Neighborhood Sociodemographic Characteristics Research Articles

Exposure to residential greenness, perceived stress, and depressive symptoms in a North American preconception cohort

BackgroundStudies suggest that greater exposure to natural vegetation (i.e., greenness) is associated with better mental health. However, there is limited research on greenness and mental health in the preconception period, a critical window of exposure in the life course. We investigated the associations of residential greenness with perceived stress and depressive symptoms using cross-sectional data from a cohort of pregnancy planners. MethodsFrom 2013 to 2019, we enrolled female-identified participants aged 21–45 years who were trying to conceive without the use of fertility treatment into a North American preconception cohort study (Pregnancy Study Online [PRESTO]). On the baseline questionnaire, participants completed the 10-item Perceived Stress Scale (PSS) and the Major Depression Inventory (MDI). Using geocoded addresses, we estimated residential greenness exposure via satellite imagery (Normalized Difference Vegetation Index [NDVI]) in a 100m buffer. We estimated mean differences and 95% confidence intervals for the association of greenness with perceived stress and depression scores using linear regression models, adjusting for individual and neighborhood sociodemographic characteristics. We also evaluated the extent to which associations were modified by urbanicity and neighborhood socioeconomic status (SES). ResultsAmong 9718 participants, mean age was 29.9 years, 81.5% identified as non-Hispanic White, 25% had household incomes <$50,000, and mean neighborhood income was $61,932. In adjusted models, higher greenness was associated with lower stress and depression scores (mean difference per interquartile range in greenness: −0.20, 95% CI: −0.39, −0.01; and −0.19, 95% CI: −0.48, 0.10, respectively). The association was stronger among residents of lower SES neighborhoods in urban areas (PSS: −0.57, 95% CI: −1.00, −0.15; MDI: −0.72, 95% CI: −1.40, −0.04). ConclusionsHigher greenness exposure was associated with lower stress and depressive symptoms among pregnancy planners, particularly in lower-SES neighborhoods.

Assessing the Association Between Neighbourhood Sociodemographic Characteristics and Adolescent Dating Violence

Assessing the Association Between Neighbourhood Sociodemographic Characteristics and Adolescent Dating Violence

A step in time: A sequence analysis and choice modelling approach to examine time allocation in individual activities and the role of neighbourhood crime

The Space-Time Budget (STB) method is used to collect spatial and temporal features of an individual’s activities and is an important technique to explore relationships between the environment and crime. This spatial and temporal approach to the study of everyday activity spaces reveals much about victimisation experiences, the relationship between place, time, and perceptions of safety, and where and when offending may occur. Few studies, however, consider the relationship between living in a criminogenic place (neighbourhood crime) and people’s daily activity patterns. Drawing on disaggregate app-based data for 50 participants tracked over a 7-day period, we use sequence analysis to first delineate time allocation to each activity on a minute-by-minute basis. Next, using result from the sequence analysis we introduce the multiple discrete-continuous extreme value (MDCEV) model to understand how neighbourhood crime and socio-demographic characteristics influence individuals’ time allocation to discrete daily activities. Results reveal that neighbourhood drug and violent incidents exert restrictions on an individual’s propensity to spend time outside the home. Females and older people appear more likely to be constrained by the presence of neighbourhood crime (in particular drug and property incidents) as they allocate less time to outdoor activities. The principal utility of the current study is its methodological advancement and the practical insights for urban planning regarding the design of crime prevention strategies to increase guardianship in public places.

Assessing structural models of neighborhood and family sociodemographic characteristics and their relations with externalizing psychopathology.

Externalizing psychopathology has been found to have small to moderate associations with neighborhood and family sociodemographic characteristics. However, prior studies may have used suboptimal operationalizations of neighborhood sociodemographic characteristics and externalizing psychopathology, potentially misestimating relations between these constructs. To address these limitations, in the current study we test different measurement models of these constructs and assess the structural relations between them. Using a population-representative sample of 2,195 twins and siblings from the Georgia Twin Study and data from the National Neighborhood Data Archive and 2000 U.S. Census, we assessed the fit of competing measurement models for family sociodemographic, neighborhood sociodemographic, and neighborhood environment characteristics. In structural models, we regressed a general externalizing dimension on different operationalizations of these variables separately and then simultaneously in a final model. Latent variable operationalizations of family sociodemographic, neighborhood sociodemographic, and neighborhood environment characteristics explained no more variance in broad externalizing psychopathology than other operationalizations. In an omnibus model, family sociodemographic characteristics showed a small association with externalizing psychopathology, while neighborhood sociodemographic and environmental characteristics did not. Family sociodemographic characteristics showed small associations with neighborhood sociodemographic and environmental characteristics, and neighborhood sociodemographic characteristics were moderately associated with neighborhood environment. These findings suggest that family sociodemographic characteristics are more associated with the development of broad externalizing psychopathology in youth than neighborhood sociodemographic characteristics and neighborhood environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Assessing Structural Models of Neighborhood and Family Sociodemographic Characteristics and Their Relations With Externalizing Psychopathology

