The Health Status of the US Veterans: A Longitudinal Analysis of Surveillance Data Prior to and during the COVID-19 Pandemic.

Chronic diseases affect a disproportionate number of United States (US) veterans, causing significant long-term health issues and affecting entitlement spending. This longitudinal study examined the health status of US veterans as compared to non-veterans pre- and post-COVID-19, utilizing the annual Center for Disease Control and Prevention (CDC) behavioral risk factor surveillance system (BRFSS) survey data. Age-adjusted descriptive point estimates were generated independently for 2003 through 2021, while complex weighted panel data were generated from 2011 and onward. General linear modeling revealed that the average US veteran reports a higher prevalence of disease conditions except for mental health disorders when compared to a non-veteran. These findings were consistent with both pre- and post-COVID-19; however, both groups reported a higher prevalence of mental health issues during the pandemic years. The findings suggest that there have been no improvements in reducing veteran comorbidities to non-veteran levels and that COVID-19 adversely affected the mental health of both populations.

College students' willingness to accept COVID-19 vaccines.

Objective To assess college students' willingness to accept COVID-19 vaccines and the factors that influence their decisions. Participants: Traditional (aged 18-23) undergraduate students at a university in central Texas. Methods: An online survey was administered in fall 2020 to 614 students stratified by sex and race/ethnicity. Results: 40.9% of students planned to take the vaccine as soon as possible, 37.1% eventually, 11.4% only if required, and 10.6% did not intend to be vaccinated. Analyses indicated that gender, major/minor, political affiliation, receiving a flu shot in the preceding 12 months, perception of risk for COVID-19, and vaccine hesitancy were all associated with willingness to accept COVID-19 vaccines. Conclusion: Results confirm that no one-size-fits-all approach to promoting COVID-19 vaccination among college students is possible. Instead, administrators interested in increasing vaccine uptake should address concerns of specific groups, while also utilizing the prosocial beliefs of college students (e.g., being vaccinated will protect others).

Determinants of Diabetes Disease Management, 2011-2019.

This study estimated the effects of Medicaid Expansion, demographics, socioeconomic status (SES), and health status on disease management of diabetes over time. The hypothesis was that the introduction of the ACA and particularly Medicaid Expansion would increase the following dependent variables (all proportions): (1) provider checks of HbA1c, (2) provider checks of feet, (3) provider checks of eyes, (4) patient education, (5) annual physician checks for diabetes, (6) patient self-checks of blood sugar. Data were available from the Behavioral Risk Factor Surveillance System for 2011 to 2019. We filtered the data to include only patients with diagnosed non-gestational diabetes of age 45 or older (n = 510,991 cases prior to weighting). Linear splines modeled Medicaid Expansion based on state of residence as well as implementation status. Descriptive time series plots showed no major changes in proportions of the dependent variables over time. Quasibinomial analysis showed that implementation of Medicaid Expansion had a statistically negative effect on patient self-checks of blood sugar (odds ratio = 0.971, p < 0.001), a statistically positive effect on physician checks of HbA1c (odds ratio = 1.048, p < 0.001), a statistically positive effect on feet checks (odds ratio = 1.021, p < 0.001), and no other significant effects. Evidence of demographic, SES, and health status disparities existed for most of the dependent variables. This finding was especially significant for HbA1c checks by providers. Barriers to achieving better diabetic care remain and require innovative policy interventions.

Exploring Health Outcomes for U.S. Veterans Compared to Non-Veterans from 2003 to 2019.

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.

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018.

BACKGROUND: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals' number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. METHODS: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. RESULTS: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran's I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. CONCLUSIONS: Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

Obesity and Morbidity Risk in the U.S. Veteran.

The obesity epidemic in the United States has been well documented and serves as the basis for a number of health interventions across the nation. However, those who have served in the U.S. military (Veteran population) suffer from obesity in higher numbers and have an overall disproportionate poorer health status when compared to the health of the older non-Veteran population in the U.S. which may further compound their overall health risk. This study examined both the commonalities and the differences in obesity rates and the associated co-morbidities among the U.S. Veteran population, utilizing data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS). These data are considered by the Centers for Disease Control and Prevention (CDC) to be the nation's best source for health-related survey data, and the 2018 version includes 437,467 observations. Study findings show not only a significantly higher risk of obesity in the U.S. Veteran population, but also a significantly higher level (higher odds ratio) of the associated co-morbidities when compared to non-Veterans, including coronary heart disease (CHD) or angina (odds ratio (OR) = 2.63); stroke (OR = 1.86); skin cancer (OR = 2.18); other cancers (OR = 1.73); chronic obstructive pulmonary disease (COPD) (OR = 1.52), emphysema, or chronic bronchitis; arthritis (OR = 1.52), rheumatoid arthritis, gout, lupus, or fibromyalgia; depressive disorders (OR = 0.84), and diabetes (OR = 1.61) at the 0.95 confidence interval level.

