An Exploratory Analysis of the Association between Healthcare Associated Infections & Hospital Financial Performance.

Purpose: Healthcare-associated infections (HAIs) place a significant financial burden on United States hospitals. HAI treatments extend hospital lengths of stay and increase hospital operational costs while significantly reducing hospital profit margins. Given these challenges, the research aim of this study was to explore the association between HAIs and hospital financial performance. A better understanding of this relationship can assist hospital leaders in optimizing the use of scarce financial resources to reduce HAI prevalence. Methods: Data for calendar year 2022 for active short-term acute care hospitals (n = 1454) in the US were analyzed using multiple linear regression analysis. We explored two derived dependent variables, operating expense per staffed bed and operating expense per discharge. The independent variables included four healthcare-associated infection rates: methicillin-resistant Staphylococcus aureus (MRSA) infection rate, Clostridium difficile (C. diff) infection rate, Catheter-Associated Urinary Tract Infection (CAUTI) rate, and Central Line Associated Blood Stream Infections (CLABSI). Appropriate organizational and market-level variables that may independently influence hospital financial performance were included as control variables. Results: The results revealed that C. diff (ß: 0.037, p < 0.05) and CAUTI (ß: 0.031, p < 0.05) rates were positively associated with an increase in operating expense per staffed bed, while increases in MRSA (ß: 0.042, p < 0.001), C. diff (ß: 0.062, p < 0.001), and CAUTI rates (ß: 0.039, p < 0.001) were correlated with increased operating expenses per discharge. Conclusions: This study demonstrates that specific HAIs may be associated with increased hospital expenses. Proactively targeting these infections through tailored interventions may lead to reduced hospital costs, improved financial performance, and economic stability.

Assessing Risk Literacy Skills: Enhancing Healthcare Management among University Students.

The purpose of this study was to explore the determinants of risk literacy among university students in the United States by utilizing the Berlin Numeracy Test. Risk literacy skills are essential for decision-making and communication of risks, but few studies consider university students. This study aims to evaluate the association of sociodemographic factors with individual risk literacy levels. An observational cross-sectional survey study was used with a convenience sample of 184 undergraduate and graduate university students. Statistical analysis revealed significant differences for demographics at risk for negative outcomes associated with lower risk literacy. For this group of students, the majority had below-average numeracy. These findings can guide healthcare professionals to focus on college-age individuals with low-risk literacy scores to enhance patient understanding, facilitate communication, and promote healthier behaviors.

When Agency Fails: An Analysis of the Association Between Hospital Agency Staffing and Quality Outcomes.

Introduction: Staffing is critical to hospital quality, but recent years have seen hospitals grappling with severe shortages, forcing them to rely on contract or agency staff for urgent patient care needs. This shift in staffing mix has raised questions about its impact on quality. Consequently, this study investigated whether the increased use of agency staff has affected healthcare quality in hospitals. Given the limited recent research on this topic, practitioners remain uncertain about the effectiveness of their staffing strategies and their potential impact on quality. Methods: Drawing from agency theory, data were obtained from Definitive Healthcare which consolidates information from numerous public access databases pertaining to hospitals such as the American Hospital Association Annual Survey (hospital profile) and the Hospital Value-Based Purchasing Program (quality data). We conducted a cross-sectional study using a multivariable linear regression model (2021-2022) with appropriate organizational and market- level control variables. Quality was measured across eight variables while the independent variable of interest was agency labor cost ratio operationalized as the percentage of net patient revenue consumed by agency labor expense. Results: Our results suggested that the employment of agency staff was significantly and negatively associated with six of eight quality measures tested, including the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) star rating, Hospital Compare rating, the hospital Total Performance Score (TPS), and three of the four sub-domains that comprise the TPS: clinical domain score, person and community engagement domain score, and the efficiency and cost reduction score. Discussion: Our results indicated that the increased use of agency labor may have a significant negative influence on quality outcomes at the hospital level. Our findings support the premise that interventions that promote full-time staffing may be more supportive of the quality of care delivered as well as patients' perceptions of care.

