Effects Of Variables Research Articles

Small Strain Stiffness of Sand‐Rubber Mixtures With Particle Size Disparity Effect

ABSTRACTThis study systematically investigates the small‐strain stiffness of sand‐rubber mixtures, focusing on combined particle disparity—both larger sand with smaller rubber and smaller sand with larger rubber—using the discrete element method. The effectiveness of various state variables in capturing stiffness behavior across different rubber contents and size disparities (SDs) is evaluated. Conventional state variables developed for natural sands, such as void ratio and mechanical void ratio were found to be less effective in describing the small‐strain stiffness characteristics of sand‐rubber mixtures due to distinct properties of rubber. This study then demonstrates that the stiffness contribution of rubber materials could be negligible, emphasizing that particle property disparity is more significant than SD between sand and rubber materials. Therefore, an adapted state variable, considering only active sand particles, shows improved performance for capturing the correlation between small‐strain stiffness with increasing rubber contents, suggesting its potential utility over conventional variables. Additionally, a refined void ratio, including inactive sand particles but excluding rubber, offers a practical alternative for capturing small‐strain stiffness in experimental and engineering practices, aligning with previous experimental observations. These findings underscore the need for developing more effective state variables that accurately reflect the interactions within heterogeneous materials like sand‐rubber mixtures.

Study of spatial spillover effects and threshold characteristics of the influence of urbanization on grain green production efficiency in China under carbon constraints.

Food security is a basic guarantee for maintaining social stability, and improving green and healthy grain production is the way to promote food security. China is currently experiencing rapid urbanization that has an impact on grain production and security, with reduced arable land, environmental pollution, insufficient agricultural population, and inadequate resource allocation. How to deal with the relationship between urbanization and grain production becomes the key to solve this problem. Therefore, this study constructs an evaluation system of grain green production efficiency (GGPE) with non-expected output containing carbon emissions, and uses the super-efficient SBM model to measure the level of GGPE, and constructs a spatial econometric model to examine the spatial correlation and spillover effect of urbanization on GGPE; then constructs a panel threshold model to analyze the nonlinear threshold characteristics between urbanization and GGPE. It was found that (1) from 2000 to 2019, China's GGPE showed an overall upward trend, and the performance of GGPE varied among different provinces. (2) According to the results of the spatial econometric model, China's GGPE shows obvious spatial characteristics, and the level of urbanization not only directly affects regional GGPE but also can indirectly affect neighboring regions' GGPE through spatial spillover effects, and the indirect effects among regions are larger than the direct effects. (3) Spatial threshold effect results, there is a significant non-linear threshold characteristic of the impact of urbanization rate on GGPE. In terms of the influence effects of other variables, the influence of each variable on GGPE is roughly in the same direction as the influence under the spatial spillover effect, but the degree of influence is slightly different.

The Relationship between Electronic Marketing and Consumer Behavior "An Applied Study on a Sample of Customers of Al-Rafidain Bank – Iraq"

The current research aims to understanding the relationship between e-marketing and consumer behavior through study applied to a sample of Rafidain Bank customers, and the current research focuses on surveying the opinions of a sample of customers dealing with Rafidain Bank in three Iraqi governorates (Babil, Karbala, and Najaf), numbering (325) Customers. the researcher examined, through a set of hypotheses, the effects of the independent variable, which is electronic marketing, on consumer behavior, and consequently the effects of the latter on the bank’s profitability. A questionnaire was designed to survey the opinions of the individuals in the aforementioned research sample, according to a scale to be respected the quintet, and these phrases included five axes distributed between the study variables, demographic information of the study sample, elements of electronic marketing, elements supporting electronic marketing, indicators of the efficiency of banking services, and finally consumer behavior. The descriptive analysis resulted in tables that displayed the initial statistical results and analytical results that address the research hypotheses, whether negative or positive. the study reached the following conclusions the most important of which is that The dimensions of consumer behavior are arranged as follows: education, attitudes, perception, motives, and finally, the study proved the existence of a strong relationship between the independent variable and the mediator on the bank's profitability clearly, so the recommendations were confirmed that this relationship should be under the attention of the relevant parties in the upper and middle management and that they should take it into account when working on developing the marketing mix strategies for banking services at Rafidain bank to enhance its pioneering role in banking work at the level of Iraq and the world.

