Multivariate Regression Methods Research Articles

Experiences of victimization before resettlement and chronic disease among foreign-born people in the United States

Stressful experiences are common among migrants and may have health implications. With the only US nationally representative data set on migration, the New Immigrant Survey, we used survey-adjusted descriptive and multivariate regression methods to examine whether victimization prior to resettlement was associated with obesity, cardiovascular disease, diabetes, arthritis, cancer, and chronic lung disease. Among foreign-born people who obtained lawful permanent residence in the US in 2003–04, 6.7 per cent reported victimization before arriving in the US. Those who had experienced victimization more often suffered from chronic conditions than people without such experiences: they were 32 per cent more likely to suffer from at least one chronic condition (p < 0.05), especially cancer (4.36, p < 0.05), arthritis (1.77, p < 0.01), and cardiovascular disease (odds ratio 1.32, p < 0.05). These relationships were in part mediated by differences in healthcare access after arriving in the US between those who had experienced victimization and those who had not. Victimization may have consequences for integration and later-life chronic disease.

FORECASTING CHANGE IN THE LEVEL OF FOREST COVERAGE USING THE GLOBAL FOREST WATCH SERVICE AND THE PROGRAMMING AND DATA ANALYSIS LANGUAGE R

The problem of calculating the level of forest cover is considered, including forecasting changes in forest cover in individual forestry. It is stated that the authors previously developed software for calculating forest cover and processing information about forest plantations using the example of the village of Spivakivka in the Izyum district of the Kharkiv region. A comparison of forest cover over a number of years was also made using the Global Forest Watch resource. From this resource, images of Prydonetsk Forestry were taken with conventional designations: areas where new forest plantations are being planted are shown in blue, and areas where cutting is taking place are shown in pink. It is proposed to divide each of the uploaded images of the selected forestry into squares, and then analyze the data for each square. The pink color saturation was calculated and stored in the table. It is noted that forecasting the change in forest stands on the selected site, that is, the change in the percentage of felling, can be done in different ways. First, use regression analysis - apply the regression equation separately to the values of each square, as well as to the entire forestry. Secondly, to form a list of input factors containing indicators on the selected plot in the two previous years and the same indicators on neighboring plots. Thus, the number of factors will be equal to 27: 26 input and 1 output (values on the studied square). Such a forecasting problem can be solved either by the method of multivariate linear regression or by the method of artificial neural networks. The R programming and data analysis language was used to perform calculations using both methods. A script was created that performs calculations by constructing regression lines and an artificial neural network, and also allows determining the best architecture of a neural network and a more effective method of its training for a certain data set. The calculation of the felling dynamics in the entire forestry (the forecast for the last year provides an error of 1 %) and the calculation of the felling dynamics in the selected square (the forecast for the last year provides an error of 3.5 %) are given. After many runs of the script, it was found that the best result is provided by a perceptron with two hidden layers and two neurons in each layer. The results of the calculations indicate a high correlation of the data for determining the percentage of forest that will be cut down in a certain square. Application of this perceptron for forecasting for the last year showed an error of 3 %.

Variable selection for both outcomes and predictors: sparse multivariate principal covariates regression

AbstractDatasets comprised of large sets of both predictor and outcome variables are becoming more widely used in research. In addition to the well-known problems of model complexity and predictor variable selection, predictive modelling with such large data also presents a relatively novel and under-studied challenge of outcome variable selection. Certain outcome variables in the data may not be adequately predicted by the given sets of predictors. In this paper, we propose the method of Sparse Multivariate Principal Covariates Regression that addresses these issues altogether by expanding the Principal Covariates Regression model to incorporate sparsity penalties on both of predictor and outcome variables. Our method is one of the first methods that perform variable selection for both predictors and outcomes simultaneously. Moreover, by relying on summary variables that explain the variance in both predictor and outcome variables, the method offers a sparse and succinct model representation of the data. In a simulation study, the method performed better than methods with similar aims such as sparse Partial Least Squares at prediction of the outcome variables and recovery of the population parameters. Lastly, we administered the method on an empirical dataset to illustrate its application in practice.

