Partnered rapid research with K-12 school districts during the COVID-19 pandemic

OBJECTIVES/GOALS: During the COVID-19 pandemic, translational scientists sought to provide scientific and data expertise to school districts serving diverse and disadvantaged students to enable equitable access to in-person learning. METHODS/STUDY POPULATION: We showcase two CTSA examples. One is a partnership with the second largest U.S. school district;the second is a national network of scientists and urban and rural school districts. In each example, CTSAs assembled expert science teams to support data-driven decision-making. The teams provided honest brokering of COVID-19 science, scientific interpretation that is sensitive to local context, and responses to community-driven questions. The teams collaborated with school district partners to design actionable data displays on key metrics including primary COVID-19 cases, school-acquired cases, quarantines, and missed school. The national ABC Science Collaborative) provided a platform for shared learning and reproducibility and credibility of science using district data. RESULTS/ANTICIPATED RESULTS: The CTSAs developed easily interpretable and actionable data displays. Partnered school districts observed data in real time to identify signals of change. Districts in the national network were able to learn in real time from variation across districts based on policies and procedures that they adopted, such as quarantine, masking, and physical distancing. DISCUSSION/SIGNIFICANCE: This scientific collaboration is a model of rapid CTSA response, informing science and real-time action. The data displays enable school districts to explain decisions regarding student and staff health and safety. These partnerships and data designs are infrastructure that can be quickly mobilized for emergent and for ongoing information needs.

Modeling COVID-19 infection dynamics and mitigation strategies for in-person K-6 instruction.

Background: U.S. school closures due to the coronavirus disease 2019 (COVID-19) pandemic led to extended periods of remote learning and social and economic impact on families. Uncertainty about virus dynamics made it difficult for school districts to develop mitigation plans that all stakeholders consider to be safe. Methods: We developed an agent-based model of infection dynamics and preventive mitigation designed as a conceptual tool to give school districts basic insights into their options, and to provide optimal flexibility and computational ease as COVID-19 science rapidly evolved early in the pandemic. Elements included distancing, health behaviors, surveillance and symptomatic testing, daily symptom and exposure screening, quarantine policies, and vaccination. Model elements were designed to be updated as the pandemic and scientific knowledge evolve. An online interface enables school districts and their implementation partners to explore the effects of interventions on outcomes of interest to states and localities, under a variety of plausible epidemiological and policy assumptions. Results: The model shows infection dynamics that school districts should consider. For example, under default assumptions, secondary infection rates and school attendance are substantially affected by surveillance testing protocols, vaccination rates, class sizes, and effectiveness of safety education. Conclusions: Our model helps policymakers consider how mitigation options and the dynamics of school infection risks affect outcomes of interest. The model was designed in a period of considerable uncertainty and rapidly evolving science. It had practical use early in the pandemic to surface dynamics for school districts and to enable manipulation of parameters as well as rapid update in response to changes in epidemiological conditions and scientific information about COVID-19 transmission dynamics, testing and vaccination resources, and reliability of mitigation strategies.

Estimating Vaccine Effectiveness by Linking Population-Based Health Registries: Some Sources of Bias.

The coronavirus disease 2019 (COVID-19) pandemic has underscored the importance of observational studies of real-world vaccine effectiveness (VE) to help answer urgent public health questions. One approach to rapidly answering questions about real-world VE relies on linking data from a population-based registry of vaccinations with a population-based registry of health outcomes. Here we consider some potential sources of bias in linked registry studies, including incomplete reporting to the registries, errors in linking individuals between registries, and errors in the assumed population size of the catchment area of the registries. We show that the direction of the bias resulting from one source of error by itself is predictable. However, if multiple sources of error are present, the direction of the bias can be either upward or downward. The biases can be so strong as to make harmful vaccines appear effective. We provide explicit formulas with which to quantify and adjust for multiple biases in estimates of VE which could be used in sensitivity analyses. While this work was motivated by COVID-19 vaccine questions, the results are generally applicable to studies that link population-based exposure registries with population-based case registries to estimate relative risks of exposures.

Interactive data displays for rapid responses to COVID-19 response in K-12 schools