The effect of COVID-19 on cancer incidences in the U.S.

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin's lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.

The bounds of meta-analytics and an alternative method.

OBJECTIVES: Meta-analysis is a statistical appraisal of the data analytic implications of published articles (Y), estimating parameters including the odds ratio and relative risk. This information is helpful for evaluating the significance of the findings. The Higgins I2 index is often used to measure heterogeneity among studies. The objectives of this article are to amend the Higgins I2 index score in a novel and innovative way and to make it more useful in practice. METHODS: Heterogeneity among study populations can be affected by many sources, including the sample size and study design. They influence the Cochran Q score and, thus, the Higgins I2 score. In this regard, the I2 score is not an absolute indicator of heterogeneity. Q changes by bound as Y increases unboundedly. An innovative methodology is devised to show the conditional and unconditional probability structures. RESULTS: Various properties are derived, including showing that a zero correlation between Q and Y does not necessarily mean that they are independent. A new alternative statistic, S2, is derived and applied to mild cognitive impairment and coronavirus disease 2019 vaccination for meta-analysis. CONCLUSIONS: A hidden shortcoming of the Higgins I2 index is overcome in this article by amending the Higgins I2 score. The usefulness of the proposed methodology is illustrated using 2 examples. The findings have potential health policy implications.

The Association Between Facility Affiliations and Revenue Generation in Skilled Nursing Facilities - An Exploratory Study.

Background: Although hospitals have been the traditional setting for interventional and rehabilitative care, skilled nursing facilities (SNFs) can offer a high-quality and less costly alternative than hospitals. Unfortunately, the financial health of SNFs is often a matter of concern. To partially address these issues, SNF leaders have increased engagement in a number of affiliations to assist in improving quality and reducing operational costs, including Accountable Care Organizations (ACOs), Health Information Exchanges (HIEs), and participation in Bundled Payment for Care Improvement (BPCI) programs. What is not well understood is what impact these affiliations have on the financial viability of the host organizations. Given these factors, this study aims to identify what association, if any, exists between SNF affiliations and revenue generation. Methods: Data from calendar year 2022 for n=13,447 SNFs in the US were assessed using multivariate regression analysis. We evaluated two separate dependent measures of revenue generation capacity: net patient revenue per bed and net patient revenue per discharge and considered three unique facility affiliations including (1) ACOs, (2) HIEs, and (3) BPCI participants. Results: Six multivariable linear regressions revealed that ACO affiliation is negatively associated with revenue generation on both dependent measures, while HIE affiliation and BPCI participation reflected mixed results. Conclusion: A better understanding of the financial impact of SNFs' affiliations may prove insightful. By carefully considering the value of each affiliation, and how each is applicable to any given market, policymakers, funding agencies, and facility leaders may be able to better position SNFs for more sustainable financial performance in a challenging economic environment.

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.

Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks.

Today's critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters.

Stochasticity among Victims of COVID-19 Pandemic.

This article provides a thorough explanation of methods and theoretical concepts to detect infectivity of COVID-19. The concept of heterogeneity is discussed and its impacts on COVID-19 pandemics are explored. Observable heterogeneity is distinguished from non-observable heterogeneity. The data support the concepts of heterogeneity and the methods to extract and interpret the data evidence for the conclusions in this paper. Heterogeneity among the vulnerable to COVID-19 is a significant factor in the contagion of COVID-19, as demonstrated with incidence rates using data of a Diamond Princess cruise ship. Given the nature of the pandemic, its heterogeneity with different social norms, pre- and post-voyage quick testing procedures ought to become the new standard for cruise ship passengers and crew. With quick testing, identification of those infected and thus, not allowing to embark on a cruise or quarantine those disembarking, and other mitigation strategies, the popular cruise adventure could become norm for safe voyage. The novel method used in this article adds valuable insight in the modeling of disease and specifically, the COVID-19 virus.

