Neighborhood Sociodemographics Research Articles

New insights into grocery store visits among east Los Angeles residents using mobility data

In this study, we employed spatially aggregated population mobility data, generated from mobile phone locations in 2021, to investigate patterns of grocery store visits among residents east and northeast of Downtown Los Angeles, in which 60% of the census tracts had previously been designated as “food deserts”. Further, we examined whether the store visits varied with neighborhood sociodemographics and grocery store accessibility. We found that residents averaged 0.4 trips to grocery stores per week, with only 13% of these visits within home census tracts, and 40% within home and neighboring census tracts. The mean distance from home to grocery stores was 2.2 miles. We found that people visited grocery stores more frequently when they lived in neighborhoods with higher percentages of Hispanics/Latinos, renters and foreign-born residents, and a greater number of grocery stores. This research highlights the utility of mobility data in elucidating grocery store use, and factors that may facilitate or be a barrier to store access. The results point to limitations of using geographically constrained metrics of food access like food deserts.

Prospective associations of neighborhood healthy food access and walkability with weight status in a regional pediatric health system

BackgroundMost neighborhood food and activity related environment research in children has been cross-sectional. A better understanding of prospective associations between these neighborhood environment factors and children’s weight status can provide stronger evidence for informing interventions and policy. This study examined associations of baseline and changes in neighborhood healthy food access and walkability with changes in children’s weight status over 5 years.MethodsHeight, weight, and home address were obtained for 4,493 children (> 75% were Black or Latinx) from primary care visits within a large pediatric health system. Eligible participants were those who had measures collected during two time periods (2012–2014 [Time 1] and 2017–2019 [Time 2]). Data were integrated with census tract-level healthy food access and walkability data. Children who moved residences between the time periods were considered ‘movers’ (N = 1052; 23.4%). Mixed-effects models, accounting for nesting of children within census tracts, were conducted to model associations of baseline and changes in the neighborhood environment variables with Time 2 weight status (BMIz and overweight or obese vs. healthy weight). Models adjusted for weight status and child and neighborhood sociodemographics at baseline.ResultsChildren living in a neighborhood with [ample] healthy food access at Time 1 had a lower BMIz at Time 2, regardless of mover status. A decrease in healthy food access was not significantly associated with children’s weight status at Time 2. Baseline walkability and improvements in walkability were associated with a lower BMIz at Time 2, regardless of mover status.ConclusionsFindings provide evidence that residing in a neighborhood with healthy food access and walkability may support a healthy weight trajectory in children. Findings on changes in the neighborhood environment suggested that improved walkability in the neighborhood may support children’s healthy weight. The greater and more consistent findings among movers may be due to movers experiencing greater changes in neighborhood features than the changes that typically occur within a neighborhood over a short period of time. Future research is needed to investigate more robust environmental changes to neighborhoods.

Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study

BackgroundCommunities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.ObjectiveThis cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.MethodsUsing baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003–2007), we calculated participants’ dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant’s census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.ResultsParticipants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was − 0.004 (SD = 2.5; min = − 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.ConclusionsThe relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.

Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications

Many global high-density cities have embraced transit-oriented development (TOD) strategies around metro stations in a strong push toward promoting transit trips. However, the general TOD principles must be adapted to a variety of local features. Thus, conducting a baseline study that deciphers the locality-specific differences and similarities in the influence of TOD on metro ridership is of paramount importance. Using the case of Shanghai in China, this paper demonstrated a novel approach to unveiling the locality-specific influence of TOD on metro ridership that integrated the node-functionality-place model with interpretable machine learning. By exploring a wide range of explanatory variables, we discovered that the relative importance of TOD structural factors (e.g., node, place and functionality) and neighborhood sociodemographics as well as their interactions presented great spatial and temporal heterogeneity for boarding and alighting on weekdays and weekends. Among the TOD structural factors, functionality was of the highest importance, and a greater contribution was observed within the central districts during peak hours. Regarding the interactions among the TOD structural factors, the interaction between node and functionality presented the highest relative importance, followed by that between place between functionality and that between node and place. Additionally, neighborhood sociodemographics also accounted for a noticeable contribution, especially in the outskirts. Based on the locality-specific estimations, the affinity propagation clustering algorithm was further used to cluster the TODs into difference groups as nuanced representations of the TOD-metro ridership relationships. The findings refreshed the knowledge base concerning the spatiotemporal heterogeneity in the nonlinear influence of TOD on metro ridership and provided new insights into spatial planning. The proposed approach showed high computational feasibility with strong theoretical underpinnings and thus offered broad potential applications into other high-density cities worldwide.