OBJECTIVES/GOALS: A UCLA Clinical and Translational Science Institute (CTSI) science team partnered with the second largest US school district, with over 500,000 K-12 students, to design and implement a statistical process control dashboard to guide COVID-19 response, including mitigation and vaccination outreach. METHODS/STUDY POPULATION: District data for students, teachers, and staff are updated daily and include COVID-19 test results, counts of quarantine after positive tests, and COVID-19 vaccination rates. Displays used a new hybrid Shewhart control chart to detect changes in test positivity rates and distinguish meaningful signals from noise (random day-to-day variation). The dashboard uses the Shiny and plotly packages in R to display interactive graphs of each data stream (cases, tests, and vaccinations) charted at multiple levels (districtwide, subdistricts, schools). Displays of variation over time show policy impacts and inequities. Selected displays use municipal COVID-19 data to complement district data. RESULTS/ANTICIPATED RESULTS: The district has used the displays to assess the impact of their COVID-19 response and to identify variation in close to real-time to suggest areas with need for additional resources for mitigation or vaccination. The CTSI team has continued to edit and add displays in response to the district’s changing operational needs and questions. DISCUSSION/SIGNIFICANCE: The UCLA CTSI team developed and implemented a robust data visualization dashboard to monitor COVID-19 case rates and plan vaccination outreach efforts. Control charts enabled the district to distinguish noise from signal, thereby rapidly identifying when specific parts of the district needed targeted support to achieve equity goals.

Using team science to support outbreak management in a large urban region during the COVID-19 pandemic

OBJECTIVES/GOALS: To describe how the UCLA Clinical and Translational Science Institute (CTSI) assembled and deployed a science team in support of a local jurisdictions effort to manage and control COVID-19 outbreaks in one of the nations largest metropolitan regions, Los Angeles County (LAC). METHODS/STUDY POPULATION: During the COVID-19 pandemic (2020-21), building an efficient data infrastructure to support outbreak management became a priority for the local health department. In response, the UCLA CTSI assembled a science team with expertise across the translational continuum: epidemiology, laboratory and microbiology, machine learning, health policy, medicine and clinical care, and community engagement. The team partnered with a new LAC Data Science Team to foster a collaborative learning environment for scientists and public health personnel, employing improvement and implementation science to help mitigate COVID-19 outbreaks in sectors including healthcare, skilled nursing facilities, and K-12 education. The goal was a public health workforce that is prepared to problem-solve complex, evolving outbreaks. RESULTS/ANTICIPATED RESULTS: The science team created a learning environment with data modeling and visualization, problem-based learning, and active knowledge and skills acquisition. First, control charts and time series methods were used to visualize COVID-19 data and find signals for action. Second, a series of 16 Grand Rounds offered interactive sessions on problem-solving of outbreak challenges in different sectors. Third, a biweekly Public Health Digest provided fieldworkers with the latest scientific studies on COVID-19. All three elements guided and empowered the workforce to implement timelier, efficient outbreak mitigation strategies in the field. The partnered team also identified barriers to adoption of selected new data and management techniques, revealing areas for further skill-building and data-driven leadership. DISCUSSION/SIGNIFICANCE: The UCLA CTSI science team offered a backbone science infrastructure for helping public health and other sector agencies manage COVID-19 outbreaks and mitigation. It showed promise in bringing and translating science into public health practice. It revealed future priorities for CTSI innovation and scientific support of public agencies.

Modeling COVID-19 infection dynamics and program interventions for K-12 school re-opening

IMPACT: This study provides public health and K-12 school districts with a pragmatic, flexible, adaptable model showing COVID-19 transmission dynamics, using local data and program elements that are modifiable and with an online model for easy use, to enable safe and equitable re-opening and maintenance of in-person learning. OBJECTIVES/GOALS: School closures resulting from the COVID-19 pandemic disrupt student education and health and exacerbate inequities. Public health agencies and school districts currently lack pragmatic models to assess the effects of potential strategies for resuming and maintaining in-person learning on outcomes such as transmission and attendance. METHODS/STUDY POPULATION: This study explored how various combinations of transmission-mitigating interventions affect health and learning outcomes in a range of underlying epidemiological conditions. The CTSA science team developed a conceptual framework and an agent-based simulation model with parameters including prevalence, transmission, testing, preventive and responsive actions, infection control, population behavior and awareness, and the potential impact of vaccine adoption and exemption policies. The team partnered with a large school district to ensure relevance of the program components to decision-making. RESULTS/ANTICIPATED RESULTS: The model shows that no single program element or condition ensures safety. Combining interventions can result in synergy in the mitigation efforts. Even without testing, an efficient health screening process with forthcoming risk reporting, combined with on-campus infection control, can reduce on-campus transmission. The resulting model is accessible online to enable exploration of likely scenarios. It is adaptable as COVID-19 science evolves, including for testing and vaccines. DISCUSSION/SIGNIFICANCE OF FINDINGS: This research provides public health agencies and school districts with a model that couples local conditions with programmatic elements to help inform the local COVID-19 response, recognizing that decisions about the school community are often complex politically, technically, and operationally when it comes to addressing a health crisis.

