The physical demands on U.S. service members have increased significantly over the past several decades as the number of military operations requiring overseas deployment have expanded in frequency, duration, and intensity. These elevated demands from military operations placed upon a small subset of the population may be resulting in a group of individuals more at-risk for a variety of debilitating health conditions. To better understand how the U.S Veterans health outcomes compared to non-Veterans, this study utilized the U.S. Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System (BRFSS) dataset to examine 10 different self-reported morbidities. Yearly age-adjusted, population estimates from 2003 to 2019 were used for Veteran vs. non-Veteran. Complex weights were used to evaluate the panel series for each morbidity overweight/obesity, heart disease, stroke, skin cancer, cancer, COPD, arthritis, mental health, kidney disease, and diabetes. General linear models (GLM’s) were created using 2019 data only to investigate any possible explanatory variables associated with these morbidities. The time series analysis showed that Veterans have disproportionately higher self-reported rates of each morbidity with the exception of mental health issues and heart disease. The GLM showed that when taking into account all the variables, Veterans disproportionately self-reported a higher amount of every morbidity with the exception of mental health. These data present an overall poor state of the health of the average U.S. Veteran. Our study findings suggest that when taken as a whole, these morbidities among Veterans could prompt the U.S. Department of Veteran Affairs (VA) to help develop more effective health interventions aimed at improving the overall health of the Veterans.

Coronavirus (COVID-19) is a potentially fatal viral infection. This study investigates geography, demography, socioeconomics, health conditions, hospital characteristics, and politics as potential explanatory variables for death rates at the state and county levels. Data from the Centers for Disease Control and Prevention, the Census Bureau, Centers for Medicare and Medicaid, Definitive Healthcare, and USAfacts.org were used to evaluate regression models. Yearly pneumonia and flu death rates (state level, 2014–2018) were evaluated as a function of the governors’ political party using a repeated measures analysis. At the state and county level, spatial regression models were evaluated. At the county level, we discovered a statistically significant model that included geography, population density, racial and ethnic status, three health status variables along with a political factor. A state level analysis identified health status, minority status, and the interaction between governors’ parties and health status as important variables. The political factor, however, did not appear in a subsequent analysis of 2014–2018 pneumonia and flu death rates. The pathogenesis of COVID-19 has a greater and disproportionate effect within racial and ethnic minority groups, and the political influence on the reporting of COVID-19 mortality was statistically relevant at the county level and as an interaction term only at the state level.

Background: As financial pressures on hospitals increase because of changing reimbursement structures and heightened focus on quality and value, the association between patient safety performance and financial outcomes remains unclear. Purpose: The purpose of this study is to investigate if hospitals with higher patient safety performance are associated with higher levels of profitability than those with lower safety performance. Methodology/Approach: Using multinomial logistic regression, we analyzed data from the spring 2014 Leapfrog Hospital Safety Score and the 2014 American Hospital Association to determine the association between Leapfrog Hospital Safety Score performance and three dimensions of organizational profitability: operating margin, net patient revenue, and operating income. Results: Our findings suggest that improved hospital safety scores are associated with a relative risk of being in the top versus bottom quartile of financial performance: 5.41 times greater (p < .001) for operating margin, 10.98 times greater (p < .001) for net patient revenue, and 4.03 times greater (p < .001) for operating income. Practice Implications: Our findings suggest that improved patient safety performance, as evaluated within the Leapfrog Hospital Safety Score, is associated with improved financial performance at the hospital level. Targeted focus on patient safety may allow hospitals to improve financial performance, maximize scarce resources, and generate additional capital to continue to positively evolve care.

Introduction: Hospital readmissions have been associated with adverse outcomes and elevated financial costs to patients, families, and hospitals across the United States. Historically, nearly 20% of all Medicare discharges had a readmission within 30 days. In an effort to address this issue, the Affordable Care Act (ACA) established the Hospital Readmission Reduction Program (HRRP) in 2012 to positively influence readmissions associated with acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, pneumonia, coronary artery bypass graft surgery, and total hip and/or knee arthroplasty. However, as recently as 2018, there were still 3.8 million 30-day all-cause adult hospital readmissions, with a 14% readmission rate and an average readmission cost of $15,200. The ACA also produced the Hospital Value-Based Purchasing (HVBP) program with the stated intent to (1) reduce mortality and complications, (2) reduce healthcare-associated infections, (3) increase patient safety, (4) improve the patient experience, and (5) increase efficiency and reduce costs. Given the costs and quality implications of an average readmission, it is logical to believe that HVBP eligible hospitals are simultaneously seeking to meet the goals of both programs. However, to date, no studies have examined if that is the case. Thus, in this study, we seek to determine if HVBP eligible hospitals are associated with a reduction in the core set of HRRP readmission rates better than the facilities that are not eligible for the HVBP program. Methods: Hospital-level data from calendar year 2019 for 3,276 short-term acute care hospitals in the United States were evaluated using multivariate regression analysis to examine the readmission rate performance between 2,719 HVBP eligible hospitals and 557 ineligible hospitals. Results: Our six separate multivariable linear regressions revealed a statistically significant and positive association between HVBP participation and readmission rates after controlling for numerous organizational, clinical complexity, and environmental factors. In each case, the magnitude of the positive directional association is moderate, ranging from and increase of +0.19% (pneumonia readmissions) to as high as +0.37% (all cause readmissions) for HVBP eligible hospitals. When considered in the context of the average number of discharges in our data set (x- = 9570), a third of a percent increase from the average 15.56% all cause readmissions to 16.08% in HVBP eligible hospitals equates to 50 additional readmissions annually (9570 × 15.56% = 1,489 vs 9570 × 16.08% = 1,539). At an average cost of $15,200 per readmission, which equates to an average additional cost to the average HVBP eligible hospital in excess of $760,000. Discussion: The fact that there is a positive association between HVBP participating hospitals and readmissions at all, and that the same effect appears to be persistent across all dependent measures is concerning. One would logically expect hospitals that are focused on quality-based care to thoroughly care for individuals in order for them to not be readmitted to the hospital. The results provided do not necessarily prove that either program is not working. But they also do not confirm that the HVBP and HRRP programs are working together and accomplishing what they were originally designed to do: improve patient care and lower health-care costs.

Introduction: The competent healing touch of a caregiver is a critical component to the care patients receive. The more skilled the provider, the higher the likelihood outcomes will be delivered in a safe and effective manner. Unfortunately, in recent years, hospitals in the United States have faced immense financial pressures that are threatening their economic sustainability and patients’ access to care in the future. Through the recent COVID-19 pandemic, the cost of delivering healthcare has continued to escalate, while the demand for patient care has exceeded many hospitals’ capacity. Most troubling is the impact that the pandemic has had on the healthcare workforce, which has resulted in many hospitals struggling to fill vacancies at ever-increasing cost – all while under immense pressure to deliver quality patient care. What remains uncertain is whether the increase in labor costs has been matched with a commensurate rise in the quality of care or if quality has deteriorated as the labor force mix has changed to include more contract and temporary staff. Thus, in the enclosed study, we sought to determine what association, if any, exists between hospitals’ cost of labor and the quality of care delivered. Methods and Models: Drawing from a representative national sample of nearly 3214 short-term acute care hospitals’ common quality measures from the year 2021, we examined the labor cost–quality relationship via multivariate linear and logistic regression and found there is a persistent negative association across all quality outcome variables studied. Discussion: These findings lead us to believe simply paying more for hospital labor does not, by itself, ensure a positive patient outcome. We contend cautious use of temporary staff, measured adoption of short-term financial incentives, and robust staff development all should be considered as features of future workforce planning.