Browsing by Subject "Linear models"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Being Called to Safety: Occupational Callings and Safety Climate in the Emergency Medical Services
    (American College of Occupational and Environmental Medicine, 2016-12) Andel, Stephanie A.; Pindek, Shani; Spector, Paul E.
    Objective: The aim of this study was to investigate the importance of safety climate in the Emergency Medical Services (EMS) and to assess occupational callings as a boundary condition for the effect of safety climate on safety behaviors. Methods: EMS professionals (n = 132) participated in a three-wave survey study. Hierarchical linear regressions were conducted to test the moderating effects of occupational callings. Results: Safety climate was significantly related to safety behavior, and occupational callings moderated this direct relationship (ΔR2 = 0.02 to 0.03, P < 0.05). Specifically, when occupational callings were high, the relationship between safety climate and safety behaviors was stronger, and when occupational callings were low, the relationship was weaker. Conclusion: In this EMS sample, safety climate was an important predictor of safety behavior. Further, occupational callings moderated this relationship, suggesting that callings may serve as a boundary condition.
  • Thumbnail Image
    Item
    Serum metabolomic analysis reveals several novel metabolites in association with excessive alcohol use - an exploratory study
    (Elsevier, 2022) Liu, Danni; Yang, Zhihong; Chandler, Kristina; Oshodi, Adepeju; Zhang, Ting; Ma, Jing; Kusumanchi, Praveen; Huda, Nazmul; Heathers, Laura; Perez, Kristina; Tyler, Kelsey; Ross, Ruth Ann; Jiang, Yanchao; Zhang, Dabao; Zhang, Min; Liangpunsakul, Suthat; Medicine, School of Medicine
    Appropriate screening tool for excessive alcohol use (EAU) is clinically important as it may help providers encourage early intervention and prevent adverse outcomes. We hypothesized that patients with excessive alcohol use will have distinct serum metabolites when compared to healthy controls. Serum metabolic profiling of 22 healthy controls and 147 patients with a history of EAU was performed. We employed seemingly unrelated regression to identify the unique metabolites and found 67 metabolites (out of 556), which were differentially expressed in patients with EAU. Sixteen metabolites belong to the sphingolipid metabolism, 13 belong to phospholipid metabolism, and the remaining 38 were metabolites of 25 different pathways. We also found 93 serum metabolites that were significantly associated with the total quantity of alcohol consumption in the last 30 days. A total of 15 metabolites belong to the sphingolipid metabolism, 11 belong to phospholipid metabolism, and 7 metabolites belong to lysolipid. Using a Venn diagram approach, we found the top 10 metabolites with differentially expressed in EAU and significantly associated with the quantity of alcohol consumption, sphingomyelin (d18:2/18:1), sphingomyelin (d18:2/21:0,d16:2/23:0), guanosine, S-methylmethionine, 10-undecenoate (11:1n1), sphingomyelin (d18:1/20:1, d18:2/20:0), sphingomyelin (d18:1/17:0, d17:1/18:0, d19:1/16:0), N-acetylasparagine, sphingomyelin (d18:1/19:0, d19:1/18:0), and 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1). The diagnostic performance of the top 10 metabolites, using the area under the ROC curve, was significantly higher than that of commonly used markers. We have identified a unique metaboloic signature among patients with EAU. Future studies to validate and determine the kinetics of these markers as a function of alcohol consumption are needed.