Background: The role of neighborhood factors in the association between adverse childhood experiences (ACEs) and body mass index (BMI) has not been widely studied. A neighborhood ACEs index (NAI) captures neighborhood environment factors associated with ACE exposure. This study examined associations between BMI and an NAI among New York City (NYC) youth. An exploratory objective examined the NAI geographic distribution across NYC neighborhoods. Methods: Data for students attending NYC public general education schools in kindergarten-12th grade from 2006-2017 (n = 1,753,867) were linked to 25 geospatial datasets capturing neighborhood characteristics for every census tract in NYC. Multivariable hierarchical linear regression tested associations between BMI and the NAI; analyses also were conducted by young (<8 years), school age (8-12 years), and adolescent (>12 years) subgroups. In addition, NAI was mapped by census tract, and local Moran\'s I identified clusters of high and low NAI neighborhoods. Results: Higher BMI was associated with higher NAI across all sex and age groups, with largest magnitude of associations for girls (medium NAI vs. low NAI: unstandardized β = 0.112 (SE 0.008), standardized β [effect size]=0.097, p < 0.001; high NAI vs. low NAI: unstandardized β = 0.195 (SE 0.008), standardized β = 0.178, p < 0.001) and adolescents (medium NAI vs. low NAI: unstandardized β = 0.189 (SE 0.014), standardized β = 0.161, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.364 (SE 0.015), standardized β = 0.334, p < 0.001 for adolescent girls; medium NAI vs. low NAI: unstandardized β = 0.122 (SE 0.014), standardized β = 0.095, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.217 (SE 0.015), standardized β = 0.187, p < 0.001 for adolescent boys). Each borough of NYC included clusters of neighborhoods with higher and lower NAI exposure, although clusters varied in size and patterns of geographic dispersion across boroughs. Conclusions: A spatial index capturing neighborhood environment factors associated with ACE exposure is associated with higher BMI among NYC youth. Findings complement prior literature about relationships between neighborhood environment and obesity risk, existing research documenting ACE-obesity associations, and the potential for neighborhood factors to be a source of adversity. Collectively, evidence suggests that trauma-informed place-based obesity reduction efforts merit further exploration as potential means to interrupt ACE-obesity associations.

BACKGROUND: Predisposing factors for traumatic injuries are complex and variable. Neighborhood environments may influence injury mechanism or outcomes. The Social Vulnerability Index (SVI) identifies areas at risk for emergencies; Area Deprivation Index (ADI) measures socioeconomic disadvantage. The objective was to assess the impact of SVI or ADI on hospital length of stay (LOS) and mortality for injured patients to determine whether SVI or ADI indicated areas where injury prevention may be most impactful.
METHODS: Adult patients who resided in Milwaukee County and were treated for injuries from 2015 to 2022 at a level I trauma center were included. Patients\' addresses were geocoded and merged with 2020 state-level SVI and ADI measures. SVI ranks census tracts 0-100 from least to most vulnerable. ADI ranks census block groups 1-10 from least to most disadvantaged. ADI and SVI rankings were converted to deciles. Statistical analyses included descriptive statistics, chi-square tests, and regression models for LOS and in-hospital mortality, adjusted for either SVI or ADI within separate models, age, sex, race or ethnicity, mechanism of injury (MOI), injury severity score (ISS).
RESULTS: 14,542 patients were included; 63 % were male. Mean total hospital LOS was 6.4 ± 9.8 days, and in-hospital mortalities occurred in 5.2 % of patients. Based on SVI and ADI, 5,280 (36 %) patients resided in high vulnerability areas and 5,576 (39 %) lived in highly disadvantaged areas, respectively. After adjusting for patient factors, SVI deciles #6, 9, 10 were associated with increased hospital LOS, and SVI decile #5 was associated with in-hospital mortality (OR = 2.22, 95 %CI:1.06-4.63; p = 0.034). When adjusted for ADI, the 7th-10th deciles were associated with increased hospital LOS. Greater age and ISS were associated with increased hospital LOS and mortality when adjusted for SVI and ADI.
CONCLUSIONS: SVI and ADI identified a similar proportion of patients in high vulnerability or disadvantaged areas. Higher SVI and ADI deciles were associated with longer hospital LOS, and only the 5th SVI decile was associated with in-hospital mortality. Highly disadvantaged or vulnerable areas may have a longer LOS, but SVI and ADI have limited influence on trauma mortality. Continued research on neighborhood and community factors and trauma outcomes is needed.