Repurposing Siddha mercurial drug for mild to moderate COVID-19 - Case series and exploration of its chemical profile.

Vajra kandi maathirai (VKM) is a mercury-based traditional Siddha drug used to treat various types of fevers and inflammatory diseases. We report our experience of using VKM successfully in the treatment of 5 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients along with its chemical profile. A family comprising of 5 members, with age ranges between 13 and 77, both male and female, one with pre-existing renal impairment, SARS-CoV-2 positive with mild to moderate category were treated with VKM along with the specific dietary practice. The drug was consumed at home quarantine. Real-Time RT-PCR from oropharynx swab, X-ray/CT scan chest, hematology, renal function, liver function, body temperature, and oxygen saturation were assessed. Blood parameters were repeated after completion of therapy to assess the safety aspect of mercury drug. With the first dose, improvement in the oxygen saturation was observed. Within 3 days of therapy, all symptoms (fever, body pain, cough, and loss of taste) were normalized and the Real-Time RT-PCR was negative for COVID-19. There were no observed side-effects and any damage to kidneys and liver were not observed. Chemical profile of the drug was done using gas chromatography-mass spectrometry and inductively coupled plasma mass spectrometry. The drug contains 22% of mercury along with a 9-Octadecenoic acid-(E), 1H-Imidazole, 4,5-dihydro-2-(phenylmethyl), and 9,12-Octadecadienoic acid (Z,Z)- as major organic compounds. VKM might be a safe drug to manage COVID-19 patients. Rigorous research is required in larger population and also for drug discovery.

Testing Kissick's Iron Triangle-Structural Equation Modeling Analysis of a Practical Theory.

BACKGROUND/PURPOSE: The purpose of this research is to determine if the tradeoffs that Kissick proposed among cost containment, quality, and access remain as rigidly interconnected as originally conceived in the contemporary health care context. Although many have relied on the Kissick model to advocate for health policy decisions, to our knowledge the model has never been empirically tested. Some have called for policy makers to come to terms with the premise of the Kissick model tradeoffs, while others have questioned the model, given the proliferation of quality-enhancing initiatives, automation, and information technology in the health care industry. One wonders whether these evolutionary changes alter or disrupt the originality of the Kissick paradigms themselves. METHODS: Structural equation modeling (SEM) was used to evaluate the Kissick hypothetical relationships among the unobserved constructs of cost, quality, and access in hospitals for the year 2018. Hospital data were obtained from Definitive Healthcare, a subscription site that contains Medicare data as well as non-Medicare data for networks, hospitals, and clinics (final n = 2766). RESULTS: Reporting significant net effects as defined by our chosen study variables, we find that as quality increases, costs increase, as access increases, quality increases, and as access increases, costs increase. Policy and Practice Implications: Our findings lend continued relevance to a balanced approach to health care policy reform efforts. Simultaneously bending the health care cost curve, increasing access to care, and advancing quality of care is as challenging now as it was when the Kissick model was originally conceived.

A Report Card on Prevention Efforts of COVID-19 Deaths in US.

COVID-19 (otherwise known as coronavirus disease 2019) is a life-threatening pandemic that has been combatted in various ways by the government, public health officials, and health care providers. These interventions have been met with varying levels of success. Ultimately, we question if the preventive efforts have reduced COVID-19 deaths in the United States. To address this question, we analyze data pertaining to COVID-19 deaths drawn from the Centers for Disease Control and Prevention (CDC). For this purpose, we employ incidence rate restricted Poisson (IRRP) as an underlying analysis methodology and evaluate all preventive efforts utilized to attempt to reduce COVID-19 deaths. Interpretations of analytic results and graphical visualizations are used to emphasize our various findings. Much needed modifications of the public health policies with respect to dealing with any future pandemics are compiled, critically assessed, and discussed.

Accreditation and Certification: Do They Improve Hospital Financial and Quality Performance?