Leisure activity engagement attenuates the risks of cognitive impairment induced by unhealthy plant-based diets: a nationwide cohort study.

We intended to reveal the joint effects between LAE and uPDI on cognition in Chinese older adults. Data were collected from the Chinese Longitudinal Healthy Longevity Survey. In total, 10,617 individuals aged 65 years and above without cognitive impairment or dementia at baseline were enrolled in 2008 and followed up in 2011, 2014, and 2018. The uPDI and the scores of LAE were derived from survey responses, and both were categorized into three groups (low, intermediate, and high). Individuals with a Mini-Mental State Examination (MMSE) score lower than 18 were considered to have cognitive impairment. Cox proportional hazards models were employed to explore the joint association of uPDI and LAE on cognitive impairment, followed by restricted cubic spline (RCS) to observe the effects of the continuous-type variable of uPDI and the scores of LAE on the risk of cognitive impairment. Stratified analysis was applied to examine the association of LAE with cognitive impairment in uPDI groups (high uPDI vs. low uPDI). Compared to participants maintained low scores of LAE and high uPDI, those who maintained high scores of LAE and low uPDI had a decreased risk of cognitive impairment (HR = 0.52, 95% CI, 0.43-0.62). The findings of the stratified analysis demonstrated that the protective effects of high scores of LAE on cognition was pronounced in individuals with low uPDI (HR = 0.61, 95% CI: 0.47-0.79) and those with high uPDI (HR = 0.63, 95% CI: 0.51-0.78). In this cohort study, a higher score of uPDI, which indicated higher intake of salt-preserved vegetables, sugars, and refined grains, was associated with an increased risk of cognitive impairment, whereas this association may be mitigated by regular engagement in leisure activities.

Regression with race-modifiers: towards equity and interpretability.

The pervasive effects of structural racism and racial discrimination are well-established and offer strong evidence that the effects of many important variables on health and life outcomes vary by race. Alarmingly, standard practices for statistical regression analysis introduce racial biases into the estimation and presentation of these race-modified effects. We advocate abundance-based constraints (ABCs) to eliminate these racial biases. ABCs offer a remarkable invariance property: estimates and inference for main effects are nearly unchanged by the inclusion of race-modifiers. Thus, quantitative researchers can estimate race-specific effects "for free"-without sacrificing parameter interpretability, equitability, or statistical efficiency. The benefits extend to prominent statistical learning techniques, especially regularization and selection. We leverage these tools to estimate the joint effects of environmental, social, and other factors on 4th end-of-grade readings scores for students in North Carolina ( n = 27, 638) and identify race-modified effects for racial (residential) isolation, PM 2.5 exposure, and mother's age at birth.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

Assessing E-Government Effectiveness

In this study, we address the pressing need to improve e-government services by conducting a comprehensive evaluation of document management practices within the Ministry of Higher Education in the Kurdistan Region of Iraq. Employing structural equation modeling and regression modeling techniques, we assess the effectiveness of key variables using the six factors (digitalization, utility, reliability, efficiency, user satisfaction, and accessibility) under the e-government assessment model. Our findings reveal that user satisfaction and ease of access are pivotal for the success of e-government services, with digitization and efficiency significantly impacting operational effectiveness. Furthermore, it also concerns that without improving the overall user satisfaction, the level of acceptance and use of e-government remains low. High user satisfaction can make a lot of difference in overall engagement besides improving service delivery among organizations. Through insights specific to the Kurdistan Region, we underscore the importance of balancing technical infrastructure with user experience. Our proposed six-factor model offers valuable guidance for policymakers and practitioners globally, aiming to enhance e-government services.

