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.

Use of outsourced nurses in long-term acute care hospitals: outcomes and leadership preferences.

When staffing effectiveness is not maintained over time, the likelihood of negative outcomes increases. This challenge is particularly problematic in long-term acute care hospitals (LTACHs) where use of outsourced temporary nurses is common when providing safe, sufficient care to medically complex patients who require longer hospital stays than normally would occur. To assess this issue, the authors discuss the outcomes of their survey of LTACH chief nursing officers that demonstrated LTACH quality indicators and overall patient satisfaction were within nationally accepted benchmarks even with higher levels of outsourced nurses used in this post-acute care setting.

Effects of outsourced nursing on quality outcomes in long-term acute-care hospitals.

Use of outsourced nurses is often a stop-gap measure for unplanned vacancies in smaller healthcare facilities such as long-term acute-care hospitals (LTACHs). However, the relationship of utilization levels (low, medium, or high percentages) of nonemployees covering staff schedules often is perceived to have negative relationships with quality outcomes. To assess this issue, the authors discuss the outcomes of their national study of LTACH hospitals that indicated no relationship existed between variations in percentage of staffing by contracted nurses and selected outcomes in this post-acute-care setting.

Examining Predictors of Myocardial Infarction.

Cardiovascular diseases are the leading cause of death in the United States. This study analyzed predictors of myocardial infarction (MI) for those aged 35 and older based on demographic, socioeconomic, geographic, behavioral, and risk factors, as well as access to healthcare variables using the Center for Disease (CDC) Control Behavioral Risk Factor Surveillance System (BRFSS) survey for the year 2019. Multiple quasibinomial models were generated on an 80% training set hierarchically and then used to forecast the 20% test set. The final training model proved somewhat capable of prediction with a weighted F1-Score = 0.898. A complete model based on statistically significant variables using the entirety of the dataset was compared to the same model built on the training set. Models demonstrated coefficient stability. Similar to previous studies, age, gender, marital status, veteran status, income, home ownership, employment status, and education level were important demographic and socioeconomic predictors. The only geographic variable that remained in the model was associated with the West North Central Census Division (in-creased risk). Statistically important behavioral and risk factors as well as comorbidities included health status, smoking, alcohol consumption frequency, cholesterol, blood pressure, diabetes, stroke, chronic obstructive pulmonary disorder (COPD), kidney disease, and arthritis. Three access to healthcare variables proved statistically significant: lack of a primary care provider (Odds Ratio, OR = 0.853, p < 0.001), cost considerations prevented some care (OR = 1.232, p < 0.001), and lack of an annual checkup (OR = 0.807, p < 0.001). The directionality of these odds ratios is congruent with a marginal effects model and implies that those without MI are more likely not to have a primary provider or annual checkup, but those with MI are more likely to have missed care due to the cost of that care. Cost of healthcare for MI patients is associated with not receiving care after accounting for all other variables.

A Monte Carlo simulation of air ambulance requirements during major combat operations.

In this study, we evaluate rules of allocation and planning factors that have an effect on requirements for Army air ambulance companies. The Army uses rules of allocation in scenarios drawn from strategic planning documents to determine how many units of each type are required. Army planners use these rules for determining the number of units required to support specific operational and tactical scenarios. Unrealistic rules result in unrealistic unit requirements. We evaluate quantitatively (via Monte Carlo simulation) planning considerations for air ambulance units during major combat operations (MCO) and estimate that 0.4 airframes per admission would be a reasonable planning factor.

Explaining and forecasting attrition in the Army pharmacy technician course.

The Army pharmacy technician (68Q) course trains--260 students per year, with a mean graduation rate of 71.3%. In support of this course, a research team conducted a study using multiple analytical methods to evaluate, to explain, and potentially to forecast failures, because the 28.7% of students who do not graduate are associated with both opportunity and real costs. Results of this study indicated that largely uncontrollable population demographic characteristics, such as rank and enrollment status, along with controllable Armed Services Vocational Aptitude Battery skilled technical test scores, were related to graduation rates. The results of this study may be used to assist individuals at risk of failure or to establish additional admission criteria to increase the likelihood of success.

Cost drivers and resource allocation in military health care systems.

This study illustrates the feasibility of incorporating technical efficiency considerations in the funding of military hospitals and identifies the primary drivers for hospital costs. Secondary data collected for 24 U.S.-based Army hospitals and medical centers for the years 2001 to 2003 are the basis for this analysis. Technical efficiency was measured by using data envelopment analysis; subsequently, efficiency estimates were included in logarithmic-linear cost models that specified cost as a function of volume, complexity, efficiency, time, and facility type. These logarithmic-linear models were compared against stochastic frontier analysis models. A parsimonious, three-variable, logarithmic-linear model composed of volume, complexity, and efficiency variables exhibited a strong linear relationship with observed costs (R(2) = 0.98). This model also proved reliable in forecasting (R(2) = 0.96). Based on our analysis, as much as $120 million might be reallocated to improve the United States-based Army hospital performance evaluated in this study.

Randomized, double-blind, comparative-effectiveness study comparing pulsed radiofrequency to steroid injections for occipital neuralgia or migraine with occipital nerve tenderness.

Occipital neuralgia (ON) is characterized by lancinating pain and tenderness overlying the occipital nerves. Both steroid injections and pulsed radiofrequency (PRF) are used to treat ON, but few clinical trials have evaluated efficacy, and no study has compared treatments. We performed a multicenter, randomized, double-blind, comparative-effectiveness study in 81 participants with ON or migraine with occipital nerve tenderness whose aim was to determine which treatment is superior. Forty-two participants were randomized to receive local anesthetic and saline, and three 120 second cycles of PRF per targeted nerve, and 39 were randomized to receive local anesthetic mixed with deposteroid and 3 rounds of sham PRF. Patients, treating physicians, and evaluators were blinded to interventions. The PRF group experienced a greater reduction in the primary outcome measure, average occipital pain at 6 weeks (mean change from baseline -2.743 ± 2.487 vs -1.377 ± 1.970; P < 0.001), than the steroid group, which persisted through the 6-month follow-up. Comparable benefits favoring PRF were obtained for worst occipital pain through 3 months (mean change from baseline -1.925 ± 3.204 vs -0.541 ± 2.644; P = 0.043), and average overall headache pain through 6 weeks (mean change from baseline -2.738 ± 2.753 vs -1.120 ± 2.1; P = 0.037). Adverse events were similar between groups, and few significant differences were noted for nonpain outcomes. We conclude that although PRF can provide greater pain relief for ON and migraine with occipital nerve tenderness than steroid injections, the superior analgesia may not be accompanied by comparable improvement on other outcome measures.