The relationship between healthcare organizational accreditation and their leaders' professional certification in healthcare management is of specific interest to institutions of higher education and individuals in the healthcare management field. Since academic program accreditation is one piece of evidence of high-quality education, and since professional certification is an attestation to the knowledge, skills, and abilities of those who are certified, we expect alumni who graduated from accredited programs and obtained professional certification to have a positive impact on the organizations that they lead, compared with alumni who did not graduate from accredited programs and who did not obtain professional certification. The authors' analysis examined the impact of hiring graduates from higher education programs that held external accreditation from the Commission on Accreditation of Healthcare Management Education (CAHME). Graduates' affiliation with the American College of Healthcare Executives (ACHE) professional healthcare leadership organization was also assessed as an independent variable. Study outcomes focused on these graduates' respective healthcare organization's performance measures (cost, quality, and access) to assess the researchers' inquiry into the perceived value of a CAHME-accredited graduate degree in healthcare administration and a professional ACHE affiliation. The results from this study found no effect of CAHME accreditation or ACHE affiliation on healthcare organization performance outcomes. The study findings support the need for future research surrounding healthcare administration professional graduate degree program characteristics and leader development affiliations, as perceived by various industry stakeholders.

Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity.

We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model's requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model's ability to estimate the community's health system memory, as future policies might reduce COVID's spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable.

Structural Zero Data of COVID-19 Discovers Exodus Probabilities.

BACKGROUND: Challenges to manage, mitigate, or prevent the COVID-19's pandemics are felt by medical, healthcare professionals and governing agencies. Health researchers conduct survey among the citizens to capture their opinion on COVID-19. In such surveys like in Hanafiah and Wan (2020), structural-zero (different from sampling zero) category occurs as they question about perception, knowledge, and communication regarding COVID-19. MATERIALS: The data were collected in a survey conducted among Malaysians by Hanafiah and Wan regarding COVID-19. The survey focused on people's response about the public communication, knowledge, and perception. METHODS: One of the four question categories in the survey is mutually exclusive with the other three questions. Consequently, there will be no entry in that category. Such group is called structurally zero category in the literature. The literature never probed the migrative split to other categories of the unknown proportion belonging to the structural zero category. In this article, the probability-based new and innovative method configures what proportion in that mutually exclusive category and it is the essence of our method. RESULTS: The mutually exclusive nature of subquestions manufactured structural zero in their data. A careful analysis of the data has created so far unknown probability concepts in the literature, which we named as "Exodus probabilities" in this article. Its discovery and utility are illustrated and elaborated with application in COVID-19. This methodology is also useful in applications in engineering, epidemiology, marketing, communication networking, etc. CONCLUSION: What is quite novel about the discovery of the exodus probability in this article is the evolution of the concepts from the structural-zero category. In such situation, when a category is eliminated, the proportions of the sample might have uncommunicatively transited to other viable categories and our research question is all about configuring their proportions. This is an innovative approach.

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.

Association of Electronic Health Record Vendors With Hospital Financial and Quality Performance: Retrospective Data Analysis.

