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Identifying socioeconomic disparities within a local area is critical for tailoring policy solutions to older adult populations. However, a comprehensive index for characterizing socioeconomic disadvantage for older adult households in the U.S. is yet to be developed. This study is the first of its kind and used secondary data from the U.S. Census to develop a new socioeconomic disadvantage index for adults older than 60 years, with a focus on food insecurity, for a large region in Central Texas. The Older Adult Socioeconomic Disadvantage Index (OASDI) includes 12 variables related to unmet needs for food, housing, healthcare access, and transportation, and others at the census tract level. For each variable, the values were ranked based on quintiles using ArcGIS Pro 3.2. An unweighted sum was used to create the OASDI, where a higher score indicated greater socioeconomic disadvantage. Choropleth maps were used to visualize the OASDI and persistent poverty for all census tracts within the study area. The OASDI was used to statistically compare two local policy regions for senior nutrition programs in the Austin and San Antonio, Texas metro areas. Results showed a greater socioeconomic disadvantage in the San Antonio region compared to the Austin region (Mann-Whitney-U = 198,303; p < 0.0001). The statistical analysis identified an area with extreme disadvantage relative to the local policy region and confirmed with member checking. Findings provided insights into local socioeconomic disparities at different levels and can be applied to advocate for policies, systems, and environmental changes for senior nutrition.

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.

Objectives: To address urgent staffing shortages, hospitals increasingly rely on agency labor. Given that staffing can significantly impact hospital performance, the aim of this study was to understand the organizational and market factors associated with agency labor utilization in US hospitals. Methods: Utilizing concepts from the Resource Dependence Theory (RDT), data for the calendar year 2022 for short-term acute care in United States hospitals (n = 2756) were analyzed with logistic regression analysis. We explored total agency labor expense (operationalized as a dichotomous variable at the 75th and 90th percentiles) in the presence of numerous organizational and market independent variables. Results: The results revealed that as markets become less complex (as measured by market concentration), the level of agency labor expense increases (β: 0.843, p < 0.001), while our chosen variables related to munificence (rural location; β: −1.634, p < 0.001) and dynamism (Medicare payor mix; β: −0.025, p < 0.001) were negatively associated with an increase in total agency labor expense. Conclusions: In general, our results appear to support the tenets of the RDT. We find that increased use of agency labor in hospitals is predicated on the hospital having the financial resources and flexibility to be able to afford the higher cost of agency labor.

Healthcare leaders are faced with many financial challenges in the contemporary environment, leading to financial distress and notable instances of bankruptcies in recent years. What is not well understood are the specific conditions that may lead to organizational economic failure. Though there are various models that predict financial distress, existing regression methods may be inadequate, especially when the finance variables follow a nonnormal frequency pattern. Furthermore, the regression approach encounters difficulties due to multicollinearity. Therefore, an alternate stochastic approach for predicting the probability of hospital bankruptcy is needed. The new method we propose involves several key steps to better assess financial health in hospitals. First, we compute and interpret the relationship between the hospital’s revenues and expenses for bivariate lognormal data. Next, we estimate the risk of bankruptcy due to the mismatch between revenues and expenses. We also determine the likelihood of a hospital’s expenses exceeding the state’s median expenses level. Lastly, we evaluate the hospital’s financial memory level to understand its level of financial stability. We believe that our novel approach to anticipating hospital bankruptcy may be useful for both hospital leaders and policymakers in making informed decisions and proactively managing risks to ensure the sustainability and stability of their institutions.

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 number of COVID-19 fatalities fluctuated widely across United States (US) counties. The number of deaths is stochastic. When the average number of deaths is equal to the dispersion, the distribution is the usual Poisson. When the average number of deaths is higher than the dispersion, the distribution is an intervened Poisson. When the average number of deaths is lower than the dispersion, the distribution is an incidence-rate-restricted Poisson (IRRP) type. Because dispersion of COVID-19 fatalities in some counties is higher than the average number of fatalities, the underlying model for the chance-oriented mechanism might be IRRP. Understanding where this overdispersion or volatility exists and the implications of it is the topic of this research. In essence, this paper focuses on the number of COVID-19 fatalities that fluctuated widely across United States (US) counties and develops an incidence-rate-restricted Poisson (IRRP) to understand where this overdispersion or volatility exists and the implications of it.

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.

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.

Background: COVID-19 efforts were often ineffective in controlling the spread of the pandemic. Thus, identifying ineffective controls during a pandemic is vital. Method: Utilizing publicly available data on COVID deaths in the counties of US states, we created an index to capture and interpret ineffectiveness in the efforts to reduce the spread of the pandemic in US counties. This index is based on the Intervened Poisson Distribution (IPD) introduced originally by Shanmugam. Motivation for the research idea occurred when we noticed the data dispersion of the COVID deaths was smaller than the average only in some counties. Under-dispersed data is common in statistical modeling. A novel approach we adapted in this article includes the estimation of an intervention parameter estimated through iterative non-linear optimization. Results: Twenty-five counties in California, Idaho, Minnesota, Mississippi, Montana, Nebraska, North Carolina, North Dakota, Texas, and Utah were found to be ineffective in controlling for fatalities based on the expected probability distribution. A review of the policies enacted in these areas would provide insight into ineffective prevention efforts, and some of these issues are documented in the current literature. Conclusions: The IPD index an innovate way to document efficacy of interventions during pandemics.