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The United States experienced at least five COVID-19 waves linked with different mutated SARS-CoV-2 variants including Alpha, Delta and Omicron. In addition to the variants, the intensity, geographical distribution, and risk factors related to those waves also vary within socio-demographic characteristics and timeframes. In this project, we have examined the spatial and temporal pattern of COVID-19 in the USA and its associations with Social Determinants of Health (SDoH) by utilizing the County Health Rankings & Roadmaps (CHRR) dataset. Our epidemiologic investigation at the county level showed that the burden of COVID-19 cases and deaths is higher in counties with high percentages of smoking, number of preventable hospital stays, primary care physician rate, the average daily density of PM 2.5 and percentages of high proportions of Hispanic residents. In addition, the analysis also demonstrated that COVID-19 incidence and mortality had distinct characteristics in their association with SDoH variables. For example, the percentages of the population 65 and older had negative associations with incidence while a significant positive association with mortality. In addition to the elderly population, median household income, unemployment, and number of drug overdose deaths showed a mixed association with COVID-19 incidence and mortality. Our findings validate several influential factors found in the existing social epidemiology literature and highlight temporal associations between SDoH variables and COVID-19 incidence and mortality not yet frequently studied., Competing Interests: Declaration of Competing Interest We declare no conflict of interest with this publication, and we did not receive any financial support for this work that could have influenced the results of this project., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Channel dimensions are important input variables for many hydrologic models. As measurements of channel geometry are not available in most watersheds, they are often predicted using bankfull hydraulic geometry relationships. This study aims at improving existing equations that relate bankfull width, depth, and cross-sectional area to drainage area ( DA) without limiting their use to well-gauged watersheds. We included seven additional variables in the equations that can be derived from data that are generally required by hydrologic models anyway and conducted several multiple regression analyses to identify the ideal combination of additional variables for nationwide and regional models for each Physiographic Division of the United States (U.S.). Results indicate that including the additional variables in the regression equations generally improves predictions considerably. The selection of relevant variables varies by Physiographic Division, but average annual precipitation ( PCP) and temperature ( TMP) were generally found to improve the models the most. Therefore, we recommend using regression equations with three independent variables ( DA, PCP, and TMP) to predict bankfull channel dimensions for hydrologic models. Furthermore, we recommend using the regional equations for watersheds within regions from which data were used for model development, whereas in all other parts of the U.S. and the rest of the world, the nationwide equations should be given preference. [ABSTRACT FROM AUTHOR]

Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern United States resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities. [ABSTRACT FROM AUTHOR]

The US portion of the Program for International Student Assessment (PISA) 2003 student questionnaire comprising of 4,733 observations was used in a multiple regression framework to predict math achievement from demographic variables, such as gender, race, and socioeconomic status, and two student-specific measures of perception, math anxiety and math self-efficacy, while controlling for age, psychological disability, and language proficiency. The results confirmed the existence of persistent racial and socioeconomic gaps in math achievement, showed that both anxiety and self-efficacy contribute significantly towards explaining variation in math achievement, and provided evidence that the gender gap disappears once important predictors of math achievement, such as math-specific self-efficacy and anxiety, are controlled for. [ABSTRACT FROM AUTHOR]

Fire history reconstructions from fire scars in tree rings have been valuable for assessing fire regime changes and their climatic controls. It has been asserted, however, that these two- to four-century long records from the western USA are unrepresentative of longer periods of the Holocene and are of limited use for understanding current or future fire regimes. The Medieval Climate Anomaly (800–1300 ce) is often suggested as a better analog for future Southwestern US climates but is beyond the chronological range of most fire-scar studies in this region. To evaluate fire regime changes over the past millennium, we build on centennial-length fire–climate studies to generate a 1416 year long reconstruction of fire activity in ponderosa pine forests of the Southern Colorado Plateau region of Arizona and New Mexico. We used a split-period calibration and verification protocol to test the reliability of a multiple regression model using annual and antecedent precipitation (reconstructed from tree-ring width chronologies) to predict the percentage of fire-scar localities (i.e. sites, N=45) that recorded extensive fires within those sites (>25% of recorder trees scarred) each year between 1700 and 1899 ce. The model explains approximately 50% of the variation in annual fire activity. Applying the model to the entire precipitation reconstruction provides a proxy for annual area burned since 572 ce. There are no statistically significant differences between the period available for fire-scar study (1600 ce–present) and the Medieval Climate Anomaly (800–1300 ce) in terms of predicted annual area burned or the frequencies of regional fire years. Multidecadal and centennial variation in the frequencies of regional fire years, however, does indicate reduced surface fire frequencies from approximately 700–800 ce and 1360–1455ce. We hypothesize that these were periods when some forests were vulnerable to altered canopy structure, accumulated fuels, and increased fire severity. [ABSTRACT FROM PUBLISHER]