Supplemental Material for Assessing Structural Models of Neighborhood and Family Sociodemographic Characteristics and Their Relations With Externalizing Psychopathology

Economic resilience during COVID-19: the case of food retail businesses in Seattle, Washington

The first year of COVID-19 tested the economic resilience of cities, calling into question the viability of density and the essential nature of certain types of services. This study examines built environment and socio-economic factors associated with the closure of customer-facing food businesses across urban areas of Seattle, Washington. The study covers 16 neighborhoods (44 census block groups), with two field audits of businesses included in cross-sectional studies conducted during the peak periods of the pandemic in 2020. Variables describing businesses and their built environments were selected and classified using regression tree methods, with relationships to business continuity estimated in a binomial regression model, using business type and neighborhood socio-demographic characteristics as controlled covariates. Results show that the economic impact of the pandemic was not evenly distributed across the built environment. Compared to grocery stores, the odds of a restaurant staying open during May and June were 24%, only improving 10% by the end of 2020. Density played a role in business closure, though this role differed over time. In May and June, food retail businesses were 82% less likely to remain open if located within a quarter-mile radius of the office-rich areas of the city, where pre-pandemic job density was greater than 95 per acre. In November and December, food retail businesses were 66% less likely to remain open if located in areas of residential density greater than 23.6 persons per acre. In contrast, median household income and percentage of non-Asian persons of color were positively and significantly associated with business continuity. Altogether, these findings provide more detailed and accurate profiles of food retail businesses and a more complete impression of the spatial heterogeneity of urban economic resilience during the pandemic, with implications for future urban planning and real estate development in the post-pandemic era.

Increased nationwide use of green spaces in Norway during the COVID-19 pandemic

In recent years, there has been growing concern about the decline in human green space use and nature-based recreation in Western countries. While some evidence suggests that the COVID-19 pandemic led to increased recreational mobility in urban green spaces, it is unclear whether the pandemic led to nationwide changes in green space use in both densely and less densely populated neighborhoods, as well as whether social inequalities in green space use were reinforced or attenuated by the pandemic. To address these questions, we used daily nationwide aggregated mobility data from more than 2 million cell phone subscribers in 14,331 geographical grids across Norway to examine potential changes in mobility in green spaces as measured by the normalized difference vegetation index (NDVI) during the pandemic. Additionally, we controlled for weather conditions, holiday periods, and neighborhood sociodemographic characteristics. The results from linear mixed model analyses showed a 9.4% increase in recreational visits in the greenest spaces during the pandemic. Notably, this increase was most prominent in neighborhoods of low socioeconomic status (SES) and was observed in both high- and low-population density neighborhoods, although the increase was somewhat stronger in neighborhoods with low population density. Our study findings suggest that the COVID-19 pandemic has played a role in increasing nationwide green space use in Norway and potentially narrowing the gap of green inequalities, thus highlighting the importance of preserving and promoting green spaces as a public health resource, particularly in disadvantaged neighborhoods.

Neighborhood sociodemographic characteristics and healthfulness of store checkouts in Northern California

Placement of products at food store checkouts has been shown to trigger impulse purchases and child purchasing requests. Therefore, food companies pay substantial amounts of money to ensure their products are placed at checkout, and these products are mostly unhealthy (e.g., sugar-sweetened beverages [SSBs], candy, chips). To improve the healthfulness of store environments, Berkeley, CA, U.S. became the first jurisdiction globally to implement a healthy checkout policy. This study examined associations between store neighborhood characteristics and healthfulness of foods and beverages offered at checkout to understand the potential for healthy checkout policies, such as Berkeley’s healthy checkout ordinance (HCO), to promote equitable food environments. Data on a near census of food and beverage facings (n = 26,758) at sampled checkouts were collected from 102 food stores (supermarkets, grocery stores, drugstores, dollar stores, specialty food stores, and mass merchandisers) across four Northern California cities (Berkeley, Oakland, Davis, and Sacramento) in February 2021. Bivariate regression analyses revealed that neighborhoods with lower socioeconomic status (SES) and higher Black and Hispanic residential composition had a higher prevalence of foods and beverages that did not meet HCO standards, including associations with a higher prevalence of sweets, higher prevalence of SSBs, and/or lower prevalence of healthy foods at checkout. Findings suggest that the checkout environment may be one of many contributors to diet-related health disparities. Additionally, healthy checkout policies may have the potential to increase nutrition equity by improving food environments across neighborhoods and especially in areas with lower SES and higher Black and Hispanic composition.