Theoretical analysis of using multiple borehole heat exchangers for production of heating and cooling energy in shallow geothermal reservoirs with underground water flow

In this work the feasibility of using the double-U borehole heat exchangers (BHE) in multiple arrangements for production of heating and cooling energy has been investigated.Theoretical case studies are given for a few hypothetical lithological cases corresponding to the lithology that can be found in City of Zagreb. The results show interference between the borehole heat exchangers and limits of use of shallow reservoir with respect to slow convection and loss of accumulated energy in the reservoir. Special emphasis is placed on the description of the groundwater flow determined by the parameters of the shallow geothermal reservoir, such as permeability, water saturation, thermal conductivity, specific heat capacity and soil density, topography, precipitation, etc. Simulations have been performed in FeFlow coupled with Python user-scripts for individual control of the BHE’s, and the surface equipment is modelled using RES2GEO. To assess the accuracy of numerical predictions in simulating thermal responses, FeFlow software was utilized to conduct Thermal Response Tests (TRT) for individual Borehole Heat Exchangers (BHEs). These numerical results were then juxtaposed with empirical TRT data collected from a site near Velika Gorica, a town within the City of Zagreb. For each case simulation is done for one year with one hour resolution. The temperature distribution analysis highlights notable differences between scenarios, where the temperature field with lower permeability experiences significant variations, particularly near the BHE. Here, temperatures can soar to 20 °C during cooling periods and plummet to 4 °C in heating seasons. These fluctuations cause overheating and subcooling of the reservoir, impairing the heat pump’s efficiency and necessitating increased electricity consumption. By using Multivariate linear regression and Multi-layer perceptron regressor method, the main parameter for determination of COP of ground source heat pump. The analysis reveals that the majority of errors fall within the range of ± 5 %, although the MLA prediction method exhibits slightly more frequent errors. Additionally, the R2 scores demonstrate strong performance: the MLA procedure achieves 0.95 for heating and 0.99 for cooling, while the MLP procedure attains 0.99 for both heating and cooling phases.

High M2-TAM Infiltration and STAT3/NF-κB Signaling Pathway as a Predictive Factor for Tumor Progression and Death in Cervical Cancer.

The tumor microenvironment (TME) plays a crucial role in the progression, invasion, and metastasis of cervical carcinoma (CC). Tumor-associated macrophages (TAMs) are significant components of the CC TME, but studies on their correlation with CC progression are still controversial. This study aimed to investigate the relationship between TAM infiltration, the STAT3/NF-κB signaling pathway, and Overall Survival (OS) in CC patients. In a retrospective study, 691 CC patients who had received a definitive histopathologic diagnosis of CC scored by the FIGO staging system and not undergone preoperative treatment were selected from a database. The effect of TAM infiltration on tumor progression biomarkers using Tissue Microarray (TMA) and immunohistochemistry was evaluated. Furthermore, the impact of the expression of these biomarkers and clinical-pathological parameters on recurrence-free (RF) and OS using Kaplan-Meier and multivariable Cox regression methods was also analyzed. High stromal CD163 + 204 + TAMs density and via STAT3 and NF-κB pathways was relevant to the expression of E-cadherin, Vimentin, MMP9, VEGFα, Bcl-2, Ki-67, CD25, MIF, FOXP3, and IL-17 (all p < 0.0001). In addition, elevated TNM staging IV had a strong association correlation with STAT3 and NF-κB pathways (p < 0.0001), CD25 (p < 0.001), VEGFα (p < 0.001), MIF (p < 0.0001), and Ki-67 (p < 0.0001). On the other hand, overall and recurrence survival was shown to be strongly influenced by the expression of SNAIL (HR = 1.52), E-cadherin (HR = 1.78), and Ki-67 (HR = 1.44). M2-TAM and via STAT3/NF-κB pathways had a strong effect on CC tumor progression which reverberated in the severity of clinicopathological findings, becoming an important factor of poor prognosis.

