Variables In Multiple Regression Model Research Articles

A Proposal for a New Python Library Implementing Stepwise Procedure

Carefully selecting variables in problems with large volumes of data are extremely important, as it reduces the complexity of the model, improves the interpretation of the results, and increases computational efficiency, ensuring more accurate and relevant analyses. This paper presents a comprehensive approach to selecting variables in multiple regression models using the stepwise procedure. As the main contribution of this study, we present the stepwise function implemented in Python to improve the effectiveness of statistical analyses, allowing the intuitive and efficient selection of statistically significant variables. The application of the function is exemplified in a real case study of real estate pricing, validating its effectiveness in improving the fit of regression models. In addition, we presented a methodological framework for treating joint problems in data analysis, such as heteroskedasticity, multicollinearity, and nonadherence of residues to normality. This framework offers a robust computational implementation to mitigate such issues. This study aims to advance the understanding and application of statistical methods in Python, providing valuable tools for researchers, students, and professionals from various areas.

A-321 Rethinking the Role of Race in Prenatal AFP Screening for Open Neural Tube Defects

Abstract Background The quantification of alpha-fetoprotein (AFP) in maternal serum is commonly used in prenatal screening to estimate the risk of fetal open neural tube defects (ONTDs). This involves comparing a patient’s measured AFP concentration to the median AFP for women of similar gestational age and weight, reported in AFP multiples of the median (MoM). Current guidelines recommend establishing population medians separately grouped by patients’ race, based on historical reports documenting differences in median AFP concentrations across different populations. We aimed to reevaluate this practice of race based ONTD risk stratification given the limited and questionable historical evidence as well as the small observed differences in AFP concentrations in our practice and in a recent report. Methods We collected maternal serum AFP screening data for patients between 12/30/19 and 2/9/24. Informed by the statistical methods used for clinical risk stratification, we developed two log-linear multiple variable regression models for estimating AFP: gestational age (GA), weight, and interaction terms (Model 1) and Model 1 predictors, race, and interaction terms (Model 2). AFP MoMs were calculated by dividing the measured AFP concentration by the model-estimated AFP. Results We analyzed maternal ONTD screening for 7,702 patients with singleton pregnancies. The current clinical process uses race and ethnicity as documented in the electronic health record, which was categorized as Black (N = 2,360), White (N = 3,734), Hispanic (N = 209), Asian (N = 641), and Other (N = 758). The addition of race in Model 2 included statistically significant predictors (p < 0.001) for Black (β = 0.2), Asian (β = 0.05), and Black:weight interaction (β = -5.9 x 10-4). Between Model 1 and Model 2, the mean change in AFP MoMs was -0.05, +0.03, 0.0, -0.02, and +0.01 for Black, White, Hispanic, Asian, and Other patients, respectively. 95% of Black patients had a change < 0.12 in AFP MoM and > 99% of all other patients had a change < 0.1. Using the clinical interpretative threshold, 56 (0.7%) results were considered elevated in both models, 4 (0.05%) elevated in Model 1 only (3 Black, 1 Other), and 4 (0.05%) elevated in Model 2 only (3 White, 1 Hispanic). Chart review of the 8 discordant interpretations showed small changes in MoMs (mean absolute difference = 0.09) and no cases of ONTDs. Conclusions In our health system’s population, maternal race appears to have a small statistically significant association with maternal AFP concentration, even after adjustment for weight and maternal GA. However, preliminary analyses indicate that this difference has a small impact on the clinical interpretation of these studies, with most patients classified similarly when race is not accounted for in the calculation. Further investigation is needed to clarify whether this small impact on estimation of risk warrants the continued inclusion of race in NTD screening, as well as whether other clinical, social, or community factors available for inclusion in this clinical risk stratification model would be better predictors than the poorly defined and discriminatory concept of race.