Residential Greenness and Depression in Older U.S. Adults: Evidence from The Health and Retirement Study

BACKGROUND AND AIM: The physical environment influences mental health. In this study, we investigated residential greenness in relation to major depressive disorder (MDD), a leading cause of disability. We also tested effect modification by climate and urbanicity given that vegetation types and human interaction with vegetation differs across these settings. METHODS: We used biennial survey data from 2008 through 2016 from a cohort of older U.S. adults. We calculated greenness at participant home addresses as the maximum normalized difference vegetation index (NDVI) for the year preceding each survey, averaged within 250m and 1km buffers. A score of &amp;#x2265;5 on the Composite International Diagnostic Interview Short Form indicated MDD. We used Köppen-Geiger classifications, which identify vegetation biomes, to characterize climate, and Beale codes for urbanicity. To estimate prevalence ratios, we used Poisson regression accounting for repeated measures and incorporated survey weights. Our models adjusted for individual and neighborhood socio-demographics, geography, sunshine, and bluespace. RESULTS: We included 27,829 participants who contributed 91,479 observations to our analysis. Participants were 66 (&amp;#xb1;11) years old on average, 83% White, and 28% completed college, with a 12-month prevalence of MDD of 8%. In fully adjusted models, an interquartile range higher NDVI within 250m was associated with a 12% lower prevalence of MDD (PR:0.88, 95% CI:0.81-0.96). This association was stronger in cold (PR:0.82, 95% CI:0.73-0.92) climates (p-interaction: 0.091), and urban (PR:0.84, 95% CI:0.76-0.93) areas (p-interaction: 0.222). We found similar results for 1km NDVI. CONCLUSIONS: More residential greenness was associated with less MDD, suggesting greening interventions could improve mental health. Since we found evidence of effect modification by climate and urbanicity, those planning interventions should consider vegetation types and prioritize areas where they may be particularly beneficial. Future studies could better identify vegetation types and communities most in need of greening interventions. KEYWORDS: Mental health, greenspace, built environment

A meta-analysis of the impact of rail stations on property values: Applying a transit planning lens

• Introduction of 10 new variables to the rail access uplift meta-analysis literature. • Transit expenditure share and network expanse positively impact rail access premiums. • Rent control policies and ethno-racial dynamics affect rail access premiums. • Tests effects of Global Financial Crisis (2007–2009) on rail access premiums. • Expansion of geo-scope of rail access premium comparisons; includes Global South. Access to transit infrastructure is valuable. In areas surrounding rail stations, some of that value gets absorbed by property markets. Since 1970, over 200 case studies worldwide have explored the extent to which transit access impacts property value uplift. However, findings from this body of research vary widely. In an attempt to explain this variation, previous meta-analytic studies have focused on testing the roles that built environment elements, temporal factors, and modelling techniques play in shaping the observed relationship between proximate positioning from rail stations and property pricing. This study expands on these meta-analyses by introducing the first examination of how transit service elements – e.g. frequency, fare, reliability – impact rail-induced residential property value uplift. It further contributes to the literature by expanding the range of geo-comparability of uplift findings across four continents, examining the potential effects of the Global Financial Crisis of the late 2000s, and adding 10 new variables of potential influence not only under the banner of ‘transit service’, but pertaining to neighborhood socio-demographics and housing policy as well. Findings reveal that factors of geography, housing data type, race and ethnicity, rent control policies, rail type, transit cost, and transit network expanse all significantly affect rail access uplift magnitude ranging from depreciating effects of 7.4 percentage points to appreciating effects of 9.6 percentage points. Beyond helping to explain variation across the literature, these findings can be a useful tool for policymakers combatting trends of increasing market-driven displacement from transit-rich areas, those pursuing financing mechanisms for transit infrastructure, and to transit planners concerned with how their decisions may affect matters of housing equity for the communities they serve.

