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Vegetation may influence asthma exacerbation through effects on aeroallergens, localized climates, air pollution, or children’s behaviors and stress levels. We investigated the association between residential vegetation and asthma exacerbation by conducting a matched case–control study based on electronic health records of asthma patients, from the Children’s Hospital of Philadelphia (CHOP). Our study included 17,639 exacerbation case events and 34,681 controls selected from non-exacerbation clinical visits for asthma, matched to cases by age, sex, race/ethnicity, public payment source, and residential proximity to the CHOP main campus ED and hospital. Overall greenness, tree canopy, grass/shrub cover, and impervious surface were assessed near children’s homes (250 m) using satellite imagery and high-resolution landcover data. We used generalized estimating equations to estimate odds ratios (OR) and 95% confidence intervals (CI) for associations between each vegetation/landcover measure and asthma exacerbation, with adjustment for seasonal and sociodemographic factors—for all cases, and for cases defined by diagnosis setting and exacerbation frequency. Lower odds of asthma exacerbation were observed in association with greater levels of tree canopy near the home, but only for children who experienced multiple exacerbations in a year (OR = 0.94 per 10.2% greater tree canopy coverage, 95% CI = 0.90–0.99). Our findings suggest possible protection for asthma patients from tree canopy, but differing results by case frequency suggest that potential benefits may be specific to certain subpopulations of asthmatic children. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11524-022-00633-7.

IntroductionThe burden of Alzheimer’s disease (AD) and AD-related dementias (ADRD) is increasing nationally and globally, with disproportionate impacts on lower-income, lower education and systematically marginalised older adults. Presence of inequalities in neighbourhood factors (eg, social context, physical and built environments) may affect risk of cognitive decline and be key for intervening on AD/ADRD disparities at the population level. However, existing studies are limited by a dearth of longitudinal, detailed neighbourhood measures linked to rich, prospective cohort data. Our main objective is to identify patterns of neighbourhood change related to prevalence of—and disparities in—cognitive decline and dementia.Methods and analysesWe describe the process of collecting, processing and linking extensive neighbourhood data to the Multi-Ethnic Study of Atherosclerosis (MESA), creating a 25+ years dataset. Within the MESA parent study, the MESA Neighborhoods and Aging cohort study will characterise dynamic, longitudinal neighbourhood social and built environment variables relevant to cognition for residential addresses of MESA participants. This includes administering new surveys, expanding residential address histories, calculating new measures derived from spatial data and implementing novel deep learning algorithms on street-level imagery. Applying novel statistical techniques, we will examine associations of neighbourhood environmental characteristics with cognition and clinically relevant AD/ADRD outcomes. We will investigate determinants of disparities in outcomes by socioeconomic position and race/ethnicity and assess the contribution of neighbourhood environments to these disparities. This project will provide new evidence about pathways between neighbourhood environments and cognitive outcomes, with implications for policies to support healthy ageing.Ethics and disseminationThis project was approved by the University of Washington and Drexel University Institutional Review Boards (protocols #00009029 and #00014523, and #180900605). Data will be distributed through the MESA Coordinating Center. Findings will be disseminated in peer-reviewed scientific journals, briefs, presentations and on the participant website.

Purpose: To examine the burden of uncontrolled hyperglycemia in patients with diabetes mellitus (DM) and their characteristics in a large urban city. Methods: A randomized sample of 4993 patients with DM ≥18 years old who received routine health care in a large university teaching hospital in the city of Philadelphia was analyzed. Uncontrolled hyperglycemia was classified as blood hemoglobin A1c >8%. The associations of uncontrolled hyperglycemia with sociodemographic and cardiovascular factors were analyzed using univariate and multivariate analysis methods. Results: The results show that patients 18–54 years had the highest prevalence of uncontrolled hyperglycemia (36.0%), followed by those at age 55–64 (30.9%), 65–74 (22.9%), and ≥75 (20.6%) years (p

