National trends and disparities in retail food environments in the USA between 1990 and 2014.

OBJECTIVE: To describe national disparities in retail food environments by neighbourhood composition (race/ethnicity and socio-economic status) across time and space. DESIGN: We examined built food environments (retail outlets) between 1990 and 2014 for census tracts in the contiguous USA (n 71 547). We measured retail food environment as counts of all food stores, all unhealthy food sources (including fast food, convenience stores, bakeries and ice cream) and healthy food stores (including supermarkets, fruit and vegetable markets) from National Establishment Time Series business data. Changes in food environment were mapped to display spatial patterns. Multi-level Poisson models, clustered by tract, estimated time trends in counts of food stores with a land area offset and independent variables population density, racial composition (categorised as predominantly one race/ethnicity (>60 %) or mixed), and inflation-adjusted income tertile. SETTING: The contiguous USA between 1990 and 2014. PARTICIPANTS: All census tracts (n 71 547). RESULTS: All food stores and unhealthy food sources increased, while the subcategory healthy food remained relatively stable. In models adjusting for population density, predominantly non-Hispanic Black, Hispanic, Asian and mixed tracts had significantly more destinations of all food categories than predominantly non-Hispanic White tracts. This disparity increased over time, predominantly driven by larger increases in unhealthy food sources for tracts which were not predominantly non-Hispanic White. Income and food store access were inversely related, although disparities narrowed over time. CONCLUSIONS: Our findings illustrate a national food landscape with both persistent and shifting spatial patterns in the availability of establishments across neighbourhoods with different racial/ethnic and socio-economic compositions.

Patient characteristics and neighborhood attributes associated with hepatitis C screening and positivity in Philadelphia.

Among patients of an urban primary care network in Philadelphia with a universal hepatitis C virus (HCV) screening policy for patients born during 1945-1965, we examined whether being unscreened and HCV positivity were associated with attributes of the census tracts where patients resided, which we considered as proxies for social health determinants. For patients with at least one clinic visit between 2014 and mid-2017, we linked demographic and HCV screening information from electronic health records with metrics that described the census tracts where patients resided. We used generalized estimating equations to estimate adjusted relative risk ratios (aRRs) for being unscreened and HCV positive. Overall, 28% of 6,906 patients were unscreened. Black race, male gender, and residence in census tracts with relatively high levels of violent crime, low levels of educational attainment and household incomes, and evidence of residential segregation by Hispanic ethnicity were associated with lower aRRs for being unscreened. Among screened patients, 9% were HCV positive. Factors associated with lower risks of being unscreened were, in general, associated with higher HCV positivity. Attributes of census tracts where patients reside are probably less apparent to clinicians than patients' gender or race but might reflect unmeasured patient characteristics that affected screening practices, along with preconceptions regarding the likelihood of HCV infection based on prior screening observations or implicit biases. Approaching complete detection of HCV-infected people would be hastened by focusing on residents of census tracts with attributes associated with higher infection levels or, if known, higher infection levels directly.

Changes in physical activity after joining a bikeshare program: a cohort of new bikeshare users.

BACKGROUND: There are hundreds of bikeshare programs worldwide, yet few health-related evaluations have been conducted. We enrolled a cohort of new bikeshare members in Philadelphia (Pennsylvania, USA) to assess whether within-person moderate and vigorous physical activity (MVPA) increased with higher use of the program and whether effects differed for vulnerable sub-groups. METHODS: During 2015-2018, 1031 new members completed baseline and one-year follow-up online surveys regarding their personal characteristics and past 7-day MVPA minutes per week (minutes per week with- and without walking). Participants were linked to their bikeshare trips to objectively assess program use. Negative binomial (for continuous outcomes) and multinomial (for categorical outcomes) regression adjusted for person characteristics (socio-demographics, health), weather, biking-infrastructure, and baseline biking. RESULTS: Participant median age was 30, 25% were of Black or Latino race/ethnicity, and 30% were socioeconomically disadvantaged. By follow-up, personal bike ownership increased and 75% used bikeshare, although most used it infrequently. Per 10 day change in past year (PY) bikeshare use, non-walking MVPA min/wk increased 3% (roughly 6 min/wk, P < 0.014). More days of bikeshare was also associated with change from inactive to more active (odds ratio for ≥ 15 days in PY vs. no bikeshare use 1.80, CI 1.05-3.09, P < 0.03). Results were consistent across vulnerable sub-groups. In general, impacts on MVPA were similar when exposure was personal bike or bikeshare. CONCLUSIONS: Bikeshare facilitated increases in cycling, slightly increased non-walking MVPA, and showed potential for activating inactive adults; however, for larger program impact, members will need to use it more frequently.

