Historical Average Research Articles

Using SMS technology to improve quality of life and symptom management in the breast cancer patient population: A feasibility study.

e13619 Background: Cancer patients experience a wide range of negative side effects/symptoms from chemotherapy and radiation including pain, dehydration, nausea/vomiting, and diarrhea/constipation. Symptoms are distressing and decrease the overall quality of life and increase unnecessary hospitalizations and emergency visits. Symptom management and quality of life may be improved by using Short Message System (SMS) texting in cardiac and diabetic patient populations. The technology may also decrease costs for unnecessary emergency department (ED) visits by breast cancer patients. The purpose of this study was to determine if an SMS texting service can improve quality of life by improving symptom management. A secondary purpose was to evaluate if texting decreases emergency visits by breast cancer patients. Methods: This IRB-approved, consented study implemented an SMS texting program for breast cancer patients. Participants received two texts per day during their chemotherapy treatment period (6 months), one text being informational regarding symptom management and the second was one of encouragement and inspiration. Participants were called bi-weekly to complete the Distress Thermometer for Patients (NCCN). ED visits and/or hospital admission rates for the sample were collected. An exit interview measured satisfaction with the program in quantitative measures and theme analysis for qualitative measures. Twenty patients participated in this study, with 13 included in the data analysis. Results: SMS texting provided a change in treatment for symptom management in 40% of patients. Overall satisfaction with the SMS program was at the 89th percentile. Almost all participants felt supported and cared for due to bi-weekly calls by the investigators. Only 10% of the participants visited the emergency department, which is lower than historical averages. Data concerning the distress thermometer proved to be non-informative. Conclusions: SMS texting is feasible and easily integrated into the care of breast cancer patients. Texting technology may provide engagement with the care team leading to changes and improvement in symptom management during chemotherapy treatment. Further study of texting technology providing interactive abilities for patient-reported outcomes is warranted.

A unified conceptual model of coastal response to accelerating sea level rise, Florida, U.S.A.

Aggregation of paleo-environmental data derived from geological investigations conducted on the shoreface and inner shelf of Florida's six coastal geomorphic sectors revealed a common and synchronous response to a decelerating rate of Holocene sea level rise: (1) early Holocene overstep and submergence (∼10–5 mm yr−1), (2) mid-Holocene erosional shoreface retreat (∼2 mm yr−1), and (3) late Holocene stabilization (<1 mm yr−1). Linear best-fit analysis of sea level data collected at 14 NOAA tide gauge stations distributed along the entire Florida coast indicates the rate of sea level rise has accelerated from a historical average of 3.1 mm yr−1 (<1972–2022; range 2.2 to 4.2) to 5.9 mm yr−1 (1993–2022; range 4.8 to 6.9) and 8.2 mm yr−1 during the 21st century (2003–2022; range 7.6 to 10.0). The 21st century rates vary between stations, however all fall within the range of values documented during the early Holocene; a time when Florida's coastline was rapidly transgressed. Recent studies have demonstrated that the destabilizing effects of this acceleration on Florida's coastal geomorphology and ecology are already evident. Rates of rise are expected to continue increasing and this will accelerate the pace and scale of landward translation and submergence of Florida's coastal environment.

An assessment of water management measures for climate change adaptation of agriculture in Seewinkel

To develop appropriate climate change adaptation plans, evidence of the effectiveness of adaptation measures is required. At a regional scale, however, this information is usually lacking. The region of Seewinkel in Austria was taken as a case study because of its extensive agricultural industry and its unique ecosystem of saline lakes. The goal of the study was to provide stakeholders with evidence to support their climate change adaptation process. Adaptation measures discussed by local stakeholders were analyzed to determine their efficacy. A system dynamics (SD) based model was developed to serve as a tool for the water policy analysis and to be used in place of advanced hydrological models. The model was calibrated using observational data and forced with bias-adjusted EURO-CORDEX climate data for three representative concentration pathways (RCPs) (2010–2100). Three parameters in the model were changed to simulate adaptation measures. The results showed that combined measures, increasing irrigation efficiency and changing crops could reduce water demand by an average of 40 %, 23 % and 23 %, respectively, for all RCPs. The local aquifer's level could be increased above the historical average by an average of 0.43 m by combined measures, 0.20 m by increasing irrigation efficiency, 0.20 m by changing crops and 0.06 m by artificially recharging the aquifer.