Effects of the subtypes of apolipoprotein E on immune inhibition and prognosis in patients with Hepatocellular Carcinoma

PurposeTo investigate whether prognosis of patients with hepatocellular carcinoma (HCC) is affected by the abundance and subgroups of myeloid-derived suppressor cells (MDSCs) as well as subtypes and expression of apolipoprotein E (apoE).Methods31 HCC patients were divided into three groups according to blood total apoE level for detecting the abundance of immunoregulatory cells by flow cytometry. Tumour tissue microarrays from 360 HCC patients were evaluated about the abundance and subgroups of MDSCs and the expression of apoE2, apoE3, apoE4 by immunofluorescence staining and immunohistochemistry staining. Survival analysis by means of univariate, multivariate COX regression and Kaplan-Meier methods of the 360 patients was performed based on clinical and pathological examinations along with 10 years’ follow-up data.ResultsThe lower apoE group presented higher abundance of MDSCs in the peripheral blood of HCC patients than higher apoE group. The abundance of monocyte-like MDSCs (M-MDSCs) was higher in the apoE low level group than high level group (p = 0.0399). Lower H-score of apoE2 (HR = 6.140, p = 0.00005) and higher H-score of apoE4 (HR = 7.001, p = 0.009) in tumour tissue were significantly associated with shorter overall survival (OS). The higher infiltration of polymorphonuclear granulocyte-like MDSCs (PMN-MDSCs, HR = 3.762, p = 0.000009) and smaller proportion of M-MDSCs of total cells (HR = 0.454, p = 0.006) in tumour tissue were independent risk factors for shorter recurrence-free survival (RFS).ConclusionThe abundance of MDSCs in HCC patients’ plasma negatively correlates with the level of apoE. The expression of apoE4 in HCC tissue indicated a poor prognosis while apoE2 might be a potential protective factor.

Early Fluid Is Less Fluid: Comparing Early Versus Late ICU Resuscitation in Severely Injured Trauma Patients.

The temporal trends of crystalloid resuscitation in severely injured trauma patients after ICU admission are not well characterized. We hypothesized early crystalloid resuscitation was associated with less volume and better outcomes than delaying crystalloid. Retrospective, observational. High-volume level 1 academic trauma center. Adult trauma patients admitted to the ICU with emergency department serum lactate greater than or equal to 4 mmol/dL, elevated lactate (≥ 2 mmol/L) at ICU admission, and normal lactate by 48 hours. None. For the 333 subjects, we analyzed patient and injury characteristics and the first 48 hours of ICU course. Receipt of greater than or equal to 500 mL/hr of crystalloid in the first 6 hours of ICU admission was used to distinguish early vs. late resuscitation. Outcomes included ICU length of stay (LOS), ventilator days, and acute kidney injury (AKI). Unadjusted and multivariable regression methods were used to compare early resuscitation vs. late resuscitation. Compared with the early resuscitation group, the late resuscitation group received more volume by 48 hours (5.5 vs. 4.1 L; p ≤ 0.001), had longer ICU LOS (9 vs. 5 d; p ≤ 0.001), more ventilator days (5 vs. 2 d; p ≤ 0.001), and higher occurrence rate of AKI (38% vs. 11%; p ≤ 0.001). On multivariable regression, late resuscitation remained associated with longer ICU LOS and ventilator days and higher odds of AKI. Delaying resuscitation is associated with both higher volumes of crystalloid by 48 hours and worse outcomes compared with early resuscitation. Judicious crystalloid given early in ICU admission could improve outcomes in the severely injured.

Prognostic significance of hemoglobin, albumin, lymphocyte, and platelet (HALP) score in breast cancer: a propensity score-matching study