Neighborhood Effects on Missed Appointments in a Large Urban Academic Multispecialty Practice.

BACKGROUND: Missed appointments diminish the continuity and quality of care. OBJECTIVE: To determine whether missing scheduled appointments is associated with characteristics of the populations in places where patients reside. DESIGN: Retrospective cross-sectional study using data extracted from electronic health records linked to population descriptors for each patient's census tract of residence. PATIENTS: A total of 58,981 patients ≥18 years of age with 275,682 scheduled appointments during 2014-2015 at a multispecialty outpatient practice. MAIN MEASURES: We used multinomial generalized linear mixed models to examine associations between the outcomes of scheduled appointments (arrived, canceled, or missed) and selected characteristics of the populations in patients' census tracts of residence (racial/ethnic segregation based on population composition, levels of poverty, violent crime, and perceived safety and social capital), controlling for patients' age, gender, type of insurance, and type of clinic service. KEY RESULTS: Overall, 17.5% of appointments were missed. For appointments among patients residing in census tracts in the highest versus lowest quartile for each population metric, adjusted odds ratios (aORs) for missed appointments were 1.27 (CI 1.19, 1.35) for the rate of violent crime, 1.27 (CI 1.20, 1.34) for the proportion Hispanic, 1.19 (CI 1.12, 1.27) for the proportion living in poverty, 1.13 (CI 1.05, 1.20) for the proportion of the census tract population that was Black, and 1.06 (CI 1.01, 1.11 for perceived neighborhood safety. CONCLUSIONS: Characteristics of the places where patients reside are associated with missing scheduled appointments, including high levels of racial/ethnic segregation, poverty, and violent crime and low levels of perceived neighborhood safety. As such, targeting efforts to improve access for patients living in such neighborhoods will be particularly important to address underlying social determinants of access to health care.

Constructing Within-City Neighborhood Health Rankings in Philadelphia by Using Data From the 500 Cities Project.

INTRODUCTION: Profound geographic disparities in health exist in many US cities. Most reporting on these disparities is based on predetermined administrative districts that may not reflect true neighborhoods. We undertook a ranking project to describe health at the neighborhood level and used Philadelphia, Pennsylvania, as our case study. METHODS: To create neighborhood health rankings, we first divided the city into neighborhoods according to groups of contiguous census tracts. Modeling our ranking methods and indicators on the Robert Wood Johnson Foundation County Health Rankings, we gathered census tract-level data from the Centers for Disease Control and Prevention's 500 Cities Project and local sources and aggregated these data, as needed, to each neighborhood. We assigned composite scores and rankings for both health outcomes and health factors to each neighborhood. RESULTS: Scores for health outcomes and health factors were highly correlated. We found clusters of neighborhoods with low rankings in Philadelphia's northern, lower northeastern, western, and southwestern regions. We disseminated information on rankings throughout the city, including through a comprehensive webpage, public communication, and a museum exhibit. CONCLUSION: The Philadelphia neighborhood health rankings were designed to be accessible to people unfamiliar with public health, facilitating education on drivers of health in communities. Our methods can be used as a model for other cities to create and communicate data on within-city geographic health disparities.

Associations between Greenspace and Gentrification-Related Sociodemographic and Housing Cost Changes in Major Metropolitan Areas across the United States.

Neighborhood greenspace may attract new residents and lead to sociodemographic or housing cost changes. We estimated relationships between greenspace and gentrification-related changes in the 43 largest metropolitan statistical areas (MSAs) of the United States (US). We used the US National Land Cover and Brown University Longitudinal Tracts databases, as well as spatial lag models, to estimate census tract-level associations between percentage greenspace (years 1990, 2000) and subsequent changes (1990-2000, 2000-2010) in percentage college-educated, percentage working professional jobs, race/ethnic composition, household income, percentage living in poverty, household rent, and home value. We also investigated effect modification by racial/ethnic composition. We ran models for each MSA and time period and used random-effects meta-analyses to derive summary estimates for each period. Estimates were modest in magnitude and heterogeneous across MSAs. After adjusting for census-tract level population density in 1990, compared to tracts with low percentage greenspace in 1992 (defined as ≤50th percentile of the MSA-specific distribution in 1992), those with high percentage greenspace (defined as >75th percentile of the MSA-specific distribution) experienced higher 1990-2000 increases in percentage of the employed civilian aged 16+ population working professional jobs (ß: 0.18, 95% confidence interval (CI): 0.11, 0.26) and in median household income (ß: 0.23, 95% CI: 0.15, 0.31). Adjusted estimates for the 2000-2010 period were near the null. We did not observe evidence of effect modification by race/ethnic composition. We observed evidence of modest associations between greenspace and gentrification trends. Further research is needed to explore reasons for heterogeneity and to quantify health implications.