Classification of Alzheimer's Disease with and without Imagery using Gradient Boosted Machines and ResNet-50.

BACKGROUND: Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

An Evaluation of Alternatives for Providing Care to Veterans.

In 2014, a whistleblower reported that many U.S. veterans died while waiting for care at the Phoenix VHA. Problems with veteran's care through 2018 reveal ongoing and systematic problem. In March 2018, the VA Inspector General identified critical deficiencies at the Washington, DC VA Medical Center including failures to track patient safety events accurately, ineffective sterile processing and more than 10 thousand open or pending prosthetic/sensory aid consults. The VHA clearly has problems with access and quality in a budget-constrained environment. In this policy analysis, four separate interventions that address the gap between the magnitude as well as the use of the VHA's fixed budget versus access and cost expectations are explored. These policy interventions include maintaining the status quo, returning to a "VHA-only" option, transitioning to a CMS central payer system and consolidating care under the DoD TRICARE insurance plans. An objective evaluation suggests that extending TRICARE to veterans during the phasing out the VHA's care responsibilities, while politically unpalatable, would likely provide the best of four possible solutions under various criterion weighting schemes. A central payer solution under the CMS would also be a viable consideration. Results suggest that TRICARE patient perceptions of quality are superior to VHA and non-VHA/non-DoD, that access provided by the TRICARE program is ranked second in terms of venue acceptance only to the CMS solution set based on primary provider acceptance and that the cost per beneficiary of a TRICARE solution ($6.5 K/beneficiary) is far better than a VHA-only solution ($14.0 K/beneficiary), the CMS central payer solution ($12.2 K/beneficiary), or the status quo (between $12.2 K and $14.0 K/beneficiary). The intent of this paper is to provoke thoughtful consideration of solutions for providing access to high-quality healthcare for veterans within or outside of the VHA.

Use of evidence-based management in healthcare administration decision-making.

Purpose The purpose of this paper is to examine whether healthcare leaders use evidence-based management (EBMgt) when facing major decisions and what types of evidence healthcare administrators consult during their decision-making. This study also intends to identify any relationship that might exist among adoption of EBMgt in healthcare management, attitudes towards EBMgt, demographic characteristics and organizational characteristics. Design/methodology/approach A cross-sectional study was conducted among US healthcare leaders. Spearman's correlation and logistic regression were performed using the Statistical Package for the Social Sciences (SPSS) 23.0. Findings One hundred and fifty-four healthcare leaders completed the survey. The study results indicated that 90 per cent of the participants self-reported having used an EBMgt approach for decision-making. Professional experiences (87 per cent), organizational data (84 per cent) and stakeholders' values (63 per cent) were the top three types of evidence consulted daily and weekly for decision-making. Case study (75 per cent) and scientific research findings (75 per cent) were the top two types of evidence consulted monthly or less than once a month. An exploratory, stepwise logistic regression model correctly classified 75.3 per cent of all observations for a dichotomous "use of EBMgt" response variable using three independent variables: attitude towards EBMgt, number of employees in the organization and the job position. Spearman's correlation indicated statistically significant relationships between healthcare leaders' use of EBMgt and healthcare organization bed size ( rs = 0.217, n = 152, p < 0.01), attitude towards EBMgt ( rs = 0.517, n = 152, p < 0.01), and the number of organization employees ( rs = 0.195, n = 152, p = 0.016). Originality/value This study generated new research findings on the practice of EBMgt in US healthcare administration decision-making.

Teaching Principal Components Using Correlations.

