Despite the association of neighborhood quality with poorer adult health, limited research has explored the association between neighborhood disadvantage, e.g. Area Deprivation Index (ADI), and older Black adults\' health, prospectively. This observational study examined the association between ADI and changes in longitudinal physical health within older Black adults. The analytic sample (n = 317) included data from waves 1 & 2 of the Baltimore Study of Black Aging: Patterns of Cognitive Aging (BSBA-PCA). Study variables included the Area Deprivation Index (ADI), objective (e.g. average heart rate) and subjective (e.g. activities of daily living) measures of physical health. Multiple linear regression models were conducted controlling for sociodemographic and social support characteristics. Participants living in more disadvantaged neighborhoods, based on national and state ADIs, were more likely to have a decreasing heart rate even after adjusting for covariates. Likewise, participants reporting increasing levels of ADL difficulty were living in a neighborhood with greater disadvantage based on national and state ADI rankings. Significant social support received and ADI (national and state) interactions were observed for average heart rate. The findings suggest that research on the effect of neighborhood quality and social support can enhance our understanding of its impact on older Black adults\' health prospectively.

UNASSIGNED: Research suggests that the neighborhood in which people live can be a risk or protective factor for various health outcomes, including cognitive decline to Alzheimer\'s disease. Similar to the impact of neighborhood on health outcomes, sleep difficulties have been linked to cognitive function in older adults. However, few studies have examined how neighborhood physical disorders moderate the effects of sleep on subjective cognitive decline (SCD).
UNASSIGNED: The study examined the moderating effect of neighborhood factors on the relationship between sleep difficulties and SCD.
UNASSIGNED: Data were obtained from 2,494 respondents (1,065 males and 1,429 females) from Wave 11 of the National Health and Aging Trends (NHATS) data. Sleep difficulties were operationalized as the presence of difficulties in falling and staying asleep. Neighborhood physical disorder (e.g., vandalism, graffiti) was based on interviewer observations of respondents\' neighborhoods. SCD was operationalized as subjective reports of increasing or worse memory loss in the past 12 months and present memory rating. We utilized Linear regression to test neighborhood physical disorder as a moderator of the relationship between sleep difficulties and SCD.
UNASSIGNED: We found a significant interaction between sleep difficulties and neighborhood physical disorder on SCD (β=0.03, SE = 0.01, 95% CI[0.00,0.51], p < 0.001). Participants who reported higher average sleep difficulties and higher levels of neighborhood physical disorder were more likely to report SCD.
UNASSIGNED: Our findings add to inform future health interventions and policy recommendations that address modifiable sources of cognitive decline and risk of Alzheimer\'s disease.

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.