Neighborhood Index Research Articles

Synergistic Integration of Local and Global Information for Critical Edge Identification.

Identifying critical edges in complex networks is a fundamental challenge in the study of complex networks. Traditional approaches tend to rely solely on either global information or local information. However, this dependence on a single information source fails to capture the multi-layered complexity of critical edges, often resulting in incomplete or inaccurate identification. Therefore, it is essential to develop a method that integrates multiple sources of information to enhance critical edge identification and provide a deeper understanding and optimization of the structure and function of complex networks. In this paper, we introduce a Global-Local Hybrid Centrality method which integrates a second-order neighborhood index, a first-order neighborhood index, and an edge betweenness index, thus combining both local and global perspectives. We further employ the edge percolation process to evaluate the significance of edges in maintaining network connectivity. Experimental results on various real-world complex network datasets demonstrate that the proposed method significantly improves the accuracy of critical edge identification, providing theoretical and methodological support for the analysis and optimization of complex networks.

Effect of disability, homelessness, and neighborhood marginalization on risk-adjustment for hospital performance measurement.

Background It is not known how disability, homelessness, or neighborhood marginalization influence risk-adjusted hospital performance measurement in a universal health care system. Methods We evaluated the effect of including these equity-related factors in risk-adjustment models for in-hospital mortality, and 7- and 30-day readmission in 28 hospitals in Ontario, Canada. We compared risk-adjustment with commonly-used clinical factors to models that also included homelessness, disability, and neighborhood indices of marginalization. We evaluated models in historical data using internal-external cross-validation. We calculated risk-standardized outcome rates for each hospital in a recent reporting period using mixed-effects logistic regression. Results The cohort included 544,805 admissions. Adjustment for disability, homelessness, and neighborhood marginalization had little impact on discrimination or calibration of risk-adjustment models. However, it influenced comparative hospital performance on risk-standardized 30-day readmission rates, resulting in 5 hospitals being reclassified between below-average, average, and above-average groups. No hospitals were reclassified for mortality and 7-day readmission. Conclusion In a system with universally insured hospital services, adjustment for disability, homelessness, and neighborhood marginalization influenced estimates of hospital performance for 30-day readmission but not 7-day readmission or in-hospital mortality. These findings can inform researchers and policymakers as they thoughtfully consider when to adjust for these factors in hospital performance measurement.

An Age-Friendly Neighbourhood Index as a Long-Term Urban Planning Decision-Making Tool

People responsible for shaping the future of cities often seek valuable tools to assist in their decision-making processes. Using objective, quantified, and analysed data proves highly beneficial when determining where to focus interventions at the city level. Various urban indexes have been established to measure different aspects of urban life, ranging from sustainability to liveability. These indexes encompass multiple dimensions of a city, including mobility and walkability, among others. The age-friendly cities initiative developed indicators for assessing the age-friendliness of cities. Some researchers further refined these indicators to focus on urban planning competencies. Building on this foundation, this article aims to present an Age-Friendly Neighbourhood Index (AFNI) validated by a panel of experts using the Delphi method. This index can serve as a valuable tool for urban planners when they need to prioritise interventions to enhance age-friendliness at neighbourhood scale. The article also outlines the necessary data and measurement techniques for these indicators. The AFNI has been applied to a real case study in the city of Santander (Spain). This application assesses the age-friendliness of various neighbourhoods in Santander, demonstrating the challenges in acquiring sub-local quality data and emphasising the need for data-driven urban management.

Individual and neighborhood-level social and deprivation factors impact kidney health in the GLOMMS-CORE study

