Two-level Hierarchical Model Research Articles

Variations in antihypertensive medication treatment and blood pressure control among Veterans with HIV and existing hypertension

BackgroundHypertension is a leading risk factor for cardiovascular disease among patients living with HIV (PLWH). Understanding the predictors and patterns of antihypertensive medication prescription and blood pressure (BP) control among PLWH with hypertension (HTN) is important to improve the primary prevention efforts for this high-risk population. We sought to assess important patient-level correlates (eg, race) and inter-facility variations in antihypertension medication prescriptions and BP control among Veterans living with HIV (VLWH) and HTN. MethodsWe studied VLWH with a diagnosis of HTN who received care in the Veterans Health Administration (VHA) from January 2018 to December 2019. We evaluated HTN treatment and blood pressure control across demographic variables, including race, and by medical comorbidities. Data were also compared among VHA facilities. Predictors of HTN treatment and control were assessed in 2-level hierarchical multivariate logistic regression models to estimate odds ratios (ORs). The VHA facility random-effects parameters from the hierarchical models were used to calculate the median odds ratios to characterize the variation across the different VHA facilities. ResultsA total of 17,468 VLWH with HTN (mean age 61 years, 97% male, 54% Black, 40% White) who received care within the VHA facilities in 2018-2019 were included. 73% were prescribed antihypertension medications with higher prescription rates among Black vs White patients (75% vs 71%) and higher prescription rates among patients with a history of cardiovascular disease, diabetes, and kidney disease (>80%), and those receiving antiretroviral therapy and with controlled HIV viral load (∼75%). Only 27% of VLWH with HTN had optimal BP control of systolic BP <130 mmHg and diastolic BP <80 mmHg, with a lower rate of control among Black vs White patients (24% v. 31%). In multivariate regression, Black patients had a higher likelihood of HTN medication prescription (OR 1.32, 95% CI: 1.22-1.42) but were less likely to have optimal BP control (OR 0.82; 0.76-0.88). Important positive correlates of antihypertensive prescription and optimal BP control included: number of outpatient visits in prior year, and histories of diabetes, coronary artery disease, and heart failure. There was about 10% variability in both antihypertensive prescription and BP control patterns between VHA facilities for patients with similar characteristics. There was increased inter-facility variation in antihypertensive prescription among those with a history of heart failure and those not receiving antiretroviral therapy. ConclusionIn a retrospective analysis of large VHA data, we found that VLWH with HTN have suboptimal antihypertensive medication prescription and BP control. Black VLWH had higher HTN medication prescription rates but lower optimal BP control.

Optimized intuitionistic fuzzy enriched honey badger algorithm for cloud network-based work load scheduling

The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters.

Facial emotion recognition in children of parents with a mental illness

ObjectiveFacial emotion recognition (FER) is a fundamental social skill essential for adaptive social behaviors, emotional development, and overall well-being. FER impairments have been linked to various mental disorders, making it a critical transdiagnostic mechanism influencing the development and trajectory of mental disorders. FER has also been found to play a role in the transgenerational transmission of mental disorders, with the majority of research suggesting FER impairments in children of parents with a mental illness (COPMI). Previous research primarily concentrated on COPMI of parents with internalizing disorders, which does not cover the full spectrum of outpatient mental health service populations. Furthermore, research focuses on varying components of FER by using different assessment paradigms, making it challenging to compare study results. To address these gaps, we comprehensively investigated FER abilities in COPMI using multiple tasks varying in task characteristics.MethodsWe included 189 children, 77 COPMI and 112 children of parents without a diagnosed mental illness (COPWMI), aged 6 to 16 years. We assessed FER using three tasks with varying task demands: an emotional Go/NoGo task, a morphing task, and a task presenting short video sequences depicting different emotions. We fitted separate two-level hierarchical Bayesian models (to account for sibling pairs in our sample) for reaction times and accuracy rates for each task. Good model fit was assured by comparing models using varying priors.ResultsContrary to our expectations, our results revealed no general FER deficit in COPMI compared to COPWMI. The Bayesian models fitted for accuracy in the morphing task and Go/NoGo task yielded small yet significant effects. However, Bayes factors fitted for the models suggested that these effects could be due to random variations or noise in the data.ConclusionsOur study does not support FER impairments as a general feature of COPMI. Instead, individual factors, such as the type of parental disorder and the timing of its onset, may play a crucial role in influencing FER development. Future research should consider these factors, taking into account the diverse landscape of parental mental disorders.