Temporal trends and determinants of COVID-19 vaccine coverage and series initiation during pregnancy in Ontario, Canada, December 2020 to December 2021: A population-based retrospective cohort study.

Population-based COVID-19 vaccine coverage estimates among pregnant individuals are limited. We assessed temporal patterns in vaccine coverage (≥1 dose before or during pregnancy) and evaluated factors associated with vaccine series initiation (receiving dose 1 during pregnancy) in Ontario, Canada. We linked the provincial birth registry with COVID-19 vaccination records from December 14, 2020 to December 31, 2021 and assessed coverage rates among all pregnant individuals by month, age, and neighborhood sociodemographic characteristics. Among individuals who gave birth since April 2021-when pregnant people were prioritized for vaccination-we assessed associations between sociodemographic, behavioral, and pregnancy-related factors with vaccine series initiation using multivariable regression to estimate adjusted risk ratios (aRR) and risk differences (aRD) with 95% confidence intervals (CI). Among 221,190 pregnant individuals, vaccine coverage increased to 71.2% by December 2021. Gaps in coverage across categories of age and sociodemographic characteristics decreased over time, but did not disappear. Lower vaccine series initiation was associated with lower age (<25 vs. 30-34years: aRR 0.53, 95%CI 0.51-0.56), smoking (vs. non-smoking: 0.64, 0.61-0.67), no first trimester prenatal care visit (vs. visit: 0.80, 0.77-0.84), and residing in neighborhoods with the lowest income (vs. highest: 0.69, 0.67-0.71). Vaccine series initiation was marginally higher among individuals with pre-existing medical conditions (vs. no conditions: 1.07, 1.04-1.10). COVID-19 vaccine coverage among pregnant individuals remained lower than in the general population, and there was lower vaccine initiation by multiple characteristics.

Recruitment of sexual minority and heterosexual colorectal cancer survivors through US cancer registries.

Describe the process, outcomes, and costs of cancer registry recruitment and enrollment of sexual minority and heterosexual non-metastatic colorectal cancer survivors into an observational survivorship study. We recruited stage I-III colorectal cancer survivors from four US cancer registries. Potential participants were screened for eligibility, and all eligible sexual minority and every 10th heterosexual survivor was invited to participate in a 45-min telephone interview. We mailed study packets to 17,855 individuals and obtained 6370 screening surveys of presumed eligible individuals. After screening, there were 182 eligible sexual minority and 5568 eligible heterosexual survivors. Of the 719 invited survivors, 127 sexual minority and 353 heterosexual individuals participated in the interview. There were some small differences in personal and neighborhood sociodemographic characteristics for the survivors who screened eligible and completed the interview relative to the registry sample. The per-participant direct costs were about $40, $120, and $1425 in the registry, screened eligible, and interviewed samples, respectively. Although we did not observe substantial selection biases, the costs of enrolling a representative sample were high. Inclusion of sexual orientation and gender identity as standard demographic questions in cancer registries is needed for reliable and cost-efficient monitoring of population health.

An Exploratory Ecological Study between COVID-19 Vaccination Rate and Racial/Ethnic and Socioeconomic Status Neighborhood Conditions in Michigan

COVID-19 vaccination rate disparities continue to persist in the U.S., despite their wide availability. A multidimensional host of sociodemographic, economic, political, and cultural factors have resulted in differences in vaccine coverage rates across the U.S. The present study descriptively explores one component of the complex relationship among what drives COVID-19 vaccination rate differences—specifically, the relationship between neighborhood sociodemographic characteristics and vaccination rates in Michigan. Data from the 2019 5-Year American Community Survey are merged with vaccine coverage rate data at the census tract level for the 18-years-and-older population for Michigan from 15 December 2020 to 19 November 2021. On the one hand, the results reveal that a neighborhood’s racial and ethnic composition and socioeconomic status characteristics are part of the complex bundle of characteristics affecting neighborhood vaccination coverage rates across Michigan. On the other hand, the findings also reveal a set of surprising patterns as they relate to the racial and ethnic composition of neighborhoods in Michigan. Future studies should further explore the extent to which race/ethnicity and class-related neighborhood characteristics are statistically relevant in examining the broader community effects on vaccine coverage rates in Michigan.

