Who Gets Sick From COVID-19? Sociodemographic Correlates of Severe Adult Health Outcomes During Alpha- and Delta-Variant Predominant Periods: September 2020-November 2021.

BACKGROUND: Because COVID-19 case data do not capture most SARS-CoV-2 infections, the actual risk of severe disease and death per infection is unknown. Integrating sociodemographic data into analysis can show consequential health disparities. METHODS: Data were merged from September 2020 to November 2021 from 6 national surveillance systems in matched geographic areas and analyzed to estimate numbers of COVID-19-associated cases, emergency department visits, and deaths per 100 000 infections. Relative risks of outcomes per infection were compared by sociodemographic factors in a data set including 1490 counties from 50 states and the District of Columbia, covering 71% of the US population. RESULTS: Per infection with SARS-CoV-2, COVID-19-related morbidity and mortality were higher among non-Hispanic American Indian and Alaska Native persons, non-Hispanic Black persons, and Hispanic or Latino persons vs non-Hispanic White persons; males vs females; older people vs younger; residents in more socially vulnerable counties vs less; those in large central metro areas vs rural; and people in the South vs the Northeast. DISCUSSION: Meaningful disparities in COVID-19 morbidity and mortality per infection were associated with sociodemography and geography. Addressing these disparities could have helped prevent the loss of tens of thousands of lives.

Association of Trends in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Seroprevalence and State-Issued Nonpharmaceutical Interventions: United States, 1 August 2020 to 30 March 2021.

BACKGROUND: We assess if state-issued nonpharmaceutical interventions (NPIs) are associated with reduced rates of severe acute respiratory syndrome coronavirus 2 (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 1 August 2020 to 30 March 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% confidence interval [CI]: 2.0-2.3) times higher for people living in a county that did not have any of the 3 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.

Timing of State and Territorial COVID-19 Stay-at-Home Orders and Changes in Population Movement - United States, March 1-May 31, 2020.

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 person-to-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-at-home 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 stay-at-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.

Provider-specific quality measurement for ERCP using natural language processing.

BACKGROUND AND AIMS: Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measures for colonoscopy. There are no systematic methods by which to track adherence to quality measures for ERCP, the highest risk endoscopic procedure widely used in practice. Our aim was to demonstrate the feasibility of using NLP to measure adherence to ERCP quality indicators across individual providers. METHODS: ERCPs performed by 6 providers at a single institution from 2006 to 2014 were identified. Quality measures were defined using society guidelines and from expert opinion, and then extracted using a combination of NLP and data mining (eg, ICD9-CM codes). Validation for each quality measure was performed by manual record review. Quality measures were grouped into preprocedure (5), intraprocedure (6), and postprocedure (2). NLP was evaluated using measures of precision and accuracy. RESULTS: A total of 23,674 ERCPs were analyzed (average patient age, 52.9 ± 17.8 years, 14,113 were women [59.6%]). Among 13 quality measures, precision of NLP ranged from 84% to 100% with intraprocedure measures having lower precision (84% for precut sphincterotomy). Accuracy of NLP ranged from 90% to 100% with intraprocedure measures having lower accuracy (90% for pancreatic stent placement). CONCLUSIONS: 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.