On the importance of integrating convolution features for Indian medicinal plant species classification using hierarchical machine learning approach

This work proposes a novel hierarchical classification framework designed to categorize hundred Indian medicinal plant species. The innovation lies in introducing a comprehensive feature representation by integrating convolutional features with geometric, texture, shape, and multispectral features for classification tasks. In this study, a two-level hierarchical plant classification model is proposed to address the challenges of inter-class similarity and intra-class variations. The first level classifies 100 medicinal plant species into 11 groups based on visual similarities among the plants. At level two, the specific plant species containing in each group are predicted using Random Forest classifier. The evaluation is performed at two levels to analyze the effectiveness of the proposed model. The performance analysis compares the effectiveness of individual feature types against the composite feature model. Performance is also evaluated based on specific groups that demonstrate high similarity between classes and intra-class variations among the plant species separately. Furthermore, the generality of the model is tested using two self-created datasets-RTL80 and RTP40, requiring more than 300 man-hours to collect. Experimental results demonstrate a promising accuracy of 94.54% on GSL100 leaf dataset and 75.46% on RTL80 and RTP40 real-time datasets reflecting the superiority of the proposed hierarchical model over state-of-the-art methods.

Telemedicine critical care availability and outcomes among mechanically ventilated patients

PurposeTelemedicine Critical Care (TCC) improves adherence to evidence based protocols associated with improved mortality among patients receiving invasive mechanical ventilation (IMV). We sought to evaluate the relationship between hospital availability of TCC and outcomes among patients receiving IMV. Materials and methodsWe performed a cross-sectional study of 66,522 adults who received IMV for non-postoperative acute respiratory failure at 318 non-federal hospitals in New York, Massachusetts, Maryland, and Florida in 2018. Hospital-level TCC availability was ascertained from the 2018 American Hospital Association Annual Survey. The primary outcome was in-hospital mortality. Secondary outcomes included the composite of tracheostomy or reintubation and duration of IMV. We used two-level hierarchical multivariable regression models to investigate the association between TCC availability and outcomes. Results20,270 (30.5%) patients were admitted into 89 TCC-available hospitals. There was no difference between TCC and non-TCC-available hospitals in mortality (odds ratio [OR] 0.94, 99% confidence interval [CI] 0.84–1.05), composite of tracheostomy or reintubation (OR 0.95 [0.82–1.11], or duration of IMV (OR 0.95 [0.83–1.09]). There was no difference in outcomes among the subgroup of patients with acute respiratory distress syndrome. ConclusionsHospital TCC availability was not associated with improved outcomes among patients receiving IMV.

Predictors of Psychological Well-Being and Quality of Life in Patients with Hypertension: A Longitudinal Study.

