Covariates In Model Research Articles

Modelling changes in the pharmacokinetics of tacrolimus during pregnancy after kidney transplantation: A retrospective cohort study.

Pregnancy after kidney transplantation is realistic but immunosuppressants should be continued to prevent rejection. Tacrolimus is safe during pregnancy and is routinely dosed based on whole-blood predose concentrations. However, maintaining these concentrations is complicated as physiological changes during pregnancy affect tacrolimus pharmacokinetics. The aim of this study was to describe tacrolimus pharmacokinetics throughout pregnancy and explain the changes by investigating covariates in a population pharmacokinetic model. Data of pregnant women using a twice-daily tacrolimus formulation following kidney transplantation were retrospectively collected from 6 months before conception, throughout gestation and up to 6 months postpartum. Pharmacokinetic analysis was performed using nonlinear mixed effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates. The final model was evaluated using goodness-of-fit plots, visual predictive checks and a bootstrap analysis. A total of 260 whole-blood tacrolimus predose concentrations from 14 pregnant kidney transplant recipients were included. Clearance increased during pregnancy from 34.5 to 41.7L/h, by 15, 19 and 21% in the first, second and third trimester, respectively, compared to prior to pregnancy. This indicates a required increase in the tacrolimus dose by the same percentage to maintain the prepregnancy concentration. Haematocrit and gestational age were negatively correlated with tacrolimus clearance (P≤ 0.01), explaining 18% of interindividual and 85% of interoccasion variability in oral clearance. Tacrolimus clearance increases during pregnancy, resulting in decreased exposure to tacrolimus, which is explained by gestational age and haematocrit. To maintain prepregnancy target whole-blood tacrolimus predose concentrations during pregnancy, increasing the dose is required.

Soil Moisture Estimation by Assimilating In‐Situ and SMAP Surface Soil Moisture Using Unscented Weighted Ensemble Kalman Filter

AbstractHighly accurate soil moisture information is necessary to understand land surface processes. However, observational techniques do not produce adequately accurate spatial‐temporal continuous regional soil moisture data. The data assimilation method can be used to improve the soil moisture estimations by merging multi‐source observed data, but its performance is affected by error covariance and the quality of assimilated data. We designed eight numerical experiments to analyze how to improve the filter performance through soil moisture assimilation using the unscented weighted ensemble Kalman filter (UWEnKF) and 1‐D Richards equation at Maqu and Erlinghu (ELH) observational sites in the source region of Yellow River (SRYR), China. The experimental results show that the filter performance improves as the quality of assimilated data increases in the soil moisture assimilation experiment when assimilating in‐situ surface soil moisture (SSM) observations, SMAP SSM data and downscaled SMAP SSM data. In other aspects, filter performance is readily affected by model and observation error covariances in soil moisture assimilation. If the SMAP SSM data are taken to be perfect (i.e., small bias), UWEnKF performs differently between different sites because of the underestimation or overestimation of SMAP SSM and model simulations compared to the in‐situ observations. Additionally, different soil moisture assimilation results can be obtained with different initial values at the beginning of the assimilation period. Overall, filter performance can be improved primarily by improving the quality of assimilated data (e.g., downscaling the remote sensing data), and by creating a reasonable and effective method for determining error covariance.

Is coronary artery calcium an independent risk factor for white matter hyperintensity?

BackgroundCardiovascular diseases have been considered the primary cause of disability and death worldwide. Coronary artery calcium (CAC) is an important indicator of the severity of coronary atherosclerosis. This study is aimed to investigate the relationship between CAC and white matter hyperintensity (WMH) in the context of diagnostic utility.MethodsA retrospective analysis was conducted on 342 patients with a diagnosis of WMH on magnetic resonance images (MRI) who also underwent chest computed tomography (CT) scans. WMH volumes were automatically measured using a lesion prediction algorithm. Subjects were divided into four groups based on the CAC score obtained from chest CT scans. A multilevel mixed-effects linear regression model considering conventional vascular risk factors assessed the association between total WMH volume and CAC score.ResultsOverall, participants with coronary artery calcium (CAC score > 0) had larger WMH volumes than those without calcium (CAC score = 0), and WMH volumes were statistically different between the four CAC score groups, with increasing CAC scores, the volume of WMH significantly increased. In the linear regression model 1 of the high CAC score group, for every 1% increase in CAC score, the WMH volume increases by 2.96%. After including other covariates in model 2 and model 3, the β coefficient in the high CAC group remains higher than in the low and medium CAC score groups.ConclusionIn elderly adults, the presence and severity of CAC is related to an increase in WMH volume. Our findings suggest an association between two different vascular bed diseases in addition to traditional vascular risk factors, possibly indicating a comorbid mechanism.