Prospective cohort studies of kidney equity are limited by a focus on advanced rather than early disease and selective recruitment. Whole population studies frequently rely on area-level measures of deprivation as opposed to individual measures of social disadvantage. Here, we linked kidney health and individual census records in the North of Scotland (Grampian area), 2011-2021 (GLOMMS-CORE) and identified incident kidney presentations at thresholds of estimated glomerular filtration rate (eGFR) under 60 (mild/early), under 45 (moderate), under 30 ml/min/1.73m2(advanced), and acute kidney disease (AKD). Household and neighborhood socioeconomic measures, living circumstances, and long-term mortality were compared. Case-mix adjusted multivariable logistic regression (living circumstances), and Cox models (mortality) incorporating an interaction between the household and the neighborhood were used. Among census respondents, there were 48546, 29081, 16116, 28097 incident presentations of each respective eGFR cohort and AKD. Classifications of socioeconomic position by household and neighborhood were related but complex, and frequently did not match. Compared to households of professionals, people with early kidney disease in unskilled or unemployed households had increased mortality (adjusted hazard ratios: 95% confidence intervals) of (1.26: 1.19-1.32) and (1.77: 1.60-1.96), respectively with adjustment for neighborhood indices making little difference. Those within either a deprived household or deprived neighborhood experienced greater mortality, but those within both had the poorest outcomes. Unskilled and unemployed households frequently reported being limited by illness, adverse mental health, living alone, basic accommodation, lack of car ownership, language difficulties, visual and hearing impairments. Thus, impacts of deprivation on kidney health are spread throughout society, complex, serious, and not confined to those living in deprived neighborhoods.

ALGORITHME DE CLUSTERING AGGLOMÉRATIF BASÉ SUR MOJO AVEC STRATÉGIE EDMT POUR LES RÉSEAUX DE CAPTEURS SANS FIL À GRANDE ÉCHELLE

The wireless sensor network (WSN) is a distributed sensor network that monitors and stores environmental data wirelessly by connecting dispersed sensor nodes. Since wireless sensor nodes rely on batteries for energy, energy consumption, and limitations are considered fundamental problems. A novel multi-objective jellyfish optimization based on energy, degree, distance, mobility, and time parameters (MOJO-ED2MT) technique has been proposed to overcome these challenges. Three phases are involved in the proposed method: selection of cluster heads, compression of data, and routing of the data. In this first phase, a fuzzy agglomerative clustering algorithm is employed to choose an optimal dual cluster head from inter-cluster and intra-cluster. In the second phase, a neighborhood indexing sequence (NIS) algorithm can compress the number of bits in the data before it is transmitted. In the third phase, jellyfish optimization selects the shortest path based on multi-objective parameters. The simulation analysis and result statistics show that the suggested MOJO-E MT approach performs better than the state-of-the-art algorithms across various performance measures. The proposed MOJO-E MT framework achieves 11.5, 15.4 %, and 17.99 % more network lifetime than EOR-iABC, C3HA, and ML-AEFA algorithms.

QSPR/QSAR study of antiviral drugs modeled as multigraphs by using TI’s and MLR method to treat COVID-19 disease

The ongoing COVID-19 pandemic continues to pose significant challenges worldwide, despite widespread vaccination. Researchers are actively exploring antiviral treatments to assess their efficacy against emerging virus variants. The aim of the study is to employ M-polynomial, neighborhood M-polynomial approach and QSPR/QSAR analysis to evaluate specific antiviral drugs including Lopinavir, Ritonavir, Arbidol, Thalidomide, Chloroquine, Hydroxychloroquine, Theaflavin and Remdesivir. Utilizing degree-based and neighborhood degree sum-based topological indices on molecular multigraphs reveals insights into the physicochemical properties of these drugs, such as polar surface area, polarizability, surface tension, boiling point, enthalpy of vaporization, flash point, molar refraction and molar volume are crucial in predicting their efficacy against viruses. These properties influence the solubility, permeability, and bio availability of the drugs, which in turn affect their ability to interact with viral targets and inhibit viral replication. In QSPR analysis, molecular multigraphs yield notable correlation coefficients exceeding those from simple graphs: molar refraction (MR) (0.9860), polarizability (P) (0.9861), surface tension (ST) (0.6086), molar volume (MV) (0.9353) using degree-based indices, and flash point (FP) (0.9781), surface tension (ST) (0.7841) using neighborhood degree sum-based indices. QSAR models, constructed through multiple linear regressions (MLR) with a backward elimination approach at a significance level of 0.05, exhibit promising predictive capabilities highlighting the significance of the biological activity IC50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$IC_{50}$$\\end{document} (Half maximal inhibitory concentration). Notably, the alignment of predicted and observed values for Remdesivir’s with obs pIC50=6.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${pIC_{50} = 6.01}$$\\end{document},pred pIC50=6.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${pIC_{50} = 6.01}$$\\end{document} (pIC50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$pIC_{50}$$\\end{document} represents the negative logarithm of IC50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$IC_{50}$$\\end{document}) underscores the accuracy of multigraph-based QSAR analysis. The primary objective is to showcase the valuable contribution of multigraphs to QSPR and QSAR analyses, offering crucial insights into molecular structures and antiviral properties. The integration of physicochemical applications enhances our understanding of factors influencing antiviral drug efficacy, essential for combating emerging viral strains effectively.

