Individual Neighborhood Research Articles

Individual socioeconomic position, neighbourhood disadvantage and mental well-being: a cross-sectional multilevel analysis of mid-age adults

BackgroundSocioeconomic disadvantage is associated with mental illness, yet its relationship with mental well-being is unclear. Mental well-being is defined as feeling good and functioning well. Benefits of mental well-being include reduced mortality, improved immune functioning and pain tolerance, and increased physical function, pro-social behaviour, and academic and job performance. This study aimed to explore the relationship between individual socioeconomic position (SEP), neighbourhood disadvantage and mental well-being in mid-age adults.MethodsMultilevel modelling was used to analyse data collected from 7866 participants from the second (2009) wave of HABITAT (How Areas in Brisbane Influence healTh and activiTy), a longitudinal study (2007–2016) of adults aged 40–65 years living in Brisbane, Australia. Mental well-being was measured using the Warwick Edinburgh Mental Well-Being Scale (WEMWBS). Exposure measures were education, occupation, household income, and neighbourhood socioeconomic disadvantage.ResultsThe lowest MWB scores were observed for the least educated (β = − 1.22, 95%CI = − 1.74, − 0.71), those permanently unable to work (β = − 5.50, 95%CI = − 6.90, − 4.10), the unemployed (β = − 2.62, 95%CI = − 4.12, − 1.13), and members of low-income households (β = − 3.77, 95%CI = − 4.59, − 2.94). Residents of the most disadvantaged neighbourhoods had lower MWB scores than those living in the least disadvantaged neighbourhoods, after adjustment for individual-level SEP (β = − 0.96, 95%CI = − 1.66, − 0.28).ConclusionsBoth individual-level SEP and neighbourhood disadvantage are associated with mental well-being although the association is stronger for individual-level SEP. This research highlights the need to address individual and neighbourhood-level socioeconomic determinants of mental well-being.

MING: An interpretative support method for visual exploration of multidimensional data

Dimensionality reduction enables analysts to perform visual exploration of multidimensional data with a low-dimensional map retaining as much as possible of the original data structure. The interpretation of such a map relies on the hypothesis of preservation of neighborhood relations. Namely, distances in the map are assumed to reflect faithfully dissimilarities in the data space, as measured with a domain-related metric. Yet, in most cases, this hypothesis is undermined by distortions of those relations by the mapping process, which need to be accounted for during map interpretation. In this paper, we describe an interpretative support method called Map Interpretation using Neighborhood Graphs (MING) displaying individual neighborhood relations on the map, as edges of nearest neighbors graphs. The level of distortion of those relations is shown through coloring of the edges. This allows analysts to assess the reliability of similarity relations inferred from the map, while hinting at the original structure of data by showing the missing relations. Moreover, MING provides a local interpretation for classical map quality indicators, since the quantitative measure of distortions is based on those indicators. Overall, the proposed method alleviates the mapping-induced bias in interpretation while constantly reminding users that the map is not the data.

Abstract 075: Sociodemographic And Pregnancy Related Differences In Blood Pressure Phenotypes A Decade Following Pregnancy