Objective and Subjective Neighborhood Crime Associated with Poor Sleep among Young Sexual Minority Men: a GPS Study.

Sleep disparities in sexual minority male (SMM) populations have received relatively little attention but they may be critical to explaining other health disparities seen among SMM, via neural or hormonal pathways. Recent research suggests that crime may be a psychosocial stressor that contributes to sleep disparities but that finding has been based on subjective measures of crime. We conducted the P18 Neighborhood Study of 250 SMM in New York City, including 211 with adequate GPS tracking data. We used the GPS tracking data to define daily path area activity spaces and tested the associations of violent crime in those activity spaces and in the subject's residential neighborhood, perceived neighborhood safety, and witnessing crime with a subjective measure of sleep. Using quasi-Poisson regression, adjusted for individual and neighborhood socio-demographics, we found that SMM who witnessed more types of crime experienced significantly more nights of poor sleep over the course of a month (RR: 1.16, 95%CI: 1.05-1.27, p-value: < 0.01). We did not find any associations between violent crime rates in either the activity area or residential area and sleep. Our findings support the conclusion that personal exposure to crime is associated with sleep problems and provide further evidence for the pathway between stress and sleep. The lack of association between neighborhood crime levels and sleep suggests that there must be personal experience with crime and ambient presence is insufficient to produce an effect.

Neighborhood Inequities in Tobacco Retailer Density and the Presence of Tobacco-Selling Pharmacies and Tobacco Shops

Studies document inequitable tobacco retailer density by neighborhood sociodemographics, but these findings may not be robust to different density measures. Policies to reduce density may be less equitable depending on how the presence of store types differs by neighborhood characteristics. We built a 2018 list of probable tobacco retailers in the United States and calculated four measures of density for all census tracts (N = 71,495), including total count, and number of retailers per 1,000 people, square mile, and kilometers of roadway. We fit multivariable regression models testing associations between each density measure and tract-level sociodemographics. We fit logistic regression models testing associations between sociodemographics and the presence of a tobacco-selling pharmacy or tobacco shop. Across all measures, tracts with a greater percentage of residents living below 150% of the federal poverty level (FPL) had higher density. A higher percentage of Black residents, Hispanic or Latino residents, and vacant housing was inconsistently associated with density across measures. Neighborhoods with a greater percentage of Black residents had a lower odds of having a pharmacy (adjusted odds ratio [aOR] = 0.96, 95% confidence interval [CI; 0.95, 0.97]) and tobacco shop (aOR = 0.87, CI [0.86, 0.89]), while those with a greater percentage of residents living below 150% FPL had greater odds of having a tobacco shop (aOR = 1.18, CI [1.16, 1.20]). Researchers and policymakers should consider how various measures of retailer density may capture different aspects of the environment. Furthermore, there may be an inequitable impact of retailer-specific policies on tobacco availability.

Relationship of Neighborhood Greenness to Alzheimer's Disease and Non-Alzheimer's Dementia Among 249,405 U.S. Medicare Beneficiaries.

Neighborhood greenness (vegetative presence) has been linked to multiple health outcomes, but its relationship to Alzheimer's disease (AD) and non-Alzheimer's (non-AD) dementia has been less studied. This study examines the relationship of greenness to both AD and non-AD dementia in a population-based sample of Medicare beneficiaries. Participants were 249,405 US Medicare beneficiaries aged > 65 years living in Miami-Dade County, FL, from 2010 to 2011. Multi-level analyses examined the relationship of greenness, assessed by mean Census block level Normalized Difference Vegetation Index (NDVI), to odds of each of AD, Alzheimer's disease and related dementias (ADRD), and non-AD dementia, respectively. Covariates included age, gender, race/ethnicity, number of comorbid health conditions, and neighborhood income. Higher greenness was associated with reduced risk of AD, ADRD, and non-AD dementia, respectively, adjusting for individual and neighborhood sociodemographics. Compared to the lowest greenness tertile, the highest greenness tertile was associated with reduced odds of AD by 20%(odds ratio, 0.80; 95%CI, 0.75-0.85), ADRD by 18%(odds ratio, 0.82; 95%CI, 0.77-0.86), and non-AD dementia by 11%(odds ratio, 0.89; 95%CI, 0.82-0.96). After further adjusting for number of comorbidities, compared to the lowest greenness tertile, the highest greenness tertile was associated with reduced odds of AD (OR, 0.94; 95%CI, 0.88-1.00) and ADRD (OR, 0.93; 95%CI, 0.88-0.99), but not non-AD dementia (OR, 1.01; 95%CI, 0.93-1.08). High neighborhood greenness may be associated with lower odds of AD and ADRD. Environmental improvements, such as increasing neighborhood vegetation, may be a strategy to reduce risk for AD and possibly other dementias.