An efficient, multi-scale neighbourhood index to quantify wildfire likelihood

Background To effectively reduce future wildfire risk, several management strategies must be evaluated under plausible future scenarios, requiring models that provide estimates of how likely wildfires are to spread to community assets (wildfire likelihood) in a computationally efficient manner. Approaches to quantifying wildfire likelihood using fire simulation models cannot practically achieve this because they are too computationally expensive. Aim This study aimed to develop an approach for quantifying wildfire likelihood that is both computationally efficient and able to consider contagious and directionally specific fire behaviour properties across multiple spatial ‘neighbourhood’ scales. Methods A novel, computationally efficient index for quantifying wildfire likelihood is proposed. This index is evaluated against historical and simulated data on a case study in South Australia. Key results The neighbourhood index explains historical burnt areas and closely replicates patterns in burn probability calculated using landscape fire simulation (ρ = 0.83), while requiring 99.7% less computational time than the simulation-based model. Conclusions The neighbourhood index represents patterns in wildfire likelihood similar to those represented in burn probability, with a much-reduced computational time. Implications By using the index alongside existing approaches, managers can better explore problems involving many evaluations of wildfire likelihood, thereby improving planning processes and reducing future wildfire risks.

Neighborhood degree sum-based molecular indices and their comparative analysis of some silicon carbide networks

Topological indices of a molecular graph are numeric quantities that characterize its numerous physico-chemical properties, chemical reactivities and biological activities. The neighborhood M-polynomial is productive for discovering neighborhood degree sum-based topological indices. This article deals with computing the neighborhood M-polynomial of silicon carbide networks Si 2 C 3-I[p, q], Si 2 C 3-II[p, q] and Si 2 C 3-III[p, q], and hence examining some standard neighborhood degree sum-based topological indices for the aforementioned networks. The obtained results are analyzed graphically. Moreover, a comparative study of the outcomes with some well-established degree-based topological indices of the silicon carbide networks is executed.

Multi-Objective ED2MT Strategy-based Clustering Algorithm for Large Scale WSN

Wireless Sensor Networks play a crucial role in everyday life, providing a wide range of networking structures to develop real-time applications. Energy consumption is limited by WSN devices, so reducing the energy consumption of malicious nodes is crucial to improving network performance. A main concern of WSNs from an application perspective is the prolonged lifetime of the network and reduced energy usage. To overcome these challenges a novel multi-objective ED2MT (Energy, Distance, Degree, Mobility, Time) driven strategy has been proposed which consists of three phases namely dual Cluster Head Selection, data compression, and routing. In the first phase, the proposed ED2MT uses a Fuzzy C-Means (FCM) clustering algorithm to select dual Cluster Head (CH) which has been performed in two stages, namely Inter-Cluster and Intra-Cluster. In the second phase, a Neighborhood Indexing Sequence (NIS) technique is utilized to compress the number of bits in the data before it is transmitted. In the third phase, a multi-objective shortest-path routing algorithm is utilized for route selection. The performance of the ED2MT method has been evaluated using network lifetime, delay, throughput, alive nodes, energy efficiency, and packet delivery ratio to provide a better result. The proposed ED2MT framework achieves 20.55%, 18.4%, and 15.2% more residual energy than C3HA, CAFL, and DHSCA algorithms.