Proposed correction to the Gamma method for estimating the bending stiffness of CLT panels

Engineered wood products, such as Cross Laminated Timber (CLT), have been widely used in civil construction. Standard calculation methods, like the Gamma method commonly used in structural design, estimate the bending stiffness of elements. However, these methods are based on one-dimensional solids (bar elements), which can be limiting. This limitation may result in inaccurate estimates, especially when the panel's dimensions, measured in the median plane, significantly differ by an order of magnitude. In this research, a parametric study was carried out using the finite element method (three-dimensional approach), varying the panel's dimensions (width and length), the layers' thickness, and the number of layers (cross-section height), and adopting elastic properties considering either the wood's orthotropy or the rolling shear estimation (108 numerical simulations in all). Based on the parameterization, multiple variable regression models were used to establish a correction coefficient to be incorporated into the Gamma method to improve its accuracy in estimating bending stiffness. Therefore, the single adjusted equation (considering the wood's orthotropy) showed values closer to the numerical ones (MAPE = 0.95 %, RMSE = 2.96 × 1011 N⋅mm2, and CV = 1.60 %) than those obtained by the Gamma method (MAPE = 2.13 %, RMSE = 7.47 × 1011 N⋅mm2, and CV = 4.04 %).

Intrinsic Factors and Psychological Safety Among Nursing Students During Simulation-Based Learning-A Correlational Design.

Well-designed simulation-based learning (SBL) experiences enhance students' self-confidence, self-efficacy, clinical judgment, and psychomotor skill development. An emerging concept in SBL research is psychological safety. There is currently no research on factors influencing psychological safety specifically related to the SBL environment, nor is there any literature found to determine whether intrinsic student characteristic, such as self-compassion and resilience, contributes to SBL learning outcomes. The aim of this study is to determine whether there is a relationship between nursing students' intrinsic characteristics (self-compassion, resilience, and anxiety sensitivity) and their psychological safety. Bivariate correlation was used to examine associations among sociodemographic variables and outcome variables. Multiple regression was used to determine the predictive nature of the sociodemographic variables. Assumptions for variables in multiple regression models were tested (normal distribution, heteroscedasticity, multicollinearity). All data were analyzed in SPSS, Version 28. The P value of significance was set at 0.05 for all analyses. Most of the 118 participants were non-Hispanic (89%), White (65%), and females (95%). Results of the demographic bivariate analysis revealed no significant differences among this diverse group or semester in the curriculum for psychological safety. The multiple regression found self-compassion (β = 29, P = 0.004), anxiety sensitivity (β = -0.16, P = 0.049), and resilience (β = 0.26, P = 0.004) predict psychological safety. The importance of creating a psychologically safe learning environment has been recognized as essential to best practices. Our findings suggest that an understanding of student characteristics that impact their perception of psychological safety will allow educators to develop strategies to better support learners in the simulation environment.

Antioxidant insights: investigating the protective role of oxidative balance in inflammatory bowel disease.

Limited studies have investigated the relationship between systemic oxidative stress and inflammatory bowel disease (IBD). The purpose of this study was to explore the relationship between oxidative balance score (OBS) and IBD. We included 175,808 participants from the UK Biobank database from 2006 to 2010. OBS scores were calculated based on 22 lifestyle and dietary factors. Multiple variable Cox proportional regression models, as well as gender stratification and subgroup analysis, were utilized to investigate the relationship between OBS and IBD. There is a significant negative correlation between OBS and the occurrence of IBD, ulcerative colitis (UC), and Crohn's disease (CD). Additionally, OBS is significantly negatively correlated with intestinal obstruction in CD patients. Gender stratified analysis suggest a significant correlation between OBS and CD in female patients, particularly pronounced in those under 60 years old. Sensitivity analysis indicates a significant negative correlation between lifestyle-related OBS and diet-related OBS with the occurrence of CD in females, diet-related OBS is negatively correlated with CD in males. OBS showed a significant negative correlation with IBD, especially in female CD patients. This study underscores the importance of antioxidant diet and lifestyle, which may provide a greater advantage for female CD patients.

An Empirical Analysis of Determinants of Exchange Rate for Economy of Pakistan (2000 – 2023)

This study will work on some core variables including exchange rate that is considered as an important variable for different nations and size of level of development. In economics different functions are impacted here by exchange rate changes and it is very important to analyze that the macroeconomic factors including interest rate, inflation rate may impact on exchange rate positively or negatively. For the economy of Pakistan covering the period 2000 to 2023 it is analyzed that the multiple regression model by using exchange rate, inflation rate, interest rate, the value of exports and the value of imports in Pakistan having some important results for country. This study has used unit root test co-integration test and ARDL for evaluation of short run and long run effect of some core variables. This research found and concluded that all the right side variables in multiple regression model having some important impact on relationship for the determination of exchange rate and it is explored that the inflation rate interest rate import and export are captured to regressive exchange rate in the economy of Pakistan.