Assessing the spatial heterogeneity in black-white differences in optimal cardiovascular health and the impact of individual- and neighborhood-level risk factors: The Multi-Ethnic Study of Atherosclerosis (MESA).

Racial disparities in cardiovascular health (CVH) continue to remain a public health concern in the United States. We use unique population-based data from the Multi-Ethnic Study of Atherosclerosis cohort to explore the black-white differences in optimal CVH. Utilizing geographically weighted regression methods, we assess the spatial heterogeneity in black-white differences in optimal CVH and the impact of both individual- and neighborhood-level risk factors. We found evidence of significant spatial heterogeneity in black-white differences that varied within and between the five sites. Initial models showed decreased odds of optimal CVH for blacks that ranged from 60% to 70% reduced odds - with noticeable variation of these decreased odds within each site. Adjusting for risk factors resulted in reductions in the black-white differences in optimal CVH. Further understanding of the reasons for spatial heterogeneities in black-white differences in nationally representative cohorts may provide important clues regarding the drivers of these differences.

Identifying neighborhood characteristics associated with diabetes and hypertension control in an urban African-American population using geo-linked electronic health records.

For health care providers, information on community-level social determinants of health is most valuable when it is specific to the populations and health outcomes for which they are responsible. Diabetes and hypertension are highly prevalent conditions whose management requires an interplay of clinical treatment and behavioral modifications that may be sensitive to community conditions. We used geo-linked electronic health records from 2016 of African American patients of a network of federally qualified health centers in Philadelphia, PA to examine cross-sectional associations between characteristics of patients' residential neighborhoods and hypertension and diabetes control (n = 1061 and n = 2633, respectively). Hypertension and diabetes control were defined to align with the Health Resources and Services Administration (HRSA) Uniform Data System (UDS) reporting requirements for HRSA-funded health centers. We examined associations with nine measures of neighborhood socioeconomic status (poverty, education, deprivation index), social environment (violent crime, perceived safety and social capital, racial segregation), and built environment (land-use mix, intersection density). In demographics-adjusted log-binomial regression models accounting for neighborhood-level clustering, poor diabetes and hypertension control were more common in highly segregated neighborhoods (i.e., high proportion of African American residents relative to the mean for Philadelphia; prevalence ratio = 1.27 [1.02-1.57] for diabetes, 1.22 [1.12-1.33] for hypertension) and less common in more walkable neighborhoods (i.e., higher retail land use). Neighborhood deprivation was also weakly associated with poor hypertension control. An important consideration in making geographic information actionable for providers is understanding how specific community-level determinants affect the patient population beyond individual-level determinants.

Personal and Neighborhood Attributes Associated with Cervical and Colorectal Cancer Screening in an Urban African American Population.

INTRODUCTION: Assessing individual social determinants of health in primary care might be complemented by consideration of population attributes in patients' neighborhoods. We studied associations between cervical and colorectal cancer screening and neighborhood attributes among an African American population in Philadelphia. METHODS: We abstracted demographic and cancer screening information from records of patients seen during 2006 at 3 federally qualified health centers and characterized patients' census tracts of residence by using census, survey, and other data to define population metrics for poverty, racial segregation, educational attainment, social capital, neighborhood safety, and violent crime. We used generalized estimating equations to obtain adjusted relative risks of screening associated with individual and census tract attributes. RESULTS: Among 1,708 patients for whom colorectal cancer screening was recommended, screening was up to date for 41%, and among 4,995 women for whom cervical cancer screening was recommended, screening was up to date for 75%. After controlling for age, sex (for colorectal cancer screening), insurance coverage, and clinic site, people living in the most racially segregated neighborhoods were nearly 10% more likely than others to be unscreened for colorectal cancer. Other census tract population attributes were not associated with differences in screening levels for either cancer. CONCLUSIONS: The association between lower rates of colorectal cancer screening and neighborhood racial segregation is consistent with known barriers to colonoscopy among African Americans combined with effects of segregation on health-related behaviors. Recognition of the association between segregation and lower colorectal cancer screening rates might be useful in informing and targeting community outreach to improve screening.

