning. Conclusion To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.

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Section: Improve Reporting and Transparency Of Cost Datamentioning

confidence: 99%

Using decision analysis to support implementation planning in research and practice

Smith

Knocke

Lich

2022

Implement Sci Commun

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“…Third, the field should consider implementation strategies that are less expensive to implement. The expense of many implementation strategies has been documented in the literature, raising questions about scalability [71][72][73]. Less expensive strategies would increase our ability to test mechanism, but more importantly, increase the resources available to recruit more organizations into studies.…”

Section: Implications For the Fieldmentioning

confidence: 99%

Required sample size to detect mediation in 3-level implementation studies

et al. 2022

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Background Statistical tests of mediation are important for advancing implementation science; however, little research has examined the sample sizes needed to detect mediation in 3-level designs (e.g., organization, provider, patient) that are common in implementation research. Using a generalizable Monte Carlo simulation method, this paper examines the sample sizes required to detect mediation in 3-level designs under a range of conditions plausible for implementation studies. Method Statistical power was estimated for 17,496 3-level mediation designs in which the independent variable (X) resided at the highest cluster level (e.g., organization), the mediator (M) resided at the intermediate nested level (e.g., provider), and the outcome (Y) resided at the lowest nested level (e.g., patient). Designs varied by sample size per level, intraclass correlation coefficients of M and Y, effect sizes of the two paths constituting the indirect (mediation) effect (i.e., X→M and M→Y), and size of the direct effect. Power estimates were generated for all designs using two statistical models—conventional linear multilevel modeling of manifest variables (MVM) and multilevel structural equation modeling (MSEM)—for both 1- and 2-sided hypothesis tests. Results For 2-sided tests, statistical power to detect mediation was sufficient (≥0.8) in only 463 designs (2.6%) estimated using MVM and 228 designs (1.3%) estimated using MSEM; the minimum number of highest-level units needed to achieve adequate power was 40; the minimum total sample size was 900 observations. For 1-sided tests, 808 designs (4.6%) estimated using MVM and 369 designs (2.1%) estimated using MSEM had adequate power; the minimum number of highest-level units was 20; the minimum total sample was 600. At least one large effect size for either the X→M or M→Y path was necessary to achieve adequate power across all conditions. Conclusions While our analysis has important limitations, results suggest many of the 3-level mediation designs that can realistically be conducted in implementation research lack statistical power to detect mediation of highest-level independent variables unless effect sizes are large and 40 or more highest-level units are enrolled. We suggest strategies to increase statistical power for multilevel mediation designs and innovations to improve the feasibility of mediation tests in implementation research.

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“…Therefore, once implementation costs are identified, financing strategies are needed to provide funding that covers those costs and additional costs that are needed to expand capacity in the EBP, which may require additional partnerships with policymakers and insurers for economic evaluations to impact practice [ 34 ]. Another paper in this special collection by Dopp and colleagues provides several case examples of how economic evaluations can be translated into implementation financing strategies, and a team science approach to economic evaluation could certainly accelerate such translation of findings [ 35 ]. Furthermore, healthcare is resplendent with examples of low-value practices that need to be de-implemented.…”

Section: The Four-phase Model Of Transdisciplinary Researchmentioning

confidence: 99%

Moving beyond Aim Three: a need for a transdisciplinary approach to build capacity for economic evaluations in implementation science

Barnett

Stadnick

Proctor

et al. 2021

Implement Sci Commun

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Background Understanding the costs and economic benefits of implementation has been identified by policymakers and researchers as critical to increase the uptake and sustainment of evidence-based practices, but this topic remains relatively understudied. Conducting team science with health economists has been proposed as a solution to increase economic evaluation in implementation science; however, these recommendations ignore the differences in goals and perspectives in these two fields. Our recent qualitative research identified that implementation researchers predominantly approach health economists to examine costs, whereas the majority of health economists expressed limited interest in conducting economic evaluations and a desire to be more integrated within implementation science initiatives. These interviews pointed to challenges in establishing fruitful partnerships when health economists are relegated to the “Third Aim” (i.e., lowest-priority research objective) in implementation science projects by their research partners. Discussion In this debate paper, we argue that implementation researchers and health economists need to focus on team science research principles to expand capacity to address pressing research questions that cut across the two fields. Specifically, we use the four-phase model of transdisciplinary research to outline the goals and processes needed to build capacity in this area (Hall et al., Transl Behav Med 2:415–30, 2012). The first phase focuses on the development of transdisciplinary research teams, including identifying appropriate partners (e.g., considering policy or public health researchers in addition to health economists) and building trust. The conceptual phase focuses on strategies to consider when developing joint research questions and methodology across fields. In the implementation phase, we outline the effective processes for conducting research projects, such as team learning. Finally, in the translation phase, we highlight how a transdisciplinary approach between health economists and implementation researchers can impact real-world practice and policy. Summary The importance of investigating the economic impact of evidence-based practice implementation is widely recognized, but efforts have been limited due to the challenges in conducting team science across disciplines. Training in team science can help advance transdisciplinary efforts, which has the potential to increase the rigor and impact of economic evaluations in implementation science while expanding the roles taken by health economists.

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Translating economic evaluations into financing strategies for implementing evidence-based practices

Cited by 15 publications

References 67 publications

Using decision analysis to support implementation planning in research and practice

Using decision analysis to support implementation planning in research and practice

Required sample size to detect mediation in 3-level implementation studies

Moving beyond Aim Three: a need for a transdisciplinary approach to build capacity for economic evaluations in implementation science

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