scholarly journals Trunk and Lower Extremity Movement Patterns, Stress Fracture Risk Factors, and Biomarkers of Bone Turnover in Military Trainees

2020 ◽  
Vol 55 (7) ◽  
pp. 724-732
Author(s):  
Timothy C. Mauntel ◽  
Stephen W. Marshall ◽  
Anthony C. Hackney ◽  
Brian G. Pietrosimone ◽  
Kenneth L. Cameron ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lower Extremity ◽  
Fracture Risk ◽  
Bone Metabolism ◽  
Bone Health ◽  
Military Service ◽  
Regression Models ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Risk Mitigation Strategies

Context Military service members commonly sustain lower extremity stress fractures (SFx). How SFx risk factors influence bone metabolism is unknown. Understanding how SFx risk factors influence bone metabolism may help to optimize risk-mitigation strategies. Objective To determine how SFx risk factors influence bone metabolism. Design Cross-sectional study. Setting Military service academy. Patients or Other Participants Forty-five men (agepre = 18.56 ± 1.39 years, heightpre = 176.95 ± 7.29 cm, masspre = 77.20 ± 9.40 kg; body mass indexpre = 24.68 ± 2.87) who completed Cadet Basic Training (CBT). Individuals with neurologic or metabolic disorders were excluded. Intervention(s) We assessed SFx risk factors (independent variables) with (1) the Landing Error Scoring System (LESS), (2) self-reported injury and physical activity questionnaires, and (3) physical fitness tests. We assessed bone biomarkers (dependent variables; procollagen type I amino-terminal propeptide [PINP] and cross-linked collagen telopeptide [CTx-1]) via serum. Main Outcome Measure(s) A markerless motion-capture system was used to analyze trunk and lower extremity biomechanics via the LESS. Serum samples were collected post-CBT; enzyme-linked immunosorbent assays determined PINP and CTx-1 concentrations, and PINP : CTx-1 ratios were calculated. Linear regression models demonstrated associations between SFx risk factors and PINP and CTx-1 concentrations and PINP : CTx-1 ratio. Biomarker concentration mean differences with 95% confidence intervals were calculated. Significance was set a priori using α ≤ .10 for simple and α ≤ .05 for multiple regression analyses. Results The multiple regression models incorporating LESS and SFx risk factor data predicted the PINP concentration (R2 = 0.47, P = .02) and PINP : CTx-1 ratio (R2 = 0.66, P = .01). The PINP concentration was increased by foot internal rotation, trunk flexion, CBT injury, sit-up score, and pre- to post-CBT mass changes. The CTx-1 concentration was increased by heel-to-toe landing and post-CBT mass. The PINP : CTx-1 ratio was increased by foot internal rotation, lower extremity sagittal-plane displacement (inversely), CBT injury, sit-up score, and pre- to post-CBT mass changes. Conclusions Stress fracture risk factors accounted for 66% of the PINP : CTx-1 ratio variability, a potential surrogate for bone health. Our findings provide insight into how SFx risk factors influence bone health. This information can help guide SFx risk-mitigation strategies.

scholarly journals Reverse logistics risk management: identification, clustering and risk mitigation strategies

2020 ◽  
Vol 58 (7) ◽  
pp. 1449-1474 ◽  
Author(s):  
Hamidreza Panjehfouladgaran ◽  
Stanley Frederick W.T. Lim
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Supply Chain ◽  
Reverse Logistics ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Content Type ◽  
Risk Mitigation Strategies

PurposeReverse logistics (RL), an inseparable aspect of supply chain management, returns used products to recovery processes with the aim of reducing waste generation. Enterprises, however, seem reluctant to apply RL due to various types of risks which are perceived as posing an economic threat to businesses. This paper draws on a synthesis of supply chain and risk management literature to identify and cluster RL risk factors and to recommend risk mitigation strategies for reducing the negative impact of risks on RL implementation.Design/methodology/approachThe authors identify and cluster risk factors in RL by using risk management theory. Experts in RL and supply chain risk management validated the risk factors via a questionnaire. An unsupervised data mining method, self-organising map, is utilised to cluster RL risk factors into homogeneous categories.FindingsA total of 41 risk factors in the context of RL were identified and clustered into three different groups: strategic, tactical and operational. Risk mitigation strategies are recommended to mitigate the RL risk factors by drawing on supply chain risk management approaches.Originality/valueThis paper studies risks in RL and recommends risk management strategies to control and mitigate risk factors to implement RL successfully.

Abstract P098: Effects Of Occupational Radiation Exposure On The Prevalence Of Hypertension And Its Complications

Circulation ◽  
2021 ◽  
Vol 143 (Suppl_1) ◽  
Author(s):  
Rithika Thirumal ◽  
Catherine Vanchiere ◽  
Ruchi Bhandari ◽  
Sania Jiwani ◽  
Ronald Horswell ◽  
...  
Keyword(s):  
Risk Factors ◽  
Radiation Exposure ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Personal Health ◽  
Electronic Survey ◽  
The Us ◽  
Risk Mitigation Strategies ◽  
Traditional Risk Factors ◽  
Reported Data

