IMPORTANCE There is limited information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and infection among pediatric patients across the United States. OBJECTIVE To describe testing for SARS-CoV-2 and the epidemiology of infected patients. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study was conducted using electronic health record data from 135 794 patients younger than 25 years who were tested for SARS-CoV-2 from January 1 through September 8, 2020. Data were from PEDSnet, a network of 7 US pediatric health systems, comprising 6.5 million patients primarily from 11 states. Data analysis was performed from September 8 to 24, 2020. EXPOSURE Testing for SARS-CoV-2. MAIN OUTCOMES AND MEASURES SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) illness. RESULTS A total of 135 794 pediatric patients (53% male; mean [SD] age, 8.8 [6.7] years; 3% Asian patients, 15% Black patients, 11% Hispanic patients, and 59% White patients; 290 per 10 000 population [range, 155-395 per 10 000 population across health systems]) were tested for SARS-CoV-2, and 5374 (4%) were infected with the virus (12 per 10 000 population [range, 7-16 per 10 000 population]). Compared with White patients, those of Black, Hispanic, and Asian race/ethnicity had lower rates of testing (Black: odds ratio [

Background:High-intensity functional training (HIFT) is a new training modality that merges high-intensity exercise with functional (multijoint) movements. Even though others exist, CrossFit training has emerged as the most common form of HIFT. Recently, several reports have linked CrossFit training to severe injuries and/or life-threatening conditions, such as rhabdomyolysis. Empirical evidence regarding the safety of this training modality is currently limited.Purpose:To examine the incidence of injuries related to CrossFit participation and to estimate the rate of injuries in a large cross-sectional convenience sample of CrossFit participants from around the world.Study Design:Descriptive epidemiology study.Methods:A total of 3049 participants who reported engaging in CrossFit training between 2013 and 2017 were surveyed.Results:A portion (30.5%) of the participants surveyed reported experiencing an injury over the previous 12 months because of their participation in CrossFit training. Injuries to the shoulders (39%), back (36%), knees (15%), elbows (12%), and wrists (11%) were most common for both male and female participants. The greatest number of injuries occurred among those who participated in CrossFit training 3 to 5 days per week (χ2 = 12.51; P = .0019). Overall, and based on the assumed maximum number of workout hours per week, the injury rate was 0.27 per 1000 hours (females: 0.28; males: 0.26), whereas the assumed minimum number of workout hours per week resulted in an injury rate of 0.74 per 1000 hours (females: 0.78; males: 0.70).Conclusion:Our findings suggest that CrossFit training is relatively safe compared with more traditional training modalities. However, it seems that those within their first year of training as well as those who engage in this training modality less than 3 days per week and/or participate in less than 3 workouts per week are at a greater risk for injuries.

While data quality is recognized as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished. This paper presents a real-world account of studying and interpreting data quality findings in a pediatric CDRN, and the lessons learned could be used by other CDRNs.

This national study evaluated trends in illness severity among 82 798 children with coronavirus disease 2019 from March 1, 2020, to December 30, 2021.

Background: Clinical data research networks (CDRNs) aggregate electronic health record data from multiple hospitals to enable large-scale research. A critical operation toward building a CDRN is conducting continual evaluations to optimize data quality. The key challenges include determining the assessment coverage on big datasets, handling data variability over time, and facilitating communication with data teams. This study presents the evolution of a systematic workflow for data quality assessment in CDRNs. Implementation: Using a specific CDRN as use case, the workflow was iteratively developed and packaged into a toolkit. The resultant toolkit comprises 685 data quality checks to identify any data quality issues, procedures to reconciliate with a history of known issues, and a contemporary GitHub-based reporting mechanism for organized tracking. Results: During the first two years of network development, the toolkit assisted in discovering over 800 data characteristics and resolving over 1400 programming errors. Longitudinal analysis indicated that the variability in time to resolution (15day mean, 24day IQR) is due to the underlying cause of the issue, perceived importance of the domain, and the complexity of assessment. Conclusions: In the absence of a formalized data quality framework, CDRNs continue to face challenges in data management and query fulfillment. The proposed data quality toolkit was empirically validated on a particular network, and is publicly available for other networks. While the toolkit is user-friendly and effective, the usage statistics indicated that the data quality process is very time-intensive and sufficient resources should be dedicated for investigating problems and optimizing data for research.