Neighborhood Deprivation and Changes in Oral Health in Older Age: A Longitudinal Population-Based Study

The aim of this study was to examine the extent to which neighborhood-level socioeconomic factors (objective and perceived) are associated with poor oral health in older adults over time, independent of individual socioeconomic position. Data for this cross-sectional and longitudinal observation study came from a socially and geographically representative cohort of men aged 71 to 92 y in 2010–12 (n = 1,622), drawn from British general practices, which was followed up in 2018–19 (aged 78–98 y; N = 667). Dental measures at both times included number of teeth, periodontal pocket depth, self-rated oral health, and dry mouth. Neighborhood deprivation was based on Index of Multiple Deprivation (IMD) and a cumulative index measuring perceptions about local environment. Individual-level socioeconomic position was based on longest-held occupation. Multilevel and multivariate logistic regressions, adjusted for relevant sociodemographic, behavioral, and health-related factors, were performed to examine the relationships of dental measures with IMD and perceived neighborhood quality index, respectively. Cross-sectionally, risks of tooth loss, periodontal pockets, and dry mouth increased from IMD quintiles 1 to 5 (least to most deprived); odds ratios (ORs) for quintile 5 were 2.22 (95% confidence interval [CI], 1.41–3.51), 2.82 (95% CI, 1.72–4.64), and 1.51 (95% CI, 1.08–2.09), respectively, after adjusting for sociodemographic, behavioral, and health-related factors. Risks of increased pocket depth and dry mouth were significantly greater in quintile 5 (highest problems) of perceived neighborhood quality index compared to quintile 1. Over the 8-y follow-up, deterioration of dentition (tooth loss) was significantly higher in the most deprived IMD quintiles after full adjustment (OR for quintile 5 = 2.32; 95% CI, 1.09–4.89). Deterioration of dentition and dry mouth were significantly greater in quintile 5 of perceived neighborhood quality index. Neighborhood-level factors were associated with poor oral health in older age, both cross-sectionally and longitudinally, particularly with tooth loss, and dry mouth, independent of individual-level socioeconomic position.

M-Polynomial and NM-Polynomial Methods for Topological Indices of Polymers

Topological indices (TIs) are numerical tools widely applied in chemometrics, biomedicine, and bioinformatics for predicting diverse physicochemical attributes and biological activities within molecular structures. Despite their significance, the challenges in deriving TIs necessitate novel approaches. This study addresses the limitations of conventional methods in dealing with dynamic molecular structures, focusing on the neighborhood M-polynomial (NM-polynomial), a pivotal polynomial for calculating degree-based TIs. Current literature acknowledges these polynomials but overlooks their limited adaptability to intricate biopolymer relationships. Our research advances by computing degree-based and neighborhood degree-based indices for prominent biopolymers, including polysaccharides, poly-γ-glutamic acid, and poly-L-lysine. Through innovative utilization of the NM-polynomial and the M-polynomial, we establish a fresh perspective on molecular structure and topological indices. Moreover, we present diverse graph representations highlighting the nuanced correlations between indices and structural parameters. By systematically investigating these indices and their underlying polynomials, our work contributes to predictive modelling in various fields. This exploration sheds light on intricate biochemical systems, offering insights into applications encompassing medicine, the food industry, and wastewater treatment. This research deepens our understanding of complex molecular interactions and paves the way for enhanced applications in diverse industries.

Unveiling novel eccentric neighborhood forgotten indices for graphs and gaph operations: A comprehensive exploration of boiling point prediction

<abstract><p>This paper marks a significant advancement in the field of chemoinformatics with the introduction of two novel topological indices: the forgotten eccentric neighborhood index (FENI) and the modified forgotten eccentric neighborhood index (MFENI). Uniquely developed for predicting the boiling points of various chemical substances, these indices offer groundbreaking tools in understanding and interpreting the thermal properties of compounds. The distinctiveness of our study lies in the in-depth exploration of the discriminative capabilities of FENI and MFENI. Unlike existing indices, they provide a nuanced capture of structural features essential for determining boiling points, a key factor in drug design and chemical analysis. Our comprehensive analyses demonstrate the superior predictive power of FENI and MFENI, highlighting their exceptional potential as innovative tools in the realms of chemoinformatics and pharmaceutical research. Furthermore, this study conducts an extensive investigation into their various properties. We present explicit results on the behavior of these indices in relation to diverse graph types and operations, including join, disjunction, composition and symmetric difference. These findings not only deepen our understanding of FENI and MFENI but also establish their practical versatility across a spectrum of chemical and pharmaceutical applications. Thus the introduction of FENI and MFENI represents a pivotal step forward in the predictive analysis of boiling points, setting a new standard in the field and opening avenues for future research advancements.</p></abstract>

