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Background As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. Methods Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. Results Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred 30%) in some datasets—limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. Conclusion Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources–a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Background Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. Methods Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. Results A total of 15,615 people were observed to have at least one serology test 14–90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79–96%) compared to non-Hispanic (60–89%) patients; in those presenting with at least one COVID-19 related symptom (69–93%) as compared to no such symptoms (63–91%); and in inpatient (70–97%) and emergency department (93–99%) compared to outpatient (63–92%) settings across datasets. PPA was highest in those with diabetes (75–94%) and kidney disease (83–95%); and lowest in those with auto-immune conditions or who are immunocompromised (56–93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59–3.86), patients with diabetes (1.49–1.56), and obesity (1.63–2.23); and lower in those with immunocompromised or autoimmune conditions (0.25–0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. Conclusion Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

BackgroundYouth in the justice system (YJS) are more likely than youth who have never been arrested to have mental health and substance use problems. However, a low percentage of YJS receive SUD services during their justice system involvement. The SUD care cascade can identify potential missed opportunities for treatment for YJS. Steps along the continuum of the cascade include identification of treatment need, referral to services, and treatment engagement. To address gaps in care for YJS, we will (1) implement a learning health system (LHS) to develop, or improve upon, alliances between juvenile justice (JJ) agencies and community mental health centers (CMHC) and (2) present local cascade data during continuous quality improvement cycles within the LHS alliances.Methods/designADAPT is a hybrid Type II effectiveness implementation trial. We will collaborate with JJ and CMHCs in eight Indiana counties. Application of the EPIS (exploration, preparation, implementation, and sustainment) framework will guide the implementation of the LHS alliances. The study team will review local cascade data quarterly with the alliances to identify gaps along the continuum. The study will collect self-report survey measures longitudinally at each site regarding readiness for change, implementation climate, organizational leadership, and program sustainability. The study will use the Stages of Implementation Completion (SIC) tool to assess the process of implementation across interventions. Additionally, the study team will conduct focus groups and qualitative interviews with JJ and CMHC personnel across the intervention period to assess for impact.DiscussionFindings have the potential to increase SUD need identification, referral to services, and treatment for YJS.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

This paper proposes the creation and application of maturity models to guide institutional strategic investment in research informatics and information technology (research IT) and to provide the ability to measure readiness for clinical and research infrastructure as well as sustainability of expertise. Conducting effective and efficient research in health science increasingly relies upon robust research IT systems and capabilities. Academic health centers are increasing investments in health IT systems to address operational pressures, including rapidly growing data, technological advances, and increasing security and regulatory challenges associated with data access requirements. Current approaches for planning and investment in research IT infrastructure vary across institutions and lack comparable guidance for evaluating investments, resulting in inconsistent approaches to research IT implementation across peer academic health centers as well as uncertainty in linking research IT investments to institutional goals. Maturity models address these issues through coupling the assessment of current organizational state with readiness for deployment of potential research IT investment, which can inform leadership strategy. Pilot work in maturity model development has ranged from using them as a catalyst for engaging medical school IT leaders in planning at a single institution to developing initial maturity indices that have been applied and refined across peer medical schools.

A robust biomedical informatics infrastructure is essential for academic health centers engaged in translational research. There are no templates for what such an infrastructure encompasses or how it is funded. An informatics workgroup within the Clinical and Translational Science Awards network conducted an analysis to identify the scope, governance, and funding of this infrastructure. After we identified the essential components of an informatics infrastructure, we surveyed informatics leaders at network institutions about the governance and sustainability of the different components. Results from 42 survey respondents showed significant variations in governance and sustainability; however, some trends also emerged. Core informatics components such as electronic data capture systems, electronic health records data repositories, and related tools had mixed models of funding including, fee-for-service, extramural grants, and institutional support. Several key components such as regulatory systems (e.g., electronic Institutional Review Board [IRB] systems, grants, and contracts), security systems, data warehouses, and clinical trials management systems were overwhelmingly supported as institutional infrastructure. The findings highlighted in this report are worth noting for academic health centers and funding agencies involved in planning current and future informatics infrastructure, which provides the foundation for a robust, data-driven clinical and translational research program.

Pregnant people face increased risk of severe COVID-19. Current guidelines recommend updated COVID-19 vaccination (2023–2024) for those aged ≥6 months, irrespective of pregnancy status. To refine recommendations for pregnant people, further data are needed. Using a test-negative design, we evaluated COVID-19 vaccine effectiveness against medically attended COVID-19 with COVID-19–like illness among pregnant people aged 18 to 45 years during June 2022 to August 2023. When doses were received during pregnancy, vaccine effectiveness was 52% (95% CI, 29%–67%); when received <6 months prior to pregnancy, 28% (95% CI, 11%–42%); and when received ≥6 months prior to pregnancy, 6% (95% CI, −11% to 21%). Pregnant people should stay up-to-date with recommended COVID-19 vaccination. [ABSTRACT FROM AUTHOR]

Introduction Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. Recommendations Here, we propose five independent fundamental dimensions of phenotyping algorithms—complexity, performance, efficiency, implementability, and maintenance—through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities. [ABSTRACT FROM AUTHOR]

