Using decision analysis to support implementation planning in research and practice.

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.

Access to outpatient services in rural communities changes after hospital closure.

OBJECTIVE: Between January 2005 and July 2020, 171 rural hospitals closed across the United States. Little is known about the extent that other providers step in to fill the potential reduction in access from a rural hospital closure. The objective of this analysis is to evaluate the trends of Federally Qualified Health Centers (FQHCs) and Rural Health Clinics (RHCs) in rural areas prior to and following hospital closure. DATA SOURCES/STUDY SETTING: We used publicly available data from Centers for Medicare and Medicaid Provider of Services files, Cecil G. Sheps Center rural hospital closures list, and Small Area Income and Poverty Estimates. STUDY DESIGN: We described the trends over time in the number of hospitals, hospital closures, FQHC sites, and RHCs in rural and urban ZIP codes, 2006-2018. We used two-way fixed effects and pooled generalized linear models with a logit link to estimate the probabilities of having any RHC and any FQHC within 10 straight-line miles. DATA COLLECTION/EXTRACTION METHODS: Not applicable. PRINCIPAL FINDINGS: Compared to hospitals that never closed, the predicted probability of having any FQHC within 10 miles increased post closure by 5.95 and 11.57 percentage points at 1 year and 5 years, respectively (p < 0.05). The predicted probability of having any RHC within 10 miles was not significantly different following rural hospital closure. A percentage point increase in poverty rate was associated with a 1.98 and a 1.29 percentage point increase in probabilities of having an FQHC or RHC, respectively (p < 0.001). CONCLUSIONS: In areas previously served by a rural hospital, there is a higher probability of new FQHC service-delivery sites post closure. This suggests that some of the potential reductions in access to essential preventive and diagnostic services may be filled by FQHCs. However, many rural communities may have a persistent unmet need for preventive and therapeutic care.

Determinants of Implementing Evidence-Based Trauma-Focused Interventions for Children and Youth: A Systematic Review.

A systematic review was conducted to identify determinants (barriers and facilitators) of implementing evidence-based psychosocial interventions for children and youth who experience emotional or behavioral difficulties due to trauma exposure. Determinants were coded, abstracted, and synthesized using the Exploration, Preparation, Implementation, and Sustainment framework. Twenty-three articles were included, all of which examined implementation of Trauma-Focused Cognitive Behavioral Therapy or Cognitive-Behavioral Intervention for Trauma in Schools. This review identified multilevel and multiphase determinants that can be addressed by implementation strategies to improve implementation and clinical outcomes, and suggests how future studies might address gaps in the evidence base.

Tapping Into Multiple Data "Springs" to Strengthen Policy Streams: A Guide to the Types of Data Needed to Formulate Local Retail Tobacco Control Policy.

In 2015, the tobacco industry spent $8.24 billion to market tobacco products in convenience stores, supermarkets, pharmacies, and other retail or point-of-sale settings. Community tobacco control partnerships have numerous evidence-based policies (eg, tobacco retailer licensing and compliance, tobacco-free-school buffer zones, eliminating price discounts) to counter point-of-sale tobacco marketing. However, deciding which point-of-sale policies to implement - and when and in what order to implement them - is challenging. The objective of this article was to describe tools and other resources that local-level tobacco use prevention and control leaders can use to assemble the data they need to formulate point-of-sale tobacco policies that fit the needs of their communities, have potential for public health impact, and are feasible in the local policy environment. We were guided by Kingdon's theory of policy change, which contends that windows of policy opportunity open when 3 streams align: a clear problem, a solution to the problem, and the political will to work for change. Community partnerships can draw on 7 data "springs" to activate Kingdon's streams: 1) epidemiologic and surveillance data, 2) macro retail environment data, 3) micro retail environment data, 4) the current policy context, 5) local legal feasibility of policy options, 6) the potential for public health impact, and 7) political will.