An interprofessional clinical experience to address social determinants of health.

ABSTRACT: The importance of effective interprofessional communication and collaboration as a strategy to improve patient outcomes has been documented in the literature. Efforts to integrate interprofessional education have been difficult to actualize due to myriad factors in academic and clinical settings. The COVID public health emergency presented an unexpected opportunity to address the needs of an underserved community through an interprofessional clinical experience between medical and APRN students. A screening tool and resource-driven algorithm were developed and launched by students in the college of medicine, for the patients of a university hospital clinic. This initiative helped meet the needs of the community, with the value-added benefit of an interprofessional clinical experience. Using a train-the-trainer model, students were oriented to the project and an online platform for real-time collaboration. Results of this initiative were positive. Approximately 100 medical and APRN students participated and contacted 1,489 patients in the community. Medical and social needs were addressed for 681 patients, and urgent social needs were addressed for 30 people. Students gained valuable clinical experience and had opportunities to identify and address social determinants of health while collaborating with medical student counterparts.

Timeliness of provisional United States mortality data releases during the COVID-19 pandemic: delays associated with electronic death registration system and weekly mortality.

All-cause mortality counts allow public health authorities to identify populations experiencing excess deaths from pandemics, natural disasters, and other emergencies. Delays in the completeness of mortality counts may contribute to misinformation because death counts take weeks to become accurate. We estimate the timeliness of all-cause mortality releases during the COVID-19 pandemic for the dates 3 April to 5 September 2020 by estimating the number of weekly data releases of the NCHS Fluview Mortality Surveillance System until mortality comes within 99% of the counts in the 19 March 19 2021 provisional mortality data release. States' mortality counts take 5 weeks at median (interquartile range 4-7 weeks) to completion. The fastest states were Maine, New Hampshire, Vermont, New York, Utah, Idaho, and Hawaii. States that had not adopted the electronic death registration system (EDRS) were 4.8 weeks slower to achieve complete mortality counts, and each weekly death per 10^8 was associated with a 0.8 week delay. Emergency planning should improve the timeliness of mortality data by improving state vital statistics digital infrastructure.

Upper extremity musculoskeletal disorders and work exposures among railroad maintenance-of-way workers.

BACKGROUND: Our objective was to examine occupational risk factors for musculoskeletal disorders of the shoulders, elbows, wrists, and hands among railroad maintenance-of-way (MOW) workers. Little systematic research on musculoskeletal disorders has been conducted in this occupational group. METHODS: In total, 3995 active members of the Brotherhood of Maintenance of Way Employes Division (BMWED) completed a standardized survey focusing on disorders caused by hand-transmitted vibration. We computed adjusted prevalence ratios (aPRs) using Poisson regression for shoulder, elbow, carpal tunnel syndrome, and vibration white finger musculoskeletal symptoms by work exposures, adjusted for age, region, race/ethnicity, smoking, potential second job, and spare time vehicle vibration exposure, and other work exposures. RESULTS: Among active male BMWED members, we found associations between >5.2 years (vs. 0.0-0.7 years) duration of full-time equivalent power tool use and shoulder pain (aPR = 2.01; 95% confidence interval [CI], 1.43-2.85), elbow pain (aPR = 2.88; 95% CI, 1.86-4.46), vibration white finger symptoms (aPR = 2.49; 95% CI, 1.06-5.85), hand/wrist pain (aPR = 2.40; 95% CI, 1.74-3.32), finger numbness or tingling (aPR = 1.86; 95% CI, 1.38-2.50) and self-reported carpal tunnel syndrome diagnosis (aPR = 2.16; 95% CI, 1.24-3.77). Associations were not consistent across outcomes for the duration of non-powered hand tool use and "repeated lifting, pushing, pulling, or bending." Positive gradients were observed for most outcomes. CONCLUSIONS: Hand-arm vibration and some other biomechanical exposures were associated with shoulder, elbow, wrist, hand, and finger symptoms. Prevention programs should address occupational risk factors for upper extremity musculoskeletal disorders among MOW workers.

Powered-hand tools and vibration-related disorders in US-railway maintenance-of-way workers.

Maintenance-of-way workers in North America who construct railroad tracks utilize specialized powered-hand tools, which lead to hand-transmitted vibration exposure. In this study, the maintenance-of-way workers were surveyed about neuro-musculoskeletal disorders, powered-hand tools and work practices. Information about vibration emission data of trade specific powered-hand tools for the North American and European Union markets was searched online to obtain respective user information of manufacturer and compared to non-commercial international data banks. The survey showed that maintenance-of-way workers frequently reported typical hand-transmitted vibration-related symptoms, and appear to be at a risk for neuro-musculoskeletal disorders of the upper extremity. Of all of the powered-hand tools used by this trade, 88% of the selected tools exceeded a=5 m/s2 and were above vibration magnitudes of common tools of other comparable industries. This may create a risk if these tools are used throughout an 8-h work day and management of vibration exposure may be needed. In the North-American market, limited or no vibration emission data is available from manufacturers or distributors. Vibration emission information for powered-hand tools, including vibration emission levels (in m/s2), uncertainty factor K, and the applied testing standard/norm may assist employers, users and occupational health providers to better assess, compare and manage risk.