Introducing principal components (PCs) to students is difficult. First, the matrix algebra and mathematical maximization lemmas are daunting, especially for students in the social and behavioral sciences. Second, the standard motivation involving variance maximization subject to unit length constraint does not directly connect to the "variance explained" interpretation. Third, the unit length and uncorrelatedness constraints of the standard motivation do not allow re-scaling or oblique rotations, which are common in practice. Instead, we propose to motivate the subject in terms of optimizing (weighted) average proportions of variance explained in the original variables; this approach may be more intuitive, and hence easier to understand because it links directly to the familiar "R-squared" statistic. It also removes the need for unit length and uncorrelatedness constraints, provides a direct interpretation of "variance explained," and provides a direct answer to the question of whether to use covariance-based or correlation-based PCs. Furthermore, the presentation can be made without matrix algebra or optimization proofs. Modern tools from data science, including heat maps and text mining, provide further help in the interpretation and application of PCs; examples are given. Together, these techniques may be used to revise currently used methods for teaching and learning PCs in the behavioral sciences.

The Resuscitative and Pharmacokinetic Effects of Humeral Intraosseous Vasopressin in a Swine Model of Ventricular Fibrillation.

Introduction The American Heart Association (AHA; Dallas, Texas USA) and European Resuscitation Council (Niel, Belgium) cardiac arrest (CA) guidelines recommend the intraosseous (IO) route when intravenous (IV) access cannot be obtained. Vasopressin has been used as an alternative to epinephrine to treat ventricular fibrillation (VF). Hypothesis/Problem Limited data exist on the pharmacokinetics and resuscitative effects of vasopressin administered by the humeral IO (HIO) route for treatment of VF. The purpose of this study was to evaluate the effects of HIO and IV vasopressin, on the occurrence, odds, and time of return of spontaneous circulation (ROSC) and pharmacokinetic measures in a swine model of VF. METHODS: Twenty-seven Yorkshire-cross swine (60 to 80 kg) were assigned randomly to three groups: HIO (n=9), IV (n=9), and a control group (n=9). Ventricular fibrillation was induced and untreated for two minutes. Chest compressions began at two minutes post-arrest and vasopressin (40 U) administered at four minutes post-arrest. Serial blood specimens were collected for four minutes, then the swine were resuscitated until ROSC or 29 post-arrest minutes elapsed. RESULTS: Fisher's Exact test determined ROSC was significantly higher in the HIO 5/7 (71.5%) and IV 8/11 (72.7%) groups compared to the control 0/9 (0.0%; P=.001). Odds ratios of ROSC indicated no significant difference between the treatment groups (P=.68) but significant differences between the HIO and control, and the IV and control groups (P=.03 and .01, respectively). Analysis of Variance (ANOVA) indicated the mean time to ROSC for HIO and IV was 621.20 seconds (SD=204.21 seconds) and 554.50 seconds (SD=213.96 seconds), respectively, with no significant difference between the groups (U=11; P=.22). Multivariate Analysis of Variance (MANOVA) revealed the maximum plasma concentration (Cmax) and time to maximum concentration (Tmax) of vasopressin in the HIO and IV groups was 71753.9 pg/mL (SD=26744.58 pg/mL) and 61853.7 pg/mL (SD=22745.04 pg/mL); 111.42 seconds (SD=51.3 seconds) and 114.55 seconds (SD=55.02 seconds), respectively. Repeated measures ANOVA indicated no significant difference in plasma vasopressin concentrations between the treatment groups over four minutes (P=.48). CONCLUSIONS: The HIO route delivered vasopressin effectively in a swine model of VF. Occurrence, time, and odds of ROSC, as well as pharmacokinetic measurements of HIO vasopressin, were comparable to IV. Burgert JM , Johnson AD , Garcia-Blanco J , Fulton LV , Loughren MJ . The resuscitative and pharmacokinetic effects of humeral intraosseous vasopressin in a swine model of ventricular fibrillation. Prehosp Disaster Med. 2017;32(3):305-310.

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.

The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

Evaluating the Impact of Hospital Efficiency on Wellness in the Military Health System.

Like all health care delivery systems, the U.S. Department of Defense Military Health System (MHS) strives to achieve top preventative care and population health outcomes for its members while operating at an efficient level and containing costs. The objective of this study is to understand the overall efficiency performance of military hospitals and investigate the relationship between efficiency and wellness. This study uses data envelopment analysis and stochastic frontier analysis to compare the efficiency of 128 military treatment facilities from the Army, Navy, and Air Force during the period of 2011 to 2013. Fixed effects panel regression is used to determine the association between the hospital efficiency and wellness scores. The results indicate that data envelopment analysis and stochastic frontier analysis efficiency scores are congruent in direction. Both results indicate that the majority of the MHS hospitals and clinics can potentially improve their productive efficiency by managing their input resources better. When comparing the performance of the three military branches of service, Army hospitals as a group outperformed their Navy and Air Force counterparts; thus, best practices from the Army should be shared across service components. The findings also suggest no statistically significant, positive association between efficiency and wellness over time in the MHS.