Characterization of upper airway and analysis of potential risk factor associated with OSA in patients with unilateral temporomandibular ankylosis and micrognathia deformities

ObjectiveTo ascertain the airway characteristics in patients with unilateral temporomandibular joint ankylosis and maxilla-mandibular deformities (UTMAMD) and investigate the potential risk factors associated with obstructive sleep apnea (OSA) among UTMAMD patients. MethodsAuthors conducted a retrospective single-center study to assess and compare study group consisting of a cohort of 32 patients diagnosed with UTMAMD between January 2011 and July 2022 with control group including 54 participants. The study group was further divided into two subgroups based on the presence or absence of OSA in patients. Parameters related to the upper airway were measured and analyzed using three-dimensional reconstruction of computed tomographic scans. The measurements of airway parameters were compared between study group and control group and between two subgroups. Pearson correlation analysis was used to identify the factors associated with the presence of OSA, and a multiple variable regression model was used to identify risk factors for OSA. ResultsAirway volume (VOL), Minimum cross-section area (min CSA), mean CSA, tilt in sagittal plane (TS), and tilt in sagittal plane (TC) in velopharynx; VOL, airway length (AL), min CSA, mean CSA, TS, TC, and airway deviation (AD) in glossopharynx; min CSA, TS, and AD in hypopharynx were found difference with significance between study group and control group. Lateral dimension/anterior-posterior dimension (LAT/AP) ratio in velopharynx and min CSA, TC, and LAT/AP ratio in glossopharynx were significant different between patients with UTMAMD with OSA and without OSA. ConclusionsThe upper airway of patients with UTMAMD exhibits significant narrowing and distortion, rendering them susceptible to suffer from OSA. Patients with UTMAMD and OSA demonstrate more elliptical airways, and the glossopharyngeal LAT/AP ratio is a predictive indicator for the occurrence of OSA.

I Read, Therefore I Am: Examining Nonmedical Reading and Its Relationship to Empathy in Anesthesia Training.

High levels of empathy among resident physicians are associated with improved patient outcomes. Empathy may be learned and practiced when reading nonmedical writing through narrative transportation, a process by which readers identify with characters and become emotionally involved in the plot. We hypothesized that residents and fellows who reported more nonmedical reading would have higher empathy levels and that empathy would decrease during training. An emailed survey was sent to program directors of Accreditation Council on Graduate Medical Education-accredited anesthesiology residency and fellowship programs, with a request to distribute the survey to trainees. The Toronto Empathy Questionnaire, reading volume, and demographics were included in the survey. Response data were analyzed using a multiple variable regression model. Of 136 responses, 119 were included for data analysis. Seventeen partially completed surveys were excluded. Higher empathy scores were reported among women (P < .0001) and residents who worked 60 to 80 hours per week (P = .039). Age, postgraduate year of training, relationship status, time spent with family, and avid reading were not significantly associated with increased empathy. In this study, we examined whether nonmedical fiction reading would increase empathy in medical trainees. Our study was not able to find any significant association with time spent reading and increased empathy; however, we found that trainees who worked more hours, specifically 60 to 80 hours, had higher empathy scores. Limitations for this study included a smaller sample size. Further research should be done in this field to determine if there are other intangible factors that affect empathy in trainees.

Early Complications after Prepectoral Tissue Expander Placement in Breast Reconstruction with and without Acellular Dermal Matrix.

Prepectoral breast reconstruction has become popularized with the concurrent use of acellular dermal matrix (ADM). The authors sought to compare 3-month postoperative complication rates and explantation rates for first-stage, tissue-expander-based, prepectoral breast reconstruction with and without the use of ADM. A single-institution retrospective chart review was performed to identify consecutive patients undergoing prepectoral tissue-expander-based breast reconstruction from August of 2020 to January of 2022. Chi-square tests were used to compare demographic categorical variables, and multiple variable regression models were used to identify variables associated with 3-month postoperative outcomes. The authors enrolled 124 consecutive patients. Fifty-five patients (98 breasts) were included in the no-ADM cohort and 69 patients (98 breasts) were included in the ADM cohort. There were no statistically significant differences between the ADM and no-ADM cohorts with regard to 90-day postoperative outcomes. On multivariable analysis, there were no independent associations between seroma, hematoma, wound dehiscence, mastectomy skin flap necrosis, infection, unplanned return to the operating room, or explantation in the ADM and no-ADM groups after controlling for age, body mass index, history of diabetes, tobacco use, neoadjuvant chemotherapy, and postoperative radiotherapy. The authors' results reveal no significant differences in odds of postoperative complications, unplanned return to the operating room, or explantation between the ADM and no-ADM cohorts. More studies are needed to evaluate the safety of prepectoral, tissue expander placement without ADM. Therapeutic, III.