Immigration bias among medical students: a randomized controlled trial.

Racial bias is found in both physicians and medical students. Immigrants in many parts of the world face challenges similar to racial minorities. Identification of immigrants might however be more subtle than identification by race, and currently, no data are available on a possible bias against the large minority group of migrants in Europe. Randomized control trial. Second-year medical students were randomized into four groups to watch a video of either a male or female patient with pain, with or without immigrant status. Students were asked whether they would administer pain medication (primary outcome). Immigrant status, patient's gender, student's gender, age, and language skills were covariates in a logistic regression model. Secondary outcomes included pain medication potency and the student's rating of the patient's pain intensity. We recruited 607 students [337 females (56%), 387 (64%) between 18 and 22 years old]. Analgesia was administered in 95% (n = 576). Immigrant status was not associated with the probability of receiving pain medication [95 vs. 95%, odds ratio (OR) 0.81, 95% confidence interval (CI) 0.39-1.70, P = 0.58]. Immigrants received high-potency analgesia less often (26 vs. 33%, OR 0.69, 95% CI 0.50-0.96, P = 0.03). Female students administered pain medication more frequently (96 vs. 93%, OR 2.29, 95% CI 1.05-5.02, P = 0.04), and rated the patients' pain higher (mean numeric rating scale 7.7, SD 0.9 vs. 7.4, SD 1.0, OR 1.36, 95% CI 1.16-1.60, P < 0.001). Medical students showed no immigration bias with regard to administering pain medication but were less likely to choose high-potency analgesia in immigrants. We also found a gender difference in pain management. These results demonstrate the importance of including knowledge about immigration bias in medical training.

Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia.

High-dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)-hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting. We compared SL-hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically-identified covariates, using a cohort study comparing real-world bleeding rates between ibrutinib- and bendamustine-rituximab (BR)-treated individuals with chronic lymphocytic leukemia in Optum's de-identified Clinformatics® Data Mart commercial claims database (2013-2020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically-identified covariate balance (absolute standardized difference [ASD] thresholds of <0.10 and <0.05) and outcome HR precision (95% confidence intervals). We identified 2423 ibrutinib- and 1102 BR-treated individuals. Including >200 empirically-identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL-hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL-hdPS than with individual hdPS models. In a real-world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.

A spatio-temporal analysis of the role of climatic drivers influencing extreme precipitation events in a Costa Rican basin

Regional patterns of rainfall are changing around the world, putting pressure on social, economic, and environmental systems, and changing the outlook for food, water, and health security. This is the case in the Tempisque-Bebedero River Basin (TBRB), located on the Pacific slope of northwest Costa Rica, which relies heavily on water resources for tourism, agriculture, ranching, and fish farming. In this study, we use non-stationary extreme value analysis to characterize the climatic drivers of extreme precipitation in the TBRB. Daily extreme precipitation events at stations across the TBRB are selected using a peak-above-threshold approach, and interannual climate variability is included as time-varying covariates in a non-stationary point process model, using three regional climate indices: the Caribbean Low-Level Jet (CLLJ) index, the Oceanic Niño Index (ONI), representative of the El Niño Southern Oscillation (ENSO), and the Atlantic Multidecadal Oscillation (AMO) index. We find that the magnitude and frequency of precipitation extremes in the basin are driven by the CLLJ and the AMO. The ONI is only a significant driver of extreme precipitation in the northwestern region of the basin. This is likely because ENSO controls the strength of the CLLJ, thus the dominant impact of ENSO on precipitation in the TBRB may be incorporated within the CLLJ component of our model.