Sociodemographic Disparities in the Tobacco Retail Environment in Washington, DC: A Spatial Perspective.

Studies assessing sociodemographic disparities in the tobacco retail environment have relied heavily on non-spatial analytical techniques, resulting in potentially misleading conclusions. We utilized a spatial analytical framework to evaluate neighborhood sociodemographic disparities in the tobacco retail environment in Washington, DC (DC) and the DC metropolitan statistical area (DC MSA). Retail tobacco availability for DC (n=177) and DC MSA (n=1,428) census tract was assessed using adaptive-bandwidth kernel density estimation. Density surfaces were constructed from DC (n=743) and DC MSA (n=4,539) geocoded tobacco retailers. Sociodemographics were obtained from the 2011-2015 American Community Survey. Spearman's correlations between sociodemographics and retail density were computed to account for spatial autocorrelation. Bivariate and multivariate spatial lag models were fit to predict retail density. DC and DC MSA neighborhoods with a higher percentage of Hispanics were positively correlated with retail density (rho = .3392, P = .0001 and rho = .1191, P = .0000, respectively). DC neighborhoods with a higher percentage of African Americans were negatively correlated with retail density (rho = -.3774, P = .0000). This pattern was not significant in DC MSA neighborhoods. Bivariate and multivariate spatial lag models found a significant inverse relationship between the percentage of African Americans and retail density (Beta = -.0133, P = .0181 and Beta = -.0165, P = .0307, respectively). Associations between neighborhood sociodemographics and retail density were significant, although findings regarding African Americans are inconsistent with previous findings. Future studies should analyze other geographic areas, and account for spatial autocorrelation within their analytic framework.

Vape shop identification, density and place characteristics in six metropolitan areas across the US.

Vaping is increasingly prevalent and controversial. Vape shops and convenience stores are common but distinct sources of vaping products, and where they locate may reflect likely target markets. This study examined the density and neighborhood demographics of vape shops and convenience stores in six metropolitan statistical areas (MSAs): Atlanta, Boston, Minneapolis, Oklahoma City, San Diego, Seattle. We identified 459 vape shops using Yelp and Google application programming interfaces and 10,777 convenience stores using ReferenceUSA and Dun & Bradstreet. Retailers were geocoded to census tracts (n=4,442), and logistic regressions were conducted using as predictors percent non-White, percent youth (5-17years or 5-20years), and median household income from the American Community Survey, 2013-2017. Per 10,000 young adults, vape shop density ranged from 0.6 (Boston, San Diego) to 1.7 (Oklahoma City), and convenience store density ranged from 12.6 (San Diego) to 26.3 (Oklahoma City). Logistic regressions indicated that vape shops more likely resided in tracts with lower percentages of youth in Boston, but higher percentages of youth in Atlanta, as well as with lower incomes in Boston and Seattle. Convenience stores more likely resided in tracts with lower percentages of non-Whites in Atlanta and Boston; lower incomes in Atlanta, Boston, San Diego, and Seattle; and higher percentages of youth in Atlanta, Boston, and Minneapolis. These common retail sources of vaping products differentially locate in relation to neighborhood sociodemographics across MSAs. Findings suggest that, in some MSAs, vape shops and convenience stores may target youth and lower income populations.

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

ABSTRACT Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of U.S. Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart city advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.

Do Denser Neighborhoods Have Safer Streets? Population Density and Traffic Safety in the Philadelphia Region

This study uses multilevel negative binomial models to investigate relationships between neighborhood socio-demographics, urban form, roadway characteristics, traffic collisions, injuries, and fatalities on the Philadelphia region’s streets from 2010 to 2014. We pay particular attention to neighborhood population density. Results indicate that streets in denser neighborhoods have fewer overall collisions, injuries, and fatalities. The association with pedestrian safety is mixed and somewhat uncertain across urban areas and model specifications. This study highlights the importance of population density in traffic safety and helps explain some of the variation in findings across studies examining the relationship between urban form and pedestrian safety.