Familiar Strangers and Crime at Transit Stations: Is Crime Lower at Train Stations Where Familiar Strangers are Present?

Familiar strangers—individuals related through regular and repeated visual encounter occurring without verbal interaction—may reduce the risk of crime at places by increasing guardianship and internal motivation for norm compliance. This study examines the association between familiar stranger presence and incidents of theft and disorder at train stations using transit smart card and crime data for 22 stations across a six-month period. Familiar strangers are operationally defined as individual transit cards that are co-present within a 30-min temporal window on more than two occasions across a one-month period at a given train station. We apply logistic regression to estimate the likelihood of disorder and theft crimes within a 400-m radius of train stations controlling for station environmental features, co-located facilities and neighbourhood socio-demographic characteristics. Our results show that the impact of familiar strangers on crime (disorder and theft) at train stations is moderated by the neighbourhood socio-demographic context. This may suggest that macro-level norms of informal social control are important for determining crime at micro-places such as transit stations. The findings also indicate that urban design and transport policies require greater flexibility to enable intra-network variability in station design and formal security to enhance ridership.

The role of neighbourhood design in cycling activity during COVID-19: An exploration of the Melbourne experience

COVID-19 restrictions imposed significant changes on human mobility patterns, with some studies finding significant increases or decreases in cycling. However, to date there is little understanding on how the neighbourhood-level built environment influenced cycling behaviour during the COVID-19 restrictions. As different neighbourhood have different built environment characteristics, it is possible that cycling trends varied across different built environment settings. We aimed to answer this question by examining recreational cycling during different stages of lockdown in Melbourne, Australia. We compared self-reported recreational cycling frequency (weekly) data from 1344 respondents between pre-COVID and two different stages in lockdown. We tested whether the built environment of their residential neighbourhood and different sociodemographic characteristics influenced leisure cycling rates and whether the effect of these factors varied between different stages of COVID-19 restriction. We found that cycling declined significantly during the two stages of COVID-19 lockdown. Cycling infrastructure density and connectivity are two built environment factors that had a significant effect on limiting the decline in leisure cycling during the pandemic. Furthermore, men and younger people had higher cycling rates in comparison to other groups, suggesting that restrictions on indoor activities and travel limits were not enough to encourage women or older people to cycle more during the pandemic.

The Availability and Price of Healthy Food in Seattle by Neighborhood Sociodemographic Characteristics.

Unequal access to healthy food in the local food retail environment contributes to diet quality disparities. We assessed whether in-store availability and prices of healthy foods differ by neighborhood-level income and racial and ethnic composition in a representative sample of food stores in Seattle, Washington. We developed and validated an in-store survey tool and surveyed 134 stores. We measured availability and prices of 19 items. For each store, we calculated a healthy food availability score (range, 0-25), and mean prices within each category. Using census tract data, we identified the median household income and proportions of Black and Hispanic residents for each store's neighborhood and grouped them by tertiles of these neighborhood characteristics across Seattle census tracts. We used Wald tests to compare mean availability scores and prices between tertiles and applied postestimation weights to reflect store-type distributions within each tertile. Neighborhoods with lower income and a larger proportion of Black residents had lower healthy food availability scores compared with neighborhoods with higher income (8.06 [95% CI, 7.04-9.07] vs 12.40 [95% CI, 10.63-14.17], P < .001) and fewer Black residents (8.88 [95% CI, 7.79-9.98] vs 12.32 [95% CI, 10.51-14.14], P = .003). Availability did not differ by Hispanic population proportions. Mean prices of grains, eggs, and meat were lower in neighborhoods with larger proportions of Black residents. We found systematic differences in healthy food availability based on neighborhood-level income and racial composition. In-store assessments of the food retail environment can inform local, tailored strategies to improve healthy food access.