Further exploration of the effects of time-varying covariate in growth mixture models with nonlinear trajectories.

Growth mixture modeling (GMM) is an analytical tool for identifying multiple unobserved sub-populations in longitudinal processes. In particular, it describes change patterns within each latent sub-population and investigates between-individual differences in within-individual change for each sub-group. A key research interest in using GMMs is examining how covariates influence the heterogeneity in change patterns. Liu & Perera (2022b) extended mixture-of-experts (MoE) models, which primarily focus on time-invariant covariates, to allow covariates to account for both within-group and between-group differences and investigate the heterogeneity in nonlinear trajectories. The present study further extends Liu & Perera, 2022b by examining the effects of time-varying covariates (TVCs) on trajectory heterogeneity. Specifically, we propose methods to decompose a TVC into an initial trait (the baseline value of the TVC) and a set of temporal states (interval-specific slopes or changes of the TVC). The initial trait is allowed to account for within-group differences in growth factors of trajectories (i.e., baseline effect), while the temporal states are allowed to impact observed values of a longitudinal process (i.e., temporal effects). We evaluate the proposed models using a simulation study and real-world data analysis. The simulation study demonstrates that the proposed models are capable of separating trajectories into several clusters and generally producing unbiased and accurate estimates with target coverage probabilities. The proposed models reveal the heterogeneity in initial trait and temporal states of reading ability across latent classes of students' mathematics performance. Additionally, the baseline and temporal effects on mathematics development of reading ability are also heterogeneous across the clusters of students.

Estimation and consequences of direct-maternal genetic and environmental covariances in models for genetic evaluation in broilers

BackgroundMaternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values.ResultsThis model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam–offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance.ConclusionsIn this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values.

Embracing Eco-Digitalization and Green Finance Policies for Sustainable Environment: Do the Engagements of Multinational Corporations Make or Mar the Target for Selected MENA Countries?

It is an undeniable fact that the digital economy and green financing are persistently gaining global attention as effective tools for achieving rapid economic growth and development. However, the environmental effects of these indicators are just evolving, leaving the research community with insufficient policies for harnessing the much-anticipated sustainability agenda. Hence, this research provides the first empirical evidence of the impacts of eco-digitalization and green financing on the sustainable environment in selected Middle East and North African countries from 1995 to 2019. The empirical model considers the roles of multinational corporations, renewable and nonrenewable energy, economic growth, and population growth as covariates models based on the STIRPAT framework. The stated hypotheses are verified based on Cross-Sectionally Augmented Mean Group, Mean Group, Common Correlated Mean Group, and Panel Quantile Regression. Findings show that from eco-digitalization, green financing, and renewable energy drive sustainable environment agenda. On the flip side, nonrenewable energy, economic growth, and population growth largely deter delivering on the blueprint. The estimated results are corroborated by findings from panel quantiles regression. Furthermore, the panel causality uncovers the existence of bidirectional and unidirectional causality in the estimated model. Policy insights that support the pathways toward sustainability in MENA economies are suggested based on the findings.

Issues in Implementing Regression Calibration Analyses.

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Humoral Immune Response to Messenger RNA Coronavirus Disease 2019 Vaccination Among Children Aged 5-11 Years in a Multisite Prospective Cohort Study, September 2021-September 2022.