Introduction: Hypertensive disorders of pregnancy (HDP) such as preeclampsia are associated with a 3-fold increased risk of chronic hypertension (HTN). Office based measurements alone results in frequent HTN misclassification, prompting the need for out-of-office measurements to ensure proper classification. Hypothesis: We hypothesized that individual and neighborhood level sociodemographic factors and adverse pregnancy outcomes are different within the BP phenotypes of persistent normotension, and sustained, white-coat, masked, and isolated diastolic HTN. Methods: Data were from 401 women who gave birth between 2008 - 2009 and participated in a research study 8-10 years following pregnancy. We determined BP phenotypes by comparing the mean office BP (3 measures) and home BP (13 measures) assessed following research protocols. HTN was defined as BP ≥130/80. Sociodemographic characteristics were age, race, education, adiposity, and perceived stress. Neighborhood characteristics were from the CDC Social Vulnerability Index (i.e., socioeconomic status, household composition, minority status, housing type) measured at the census tract level, with higher values representing greater vulnerability. Chi-square determined differences in BP phenotypes for categorical data, and ANOVA for continuous data, both with Tukey correction. Results: Participants had a mean age of 37.6 (5.9), 15.1 (2.6) years of education, 34.7% (n=155) were Black and 22.8% (n=102) had a history of HDP. BMI and central adiposity were higher in all BP phenotypes compared to the normotensive group. Women with sustained HTN ( 6.2%, n=25) and isolated diastolic HTN (18.2%, n=73,) were more likely to be Black and have a history of HDP. However, only women with isolated diastolic HTN were more likely to have less education and higher stress scores. Compared to the normotensive women, those with masked HTN (n=25, 6.2%) tended to have a history of HDP. The BP phenotype groups did not differ by neighborhood level factors, although there was a trend toward lower neighborhood socioeconomic status among the isolated diastolic and masked HTN groups (p< .10). Conclusion: Isolated diastolic HTN was the most common adverse BP phenotype in this sample of young women, and was more common among Black women and those with a history of HDP. Isolated diastolic HTN may be more prevalent than generally recognized in clinical practice. Research is needed to determine the long-term implications of this phenotype, especially among women with a history of HDP. BP phenotypes differed on individual characteristics and neighborhood socioeconomic status, however, neighborhood characteristics such as violence, air quality, and cohesiveness should be considered in future research. This research was supported by T32HL007560 (PIs: Thurston & Gianaros, Trainee: Scott) and AHA award 16SFRN28930000 (PI: Catov).

Effect of socioeconomic status on survival in patients on the Diabetes Shared Care Program: Finding from a Taiwan nationwide cohort.

The Diabetes Shared Care Program (DSCP) is an integrated care model in Taiwan to improve the care quality of patients with diabetes. Socioeconomic status (SES) is one of the important factors affecting health, and it is confirmed as a predictor of various diseases and deaths.This study aimed to determine the relationship between survival rate and SES among patients who participated in the DSCP. A cohort population-based study was conducted using the National Health Insurance Research Database of Taiwan from 2008 to 2013. The study subjects were type 2 diabetes. We defined individual SES and neighborhood SES by each patient's job category and household income, which were characterized as advantaged or disadvantaged. Then we compared the survival rates of SES groups by Cox proportional hazards model to adjust risk factors. This study included 16 614 patients with type 2 diabetes who participated in the DSCP program. The DSCP cohort showed a high hospitalization rate in low individual SES. In terms of 10-year overall survival, DSCP participants with high individual SES living in advantaged and disadvantaged neighborhoods had lower risk of mortality than those with low SES living in advantaged and disadvantaged neighborhoods, after adjustment for age and comorbidity. DSCP participants with low individual SES living in disadvantaged neighborhoods had no significant difference of mortality as those with low individual SES living in advantaged neighborhoods. In this study, we found that low individual SES, but not neighborhood SES, was associated with an increased mortality rate among DSCP participants.

Assessment of Racial Disparity in Survival Outcomes for Early Hormone Receptor–Positive Breast Cancer After Adjusting for Insurance Status and Neighborhood Deprivation