Previous research has highlighted the positive impact of greater health-related quality of life (Hr-QoL) and subjective well-being (SWB) on chronic diseases' severity and progression. There is a paucity of studies investigating the long-term trajectories of these variables among hypertensive patients. The present study aims to investigate the relationships between psychological variables (Type A and D personality, locus of control-LoC, self-esteem, and trait anxiety) with SWB and Hr-QoL in patients with hypertension and comorbid metabolic syndrome. A total of 185 volunteer patients (130 males, 70.3%; mean age 54 ± 10.93) were enrolled. Patients filled out measures of Hr-QoL and SWB, LoC, and self-esteem at three time points-Type A and D behaviors and anxiety measures only at baseline. Analyses were run through two-level hierarchical mixed models with repeated measures (Level 1) nested within participants (Level 2), controlling for sociodemographic and clinical confounders. Neither Hr-QoL nor SWB changed over time. Patients with greater self-esteem and internal LoC (and lower external LoC) increased their SWB and Hr-QoL up to 1-year follow-up. A greater Type A behavior and trait anxiety at baseline predicted a longitudinal increase in most of the dependent variables. Results suggest that it could be useful to tailor interventions targeting specific variables to increase Hr-QoL and SWB among hypertensive patients.

Relation between school education aims and Finnish adolescents’ global mindedness: The mediating role of tolerance

Global mindedness is increasingly crucial for individual, national, and global flourishing, and thus obtains great emphasis in school education. This study aimed to examine the associations between adolescents’ global mindedness and three-dimensional school education aims (aims for academic achievement, employability, and narrow-sense holistic development). We further explored the role of adolescents’ tolerance that might explain the associations. A total of 2448 15-year-old Finnish adolescents were selected and two-level hierarchical liner modeling was employed. Results showed that (1) academic achievement aim played a positive role in adolescents’ global mindedness, whereas narrow-sense holistic development aim and employability aim showed insignificant and negative effect on it, respectively; and (2) adolescents’ tolerance mediated the associations of these three school education aims and global mindedness. Findings identified the fundamental contribution of academic achievement aim to global mindedness, and imply schools to integrate global education with academic education, as well as to forbid the discouragement for tolerance and global mindedness in employability education.

A Hierarchical Framework for Video-Based Human Activity Recognition Using Body Part Interactions

Human Activity Recognition (HAR) is an important field with diverse applications. However, video-based HAR is challenging because of various factors, such as noise, multiple people, and obscured body parts. Moreover, it is difficult to identify similar activities within and across classes. This study presents a novel approach that utilizes body region relationships as features and a two-level hierarchical model for classification to address these challenges. The proposed system uses a Hidden Markov Model (HMM) at the first level to model human activity, and similar activities are then grouped and classified using a Support Vector Machine (SVM) at the second level. The performance of the proposed system was evaluated on four datasets, with superior results observed for the KTH and Basic Kitchen Activity (BKA) datasets. Promising results were obtained for the HMDB-51 and UCF101 datasets. Improvements of 25%, 25%, 4%, 22%, 24%, and 30% in accuracy, recall, specificity, Precision, F1-score, and MCC, respectively, are achieved for the KTH dataset. On the BKA dataset, the second level of the system shows improvements of 8.6%, 8.6%, 0.85%, 8.2%, 8.4%, and 9.5% for the same metrics compared to the first level. These findings demonstrate the potential of the proposed two-level hierarchical system for human activity recognition applications.

Separating the consumption and investment demands for housing: Evidence from urban China

Over the last two decades, housing price-oriented regulation of the real estate market has sparked various debates in several contexts, including policy effectiveness and institutional bias. Scholars have explored the broad impact of policies such as land use controls, property taxes, and financial leverage on housing prices. However, very few studies have focused on the role of separating the consumption and investment demands for housing, especially when the housing bubble prevails and the basic living demands of residents are not fully met. This paper proposes a theoretical model and an empirical method to separate the consumption and investment demands for housing. Using data on housing sales prices and housing rental prices in 70 cities in China, we apply the CES effect function to exhaustively estimate the size of consumption and investment demand, and use a Two-level Hierarchical Liner Model to measure the heterogeneous effects of consumption demands and investment demands on housing prices and economic growth. Our study provides an insightful snapshot of the gradual housing market reform that is unfolding and has yet to be studied in contemporary China.

