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BackgroundIt is increasingly recognized that policies have played a role in both alleviating and exacerbating the health and economic consequences of the COVID-19 pandemic. There has been limited systematic evaluation of variation in U.S. local COVID-19-related policies. This study introduces the U.S. COVID-19 County Policy (UCCP) Database, whose objective is to systematically gather, characterize, and assess variation in U.S. county-level COVID-19-related policies.MethodsIn January-March 2021, we collected an initial wave of cross-sectional data from government and media websites for 171 counties in 7 states on 22 county-level COVID-19-related policies within 3 policy domains that are likely to affect health: (1) containment/closure, (2) economic support, and (3) public health. We characterized the presence and comprehensiveness of policies using univariate analyses. We also examined the correlation of policies with one another using bivariate Spearman's correlations. Finally, we examined geographical variation in policies across and within states.ResultsThere was substantial variation in the presence and comprehensiveness of county policies during January-March 2021. For containment and closure policies, the percent of counties with no restrictions ranged from 0% (for public events) to more than half for public transportation (67.8%), hair salons (52.6%), and religious gatherings (52.0%). For economic policies, 76.6% of counties had housing support, while 64.9% had utility relief. For public health policies, most were comprehensive, with 70.8% of counties having coordinated public information campaigns, and 66.7% requiring masks outside the home at all times. Correlations between containment and closure policies tended to be positive and moderate (i.e., coefficients 0.4-0.59). There was variation within and across states in the number and comprehensiveness of policies.ConclusionsThis study introduces the UCCP Database, presenting granular data on local governments' responses to the COVID-19 pandemic. We documented substantial variation within and across states on a wide range of policies at a single point in time. By making these data publicly available, this study supports future research that can leverage this database to examine how policies contributed to and continue to influence pandemic-related health and socioeconomic outcomes and disparities. The UCCP database is available online and will include additional time points for 2020-2021 and additional counties nationwide.

Objective: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.Material and Methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient. 82.1% of patients were found in a single partner and 14.7% were in 2. The percentage overlap between Partners ranged between 0% and 80% with a median of 0%. Linking patients' electronic health records with claims increased disease prevalence in every clinical characteristic, ranging between 63% and 173%.Discussion: The overlap between Partners was variable and depended on timeframe. However, patient data linkage changed the prevalence profile of the PCORnet patient population.Conclusions: This project was one of the largest linkage efforts of its kind and demonstrates the potential value of record linkage. Linkage between Partners may be most useful in cases where there is geographic proximity between Partners, an expectation that potential linkage Partners will be able to fill gaps in data, or a longer study timeframe. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network. Results: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet®). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens. We queried the sites for patients with a clinical encounter in 2018 or 2019 and matched their tokens to determine whether overlap existed. We described patient overlap among the sites and generated a "deduplicated" table of patient demographic characteristics. Overlapping patients were found in 3 of the 6 site-pairs. Following deduplication, the total patient count was 3,108,515 (0.11% reduction), with the largest reduction in count for patients with an "Other/Missing" value for Sex; from 198 to 163 (17.6% reduction). The PPRL solution successfully links patients across data sources using distributed queries without directly accessing patient PII. The overlap queries and analysis performed in this pilot is being replicated across the full network to provide additional insight into patient linkages among a distributed research network. [ABSTRACT FROM AUTHOR]

Background: The US Food and Drug Administration's Sentinel Initiative is well positioned to support pragmatic clinical trials. FDA-Catalyst combines direct contact with health plan members and/or providers with data in the Sentinel infrastructure. Here, we describe the rationale, feasibility analyses, and lessons learned from the planning phase of the first large pragmatic trial conducted using the Sentinel Initiative's delivery system capabilities—IMplementation of a randomized controlled trial to imProve treatment with oral AntiCoagulanTs in patients with Atrial Fibrillation (the IMPACT-AFib trial). Methods: During the planning phase, we convened representatives from five commercial health plans, FDA, study coordinating centers, and a patient representative for protocol development, institutional review board preparation, and other activities. Administrative claims data from the plans were included in a retrospective cohort analysis to assess sample size for the trial. Members ≥30 years old with ≥365 days of medical/pharmacy coverage, ≥2 diagnosis codes for atrial fibrillation, a guideline-based indication for oral anticoagulant use for stroke prevention, and no evidence of oral anticoagulant use in the 365 days prior to the index atrial fibrillation diagnosis in 2013 were included. Exclusions for the analysis included other conditions requiring anticoagulation, history of intracranial hemorrhage, and gastrointestinal bleed. We calculated rates of oral anticoagulant use, transient ischemic attack or stroke, and bleeding in the 365 days following the index atrial fibrillation diagnosis. Results: A total of 44,786 members with atrial fibrillation with no evidence of recent oral anticoagulant use were identified. In total, 87% (n = 38,759) were classified as having a guideline-based indication for oral anticoagulants. Of those, 33% (n = 12,867) had a new oral anticoagulant dispensed during the following year, 15% (n = 5917) were hospitalized for stroke or transient ischemic attack, and 9% (n = 3469) for bleeding events. This information was used to develop the trial protocol including sample size, power calculations, and level of randomization. Conclusion: Sentinel infrastructure generated preliminary data that supported planning and implementation of a large pragmatic trial embedded in health plans. This planning identified unanticipated challenges that must be addressed in similar trials. [ABSTRACT FROM AUTHOR]