BackgroundThe hemoglobin-albumin-lymphocyte-platelet (HALP) score functions as a comprehensive index that assesses the systemic inflammatory response, nutritional, and immune status. This study aimed to explore the relationship between preoperative HALP score and the prognosis of BC patients and to develop predictive nomograms.MethodsClinicopathological data were collected for BC patients who underwent mastectomy between December 2010 and April 2014 from Sun Yat-sen University Cancer Center. The optimal cutoff value for HALP was determined by maximally selected rank statistics for overall survival data. Propensity score matching (PSM) was applied to develop comparable cohorts of high-HALP group and low-HALP group. Kaplan–Meier curves and Cox regression analyses were performed to determine the impact of HALP on BC patients. Prognostic nomograms were developed based on the multivariate Cox regression method. Then, the concordance index (C-index), calibration plots, and decision curves analysis (DCA) were applied to evaluate the prognostic performance of the nomograms.ResultsA total of 1,856 patients were included as the primary cohort, and 1,470 patients were matched and considered as the PSM cohort. In the primary cohort, the 5-year overall survival (OS) and progression-free survival (PFS) rates for high-HALP group (≥ 47.89) and low-HALP group (< 47.89) were 94.4% vs. 91.0% (P = 0.005) and 87.8% vs. 82.1% (P = 0.005), respectively. Similar results were observed in PSM cohort (5-year OS, 94.3% vs. 90.8%, P = 0.015; 5-year PFS, 87.5% vs. 83.2%, P = 0.036). Notably, multivariate Cox regression analysis in the PSM cohort showed that HALP could independently predict BC patient prognosis in both OS (HR: 0.596, 95%CI [0.405–0.875], P = 0.008) and PFS (HR: 0.707, 95%CI [0.538–0.930], P = 0.013). OS and PFS nomograms showed excellent predictive performance with the C-indexes of 0.783 and 0.720, respectively. The calibration plots and DCA also indicated the good predictability of the nomograms. Finally, subgroup analysis further demonstrated a favorable impact of HALP on both OS and PFS.ConclusionPreoperative HALP score can be used as a reliable independent predictor of OS and PFS in BC patients, and the nomograms may provide a personalized treatment strategy.

Artificial intelligence probabilities scheme for disease prevention data set construction in intelligent smart healthcare scenario

In the face of an aging population, smart healthcare services are now within reach, thanks to the proliferation of high-speed internet and other forms of digital technology. Data problems in smart healthcare, unfortunately, put artificial intelligence in this area to serious limitations. There are several issues, including a lack of standard samples, noisy data interference, and actual data that is missing. A three-stage AI-based data generating strategy is suggested to handle missing datasets, using a small sample dataset obtained from a smart healthcare program community in a specific city: Step one involves generating the dataset's basic attributes using a tree-based generation strategy that takes the original data distribution into account. Step two involves using the Naive Bayes algorithm to create basic indicators of behavioural capability assessment for the samples. Step three builds on stage two and uses a multivariate linear regression method to create evaluation criteria and indicators of high-level behavioural capability. Six problems involving multiple classifications and two tasks using multiple labels are implemented using various neural network-based training strategies on the obtained data to assess the usefulness of the dataset for downstream tasks. To ensure that the data collected is genuine and useful, the experimental data must be analysed and expert knowledge must be included.

Proportion and Potential Risk Factors of Poor Glycemic Control among Patients with Type 2 Diabetes Mellitus: Experience of a Tertiary Center in Baghdad, Iraq, 2020

Background: Diabetes Mellitus is the most prevalent metabolic disorder worldwide. Effective diabetes self-management and keeping the Glycosylated hemoglobin level within the normal range could decrease the burden on the health system by reducing hospital admissions and diabetic complications, lowering the financial strain on the health system. Objective: To recognize the potential risk factors of poor glycemic control in patients having type 2 Diabetes Mellitus in Baghdad, Iraq. Patients and Methods: This cross-sectional study was conducted in the Diabetes and Endocrine Center at Al-Kindy Teaching Hospital, Baghdad, Iraq, from June to November 2020. The total number of the study's participants was 234. Based on the cutoff point of glycosylated hemoglobin of 7, the patients with glycosylated hemoglobin of ≥7 were considered uncontrolled, and those with glycosylated hemoglobin of &lt;7 were considered controlled. Results: The proportion of cases with uncontrolled diabetes was 68.4% (160). The remaining 74 patients had controlled diabetes. Age, sex, marital status, and employment status were not significantly associated with the control status (P&gt;0.05). The binary analysis showed a significant association observed between the control status and level of education (P=0.001), income (P=0.001), presence of comorbidities (P=0.028), positive family history (P=0.03), dyslipidemia (P=0.001), cholesterol level (P=0.002), high triglyceride level (P=0.001), and low-density lipoprotein-cholesterol level (P=0.025). The smoking status, body mass index, and high-density lipoprotein-cholesterol level were not significantly associated with the control status (P&gt;0.05). The disease characteristics, including the disease duration, fasting blood glucose, type of medication, self-monitoring, healthy diet, physical activity, and medication adherence, were significant factors (P&lt;0.001). The multivariate regression method showed that dyslipidemia, FBS≥130, physical inactivity, and poor medication adherence were significant predictors for uncontrolled DM (P-value = 0.03, 0.001, 0.03, and 0.043, respectively). Conclusion: The most important potentially modifiable risk factors for poor diabetes control were dyslipidemia, physical inactivity, and poor adherence to the management protocol. Enhancing the education of patients and their healthcare providers on these factors is crucial to improving the patients’ control status.