Comparison of Resuscitative Protocols for Bupropion Overdose Using Lipid Emulsion in a Swine Model.

AIMS: The aim of this study was to investigate the effects of different treatment combinations on bupropion recovery as well as time to return of spontaneous circulation. METHODS: We conducted an eight group, randomized, experiment to evaluate combinations of epinephrine, vasopressin, and lipids on the restoration of cardiac function in Yorkshire pigs. After tracking the animals' baseline vitals for 10 minutes, we injected the animals with bupropion (35 mg/kg) and initiated a randomized protocol 2 minutes after cardiac arrest. RESULTS: Results demonstrated that animal survival given treatment combinations including epinephrine were statistically superior to any other group (p < 0.001, Fishers' exact test). The odds of survival with use of epinephrine vs. other options were 22:1 (5.47, 88.43). Further, all animals receiving only lipids died. Cox survival analysis with bootstrapped parameter estimates provided evidence that the rapidity of cardiac recovery was maximized with a combination of epinephrine and lipids (p < 0.05). CONCLUSIONS: Lipids may require an additional chemical catalyst in order to be effective in cardiac recovery. Epinephrine and lipids combined shortened recovery time for surviving animals.

Comparative Resuscitative Methods for Venlafaxine Toxicity in a Swine Model.

OBJECTIVES: Venlafaxine overdose can lead to cardiovascular collapse that is difficult to resuscitate with traditional Advanced Cardiovascular Life Support protocols. Evidence has suggested that lipid emulsion infusion therapy has been successful in the treatment of antidepressant overdose. No studies have determined the optimal combination of lipid/advanced cardiovascular life support therapy for treatment. METHODS: This study was a prospective, experimental, between subjects design with a swine model investigating the effectiveness of drug combinations administered with cardiopulmonary resuscitation (CPR) postvenlafexine overdose. Subjects were randomly assigned to 1 of eight groups containing seven subjects. The groups tested were CPR only and CPR with epinephrine alone; vasopressin alone; lipid alone; epinephrine and vasopressin; epinephrine and lipid; vasopressin and lipid; and epinephrine, vasopressin, and lipid. The outcomes of interest were survival odds and time to return of spontaneous circulation. RESULTS: Results on these swine models indicate that the use of vasopressin coupled with lipids for venlafaxine overdose resulted in a higher survival rate when compared to the control group (p = 0.023). Groups receiving vasopressin experienced statistically faster times to return of spontaneous circulation than other groups (p = 0.019). CONCLUSIONS: The results suggest that in swine models, the optimal treatment for venlafaxine overdose would include vasopressin with lipids.

Effects of the ResQPOD on Kinetics, Hemodynamics of Vasopressin, and Survivability in a Porcine Cardiac Arrest Model.

BACKGROUND: Ventilation through an impedance threshold device (ITD) purportedly improves hemodynamics and survivability and is given a Class IIb recommendation by the American Heart Association/American College of Cardiology for adult cardiac arrest. No studies have investigated the effects of an ITD with vasopressin. METHODS AND RESULTS: This study compared return of spontaneous circulation (ROSC), time to ROSC, hemodynamics, and pharmacokinetics with and without the use of a ResQPOD ITD. Swine were randomized to three groups: cardiopulmonary resuscitation and defibrillation alone, vasopressin with ResQPOD, and vasopressin without ResQPOD. Survival differences between the cardiopulmonary resuscitation and defibrillation group versus with and without ResQPOD groups were found (p = 0.001, FET; p = 0.021, FET, respectively) but no differences between with and without ResQPOD groups (p = 0.462). A test of Cmax between the IV and IV/ResQPOD group provided limited evidence that the IV/ResQPOD group attained higher Cmax than then IV only group (U = 11.00, p = 0.097). Median Tmax and ROSC were not statistically different between the groups (U = 11.00, p = 0.314). CONCLUSIONS: Our data suggest that there is no difference in drug kinetics or clinical outcomes in terms of survivability with or without the ResQPOD.