CTSA collaboration to support K-12 school re-opening in the COVID-19 pandemic

IMPACT: The mobilization of a CTSA-sponsored team with multi-disciplinary translational science expertise enabled the university to provide a range of T1-T4 expertise to a large, complex school district that resulted in permanent learning and data science infrastructure. OBJECTIVES/GOALS: The Clinical Translational Science Institute (CTSI) formed a multidisciplinary science team to provide expertise in support of the re-opening of in-person learning in the second-largest U.S. school district during the COVID-19 pandemic. METHODS/STUDY POPULATION: The assembled interdisciplinary science team provided expertise in epidemiology, machine learning, causal inference and agent-based modeling, data and improvement science, biostatistics, clinical and laboratory medicine, health education, community engagement, and experience in outbreak investigation and management. The team included TL1 pre and postdoctoral fellows and mobilized scientists from multiple professional schools and T1-T4 stages of translational research. RESULTS/ANTICIPATED RESULTS: Tangible outcomes achieved using this team approach included the development of practical metrics for use in the school community, a learning process, the integration of preventive design elements into a testing and tracing program, and targeted and data-driven health education. The team, for example, generated new data displays for community engagement and collaborated with the school district in their use to visualize, learn from, and act on variation across a 700 square mile region. DISCUSSION/SIGNIFICANCE OF FINDINGS: Novel translational methods can be used to establish a learning environment and data science infrastructure that complements efforts of public health agencies to aid schools in the COVID-19 pandemic. These new capabilities apply to COVID-19 testing and vaccines and can be mobilized for future population health challenges faced by school districts.

Impacts of COVID-19 and elective surgery cancellations on platelet supply and utilization in the Canadian Province of British Columbia.

BACKGROUND AND OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic raised concerns about the vulnerability of platelet supply and the uncertain impact of the resumption of elective surgery on utilization. We report the impact of COVID-19 on platelet supply and utilization across a large, integrated healthcare system in the Canadian province of British Columbia (BC). MATERIALS AND METHODS: Historical platelet use in BC by indication was compiled for fiscal year 2010/2011-2019/2020. Platelet collections, initial daily inventory and disposition data were assessed pre-COVID-19 (1 April 2018-15 March 2020) and for two COVID-19 time periods in BC: a shutdown phase with elective surgeries halted (16 March-17 May, 2020) and a renewal phase when elective surgeries resumed (18 May-27 September 2020); comparisons were made provincially and for individual health authorities. RESULTS: Historically, elective surgeries accounted for 10% of platelets transfused in BC. Initial daily supplier inventory increased from baseline during both COVID-19 periods (93/90 units vs. 75 units pre-COVID-19). During the shutdown phase, platelet utilization decreased 10.4% (41 units/week; p < 0.0001), and remained significantly decreased during the ensuing renewal period. Decreased platelet utilization was attributed to fewer transfusions during the shutdown phase followed by a decreased discard/expiry rate during the renewal phase compared to pre-COVID-19 (15.2% vs. 18.9% pre-COVID-19; p < 0.0001). Differences in COVID-19 platelet utilization patterns were noted between health authorities. CONCLUSION: Decreased platelet utilization was observed in BC compared to pre-COVID-19, likely due to a transient reduction in elective surgery as well as practice and policy changes triggered by pandemic concerns.

Adaptations of transfusion systems to the COVID-19 pandemic in British Columbia, Canada: Early experiences of a large tertiary care center and survey of provincial activities.

BACKGROUND: In March 2020, a state of emergency was declared to facilitate organized responses to the coronavirus disease 2019 (COVID-19) pandemic in British Columbia, Canada. Emergency blood management committees (EBMCs) were formed regionally and provincially to coordinate transfusion service activities and responses to possible national blood shortages. STUDY DESIGN AND METHODS: We describe the responses of transfusion services to COVID-19 in regional health authorities in British Columbia through a collaborative survey, contingency planning meeting minutes, and policy documents, including early trends observed in blood product usage. RESULTS: Early strategic response policies were developed locally in collaboration with members of the provincial EBMC and focused on three key areas: utilization management strategies, stakeholder engagement (collaboration with frequent users of the transfusion service, advance notification of potential inventory shortage plans, and development of blood triage guidance documents), and laboratory staffing and infection control procedures. Reductions in transfusion volumes were observed beginning in mid-March 2020 for red blood cells and platelets relative to the prepandemic baseline (27% and 26% from the preceding year, respectively). There was a slow gradual return toward baseline beginning one month later; no product shortage issues were experienced. CONCLUSION: Provincial collaborative efforts facilitated the development of initiatives focused on minimizing potential COVID-19-related disruptions in transfusion services in British Columbia. While there have been no supply issues to date, the framework developed early in the pandemic should facilitate timely responses to possible disruptions in future waves of infection.

Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis.

BACKGROUND: Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. OBJECTIVE: The aim of this study is to investigate whether excess visits where the word "cough" was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. METHODS: A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. RESULTS: The percentage of patients presenting with an EHR reason for visit containing the word "cough" to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. CONCLUSIONS: A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.