SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to spread from person to person primarily by the respiratory route and mainly through close contact (1). Community mitigation strategies can lower the risk for disease transmission by limiting or preventing personto-person interactions (2). U.S. states and territories began implementing various community mitigation policies in March 2020. One widely implemented strategy was the issuance of orders requiring persons to stay home, resulting in decreased population movement in some jurisdictions (3). Each state or territory has authority to enact its own laws and policies to protect the public's health, and jurisdictions varied widely in the type and timing of orders issued related to stay-at-home requirements. To identify the broader impact of these stay-athome orders, using publicly accessible, anonymized location data from mobile devices, CDC and the Georgia Tech Research Institute analyzed changes in population movement relative to stay-at-home orders issued during March 1-May 31, 2020, by all 50 states, the District of Columbia, and five U.S. territories.* During this period, 42 states and territories issued mandatory stay-at-home orders. When counties subject to mandatory state-and territory-issued stay-at-home orders were stratified along rural-urban categories, movement decreased significantly relative to the preorder baseline in all strata. Mandatory stayat-home orders can help reduce activities associated with the spread of COVID-19, including population movement and close person-to-person contact outside the household. Data on state and territorial stay-at-home orders were obtained from government websites containing executive or administrative orders or press releases for each jurisdiction. Each order was analyzed and coded into one of five mutually exclusive categories: 1) mandatory for all persons; 2) mandatory only for persons in certain areas of the jurisdiction; 3) mandatory only for persons at increased risk in the jurisdiction; 4) mandatory only for persons at increased risk in certain areas of the jurisdiction; or 5) advisory or recommendation (i.e., nonmandatory). Jurisdictions that did not issue an order were coded as having no state-or territory-issued

NLP in conjunction with data mining facilitates individualized tracking of ERCP providers for quality metrics without the need for manual medical record review. Incorporation of these tools across multiple centers may permit tracking of ERCP quality measures through national registries.

Objectives To assess if state-issued nonpharmaceutical interventions (NPIs) are associated with reduced rates of SARS-CoV-2 infection as measured through anti-nucleocapsid (anti-N) seroprevalence, a proxy for cumulative prior infection that distinguishes seropositivity from vaccination). Methods Monthly anti-N seroprevalence during August 1, 2020 – March 30, 2021 was estimated using a nationwide blood donor serosurvey. Using multivariable logistic regression models, we measured the association of seropositivity and state-issued, county-specific NPIs for mask mandates, gathering bans, and bar closures. Results Compared with individuals living in a county with all three NPIs in place, the odds of having anti-N antibodies were 2.2 (95% CI: 2.0-2.3) times higher for people living in a county that did not have any of the three NPIs, 1.6 (95% CI: 1.5-1.7) times higher for people living in a county that only had a mask mandate and gathering ban policy, and 1.4 (95% CI: 1.3-1.5) times higher for people living in a county that had only a mask mandate. Conclusions Consistent with studies assessing NPIs relative to COVID-19 incidence and mortality, the presence of NPIs were associated with lower SARS-CoV-2 seroprevalence indicating lower rates of cumulative infections. Multiple NPIs are likely more effective than single NPIs.

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The relative efficacy of natural language processing (NLP) of text reports compared to structured data queries for identifying patients from electronic health records (EHRs) with metastatic cancer remains unclear. Such identification is critical for identifying and recruiting potential study candidates for cancer trials, particularly trials of cancer chemotherapy. For such purposes, we performed a direct comparison between NLP and structured data query methods for identifying patients with metastatic melanoma. Using EHR data from two large institutions, we found that NLP of text reports identified close to three times as many patients with metastatic melanoma compared to a structured data query algorithm (1,727 vs. 607 patients). Using an external tumour registry, we also found NLP had much higher sensitivity than structured query for identifying such patients (67% vs. 35%). Our results emphasise the importance of employing NLP criteria when identifying potential cancer study candidates with metastatic disease.