Straightening machine preventive maintenance intervention plan based on AHP: a case study in a steel company in Brazil

Equipment failure and production shortfalls can cause significant impacts on companies. Preventive maintenance can potentially increase equipment reliability and availability, helping companies achieve their corporate goals while reducing operation and maintenance costs. However, determining the optimal interval between maintenance interventions involves balancing various factors such as maintenance costs, equipment availability, risk of failures, and the need for corrective maintenance, as well as potential operational and financial impacts. Under this context, this paper aims to define the interval between preventive maintenance interventions for a key production equipment in a steel company located in Southeast region of Brazil. Upon documental analysis and interviews with experts, an AHP was applied to hierarchize the maintenance periodicity, based on the following criteria: part replacement based on service life, downtime hours for preventive maintenance, equipment reliability, and cost of materials and labour. A biweekly preventive maintenance periodicity was identified as the most adequate for the analysed device. An implementation plan was approved and preliminary results indicate no availability deterioration, while maintenance costs are below historical average.

PD-1 Inhibitors Prolong Survival in Leptomeningeal Disease.

According to interim results of a phase I trial, patients with melanoma and leptomeningeal disease who received intrathecal and intravenous nivolumab, a PD-1 inhibitor, experienced a median overall survival of 4.9 months compared with a historical average of about 6 weeks with radiation and chemotherapy. No dose-limiting toxicities were reported.

The 2022 energy crisis: Fuel poverty and the impact of policy interventions in Australia's National Electricity Market

The war in Ukraine and associated 2022 energy crisis had far-reaching effects, with seaborne prices for coal and gas reaching multiples of their historic averages. While Europe was the epicentre, countries as far away as Australia were impacted. Governments across multiple jurisdictions have since orchestrated very material energy subsidies for households to reduce the adverse effects, with fiscal estimates across EU member states ranging from 0.5 to 7% of GDP. As a major energy exporter, the Australian Government took a different policy pathway by targeting the source of electricity tariff increases, viz. imposing price caps of $125/t and $12/GJ for coal and gas, respectively. Some state governments in Australia also revised their household energy hardship policies. This article analyses the level of fuel poverty before- and after- the array of Commonwealth and State policy interventions. Commonwealth policy had the effect of moderating forecast electricity tariff increases from 35% to 16.5%, which benefits all customers. However, State Government customer hardship policy remains vitally important and makes the dominant contribution in reducing fuel poverty.

Graph Neural Networks and Open-Government Data to Forecast Traffic Flow

Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown great promise in traffic forecasting due to their ability to capture complex spatio–temporal dependencies within traffic networks. Additionally, public authorities around the world have started providing real-time traffic data as open-government data (OGD). This large volume of dynamic and high-value data can open new avenues for creating innovative algorithms, services, and applications. In this paper, we investigate the use of traffic OGD with advanced deep-learning algorithms. Specifically, we deploy two GNN models—the Temporal Graph Convolutional Network and Diffusion Convolutional Recurrent Neural Network—to predict traffic flow based on real-time traffic OGD. Our evaluation of the forecasting models shows that both GNN models outperform the two baseline models—Historical Average and Autoregressive Integrated Moving Average—in terms of prediction performance. We anticipate that the exploitation of OGD in deep-learning scenarios will contribute to the development of more robust and reliable traffic-forecasting algorithms, as well as provide innovative and efficient public services for citizens and businesses.

Beef and pork processing plant labor costs

AbstractFederal and state legislators recently enacted policies to fund new and renovated small and local meat processors expansion with the aim of increasing the meat‐processing sector's resiliency. Wages must be competitive to attract employees for these new and renovated plants to be competitive. No previous studies have examined what has happened to employee labor costs at United States (US) meat‐processing plants since the early 2000s. This study estimates how meat‐processing firm size affects employee wages in the US. We use average employee wages for beef and pork processing plants from 2007 to 2019. We find larger plants pay higher wages than smaller ones, which is likely attributable to lower fixed costs resulting from economies of scale. Findings suggest that facilities with more than 500+ employees will most likely offer wage that are high enough to recruit workers in for this industry. Small plants will need to increase real wages that are higher than historical averages. Thus, if smaller facilities increase the sector's resiliency, then it will likely come at a cost in terms of higher wage bills. [EconLit Citations: Q12, Q18].

Different Responses of Growing Season Ecosystem CO2 Fluxes to Rain Addition in a Desert Ecosystem.