Examining the adaptive elastic anisotropy of granular materials

Changes of granular material anisotropy with stress are a function of numerous microscale attributes, such as contact network, particle size uniformity and shape. Such influences are often represented with fabric variables. Although multiple choices for fabric exist, the question of which variable best explains why and how granular materials adapt to stress is still open. In this study, the effectiveness of different fabric variables in tracking stress-induced anisotropy is compared. The analysis is restricted to a simple, yet fundamental, range of behavioir: elastic deformation. Both particle-scale simulations and continuum analyses are deployed. Analyses based on the discrete-element method (DEM) reveal that, depending on the contact behaviour, anisotropy may emerge even if the topology of the grain contact network is unchanged. The inspection of these results through a continuum-scale hyperelastic constitutive law further reveals that the computed trends cannot be captured without an evolving fabric variable. A comparison between such fabric and multiple microstructural indicators extracted from DEM simulations suggests that elastic stress-induced anisotropy is primarily a manifestation of changes in the anisotropic particle contact area distribution in response to the current magnitude and orientation of force chains. These results not only offer opportunities to enhance current continuum elastic models for granular materials, but can also be used to design testing strategies quantifying changes in fabric anisotropy through multi-scale measurements.

Suicidal ideation in adolescence: an explanation from three of its associated variables in Bogotá, 2009

Objective: To determine the relationship between hopelessness, loneliness and the level of family health in school-age adolescents with and without suicidal ideation to deepen in the knowledge of this phenomenon from a nursing point of view and to pose promotion and prevention options in Bogotá, Colombia. Method: This was a cross-sectional, comparative, correlational, descriptive and quantitative study. The sample was selected in the period between February and April 2009, through mixed sampling and 482 school-attending 14-17 year-old adolescents were included. Widely recognized and highly qualified psychometric tools were used to measure the variables concerned. Univariate, bivariate and multivariate statistic analysis were used to describe the behavior of each variable, to explore the relationships and contrasts between these variables and to estimate the effect of the independent variables on the presence of suicidal thoughts. This study has followed the ethical considerations necessary for research involving human beings.Results: 20% of the sample used reported the presence of suicidal thoughts or desires. All variables studied influence the presence of suicidal thoughts except age, socioeconomic strata, borough and type of school. However, with the use of logistic regression methods, the variables that best express the presence of suicidal thoughts are: previous suicide attempt, low self-esteem, depression and belonging to an unhealthy family.Conclusions: Data on suicidal ideation and attempts made in this study support the idea of implementing greater preventive efforts within families and schools. Identifying adolescents at risk of suicide or mental health disorders, and build effective prevention and treatment programs is a priority.

Computational Modeling of Bone Fracture Healing Under Different Initial Conditions and Mechanical Load.

Computational model of bone healing can replace animal experiments to study the parameters affecting the bone healing process, thus reducing the damage to experimental animals and saving a lot of time. We propose a computational model for continuous simulation of four phases of bone healing to study the effects of mechanical environmental and biological factors, including initial conditions at the fracture site, mechanical stimulus loading, and vascular growth rate. A finite element model of mechanobiological fracture healing containing several pre-determined variables was developed for bone healing after fracture in sheep, which included many relevant parameters and biological effects during fracture healing, such as the effects of mechanical environment, blood supply level in the local fracture area, cell migration and diffusion, and resorption effects of fracture healing. The effects of several parameters on indices such as Young's modulus of the callus during bone healing were obtained by simulation. The initial geometry of the healing tissue and mechanical loading had the greatest effect on fracture healing, and different preset values were likely to cause delayed or non-healing fractures. Changed initial tissue properties of the healing tissue showed a nonlinear effect on fracture healing rather than a linear delay or advancement. Parameters related to angiogenesis had a greater effect on fracture healing compared to those related to cell migration. This paper quantified the effect of fracture healing pre-determined variables on fracture healing to better understand the application of mechanobiology in fracture healing simulation models and optimization of treatment strategies. The importance of initial conditions and loads on fracture healing has been shown to help physicians treat bone nonunion or delayed bone healing after a fracture.

Can U.S. macroeconomic indicators forecast cryptocurrency volatility?

This research examines the ability of 28 U.S. macroeconomic variables to forecast the volatility of six cryptocurrencies. In- and out-of-sample analyses are performed to validate their forecasting ability. Our analysis shows that during the full-sample period, 15 variables display forecasting ability, while post-COVID-19 period, this number is 17. Among these variables, the most influential include the consumer confidence index, leading economic index, consumer price index, U.S. exports and U.S. imports. Importantly, the predictive ability of these variables appears to have strengthened during the post-COVID-19 period. The out-of-sample results confirm the effectiveness of those macroeconomic variables in the in-sample tests. Furthermore, the robustness test reveals that incorporating these U.S. macroeconomic variables can enhance the performance of the GARCH volatility model. In this study, combination methods are used to enhance forecasting stability and are proven to have good forecasting ability. Our research also indicates that integrating global macroeconomic variables can enhance forecasting ability while recognizing the valuable information provided by U.S. macroeconomic variables. Additionally, we find that variables such as the short-term government bond yield and the M1 money supply emerge as important predictors of cryptocurrency bubbles.

