Background: Computerized morbidity registration networks might serve as early warning systems in a time where natural epidemics such as the H1N1 flu can easily spread from one region to another.
Methods: In this contribution we examine whether general practice based broad-spectrum computerized morbidity registration networks have the potential to act as a valid surveillance instrument of frequently occurring diseases. We compare general practice based computerized data assessing the frequency of influenza-like illness (ILI) and acute respiratory infections (ARI) with data from a well established case-specific sentinel network, the European Influenza Surveillance Scheme (EISS). The overall frequency and trends of weekly ILI and ARI data are compared using both networks.
Results: Detection of influenza-like illness and acute respiratory illness occurs equally fast in EISS and the computerized network. The overall frequency data for ARI are the same for both networks, the overall trends are similar, but the increases and decreases in frequency do not occur in exactly the same weeks. For ILI, the overall rate was slightly higher for the computerized network population, especially before the increase of ILI, the overall trend was almost identical and the increases and decreases occur in the same weeks for both networks.
Conclusions: Computerized morbidity registration networks are a valid tool for monitoring frequent occurring respiratory diseases and the detection of sudden outbreaks.
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http://dx.doi.org/10.1186/1471-2296-11-24 | DOI Listing |
Afr J Prim Health Care Fam Med
December 2024
Department of Family Medicine, Federal Medical Centre, Abeokuta.
The training of Family Medicine residents in the West Africa College of Physicians (WACP) has steadily upscaled to a competency-based approach over the years. The latest review of the curriculum (2022) includes self-directed online modules on clinical postings, health management, patient safety, quality assurance research and medical education among others. The operationalisation of the revised curriculum involves the use of workplace-based tools for formative assessments.
View Article and Find Full Text PDFTrauma Surg Acute Care Open
January 2025
Division of Healthcare Engineering, Department of Radiation Oncology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Background: Burnout negatively impacts healthcare professionals' well-being, leading to an increased risk of human errors and patient harm. There are limited assessments of burnout and associated stressors among acute care and trauma surgery teams.
Methods: Acute care and trauma surgery team members at a US academic medical center were administered a survey that included a 2-item Maslach Burnout Inventory and 21 workplace stressors based on the National Academy of Medicine's systems model of clinician burnout and professional well-being.
Front Immunol
January 2025
Department of Pathology, Microbiology & Immunology, New York Medical College, Valhalla, NY, United States.
Rationale: Approximately 32 million people in the United States suffer from food allergies. Some food groups, such as legumes - peanuts, tree nuts, fish, and shellfish, have a high risk of cross-reactivity. However, the murine model of multiple food group cross-reactivity is limited.
View Article and Find Full Text PDFJ Am Stat Assoc
April 2021
University of Geneva, Geneva, Switzerland.
Latent time series models such as (the independent sum of) ARMA(, ) models with additional stochastic processes are increasingly used for data analysis in biology, ecology, engineering, and economics. Inference on and/or prediction from these models can be highly challenging: (i) the data may contain outliers that can adversely affect the estimation procedure; (ii) the computational complexity can become prohibitive when the time series are extremely large; (iii) model selection adds another layer of (computational) complexity; and (iv) solutions that address (i), (ii), and (iii) simultaneously do not exist in practice. This paper aims at jointly addressing these challenges by proposing a general framework for robust two-step estimation based on a bounded influence M-estimator of the wavelet variance.
View Article and Find Full Text PDFSurg Pract Sci
September 2023
Department of Surgery, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, Michigan 48109, United States.
Introduction: Case-based learning (CBL) utilizes authentic clinical cases that connect theory to practice. CBL has been shown to result in deeper learning and high engagement of adult learners. An open-source, web-based CBL module was created to help learners develop the cognitive foundation of ectopic pregnancy management in the low-resource setting.
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