Desert ecosystem CO2 exchange may play an important role in global carbon cycling. However, it is still not clear how the CO2 fluxes of shrub-dominated desert ecosystems respond to precipitation changes. We performed a 10-year long-term rain addition experiment in a Nitraria tangutorum desert ecosystem in northwestern China. In the growing seasons of 2016 and 2017, with three rain addition treatments (natural precipitation +0%, +50%, and +100% of annual average precipitation), gross ecosystem photosynthesis (GEP), ecosystem respiration (ER), and net ecosystem CO2 exchange (NEE) were measured. The GEP responded nonlinearly and the ER linearly to rain addition. The NEE presented a nonlinear response along the rain addition gradient, with a saturation threshold by rain addition between +50% and +100%. The growing season mean NEE ranged from -2.25 to -5.38 μmol CO2 m-2 s-1, showing net CO2 uptake effect, with significant enhancement (more negative) under the rain addition treatments. Although natural rainfall fluctuated greatly in the growing seasons of 2016 and 2017, reaching 134.8% and 44.0% of the historical average, the NEE values remained stable. Our findings highlight that growing season CO2 sequestration in desert ecosystems will increase against the background of increasing precipitation levels. The different responses of GEP and ER of desert ecosystems under changing precipitation regimes should be considered in global change models.

Racial Disparities in Breast Imaging Wait Times Before and After the Implementation of a Same-Day Biopsy Program.

To examine time from screening to diagnostic workup, biopsy, and surgery for non-Hispanic White (NHW) and Black women following implementation of a same-day biopsy program. All NHW and Black women with BI-RADS category 0 screening mammogram at Duke University Hospital were identified between August 1, 2020, and August 1, 2021. Patient characteristics were recorded. Time between screening mammogram, diagnostic workup, breast biopsy, surgical consultation, and surgery were recorded. Comparisons were made between NHW and Black women using a multivariable regression model. Diagnostic imaging to biopsy time interval was compared to historical averages before same-day biopsy implementation. There were 2156 women: 69.9% NHW (1508/2156) and 30.1% Black (648/2156). Mean ± standard deviation time from screening to diagnostic imaging overall was 13.5 ± 32.5 days but longer for Black (18.0 ± 48.3 days) than for NHW women (11.5 ± 22.2 days) (P < 0.001). The mean time from diagnostic mammogram to biopsy was 5.9 ± 18.9 days, longer for Black (9.0 ± 27.9 days) than for NHW women (4.4 ± 11.8 days) (P = 0.017). The same-day biopsy program shortened the time from diagnostic imaging to biopsy overall (12.5 ± 12.4 days vs 5.9 ± 18.9 days; P < 0.001), with a significant reduction for NHW women (12.4 ± 11.7 days vs 4.4 ± 11.8 days) (P < 0.001) but not Black women (11.5 ± 9.9 days vs 9.0 ± 27.9 days) (P = 0.527). Disparities exist along the breast imaging pathway. A same-day biopsy program benefited NHW women more than Black women.

Temperature, storage, and natural gas futures prices

AbstractPrevious literature suggests that daily changes in US natural gas (NG) futures prices and their variance can be explained by changes in oil futures prices and volatilities, storage announcements, and temperature shocks. These studies measure temperature shocks using the National Oceanic and Atmospheric Administration definitions of heating degree day (HDD) and cooling degree day (CDD). We show how hourly temperatures can be used to better measure how long as well as how much temperatures depart above and/or below the “comfort level” within a day, the effects of temperature variability within a day, and the effects of thermal inertia. Hourly temperatures also allow us to construct measures of extreme temperatures that better match the timing of release of other market information. Another innovation is that we use more economically relevant weights to convert regional temperature shocks to national averages, and measure expected values of variables via econometric models instead of historical averages. Our results show that the proposed measures of HDD and CDD and temperature changes are important for explaining weekly changes in NG in storage and daily changes in NG futures prices and their variance.

