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.

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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.

Research Objective We sought to identify the degree to which COVID‐19 burden varied by the risk of financial distress of the hospital and community vulnerability. Study Design Community burden of COVID‐19 was assessed using three per‐capita measures calculated from the New York Times GitHub using data through 12/31/2020 – cumulative identified cases and cumulative deaths measure the cumulative burden on the community and peak case date. Hospital burden was measured using occupancy measures calculated from the US HHS data on COVID‐19 Reported Patient Impact and Hospital Capacity by Facility. The Financial Distress Index (FDI) is an algorithm that uses historical data about hospital financial performance, government reimbursement, organizational characteristics, and market characteristics to predict the current risk of financial distress. The model assigns every rural hospital to one of four financial risk categories: high, mid‐high, mid‐low, or low. Rural hospitals were characterized by their 2020 risk of financial distress level. The Centers for Disease Control and Prevention Social Vulnerability Index (SVI) indicates US counties' relative vulnerability based on 15 social factors covering economy, infrastructure, and community composition. Population Studied We include all rural hospitals defined by the Federal Office of Rural Health Policy. Principal Findings The analytical dataset included 2266 rural hospitals with financial distress, including High (N = 228, 10.1%), Mid‐High (N = 399, 17.6%), Mid‐Low (N = 997, 44.0%) and Low (N = 642, 28.3%). While there were no significant differences in cumulative or peak cases among communities with low or high financial distress hospitals, communities with hospitals in high financial distress had higher death rates per capita (13.4 deaths per 10,000 for High distress vs. 10.6 deaths for Low distress (p < 0.0001). During the last week of December, more distressed hospitals had a higher percent of occupied beds with COVID‐19 patients and lower reported occupancy (both p < 0.01). In regression analyses, more vulnerable communities, as measured by the SVI, had higher death rates (p < 0.001) and lower peak rates (p < 0.001) but no significant difference in cumulative cases per capita. Conclusions More vulnerable rural counties have faced a higher COVID‐19 burden in terms of deaths per capita, and they were also more likely to have financially stressed hospitals. Implications for Policy or Practice Financially stressed hospitals serving vulnerable communities may need additional support to provide services to their populations, which have been the most burdened by COVID‐19 deaths.

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.