Background: Advancement of fluoroscopy-assisted procedures in the field of medicine has led to an increase in the frequency of their use among cardiologists, radiologists and surgeons. The personal health risk involved with radiation exposure is of concern and has come under the limelight in recent times. In addition to other consequences, radiation has been linked to cardiovascular disease, but its significance is not well established. Methods: Self-reported demographic, social, occupational, and medical data was collected from board-certified cardiologists via an electronic survey. Bivariate and multivariable logistic regression analyses were performed. Results: 1478 responses were collected from board-certified cardiologists; 85.4% were males, 79% were Caucasian and 66.1% were ≤65 yrs of age. 35.6% of respondents were interventional cardiologists and 16.4% were electrophysiologists. Of those who performed procedures, 92.2% wore lead apparel during all times of radiation exposure. Exposure hours, stratified by less or more than 20,000, correlated positively with the presence of hypertension, and remained significant when adjusted for common risk factors such as age, sex, race, DM, OSA, and alcohol/tobacco use (OR 1.63 CI 1.16 to 2.29, p = 0.005). Conclusion: This study captures self-reported data of just over 4% of cardiologists in the US, and demonstrates a positive correlation between hypertension and procedural radiation exposure hours even post-adjustment for traditional risk factors. As the use of fluoroscopy-assisted procedures continues to grow, further research is necessary to inform operators of the personal health risks of radiation exposure and drive progress in protective attire and risk mitigation strategies.

RISK FACTORS FOR BACK, NECK AND SHOULDER MUSCULOSKELETAL INJURIES AND CLAIMS IN AMBULANCE OFFICERS

2012 ◽  
Vol 15 (01) ◽  
pp. 1250009 ◽  
Author(s):  
Monica Broniecki ◽  
Adrian Esterman ◽  
Hugh Grantham
Keyword(s):  
Risk Factors ◽  
Perceived Risk ◽  
Injury Risk ◽  
Risk Mitigation ◽  
Cross Sectional Study ◽  
Musculoskeletal Injuries ◽  
Mitigation Strategies ◽  
Cross Sectional ◽  
Risk Mitigation Strategies ◽  
Manual Handling

Relatively little has been published on the range of risk factors contributing to musculoskeletal injuries in ambulance officers. This study aims to identify perceived risk factors for back, neck and shoulder musculoskeletal injuries and claims in relation to working conditions, and the physical and psychological demands of the job. This was a cross-sectional study using an internet-based survey in an Australian ambulance service. The survey included demographic questions and questions on psychosocial factors related to the job and the way in which work is organized, musculoskeletal injuries sustained and claims submitted in the previous 12 months; and two open ended questions on perceived risk factors for injury and injury risk mitigation strategies. Ambulance officers who felt they were able to take sufficient breaks were less likely to sustain a back, neck or shoulder musculoskeletal injury, and those who perceived their work required high levels of physical effort were more likely to submit a claim for these injuries. Two important perceived causal factors contributing to musculoskeletal injuries were the uncontrolled environment and non-adherence to manual handling techniques. However, suggested risk mitigation strategies of improving fitness and manual handling training, were not supported by the quantitative analysis.

scholarly journals Use of failure modes and effects analysis to mitigate potential risks prior to implementation of an intravenous compounding technology

2021 ◽  
Author(s):  
Agnes Ann Feemster ◽  
Melissa Augustino ◽  
Rosemary Duncan ◽  
Anand Khandoobhai ◽  
Meghan Rowcliffe
Keyword(s):  
Medication Safety ◽  
Failure Modes ◽  
Risk Mitigation ◽  
New Technology ◽  
Hazard Index ◽  
Mitigation Strategies ◽  
Investigational Drug ◽  
Workflow Optimization ◽  
Proactive Approach ◽  
Risk Mitigation Strategies

Abstract Disclaimer In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The purpose of this study was to identify potential failure points in a new chemotherapy preparation technology and to implement changes that prevent or minimize the consequences of those failures before they occur using the failure modes and effects analysis (FMEA) approach. Methods An FMEA was conducted by a team of medication safety pharmacists, oncology pharmacists and technicians, leadership from informatics, investigational drug, and medication safety services, and representatives from the technology vendor. Failure modes were scored using both Risk Priority Number (RPN) and Risk Hazard Index (RHI) scores. Results The chemotherapy preparation workflow was defined in a 41-step process with 16 failure modes. The RPN and RHI scores were identical for each failure mode because all failure modes were considered detectable. Five failure modes, all attributable to user error, were deemed to pose the highest risk. Mitigation strategies and system changes were identified for 2 failure modes, with subsequent system modifications resulting in reduced risk. Conclusion The FMEA was a useful tool for risk mitigation and workflow optimization prior to implementation of an intravenous compounding technology. The process of conducting this study served as a collaborative and proactive approach to reducing the potential for medication errors upon adoption of new technology into the chemotherapy preparation process.

A Low-Fidelity Stochastic Model of Viral Spread in Aircraft to Assess Risk Mitigation Strategies

2021 ◽  
Author(s):  
Leigh McCue
Keyword(s):  
Large Scale ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Traffic Network ◽  
Computationally Efficient ◽  
Passenger Compartment ◽  
Stochastic Effects ◽  
Viral Spread ◽  
Risk Mitigation Strategies ◽  
Baseline Health

Abstract The purpose of this work is to develop a computationally efficient model of viral spread that can be utilized to better understand influences of stochastic factors on a large-scale system - such as the air traffic network. A particle-based model of passengers and seats aboard a single-cabin 737-800 is developed for use as a demonstration of concept on tracking the propagation of a virus through the aircraft's passenger compartment over multiple flights. The model is sufficiently computationally efficient so as to be viable for Monte Carlo simulation to capture various stochastic effects, such as number of passengers, number of initially sick passengers, seating locations of passengers, and baseline health of each passenger. The computational tool is then exercised in demonstration for assessing risk mitigation of intervention strategies, such as passenger-driven cleaning of seating environments and elimination of middle seating.

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