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BackgroundAccurately 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. ObjectiveThe 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. MethodsA 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. ResultsThe 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. ConclusionsA 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.

BackgroundU.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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.

Introduction: Cross-sectional methods can be used to estimate HIV incidence for surveillance and prevention studies. We evaluated assays and multi-assay algorithms (MAAs) for incidence estimation in subtype C settings. Methods: We analysed samples from individuals with subtype C infection with known duration of infection (2442 samples from 278 adults; 0.1 to 9.9 years after seroconversion). MAAs included 1-4 of the following assays: Limiting Antigen Avidity assay (LAg-Avidity), BioRad-Avidity assay, CD4 cell count and viral load (VL). We evaluated 23,400 MAAs with different assays and assay cutoffs. We identified the MAA with the largest mean window period, where the upper 95% confidence interval (CI) of the shadow was 1000 copies/mL), and two MAAs previously optimized for subtype B settings. We compared these cross-sectional incidence estimates to observed incidence in an independent longitudinal cohort. Results: The optimal MAA was LAg-Avidity 400 copies/mL. This MAA had a mean window period of 248 days (95% CI: 218, 284), a shadow of 306 days (95% CI: 255, 359), and provided the most accurate and precise incidence estimate for the independent cohort. The widely used LAg algorithm had a shorter mean window period (142 days, 95% CI: 118, 167), a longer shadow (410 days, 95% CI; 318, 491), and a less accurate and precise incidence estimate for the independent cohort. Conclusions: An optimal MAA was identified for cross-sectional HIV incidence in subtype C settings. The performance of this MAA is superior to a testing algorithm currently used for global HIV surveillance. Keywords: Cross-sectional incidence testing; Southern Africa; Subtype C; Women; Epidemiology, 1 | INTRODUCTION Accurate methods for estimating HIV incidence are critical for HIV surveillance and for evaluating the effectiveness of HIV prevention efforts [1]. Traditional longitudinal cohort studies are costly [...]

Researchers from Cornell University, New York conducted a study on the bioavailability of the pesticides DDT, DDE, DDD in soil treated with DDT 49 years earlier. Soil samples taken from the earthworm Eisenia foetida were analyzed for the presence of the persistent pesticides.