Gender Differences of Endowed Professorship in Obstetrics and Gynecology Departments at Top Academic Institutions.

Background: Gender equity is a critical issue in academic medicine. Whether there is equitable access to the prestige and resources of endowed professorships merits evaluation. We investigated this question in obstetrics and gynecology, a field that focuses on the health of women and in which women are much better represented than other specialties of medicine. Materials and Methods: We compiled a list of the top 25 United States departments of obstetrics and gynecology and contacted department chairs (and used department websites) to obtain lists of faculty and their positions. Scopus, department websites, and National Institutes of Health (NIH) RePORTER were used to collect h-Index, number of publications and citations, graduation year, degrees, gender, and NIH-funding. We conducted a bivariate comparison of endowed professorship attainment by gender using a chi-square test and created a multiple variable regression model. Results: Of the 680 obstetrics and gynecology faculty across 23 departments that had endowed chairs, 64 out of 400 women (16%) and 66 out of 280 men (24%) held endowed chairs (p = 0.01). The multivariable model suggested no independent gender difference in attainment of an endowed chair after adjusting for covariates. Conclusion: To our knowledge, this study is the first to examine gender as a variable in endowed chair allocation in top obstetrics and gynecology academic departments. Our findings suggest a significant gender difference in the allocation of endowed chairs. That difference is driven by gender differences in academic rank, graduation year, publications, and funding. To promote the intraprofessional equity necessary to optimally advance women's health, further research and intervention are necessary.

Climate in Africa sequentially shapes spring passage of Willow Warbler Phylloscopus trochilus across the Baltic coast.

BackgroundMany migrant birds have been returning to Europe earlier in spring since the 1980s. This has been attributed mostly to an earlier onset of spring in Europe, but we found the timing of Willow Warblers’ passage to be influenced by climate indices for Africa as much as those for Europe. Willow Warblers’ spring passage through northern Europe involves populations from different wintering quarters in Africa. We therefore expected that migration timing in the early, middle and late periods of spring would be influenced sequentially by climate indices operating in different parts of the winter range.MethodsUsing data from daily mistnetting in 1 April–15 May over 1982–2017 at Bukowo (Poland, Baltic Sea coast), we derived an Annual Anomaly (AA, in days) of Willow Warbler spring migration. We decomposed this anomaly into three main periods (1–26 April, 27 April–5 May, 6–15 May); one-third of migrants in each period. We modelled three sequential time series of spring passage using calendar year and 15 large-scale climate indices averaged over the months of Willow Warblers’ life stages in the year preceding spring migration as explanatory variables in multiple regression models. Nine climate variables were selected in the best models. We used these nine explanatory variables and calculated their partial correlations in models for nine overlapping sub-periods of AA. The pattern of relationships between AA in these nine sub-periods of spring and the nine climate variables indicated how spring passage had responded to the climate. We recommend this method for the study of birds’ phenological responses to climate change.ResultsThe Southern Oscillation Index and Indian Ocean Dipole in Aug–Oct showed large partial correlations early in the passage, then faded in importance. For the Sahel Precipitation Index (PSAH) and Sahel Temperature Anomaly (TSAH) in Aug–Oct partial correlations occurred early then peaked in mid-passage; for PSAH (Nov–March) correlations peaked at the end of passage. NAO and local temperatures (April–May) showed low correlations till late April, which then increased. For the Scandinavian Index (Jun–Jul) partial correlations peaked in mid-passage. Year was not selected in any of the best models, indicating that the climate variables alone accounted for Willow Warblers’ multiyear trend towards an earlier spring passage.DiscussionClimate indices for southern and eastern Africa dominated relationships in early spring, but western African indices dominated in mid- and late spring. We thus concluded that Willow Warblers wintering in southern and eastern Africa dominated early arrivals, but those from western Africa dominated later. We suggest that drivers of phenological shifts in avian migration are related to changes in climate at remote wintering grounds and at stopovers, operating with climate change in the north, especially for species with complex and long-distance migration patterns.

