To examine trajectories of employment probability up to 10 years following moderate-to-severe traumatic brain injury (TBI) and identify significant predictors from baseline socio-demographic and injury characteristics. A longitudinal observational study followed 97 individuals with moderate-to-severe TBI for their employment status up to 10 years post injury. Participants were enrolled at the Trauma Referral Center in South-Eastern Norway between 2005 and 2007. Socio-demographic and injury characteristics were recorded at baseline. Employment outcomes were assessed at 1, 2, 5, and 10 years. Hierarchical linear modeling (HLM) was used to examine employment status over time and assess the predictors of time, gender, age, relationship status, education, employment pre-injury, occupation, cause of injury, acute Glasgow Coma Scale (GCS) score, duration of post-traumatic amnesia (PTA), CT findings, and injury severity score, as well as the interaction terms between significant predictors and time. The linear trajectory of employment probabilities for the full sample remained at ~50% across 1, 2, 5, and 10-years post-injury. Gender ( = 0.016), relationship status ( = 0.002), employment ( < 0.001) and occupational status at injury ( = 0.005), and GCS ( = 0.006) yielded statistically significant effects on employment probability trajectories. Male gender, those in a partnered relationship at the time of injury, individuals who had been employed at the time of injury, those in a white-collar profession, and participants with a higher acute GCS score had significantly higher overall employment probability trajectories across the four time points. The timegender interaction term was statistically significant ( = 0.002), suggesting that employment probabilities remained fairly stable over time for men, but showed a downward trend for women. The timeemployment at injury interaction term was statistically significant ( = 0.003), suggesting that employment probabilities were fairly level over time for those who were employed at injury, but showed an upward trend over time for those who had been unemployed at injury. Overall employment probability trajectories remained relatively stable between 1 and 10 years. Baseline socio-demographic and injury characteristics were predictive of employment trajectories. Regular follow-up is recommended for patients at risk of long-term unemployment.
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http://dx.doi.org/10.3389/fneur.2018.01051 | DOI Listing |
BMC Infect Dis
January 2025
Infectious Diseases Research Center, Birjand University of Medical Sciences, Birjand, P.O. Box: 9717853577, Iran.
Background: Toxoplasma gondii (T. gondii) is the most successful obligate protozoan that can infect warm-blooded vertebrate hosts. Some researchers suggest that the presence of Toxoplasma cysts in the brain can lead to mental disorders.
View Article and Find Full Text PDFBMC Public Health
December 2024
Finnish Institute of Occupational Health, TYÖTERVEYSLAITOS, PL 18, Helsinki, 00032, Finland.
Background: The COVID-19 pandemic was a significant health risk and resulted in increased sickness absence during the pandemic. This study examines whether a history of COVID-19 infection is associated with a higher risk of subsequent sickness absence.
Methods: In this prospective cohort study, 32,124 public sector employees responded to a survey on COVID-19 infection and lifestyle factors in 2020 and were linked to sickness absence records before (2019) and after (2021-2022) the survey.
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFIndian J Med Res
November 2024
Department of Community Medicine, Burdwan Medical College & Hospital, Kolkata, West Bengal, India.
Background & objectives Non communicable diseases (NCD) have emerged as one of the leading causes of mortality and morbidity in India in the past few decades. This study was undertaken to determine the prevalence of NCD risk factors among adults residing in urban slums of West Bengal, India. Methods A community based cross-sectional study was conducted among adult population aged 15-69 yr in urban slums of Purba Burdwan district, West Bengal over a period of two months.
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