Background During the 2022–2023 influenza season, the United States experienced the highest influenza-associated pediatric hospitalization rate since 2010–2011. Influenza A/H3N2 infections were predominant. Methods We analyzed acute respiratory illness (ARI)–associated emergency department or urgent care (ED/UC) encounters or hospitalizations at 3 health systems among children and adolescents aged 6 months–17 years who had influenza molecular testing during October 2022–March 2023. We estimated influenza A vaccine effectiveness (VE) using a test-negative approach. The odds of vaccination among influenza-A–positive cases and influenza-negative controls were compared after adjusting for confounders and applying inverse-propensity-to-be-vaccinated weights. We developed overall and age-stratified VE models. Results Overall, 13 547 of 44 787 (30.2%) eligible ED/UC encounters and 263 of 1862 (14.1%) hospitalizations were influenza-A–positive cases. Among ED/UC patients, 15.2% of influenza-positive versus 27.1% of influenza-negative patients were vaccinated; VE was 48% (95% confidence interval [CI], 44–52%) overall, 53% (95% CI, 47–58%) among children aged 6 months–4 years, and 38% (95% CI, 30–45%) among those aged 9–17 years. Among hospitalizations, 17.5% of influenza-positive versus 33.4% of influenza-negative patients were vaccinated; VE was 40% (95% CI, 6–61%) overall, 56% (95% CI, 23–75%) among children ages 6 months–4 years, and 46% (95% CI, 2–70%) among those 5–17 years. Conclusions During the 2022–2023 influenza season, vaccination reduced the risk of influenza-associated ED/UC encounters and hospitalizations by almost half (overall VE, 40–48%). Influenza vaccination is a critical tool to prevent moderate-to-severe influenza illness in children and adolescents. [ABSTRACT FROM AUTHOR]

Background: Although psychiatric disorders have been associated with reduced immune responses to other vaccines, it remains unknown whether they influence COVID‐19 vaccine effectiveness (VE). This study evaluated risk of COVID‐19 hospitalization and estimated mRNA VE stratified by psychiatric disorder status. Methods: In a retrospective cohort analysis of the VISION Network in four US states, the rate of laboratory‐confirmed COVID‐19‐associated hospitalization between December 2021 and August 2022 was compared across psychiatric diagnoses and by monovalent mRNA COVID‐19 vaccination status using Cox proportional hazards regression. Results: Among 2,436,999 adults, 22.1% had ≥1 psychiatric disorder. The incidence of COVID‐19‐associated hospitalization was higher among patients with any versus no psychiatric disorder (394 vs. 156 per 100,000 person‐years, p < 0.001). Any psychiatric disorder (adjusted hazard ratio [aHR], 1.27; 95% CI, 1.18–1.37) and mood (aHR, 1.25; 95% CI, 1.15–1.36), anxiety (aHR, 1.33, 95% CI, 1.22–1.45), and psychotic (aHR, 1.41; 95% CI, 1.14–1.74) disorders were each significant independent predictors of hospitalization. Among patients with any psychiatric disorder, aHRs for the association between vaccination and hospitalization were 0.35 (95% CI, 0.25–0.49) after a recent second dose, 0.08 (95% CI, 0.06–0.11) after a recent third dose, and 0.33 (95% CI, 0.17–0.66) after a recent fourth dose, compared to unvaccinated patients. Corresponding VE estimates were 65%, 92%, and 67%, respectively, and were similar among patients with no psychiatric disorder (68%, 92%, and 79%). Conclusion: Psychiatric disorders were associated with increased risk of COVID‐19‐associated hospitalization. However, mRNA vaccination provided similar protection regardless of psychiatric disorder status, highlighting its benefit for individuals with psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background The epidemiology of coronavirus disease 2019 (COVID-19) continues to develop with emerging variants, expanding population-level immunity, and advances in clinical care. We describe changes in the clinical epidemiology of COVID-19 hospitalizations and risk factors for critical outcomes over time. Methods We included adults aged ≥18 years from 10 states hospitalized with COVID-19 June 2021–March 2023. We evaluated changes in demographics, clinical characteristics, and critical outcomes (intensive care unit admission and/or death) and evaluated critical outcomes risk factors (risk ratios [RRs]), stratified by COVID-19 vaccination status. Results A total of 60 488 COVID-19–associated hospitalizations were included in the analysis. Among those hospitalized, median age increased from 60 to 75 years, proportion vaccinated increased from 18.2% to 70.1%, and critical outcomes declined from 24.8% to 19.4% (all P <.001) between the Delta (June–December, 2021) and post-BA.4/BA.5 (September 2022–March 2023) periods. Hospitalization events with critical outcomes had a higher proportion of ≥4 categories of medical condition categories assessed (32.8%) compared to all hospitalizations (23.0%). Critical outcome risk factors were similar for unvaccinated and vaccinated populations; presence of ≥4 medical condition categories was most strongly associated with risk of critical outcomes regardless of vaccine status (unvaccinated: adjusted RR, 2.27 [95% confidence interval {CI}, 2.14–2.41]; vaccinated: adjusted RR, 1.73 [95% CI, 1.56–1.92]) across periods. Conclusions The proportion of adults hospitalized with COVID-19 who experienced critical outcomes decreased with time, and median patient age increased with time. Multimorbidity was most strongly associated with critical outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Research driven by real‐world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID‐19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy‐preserving record linkage (PPRL) methods to ensure that patient‐level EHR data remains secure and private when governance‐approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear‐text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross‐linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large‐scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions. [ABSTRACT FROM AUTHOR]

Adults with disabilities are at increased risk for severe coronavirus disease 2019 (COVID-19). Using data across 9 states during Delta- and Omicron-predominant periods (June 2021–September 2022), we evaluated the effectiveness of the original monovalent COVID-19 messenger RNA vaccines among 521 206 emergency department/urgent care encounters (11 471 [2%] in patients with a documented disability) and 139 548 hospitalizations (16 569 [12%] in patients with a disability) for laboratory-confirmed COVID-19 illness in adults (aged ≥18 years). Across variant periods and for the primary series or booster doses, vaccine effectiveness was similar in those with and those without a disability. These findings highlight the importance of adults with disabilities staying up to date with COVID-19 vaccinations. [ABSTRACT FROM AUTHOR]