We describe the methodology and impact of merging detailed statewide mortality data into the master patient index tables of the clinical data repository (CDR) of the University of Virginia Health System (UVAHS). We employ three broadly inclusive linkage passes (designed to result in large numbers of false positives) to match the patients in the CDR to those in the statewide files using the following criteria: a) Social Security Number; b) Patient Last Name and Birth Date; c) Patient Last Name and Patient First Name. The results from these initial matches are refined by calculation and assignment of a total score comprised of partial scores depending on the quality of matching between the various identifiers. In order to validate our scoring algorithm, we used those patients known to have died at UVAHS over the eight year period as an internal control. We conclude that we are able to update our CDR with 97% of the deaths from the state source using this scheme. We illustrate the potential of the resulting system to assist caregivers in identification of at-risk patient groups by description of those patients in the CDR who were found to have committed suicide. We suggest that our approach represents an efficient and inexpensive way to enrich hospital data with important outcomes information.
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Sports 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.
View Article and Find Full Text PDFJMIR Med Inform
January 2025
Department of Endocrinology and Metabolism, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Background: Many tools have been developed to predict the risk of diabetes in a population without diabetes; however, these tools have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, and low sensitivity or specificity.
Objective: We aimed to develop and validate an easy, systematic index for predicting diabetes risk in the Asian population.
Methods: We collected the data from the NAGALA (NAfld [nonalcoholic fatty liver disease] in the Gifu Area, Longitudinal Analysis) database.
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