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.

Puff, Puff, Don't Pass: harm reduction for cannabis use during a viral respiratory pandemic.

BACKGROUND: Prior to the COVID-19 pandemic, cannabis use social practices often involved sharing prepared cannabis (joints/blunts/cigarettes) and cannabis-related paraphernalia. Previous studies have demonstrated that sharing paraphernalia for cannabis, tobacco, and crack cocaine is a risk factor for respiratory viral and bacterial infections. Although COVID-19 is a respiratory viral infection that spreads through droplets and airborne transmission, it is unclear if many individuals adopted harm reduction practices around sharing cannabis. This study: quantifies the prevalence of sharing prepared non-medical cannabis and cannabis-related paraphernalia reported before and during the pandemic; assesses changes in sharing of non-medical cannabis from before to during the pandemic; assess the association between frequency of non-medical cannabis use and sharing of cannabis during the pandemic; and describes how respondents obtained their cannabis and the reasons for changing their cannabis use during the pandemic to explain differences in sharing patterns. METHODS: This cross-sectional study used data collected from an anonymous, US-based web survey on cannabis-related behaviors from August to September 2020 (n = 1833). Participants were included if they reported using a mode of inhalation for non-medical cannabis consumption. We calculated proportional changes in sharing cannabis before/during the COVID-19 pandemic. Associations between frequency of cannabis use and cannabis sharing during the COVID-19 pandemic were assessed using logistic regression analysis. RESULTS: Overall, 1,112 participants reported non-medical cannabis use; 925 (83.2%) reported a mode of cannabis inhalation. More respondents reported no sharing during (24.9%) than before the pandemic (12.4%; p < 0.01); less respondents shared most of the time (19.5% before; 11.2% during; p < 0.01) and always during the pandemic (5.2% before; 3.1% during; p < 0.01). After adjusting for covariates, the odds of any sharing during the pandemic for those who reported ≥ weekly cannabis use was 0.53 (95% CI 0.38, 0.75) compared to those who reported ≤ monthly. CONCLUSIONS: Sharing of prepared cannabis and cannabis-related paraphernalia decreased during the COVID-19 pandemic compared to before the pandemic. This finding suggests potential risk mitigation strategies taken by participants for COVID-19 prevention either directly through behavior change or indirectly through adherence to COVID-19 prevention recommendations. Harm reduction messaging around sharing of cannabis during surges of COVID-19 or other respiratory infections may provide benefit in reducing infection among those who use cannabis, especially as cannabis use in the USA continues to increase.

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.

Performance of diagnostic coding and laboratory testing results to measure COVID-19 during pregnancy and associations with pregnancy outcomes.

BACKGROUND: Large-scale evaluation of COVID-19 is likely to rely on the quality of ICD coding. However, little is known about the validity of ICD-coded COVID-19 diagnoses. OBJECTIVES: To evaluate the performance of diagnostic codes in detecting COVID-19 during pregnancy. METHODS: We used data from a national cohort of 78,283 individuals with a pregnancy ending between 11 March 2020 and 31 January 2021 in the OptumLabs® Data Warehouse (OLDW). OLDW is a longitudinal, real-world data asset with de-identified administrative claims and electronic health record data. We identified all services with an ICD-10-CM diagnostic code of U07.1 and all laboratory claims records for COVID-19 diagnostic testing. We compared ICD-coded diagnoses to testing results to estimate positive and negative predictive values (PPV and NPV). To evaluate impact on risk estimation, we estimated risk of adverse pregnancy outcomes by source of exposure information. RESULTS: Of 78,283 pregnancies, 5644 had a laboratory test result for COVID-19. Testing was most common among older individuals, Hispanic individuals, those with higher socioeconomic status and those with a diagnosed medical condition or pregnancy complication; 52% of COVID-19 cases was identified through ICD-coded diagnosis alone, 19% from laboratory test results alone and 29% from both sources. Agreement between ICD-coded diagnosis and laboratory testing records was high 91% (95% confidence interval [CI] 90, 92). However, the PPV of ICD-code diagnosis was low (36%; 95% CI 33, 39). We observed up to a 50% difference in risk estimates of adverse pregnancy outcomes when exposure was based on laboratory testing results or diagnostic coding alone. CONCLUSIONS: More than one-in-five COVID-19 cases would be missed by using ICD-coded diagnoses alone to identify COVID-19 during pregnancy. Epidemiological studies exclusively relying on diagnostic coding or laboratory testing results are likely to be affected by exposure misclassification. Research and surveillance should draw upon multiple sources of COVID-19 diagnostic information.