Change orders are very common in almost every construction project nowadays, often resulting in increases of 5–10% in the contract price. Understanding the consequences of such trends, several studies have attempted to quantify the impact of change orders on the project cost. Most of the studies aimed at the quantification of the change orders were sponsored by contractors’ organizations, where statistical models used to quantify the impact of the change orders on the project cost were based on data supplied by the contractors; a situation that can lead to owner-contractor disagreements related to the quantification method used. In addition, most of the studies tackled commercial and electromechanical work, and very rare studies tackled the field of heavy construction; a field that suffers from change orders because of errors and omissions, scope of work changes, or changes because of unforeseen conditions. This study addresses the need for a statistical model to quantify the increase of the contract price due to change orders in heavy construction projects in Florida. The model is based on data collected from 16 Florida DOT projects with contract values that ranged between $10–$25 million, and that encountered an increase in the contract price from 0.01 to 15%. Eleven variables were analyzed to test their impact on the cost of the change orders. The study concluded that most significant variables that impact the value of the change order, which are (1) the timing of the change order and (2) when the reason for issuing the change order is unforeseen conditions. Two regression models are developed and validated as follows: (1) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 0.01 to 5% and (2) a model to quantify the percentage increase in the contract price due to the change orders that increase the contract price from 5 to 15%. Those models will provide the owner with a retrospective or forward pricing of the change orders, and hence, allow the owner to estimate and utilize contingency amounts. [ABSTRACT FROM AUTHOR]

Aim To examine range-wide patterns of genetic diversity in association with range limits in a broadly distributed temperate lizard, and to identify the contributions of a series of environmental, geographical and historical variables to the observed patterns. Reduced genetic diversity may limit local adaptation in peripheral populations, thereby limiting their ability to adapt to marginal environmental conditions, possibly explaining the existence of temporally stable range limits. Location Various sampling locales throughout eastern and central USA and southern Ontario, Canada. Methods Genetic diversity of nuclear DNA microsatellites was estimated for each of 38 populations from across the range of eastern North America’s most broadly distributed lizard, the five-lined skink, Plestiodon fasciatus (Linnaeus, 1758). Results Local climatic conditions and an interaction between distance from range border and glaciation history best predicted a population’s present-day genetic diversity. Overall, peripheral populations had reduced genetic diversity relative to that of central populations, but this difference was attributable to the reduced genetic diversity in peripheral populations to the north and west that are not bordered by any obvious physical boundaries. Main conclusions Some, but not all, peripheral populations had reduced genetic diversity relative to that of more central populations, which probably arose through an interaction of ecological and historical factors. Peripheral populations that were bordered by an obvious boundary (e.g. an ocean) had higher diversity than peripheral populations that were not bordered by an obvious physical barrier to range expansion, suggesting that reduced intrapopulation genetic diversity is associated with range limits in the five-lined skink. [ABSTRACT FROM AUTHOR]

The objective of this study is to identify the determinants of the travel demand and to assess their impacts on the number of inter- national tourist arrivals from Canada to the U.S. Annual time data (1960-2002) of tourist arrivals, real per capita income, relative prices and real exchange rates were used throughout the study. The Dummy variables of 9-11, the Olympic Games, the Open Skies Agreement, the Gulf War, the Free Trade Agreement, the World Economic Recession and the Oil Crisis were used to measure the impacts of special incidents on the number of Canadian arrivals. Multiple regression analysis was used to analyze the data. The results showed that real per capita income, relative price, the Gulf War and the Oil Crisis were the determinants of the travel demand from Canada to the U.S. [ABSTRACT FROM AUTHOR]

This study used prescription claims data to evaluate whether average cost of pharmaceuticals differed among various plan characteristics after controlling for covariates and to examine the relationships among plan characteristics and cost of pharmaceuticals within various therapeutic categories. Data for the study were obtained from 1996 prescription claims information for a commercial population administered by a Rhode Island-based PBM. Six therapeutic categories were analyzed. Significant associations were found among plan characteristics and cost of pharmaceuticals. Average cost of pharmaceuticals differed among various plan characteristics such as copayment, mode of payment, formulary status, and pharmacy type after controlling for average wholesale price and days' supply. [ABSTRACT FROM PUBLISHER]

Objectives: The purpose of this study was to determine whether frequency of participation in activities offered in a student response system was associated with students' final course grades in four different nursing courses and to explore whether that effect was dependent on the specific pedagogical context of each course., Design: An observational approach with a predictive model was used to examine the effect of a student's frequency of responses to prompts in a student response system (Nearpod) on final grade., Setting: This investigation was carried out in four high-enrollment nursing courses in a large Midwestern university in the United States., Participants: Students who participated in this study were enrolled in one of four core nursing courses: Pathophysiology, Health Assessment, Nursing Care of the Adult at the undergraduate level, and Pathophysiology at the graduate level., Methods: Multiple regression analysis was conducted to examine the association between a student's frequency of Nearpod use and their final grade., Results: The regression model explained 25.42% of the variance in final grade when controlling for course differences. The key finding from this study is that the more a student responded to prompts in Nearpod, the more likely they were to earn a higher final grade, but this effect was statistically significant only in undergraduate Pathophysiology., Conclusions: The positive association between more frequent Nearpod use and higher final grade appears to be dependent on contextual factors. These factors may include consistent use of Nearpod in class, detailed feedback and elaboration on student responses, and required participation in Nearpod activities., Competing Interests: Declaration of competing interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)