Background Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States. Objective The purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases. Methods Using a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties. Results This open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states ( P <.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases ( P =.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22). Conclusions This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.
BACKGROUND Worldwide, non-pharmacologic interventions (NPIs) have been the main tool used to mitigate the Coronavirus Disease (COVID-19) pandemic. While preliminary research across the globe has shown NPI policy to be effective, there is currently a lack of information on NPI effectiveness in the United States. OBJECTIVE The purpose of this study was to create a granular NPI dataset at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases. METHODS Using a standardized crowdsourcing methodology, we collected time series data on seven key NPIs for 1,320 U.S. counties. RESULTS This open source dataset is the largest and most comprehensive county NPI policy dataset and meets the need for higher resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P < 0.001). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R = 0.21) and elected leadership (R = 0.22). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P = 0.0043). CONCLUSIONS This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this dataset will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.
Objective: Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods: We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results: From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures.
were more likely to have intermediate (44.6%) and inadequate (18.9%) prenatal care (P5.04). Adolescents also had higher antepartum admissions (P5.006) and a higher access ratio (P5.01). Outcomes were similar except for higher rates of preterm contractions (P5.03). CONCLUSION:The pattern of resource utilization demonstrates an overall favoring of inpatient services among adolescent mothers with inadequate access to outpatient resources. Strategies to improve access to prenatal outpatient visits are essential.
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