SARS-CoV-2 Infection During Pregnancy and Associated Perinatal Health Outcomes: A National US Cohort Study.

BACKGROUND: Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been associated with increased risk of adverse perinatal health outcomes, few large-scale, community-based epidemiological studies have been conducted. METHODS: We conducted a national cohort study using deidentified administrative claims data for 78 283 pregnancies with estimated conception before 30 April 2020 and pregnancy end after 11 March 2020. We identified SARS-CoV-2 infections using diagnostic and laboratory testing data, and compared the risk of pregnancy outcomes using Cox proportional hazard models treating coronavirus disease 2019 (COVID-19) as a time-varying exposure and adjusting for baseline covariates. RESULTS: Of the pregnancies, 2655 (3.4%) had a documented SARS-CoV-2 infection. COVID-19 during pregnancy was not associated with risk of miscarriage, antepartum hemorrhage, or stillbirth, but was associated with 2-3 fold higher risk of induced abortion (adjusted hazard ratio [aHR], 2.60; 95% confidence interval [CI], 1.17-5.78), cesarean delivery (aHR, 1.99; 95% CI, 1.71-2.31), clinician-initiated preterm birth (aHR, 2.88; 95% CI, 1.93-4.30), spontaneous preterm birth (aHR, 1.79; 95% CI, 1.37-2.34), and fetal growth restriction (aHR, 2.04; 95% CI, 1.72-2.43). CONCLUSIONS: Prenatal SARS-CoV-2 infection was associated with increased risk of adverse pregnancy outcomes. Prevention could have fetal health benefits.

Optimal uses of pooled testing for COVID-19 incorporating imperfect test performance and pool dilution effect: An application to congregate settings in Los Angeles County.

INTRODUCTION: Pooled testing is a potentially efficient alternative strategy for COVID-19 testing in congregate settings. We evaluated the utility and cost-savings of pooled testing based on imperfect test performance and potential dilution effect due to pooling and created a practical calculator for online use. METHODS: We developed a 2-stage pooled testing model accounting for dilution. The model was applied to hypothetical scenarios of 100 specimens collected during a one-week time-horizon cycle for varying levels of COVID-19 prevalence and test sensitivity and specificity, and to 338 skilled nursing facilities (SNFs) in Los Angeles County (Los Angeles) (data collected and analyzed in 2020). RESULTS: Optimal pool sizes ranged from 1 to 12 in instances where there is a least one case in the batch of specimens. 40% of Los Angeles SNFs had more than one case triggering a response-testing strategy. The median number (minimum; maximum) of tests performed per facility were 56 (14; 356) for a pool size of 4, 64 (13; 429) for a pool size of 10, and 52 (11; 352) for an optimal pool size strategy among response-testing facilities. The median costs of tests in response-testing facilities were $8250 ($1100; $46,100), $6000 ($1340; $37,700), $6820 ($1260; $43,540), and $5960 ($1100; $37,380) when adopting individual testing, a pooled testing strategy using pool sizes of 4, 10, and optimal pool size, respectively. CONCLUSIONS: Pooled testing is an efficient strategy for congregate settings with a low prevalence of COVID-19. Dilution as a result of pooling can lead to erroneous false-negative results.