Racial disparities in survival outcomes among Black women with hormone receptor-positive breast cancer have been reported. However, the association between individual-level and neighborhood-level social determinants of health on such disparities has not been well studied. To evaluate the association between race and clinical outcomes (ie, relapse-free interval and overall survival) adjusting for individual insurance coverage and neighborhood deprivation index (NDI), measured using zip code of residence, in women with breast cancer. This was a post hoc analysis of 9719 women with breast cancer in the Trial Assigning Individualized Options for Treatment, a randomized clinical trial conducted from April 7, 2006, to October 6, 2010. All participants received a diagnosis of hormone receptor-positive, ERBB2-negative, axillary node-negative breast cancer. The present data analysis was conducted from April 1 to October 22, 2021. A multivariate model was developed to evaluate the association between race and relapse-free interval and overall survival adjusting for insurance and NDI level at study entry, early discontinuation of endocrine therapy 4 years after initiation, and clinicopathologic characteristics of cancer. Median follow-up for clinical outcomes was 96 months. A total of 9719 women (4.2% [n = 405] Asian; 7.1% [n = 693] Black; 84.3% [n = 8189] White; 4.4% [n = 403] others/not specified) were included; 9.1% of included women [n = 889] were Hispanic or Latino. Median (SD) age was 56 (9.2) years. In multivariate models, Black race compared with White race was associated with statistically significant shorter relapse-free interval (hazard ratio [HR], 1.39; 95% CI, 1.05-1.84; P = .02) and overall survival (HR, 1.49; 95% CI, 1.10-2.99; P = .009), adjusting for insurance and NDI level at study entry and other factors. Although uninsured status was not associated with clinical outcomes, patients with Medicare (HR, 1.30; 95% CI, 1.01-1.68; P = .04) and Medicaid (HR, 1.44; 95% CI, 1.01-2.05; P = .05) had shorter overall survival compared with those with private insurance. Participants living in neighborhoods in the highest NDI quartile experienced shorter overall survival compared with those in the lowest quartile (HR, 1.34; 95% CI, 1.01-1.77; P = .04), regardless of self-identified race. The findings of this post hoc analysis of a randomized clinical trial suggest that Black women with breast cancer have significantly shorter relapse-free interval and overall survival compared with White women. Early discontinuation of endocrine therapy, clinicopathologic characteristics, insurance coverage, and NDI do not fully explain the observed disparity. ClinicalTrials.gov Identifier: NCT00310180.

Considering urban uses at a fine spatial resolution to understand the distribution of invasive plant species in cities

Cities are heterogeneous landscapes, composed of different urban uses with diverse histories. A thorough description of these urban landscapes is required to understand ecological patterns, particularly concerning high-stakes species such as invasive alien plants (IAP) which are abundant in cities. We assessed the effects of urban uses and linear structural elements on the distributions of seven IAP in a medium-sized French city (Blois). We examined how the relative contributions of these variables vary between three spatial scales of analysis (100 m, 200 m and 400 m) and three urban classifications. We characterized the use and age of urban neighborhoods through three classifications with different levels of details and described road and rail networks of Blois. We analyzed their effects on the distribution of seven invasive plants recorded throughout the whole city using GLM models. Urban use was the most significant variable in explaining IAP distribution. This variable was especially important at the finest spatial scale which allowed a detailed urban classification. Individual residential neighborhoods of different ages explained the distribution of Buddleja davidii, Berberis aquifolium and Acer negundo, whereas industrial areas explained Ailanthus altissima distribution. The effects of linear structural elements were lower and differed between species. We show that combining fine-scale spatial analyses with detailed characterization of urban use diversity is critical to understanding ecological patterns at the city scale. Investigating planting choices and dispersal process of IAP could complete our results. Urban descriptions based on explicit urban forms could also contribute to understanding species distribution in cities.

Energy Systems and Energy Sharing in Traditional and Sustainable Archetypes of Urban Developments

Diverse factors influence the energy profile of an urban development including density, shape of buildings and their types, energy demand, and available energy resources. A systematic investigation of the energy characteristics of urban areas, involves the determination of representative archetypes of urban developments. This study presents a comparison of energy performance and resources between two categories of traditionally built urban development building clusters (BCs) in the North American urban context, and neighborhood units (NUs) designed with various sustainable principles and considerations. The study presents a methodology to optimize the mix of energy resources of individual building clusters and neighborhoods, as well as the optimization of energy sharing among the individual urban units of each category. Optimal energy sharing is determined based on the best combination of energy deficit and energy surplus of various clusters and neighborhoods. The study shows that in general neighborhood units encompassing diverse building uses and designed to allow different amenities within a walking distance perform better than commonly built building clusters with low usage diversity. Highly diverse neighborhoods that combine large commercial areas to high density residential buildings can generate up to 84% of their annual electrical and up to 37% of their annual thermal consumption. PV generation accounts for major part of the electrical energy generation of both individual urban units (BCs and NUs) and combination of these units. This can reach up to 92% of the total energy consumption of some combinations of NUs, while the remaining energy requirement is fulfilled by wind and waste to energy (3.4% and 4.9%, respectively). On the other hand, the study shows that thermal energy is mostly supplied by alternative energy sources, since building surfaces prioritize the accommodation of PV modules.

CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization

In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversity and convergence. Specifically, the cellular automata model arranges the population of individuals in a 2D grid spatial structure where the concepts of cells and their neighbors are considered to drive the evolution step. A recent population-based algorithm is proposed called the crow search algorithm (CSA). It stimulates the behavior of crows in keeping their food in hidden places to be retrieved when needed. Like other population-based algorithms, CSA endures from slow convergence because of inadequate diversity. In this paper, the cellular automata model is incorporated with the optimization framework of CSA to control its diversity during the search, thus boosts its efficiency. The proposed method is abbreviated as CCSA. In CCSA, the population is structured as a 2D grid where the active population is iteratively determined. At each iteration, each individual is updated based on current neighboring individuals determined by the topological neighborhood shapes and follows the best neighboring individual in its active population. The CCSA is evaluated using 23 standard benchmark functions well-circulated in the literature. Initially, six cellular topological neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are studied with various problem sizes to investigate their impact on the CCSA convergence. Comparative evaluations against twelve state-of-the-art methods, including four CSA versions, are conducted. The comparative results prove the effectiveness of the proposed CCSA. The diversity of the proposed CCSA has also been stressed using convergence analysis and hamming distance. For further validations, the proposed algorithm is evaluated using three real-world problems published in IEEE-CEC2011 and welded beam design problems. Again, the proposed CCSA yields more fruitful results than other well-established methods using real-world problems. In conclusion, this paper provides an efficient alternative of CSA to perfectly maintain the diversity of the search, which can be further tested using other optimization problems.

Electronic Media Review

Electronic Media Review

Role of solar energy in achieving net zero energy neighborhoods

This paper aims at determining and highlighting the role of solar energy in achieving net zero energy status, in various neighborhood archetypes. The neighborhoods considered in this study include small neighborhood units and combinations of these units, designed according to sustainable practices in a North American context. The role of solar energy to fulfill the thermal and electrical energy of the studied neighborhoods is compared to other renewable and alternative energy resources such as wind energy, and waste-based energy. Results show that solar energy can play very significant role in fulfilling all electrical needs, even in high density and highly mixed developments, supplying between 36%-100% of the total electrical energy consumption of individual neighborhood units. Optimal combinations of neighborhood units designed to share excess on-site energy generation, assist in achieving net zero electrical energy status of all combinations, relying on roof integrated PV, on the buildings of these neighborhood combinations. Additional landscape area is required in most of the neighborhoods, especially those with higher residential density, to integrate solar thermal collectors that can fulfill the thermal loads of the neighborhood units. The required land area for thermal collectors’ integration changes significantly among neighborhoods, becoming relatively small as compared to the total land of the neighborhood (maximum of 15%), in large neighborhoods composed of optimal combinations of individual neighborhood units.

Housing, Neighborhood Factors, and Cognitive Status of Older Adults Living in Houses Versus Apartments

This study examines associations between housing, neighborhood factors and cognitive status among community-dwelling older adults, and how the associations differ between older adults who live in houses and in apartment buildings. Specifically, using the neighborhood stressor theory, three research questions are examined: 1) What individual-level factors predict late-life cognitive status? 2) After controlling for individual-level factors, what housing and neighborhood factors are significant in predicting older adults’ cognitive status? 3) How do individual, housing, neighborhood predictors of cognitive status differ between house and apartment residents? Using data from the Wave 3 NSHAP, multilevel linear regression analyses are conducted with the total sample. Results suggest that individual-level factors including young-age, female, white, and having a bachelor’s degree are associated with better cognitive status. After controlling for individual-level factors, housing and neighborhood factors including quality maintenance and high level of community safety are associated with higher cognitive scores. In addition to the additive model, we also test the interactive effect between housing type and three level of factors –individual level, housing, and neighborhood factors. Findings suggest that the joint effect of depression and housing type on cognitive status is significant. To explore the last research question, we conduct parallel regression analyses by housing type. Findings suggest that quality maintenance and high level of community safety are associated with higher cognitive scores among house residents only. Findings highlight the predictors of cognitive health vary between older adults living in different residential environments.