Increased Number and Severity of Open Globe Injuries during COVID-19 Stay-at-home Orders

ABSTRACT Purpose To evaluate the occurrence and outcome of open globe injury during state-mandated COVID-19 stay-at-home orders compared to historical averages at a tertiary medical center in Portland, Oregon. Methods Open globe injury between January 1, 2015 and December 31, 2020 was identified using relevant procedure codes. The number of injuries, mechanism of trauma, and short-term outcomes of globes repaired during the study period of March 23, 2020 to July 6, 2020 when stay-at-home orders were in effect were compared to a cohort from the same 15-week time frame in 2015–2019. We also evaluated injuries occurring throughout 2020 as compared to the prior 5 years. Results 263 consecutive open globe injuries were identified between January 2015 and December 2020. While Oregon’s stay-at-home orders were in effect, we observed a significant increase in the number of open globe injuries treated compared to the prior 5 years (p = .004). Twenty-four cases identified during the study period represent a 2-fold increase over the 2015–2019 average of 11.8 globe repairs during the same 15-week time period. Visual acuity < 20/200 at 6 months (p = .008) and secondary enucleation (p < .001) were more frequent during stay-at-home orders, and severity of injury as calculated by the Ocular Trauma Scores (OTS) was higher. Time-to-repair was similar between the two cohorts. Conclusion At our center, there was an increased number and severity of open globe injury during the period of mandatory COVID-19 stay-at-home orders. Visual acuity outcomes and risk for secondary enucleation were poorer compared to the reference cohort. Abbreviations: Ocular Trauma Score (OTS), Open globe injury (OGI), Emergency department (ED), Oregon Health and Science University (OHSU)

Balance Sheet Policies in an Evolving Economy: Some Modelling Advances and Illustrative Simulations

Once considered "unconventional," balance sheet policies have become an integral part of the toolkit of many central banks. Increased reliance on balance sheet policies reflects in part a decline in the neutral level of interest rates, which limits central banks' ability to cut their policy rates to support the economy during downturns, and many observers expect that neutral level to remain low relative to its historical average in the coming decades.

From Economics to Earnings: A Macro-Based Equity Earnings Growth Forecasting Model

Earnings growth measures the change in a company’s reported net income through time. It is arguably the most widely observed measure of the growth and the profitability of a business, and a critical driver of equity returns. Though many practitioners have relied on historical averages to inform earnings growth expectations, research has found a relationship between earnings growth and prevailing economic growth. Building on prior research that connects earnings growth with real GDP growth, the authors split the earnings growth into two parts—revenue growth and changes in profit margins—and identifies multiple macroeconomic factors that have historical relationships separately with each. The authors find that revenue growth can historically be explained by world GDP growth, US GDP growth and payout ratios, and changes in profit margins can be explained by labor costs trade intensity. Upon conducting the out of sample test, this article offers a robust solution on predicting future earnings growth with macroeconomic factors and provides an important framework for understanding a key component of equity returns.

Gender differences in COVID-19-related manuscript authorship by hospitalists during the pandemic: A bibliometric analysis.

Hospital medicine (HM) has a well-described gender disparity related to academic work and promotion. During the COVID-19 pandemic, female authorship across medicine fell further behind historical averages. Examine how COVID-19 affected the publication gender gap for hospitalists. Bibliometric analysis to determine gender and specialty of US-based physician first and last authors of COVID-19 articles published March 1, 2020 to February 28, 2021 in the four highest impact general medical journals and two highest impact HM-specific journals. We characterized the percentage of all physician authors that were women, the percentage of physician authors that were hospitalists, and the percentage of HM authors that were women. We compared author gender between general medical and HM-specific journals. During the study period, 853 manuscripts with US-based first or last authors were published in eligible journals. Included manuscripts contained 1124 US-based physician first or last author credits, of which 34.2% (384) were women and 8.8% (99) were hospitalists. Among hospitalist author credits, 43.4% (n = 43/99) were occupied by women. The relative gender equity for hospitalist authors was driven by the two HM journals where, compared to the four general medical journals, hospitalist authors (54.1% [33/61] vs. 26.3% [10/38] women, respectively, p = .002) and hospitalist last authors (51.9% [14/27] vs. 20% [4/20], p = .03) were more likely to be women. Across COVID-19-related manuscripts, disparities by gender were driven by the high-impact general medical journals. HM-specific journals had more equitable inclusion of women authors, demonstrating the potential impact of proactive editorial policies on diversity.

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.

Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration. The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance. A total of 3189 patients who underwent spine surgery were included. The institution's current method of predicting case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.

The Kidney Failure Risk Equation: Evaluation of Novel Input Variables including eGFR Estimated Using the CKD-EPI 2021 Equation in 59 Cohorts.