The PROTECT study is a longitudinal cohort study initiated in July 2021 with weekly testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 4 states: Arizona, Florida, exas, and Utah. This study aims to examine vaccine-elicited antibody response against postvaccination SARS-CoV-2 infections. Children aged 5-11 years had serum collected 14-59 days after their second dose of monovalent Pfizer-BioNTech coronavirus disease 2019 messenger RNA vaccine. Vaccine-elicited antibodies were measured using the area under the curve (AUC) and end-point titer using enzyme-linked immunosorbent assay (receptor-binding domain [RBD] and S2) and surrogate neutralization assays against ancestral (WA1) and Omicron (BA.2). 79 vaccinated participants (33 [41.7%] female; median age, 8.8 years [standard deviation, 1.9 years]), 48 (60.8%) were from Tucson, Arizona; 64 (81.0%) were non-Hispanic white; 63 (80.8%) attended school in person; 68 (86.1%) did not have any chronic conditions; and 47 (59.5%) were infected after vaccination. Uninfected children had higher AUCs against WA1 (P = .009) and Omicron (P = .02). The geometric mean and surrogate neutralization titer above the limit of detection was 346.0 for WA1 and 39.7 for Omicron, an 8.7-fold decrease (P < .001). After adjustment of covariates in the WA1-specific model, we observed a 47% reduction in the odds of postvaccination infection for every standard deviation increase in RBD AUC (aOR, 0.53 [95% confidence interval, .29-.97) and a 69% reduction in the odds of infection for every 3-fold increase in RBD end titer (0.31 [.06-1.57]). Children with higher antibody levels experienced a lower incidence of postvaccination SARS-CoV-2 infection.

Predictors of Viral Load and Medication Adherence Among HIV-Positive Adults Under Treatment at Felege-Hiwot Comprehensive Specialized Hospital, North-West, Ethiopia.

Maintaining good medication adherence and decreasing viral load in patients living with HIV/AIDS are critical to ensuring antiretroviral therapy's preventive and therapeutic benefits. The main objective of this study was to assess the predictors of viral load and medication adherence among HIV-positive adults under treatment at Felege Hiwot Comprehensive Specialized Hospital (FHCSH). A retrospective cohort study design was conducted from a random sample of 281 adult HIV-infected patients under treatment at FHCSH in northwest Ethiopia from June 2017 to June 2021. Separate GLMM was used in analysis of viral load and medication adherence, and joint mode was applied to fit those two outcomes jointly. The potential correlation of those two outcomes was linked by random intercepts. Information criteria (AIC and BIC) were used for model comparison and covariance structure selection. The small standard error of significant predictors and significant correlation between viral load and medication adherence over time provide evidence for joint model selection. The correlation between viral load and medication adherence was -0.7688 (P-value=< 0.05), which indicates that the decrement of viral load tends to increase good medication adherence. Patient substance use, visit time, baseline CD4 cell, baseline hemoglobin, and the interaction of visit time by substance use were significantly associated with viral load and medication adherence jointly. The study revealed that substance user adult patients, patients with low baseline CD4 cells, and patients with low baseline hemoglobin were with high viral loads and poor medication adherence. Therefore, health officials and other concerned bodies should give special attention and high intervention to patients with low baseline hemoglobin; poor adherence and low baseline CD4 cell count.

A calibration method to stabilize estimation with missing data

AbstractThe augmented inverse weighting (AIW) estimator is commonly used to estimate the marginal mean of an outcome because of its doubly robust property. However, the AIW estimator can be severely biased if both the propensity score (PS) and the outcome regression (OR) models are misspecified. One possible reason is that misspecification of the PS or OR model yields extreme values in these models, which can have a great influence on the marginal mean estimate. In this article, we propose a calibrated AIW estimator for the marginal mean, which can control the influence of these extreme values and provide a stable marginal mean estimator. The proposed estimator also enjoys the doubly robust property. We also extend this method to handle high‐dimensional covariates in PS and OR models. Asymptotic results are also developed. Extensive simulation studies show that the proposed method performs better in most cases than existing approaches by providing a more stable estimate. We apply this method to an AIDS clinical trial study.

Refining analytic approximation based estimation of mixed multinomial probit models by parameter selection

Applying analytic approximations for computing multivariate normal cumulative distribution functions has led to a substantial improvement in the estimability of mixed multinomial probit models, both in terms of accuracy and especially in terms of computation time. This paper makes a contribution by presenting a possible way to improve the accuracy of estimating mixed multinomial probit model covariances based on the idea of parameter selection using cross-validation. Comparisons to the MACML approach indicate that the proposed parameter selection approach is able to recover covariance parameters more accurately, even when there is a moderate degree of independence between the random coefficients. The approach also estimates parameters efficiently, with standard errors tending to be smaller than those of the MACML approach, which can be observed by means of a real data case.