Understanding the Density and Distribution of Restaurants in Los Angeles County to Inform Local Public Health Practice.

IntroductionTo describe the potential reach of restaurant-based strategies that seek to improve the healthfulness of menu options, it is important to understand the local restaurant environment, including the extent to which restaurants subject to policy mandates are located in communities disproportionately affected by diet-related diseases.MethodsThis cross-sectional study examined the restaurant environment in Los Angeles County, a large jurisdiction with diverse geographic and socioeconomic characteristics, specifically 1) the number and characteristics of restaurants; 2) the association between neighborhood sociodemographics and restaurant density; and 3) the association between neighborhood sociodemographics and restaurant characteristics, including chain status (large chain, small chain, independent restaurant). Data sources were 1) industry data on restaurant location and characteristics (N = 24,292 restaurants) and 2) US Census data on neighborhood sociodemographics (N = 247 neighborhoods). We conducted descriptive and bivariate analyses at the restaurant and neighborhood level.ResultsCountywide, only 26.5% of all restaurants were part of a large chain (a chain with ≥20 locations). We found positive associations between restaurant density and neighborhood proportions of non-Hispanic white residents and residents with more than a high school education. We found limited support to suggest a greater density of large chains in neighborhoods with lower socioeconomic status.ConclusionResults highlight the potentially limited reach of strategies targeting chain restaurants and point to the importance of including small chain restaurants and independent restaurants in public health efforts to improve the healthfulness of restaurants. Understanding where restaurants are in relation to priority populations is a critical step to planning strategies that address diet-related disparities.

Assessing quality of life using WHOQOL-BREF: a cross-sectional study on the association between quality of life and neighborhood environmental satisfaction, and the mediating effect of health-related behaviors

BackgroundQuality of life (QOL) is an important component in assessing people’s health. Environmental quality can influence people’s QOL in the physical health, psychological, social relationships and environment domains. QOL in the four domains, overall QOL and general heath of residents living in the Kowloon Peninsula of Hong Kong were assessed. The association between satisfaction with the neighborhood environment and QOL, and health-related behaviors which mediated the effect were investigated.MethodsA sample of 317 residents completed a questionnaire which comprised the WHOQOL-BREF (Hong Kong version) to assess QOL, the International Physical Activity Questionnaire (IPAQ) to study physical activities, and questions on satisfaction with the neighborhood environment, health-related behaviors and socio-demographics. One-way ANOVA and linear regression were used to study the associations between environmental satisfaction and QOL in the four domains, overall QOL and general health, followed by assessing the relationships between environmental satisfaction and the potential health-related behavior mediators with regression tests. Mediation analysis was conducted using multiple linear regressions to study the effects of environmental satisfaction on QOL in the four domains, overall QOL and general health, as well as the potential mediating roles played by various health-related behaviors. A P-value of < 0.05 was considered as statistically significant.ResultsThe residents had a relatively higher physical health mean score of 70.83 ± 12.69, and a lower environmental mean score of 61.98 ± 13.76. Moderate satisfaction with the neighborhood environment had a significant relationship with QOL in the psychological domain (β = 0.170, P = 0.006), however, this effect was partially mediated by the non-smoking behavior of the residents (β = 0.143, P = 0.022).ConclusionsOur residents had lower QOL in the physical health and psychological domains but similar QOL in the social relationships and environmental domains compared to other countries. Only QOL in the psychological domain could be predicted by the satisfaction with the neighborhood environment, and non-smoking status was a partial mediator of the effect of moderate environmental satisfaction on QOL in the psychological domain. Refrain from smoking seems to be able to lower the influence of neighborhood environment on people’s QOL in the psychological domain to a certain extent.