The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict 2- and 5-year risk of kidney failure in populations with eGFR <60 ml/min per 1.73 m 2 . However, the CKD-EPI 2021 creatinine equation for eGFR is now recommended for use but has not been fully tested in the context of KFRE. In 59 cohorts comprising 312,424 patients with CKD, the authors assessed the predictive performance and calibration associated with the use of the CKD-EPI 2021 equation and whether additional variables and accounting for the competing risk of death improves the KFRE's performance. The KFRE generally performed well using the CKD-EPI 2021 eGFR in populations with eGFR <45 ml/min per 1.73 m 2 and was not improved by adding the 2-year prior eGFR slope and cardiovascular comorbidities. The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict kidney failure risk in people with GFR <60 ml/min per 1.73 m 2 . Using 59 cohorts with 312,424 patients with CKD, we tested several modifications to the KFRE for their potential to improve the KFRE: using the CKD-EPI 2021 creatinine equation for eGFR, substituting 1-year average ACR for single-measure ACR and 1-year average eGFR in participants with high eGFR variability, and adding 2-year prior eGFR slope and cardiovascular comorbidities. We also assessed calibration of the KFRE in subgroups of eGFR and age before and after accounting for the competing risk of death. The KFRE remained accurate and well calibrated overall using the CKD-EPI 2021 eGFR equation. The other modifications did not improve KFRE performance. In subgroups of eGFR 45-59 ml/min per 1.73 m 2 and in older adults using the 5-year time horizon, the KFRE demonstrated systematic underprediction and overprediction, respectively. We developed and tested a new model with a spline term in eGFR and incorporating the competing risk of mortality, resulting in more accurate calibration in those specific subgroups but not overall. The original KFRE is generally accurate for eGFR <45 ml/min per 1.73 m 2 when using the CKD-EPI 2021 equation. Incorporating competing risk methodology and splines for eGFR may improve calibration in low-risk settings with longer time horizons. Including historical averages, eGFR slopes, or a competing risk design did not meaningfully alter KFRE performance in most circumstances.

National influenza mid-season report, 2022-2023: A rapid and early epidemic onset.

Canada's 2022-2023 national influenza epidemic was declared in epidemiological week 43 (week ending October 29, 2022), relatively early in comparison to historical seasons. This year marks the return to pre-pandemic-like influenza circulation, following the brief and delayed influenza epidemic declared in the spring of the 2021-2022 season. To date this season, 59,459 detections of influenza have been reported out of 456,536 tests; both values exceeding historical averages. This epidemic is being fundamentally driven by influenza A, with influenza A(H3N2) accounting for 94% of subtyped detections. This season to date has had a significant impact on adolescents and young children, with a high proportion of detections occurring in those aged 0-19 years (42%). Provinces and territories have reported higher than usual influenza-associated hospitalizations, intensive care unit admissions, and deaths in comparison with previous seasons; in particular, paediatric hospitalization incidence was persistently far above historical peak levels for several weeks. The return of seasonal influenza circulation highlights the importance of sustained vigilance with regard to influenza and employment of available mitigation measures, especially of annual seasonal influenza vaccination.

Progress and opportunities in advancing near-term forecasting of freshwater quality.

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

Materials availability and supply chain considerations for vanadium in grid-scale redox flow batteries

Redox flow batteries (RFBs) are a promising electrochemical storage solution for power sector decarbonization, particularly emerging long-duration needs. While the battery architecture can host many different redox chemistries, the vanadium RFB (VRFB) represents the current state-of-the-art due to its favorable combination of performance and longevity. However, the relatively high and volatile price of vanadium has hindered VRFB financing and deployment opportunities. Here we evaluate the vanadium supply chain to understand how it enables or constrains VRFB advancement and assess opportunities for accelerated growth. We find that – while vanadium may not be scarce – its abundance is confounded by highly concentrated production coupled with the disperse nature of sources suitable for potential supply increase. These factors challenge rapid growth, limiting deployment rate and magnitude. We estimate gigawatt-hour deployment scales are feasible over the next decade, which would represent marked expansion of the RFB industry and drive down system costs substantially, though this would require growth rates to vanadium production above historical averages. Accordingly, we review opportunities to accelerate supply chain growth and economic strategies to stabilize the market. Finally, we posit terawatt-hour deployment scales will be challenged by vanadium market conditions and resource availability, motivating the continued efforts developing next-generation RFB chemistries.