Spatial accessibility to pediatric primary care in Philadelphia: an area-level cross sectional analysis.

BACKGROUND: Pediatric primary care visits are a foundational element in the health maintenance of children. Differential access may be a driver of racial inequities in health. We hypothesized that pediatric primary care accessibility would be lowest in neighborhoods with higher proportion of non-Hispanic Black residents. METHODS: Annual ratios (2008-2016) of providers to pediatric population were calculated by census tract in Philadelphia, Pennsylvania. Marginal logistic regression was used to estimate the independent association between neighborhood racial composition and access to pediatric primary care controlling for confounders. RESULTS: In general, low access to care was associated with greater neighborhood disadvantage (e.g., SES, % poverty, % public insurance). After controlling for neighborhood indicators of disadvantage, risk of being in the lowest quintile of access significantly increased as the percent of non-Hispanic Black residents increased. CONCLUSION: A new measure of pediatric primary care accessibility demonstrates a persistent disparity in primary care access for predominantly non-Hispanic Black neighborhoods.

The role of neighborhood characteristics and the built environment in understanding racial/ethnic disparities in childhood obesity.

BACKGROUND: Childhood obesity prevalence remains high and racial/ethnic disparities may be widening. Studies have examined the role of health behavioral differences. Less is known regarding neighborhood and built environment mediators of disparities. The objective of this study was to examine the extent to which racial/ethnic disparities in elevated child body mass index (BMI) are explained by neighborhood socioeconomic status (SES) and built environment. METHODS: We collected and analyzed race/ethnicity, BMI, and geocoded address from electronic health records of 44,810 children 4 to 18years-old seen at 14 Massachusetts pediatric practices in 2011-2012. Main outcomes were BMI z-score and BMI z-score change over time. We used multivariable linear regression to examine associations between race/ethnicity and BMI z-score outcomes, sequentially adjusting for neighborhood SES and the food and physical activity environment. RESULTS: Among 44,810 children, 13.3% were black, 5.7% Hispanic, and 65.2% white. Compared to white children, BMI z-scores were higher among black (0.43units [95% CI: 0.40-0.45]) and Hispanic (0.38 [0.34-0.42]) children; black (0.06 [0.04-0.08]), but not Hispanic, children also had greater increases in BMI z-score over time. Adjusting for neighborhood SES substantially attenuated BMI z-score differences among black (0.30 [0.27-0.34]) and Hispanic children (0.28 [0.23-0.32]), while adjustment for food and physical activity environments attenuated the differences but to a lesser extent than neighborhood SES. CONCLUSIONS: Neighborhood SES and the built environment may be important drivers of childhood obesity disparities. To accelerate progress in reducing obesity disparities, interventions must be tailored to the neighborhood contexts in which families live.

Racial differences in the built environment--body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods.

BACKGROUND: Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). METHODS: Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran's I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. RESULTS: We found significant positive spatial autocorrelation in the built environment features examined (Global Moran's I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran's I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. CONCLUSION: Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents.

Program practices and demographic factors associated with federal funding for the Safe Routes to School program in the United States.

In 2005, the United States Congress authorized $612 million for use in implementing the US Safe Routes to School program to address physical inactivity, air quality, safety and traffic near schools. Each US state developed administrative practices to implement the program. Based on state-specific annual obligations, on average, states have obligated 44% of available funds. State project obligations were directly associated with programmatic factors, including broader adherence to federal agency administrative guidance objectives and the number of years for which the states obligated new projects and indirectly associated with student enrollment and state child poverty. Research and policy recommendations are discussed.

Validation of walk score for estimating neighborhood walkability: an analysis of four US metropolitan areas.

Neighborhood walkability can influence physical activity. We evaluated the validity of Walk Score(®) for assessing neighborhood walkability based on GIS (objective) indicators of neighborhood walkability with addresses from four US metropolitan areas with several street network buffer distances (i.e., 400-, 800-, and 1,600-meters). Address data come from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCA-administered, after-school programs located in four geographically diverse metropolitan areas in the US (n = 733). GIS data were used to measure multiple objective indicators of neighborhood walkability. Walk Scores were also obtained for the participant's residential addresses. Spearman correlations between Walk Scores and the GIS neighborhood walkability indicators were calculated as well as Spearman correlations accounting for spatial autocorrelation. There were many significant moderate correlations between Walk Scores and the GIS neighborhood walkability indicators such as density of retail destinations and intersection density (p < 0.05). The magnitude varied by the GIS indicator of neighborhood walkability. Correlations generally became stronger with a larger spatial scale, and there were some geographic differences. Walk Score(®) is free and publicly available for public health researchers and practitioners. Results from our study suggest that Walk Score(®) is a valid measure of estimating certain aspects of neighborhood walkability, particularly at the 1600-meter buffer. As such, our study confirms and extends the generalizability of previous findings demonstrating that Walk Score is a valid measure of estimating neighborhood walkability in multiple geographic locations and at multiple spatial scales.