Occupational risk factors for musculoskeletal disorders among railroad maintenance-of-way workers.

BACKGROUND: Our objective was to examine occupational risk factors for musculoskeletal disorders of the neck, back, and knee among railroad maintenance-of-way (MOW) workers. METHODS: Four thousand eight-hundred sixteen active, retired, and disabled members of the Brotherhood of Maintenance of Way Employes Division (BMWED) completed a survey. We computed adjusted prevalence ratios (aPRs) using Poisson regression for neck, back, and knee musculoskeletal symptoms by work exposures, adjusted for age, region, race/ethnicity, smoking, and potential second job and spare time vehicle vibration exposure. RESULTS: Among active male BMWED members, we found associations between use of high-vibration vehicles and neck pain (aPR = 1.47, 95% confidence interval (CI): 1.07-2.03) and knee pain (aPR = 1.38, 95% CI: 1.04-1.82) for more than 1.9 years (vs 0) of full-time equivalent use; but not back pain. Back pain radiating below the knee (sciatica indicator) was associated with high-vibration vehicle use greater than 0.4 and less than 1.9 years (aPR = 1.58, 95% CI: 1.15-2.18). We also found significant associations between often or always lifting, pushing, pulling, or bending on the job (vs seldom or never) and neck pain (aPR = 2.43, 95% CI: 1.20-4.90), back pain (aPR = 1.94, 95% CI: 1.24-3.03), the sciatica indicator (aPR = 5.18, 95% CI: 1.28-20.95), and knee pain (aPR = 2.84, 95% CI: 1.47-5.51), along with positive gradients in the outcome by exposure time. CONCLUSIONS: Biomechanical work exposures, including force and nonneutral postures, were associated with neck, lower back, and knee pain. Whole-body vibration, as measured by the duration of use of high-vibration vehicles, was associated with neck pain, knee pain, and sciatica. Prevention programs should address occupational risk factors for musculoskeletal disorders among MOW workers.

Work Exposures and Musculoskeletal Disorders Among Railroad Maintenance-of-Way Workers.

OBJECTIVE: The aim of this study was to measure musculoskeletal disorders and occupational risk factors among railroad maintenance-of-way (MOW) workers. METHODS: Four thousand eight hundred sixteen active, retired, and disabled members of the Brotherhood of Maintenance of Way Employes Division (BMWED) completed a survey. RESULTS: Compared with U.S. employed men, adjusting for age, race, and region, active male MOW workers were more likely to report "repeated lifting, pushing, pulling, or bending" at work (74.6% vs 46.9%), not enough staff (88.1% vs 65.2%), and a diagnosis of carpal tunnel syndrome (7.9% vs 3.6%). They were less likely to report management priority on workplace health and safety (59.37% vs 94.8%), ability to make job decisions on their own (68.4% vs 87.7%), and supervisor support (60.3% vs 90.8%) (all comparisons, P < 0.001). CONCLUSION: Prevention programs should address risk of musculoskeletal disorders and occupational hazards faced by MOW workers.

Learning curve estimation in medical devices and procedures: hierarchical modeling.

In the use of medical device procedures, learning effects have been shown to be a critical component of medical device safety surveillance. To support their estimation of these effects, we evaluated multiple methods for modeling these rates within a complex simulated dataset representing patients treated by physicians clustered within institutions. We employed unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE) and generalized linear mixed effect models. We found that both methods performed well, but that the GEE may have some advantages over the generalized linear mixed effect models for ease of modeling and a substantially lower rate of model convergence failures. We then focused more on using GEE and performed a separate simulation to vary the shape of the learning curve as well as employed various smoothing methods to the plots. We concluded that while both hierarchical methods can be used with our mathematical modeling of the learning curve, the GEE tended to perform better across multiple simulated scenarios in order to accurately model the learning effect as a function of physician and hospital hierarchical data in the use of a novel medical device. We found that the choice of shape used to produce the 'learning-free' dataset would be dataset specific, while the choice of smoothing method was negligibly different from one another. This was an important application to understand how best to fit this unique learning curve function for hierarchical physician and hospital data. Copyright © 2017 John Wiley & Sons, Ltd.