Disproportionate increase in cannabis use among people with serious psychological distress and associations with psychiatric service use in the United States, 2009–2019

BackgroundCannabis use is on the rise, but it is unclear how use is changing among individuals with serious psychological distress (SPD) compared to the general population as well as what associations this may have with mental health service use. MethodsRetrospective cohort study using the National Survey on Drug Use and Health (NSDUH) 2009–19 public use files of 447,228 adults aged ≥ 18 years. Multivariable logistic regression and predictive margin methods were used to estimate linear time trends in any and greater-than-weekly levels of cannabis use by year and SPD status and rates of psychiatric hospitalization and outpatient mental health care.Findings: Rates of any and weekly-plus cannabis use increased similarly among individuals with SPD compared to those without from 200 to 2014 but more rapidly in SPD every year from 2015 to 2019 (p < 0.001). Among individuals with SPD, no use was associated with a 4.2 % probability of psychiatric hospitalization, significantly less than less-than-weekly (5.0 %, p = 0.037) and weekly-plus cannabis use (5.1 %, p = 0.028). For outpatient mental health care, no use was associated with a 27.4 % probability (95 % CI 26.7–28.1 %) of any outpatient care, significantly less than less than weekly use (32.6 % probability, p < 0.001) and weekly-plus use (29.9 % probability, p = 0.01). ConclusionsCannabis use is increasing more rapidly among individuals with SPD than the general population, and is associated with increased rates of psychiatric hospitalization and outpatient service use. These findings can inform policy makers looking to tailor regulations on advertising for cannabis and develop public health messaging on cannabis use by people with mental illness.

Theoretical Studies on the Insertion Reaction of Polar Olefinic Monomers Mediated by a Scandium Complex

This study aimed to investigate the insertion reaction of the polar monomers mediated by the cationic rare earth metal complex [(C5H5)Sc(NMe2CH2C6H4-o)]+ utilizing a combination of density functional theory (DFT) calculations and multivariate linear regression (MLR) methods. The chain initiation step of the insertion reaction could be described by the poisoning effect and the ease of monomer insertion, which could be represented via the DFT-calculated energy difference between σ- and π-coordination complexes (ΔΔE) and insertion energy barrier (ΔG≠), respectively. The results indicate that ΔΔE and ΔG≠ can be predicted by only several descriptors using multiple linear regression methods, with a root mean squared error (RMSE) of less than 2.5 kcal/mol. Furthermore, the qualitative analysis of the MLR models provided effective information on the key factors governing the insertion reaction chain initiation.

Remotely monitored physical activity from older people with cardiac devices associates with physical functioning

IntroductionAccelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning.MethodsThe PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, β = beta coefficient, CI = confidence intervals).ResultsData were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8–195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (β = 0.04, 95% CI 0.01–0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57–0.92, p = 0.01, SF-36 physical functioning: β = 4.60, 95% CI 1.38–7.83, p = 0.005).ConclusionsPA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways.