Aeromedical evacuation planning using geospatial decision-support.

In this study, we proffer an algorithmic, geospatial-based decision-support methodology that assists military decision-makers in determining which aeromedical evacuation (MEDEVAC) assets to launch after receiving an injury location, given knowledge only of terrain, aircraft location, and aircraft capabilities. The objective is for military medical planners to use this decision-support tool (1) to improve real-time situational awareness by visualization of MEDEVAC coverage, showing which areas can be reached within established timelines; (2) to support medical planning by visualizing the impact of changes in the medical footprint to the MEDEVAC coverage; and (3) to support decision-making by providing a time-sorted list of MEDEVAC asset packages to select from, given the location of the patients. This same geospatial-based decision tool can be used for proper emplacement of evacuation assets such that the theater is covered within a truly representative 1-hour response time. We conclude with a discussion of applicability of this tool in medical force structure planning.

Analyzing the future of army aeromedical evacuation units and equipment: a mixed methods, requirements-based approach.

We utilize a mixed methods approach to provide three new, separate analyses as part of the development of the next aeromedical evacuation (MEDEVAC) platform of the Future of Vertical Lift (FVL) program. The research questions follow: RQ1) What are the optimal capabilities of a FVL MEDEVAC platform given an Afghanistan-like scenario and parameters associated with the treatment/ground evacuation capabilities in that theater?; RQ2) What are the MEDEVAC trade-off considerations associated with different aircraft engines operating under variable conditions?; RQ3) How does the additional weight of weaponizing the current MEDEVAC fleet affect range, coverage radius, and response time? We address RQ1 using discrete-event simulation based partially on qualitative assessments from the field, while RQ2 and RQ3 are based on deterministic analysis. Our results confirm previous findings that travel speeds in excess of 250 knots and ranges in excess of 300 nautical miles are advisable for the FVL platform design, thereby reducing the medical footprint in stability operations. We recommend a specific course of action regarding a potential engine bridging strategy based on deterministic analysis of endurance and altitude, and we suggest that the weaponization of the FVL MEDEVAC aircraft will have an adverse effect on coverage capability.

The Future of Vertical Lift: initial insights for aircraft capability and medical planning.

The U.S. Army continues to evaluate capabilities associated with the Future of Vertical Lift (FVL) program-a futures program (with a time horizon of 15 years and beyond) intended to replace the current helicopter fleet. As part of the FVL study, we investigated required capabilities for future aeromedical evacuation platforms. This study presents two significant capability findings associated with the future aeromedical evacuation platform and one doctrinal finding associated with medical planning for future brigade operations. The three results follow: (1) Given simplifying assumptions and constraints for a scenario where a future brigade is operating in a 300 x 300 km2, the zero-risk aircraft ground speed required for the FVL platform is 350 nautical miles per hour (knots); (2) Given these same assumptions and constraints with the future brigade projecting power in a circle of radius 150 km, the zero-risk ground speed required for the FVL platform is 260 knots; and (3) Given uncertain casualty locations associated with future brigade stability and support operations, colocating aeromedical evacuation assets and surgical elements mathematically optimizes the 60-minute set covering problem.

Financial performance monitoring of the technical efficiency of critical access hospitals: a data envelopment analysis and logistic regression modeling approach.

From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital--the critical access hospital (CAH). The conversion to CAH and the associated cost-based reimbursement scheme significantly slowed the closure rate of rural hospitals. This work investigates which methods can ensure the long-term viability of small hospitals. This article uses a two-step design to focus on a hypothesized relationship between technical efficiency of CAHs and a recently developed set of financial monitors for these entities. The goal is to identify the financial performance measures associated with efficiency. The first step uses data envelopment analysis (DEA) to differentiate efficient from inefficient facilities within a data set of 183 CAHs. Determining DEA efficiency is an a priori categorization of hospitals in the data set as efficient or inefficient. In the second step, DEA efficiency is the categorical dependent variable (efficient = 0, inefficient = 1) in the subsequent binary logistic regression (LR) model. A set of six financial monitors selected from the array of 20 measures were the LR independent variables. We use a binary LR to test the null hypothesis that recently developed CAH financial indicators had no predictive value for categorizing a CAH as efficient or inefficient, (i.e., there is no relationship between DEA efficiency and fiscal performance).