Housing, Neighborhood Factors, and Cognitive Status of Older Adults Living in Houses Versus Apartments

This study examines associations between housing, neighborhood factors and cognitive status among community-dwelling older adults, and how the associations differ between older adults who live in houses and in apartment buildings. Specifically, using the neighborhood stressor theory, three research questions are examined: 1) What individual-level factors predict late-life cognitive status? 2) After controlling for individual-level factors, what housing and neighborhood factors are significant in predicting older adults’ cognitive status? 3) How do individual, housing, neighborhood predictors of cognitive status differ between house and apartment residents? Using data from the Wave 3 NSHAP, multilevel linear regression analyses are conducted with the total sample. Results suggest that individual-level factors including young-age, female, white, and having a bachelor’s degree are associated with better cognitive status. After controlling for individual-level factors, housing and neighborhood factors including quality maintenance and high level of community safety are associated with higher cognitive scores. In addition to the additive model, we also test the interactive effect between housing type and three level of factors –individual level, housing, and neighborhood factors. Findings suggest that the joint effect of depression and housing type on cognitive status is significant. To explore the last research question, we conduct parallel regression analyses by housing type. Findings suggest that quality maintenance and high level of community safety are associated with higher cognitive scores among house residents only. Findings highlight the predictors of cognitive health vary between older adults living in different residential environments.

Causal evaluation of the health effects of light rail line: A natural experiment

Background and objectiveNo research to date has causally linked built environment data with health care costs derived from clinically assessed health outcomes within the framework of longitudinal intervention design. This study examined the impact of light rail transit (LRT) line intervention on health care costs after controlling for mode-specific objectively assessed moderate-to-vigorous physical activity (MVPA), participant-level neighborhood environmental measures, demographics, attitudinal predispositions, and residential choices. Data and methodsBased on a natural experiment related to a new LRT line in Portland – 282 individuals divided into treatment and control groups were prospectively followed during the pre- and post-intervention periods. For each individual, we harness high-resolution data on Electronic Medical Record (EMR) based health care costs, mode-specific MVPA, survey-based travel behavior, attitudinal/perception information, and objectively assessed built environment measures. Simulation-assisted longitudinal grouped random parameter models are developed to gain more accurate insights into the effects of LRT line intervention. ResultsRegarding the “average effect” of the LRT line intervention, no statistically significant reductions in health care costs were observed for the treated individuals over time. However, substantial heterogeneity was observed not only in the magnitude of effects but its direction as well after controlling for the within- and between-individual variations. For a subgroup of treated individuals, the LRT line opening decreased health care costs over time relative to the control group. Further comparative analysis based on the findings of heterogeneity-based models revealed that the effect of LRT intervention for the treated individuals differed by individual characteristics, attitudes/perceptions, and neighborhood level environmental features. ConclusionsThe study revealed the presence of significant effect modifiers and distinct subgroup structures in the data related to the effects of LRT line intervention on health care costs. Severe implications of ignoring unobserved heterogeneity are highlighted. Limitations and potential avenues for future research are discussed.