Testing for SARS-CoV-2 infection has been a key strategy to mitigate and control the COVID-19 pandemic. Wide spatial and racial/ethnic disparities in COVID-19 outcomes have emerged in US cities. Previous research has highlighted the role of unequal access to testing as a potential driver of these disparities. We described inequities in spatial accessibility to COVID-19 testing locations in 30 large US cities. We used location data from Castlight Health Inc corresponding to October 2021. We created an accessibility metric at the level of the census block group (CBG) based on the number of sites per population in a 15-minute walkshed around the centroid of each CBG. We also calculated spatial accessibility using only testing sites without restrictions, i.e., no requirement for an appointment or a physician order prior to testing. We measured the association between the social vulnerability index (SVI) and spatial accessibility using a multilevel negative binomial model with random city intercepts and random SVI slopes. Among the 27,195 CBG analyzed, 53% had at least one testing site within a 15-minute walkshed, and 36% had at least one site without restrictions. On average, a 1-decile increase in the SVI was associated with a 3% (95% Confidence Interval: 2% - 4%) lower accessibility. Spatial inequities were similar across various components of the SVI and for sites with no restrictions. Despite this general pattern, several cities had inverted inequity, i.e., better accessibility in more vulnerable areas, which indicates that some cities may be on the right track when it comes to promoting equity in COVID-19 testing. Testing is a key component of the strategy to mitigate transmission of SARS-CoV-2 and efforts should be made to improve accessibility to testing, particularly as new and more contagious variants become dominant.

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.

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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11606-021-06935-x.

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 &gt, 75th percentile of the MSA-specific distribution) experienced higher 1990–2000 increases in percentage of the employed civilian aged 16+ population working professional jobs (β: 0.18, 95% confidence interval (CI): 0.11, 0.26) and in median household income (β: 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.

Fine particulate matter (PM2.5) and ozone (O3) air pollutants are known risk factors for asthma exacerbation. We studied the association of these air pollutants with pediatric asthma exacerbation in the Philadelphia metropolitan region, and evaluated potential effect modification by children's characteristics (e.g., race/ethnicity, atopic conditions) and environmental factors (e.g., neighborhood tree canopy, meteorological factors, aeroallergens). We conducted a time-stratified case-crossover study of 54,632 pediatric (age ≤18 years) asthma exacerbation cases occurring from 2011 to 2014, identified through electronic health records (EHR) of the Children's Hospital of Philadelphia (CHOP) health system. We applied conditional logistic regression to estimate associations between air pollution and asthma exacerbation, using daily census-tract level pollutant concentrations estimated from the EPA Fused Air Quality Surface Using Downscaling (FAQSD) files. The associations were estimated within warm (Apr-Sep) and cold (Oct-Mar) months for unlagged exposure and for cumulative effects up to 5 days after exposure, with adjustment for temperature, relative humidity, and holidays. We found small increases in odds of asthma exacerbation with higher pollutant concentrations, with positive associations (OR, comparing concentrations of 75th to 25th percentile) observed for PM2.5 during both warm (1.03, 95% CI: 0.98-1.08) and cold months (1.05, 95% CI: 1.02-1.07), and for O3 during cold months (1.08, 95% CI: 1.02-1.14). The exposure-response relationship with PM2.5 during the cold months was essentially linear, whereas thresholds of effect were observed for the other associations at low-medium pollutant concentrations. Results were robust to multi-pollutant modeling and adjustment for additional covariates. We found no effect modification by most children's characteristics, while effect sizes were higher on days with detected tree and grass pollens during warm months. Our results suggest that even small decreases in pollutant concentrations could potentially reduce risk of childhood asthma exacerbation - an important finding, given the high burden of childhood asthma and known disparities in asthma control.

Stress has been shown to adversely affect pregnancy outcomes. Neighborhood crime rates may serve as one publicly available social determinant of health for pregnancy studies that use registry or electronic health record datasets in which individual-level stress data are not available. We sought to determine whether neighborhood violent crime incidents were associated with measured perceived stress in a largely minority, urban pregnancy cohort. We performed a secondary analysis of the 1309 Philadelphia residents participating in the Motherhood and Microbiome cohort (n = 2000) with both neighborhood violent crime and Cohen&rsquo, s Perceived Stress Scale (PSS-14) data. Generalized linear mixed models accounting for confounding variables and geographic clustering demonstrated that, regardless of race, women with the highest quartile of neighborhood violent crime had significantly elevated odds of high stress compared to women with lower crime. We also found that Black women were more likely to have both the highest quartile of neighborhood violent crime and high stress than non-Black women. Overall, this study demonstrates that neighborhood violent crime is associated with perceived stress in pregnancy. Given disparate exposure to crime and prenatal stress by race, future work is warranted to determine whether urban neighborhood violence and/or stress reduction strategies would improve birth outcome racial disparities.