Cross-level assessment of process innovation practices: an empirical approach to small and medium manufacturing enterprises in Southeast Asia

Process innovation is the core that helps both firms and countries achieve development goals quickly and sustainably. However, how a country’s ecosystem aligns with the innovation development of domestic firms and reverses backwards is less discussed. This study examines how process innovation practices at different levels affect the firms’ and governments’ performance to build a global competitiveness roadmap. The data of 4,859 small and medium manufacturing firms in nine Southeast Asian countries are used to examine the critical process innovation practices at the firm level, then utilized the Global Competitiveness Index to determine whether the cross-level effects the relationship between process innovation practices and firms, and national competitiveness performance. A two-level hierarchical model shows that innovation investment positively impacts process innovation practices. Higher education, training, and labor market efficiency also have a cross-level influence on process innovation. Finally, firm performance is boosted by integrating process innovation with financial market growth and size. The contributions are: (1) demonstrating a direct and indirect impact of innovation investment on process innovation practices and (2) providing an in-depth look at the cross-level effect of process innovation on global competitiveness.

Meso Safety Analysis of Major Arterials: The Impact of Intersection Spacing and Network Connection Compatibility

With the increase in high-density road network construction in many urban areas, neither macro- nor micro-level safety analysis can fully explain the impact of network characteristics on safety. This study employed a meso-level analysis unit, which combines intersections with adjacent road segments, to investigate the safety impact of intersection spacing and network connection compatibility along 19 major arterials in Suzhou, China. Three main intersection spacing characteristics, the density of intersection spacing (DOIS), the standard deviation of intersection spacing (SDOIS), and the variation of intersection spacing (VOIS), were used to represent the interaction between arterials and other roads in the network. This study introduced the concept of hierarchical differences to quantitatively analyze the safety effects of connections between roads with different accessibility and mobility functions. To manage the hierarchical structure of the data (which has both within-arterial and between-arterial correlations), a two-level hierarchical negative binomial conditional autoregressive model was adopted to examine the safety effects of network characteristics at the meso level. The results showed the existence of unobserved heterogeneities among the selected arterials. The SDOIS and VOIS were found to have positive effects on crash frequency (more crashes on units with higher SDOIS and VOIS), while the DOIS was negatively associated with crash frequency. The meso units with moderately compatible network connections were found to have more crashes than those with compatible connections, while the units with the highest hierarchical differences had fewer crashes. The results from this study are expected to provide suggestions on potential safety hazard screening and crash prevention.

Hierarchical Reliability Modelling and Analysis of Life Support System of Fighter Aircraft

The paper proposes a hierarchical reliability modelling and assessment approach for a life support system (LSS) that provides oxygen to the pilot and is employed in a combat aircraft. The system has the primary function of generating oxygen onboard, and it has a backup gaseous oxygen tank as redundancy. An emergency oxygen bottle is also part of the ejection seat for emergency use. Both backup oxygen and emergency oxygen have a fixed capacity and a fixed duration of oxygen supply. Therefore, it is crucial to assess the reliability of the LSS to ensure its safety and effectiveness of this LSS during a mission by the combat aircraft. The proposed reliability model of LSS is developed as a two-level hierarchical model, that captures the inherent randomness in the operation of the system. At the lowest level of the hierarchy, Markov chains are used to model the events that may lead to the failure of the LSS. The events include the failure of individual components, the depletion of backup oxygen, and the depletion of emergency oxygen. The Markov chains consider the interactions between individual components and events during the mission profile. At the top level of the hierarchy, a fault tree is used to model the interactions between various events during the mission profile. The fault tree considers the interactions between individual events and the effects of redundancy on the reliability of the LSS. The results of the Markov chains at the lower level are exported to the higher level modelled via fault tree to find the overall system reliability. The reliability model is further extended to incorporate the deterministic nature of the LSS due to the fixed capacity of the backup tank and emergency bottle. The work addresses the modelling of six different scenarios of LSS operations. The modelling of these scenarios is achieved using Semi-Markov Processes (SMP), which allow the state holding time to be a general distribution.