The urban exposome and type 2 diabetes: A deep learning approach

BACKGROUND AND AIM: Type 2 diabetes (T2D) is a chronic disease with high individual and societal burden. Risk of T2D is partly influenced by urban characteristics, such as air pollution, or indirectly through their relation with lifestyle behaviours (e.g. walkability, food environment). Environmental characteristics are generally studied individually in their association with T2D, but occur simultaneously in real life, and may associate with T2D in nonlinear and non-additive ways. We aimed to identify which factors of the urban exposome are associated with T2D by applying artificial neural networks (ANN). In addition, we compared results of ANN model with penalized regression LASSO, as a more conventional method. METHODS: We analyzed baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study living across the Netherlands. Self-reported questionnaire data were used to identify participants diagnosed with T2D (n=676(4.6%)). Exposome variables (n=86) were linked to individual home addresses, including air pollution, traffic noise, green-space, chemicals in drinking water, built environmental and neighborhood socio-demographic characteristics. Models were adjusted for individual socio-demographic variables. Nested cross-validation was used to determine the optimal model parameters of both approaches (ANN and LASSO), and the cross-validated predictive accuracies were compared. RESULTS: One exposure was selected by each approach. Living in neighborhoods with a higher share of non-Western immigrants (selected by ANN) was associated with a higher risk of T2D. Higher average home value in residential neighborhood (selected by LASSO) was associated with a lower risk of T2D. Cross-validated prediction error logLoss (sd) was 0.177 (0.0060) for ANN and 0.167 (0.0022) for LASSO. CONCLUSIONS: Neighborhood socio-demographic characteristics are associated with the risk of T2D. Accuracy of the ANN model was lower than LASSO, which might be due to the low prevalence of diabetes and relatively weak signal in the data. KEYWORDS: Multiple exposures, Machine learning, Neighborhood socio-demographic characteristics

Impact of Neighborhood Sociodemographic Characteristics on Food Store Accessibility in the United States Based on the 2020 US Census Data.

BackgroundIt has been previously reported that access to quality food is greatly impacted by neighborhood food store quality and availability, which in turn is determined by a complex interplay of sociodemographic factors. Low-income predominantly Black neighborhoods face the most limited access to quality food. The purpose of this study was to examine the newly available 2020 Census to see if any significant change has occurred to this pattern and if any new insights can be gained by analyzing these data.Methods2020 US Census and current ReferenceUSATM food store data were merged and multivariate Negative Binomial Count Regression Models were used to establish the relationship between different types of food stores (high, medium, and low quality) and neighborhood characteristics including urbanicity, poverty level, and race/ethnicity.Results11.5% of the predominantly Non-Hispanic (NH) White census tracts (CT) (6,486 out of 56,192), 61.3% (4,002 out of 6,531) of the predominantly Non-Hispanic Black CTs, and 44.1% (3,644 out of 8,258) of the predominantly Hispanic CTs were in the high poverty category. Compared to the reference group of NH White/low-poverty group, the incident rate ratio (IRR) and 95% Confidence interval [CI] of having access to high quality food stores for NH Black was significantly lower starting at the low poverty level (0.57 [0.48, 0.67], p<0.001) and decreasing further with increasing poverty: NH Black/medium poverty (0.48 [0.42, 0.55], p<0.001); NH Black/high poverty (0.38, [0.34, 0.42], p<0.001). A similar pattern was seen with the Hispanic groups as well, though to a lesser degree. We further examined access to computer/electronic devices including smartphones from 2017 to 2020. High poverty NH Black households experienced the fastest growth from 73.6% access rate in 2017 to 82.6% in 2020, compared with 87.0% to 92.0% in the total population.ConclusionAnalyses of the 2020 Census data reveal that access to high-quality food stores in high-poverty minority neighborhoods, NH Black neighborhoods in particular, remains severely limited. Innovative interventions and emerging technologies, online grocery shopping for example, warrant further evaluation as potential strategies to improve access and decrease disparities in social determinants of healthy eating.

Paws on the Street: Neighborhood-Level Concentration of Households with Dogs and Urban Crime.