Background The availability of marijuana products is becoming increasingly prevalent across the United States (US), many states are allowing for the production, processing, and retailing of these products for medical and/or recreational use. The purpose of this study is to: (1) examine the spatial patterning of marijuana licenses, and (2) examine the impact of alcohol outlets in addition to other neighborhood characteristics on marijuana licenses within the state of Washington. Methods This cross-sectional observational study examined 1458 census tracts in Washington state from 2017, using marijuana and alcohol data from the Washington State Liquor and Cannabis Board as well as neighborhood characteristics data from the American Community Survey 2011–2015 5-year estimates. We used exploratory and formal spatial regression methods, including integrated nested Laplace approximation within a Bayesian statistical framework, to address the study aims. Results Our results indicate there is significant spatial patterning of marijuana producers and processors across the state. We also found that all marijuana licenses are located in poorer census tracts, and marijuana retailers are co-located in census tracts with off-premises alcohol outlets. Conclusions Our study provides empirical evidence of the relationship between marijuana licenses, alcohol outlets, and neighborhood characteristics, and has important implications for policymakers in other states currently considering legalizing marijuana-products for medical and/or recreational use.

Introduction It has been posited that policies to promote child health and prevent obesity should target neighborhood environments but evidence on the impact of neighborhoods on child weight is conflicting and longitudinal studies (which have benefits for causal inference) are scarce. Methods We used electronic health records (2007–2016) from an urban, pediatric integrated delivery system and linked children (N = 51,873, ages 6–19 years, 77% African American) to neighborhood-level data to investigate how changes in neighborhood environments relate to changes in body mass index (BMI). Measures of neighborhood environment were resources for healthy foods and physical activity (‘resources’), greenness, violent crime rate, perceived safety and social cohesion. Fixed effects models estimated associations between changes in neighborhood environment exposures and changes in BMI z-score and whether effects differed by sex, baseline age, neighborhood socioeconomic status and population density. Results Approximately 22% of the cohort was obese (BMI z-score ≥ 95th percentile). In adjusted models, increases in neighborhood greenness and perceived safety were associated with decreases in BMI z-score (mean change in BMI z-score for 1-SD increase for both: -0.012; 95% CI= (−0.018, −0.007)). Increases in neighborhood safety had a stronger effect in children ages 6–10 years than in older children. Increases in social cohesion were associated with increases in BMI z-score (mean change: 0.005 95% CI = (0.003, 0.008)) especially in boys. Increases in food and physical activity resources were not associated with changes in BMI. Conclusions This study suggests that increasing neighborhood greenness and safety are potential approaches to reduce children's BMI.

Objective. To quantify the association between heat and infant mortality and identify factors that influence infant vulnerability to heat. Methods. We conducted a time-stratified case–crossover analysis of associations between ambient temperature and infant mortality in Philadelphia, Pennsylvania, during the warm months of 2000 through 2015. We used conditional logistic regression models to estimate associations of infant mortality with daily temperatures on the day of death (lag 0) and for averaging periods of 0 to 1 to 0 to 3 days before the day of death. We explored modification of associations by individual and census tract–level characteristics and by amounts of green space. Results. Risk of infant mortality increased by 22.4% (95% confidence interval [CI] = 5.0%, 42.6%) for every 1°C increase in minimum daily temperature over 23.9°C on the day of death. We observed limited evidence of effect modification across strata of the covariates. Conclusions. Our results contribute to a growing body of evidence that infants are a subpopulation that is particularly vulnerable to climate change effects. Further research using large data sets is critically needed to elucidate modifiable factors that may protect infants against heat vulnerability.

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.

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.

Objective To examine the sociodemographic characteristics of one population of young, febrile infants and identify associations between neighborhood-level social determinants of health (SDHs) with bacterial infections. Study design This was a retrospective cross sectional study of all infants ≤90 days old with a temperature of ≥38°C who presented in 2014 to the emergency department of an urban children's hospital in a large east coast city. The primary outcome was the presence of a bacterial infection, defined as a positive urine, blood, or cerebrospinal fluid culture that was treated clinically as a pathogen. The home address of each infant was geocoded and linked to neighborhood data based on census tract. Neighborhood-level SDHs included deprivation index, median household income, poverty, childhood poverty, social capital, and crowded housing. Associations were estimated using generalized estimating equations and negative binomial regression analysis. Models were adjusted for age, prematurity, and race/ethnicity. Results Of 232 febrile infants, the median age was 54 days, 58% were male, 49% were Hispanic, and 88% had public health insurance; 31 infants (13.4%) had a bacterial infection. In the adjusted analyses, the risk of bacterial infection among infants from neighborhoods with high rates of childhood poverty was >3 times higher (relative risk, 3.16; 95% CI, 1.04-9.6) compared with infants from neighborhoods with low rates of childhood poverty. Conclusions Our findings suggest that SDHs may be associated with bacterial infections in young, febrile infants. If confirmed in subsequent studies, the inclusion of SDHs in predictive tools may improve accuracy in detecting bacterial infections among young, febrile infants.

