Objective: The objective of this study was to examine the predictive relationships between employee health risk factors (HRFs) and workers' compensation (WC) claim occurrence and costs.
Methods: Logistic regression and generalised linear models were used to estimate the predictive association between HRFs and claim occurrence and cost among a cohort of 16 926 employees from 314 large, medium and small businesses across multiple industries. First, unadjusted (HRFs only) models were estimated, and second, adjusted (HRFs plus demographic and work organisation variables) were estimated.
Results: Unadjusted models demonstrated that several HRFs were predictive of WC claim occurrence and cost. After adjusting for demographic and work organisation differences between employees, many of the relationships previously established did not achieve statistical significance. Stress was the only HRF to display a consistent relationship with claim occurrence, though the type of stress mattered. Stress at work was marginally predictive of a higher odds of incurring a WC claim (p<0.10). Stress at home and stress over finances were predictive of higher and lower costs of claims, respectively (p<0.05).
Conclusions: The unadjusted model results indicate that HRFs are predictive of future WC claims. However, the disparate findings between unadjusted and adjusted models indicate that future research is needed to examine the multilevel relationship between employee demographics, organisational factors, HRFs and WC claims.
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http://dx.doi.org/10.1136/oemed-2015-103334 | DOI Listing |
Front Endocrinol (Lausanne)
January 2025
Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea.
Introduction: The global burden of gout, a severe and painful arthralgia, is of note and is expected to increase in the future. We aimed to investigate the association between the triglyceride/high-density lipoprotein (TG/HDL) ratio, a simple and validated biomarker for insulin resistance, and the incidence of gout in a longitudinal setting in the general population.
Methods: Our study was conducted using the National Health Insurance Service-Health Screening Cohort database of Republic of Korea (2002-2019).
Shoulder Elbow
January 2025
Rothman Orthopaedic Institute, Paramus, NJ, USA.
Background: The purpose of this study is to characterize malpractice claims against orthopedic surgeons treating humeral fractures and determine factors associated with plaintiff verdicts and settlements.
Methods: The Westlaw legal database was queried for all cases involving humeral fractures. Patient demographics, causes cited for litigation, case outcomes, and indemnity payments were collected to determine common factors that lead plaintiffs to pursue legal action.
Int J Med Inform
January 2025
Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address:
Background: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.
View Article and Find Full Text PDFBMJ Open
January 2025
Department of General Practice, University College Cork, Cork, Ireland
Objectives: To describe the prevalence of sub-optimal monitoring for selected higher-risk medicines in older community-dwelling adults and to evaluate patient characteristics and outcomes associated with sub-optimal monitoring.
Study Design: Retrospective observational study (2011-2015) using historical general practice-based cohort data and linked dispensing data from a national pharmacy claims database.
Setting: Irish primary care.
HGG Adv
January 2025
Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada; Department of Human Genetics, McGill University, Montréal, Québec, Canada; 5 Prime Sciences Inc, Montréal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada; Department of Medicine, McGill University, Montréal, Québec, Canada; Department of Twin Research, King's College London, London, UK. Electronic address:
Identifying novel, high-yield drug targets is challenging and often results in a high failure rate. However, recent data indicates that leveraging human genetic evidence to identify and validate these targets significantly increases the likelihood of success in drug development. Two recent papers from Open Targets claimed that around half of FDA-approved drugs had targets with direct human genetic evidence.
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