The formative work of Jane Jacobs underscores the combination of "eyes on the street" and trust between residents in deterring crime. Nevertheless, little research has assessed the effects of residential street monitoring on crime due partly to a lack of data measuring this process. We argue that neighborhood-level rates of households with dogs captures part of the residential street monitoring process core to Jacobs' hypotheses and test whether this measure is inversely associated with property and violent crime rates. Data from a large-scale marketing survey of Columbus, OH, USA residents (2013; n=43,078) are used to measure census block group-level (n=595) rates of households with dogs. Data from the Adolescent Health and Development in Context study are used to measure neighborhood-level rates of trust. Consistent with Jacobs' hypotheses, results indicate that neighborhood concentration of households with dogs is inversely associated with robbery, homicide, and, to a less consistent degree, aggravated assault rates within neighborhoods high in trust. In contrast, results for property crime suggest that the inverse association of dog concentration is independent of levels of neighborhood trust. These associations are observed net of controls for neighborhood sociodemographic characteristics, temporally lagged crime, and spatial lags of trust and dog concentration. This study offers suggestive evidence of crime deterrent benefits of local street monitoring and dog presence and calls attention to the contribution of pets to other facets of neighborhood social organization.

Sociodemographic Disparities in Tobacco Retailer Density in the United States, 2000-2017.

Studies find differences in tobacco retailer density according to neighborhood sociodemographic characteristics, raising issues of social justice, but not all research is consistent. This study examined associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints (2000, 2007, 2012, and 2017) and investigated if associations remained stable over time. Data on tobacco retailers came from the National Establishment Time-Series Database. Adjusted log-linear models examined the relationship between retailer density and census tract sociodemographic characteristics (% non-Hispanic Black [Black], % Hispanic, % vacant housing units, median household income), controlling for percentage of youth, urbanicity, and US region. To examine whether the relationship between density and sociodemographic characteristics changed over time, additional models were estimated with interaction terms between each sociodemographic characteristic and year. Tobacco retailer density ranged from 1.22 to 1.44 retailers/1000 persons from 2000 to 2017. There were significant, positive relationships between tobacco retailer density and the percentage of Black (standardized exp(b) = 1.05 [95% CI: 1.04% to 1.07%]) and Hispanic (standardized exp(b) = 1.06 [95% CI: 1.05% to 1.08%]) residents and the percentage of vacant housing units (standardized exp(b) =1.08 [95% CI: 1.07% to 1.10%]) in a census tract. Retailer density was negatively associated with income (standardized exp(b) = 0.84 [95% CI: 0.82% to 0.86%]). From 2000 to 2017, the relationship between retailer density and income and vacant housing units became weaker. Despite the weakening of some associations, there are sociodemographic disparities in tobacco retailer density from 2000 to 2017, which research has shown may contribute to inequities in smoking. This study examines associations between tobacco retailer density and neighborhood sociodemographic characteristics in the United States at four timepoints from 2000 to 2017. Although some associations weakened, there are sociodemographic disparities in tobacco retailer density over the study period. Research suggests that sociodemographic disparities in retailer density may contribute to inequities in smoking. Findings from this study may help identify which communities should be prioritized for policy intervention and regulation.

Machine learning approaches to characterize the obesogenic urban exposome

Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously. We aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches. A cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations. The most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households, smaller amounts of healthy food retailers and higher OP levels. Across the approaches, we observed consistent patterns of results based on model's capacity to incorporate linear or nonlinear associations. The pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.

Social Capital and Sustainable Social Development—How Are Changes in Neighbourhood Social Capital Associated with Neighbourhood Sociodemographic and Socioeconomic Characteristics?

The development of social capital is acknowledged as key for sustainable social development. Little is known about how social capital changes over time and how it correlates with sociodemographic and socioeconomic factors. This study was conducted in 46 neighbourhoods in Umeå Municipality, northern Sweden. The aim was to examine neighbourhood-level characteristics associated with changes in neighbourhood social capital and to discuss implications for local policies for sustainable social development. We designed an ecological study linking survey data to registry data in 2006 and 2020. Over 14 years, social capital increased in 9 and decreased in 15 neighbourhoods. Higher levels of social capital were associated with specific sociodemographic factors, but these differed in urban and rural areas. Urban neighbourhoods with a higher proportion of older pensioners (OR = 1.49, CI: 1.16–1.92), children under 12 (OR= 2.13, CI: 1.31–3.47), or a lower proportion of foreign-born members (OR= 0.32, CI: 0.19–0.55) had higher odds for higher social capital levels. In rural neighbourhoods, a higher proportion of single-parent households was associated with higher levels of social capital (OR = 1.44, 95% CI = 1.04–1.98). Neighbourhood socioeconomic factors such as income or educational level did not influence neighbourhood social capital. Using repeated measures of social capital, this study gives insights into how social capital changes over time in local areas and the factors influencing its development. Local policies to promote social capital for sustainable social development should strive to integrate diverse demographic groups within neighbourhoods and should increase opportunities for inter-ethnic interactions.