Determining push-off forces in speed skating imitation drills

Speed skating takes place on ice rinks and is, therefore, dependent on seasonal conditions. To be able to train all year round, training in the summer months, when no ice rinks are available, consists mainly of athletics and endurance training as well as imitation drills. Imitation drills are exercises, e.g. on a slide board, which imitate the actual skating movement. To objectively evaluate the quality of the execution of these exercises, key performance indicators such as push-off forces need to be quantified. The aim of this work was to determine the push-off forces during speed skating imitation drills using pressure insoles in combination with machine-learning methods. A slide board is usually not instrumented. Here, the slide board was equipped with force plates to record the target variables, i.e. the push-off forces. The input variables to determine the push-off forces were recorded using plantar pressure insoles and triaxial accelerometers. Seven participants took part in the study. Two different machine-learning algorithms were compared. A non-linear deep neural network model and a linear multiple variable regression model. The models were trained using the obtained force–time curves. The linear regression model proved sufficient to predict the push-off forces. The relative difference between the measured and modelled maximum push-off force remained below 5%. This approach, based on a mobile and low-cost measurement system, allows a quantitative analysis of the athlete’s technique/performance. Therefore, we expect the instrument to be a helpful tool for the training of speed skaters.

Maximum surface settlement prediction in EPB TBM tunneling using soft computing techniques

Earth pressure balance (EPB) TBMs are commonly used for soft ground tunneling in urban areas. In metro tunnels’ excavation, designing a comprehensive monitoring system to control surface settlement is essential to prevent damage to surface structures. The present study aims to develop new prediction models to estimate the ground surface settlement using two soft computing techniques, SVM and ANN-MLP, and a multiple variable regression model to develop the new empirical formulas. The TBM operational parameters collected from the Tehran metro line 6, South extension (TML6-SE) project have been applied to confirm the provided models. In the data analysis process, the relationships between various parameters (torque index, thrust index, and earth pressure) and the ground surface settlement are investigated. Moreover, several statistical evaluation criteria are implemented to evaluate the performance of the developed models. The results show that the predicted values are in good agreement with the real data. The results can be used for similar ground and TBM tunneling conditions.

HR Max Prediction Based on Age, Body Composition, Fitness Level, Testing Modality and Sex in Physically Active Population.

Maximal heart rate (HRmax) is associated mostly with age, but age alone explains the variance in HRmax to a limited degree and may not be adequate to predict HRmax in certain groups. The present study was carried out on 3374 healthy Caucasian, Polish men and women, clients of a sports clinic, mostly sportspeople, with a mean age of 36.57 years, body mass 74.54 kg, maximum oxygen uptake (VO2max, ml∗kg–1 ∗min–1) 50.07. Cardiopulmonary exercise tests (CPET) were carried out on treadmills or cycle ergometers to evaluate HRmax and VO2max. Linear, multiple linear, stepwise, Ridge and LASSO regression modeling were applied to establish the relationship between HRmax, age, fitness level, VO2max, body mass, age, testing modality and body mass index (BMI). Mean HRmax predictions calculated with 5 previously published formulae were evaluated in subgroups created according to all variables. HRmax was univariately explained by a 202.5–0.53∗age formula (R2 = 19.18). The weak relationship may be explained by the similar age with small standard deviation (SD). Multiple linear regression, stepwise and LASSO yielded an R2 of 0.224, while Ridge yielded R2 0.20. Previously published formulae were less precise in the more outlying groups of the studied population, overestimating HRmax in older age groups and underestimating in younger. The 202.5–0.53∗age formula developed in the present study was the best in the studied population, yielding lowest mean errors in most groups, suggesting it could be used in more active individuals. Tanaka’s formula offers the second best overall prediction, while the 220-age formula yields remarkably high mean errors of up to 9 bpm. In conclusion, adding the studied variables in multiple regression models improves the accuracy of prediction only slightly over age alone and is unlikely to be useful in clinical practice.