Trends in the ability of socioeconomic position to predict individual body mass index: an analysis of repeated cross-sectional data, 1991-2019.

The widening of group-level socioeconomic differences in body mass index (BMI) has received considerable research attention. However, the predictive power of socioeconomic position (SEP) indicators at the individual level remains uncertain, as does the potential temporal variation in their predictive value. Examining this is important given the increasing incorporation of SEP indicators into predictive algorithms and calls to reduce social inequality to tackle the obesity epidemic. We thus investigated SEP differences in BMI over three decades of the obesity epidemic in England, comparing population-wide (SEP group differences in mean BMI) and individual-level (out-of-sample prediction of individuals' BMI) approaches to understanding social inequalities. We used repeated cross-sectional data from the Health Survey for England, 1991-2019. BMI (kg/m2) was measured objectively, and SEP was measured via educational attainment, occupational class, and neighbourhood index of deprivation. We ran random forest models for each survey year and measure of SEP adjusting for age and sex. The mean and variance of BMI increased within each SEP group over the study period. Mean differences in BMI by SEP group also increased: differences between lowest and highest education groups were 1.0kg/m2 (0.4, 1.6) in 1991 and 1.3kg/m2 (0.7, 1.8) in 2019. At the individual level, the predictive capacity of SEP was low, though increased in later years: including education in models improved predictive accuracy (mean absolute error) by 0.14% (- 0.9, 1.08) in 1991 and 1.05% (0.18, 1.82) in 2019. Similar patterns were obtained for occupational class and neighbourhood deprivation and when analysing obesity as an outcome. SEP has become increasingly important at the population (group difference) and individual (prediction) levels. However, predictive ability remains low, suggesting limited utility of including SEP in prediction algorithms. Assuming links are causal, abolishing SEP differences in BMI could have a large effect on population health but would neither reverse the obesity epidemic nor reduce much of the variation in BMI.

Extremal topological indices of some nanostructures

In this work, general formulas of degree-based and neighborhood degree sum-based topological indices for a 2D lattice of H-Naphtalenic nanotubes and pent-heptagonal nanosheets are determined. As an example, six neighborhood degree sum-based topological indices are computed using the obtained formula. Furthermore, the extremal cases of these nanostructures with a fixed number of blocks are characterized for a given degree-based or neighborhood degree sum-based topological index. Also a visual comparison of these indices is shown. Additionally, it is demonstrated how beneficial the indices are for representing structure-property relationships.

Resampling Technique-Based Demodulation Analysis for Planet Bearing Cage Fault Diagnosis Under Nonstationary Conditions

Vibration signals of the planetary transmission system usually contain excitation responses from different structures. The fault characteristic of the planet bearing cage is weak, which is often overwhelmed by strong noise and other irrelevant vibration components. Therefore, fault feature extraction of the planet bearing cage is a challenging topic. In this paper, a novel fault feature extraction method is proposed to extract weak fault features of the planet bearing cage. The general parameterized time-frequency transform is employed to accurately extract instantaneous rotational speed information from the planet bearing vibration signal for resampling. It can obtain the accurate rotational speed result even though under power frequency interference conditions. The influence of the deterministic components excited by the gear can be reduced by using the spectral pre-whitening and the interference of noise and random impact can be reduced in the selection of the optimal resonance frequency band by using autogram analysis. The fault characteristic components of the planet bearing cage can be extracted from the square envelop spectrum. The neighborhood power density ratio index is further constructed to distinguish the health bearing state from the cage fault bearing state and evaluate the degree of faults. Experiments designed in a planetary transmission system test rig of an armored vehicle have verified the potential and effectiveness of the proposed fault feature extraction method in exhibiting planet bearing cage fault features.