Effect of individual, household and regional socioeconomic factors and PM2.5 on anaemia: A cross-sectional study of sub-Saharan African countries

There is limited knowledge on the effect of contextual and environmental factors on the risk of anaemia, as well as the spatial distribution of anaemia in the Sub-Saharan Africa region. In this study, we used multi-country data from the Demographic & Health survey (DHS) with 270,011 observations and PM2.5 data from NASA, applied to the spatial risk pattern of anaemia in the SSA region. The prevalence of anaemia amongst women (41%) was almost twice that of men (22%). A Bayesian hierarchical model showed that individual household, neighbourhood and regional socioeconomic factors were significantly associated with the likelihood of being anaemic. 1 μg/m3 increase in cumulative lifetime PM2.5 exposure accounted for 1% (β = 0.011, CI = 0.008 – 0.015) increase in the likelihood of being anaemic. The results suggest the need for a multidimensional approach to tackle anaemia in the Sub-Saharan African region and identify high-risk areas for target intervention policies or programs.

Multi-Strategy Ensemble Non-Dominated Sorting Genetic Algorithm and Application in Energy-Enviro-Economic Multi-Objective Optimization of Isopropyl Alcohol/Isopropyl Ether Separation Process

The separation of isopropyl alcohol (IPA)/isopropyl ether (IPE) is crucial for the recovery of valuable solvents from alcohol ether waste liquids, but involves high energy consumption and emissions. Therefore, the multi-objective optimization of energy, economy and environment in the IPA/IPE separation process is conducive to the sustainable development of the organic solvent industry. Considering the slow convergence and the tendency to fall into local optimum for the most commonly used multi-objective optimization method, namely non-dominant sorting genetic algorithm-II (NSGA-II), a novel multi-strategy ensemble non-dominated sorting genetic algorithm (MENSGA-II) is proposed. In this algorithm, two evolution strategies based on individual neighborhood guidance and random walk are developed, aiming at accelerating convergence speed and enhancing search ability, and a selection strategy based on double space is proposed to improve the distribution of the Pareto front obtained. The MENSGA-II is tested on the benchmark functions and the actual separation process of IPA/IPE. The results prove that the proposed MENSGA-II algorithm has superiority in robustness, convergence speed and distribution of Pareto front. Compared with the actual operating condition, the annual exergy destruction and carbon emission can be reduced by 18.49% and 15.47% when the annual gross profit increases by 3.24% via multi-objective optimization.

Exploring neighbourhood-level mobility inequity in Chicago using dynamic transportation mode choice profiles

This paper develops a method to dynamically model urban passenger mode trade-offs at fine-grained spatial and temporal scales using data from OpenTripPlanner (OTP) and the City of Chicago’s Transportation Network Providers (TNP) dataset. This approach can be used to calculate dynamic modal cost-distance trade-offs for specific times, routes, and geographic areas of interest, providing a framework for creating aggregate mode choice profiles for individual cities and neighbourhoods that can be used to assess structural differences in transportation investment and mobility, as well as to test various assumptions about travel behaviour, observe temporal changes in modal trade-offs, and model the system-wide implications of changes to the transportation system to modal trade-offs. Using this dynamic mode choice framework, this paper explores the features underlying observed structural heterogeneity in the ratio of cost to distance (i.e., speed or potential mobility) for observed flows across the city for each mode. It finds that Census tracts with larger proportions of Black and Hispanic population tend to have significantly larger cost-distance ratios (i.e., slower speeds/lower potential mobility) for non-auto modes, while Census tracts with higher proportions of “creative class” employment and features of walkable built environments have significantly lower cost-distance ratios (i.e., faster speeds/higher potential mobility).

Dual Use of Off-Premise Outlets for Alcohol and Grocery Purchases: Results From the East Bay Neighborhoods Study

Alcohol privatization efforts have enabled consumers in many states in the United States to purchase alcohol in a variety of off-premise outlets, including grocery stores. This study examines the dual use of outlets to purchase both alcohol and groceries and the extent to which dual use is related to individual income, neighborhood income, and local physical availability of alcohol. The East Bay Neighborhoods Study surveyed residents from 72 microecological neighborhoods across six cities in Alameda County, California. Eligible respondents who purchased alcohol in the last year (n = 707) were asked about the off-premise outlet in which they most often purchase alcohol. Purchasing behaviors within this outlet, including purchasing groceries and frequency of visits to the outlet, were assessed. Multilevel logistic regression and negative binomial models assessed outcomes. Seventy-three percent of participants reported dual use of their most used outlet for groceries and alcohol. In adjusted models, dual use of the outlet was not associated with individual income, alcohol use, or neighborhood outlet density but was positively associated with neighborhood income and the number of outlet visits. After adjustment, dual use of the most used outlet was associated with 0.77 more visits per 28 days. Results suggest that convenience and reduced time costs for purchasing alcohol encourage the dual use of outlets, particularly in high-income neighborhoods. Differences in the dual use of outlets and frequency in outlet use by neighborhood environments highlight the importance of understanding the role of alcohol availability in distinct neighborhood income and outlet-level contexts.