Multi-objective analysis of solid-state joints: Effect of tool pin shoulder clearance on thick dissimilar aluminum plates

Abstract Friction stir welding (FSW) is a well-established technique developed extensively over the past two decades for various applications. This study investigates the effect of repositioning the tool pin shoulder (0.18 mm) above the surface of a 12 mm thick dissimilar aluminum alloy (AA6063-AA5083) on tensile strength, welding temperature, and micro-hardness. Trials were conducted according to the L9 Taguchi method and further simulated using ABAQUS software. The input parameters spindle speed (SS), tool pin length (TPL), and traversing speed (TS) were each tested at three levels. The impact of these parameters on weld quality was analyzed using ANOVA. Optimization of the process to achieve desired temperature and tensile strength was carried out using grey relational analysis (GRA) for both experimental and simulated results. Mathematical models were generated using the multi-variable regression method (MVRM) and response surface method (RSM). Predicted data from the RSM model were compared to experimental outcomes, revealing a maximum deviation of 8.69% for temperature and 5.17% for tensile strength. Furthermore, the study demonstrated that ABAQUS accurately simulates the FSW process with an accuracy of up to 93.71%.

Effect of smoking on prostate cancer: Results from the National Health and Nutrition Examination Survey 2003-2018 and Mendelian randomization analyses.

The controversial relationship between smoking and prostate cancer (PCa) risk prompted us to conduct a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database and apply Mendelian randomization (MR) analyses in order to clarify the possible causal effect of smoking on PCa risk. Using univariate and multivariate logistic regression methods, a secondary analysis of the pooled 2003-2018 NHANES dataset was performed to explore the association between smoking and PCa risk. Propensity-score matching was used to reduce selection bias. Then, we conducted subsequent MR analysis study to investigate the potential causal effect of smoking on PCa risk, with genetic variants of four exposure factors including the lifetime smoking index, light smoking, smoking initiation, and the amount of smoking per day obtained from genome-wide association studies, and PCa summary statistics obtained from three database populations. Inverse-variance weighting was the primary analytical method, and weighted median and MR-Egger regression were used for sensitivity analyses. The MR results for the three PCa databases were combined using meta-analysis. The study included 16073 NHANES subjects, comprising 554 with PCa and 15519 without PCa. Logistic regression before and after matching did not reveal any significant association. Meta-analysis of the MR results also did not support an association of PCa risk with lifetime smoking index (OR=0.95; 95% CI: 0.83-1.09), light smoking (OR=1.00; 95% CI: 0.95-1.06), smoking initiation (OR=0.99, 95% CI=0.99-1.00), or the amount of smoking per day (OR=1.00; 95% CI: 0.99-1.00) and PCa risk. There was no evidence for an association between smoking and the risk of PCa. Further studies are needed to determine if there are any associations of other forms of smoking with the risk of PCa at different stages.

Statistical Downscaling of Remote Sensing Precipitation Estimates Using MODIS Cloud Properties Data over Northeastern Greece

The aim of this study is to spatially downscale the daily precipitation data from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG), utilizing cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Cloud optical thickness (COT), cloud effective radius (CER), and cloud water path (CWP) are used to statistically downscale IMERG precipitation estimates from 0.1 to 0.01° spatial resolution, using the Multivariate Linear Regression (MLR) and residual correction methods. The downscaled precipitation estimates were subsequently validated using in situ rain gauge measurements. The residual corrected IMERG downscaled precipitation estimates were found to be more accurate than the downscaled predicted precipitation without the implementation of the residual correction algorithm (up to 37%), with a respective decrease of the Root Mean Square Error (RMSE) (up to 75%), Normalized Root Mean Square Error (NRMSE) (up to 79%), and the Percent Bias (PB) (up to 98%). In addition, the final downscaled product after the MLR method implementation with residual correction was better correlated with the rain gauge observations than the initial IMERG product (up to 20%). Thus, the implementation of the MLR method in conjunction with the residual correction algorithm is an efficient tool for downscaling remote sensing products with a coarse spatial resolution.