BACKGROUND: Population-based surveys suggest that low socioeconomic status (SES) is associated with higher prevalence of depressive symptoms, while their healthcare utilization is not necessarily higher.
OBJECTIVE: To investigate the association between neighborhood socioeconomic status (NSES) and healthcare utilization among individuals diagnosed with major depressive disorder (MDD).
METHODS: This was a retrospective longitudinal study of all adults with a first MDD diagnosis within primary care during 2010-2018. NSES was defined by the household area of residence using the Mosaic™ classification. Outcomes were AD (antidepressants) (N06A) dispensation and psychiatric outpatient visit, both of which are outlined as options in depression guidelines. Cox multivariable regression was used for the time to event analyses.
RESULTS: A total of 117,193 individuals were included, of which 87,499 (75 %) were dispensed an AD and 35,989 (31 %) had a recorded psychiatric outpatient visit. Low NSES was associated with lower rate of AD dispensation in the first-year post-diagnosis (HR: 0.95, 95 % CI: 0.93-0.96, p < 0.001) and higher rate of psychiatric visit (HR: 1.10, 95 % CI: 1.07-1.12, p < 0.001) compared with high NSES.
CONCLUSIONS: Data sources have high coverage. A minority of psychiatric care provided by non-publicly financed providers was not included. It was not possible to adjust for depression severity.
CONCLUSIONS: Socioeconomic status as measured by the neighborhood of residency was associated with AD dispensation and psychiatric outpatient visit in MDD, also in a healthcare system with virtually free access. This is of relevance for clinical practice, considering the focus on equity of care and the increase in depression prevalence worldwide.

OBJECTIVE: To investigate ethnic inequities in, and social determinants of, adolescent sleep health in Aotearoa New Zealand.
METHODS: Analysis of self-report data from a cross-sectional survey of secondary school students (12- to 18-year-olds). Analyses included weighted prevalence estimates of good and poor sleep health stratified by ethnicity, and multivariable logistic regression models concurrently adjusted for ethnicity, school year, gender, rurality, neighborhood deprivation, school decile, housing deprivation, sleeping elsewhere due to lack of adequate housing, unsafe environment, and racism.
RESULTS: Inequities in social determinants of health were evident for Māori (Indigenous peoples of Aotearoa New Zealand; n = 1528) and minoritized (Pacific n = 1204; Asian n = 1927; Middle Eastern, Latin American, and African [MELAA] n = 210; and \'Other\' ethnicity n = 225) adolescents. A greater proportion of Māori, Pacific, Asian, MELAA, and \'Other\' adolescents had short sleep, compared to European (n = 3070). Māori, Pacific, Asian, and MELAA adolescents were more likely to report late bedtimes (after midnight), and Māori, Pacific, and \'Other\' adolescents were more likely to report early waketimes (5 AM-6 AM or earlier), on school days. Rurality, neighborhood deprivation, school-level deprivation, housing deprivation, sleeping elsewhere due to inadequate housing, unsafe environments, and racism partially, but not fully, explained associations between ethnicity and short sleep, late bedtimes, and early waketimes.
CONCLUSIONS: Ethnic inequities exist in adolescent sleep health in Aotearoa New Zealand. Socio-political actions are needed to address racism and colonialism as root causes of ethnic inequities in adolescent sleep, to ensure all young people are afforded the basic human right of good sleep health and associated mental and physical well-being.

Effect modification of integrated neighborhood environment on associations of air pollution with mortality remained unclear. We analyzed data from UK biobank prospective study (n = 421,650, median 12.5 years follow-up) to examine disparities of mortality risk associated with air pollution among varied neighborhood settings. Fine particulate matter (PM2.5), PM10 and nitrogen dioxide (NO2) were measured and assigned to each participants\' address. Diverse ecological and societal settings of neighborhoods were integrated with principal component analysis and categorized into disadvantaged, intermediate and advantaged levels. We estimated mortality risk associated with air pollution across diverse neighborhoods using Cox regression. We calculated community-level proportions of mortality attributable to air pollutants. There was evidence of higher all-cause and respiratory disease mortality risk associated with PM2.5 and NO2 among those in disadvantaged neighborhoods. In disadvantaged communities, air pollutants explained larger proportions of deaths and such disparities persisted over past decades. Across 2010-2021, reducing PM2.5 and NO2 to 10 μg/m3 (World Health Organization limits) would save 87,000 (52,000-120,000) and 91,000 (37,000-145,000) deaths of populations aged ≥ 40 years, with 150 000 deaths occurred in disadvantaged neighborhood settings. These findings suggested that disadvantaged neighborhoods can exacerbate mortality risk associated with air pollution.

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback-Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

UNASSIGNED: Racial disparities in health outcomes are a persistent threat in gentrifying neighborhoods. A contributor to health outcomes is health services utilization, the extent to which people receive care from a medical professional. There are documented racial disparities in health services utilization in the general population. We aim to determine whether racial disparities in health services utilization exist in gentrifying neighborhoods.
UNASSIGNED: We used data from the American Community Survey to identify gentrifying neighborhoods across the United States from 2006 to 2017. We collected data on three measures of healthcare services utilization (office-based physician visits, office-based nonphysician visits, and having a usual source of care) for 247 Black and 689 White non-Hispanic respondents of the 2014 Medical Expenditure Panel Survey living in gentrifying neighborhoods. We used modified Poisson models to determine whether there is a difference in the prevalence of health services utilization by race among residents of gentrifying neighborhoods.
UNASSIGNED: After adjusting for age, gender, education, income, employment, insurance, marital status, region, and self-rated health, Black residents of gentrifying neighborhoods demonstrated a similar prevalence of having an office-based physician visit, a lower prevalence of having an office-based nonphysician visit (prevalence ratio: 0.74; 95% confidence interval, 0.60 to 0.91), and a lower prevalence of having a usual source of care (prevalence ratio: 0.87; 95% confidence interval, 0.77 to 0.98) than White residents.
UNASSIGNED: The existence of racial disparities in health services utilization in US gentrifying neighborhoods demonstrates a need for policy-relevant solutions to create a more equitable distribution of health resources.

