Muscle Health: The Gateway to Population Health Management

The muscle on your frame is a prime indicator of health and longevity. Dr. Paul Terpeluk with the Cleveland Clinic has stated that muscular strength is the new vital sign of workplace health and safety. Research studies focusing on Type II diabetes, cardiovascular disease, musculo-skeletal injuries, certain cancers and the delay of dementia have shown a strong correlation between disease prevention and muscular strength. IPCS’ database of over 500,000 strength tests have shown a workers’ absolute strength today is at least 14% weaker than the worker 15 years ago and weighs about 8 pounds more. Over the last 10 years, there has been a significant shift by 52% with an increase in the number of workers with a BMI of 35 or greater. The Cleveland Clinic implemented a new hire muscular strength assessment to place new hire applicants into jobs that match their physical capability in 2011. The outcomes show a statistically significant reduction in number of employee health, pharmacy and workers’ compensation claims and costs with overall savings near $25 million. Musculo-skeletal health of the worker can be improved. When a worker maintains good muscular strength, the worker is more productive, has fewer medical claims and workers’ compensation claims

Delineating a Retesting Zone Using Receiver Operating Characteristic Analysis on Serial QuantiFERON Tuberculosis Test Results in US Healthcare Workers

Objective. To find a statistically significant separation point for the QuantiFERON Gold In-Tube (QFT) interferon gamma release assay that could define an optimal “retesting zone” for use in serially tested low-risk populations who have test “reversions” from initially positive to subsequently negative results. Method. Using receiver operating characteristic analysis (ROC) to analyze retrospective data collected from 3 major hospitals, we searched for predictors of reversion until statistically significant separation points were revealed. A confirmatory regression analysis was performed on an additional sample. Results. In 575 initially positive US healthcare workers (HCWs), 300 (52.2%) had reversions, while 275 (47.8%) had two sequential positive tests. The most statistically significant (Kappa = 0.48, chi-square = 131.0, P<0.001) separation point identified by the ROC for predicting reversion was the tuberculosis antigen minus-nil (TBag-nil) value at 1.11 International Units per milliliter (IU/mL). The second separation point was found at TBag-nil at 0.72 IU/mL (Kappa = 0.16, chi-square = 8.2, P<0.01). The model was validated by the regression analysis of 287 HCWs. Conclusion. Reversion likelihood increases as the TBag-nil approaches the manufacturer's cut-point of 0.35 IU/mL. The most statistically significant separation point between those who test repeatedly positive and those who revert is 1.11 IU/mL. Clinicians should retest low-risk individuals with initial QFT results < 1.11 IU/mL

Impact of the COVID-19 Pandemic on Healthcare Workers\u27 Risk of Infection and Outcomes in a Large, Integrated Health System

BACKGROUND: Understanding the impact of the COVID-19 pandemic on healthcare workers (HCW) is crucial. OBJECTIVE: Utilizing a health system COVID-19 research registry, we assessed HCW risk for COVID-19 infection, hospitalization, and intensive care unit (ICU) admission. DESIGN: Retrospective cohort study with overlap propensity score weighting. PARTICIPANTS: Individuals tested for SARS-CoV-2 infection in a large academic healthcare system (N = 72,909) from March 8-June 9, 2020, stratified by HCW and patient-facing status. MAIN MEASURES: SARS-CoV-2 test result, hospitalization, and ICU admission for COVID-19 infection. KEY RESULTS: Of 72,909 individuals tested, 9.0% (551) of 6145 HCW tested positive for SARS-CoV-2 compared to 6.5% (4353) of 66,764 non-HCW. The HCW were younger than the non-HCW (median age 39.7 vs. 57.5, p \u3c 0.001) with more females (proportion of males 21.5 vs. 44.9%, p \u3c 0.001), higher reporting of COVID-19 exposure (72 vs. 17%, p \u3c 0.001), and fewer comorbidities. However, the overlap propensity score weighted proportions were 8.9 vs. 7.7 for HCW vs. non-HCW having a positive test with weighted odds ratio (OR) 1.17, 95% confidence interval (CI) 0.99-1.38. Among those testing positive, weighted proportions for hospitalization were 7.4 vs. 15.9 for HCW vs. non-HCW with OR of 0.42 (CI 0.26-0.66) and for ICU admission: 2.2 vs. 4.5 for HCW vs. non-HCW with OR of 0.48 (CI 0.20-1.04). Those HCW identified as patient facing compared to not had increased odds of a positive SARS-CoV-2 test (OR 1.60, CI 1.08-2.39, proportions 8.6 vs. 5.5), but no statistically significant increase in hospitalization (OR 0.88, CI 0.20-3.66, proportions 10.2 vs. 11.4) and ICU admission (OR 0.34, CI 0.01-3.97, proportions 1.8 vs. 5.2). CONCLUSIONS: In a large healthcare system, HCW had similar odds for testing SARS-CoV-2 positive, but lower odds of hospitalization compared to non-HCW. Patient-facing HCW had higher odds of a positive test. These results are key to understanding HCW risk mitigation during the COVID-19 pandemic