The effect of political connections on the relationship between managers’ personality traits and corporate social responsibility disclosure

Purpose Corporate social responsibility (CSR) might be among the primary factors ensuring any organization’s survival, and disclosing its related information is very important. This research initially investigates the effect of managers’ behavior characteristics, including overconfidence, myopia and narcissism and corporate political ties on the disclosure of CSR. This study also aims to assess the mediating impact of political connections on the association between managerial personality traits and CSR. Design/methodology/approach The research sample included 129 listed companies on the Tehran Stock Exchange from 2013 to 2020. Behavioral managerers charecteristics. A multivariate regression method with combined data (firm-year) was used to test the research hypotheses. Findings The results show that overconfidence and managerial myopia cause the disclosure of CSR to decrease. Managers’ overconfidence and short-term attitudes lead to a decrease in the level of CSR activities of the companies and their disclosure, respectively, 0.021 and 0.025. However, the existence of narcissism in managers and having political ties by companies may lead to an increase in the disclosure of the CSR, respectively, around 0.089 and 0.02. Further findings also indicate that political connections may motivate narcissistic managers to increase CSR disclosure near 0.037. However, the results document no significant impact of political ties on the relationship between managerial overconfidence and myopia with CSR involvement. Research limitations/implications According to the findings, the authors recommend to stockholders that employing narcissistic managers and improving political connections might be two effective strategies to enhance the level of CSR engagement. One of the critical limitations of the current paper might be its generalizability. As Iran is an emerging and fossil fuel seller country, its institutional settings may significantly differ from those of developed and industrial nations. Thus, the readers of these nations must consider such an important issue. Originality/value For the first time, to the best of the authors’ knowledge, this research has investigated the moderating effect of political ties on the association between management behavioral characteristics and the level of fulfilling CSR by listed companies.

Association of admission serum triglyceride levels with intensive care unit hospitalization rates in acute pancreatitis patients: A retrospective study.

Acute pancreatitis (AP) is a complex and unpredictable condition, of which hypertriglyceridemia (HTG) is the third most prevalent cause. This study aimed to conduct a retrospective analysis of clinical data from hospitalized AP patients to uncover a potential correlation between triglyceride (TG) levels and the necessity for intensive care unit (ICU) admission. This retrospective cohort study utilized the Medical Information Mart for Intensive Care IV 2.2 (MIMIC-IV) critical care dataset, incorporating data from 698 patients with hypertriglyceridemic acute pancreatitis (HTG-AP). The analysis employed the RCS model along with univariate and multivariate logistic regression methods to affirm the association between triglyceride levels and ICU admission. Subgroup analysis was performed to investigate specific populations. The study included 698 patients with AP, 42.41% of whom experienced HTG during hospitalization. RCS analysis revealed a linear association between TG levels and risk of ICU admission (p for nonlinear = .219, p for overall = .009). Multivariate logistic regression analysis indicated an increased risk of ICU admission in the TG range of 1.7-5.65 mmol/L (aOR = 1.83, 95% CI 1.12-2.99, P = .015) and TG >11.3 mmol/L (aOR = 5.69, 95% CI 2.36-13.74, P < .001) compared to the normal group. Similar results were observed across the various subgroups. As triglyceride levels increased, there was a corresponding increase in ICU admissions. Patients within the 1.7 to 5.65 mmol/L and > 11.3 mmol/L triglyceride groups exhibited higher rates of ICU admissions. Moreover, we observed a higher risk of ICU hospitalization even with mild TG elevation.

#1858 Delirium among patients admitted to a Nephrology ward-risk factors and association with short-term outcomes: an observational prospective cohort study