A Case Study on Hierarchical Linear Models Applied to the UN’s Sustainable Development Goals (SDGs): A Perspective Using the World and Brazil’s Data

This study analyzed the statistical relation between the Sustainable Development Goals and their relative indicators for the UN’s 2030 Agenda through the implementation of a two-level linear hierarchical model (HLM2) using STATA/SE 16 statistical software. The objective of this model was to address priorities by saying how much and where each country should invest so that they can achieve these goals by the end of the decade. Intrinsically, it was checked whether the indicators listed by the UN are statistically capable of describing the expected output. After analyzing the results, SDGs 8, 9 and 7 were, respectively, identified as priorities. The HLM2 also pointed out that economic growth is the most important variable amongst all considered. Finally, it was concluded that a generic answer does not serve to respond to the complexities worldwide, and therefore, it would be more appropriate to direct actions on a case-by-case basis.

A hierarchical multinomial logit model to examine the effects of signal strategies on right-turn crash injury severity at signalised intersections

The severity of right-turn crashes (or left-turn crashes for the roads in the US) at signalised intersections tends to be high because of the relatively high conflicting speeds and angle of impact. However, right-turn crash injury severity at signalised intersections was not sufficiently studied. In particular, the effects of signal control strategies on crash injury severity are not known. This study developed crash injury severity models for right-turn crashes at signalised intersections with a novel approach of linking crashes with signal strategies which enabled assessing the effects of signal strategies on crash injury severity. The study provided a comprehensive understanding of the impacts of signal strategies, intersection geometry and traffic factors on crash injury severity of right-turn crashes at signalised intersections. Crash injury severity models were estimated with crash data from 221 signalised intersections in Queensland from 2012 to 2018. To address the hierarchical structure of crash data, two-level hierarchical Multinomial Logit models were applied, hypothesising that the first level includes individual crash characteristics while the second level includes intersection characteristics. The applied hierarchical model accounts for the correlation among crashes within intersections. Results showed that crashes during Lagging right-turn and Diamond overlap turns are likely to be more severe than other signal strategies at intersections, with the Lagging right-turn signal being the most hazardous. The results also illustrate that the probability of severe injuries increases with the number of conflicting lanes, whereas the corresponding probability decreases with the occupancy of the conflicting lane.

Supply and quality of colonoscopy according to the characteristics of gastroenterologists in the French population-based colorectal-cancer screening program.

Since its complete roll-out in 2009, the French colorectal cancer screening program (CRCSP) experienced 3 major constraints [use of a less efficient Guaiac-test (gFOBT), stopping the supply of Fecal-Immunochemical-Test kits (FIT), and suspension of the program due to the coronavirus disease 2019 (COVID-19)] affecting its effectiveness. To describe the impact of the constraints in terms of changes in the quality of screening-colonoscopy (Quali-Colo). This retrospective cohort study included screening-colonoscopies performed by gastroenterologists between Jan-2010 and Dec-2020 in people aged 50-74 living in Ile-de-France (France). The changes in Quali-colo (Proportion of colonoscopies performed beyond 7 mo (Colo_7 mo), Frequency of serious adverse events (SAE) and Colonoscopy detection rate) were described in a cohort of Gastroenterologists who performed at least one colonoscopy over each of the four periods defined according to the chronology of the constraints [gFOBT: Normal progress of the CRCSP using gFOBT (2010-2014); FIT: Normal progress of the CRCSP using FIT (2015-2018); STOP-FIT: Year (2019) during which the CRCSP experienced the cessation of the supply of test kits; COVID: Program suspension due to the COVID-19 health crisis (2020)]. The link between each dependent variable (Colo_7 mo; SAE occurrence, neoplasm detection rate) and the predictive factors was analyzed in a two-level multivariate hierarchical model. The 533 gastroenterologists (cohort) achieved 21509 screening colonoscopies over gFOBT period, 38352 over FIT, 7342 over STOP-FIT and 7995 over COVID period. The frequency of SAE did not change between periods (gFOBT: 0.3%; FIT: 0.3%; STOP-FIT: 0.3%; and COVID: 0.2%; P = 0.10). The risk of Colo_7 mo doubled between FIT [adjusted odds ratio (aOR): 1.2 (1.1; 1.2)] and STOP-FIT [aOR: 2.4 (2.1; 2.6)]; then, decreased by 40% between STOP-FIT and COVID [aOR: 2.0 (1.8; 2.2)]. Regardless of the period, this Colo_7 mo's risk was twice as high for screening colonoscopy performed in a public hospital [aOR: 2.1 (1.3; 3.6)] compared to screening-colonoscopy performed in a private clinic. The neoplasm detection, which increased by 60% between gFOBT and FIT [aOR: 1.6 (1.5; 1.7)], decreased by 40% between FIT and COVID [aOR: 1.1 (1.0; 1.3)]. The constraints likely affected the time-to-colonoscopy as well as the colonoscopy detection rate without impacting the SAE's occurrence, highlighting the need for a respectable reference time-to-colonoscopy in CRCSP.

Hierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids

Aiming to take full advantage of Electric Vehicles’ (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles.

254: TELEMEDICINE CRITICAL CARE AVAILABILITY AND OUTCOMES AMONG MECHANICALLY VENTILATED PATIENTS

Introduction: Mechanical ventilation (MV) interventions like low tidal volume ventilation and daily spontaneous awakening and breathing trials are associated with improved survival but are unevenly implemented in real-world practice. Telemedicine Critical Care (TCC) improves adherence to these interventions. Whether or not hospitals with TCC have better MV outcomes than hospitals without TCC is unknown. We evaluated the association of hospital availability of TCC and outcomes among MV patients. Methods: We analyzed 19,679 adult non-surgical, mechanically ventilated patients in an Intensive Care Unit (ICU) in 2018 at 310 non-federal State Inpatient Database hospitals in Florida, Massachusetts, Maryland, and New York. The primary predictor variable was availability of TCC at a study hospital. The primary outcome was mortality defined as hospital death or discharge to hospice. Secondary outcomes were a composite of tracheostomy or re-intubation and duration of mechanical ventilation. We used a two-level hierarchical multivariable logistic regression model to investigate whether TCC availability was associated with study outcomes. Results: Among 19,679 mechanically ventilated patients, 6,177 (31.4%) occurred in TCC hospitals. Compared to non-TCC hospitals, TCC hospitals had reduced but insignificant odds of mortality (odds ratio [OR] 0.97, 95% CI 0.85–1.09), composite of tracheostomy/re-intubation (OR 0.93 [0.78–1.0]), and duration of mechanical ventilation (OR 0.95 [0.85-1.07]). Hospital size and annual mechanical ventilation case volume did not modify the association between TCC availability and mortality (P=0.75 and P=0.44 for interactions, respectively). Conclusions: Hospital availability of TCC was not associated with lower odds of mortality among mechanically ventilated patients.

On the yield criterion of two-scale porous materials by using Eshelby-type velocity field and Steigmann–Ogden surface model

