Purpose During the COVID‐19 epidemic, it is critical to understand how the need for hospital care in rural areas aligns with the capacity across states. Methods We analyzed data from the 2018 Behavioral Risk Factor Surveillance System to estimate the number of adults who have an elevated risk of serious illness if they are infected with coronavirus in metropolitan, micropolitan, and rural areas for each state. Study data included 430,949 survey responses representing over 255.2 million noninstitutionalized US adults. For data on hospital beds, aggregate survey data were linked to data from the 2017 Area Health Resource Files by state and metropolitan status. Findings About 50% of rural residents are at high risk for hospitalization and serious illness if they are infected with COVID‐19, compared to 46.9% and 40.0% in micropolitan and metropolitan areas, respectively. In 19 states, more than 50% of rural populations are at high risk for serious illness if infected. Rural residents will generate an estimated 10% more hospitalizations for COVID‐19 per capita than urban residents given equal infection rates. Conclusion More than half of rural residents are at increased risk of hospitalization and death if infected with COVID‐19. Experts expect COVID‐19 burden to outpace hospital capacity across the country, and rural areas are no exception. Policy makers need to consider supply chain modifications, regulatory changes, and financial assistance policies to assist rural communities in caring for people affected by COVID‐19.

Background Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches and can reveal new empirical findings related to implementation that might otherwise have gone undetected. Methods We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings. Results The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage. Conclusions CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) infected over 5 million United States (US) residents resulting in more than 180,000 deaths by August 2020. To mitigate transmission, most states ordered shelter-in-place orders in March and reopening strategies varied. OBJECTIVE: To estimate excess COVID-19 cases and deaths after reopening compared with trends prior to reopening for two groups of states: (1) states with an evidence-based reopening strategy, defined as reopening indoor dining after implementing a statewide mask mandate, and (2) states reopening indoor dining rooms before implementing a statewide mask mandate. DESIGN: Interrupted time series quasi-experimental study design applied to publicly available secondary data. PARTICIPANTS: Fifty United States and the District of Columbia. INTERVENTIONS: Reopening indoor dining rooms before or after implementing a statewide mask mandate. MAIN MEASURES: Outcomes included daily cumulative COVID-19 cases and deaths for each state. KEY RESULTS: On average, the number of excess cases per 100,000 residents in states reopening without masks is ten times the number in states reopening with masks after 8 weeks (643.1 cases; 95% confidence interval (CI) = 406.9, 879.2 and 62.9 cases; CI = 12.6, 113.1, respectively). Excess cases after 6 weeks could have been reduced by 90% from 576,371 to 63,062 and excess deaths reduced by 80% from 22,851 to 4858 had states implemented mask mandates prior to reopening. Over 50,000 excess deaths were prevented within 6 weeks in 13 states that implemented mask mandates prior to reopening. CONCLUSIONS: Additional mitigation measures such as mask use counteract the potential growth in COVID-19 cases and deaths due to reopening businesses. This study contributes to the growing evidence that mask usage is essential for mitigating community transmission of COVID-19. States should delay further reopening until mask mandates are fully implemented, and enforcement by local businesses will be critical for preventing potential future closures.

Background and Objective To characterize health care use and costs among new Medicaid enrollees before and during the COVID pandemic. Results can help Medicaid non-expansion states understand health care use and costs of new enrollees in a period of enrollment growth. Research Design Retrospective cross-sectional analysis of North Carolina Medicaid claims data (January 1, 2018 - August 31, 2020). We used modified Poisson and ordinary least squares regression analysis to estimate health care use and costs as a function of personal characteristics and enrollment during COVID. Using data on existing enrollees before and during COVID, we projected the extent to which changes in outcomes among new enrollees during COVID were pandemic-related. Subjects 340,782 new enrollees pre-COVID (January 2018 – December 2019) and 56,428 new enrollees during COVID (March 2020 – June 2020). Measures We observed new enrollees for 60-days after enrollment to identify emergency department (ED) visits, nonemergent ED visits, primary care visits, potentially-avoidable hospitalizations, dental visits, and health care costs. Results New Medicaid enrollees during COVID were less likely to have an ED visit (-46 % [95 % CI: -48 %, -43 %]), nonemergent ED visit (-52 % [95 % CI: -56 %, -48 %]), potentially-avoidable hospitalization (-52 % [95 % CI: -60 %, -43 %]), primary care visit (-34 % [95 % CI: -36 %, -33 %]), or dental visit (-36 % [95 % CI: -41 %, -30 %]). They were also less likely to incur any health care costs (-29 % [95 % CI: -30 %, -28 %]), and their total costs were 8 % lower [95 % CI: -12 %, -4 %]. Depending on the outcome, COVID explained between 34 % and 100 % of these reductions. Conclusions New Medicaid enrollees during COVID used significantly less care than new enrollees pre-COVID. Most of the reduction stems from pandemic-related changes in supply and demand, but the profile of new enrollees before versus during COVID also differed.

Background: Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches, and can reveal new empirical findings related to implementation that might otherwise have gone undetected. Methods: We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings. Results: The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage. Conclusions: CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.Contributions to the literature· Coincidence Analysis (CNA) represents a new mathematical, cross-case method for researchers evaluating the implementation of complex interventions in dynamic settings.· CNA can address multiple dimensions of real-world complexity, including conjunctivity (where several conditions must be jointly present to bring about an outcome) and equifinality (where different paths can lead to the same outcome). CNA can also detect causal chains, where conditions lead to an intermediary outcome, which then leads to the final outcome. · Intentionally designed to investigate different hypotheses and uncover different properties of causal structures than more traditional approaches, CNA can identify implementation-related findings that might otherwise go undetected.