Abstract Background and Aims To date, delirium is considered one of the most frequent neuropsychiatric acute syndromes among hospitalized populations. In general medicine, the risk factors for delirium are well-established but there are limited data concerning the incidence and predictors of delirium in nephrological patients. The aims of the study were (i) to assess the prevalence and the risk factors for delirium in hospitalized patients with acute kidney injury (AKI) and chronic kidney disease (CKD), and (ii) to evaluate the effect of delirium on in-hospital mortality. Method A cohort of 942 patients admitted to the Nephrology Department of a tertiary-care Hospital from January 2023 to December 2023 were enrolled in a prospective observational study. Delirium was diagnosed by a psychiatrist during the hospitalization. The endpoint was defined as hospital death or hospital discharge. Relevant sociodemographic, nephrological- and medical-related clusters were assessed with logistic uni- and multivariable regression methods to evaluate their association with delirium and in-hospital mortality. A two-tailed value &amp;lt;0.05 was considered significant. Results In the studied group, mean age was 62.85 years, 519 (55.09%) patients being males. The prevalence of delirium was 5.41% (51/942 patients). The delirious patients were older (mean 70.04 vs. 62.44 years; P&amp;lt;0.001) and with a longer duration of hospitalization (13.12 ± 7.90 days vs. 10.76 ± 8.92 days; P=0.007). Univariable regression analysis showed that delirium was associated with age (r=0.117, P&amp;lt;0.001), alcohol abuse (r=0.144, P&amp;lt;0.001), history of stroke (r=0.184, P&amp;lt;0.001), vascular dementia (r=0.125, P&amp;lt;0.001), dehydration (r=0.175, P&amp;lt;0.001), sepsis (r=0.144, P&amp;lt;0.001), and acute kidney injury (AKI) stage 2 and 3 (r=0.155, P&amp;lt;0.001). In multivariable regression analysis, the independent predictors of delirium were: age (adjusted odds ratio (OR) 1.029, 95% confidence interval (CI): 1.002–1.056, P=0.034), alcohol abuse (OR=4.728, 95% CI: 1.968-11.359, P=0.001), history of stroke (OR=3.493, 95% CI: 1.849-6.598, P&amp;lt;0.001), and AKI stage 2 and 3 (OR=2.175, 95% CI: 1.152-4.105, P=0.017). Chronic kidney disease was not associated with a higher risk of delirium (OR=0.583, 95% CI: 0.308-1.110, P=0.097). Delirium was associated with increased in-hospital mortality (47.05% vs. no-delirium 14.36%, P&amp;lt;0.001) and was an independent predictor of in-hospital mortality (OR=3.074, 95% CI: 1.630-5.797, P=0.001). Conclusion In this study conducted on patients hospitalized in a nephrology ward age, alcohol abuse, history of stroke, and AKI stage 2 and 3 were independent risk factors for delirium. Delirium significantly increased in-hospital mortality. The findings of the study emphasize the strong connectivity between AKI and acute brain dysfunction.

Developmental trajectory of care dependency in older stroke patients.

Stroke is the leading cause of death in China. Older stroke survivors often have other chronic conditions, not only musculoskeletal deterioration due to age, but also changes in body image that can be brought on by stroke and other diseases, making them unable to take good care of themselves and dependent on others. The degree of dependency affects the rehabilitation progress of stroke survivors and shows dynamic changes that need to be recognized. This study investigates the trajectory of dependency changes in older stroke patients with comorbidities and analyze the influencing factors. Grounded in the Chronic Illness Trajectory Framework (CITF), a longitudinal study was conducted from February 2023 to October 2023, tracking 312 older stroke patients with comorbidities admitted to two tertiary hospitals in Guangzhou. Care dependency levels were assessed using Care Dependency Scale on admission day 5 (T0), at discharge (T1), 1 month post-discharge (T2), and 3 months post-discharge (T3). Growth Mixture Model were utilized to identify trajectory categories, and both univariate analysis and multivariate logistic regression methods were employed to explore factors associated with different developmental trajectories. A total of four developmental trajectories were fitted, C1 (high independence-slow increased group, 52.0%), C2 (moderate independence-rapid increased group, 13.0%), C3 (moderate independence-slow increased group, 25.0%), and C4 (low independence-increased and decreased group, 10.0%). Length of hospital stay, place of residence, level of social support, residual functional impairments, NIHSS score, and BI index independently influence the trajectory categories. There is heterogeneity in care dependency among older stroke patients with comorbidities. Most patients gradually reduce their dependency and become more independent, but others remain dependent for an extended period of time. It is recommended to focus on patients who live in rural areas, have low social support, have high admission NIHSS scores and have residual functional impairment, and provide them with personalized continuity of care and rehabilitation services in order to reduce care dependency and the burden of care, and to improve patients' quality of life.