Using Appropriate Functional Forms for Continuous Variables and Improving Predictive Accuracy in Developing the Risk Model of Clostridium Difficile Infection

Simple parametric functional forms, if appropriate, are preferred over more complicated functional forms in clinical prediction models. In this paper, we illustrate our practical approach to obtaining the appropriate functional forms for continuous variables in developing a clinical prediction model for risk of Clostridium dicile infection. First, we used a nonpara- metric regression smoother to establish the reference curve. Then, we used regression spline function-restricted cubic spline (RCS) and simple para- metric forms to approximate the reference curve. Based on the shape of the reference curve, the model t information (AIC), and the formal statistical test (Vuong test), we selected the simple parametric forms to replace the more elaborated RCS functions. Finally, we rened the simple parametric forms in the multiple variable regression model using the Wald test and the likelihood-ratio test. In addition, we compared the calibration and discrim- ination aspects between the model with appropriate functional forms and the model with simple linear terms. The calibration 2 (8.4 versus 10) and calibration plot, the area under ROC curve (0.88 vs 0.84,p < 0:05), and inte- grated discrimination improvement (0.0072, p < 0:001) indicated the model with appropriate forms was better calibrated and had higher discrimination ability.

Effects of different types of entrances on natural ventilation in a subway station

In this study, the natural ventilation of a horizontal entrance of a typical subway station is investigated based on numerical simulations and experiments. In addition, a renormalization group k-ɛ model (RNG k-ɛ model) is applied to compute both the internal and external airflow patterns. Computational fluid dynamics (CFD) simulations are validated based on the experimental results. Furthermore, a multiple variable regression model is employed to study how the different parameters affect the internal airflow rates of the subway station statistically, according to the experimental results. For this typical model, the parameter importance can be ranked as follows: (1) outdoor wind speed; (2) flow resistance of the subway station; (3) height of the wind catcher; and (4) length of the wind catcher. To understand the detailed pressure distribution of the horizontal entrance of the subway station with and without the wind catcher, 3D numerical simulations are conducted for different scenarios. We attempt to alter the size of the wind catcher (including the length and height) to study the characteristics of the pressure on the horizontal entrance of the subway station under outdoor wind-driven conditions. The pressure distributions for the entrance for different scenarios are compared and analyzed. Finally, the interactions between the internal and external flows are investigated by changing the resistance of the subway station. When the internal flow resistance is changed, the pressure coefficient (Cp) of the entrance is different as well. Hence, the Cp is not only affected by the outdoor environment, but is also influenced by the internal airflows.

Previous experience, aerobic capacity and body composition are the best predictors for Olympic distance triathlon performance: Predictors in amateur triathlon

Objective: Present study examines predictors of the overall race time and disciplines in the Olympic distance triathlon. Methods: Thirty-nine male and six female triathletes were evaluated for anthropometric, physiological, genetic, training, clinical and circadian characteristics. Body composition, maximum capacity for oxygen uptake (V˙O2max), maximum aerobic velocity (MAV), anaerobic threshold (AT), triathlon experience (TE) and XX genotype for α-actinin 3 affected total race time (p<0.05). Results: Total race time can be predicted by MAV (ß = -0.430, t = -3.225, p = 0.003), TE (ß = -0.378, t = -3.605, p = 0.001), and percentage of lean mass (%LM) (ß = -0.332, t = -2.503, p = 0.017). Swimming can be predicted by MAV (ß = -0.403, t = -3.239, p = 0.002), TE (ß = -0.339, t = -2.876, p = 0.007), and AT%V˙O2max (ß = 0.281, t = 2.278, p = 0.028). Cycling can be predicted by MAV (ß = -0.341, t = -2.333, p = 0.025), TE (ß = -0.363, t = -3.172, p = 0.003), and %LM (ß = -0.326, t = -2.265, p = 0.029). In running split, MAV (ß = -0.768, t = -6.222, p < 0.001) was the only parameter present in the best multiple linear regression model. Conclusion: The most important variables in multiple regression models for estimating performance were MAV, TE, AT and %LM.