Effect of pre- and postpartum supplementation of a pure glycerol product to dairy cows on feed intake, metabolic markers, and milk yield and components

The objective of this study was to quantify the effects of supplementing transition dairy cows with a low inclusion dry glycerol product in the pre- and postpartum periods on feed intake, metabolic markers, and milk yield and components. Multiparous Holstein dairy cows (n = 60) were enrolled in a 2-by-2 factorial design study. Starting 21 d before expected parturition, cows individually received a dry cow diet with (1) 250 g/d glycerol product supplementation [66% pure glycerol (United States Pharmacopeia grade); GLY], or (2) no supplementation (CON) mixed to their total mixed ration. After parturition, cows, again, were individually assigned to either GLY, or (2) no supplementation (CON) to their partial mixed ration for the first 21 d in milk (DIM). Cows were milked by an automated milking system and offered a target of 5.4 kg DM/d pellet (23% of target total dry matter intake, DMI) in the automated milking system and followed for 42 d into lactation. Blood samples were collected 6.3 ± 3.47 d before calving for all blood measures and 3, 7, 10, and 14 DIM for analysis of glucose and β-hydroxybutyrate, as well as 3 and 7 DIM for nonesterified fatty acids (NEFA) and haptoglobin. Initial dry cow body weight (BW), calf birth weight, previous 305-d milk, and month of parturition were used as covariates in the statistical model. Cows supplemented with GLY prepartum lost less BW and consumed more DMI pre- and postpartum, as well as had lower postpartum blood β-hydroxybutyrate and NEFA concentrations compared with those fed the CON treatment prepartum. Cows supplemented with GLY postpartum had lesser DMI in the first 42 DIM than cows fed CON postpartum, but also had reduced blood NEFA concentrations, odds of a high haptoglobin test, odds of a low blood glucose test, and lesser preformed fatty acid concentrations and yields in their milk. Cows supplemented glycerol both pre- and postpartum lost the least total BW from -21 to 21 DIM. No treatment effects were detected for milk yield; however, cows receiving GLY postpartum had lower milk fat. Overall, glycerol supplementation during the transition period, particularly during the 21 d before calving, was associated with markers of improved metabolic status.

Could Airborne Geophysical Data Be Used to Improve Predictive Modeling of Agronomic Soil Properties in Tropical Hillslope Area?

Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (μR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0–30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model’s performance was evaluated through the R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R2 values ranging from 0.15 to 0.23), as well as the SVM model (R2 values ranging from 0.08 to 0.23) when compared to RF (R2 values ranging from 0.10 to 0.20) and SVM (R2 values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping.

Association of underlying untreated cardiovascular risk factors with mortality and functional outcome in ischaemic stroke patients

ObjectivesThe study aimed to determine the prevalence of untreated pharmacologically modifiable cardiovascular risk factors (PMRF) among patients with ischaemic stroke and the association with in-hospital mortality and functional outcome. MethodsWe analysed the data from ischaemic stroke cases admitted to Sarawak General Hospital between June 2013 and June 2021. We matched the underlying PMRFs with prior medications and categorised them as treated, untreated, or no PMRF. We calculated the prevalence and assessed the association between untreated PMRFs and in-hospital mortality or favourable functional outcome (FFO) at discharge, which was adjusted for age, sex, and other covariates in multivariable models. ResultsWe included 1963 patients [65.4% male, 59.8 (SD 13.4) years]; 43.8% who had at least one untreated PMRF had triple the odds of in-hospital mortality [adjusted OR (aOR) 2.86, (95%CI 1.44, 5.70)], whereas 30.2% who had all PMRFs treated showed no significant association. Untreated hypertension [aOR 2.19 (95%CI 1.21, 3.98)], treated [aOR 3.02 (95%CI 1.32, 6.92)], and untreated atrial fibrillation [aOR 1.89 (95%CI 1.18, 3.03)] were significantly associated with more in-hospital death, whereas treated prior stroke was associated with fewer in-hospital death [aOR 0.31 (95%CI 0.11, 0.84)]. Treated diabetes [aOR 0.66 (95%CI 0.49, 0.88)] and untreated prior stroke [aOR 0.53 (95%CI 0.33, 0.83)] were associated with fewer FFO. ConclusionThe high prevalence of untreated underlying PMRFs was significantly associated with poorer outcomes among Malaysian patients with ischaemic stroke in Sarawak. Efforts are needed to promote early screening and treatment of cardiovascular risk factors to reduce the burdens and improve stroke outcomes in this region.