In spite of a dramatic decrease in anthropogenic emissions, ambient concentrations of major pollutants have not changed within many urban locations. To clarify the relationship between ambient air quality trend and the population exposures, we compared the intraurban versus temporal variability of the collocated measurements of five major air pollutants including particulate matter (PM) with an aerodynamic diameter &lt, 10 µm (PM10), &lt, 2.5 µm (PM2.5), tropospheric ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2), in Kraków, Poland, during the 2000-2010 period. Strong seasonal trends and overall absence of spatial heterogeneity in PM10 and PM2.5, except in the traffic monitoring site, were observed across the monitoring network. The range of median PM2.5 concentrations during winter (54–64 µg/m3) was 3- to 4-times higher than the summer medians (15–26 µg/m3) across the sites during 2009-2010. Furthermore, large proportion of PM10 appears to be comprised of PM2.5 (PM2.5/PM10 concentration ratios range, 0.5–0.7). At each monitoring site, the Pearson’s correlation coefficients between PM2.5 and PM10 ranged between 0.944 and 0.963, suggesting a health-relevance of PM10 monitoring. One ln-unit increase in PM10 was associated with 92%–100% increase in PM2.5 concentrations in the same location. While PM10 did not demonstrate a clear temporal trend, SO2 concentrations steadily declined by 40% during the 2000–2010 period. Summertime median NO2 concentration was acutely elevated ‎(70 mg/m3 vs. 22 mg/m3) at the traffic oriented site compared to the city’s central monitoring site. The traffic and the industrial sites were associated with highest number of days during which 24-hour mean PM10 and PM2.5 concentrations exceeded the European Union standard. Steadily growing contributions by vehicular emissions appear to be associated with the absence of clear trend in PM10. Current practices of air quality control within Kraków may not be adequate for the protection of the public’s health.

The built environment predicts walking in older adults, but the degree to which associations between the objective built environment and walking for different purposes are mediated by environmental perceptions is unknown. We examined associations between the neighborhood built environment and leisure and utilitarian walking and mediation by the perceived environment among older women. Women (N = 2732, M age = 72.8 ± 6.8 years) from Massachusetts, Pennsylvania, and California completed a neighborhood built environment and walking survey. Objective population and intersection density and density of stores and services variables were created within residential buffers. Perceived built environment variables included measures of land use mix, street connectivity, infrastructure for walking, esthetics, traffic safety, and personal safety. Regression and bootstrapping were used to test associations and indirect effects. Objective population, stores/services, and intersection density indirectly predicted leisure and utilitarian walking via perceived land use mix (odds ratios (ORs) = 1.01–1.08, 95 % bias corrected and accelerated confidence intervals do not include 1). Objective density of stores/services directly predicted ≥150 min utilitarian walking (OR = 1.11; 95% CI = 1.02, 1.22). Perceived land use mix (ORs = 1.16–1.44) and esthetics (ORs = 1.24–1.61) significantly predicted leisure and utilitarian walking, Perceived built environment mediated associations between objective built environment variables and walking for leisure and utilitarian purposes. Interventions for older adults should take into account how objective built environment characteristics may influence environmental perceptions and walking.

Introduction: Air pollution exposure has been associated with increased blood pressure (BP) in adults. Previously, we found that higher exposures to particle mass (PM2.5) and black carbon in the th...

Numerous studies have demonstrated that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM2.5 to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM2.5 exposures. In this paper, we used AOD data with other PM2.5 variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM2.5 at a 1-km(2) resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM2.5 measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R(2) values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 μg/m(3) for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM2.5 concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM2.5. Our model results will also extend the existing studies on PM2.5 which have mostly focused on urban areas because of the paucity of monitors in rural areas.