The influence of antidepressant and psychotherapy treatment adherence on future work leaves for patients with major depressive disorder

BackgroundDepression is the greatest contributor to worldwide disability. The purpose of this study was to understand the influence of antidepressant and psychotherapy treatment adherence on future work leaves for patients with major depressive disorder.MethodsPatients with a newly diagnosed major depressive disorder (n = 26,256) were identified in IBM® Watson™ MarketScan® medical and disability claims databases. Antidepressant and psychotherapy adherence metrics were evaluated in the acute phase of treatment, defined as the 114 days following the depression diagnosis. Multiple variable Cox proportional hazards regression models evaluated the influence of antidepressant and/or psychotherapy adherence on future injury or illness work leaves.ResultsThe majority of work leaves in the 2-year follow-up period occurred in the acute phase of treatment (71.2%). Among patients without a work leave in the acute phase and who received antidepressants and/or psychotherapy (n = 19,994), those who were adherent to antidepressant or psychotherapy treatment in the acute phase had a 16% (HR = 0.84, 95% CI = 0.77–0.91) reduced risk of a future work leave compared to treatment non-adherent patients. Patients who were non-adherent or adherent to antidepressant treatment had a 22% (HR = 1.22, 95% CI = 1.11–1.35) and 13% (HR = 1.13, 95% CI = 1.01–1.27) greater risk of a future work leave, respectively, than patients not receiving antidepressant treatment. Conversely, patients who were non-adherent or adherent to psychotherapy treatment had a 9% (HR = 0.91, 95% CI = 0.81–1.02) and 28% (HR = 0.72, 95% CI = 0.64–0.82) reduced risk of a future work leave, respectively, than patients not receiving psychotherapy treatment.ConclusionsThis analysis suggests that treatment adherence may reduce the likelihood of a future work leave for patients with newly diagnosed major depressive disorder. Psychotherapy appears more effective than antidepressants in reducing the risk of a future work leave.

Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK

In this article, monthly and yearly electricity consumption predictions for the German power market were calculated using the multiple variable regression model. This model accounts for several factors that are often neglected when forecasting electricity demand in practice, in particular the role of the higher efficiency of electricity usage from year to year. The analysis performed in this paper helps to explain why no growth in power consumption has been observed in Germany during the last decade. It shows that the electricity efficiency usage dataset is a relevant input for the model, which mitigates the combined impact of other factors on the final electricity consumption. The electricity demand forecasting model presented in this article was built in the year 2013 with forecasts for the future years’ electricity demand in Germany provided until 2020. These forecasts and related findings are also evaluated in this article.

Climatic variation in Africa and Europe has combined effects on timing of spring migration in a long-distance migrant Willow Warbler Phylloscopus trochilus.

BackgroundThe arrival of many species of migrant passerine in the European spring has shifted earlier over recent decades, attributed to climate change and rising temperatures in Europe and west Africa. Few studies have shown the effects of climate change in both hemispheres though many long-distance migrants use wintering grounds which span Africa. The migrants’ arrival in Europe thus potentially reflects a combination of the conditions they experience across Africa. We examine if the timing of spring migration of a long-distance migrant, the Willow Warbler, is related to large-scale climate indices across Africa and Europe.MethodsUsing data from daily mistnetting from 1 April to 15 May in 1982–2017 at Bukowo (Poland, Baltic Sea coast), we developed an Annual Anomaly metric (AA, in days) to estimate how early or late Willow Warblers arrive each spring in relation to their multi-year average pattern. The Willow Warblers’ spring passage advanced by 5.4 days over the 36 years. We modelled AA using 14 potential explanatory variables in multiple regression models. The variables were the calendar year and 13 large-scale indices of climate in Africa and Europe averaged over biologically meaningful periods of two to four months during the year before spring migration.ResultsThe best model explained 59% of the variation in AA with seven variables: Northern Atlantic Oscillation (two periods), Indian Ocean Dipole, Southern Oscillation Index, Sahel Precipitation Anomaly, Scandinavian Index and local mean temperatures. The study also confirmed that a long-term trend for Willow Warblers to arrive earlier in spring continued up to 2017.DiscussionOur results suggest that the timing of Willow Warbler spring migration at the Baltic Sea coast is related to a summation of the ecological conditions they had encountered over the previous year during breeding, migration south, wintering in Africa and migration north. We suggest these large-scale climate indices reflect ecological drivers for phenological changes in species with complex migration patterns and discuss the ways in which each of the seven climate indices could be related to spring migration at the Baltic Sea coast.