Effects of different slopes on hip and ankle biomechanics of replaced and non-replaced limbs of patients with total knee arthroplasty during incline ramp walking

Although knee biomechanics has been examined, hip and ankle biomechanics in incline ramp walking has not been explored for patients with total knee arthroplasty (TKA). The purpose of this study was to investigate the hip and ankle joint kinematic and kinetic biomechanics of different incline slopes for replaced limbs and non-replaced limbs in individuals with TKA compared to healthy controls. Twenty-five patients with TKR and ten healthy controls performed walking trials on four slope conditions of level (0°), 5°, 10° and 15° on a customized instrumented ramp system. A 3x4 (limb x slope) repeated analysis of variance was used to evaluate selected variables. The results showed a greater peak ankle dorsiflexion angle in the replaced limbs compared to healthy limbs. No significant interactions or limb main effect for other ankle and hip variables. The peak dorsiflexion angle, eversion angle and dorsiflexion moment were progressively higher in each comparison from level to 15°. The peak plantarflexion moment was also increased with each increase of slopes. Both the replaced and non-replaced limbs of patients with TKA had lower hip flexion moments than the healthy control limbs. Hip angle at contact and hip extension range of motion increased with each increase of slopes. Peak hip loading-response internal extension moment increased with each increase in slope and peak hip push-off internal flexion moment decreased with each increase of slope. Our results showed increased dorsiflexion in replaced limbs but no other compensations of hip and ankle joints of replaced limbs compared to non-replaced limbs and their healthy controls during incline walking, providing further support of using incline walking in rehabilitation for patients with TKA.

A case study on effect of variable viscosity on non-newtonian nanofluid over an extendable cylindrical surface by utilizing Reynold’s model

Current artifact aims to investigate attributes of nanofluidic transport mechanism developed for dynamics of Williamson liquid over an extendable cylinder with novel physical aspects of magnetic field. Additionally, Newtonian heating is accounted at cylinder surface and Reynold viscosity model is implemented to delineate the aspects of temperature dependent viscosity. Thermophoresis, Brownian motion, and variable viscosity effects have been studied for heat and mass transfer analysis. Formulation of problems characterized by governing physical laws is conceded in the form of dimensional partial differential setup by executing boundary layer approach. Conversion of attained differential system into ODE’s is engrossed by capitalizing similarity transformations. Numerical procedures (Runge-Kutta and shooting) are implemented to resolve the problem and to attain solution. Effectiveness of sundry variables on associated distributions is revealed through graphical and tabular manner. Quantities of interest are also manipulated against the involved variables. Comparison of results to certify the numerical code is checked by making assessment with existing studies and found remarkable agreement.

Analytical modeling of perceived authenticity in AI assistants: application of PLS-predict algorithm and importance-performance map analysis

PurposeThe present study aimed to explore the consequences of perceived authenticity in artificial intelligence (AI) assistants and develop a serial-mediation architecture specifying causation of loyalty in human–AI relationships. It intended to assess the predictive power of the developed model based on a training-holdout sample procedure. It further attempted to map and examine the predictors of loyalty, strengthening such relationship.Design/methodology/approachPartial least squares structural equation modeling (PLS-SEM) based on bootstrapping technique was employed to examine the higher-order effects pertaining to human–AI relational intricacies. The sample size of the study comprised of 412 AI assistant users belonging to millennial generation. PLS-Predict algorithm was used to assess the predictive power of the model, while importance-performance analysis was executed to assess the effectiveness of the predictor variables on a two-dimensional map.FindingsA positive relationship was found between “Perceived Authenticity” and “Loyalty,” which was serially mediated by “Perceived-Quality” and “Animacy” in human–AI relational context. The construct “Loyalty” remained a significant predictor of “Emotional-Attachment” and “Word-of-Mouth.” The model possessed high predictive power. Mapping analysis delivered contradictory result, indicating “authenticity” as the most significant predictor of “loyalty,” but the least effective on performance dimension.Practical implicationsThe findings of the study may assist marketers to understand the relevance of AI authenticity and examine the critical behavioral consequences underlying customer retention and extension strategies.Originality/valueThe study is pioneer to introduce a hybrid AI authenticity model and establish its predictive power in explaining the transactional and communal view of human reciprocation in human–AI relationship. It exclusively provided relative assessment of the predictors of loyalty on a two-dimensional map.