OBJECTIVE: Though research indicates that certain aspects of adverse neighborhood conditions may influence weight development in childhood and adolescence, it is unknown if the Child Opportunity Index (COI), a composite measure of 29 indicators of neighborhood conditions, is associated with weight outcomes in adolescence. We hypothesized that lower COI would be associated with higher overweight and obesity in cross-sectional and longitudinal modeling in a national sample of 9 year olds and 10 year olds and that this association would be different by sex.
METHODS: Using data from the Adolescent Brain Cognitive Development study (n = 11,857), we examined the cross-sectional association between COI quintile and overweight and obesity in 9 year olds and 10 year olds. Additionally, we used hazard ratios to examine incident overweight and obesity across three waves of data collection.
RESULTS: Due to the interaction between sex and COI (p < .05), we present sex-specific models. There was a stepwise bivariate association, in which higher COI was associated with lower obesity prevalence. This pattern held in multilevel models, with a stronger association in females. In models adjusted for individual and household characteristics, female adolescents in the lowest quintile COI neighborhoods had 1.81 (95% confidence interval: 1.32, 2.48) times the odds of obesity compared to those in the highest quintile. In longitudinal models, the COI was associated with incident obesity in females only: adjusted hazard ratio = 4.27 (95% confidence interval: 1.50, 12.13) for lowest compared to highest COI.
CONCLUSIONS: Neighborhood opportunity is associated with risk of obesity in pre-adolescence into mid-adolescence. Females may be particularly influenced by neighborhood conditions.

BACKGROUND: The extent to which incidence rates of asthma-related emergency department (ED) visits vary from neighborhood to neighborhood and predictors of neighborhood-level asthma ED visit burden are not well understood.
OBJECTIVE: We aimed to describe the census tract-level spatial distribution of asthma-related ED visits in Central Texas and identify neighborhood-level characteristics that explain variability in neighborhood-level asthma ED visit rates.
METHODS: Conditional autoregressive models were used to examine the spatial distribution of asthma-related ED visit incidence rates across census tracts in Travis County, Texas, and assess the contribution of census tract characteristics to their distribution.
RESULTS: There were distinct patterns in ED visit incidence rates at the census tract scale. These patterns were largely unexplained by socioeconomic or selected built environment neighborhood characteristics. However, racial and ethnic composition explained 33% of the variability of ED visit incidence rates across census tracts. The census tract predictors of ED visit incidence rates differed by racial and ethnic group.
CONCLUSIONS: Variability in asthma ED visit incidence rates are apparent at smaller spatial scales. Most of the variability in census tract-level asthma ED visit rates in Central Texas is not explained by racial and ethnic composition or other neighborhood characteristics.

UNASSIGNED: Research on links between social, geographic, and cultural determinants of health has been thwarted by inadequate measures of culture. The purpose of this study was to improve the measurement of community culture, defined as shared patterns of attitudes and behaviors among people within a neighborhood that distinguish it from others, and to examine dimensions of culture, independent of socioeconomic and demographic factors, and their relationships with health.
UNASSIGNED: A survey research design with correlational analyses was used.
UNASSIGNED: A survey packet including the Community Culture Survey - Revised (CCS-R), demographic, health, and other individual-level measures was administered through convenience sampling across the United States (US) and to a sample in Thailand from 2016 to 2018. US county-level variables were obtained from zip codes.
UNASSIGNED: 1930 participants from 49 US states (n = 1592) and Thailand (n = 338) completed all CCS-R items, from which 12 subscales were derived: Social Support & Connectedness, Responsibility for Self & Others, Family Ties & Duties, Social Distress, Urban Diversity, Discontinuity, Church-Engaged, External Resource-Seeking, Locally Owned Business-Active, Power Deference, Next Generation Focus, and Self-Reliance. Neighborhood culture subscale scores varied more by geography than by participant\'s demographics. All subscales predicted one or more health indicator, and some of these relationships were significant after adjusting for participant age and county-level socioeconomic variables. Most of the significant differences on subscales by race/ethnicity were no longer significant after adjusting for participant\'s age and county-level socioeconomic variables. Most rural/urban and regional differences in culture within the US persisted after these adjustments. Based on correlational analyses, Social Support & Connectedness and Responsibility for Self & Others were the best predictors of participants\' overall health and quality of life, and Responsibility for Self & Others was the best predictor (inversely) of the CDC\'s measures of social vulnerability.
UNASSIGNED: Neighborhood culture is measurable, multi-dimensional, distinct from race/ethnicity, and related to health even after controlling for age and socioeconomic factors. The CCS-R is useful for advancing research and practice addressing the complex interactions between individuals, their neighborhood communities, and health outcomes.