Factors associated with federal transportation funding for local pedestrian and bicycle programming and facilities.

Providing safe, convenient places for walking and bicycling can reduce barriers to participating in regular physical activity. We examined bicycle- and pedestrian-related investments authorized by federal transportation legislation in 3,140 counties in the United States by region, population size and urbanization, social and economic characteristics, and indicators of travel-related walking and bicycling. From 1992 to 2004, states and counties implemented 10,012 bicycle- and pedestrian-related projects representing $3.17 billion in federal expenditures. We found disparities in implementation and system-building outcomes according to population size and location and social and economic indicators. Counties characterized by persistent poverty (odds ratio=0.69, 95% confidence interval 0.53-0.91) or low educational status (odds ratio=0.66, 95% confidence interval 0.52-0.84) were less likely to implement projects. Three key policy recommendations for improving public health outcomes are drawn from this research: Improved data tracking, more explicit linkages between transportation projects and public health, and improved planning assistance to underserved communities are all seen as essential steps.

Evaluating efficiency-equality tradeoffs for mobile source control strategies in an urban area.

In environmental risk management, there are often interests in maximizing public health benefits (efficiency) and addressing inequality in the distribution of health outcomes. However, both dimensions are not generally considered within a single analytical framework. In this study, we estimate both total population health benefits and changes in quantitative indicators of health inequality for a number of alternative spatial distributions of diesel particulate filter retrofits across half of an urban bus fleet in Boston, Massachusetts. We focus on the impact of emissions controls on primary fine particulate matter (PM(2.5)) emissions, modeling the effect on PM(2.5) concentrations and premature mortality. Given spatial heterogeneity in baseline mortality rates, we apply the Atkinson index and other inequality indicators to quantify changes in the distribution of mortality risk. Across the different spatial distributions of control strategies, the public health benefits varied by more than a factor of two, related to factors such as mileage driven per day, population density near roadways, and baseline mortality rates in exposed populations. Changes in health inequality indicators varied across control strategies, with the subset of optimal strategies considering both efficiency and equality generally robust across different parametric assumptions and inequality indicators. Our analysis demonstrates the viability of formal analytical approaches to jointly address both efficiency and equality in risk assessment, providing a tool for decisionmakers who wish to consider both issues.

Residential exposure to traffic-related air pollution and survival after heart failure.

BACKGROUND: Although patients with heart failure (HF) have been identified as particularly susceptible to the acute effects of air pollution, the effects of long-term exposure to air pollution on patients with this increasingly prevalent disease are largely unknown. OBJECTIVE: This study was designed to examine the mortality risk associated with residential exposure to traffic-related air pollution among HF patients. METHODS: A total of 1,389 patients hospitalized with acute HF in greater Worcester, Massachusetts, during 2000 were followed for survival through December 2005. We used daily traffic within 100 and 300 m of residence as well as the distance from residence to major roadways and to bus routes as proxies for residential exposure to traffic-related air pollution. We assessed mortality risks for each exposure variable using Cox proportional hazards models adjusted for prognostic factors. RESULTS: After the 5-year follow-up, only 334 (24%) subjects were still alive. An interquartile range increase in daily traffic within 100 m of home was associated with a mortality hazard ratio (HR) of 1.15 [95% confidence interval (CI), 1.05-1.25], whereas for traffic within 300 m this association was 1.09 (95% CI, 1.01-1.19). The mortality risk decreased with increasing distance to bus routes (HR = 0.88; 95% CI, 0.81-0.96) and was larger for those living within 100 m of a major roadway or 50 m of a bus route (HR = 1.30; 95% CI, 1.13-1.49). Adjustment for area-based income and educational level slightly attenuated these associations. CONCLUSIONS: Residential exposure to traffic-related air pollution increases the mortality risk after hospitalization with acute HF. Reducing exposure to traffic-related emissions may improve the long-term prognosis of HF patients.