Neighborhood Number-Based Topological Indices of Graphene

The study of topological indices prompted mathematicians to venture into mathematical chemistry, specifically chemical graph theory. They are quantitative measures that reflect the topology of the molecular graph of a compound, without taking into account of any information about the chemical bonds or the atom positions. Graphene is a monolayer carbon allotrope that is tightly bound in a hexagonal honeycomb lattice. It is an eco-friendly compound and a sustainable solution for almost countless applications. In this paper, we estimate three neighborhood number-based topological indices; Mostar index, PI index and Szeged index of Graphene structure, considering all possible cases in the number of rows and hexagons. The results are obtained and corresponding graphs are plotted along with the contour plot.

Iot-Based Secure Healthcare Framework Using Blockchain Technology with A Novel Simplified Swarm-Optimized Bayesian Normalized Neural Networks

The Internet of Things (IoT) is growing in popularity nowadays and has many potential uses, particularly in healthcare. A large amount of sensing data is generated from a variety of sensing devices as a result of the growing needs of the IoT. The use of artificial intelligence (AI) methods is crucial for real-time, scalable, and accurate data processing. However, there are certain obstacles in the way of the layout and implementation of a successful analysis of the big data approach. These include a lack of suitable training data, resource limits, and a centralized architecture for the data. However, emerging blockchain technology provides a distributed system. It is advocated for getting rid of centralized control and solving AI issues, and it allows for safe data and resource exchange across the many nodes of the IoT network. Thus, this research develops a novel simplified swarm-optimized Bayesian normalized neural network (SSO-BNNN) for the secret transmission of medical images to address the aforementioned challenge. The neighborhood indexing sequence (NIS) approach is also used to encrypt the hash value. Several experiments were conducted to verify the outcomes of the suggested approach, and several facets of those results are discussed. Experimental results show that the proposed excellent findings with the best accuracy, sensitivity, and specificity were produced by the model.

Dynamic Set Similarity Join: An Update Log Based Approach

The set similarity join finds all pairs of similar sets from two collections of sets. It has many real world applications, such as personalized recommendation and community mining. In this paper, we study the problem of computing the similarity join in a dynamic context, where the sets are updated dynamically. This, however, is inefficient with the state-of-the-art join methods, because they usually assume that data collections are static and have to compute the join result from scratch whenever a set is updated. To address this issue, we propose <inline-formula><tex-math notation="LaTeX">${{\sf ALJoin}}$</tex-math></inline-formula> , an adaptive filtering approach that computes the join result incrementally based on the update logs. We first investigate the effect of set updates on the similarity values, and on this basis we propose to build a neighborhood index for each set. The neighborhood index of a specific set consists of any other sets that can be transformed into its similar sets within a threshold number of update operations. <inline-formula><tex-math notation="LaTeX">${{\sf ALJoin}}$</tex-math></inline-formula> then uses this index to effectively identify both similar and dissimilar set pairs based on their update logs. To efficiently build the neighborhood index, we devise several filtering techniques and propose a “lazy-forward” method to reduce the computational cost. In addition, to improve the efficiency on varying workloads, we propose an analytical cost model, and design an online algorithm with performance guarantees to dynamically consolidate the update logs and adapt the neighborhood indexes. We evaluated our method using four real-world datasets. Experimental results show that our approach outperforms existing methods by up to <inline-formula><tex-math notation="LaTeX">$3.7\times$</tex-math></inline-formula> .