Transportation Disadvantage and Neighborhood Sociodemographics: A Composite Indicator Approach to Examining Social Inequalities

Transportation disadvantage refers to the barriers to or limits on the participation in daily socioeconomic and political life due to the reduced physical accessibility to services and opportunities. The transportation disadvantaged groups not only face the problems of social exclusion in location, but also experience greater exposure to a diversity of social and environmental threat. Under this context, identifying the transportation disadvantaged neighborhoods is of great interest for urban planners. This paper examines the transportation disadvantage in association with neighborhood sociodemographics in Shenzhen, China. A set of multidimensional indicators are initially selected to measure the transportation disadvantage (physical conditions, public transport services, education opportunity, employment opportunity, medical care opportunity, food opportunity, and commercial opportunity). Based on these indicators, principal component analysis produces four composite indicators of different types of transportation disadvantage: transportation disadvantage in physical infrastructure and public services, transportation disadvantage in food and education accessibility, transportation disadvantage in medical care accessibility, and transportation disadvantage in commercial and employment accessibility. In space, the transportation disadvantaged neighborhoods are generally located in the outskirts. Transportation disadvantage is more likely to be observed in neighborhoods that are economically, educationally, residentially and occupationally disadvantaged. These findings reveal significant inequalities in transportation opportunity.

Enhancing Administrative Data to Predict Emergency Department Utilization: The Role of Neighborhood Sociodemographics.

Much of emergency department use is avoidable, and high-quality primary care can reduce it, but performance measures related to ED use may be inadequately risk-adjusted. To explore associations between emergency department (ED) use and neighborhood poverty, we conducted a secondary analysis of Massachusetts managed care network data, 2009-2011. For enrollees with commercial insurance (n = 64,623), we predicted any, total, and total primary-care-sensitive (PCS) ED visits using claims/enrollment (age, sex, race, morbidity, prior ED use), network (payor, primary care provider [PCP] type and quality), and census-tract-level characteristics. Overall, 14.6% had any visit; mean visits per 100 persons were 18.8 (±0.2) total and 7.6 (±0.1) PCS. Neighborhood poverty predicted all three outcomes (all P&lt; .001). Holding providers accountable for their patients' ED use should avoid penalizing PCPs who care for poor and otherwise vulnerable populations. Expected use targets should account for neighborhood-level variables such as income, as well as other risk factors.

Neighborhood sociodemographics and change in built infrastructure

While increasing evidence suggests an association between physical infrastructure in neighborhoods and health outcomes, relatively little research examines how neighborhoods change physically over time and how these physical improvements are spatially distributed across populations. This paper describes the change over 25 years (1985–2010) in bicycle lanes, off-road trails, bus transit service and parks, and spatial clusters of changes in these domains relative to neighborhood sociodemographics in four US cities that are diverse in terms of geography, size, and population. Across all four cities we identified increases in bicycle lanes, off-road trails, and bus transit service, with spatial clustering in these changes that related to neighborhood sociodemographics. Overall, we found evidence of positive changes in physical infrastructure commonly identified as supportive of physical activity. However, the patterning of infrastructure change by sociodemographic change encourages attention to the equity in infrastructure improvements across neighborhoods.

Examining social inequalities in urban public leisure spaces provision using principal component analysis

Nowadays, urban planners are discovering the potential of public leisure spaces (PLSs) in building livable cities. However, the PLSs provision is always inappropriately distributed over space in urban cities, leading to the inequities that some residents have overwhelmingly superior access to PLSs but another are in considerably restricted proximity of PLSs. In this context, examining the inequalities in PLSs provision should provide critical management implications. This paper examines the PLSs provision in association with neighborhood sociodemographics in Shenzhen, China. A set of mixed indicators is developed to measure the PLSs provision in an integrated manner (physical amount, spatial extent, and accessibility), and 19 variables in total are used to describe neighborhood sociodemographics. Principle component analysis (PCA) is employed to produce the neighborhood sociodemographic profiles of PLSs provision. PCA generates four categories of neighborhood sociodemographic profiles in relation to PLSs provision: the socioeconomically disadvantaged neighborhood of lower PLSs amount, the educationally disadvantaged neighborhood of lower PLSs accessibility, the housing disadvantaged neighborhood of higher PLSs fragmentation, and the occupationally advantaged neighborhood of greater PLSs intact. In space, the disadvantaged neighborhoods of PLSs provision are generally located in the outskirts. The findings evidence the significant inequalities of PLSs provision in physical amount, accessibility and spatial extent. Our study highlights the promising potential of PCA in assisting urban managers and planners to better the PLSs provision patterns.