A digital teaching model for jurisprudence courses based on a linear regression model

Abstract To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.

Features associated with travel distance for radical cystectomy in Florida: Implications for access to care

IntroductionCharacteristics associated with travel distance for radical cystectomy (RC) remain incompletely defined but are needed to inform efforts to bridge gaps in care. Therefore, we assessed features associated with travel distance for RC in a statewide dataset. MethodsWe identified RC patients in the Florida Inpatient Discharge dataset from 2013 to 2019. Travel distance was estimated using zip code centroids. The primary outcome was travel >50 miles for RC. Secondary outcomes included inpatient mortality, nonhome discharge, and inpatient complications. U.S. County Health Rankings were included as model covariates. Mixed effects logistic regression models accounting for clustering within hospitals were utilized. ResultsWe identified 4,209 patients, of whom 2,284 (54%) traveled <25 miles, 654 (16%) traveled 25 to 50 miles, and 1271 (30%) traveled >50 miles. Patients who traveled >50 miles primarily lived in central and southwest Florida. Following multivariable adjustment, patients traveling >50 miles were less likely to be Hispanic/Latino (odds ratio [OR] 0.35, 95% CI: 0.23–0.51), and more likely to reside in a county with the lowest health behavior (OR 6.48, 95% CI: 3.81–11.2) and lowest socioeconomic (OR 7.63, 95% CI: 5.30–11.1) rankings compared to those traveling <25 miles (all P < 0.01). Travel distance >50 miles was associated with treatment at a high-volume center and significantly lower risks of inpatient mortality, nonhome discharge, and postoperative complications (all P < 0.02). ConclusionThese data identify characteristics of patients and communities in the state of Florida with potentially impaired access to RC care and can be used to guide outreach efforts designed to improve access to care.

Maternal Diet May Modulate Breast Milk Microbiota-A Case Study in a Group of Colombian Women.

There is increasing evidence that the diet and nutritional status of women during pregnancy and lactation can modulate the microbiota of their milk and, therefore, the microbiota of the infant. An observational, descriptive, and cross-sectional study was carried out in a group of lactating women. Dietary intake during gestation and the first trimester of lactation was evaluated, and the microbiota was analyzed by 16S ribosomal RNA (rRNA) sequencing using the Illumina platform. Globally, Streptococcus spp. (32%), Staphylococcus spp. (17.3%), Corynebacterium spp. (5.1%) and Veillonella spp. (3.1%) were the predominant bacterial genera. The consumption of simple carbohydrates in gestation (rho = 0.55, p ≤ 0.01) and lactation (rho = 0.50, p ≤ 0.01) were positively correlated with Enterobacter spp. In lactation, a negative correlation was observed between the intake of simple carbohydrates and the genus Bifidobacterium spp. (rho = -0.51 p ≤ 0.01); furthermore, a positive correlation was identified between the intake of folic acid and Akkermansia spp. (rho = 0.47, p ≤ 0.01). Amplicon sequence variants (ASVs) associated with the delivery mode, employment relationship, the baby's gender, birth weight, the Body Mass Index (BMI) of the breastfeeding woman, and gestational weight gain were recovered as covariates in a linear mixed model. The results of this research showed that the maternal nutritional status and diet of women during gestation and lactation could modulate the microbiota of breast milk.