Inclusion of Race and Ethnicity With Neighborhood Socioeconomic Deprivation When Assessing COVID-19 Hospitalization Risk Among California Veterans Health Administration Users

Despite complexities of racial and ethnic residential segregation (hereinafter referred to as segregation) and neighborhood socioeconomic deprivation, public health studies, including those on COVID-19 racial and ethnic disparities, often rely on composite neighborhood indices that do not account for residential segregation. To examine the associations by race and ethnicity among California's Healthy Places Index (HPI), Black and Hispanic segregation, Social Vulnerability Index (SVI), and COVID-19-related hospitalization. This cohort study included veterans with positive test results for COVID-19 living in California who used Veterans Health Administration services between March 1, 2020, and October 31, 2021. Rates of COVID-19-related hospitalization among veterans with COVID-19. The sample available for analysis included 19 495 veterans with COVID-19 (mean [SD] age, 57.21 [17.68] years), of whom 91.0% were men, 27.7% were Hispanic, 16.1% were non-Hispanic Black, and 45.0% were non-Hispanic White. For Black veterans, living in lower-HPI (ie, less healthy) neighborhoods was associated with higher rates of hospitalization (odds ratio [OR], 1.07 [95% CI, 1.03-1.12]), even after accounting for Black segregation (OR, 1.06 [95% CI, 1.02-1.11]). Among Hispanic veterans, living in lower-HPI neighborhoods was not associated with hospitalization with (OR, 1.04 [95% CI, 0.99-1.09]) and without (OR, 1.03 [95% CI, 1.00-1.08]) Hispanic segregation adjustment. For non-Hispanic White veterans, lower HPI was associated with more frequent hospitalization (OR, 1.03 [95% CI, 1.00-1.06]). The HPI was no longer associated with hospitalization after accounting for Black (OR, 1.02 [95% CI, 0.99-1.05]) or Hispanic (OR, 0.98 [95% CI, 0.95-1.02]) segregation. Hospitalization was higher for White (OR, 4.42 [95% CI, 1.62-12.08]) and Hispanic (OR, 2.90 [95% CI, 1.02-8.23]) veterans living in neighborhoods with greater Black segregation and for White veterans in more Hispanic-segregated neighborhoods (OR, 2.81 [95% CI, 1.96-4.03]), adjusting for HPI. Living in higher SVI (ie, more vulnerable) neighborhoods was associated with greater hospitalization for Black (OR, 1.06 [95% CI, 1.02-1.10]) and non-Hispanic White (OR, 1.04 [95% CI, 1.01-1.06]) veterans. In this cohort study of US veterans with COVID-19, HPI captured neighborhood-level risk for COVID-19-related hospitalization for Black, Hispanic, and White veterans comparably with SVI. These findings have implications for the use of HPI and other composite neighborhood deprivation indices that do not explicitly account for segregation. Understanding associations between place and health requires ensuring composite measures accurately account for multiple aspects of neighborhood deprivation and, importantly, variation by race and ethnicity.

A New Tree-Level Multi-Objective Forest Harvest Model (MO-PSO): Integrating Neighborhood Indices and PSO Algorithm to Improve the Optimization Effect of Spatial Structure

Accurate, efficient, impersonal harvesting models play a very important role in optimizing stand spatial structural and guiding forest harvest practices. However, existing studies mainly focus on the single-objective optimization and evaluation of forest at the stand- or landscape-level, lacking considerations of tree-level neighborhood interactions. Therefore, the study explored the combination of the PSO algorithm and neighborhood indices to construct a tree-level multi-objective forest harvest model (MO-PSO) covering multi-dimensional spatial characteristics of stands. Taking five natural secondary forest plots and thirty simulated plots as the study area, the MO-PSO was used to simulate and evaluate the process of thinning operations. The results showed that the MO-PSO model was superior to the basic PSO model (PSO) and random thinning model Monte Carlo-based (RD-TH), DBH dominance (DOMI), uniform angle (ANGL), and species mingling (MING) were better than those before thinning. The multi-dimensional stand spatial structure index (L-index) increased by 1.0%~11.3%, indicating that the forest planning model (MO-PSO) could significantly improve the spatial distribution pattern, increase the tree species mixing, and reduce the degree of stand competition. In addition, under the four thinning intensities of 0% (T1), 15% (T2), 30% (T3), and 45% (T4), L-index increased and T2 was the optimal thinning intensity from the perspective of stand spatial structure overall optimization. The study explored the effect of thinning on forest spatial structure by constructing a multi-objective harvesting model, which can help to make reasonable and scientific forest management decisions under the concept of multi-objective forest management.