Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.
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Background: Shoulder injuries account for a large portion of all recorded injuries in professional baseball. Much is known about other shoulder pathologies in the overhead athlete, but the incidence and impact of acromioclavicular (AC) joint injuries in this population are unknown. We examined the epidemiology of AC joint injuries in Major League Baseball (MLB) and Minor League Baseball (MiLB) players and determined the impact on time missed. Methods: The MLB Health and Injury Tracking System was used to compile records of all MLB and MiLB players from 2011 to 2017 with documented AC joint injuries. These injuries were classified as acute (sprain or separation) or chronic (AC joint arthritis or distal clavicular osteolysis), and associated data extracted included laterality, date of injury, player position, activity, mechanism of injury, length of return to play, and need for surgical intervention. Results: A total of 312 AC joint injuries (183 in MiLB players and 129 in MLB players; range, 39-60 per year) were recorded: 201 acute (64.4%) and 111 chronic (35.6%). A total of 81% of acute and 59% of chronic injuries resulted in time missed, with a mean length of return to play of 21 days for both. Of the injuries in outfielders, 79.6% were acute (P < .0001), as were 66.3% of injuries in infielders (P ¼ .004). Pitchers and catchers had more equal proportions of acute and chronic AC injuries (P > .05 for all). Acute AC injuries occurred most often while fielding (n ¼ 100, 84.7%), running (n ¼ 25, 80.6%), and hitting (n ¼ 19, 61.3%), whereas chronic injuries tended to be more common while pitching (n ¼ 26, 68.4%). Of contact injuries, 82.5% were acute (P < .0001), whereas 59.0% of noncontact injuries were chronic (P ¼ .047). MLB players showed consistently higher regular-season rates of both acute and chronic AC injuries than MiLB players (P < .0001 for each). Conclusion: Acute AC joint injuries are contact injuries occurring most commonly among infielders and outfielders while fielding that result in 3 weeks missed before return to play, whereas chronic AC joint injuries occur more commonly in pitchers and catchers from noncontact repetitive overhead activity. Knowledge of these data can better guide expectation management in this elite population to better elucidate the prevalence of 2 common injury patterns in the AC joint. The Institutional Review Board of the Johns Hopkins University approved this study; the study procedures have been approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board.
Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.
Background Malaria is a leading cause of morbidity and mortality in refugee children in high-transmission parts of Africa. Characterizing the clinical features of malaria in refugees can inform approaches to reduce its burden. Methods The study was conducted in a high-transmission region of northern Zambia hosting Congolese refugees. We analyzed surveillance data and hospital records of children with severe malaria from refugee and local sites using multivariable regression models and geospatial visualization. Findings Malaria prevalence in the refugee settlement was similar to the highest burden areas in the district, consistent with the local ecology and leading to frequent rapid diagnostic test (RDT) stockouts. We identified 2,197 children hospitalized for severe malaria during the refugee crisis in 2017 and 2018. Refugee children referred from a refugee transit center (n = 63) experienced similar in-hospital mortality to local children and presented with less advanced infection. However, refugee children from a permanent refugee settlement (n = 110) had more than double the mortality of local children (p < 0.001), had lower referral rates, and presented more frequently with advanced infection and malnutrition. Distance from the hospital was an important mediator of the association between refugee status and mortality, but did not account for all of the increased risk. Interpretation Malaria outcomes were more favorable in refugee children referred from a highly outfitted refugee transit center than those referred later from a permanent refugee settlement. Refugee children experienced higher in-hospital malaria mortality due in part to delayed presentation and higher rates of malnutrition. Interventions tailored to the refugee context are required to ensure capacity for rapid diagnosis and referral to reduce malaria mortality.
With the incidence of Lyme and other tickborne diseases on the rise in the US and globally, there is a critical need for data-driven tools that communicate the magnitude of this problem and help guide public health responses. We present the Johns Hopkins Lyme and Tickborne Disease Dashboard (https://www.hopkinslymetracker.org/), a new tool that harnesses the power of geography to raise awareness and fuel research and scientific collaboration. The dashboard is unique in applying a geographic lens to tickborne diseases, aiming not only to become a global tracker of tickborne diseases but also to contextualize their complicated geography with a comprehensive set of maps and spatial data sets representing a One Health approach. We share our experience designing and implementing the dashboard, describe the main features, and discuss current limitations and future directions.
Vibrio parahaemolyticus (V. parahaemolyticus) is a naturallyoccurring bacterium found in estuaries, such as the Chesapeake Bay (USA), that can cause vibriosis, a food - and waterborne illness, in humans. Tracking the spatial and temporal distribution of V. parahaemolyticus in the Chesapeake Bay, which varies in part due to water temperature, salinity, and other environmental variables, can help identify areas and time periods of high risk. These observations can support interventions used to reduce the burden of vibriosis. Spatial and spatiotemporal clusters of high V. parahaemolyticus abundance were identified among surface water samples in the Chesapeake Bay between 2007 and 2010. While Euclidean distances between geographic points in spatial analyses are often used for cluster detection, non-Euclidean distances should be considered for cluster detection due to the complex nature of the Chesapeake Bay shoreline. Comparison of both methods consistently showed the non-Euclidean cluster detection providing unique and more reasonable clusters than the Euclidean approach. Residuals from univariate and multivariate models were used to identify how clusters changed after controlling for environmental variables. Most clusters tended to decrease in space, time, or significance after adjustment, suggesting these covariates contributed to the original formation of the clusters and as such are useful observation tools for vibriosis risk managers. Clusters that remained after adjustment suggest areas for further study and intervention. These findings reinforce the importance of using non-Euclidean distances when tracking the spatiotemporal variation of V. parahaemolyticus as well as the benefits of cluster detection methods for V. parahaemolyticus risk management in estuaries.
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