Determining if outbreak data collected by regional or international organizations can reflect patterns observed in more detailed data collected by national veterinary services is a necessary first step if global databases are to be used for making inference about determinants of disease maintenance and spread and for emergency planning and response. We compared two data sources that capture spatial and temporal information about H5N1 highly pathogenic avian influenza outbreaks reported since 2004 in four countries: Bangladesh, Egypt, Turkey, and Vietnam. One data source consisted of reports collected as part of each country's national veterinary services surveillance program, while the other data source included reports collected using the Emergency Prevention System for Priority Animal and Plant Pests and Diseases (EMPRES-i) global animal health information system. We computed Spearman rank-order correlation statistics to compare spatial and temporal outbreak distributions, and applied a space-time permutation test to check for consistency between the two data sources. Although EMPRES-i typically captured fewer outbreaks than detailed national reporting data, the overall similarity in space and time, particularly after 2006, reflect the ability of the EMPRES-i system to portray disease patterns comparable to those observed in national data sets. Specifically, we show that the two datasets exhibit higher positive correlations in outbreak timing and reported locations after 2006 when compared to December 2003 through 2006. Strengthening the capacity of global systems to acquire data from national and regional databases will improve global analysis efforts and increase the ability of such systems to rapidly alert countries and the international community of potential disease threats.
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http://dx.doi.org/10.1016/j.prevetmed.2010.03.012 | DOI Listing |
JMIR Mhealth Uhealth
January 2025
Department of Learning and Workforce Development, The Netherlands Organisation for Applied Scientific Research, Soesterberg, Netherlands.
Background: Wearable sensor technologies, often referred to as "wearables," have seen a rapid rise in consumer interest in recent years. Initially often seen as "activity trackers," wearables have gradually expanded to also estimate sleep, stress, and physiological recovery. In occupational settings, there is a growing interest in applying this technology to promote health and well-being, especially in professions with highly demanding working conditions such as first responders.
View Article and Find Full Text PDFSports Health
January 2025
Department of Orthopaedic Surgery, Hackensack Meridian Health, Hackensack, New Jersey.
Background: The elderly US population is growing quickly and staying active longer. However, there is limited information on sports-related injuries in older adults.
Hypotheses: (1) National estimate and incidence of sports-related orthopaedic injuries in the US elderly population have increased over the last 10 years, (2) types and causes of sports-related injuries in the elderly have changed, and (3) elderly sports-related injuries will increase more than the number of treating physicians by 2040.
JMIR Public Health Surveill
January 2025
School of Public Health, National Defense Medical Center, Taipei City, Taiwan.
Background: Japanese encephalitis (JE) is a zoonotic parasitic disease caused by the Japanese encephalitis virus (JEV), and may cause fever, nausea, headache, or meningitis. It is currently unclear whether the epidemiological characteristics of the JEV have been affected by the extreme climatic conditions that have been observed in recent years.
Objective: This study aimed to examine the epidemiological characteristics, trends, and potential risk factors of JE in Taiwan from 2008 to 2020.
JMIR Med Inform
January 2025
INSERM U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 30 Bd Jean Monnet, Nantes, 44093, France, 33 2 40 08 74 10.
Precision medicine involves a paradigm shift toward personalized data-driven clinical decisions. The concept of a medical "digital twin" has recently become popular to designate digital representations of patients as a support for a wide range of data science applications. However, the concept is ambiguous when it comes to practical implementations.
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