Deep learning–based prediction of compressive strength of eco-friendly geopolymer concrete

The greenhouse gases cause global warming on Earth. The cement production industry is one of the largest sectors producing greenhouse gases. The geopolymer is produced with synthesized by the reaction of an alkaline solution and the waste materials such as slag and fly ash. The use of eco-friendly geopolymer concrete decreases energy consumption and greenhouse gases. In this study, the fc (compressive strength) of eco-friendly geopolymer concrete was predicted by the deep long short-term memory (LSTM) network model. Moreover, the support vector regression (SVR), least squares boosting ensemble (LSBoost), and multiple linear regression (MLR) models were devised to compare the forecast results of the deep LSTM algorithm. The input variables of the models were used as the mole ratio, the alkaline solution concentration, the curing temperature, the curing days, and the liquid-to-fly ash mass ratio. The output variable of the proposed models was chosen as the compressive strength (fc). Furthermore, the effects of the input variable on the fc of eco-friendly geopolymer concrete were determined by the sensitivity analysis. The fc of eco-friendly geopolymer concrete was predicted by the deep LSTM, LSBoost, SVR, and MLR models with 99.23%, 98.08%, 78.57%, and 88.03% accuracy, respectively. The deep LSTM model forecasted the fc of eco-friendly geopolymer concrete with higher accuracy than the SVR, LSBoost, and MLR models. The sensitivity analysis obtained that the curing temperature was the most important experimental variable that affected the fc of geopolymer concrete.

Activated partial thromboplastin time predicts mortality in patients with severe fever with thrombocytopenia syndrome: A multicenter study in north China

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high lethality. This study aimed to determine whether prolonged activated partial thromboplastin time (APTT) predicted SFTS mortality. MethodsSFTS patients were enrolled from 6 hospitals in the north China. Subjects were divided into training cohort and 5 externally validation cohorts. The least absolute shrinkage and selection operator Cox regression model was performed to screen potential prognostic factors. Risk factors were analyzed using multivariable regression models. Prognostic models were established by Cox regression and random survival forest (RSF) methods, and evaluated regarding discrimination, validity and clinical benefit. Time-dependent receiver operating characteristic (ROC) curve was used to evaluate the predictive effectiveness of variables. Results1332 SFTS cases were included, in which 211 patients died. Six potential prognostic factors were screened, and pulse, breath, APTT and aspartic transaminase (AST) were independently associated with mortality in both training cohort (Yantai, N = 791) and external validation cohort (N = 541). APTT was steadily correlated with the fatality (HR: 1.039–1.144; all P < 0.01) in each five sub-validation cohorts (Dandong, Dalian, Tai'an, Qingdao and Beijing). RSF model with variables of APTT, AST, pulse and breath had considerable prognostic effectiveness, which APTT showed the highest prognostic ability with the area under the curve of 0.848 and 0.787 for 7-day and 14-day survival, respectively. Survival differences were found between high and low levels of APTT for mortality using 50s as the optimal cut-off. ConclusionsSFTS patients have prolonged APTT, which is an independent risk factor for fatality. APTT≥50s was recommended as a biomarker to remind physicians to monitor and treat patients more aggressively to improve clinical prognosis.