BACKGROUND: Electronic health records (EHRs) are a central feature of care delivery in acute care hospitals; however, the financial and quality outcomes associated with system performance remain unclear. OBJECTIVE: In this study, we aimed to evaluate the association between the top 3 EHR vendors and measures of hospital financial and quality performance. METHODS: This study evaluated 2667 hospitals with Cerner, Epic, or Meditech as their primary EHR and considered their performance with regard to net income, Hospital Value-Based Purchasing Total Performance Score (TPS), and the unweighted subdomains of efficiency and cost reduction; clinical care; patient- and caregiver-centered experience; and patient safety. We hypothesized that there would be a difference among the 3 vendors for each measure. RESULTS: None of the EHR systems were associated with a statistically significant financial relationship in our study. Epic was positively associated with TPS outcomes (R2=23.6%; ß=.0159, SE 0.0079; P=.04) and higher patient perceptions of quality (R2=29.3%; ß=.0292, SE 0.0099; P=.003) but was negatively associated with patient safety quality scores (R2=24.3%; ß=-.0221, SE 0.0102; P=.03). Cerner and Epic were positively associated with improved efficiency (R2=31.9%; Cerner: ß=.0330, SE 0.0135, P=.01; Epic: ß=.0465, SE 0.0133, P<.001). Finally, all 3 vendors were associated with positive performance in the clinical care domain (Epic: ß=.0388, SE 0.0122, P=.002; Cerner: ß=.0283, SE 0.0124, P=.02; Meditech: ß=.0273, SE 0.0123, P=.03) but with low explanatory power (R2=4.2%). CONCLUSIONS: The results of this study provide evidence of a difference in clinical outcome performance among the top 3 EHR vendors and may serve as supportive evidence for health care leaders to target future capital investments to improve health care delivery.

The lengths of spinal curvature stretch due to the angles of sitting on saddle chair to alleviate back pain: A statistical analysis.

BACKGROUND: By dividing the burden of one's weight between the shins and the buttocks in the sitting position on an office or saddle chair, a person can avoid back pain. In this 21st century, sitting on a chair for long hours in workplace on office chair is unavoidable necessity and hence, millions in different countries undergo a risk for backpain. Is there a right sitting position? OBJECTIVE: The aim of this article is to find out how much a correlation exists between the angle of sitting and the length of spinal curvature which is the source of backpain. An experiment can be designed and carried out to measure various angles in sitting and the changing length of the person's spinal cord curvature. METHOD: The usual statistical methodology requires a pair of values namely x and y to quantify the correlation. The data on sitting angles and the length of spinal curvature do not have such pairing, and hence, the traditional approach to find the correlation between the sitting angle and length of spinal curvature is not applicable. Yet, an approach is necessary. This article constructs an innovative statistical approach to fulfil this need. RESULTS: Our approach yields a correlation of 0.998 for sitting on office chair and an increased correlation of 0.999 on saddle chair, according to the Truszczynska-Baszaka et al.'s data. CONCLUSIONS: An adjustment is made in various angles of sitting on office chair to transform the comfortable sitting on a saddle chair. In consequence, the proportional effect on the spinal curvature is estimable with the data and it is phenomenal (that is significantly more than one). No wonder people prefer saddle chair over office chair when it comes to avoid back pain and this article proves the convenience statistically.

Restricted Prevalence Rates of COVID-19's Infectivity, Hospitalization, Recovery, Mortality in the USA and Their Implications.

This article constructs and demonstrates an alternate probabilistic approach (using incidence rate restricted model), compared with the deterministic mathematical models such as SIR, to capture the impact of healthcare efforts on the prevalence rate of the COVID-19's infectivity, hospitalization, recovery, and mortality in the eastern, central, mountain, and pacific time zone states in the USA. We add additional new properties for the incidence rate restricted Poisson probability distribution. With new properties, our method becomes feasible to comprehend not only the patterns of the prevalence rate of the COVID-19's infectivity, hospitalization, recovery, and mortality but also to quantitatively assess the effectiveness of social distancing, healthcare management's efforts to hospitalize the patients, the patient's immunity to recover, and lastly the unfortunate mortality itself. To make regional comparisons (as the people's movement is far more frequent within than outside the regional zone on daily basis), we group the COVID-19 data in terms of eastern, central, mountain, and pacific zone states. Several non-intuitive findings in the data results are noticed. They include the existence of imbalance, different vulnerability, and risk reduction in these four regions. For example, the impact of healthcare efforts is high in the recovery category in the pacific states. The impact is less in the hospitalization category in the mountain states. The least impact is seen in the infectivity category in the eastern zone states. A few thoughts on future research work are cited. It requires collecting rich data on COVID-19 and extracting valuable information for better public health policies.

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.