Neighborhood greenspace and cardiometabolic risk factors: Cross-sectional and longitudinal analysis in ELSA-Brasil participants.

Greater neighborhood greenspace has been associated with better cardiometabolic risk factors, especially in high-income countries. This cross-sectional and longitudinal study assessed this association in approximately 2000 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) residing in Belo Horizonte, a large Brazilian capital city. Neighborhood greenspace was studied through the Normalized Difference Vegetation Index (NDVI) and two additional types, percentages of tree cover and herbaceous cover. Multivariable logistic regression models estimated the cross-sectional and longitudinal associations of neighborhood greenspace with three metabolic factors - (i) obesity, (ii) abdominal obesity, and (iii) low HDL-cholesterol - after adjustment for individual sociodemographic factors and neighborhood average household income per capita. Cross-sectional results showed that higher neighborhood greenspace was associated with lower odds of obesity, abdominal obesity and low HDL-c. However, neighborhood greenspace was not associated with the incidence of any of these risk factors. The percentage of tree cover seemed to contribute more to the associations found with NDVI than the percentage of herbaceous cover. The results support the evidence that increased neighborhood greenspace contributes to maintain a better cardiometabolic health.

Linking urban scenarios with energy simulations for dense urban planning under climate change

This research aims at linking Urban Planning, Energy Simulations and Climate Change projections into the year 2100 for hot climates. The workflow of going back and forth between urban and city scale plans and individual neighborhood parcels to building scale, for the sake of simulating energy demand for a given city into the future is complex. It is prone to rely on many assumptions and simplifications in order to aid the simulations. In this work, we streamline the process with new computational tools, with the goal of communicating a more precise impact of building scale and neighborhood morphological scale design and retrofit strategies in order to meet energy reduction and carbon emission targets focusing on 2030, 2050 and 2100. Urban scenarios are developed using Envision Tomorrow. The building archetypes used therein are associated with energy demand profiles which we simulate using EnergyPlus for various climate change scenarios to improve the forecasting ability of Envision Tomorrow. Denser developments yield far lower neighborhood energy use.

Neighborhood-level predictors of African American and Latinx parents' ethnic-racial socialization.

Few studies examine how neighborhood structural factors (e.g., socioeconomic status [SES] and diversity) and perceived disorder may influence the messages parents communicate to their youth about race/ethnicity. Guided by the integrative model and social disorganization theory, this study examines how parents' ethnic-racial socialization messages (ERS) are shaped by the broader environment. Data come from the MacArthur Studies of Diversity in Midlife. Latinx and African American parents N = 508 (Mage = 39) with a child between 6- and 17-years old living in two urban US cities were included. Cultural perceptions were assessed at both the individual level (e.g., perceived discrimination and ethnic affirmation) and neighborhood level along with demographic and structural neighborhood characteristics at the individual and neighborhood level, respectively. Multilevel analyses revealed that at both the individual level and neighborhood levels, perceived neighborhood problems were associated with more frequent messages about ethnicity and race (e.g., cultural socialization and preparation for bias). In addition, neighborhood-level affirmation promoted cultural messages; in contrast, neighborhood-level discrimination experiences positively impacted preparation for unfair treatment. Results reveal how parents' ERS is informed by their own characteristics as well as neighborhood factors. Further, cross-level interactions were found. Findings are discussed in terms of contextual and cultural-developmental theorizing about parenting.