Indonesian Teachers' Acceptance on Online Teaching Technology During the COVID-19 Pandemic

&lt;p style="text-align:justify"&gt;This study investigates the impact of external variables, technological pedagogical and content knowledge (TPACK) self-efficacy, and facilitating conditions on teacher adoption of online teaching technology during the COVID-19 pandemic. It employs explanatory research to characterize the effect of external variables on the variables of the technology acceptance model. 240 high school teachers filled out Google Forms survey questionnaires for six research variables and analyzed by the SmartPLS program. The result indicates that attitude and perceived usefulness significantly and positively influence behavioral intention. Perceived usefulness and ease of use have a strong positive effect on attitude. Furthermore, perceived ease of use has a considerable effect on perceived usefulness. Perceived usefulness and ease of use are not significantly influenced by external variables. Facilitating conditions significantly positively affect behavioral intention, whereas TPACK self-efficacy negatively affects behavioral intention.&lt;/p&gt;

Effect of Demographic Variables on the Accuracy of Teacher

Effect of Demographic Variables on the Accuracy of Teacher

Public Perception of the Effectiveness of Vaccination against the Level of Anxiety Exposed to Covid-19

This research aims to find out how people's perceptions about effectiveness of vaccination on the level of anxiety exposed to Covid-19 which is influenced by knowledge. The sampling technique used accidental sampling. Nine districts and cities in Bali were included in the samples and 190 respondents in all. Questionnaires as research instruments and primary data are the types of data used. The Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis approach was used in this study. The variables in this research consist of exogenous latent variables (knowledge), mediating latent variables (effectiveness), and endogenous latent variables (anxiety). By considering the results of hypothesis testing carried out through the bootstrapping process, the study revealed that better knowledge about Covid-19 can increase the effectiveness of vaccination by 78.9%, better knowledge about Covid-19 can reduce anxiety levels by 23.2%, increased vaccination effectiveness results in a decrease in anxiety levels by 29.4%, and better knowledge about Covid-19 can indirectly reduce anxiety levels through vaccination effectiveness by 59.3%. As a result, H0 gets dismissed in the hypothesis test, which indicates that knowledge significantly affects anxiety, both directly and indirectly through the mediation of vaccination effectiveness.

Abstract 6479: Metastases in uveal melanoma patients: Trends and risk factors

Abstract Purpose: Uveal melanoma has a high tendency to metastasize. Due to its rarity, there has not been large population studies that have demonstrated the effect of demographic variables on a patient’s risk to have metastases. Methods: The Nationwide Emergency Department Sample was queried from the last quarter of 2015 to 2019 to identify patients with a diagnosis of uveal melanoma. Multivariable logistic regression, with patient- and hospital-level characteristics as covariates, was used to identify factors associated with a higher chance of developing a metastasis and specific metastasis locations. Results: From the last quarter of 2015 to 2019, there were 7,561 ED encounters with a diagnosis of uveal melanoma. Of these patients, 5.8% had at least one metastasis. Most patients with a metastasis were at least 65 years of age (47.1%), male (54.4%), a Medicare beneficiary (45.2%), and belonged to a family in the highest income quartile (29.7%). The median age of these patients was 61 years (IQR: 32-74). Most patients presented to hospitals in the South (40.7%), with a Trauma Level I designation (56.1%), and to a metropolitan teaching hospital (72.9%). The most common sites of metastasis were liver (17.0%), brain (5.3%), and bone (2.0%). Multivariable logistic regression identified the following factors to be associated with increased risk of developing a metastasis: private insurance (odds ratio [OR]=1.62; p=0.012), highest income quartile (OR=2.30; p&amp;lt;0.001), and 2nd highest income quartile (OR=1.78; p=0.002). The following factors were associated with higher odds of developing a liver metastasis specifically: no-charge/charity work (OR=15.8; p=0.012), highest income quartile (OR=1.99; p=0.003), and second highest income quartile (OR=1.66; p=0.035). Presenting to a Trauma Level I hospital was associated with decreased risk of developing a liver metastasis (OR=0.57; p=0.016). Belonging to the highest income quartile was associated with a higher chance of developing a brain metastasis (OR=2.13; p=0.023) or a bone metastasis (OR=2.74; p=0.045). Among patients with a metastasis, the in-hospital mortality rate was 8.3%. Conclusion: Metastasis is an important complication to monitor in uveal melanoma patients. In this study, we have demonstrated that income and insurance status are factors that are associated with higher rates of metastases. Physicians should place special attention to these groups to ensure that metastases are minimized. Citation Format: Alice A. Beneke, Kamil Taneja, Lauren Ladehoff, Kevin Root, Eric Toloza. Metastases in uveal melanoma patients: Trends and risk factors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6479.