In this work, we investigate the yield strength of porous metallic materials in the presence of both microvoids and nanovoids. A two-level hierarchical model composed of both microscopic and macroscopic representative volume elements (RVEs) are proposed for this purpose. The lower level RVE is made up of an incompressible, perfectly elastoplastic, von Mises matrix and multiple dilute nanovoids. By using a micromechanics-based homogenization method, a microscopic yield criterion is first established in terms of the overall equivalent stress, uniaxial yield strength of the microscopic matrix, Gurtin–Murdoch and Steigmann–Ogden nanovoids surface constants, as well as the microscopic porosity. The lower level RVE is then treated as a material point in the upper level RVE matrix. The macroscopic dissipation rate is evaluated from the conventional deviatoric strain rate used in Gurson’s classical criterion, an Eshelby-type exterior velocity field and the compressibility rate of the macroscopic matrix. Closed-form solutions are eventually determined for such a velocity field and the macroscopic yield function, by simultaneously satisfying the arbitrary macroscopic strain rate boundary conditions and enforcing the principle of minimum dissipation rate. Compared to similar criteria available in the literature, two distinct features can be identified. First, the full version Steigmann–Ogden surface model is considered on nanovoids surface. Second, both the hydrostatic and deviatoric deformation of the microvoid are considered in the velocity field. Extensive parametric studies are conducted to investigate the effects of nanovoids surface bulk modulus, surface shear modulus, surface flexural rigidity, nanovoids radius, and both levels of porosities on the macroscopic yield loci. The results obtained in this article are helpful to the better design and manufacturing of nanoporous metallic materials.

Abstract 261: Association Of Neighborhood Social Determinants Of Health With Adverse Out-of-hospital Cardiac Arrest (OHCA) Outcomes Among Medicare Beneficiaries

Introduction: Out-of-hospital cardiac arrest (OHCA) is a leading cause of death with prior studies demonstrated worse OHCA outcomes in neighborhoods with higher proportion of Blacks and lower socioeconomic status. The association between OHCA outcomes and other social determinants of health (SDOHs) like limited English proficiency (LEP) and education is not well studied. Hypothesis: We hypothesized patients living in neighborhoods with high proportion of LEP had worse OHCA outcomes. We chose survival to hospital admission as the primary outcome due to its proximity to reflect a neighborhood’s characteristics. Methods: We performed a two-level hierarchical logistic-regression model. Patient data were Medicare fee-for-service claims between January 1, 2013, and December 31, 2015 (level 1). Neighborhood characteristics for the area in which each OHCA occurred were derived from the 2014 American Community Survey at the ZCTA level (level 2). We identified six neighborhood SDOH predictors of interest including LEP, median household income, racial and ethnic-group composition, percent unemployment, less than high school education, and population density. Results: Among the 194,231 OHCA events identified, 60,760 (31.3%) survived to hospital admission. Contrary to our hypothesis, Patient who had a cardiac arrest in the highest proportion of LEP neighborhoods were more likely to survive to hospital admission as compared to those living in predominantly English-speaking neighborhoods (OR 1.16; 95% CI, 1.10 to 1.23). Household income, racial composition, and unemployment had no effect on the likelihood of survival to hospital admission in OHCA while education and population density appeared to be protective (Table) . Conclusions: In a large cohort study, we found that patients who had an out of hospital cardiac arrest were more likely to survive to hospital admission in higher LEP, higher education, and higher population density neighborhoods.

The role of destination contextual effects in driving the expenditure of tourists: a multilevel spatial modelling approach

ABSTRACT Tourist’s expenditure is a key variable for the tourism industry with a high presence in the literature. Recent micro-econometric approaches estimating the factors driving the expenditure of visitors do not confer much relevance to destination features, despite its relevance shown by the theoretical tourism literature. The present investigation proposes a novel framework in tourism demand studies, expanding the previous modelling approach, in order to take into account such contextual destination effects. Particularly, we define a two-level hierarchical spatial model that includes spatial effects, operating at both the level of the destination and the neighbouring clusters, accommodating in this way for a spatial local dependence structure in the spending choices of visitors. Empirical evidence on the performance of this modelling framework is obtained by building on a relevant dataset for more than 136,000 international visitors reaching 1872 destinations in Spain. This allows for a multi-destination analysis showing that the destination contextual and local neighbourhood effects could account up to 50% of the variance of the expenditure of tourists in the case of most popular destinations. However, such effects do not show this salient role in the case of less developed destinations. Policy recommendations emerging from the results of the investigation are discussed further.