Background

Studies of the built environment and physical activity have implicitly assumed that a substantial amount of activity occurs near home, but in fact the location is unknown.

Purpose

Examine associations between built environment variables within home and work buffers and moderate-vigorous physical activity occurring within these locations.

Methods

Adults (n= 148) from Massachusetts wore an accelerometer and GPS unit for up to four days. Moderate and vigorous intensity activity was quantified within 50 m and 1 km home and work buffers. Multiple regression models were used to examine associations between five objective built environment variables within 1 km home and work buffers (intersection density, land use mix, population and housing unit density, vegetation index) and moderate-vigorous physical activity within those areas.

Results

The mean daily minutes of moderate-vigorous physical activity accumulated in all locations = 61.1 ± 32.8, while duration within 1 km home buffers = 14.0 ± 16.4 min. Intersection density, land use mix, and population and housing unit density within 1 km home buffers were positively associated with moderate-vigorous physical activity in the buffer, while a vegetation index showed an inverse relationship (all p< 0.05). None of these variables showed associations with total moderate-vigorous activity. Within 1 km of work, only population and housing unit density were significantly associated with moderate-vigorous physical activity within the buffer.

Conclusions

Findings are consistent with studies showing that certain attributes of the built environment around homes are positively related to physical activity, but in this case only when the outcome was “location-based”. Simultaneous accelerometer-GPS monitoring shows promise as a method to improve understanding of how the built environment influences physical activity behaviors by allowing activity to be quantified in a range of physical contexts and thereby provide a more explicit link between physical activity outcomes and built environment exposures.

Background

Adverse birth outcomes such as low birth weight and premature birth have been previously linked with exposure to ambient air pollution. Most studies relied on a limited number of monitors in the region of interest, which can introduce exposure error or restrict the analysis to persons living near a monitor, which reduces sample size and generalizability and may create selection bias.

Methods

We evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM2.5) levels during pregnancy in Massachusetts for a 9-year period (2000–2008). Building on a novel method we developed for predicting daily PM2.5 at the spatial resolution of a 10x10km grid across New-England, we estimated the average exposure during 30 and 90 days prior to birth as well as the full pregnancy period for each mother. We used linear and logistic mixed models to estimate the association between PM2.5 exposure and birth weight (among full term births) and PM2.5 exposure and preterm birth adjusting for infant sex, maternal age, maternal race, mean income, maternal education level, prenatal care, gestational age, maternal smoking, percent of open space near mothers residence, average traffic density and mothers health.

Results

Birth weight was negatively associated with PM2.5 across all tested periods. For example, a 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy was significantly associated with a decrease of 13.80 g [95% confidence interval (CI) = −21.10, -6.05] in birth weight after controlling for other factors, including traffic exposure. The odds ratio for a premature birth was 1.06 (95% confidence interval (CI) = 1.01–1.13) for each 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy period.

Conclusions

The presented study suggests that exposure to PM2.5 during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.

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.

Background

Limited availability of desirable destinations within walkable distances and unsuitable weather may adversely affect physical activity among adolescents on weekends.

Methods

Students (n=152, 59% male, mean age 13.7 years) from 10 neighborhoods with schools in four communities wore TriTrac-R3D accelerometers recording physical movements on weekends. Minute-by-minute data were summed over 15-minute intervals providing estimates of proportion of time spent in moderate and vigorous physical activity (MVPA) and (log) mean physical activity levels on weekends (n=7506 intervals). Objective measures of neighborhood characteristics were calculated using geographic information systems including average daily traffic, housing density, open space, and density of employees/ km2 in youth destinations. Linear mixed models were fit examining associations between neighborhood environmental variables and accelerometer measures of physical activity, controlling for time, day, age, BMI, sex of respondent, race/ethnicity, precipitation, and temperature deviation.

Results

On weekends, greater densities of employees in neighborhood destinations serving youth (β=3.96, p=0.050) was directly associated with MVPA, independent of student characteristics.

Conclusions

Young people attending schools in neighborhoods characterized by greater densities of employees in destinations for youth are more physically active on weekends. Compared with neighborhoods with lower densities, attending a school in neighborhoods with higher densities of employees in potential destinations for youth may contribute to participation in an additional 30 minutes of MVPA per day on weekends.

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.

Background

There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations.

Methods

Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored.

Results

Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches.

Conclusion

Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.

Background

Particulate air pollution has been consistently linked to increased risk of arterial cardiovascular disease. Few data on air pollution exposure and risk of venous thrombosis are available. We investigated whether living near major traffic roads increases the risk of deep vein thrombosis (DVT), using distance from roads as a proxy for traffic exposure.

Methods and Results

Between 1995-2005, we examined 663 patients with DVT of the lower limbs and 859 age-matched controls from cities with population>15,000 inhabitants in Lombardia Region, Italy. We assessed distance from residential addresses to the nearest major traffic road using geographic information system methodology. The risk of DVT was estimated from logistic regression models adjusting for multiple clinical and environmental covariates.

The risk of DVT was increased (Odds Ratio [OR]=1.33; 95% CI 1.03-1.71; p=0.03 in age-adjusted models; OR=1.47; 95%CI 1.10-1.96; p=0.008 in models adjusted for multiple covariates) for subjects living near a major traffic road (3 meters, 10th centile of the distance distribution) compared to those living farther away (reference distance of 245 meters, 90th centile). The increase in DVT risk was approximately linear over the observed distance range (from 718 to 0 meters), and was not modified after adjusting for background levels of particulate matter (OR=1.47; 95%CI 1.11-1.96; p=0.008 for 10th vs. 90th distance centile in models adjusting for area levels of particulate matter <10 μm in aerodynamic diameter [PM10] in the year before diagnosis).

Conclusions

Living near major traffic roads is associated with increased risk of DVT.

Background

Previous research suggests that school characteristics may influence physical activity. However, few studies have examined associations between school building and campus characteristics and objective measures of physical activity among middle school students.

Methods

Students from ten middle schools (n=248, 42% female, mean age 13.7 years) wore TriTrac-R3D accelerometers in 1997 recording measures of minute-by-minute physical movements during the school day that were then averaged over 15-minute intervals (n=16,619) and log-transformed. School characteristics, including school campus area, play area, and building area (per student) were assessed retrospectively in 2004–2005 using land-use parcel data, site visits, ortho-photos, architectural plans, and site maps. In 2006, linear mixed models using SAS PROC MIXED were fit to examine associations between school environmental variables and physical activity, controlling for potentially confounding variables.

Results

Area per enrolled student ranged from 8.8 to 143.7 m2 for school campuses, from 12.1 to 24.7 m2 for buildings, and from 0.4 to 58.9 m2 for play areas. Play area comprised from 3% to 62% of total campus area across schools. In separate regression models, school campus area per student (β=0.2244, p<0.0001); building area per student (β=2.1302, p<0.02); and play area per student (β=0.347, p<0.0001) were each directly associated with log-TriTrac-R3D vector magnitude. Given the range of area density measures in this sample of schools, this translates into an approximate 20% to 30% increase in average vector magnitude, or walking 2 extra miles over the course of a week.

Conclusions

Larger school campuses, school buildings, and play areas (per enrolled student) are associated with higher levels of physical activity in middle school students.

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.