The objective of this study is to examine the relationship between measured traffic density near the homes of children and attained body mass index (BMI) over an eight-year follow up.
Children aged 9–10 years were enrolled across multiple communities in Southern California in 1993 and 1996 (n = 3318). Children were followed until age 18 or high school graduation to collect longitudinal information, including annual height and weight measurements. Multilevel growth curve models were used to assess the association between BMI levels at age 18 and traffic around the home.
For traffic within 150 m around the child’s home, there were significant positive associations with attained BMI for both sexes at age 18. With the 300 m traffic buffer, associations for both male and female growth in BMI were positive, but significantly elevated only in females. These associations persisted even after controlling for numerous potential confounding variables.
This analysis yields the first evidence of significant effects from traffic density on BMI levels at age 18 in a large cohort of children. Traffic is a pervasive exposure in most cities, and our results identify traffic as a major risk factor for the development of obesity in children.
Traffic; built environment; children; overweight and obesity; geographic information systems; multilevel models; cohort study
The role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors. We evaluated the extent to which spatial clustering of extreme body mass index (BMI) values among a large sample of adults with diabetes was explained by individual characteristics and contextual factors.
We quantified spatial clustering of BMI among 15,854 adults with diabetes from the Diabetes Study of Northern California (DISTANCE) cohort using the Global and Local Moran’s I spatial statistic. As a null model, we assessed the amount of clustering when BMI values were randomly assigned. To evaluate predictors of spatial clustering, we estimated two linear models to estimate BMI residuals. First we included individual factors (demographic and socioeconomic characteristics). Then we added contextual factors (neighborhood deprivation, food environment) that may be associated with BMI. We assessed the amount of clustering that remained using BMI residuals.
Global Moran’s I indicated significant clustering of extreme BMI values; however, after accounting for individual socioeconomic and demographic characteristics, there was no longer significant clustering. Twelve percent of the sample clustered in extreme high or low BMI clusters, whereas, only 2.67% of the sample was clustered when BMI values were randomly assigned. After accounting for individual characteristics, we found clustering of 3.8% while accounting for neighborhood characteristics resulted in 6.0% clustering of BMI. After additional adjustment of neighborhood characteristics, clustering was reduced to 3.4%, effectively accounting for spatial clustering of BMI.
We found substantial clustering of extreme high and low BMI values in Northern California among adults with diabetes. Individual characteristics explained somewhat more of clustering of the BMI values than did neighborhood characteristics. These findings, although cross-sectional, may suggest that selection into neighborhoods as the primary explanation of why individuals with extreme BMI values live close to one another. Further studies are needed to assess causes of extreme BMI clustering, and to identify any community level role to influence behavior change.
Electronic supplementary material
The online version of this article (doi:10.1186/1476-072X-13-48) contains supplementary material, which is available to authorized users.
Body mass index; Diabetes; Spatial clustering; Moran’s I; Spatial autocorrelation; Neighborhood characteristics; Geographical epidemiology
Exposure to air pollution during pregnancy has been linked to the risk of childhood cancer, but the evidence remains inconclusive. In the present study, we used land use regression modeling to estimate prenatal exposures to traffic exhaust and evaluate the associations with cancer risk in very young children. Participants in the Air Pollution and Childhood Cancers Study who were 5 years of age or younger and diagnosed with cancer between 1988 and 2008 were had their records linked to California birth certificates, and controls were selected from birth certificates. Land use regression–based estimates of exposures to nitric oxide, nitrogen dioxide, and nitrogen oxides were assigned based on birthplace residence and temporally adjusted using routine monitoring station data to evaluate air pollution exposures during specific pregnancy periods. Logistic regression models were adjusted for maternal age, race/ethnicity, educational level, parity, insurance type, and Census-based socioeconomic status, as well as child's sex and birth year. The odds of acute lymphoblastic leukemia increased by 9%, 23%, and 8% for each 25-ppb increase in average nitric oxide, nitrogen dioxide, and nitrogen oxide levels, respectively, over the entire pregnancy. Second- and third-trimester exposures increased the odds of bilateral retinoblastoma. No associations were found for annual average exposures without temporal components or for any other cancer type. These results lend support to a link between prenatal exposure to traffic exhaust and the risk of acute lymphoblastic leukemia and bilateral retinoblastoma.
air pollution; epidemiology; leukemia; neoplasms; retinoblastoma
Manganese (Mn) is an essential nutrient, but overexposure can be neurotoxic. Over 800 000 kg of Mn-containing fungicides are applied each year in California. Manganese levels in teeth are a promising biomarker of perinatal exposure. Participants in our analysis included 207 children enrolled in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS), a longitudinal birth cohort study in an agricultural area of California. Mn was measured in teeth using laser-ablation-inductively coupled plasma-mass spectrometry. Our purpose was to determine environmental and lifestyle factors related to prenatal Mn levels in shed teeth. We found that storage of farmworkers’ shoes in the home, maternal farm work, agricultural use of Mn-containing fungicides within 3 km of the residence, residence built on Antioch Loam soil and Mn dust loading (μg/m2 of floor area) during pregnancy were associated with higher Mn levels in prenatal dentin (p < 0.05). Maternal smoking during pregnancy was inversely related to Mn levels in prenatal dentin (p < 0.01). Multivariable regression models explained 22–29% of the variability of Mn in prenatal dentin. Our results suggest that Mn measured in prenatal dentin provides retrospective and time specific levels of fetal exposure resulting from environmental and occupational sources.
Recent studies suggest that chronic exposure to air pollution can promote the development of diabetes. However, whether this relationship actually translates into an increased risk of mortality attributable to diabetes is uncertain.
RESEARCH DESIGN AND METHODS
We evaluated the association between long-term exposure to ambient fine particulate matter (PM2.5) and diabetes-related mortality in a prospective cohort analysis of 2.1 million adults from the 1991 Canadian census mortality follow-up study. Mortality information, including ∼5,200 deaths coded as diabetes being the underlying cause, was ascertained by linkage to the Canadian Mortality Database from 1991 to 2001. Subject-level estimates of long-term exposure to PM2.5 were derived from satellite observations. The hazard ratios (HRs) for diabetes-related mortality were related to PM2.5 and adjusted for individual-level and contextual variables using Cox proportional hazards survival models.
Mean PM2.5 exposure levels for the entire population were low (8.7 µg/m3; SD, 3.9 µg/m3; interquartile range, 6.2 µg/m3). In fully adjusted models, a 10-µg/m3 elevation in PM2.5 exposure was associated with an increase in risk for diabetes-related mortality (HR, 1.49; 95% CI, 1.37–1.62). The monotonic change in risk to the population persisted to PM2.5 concentration <5 µg/m3.
Long-term exposure to PM2.5, even at low levels, is related to an increased risk of mortality attributable to diabetes. These findings have considerable public health importance given the billions of people exposed to air pollution and the worldwide growing epidemic of diabetes.
Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide.
Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada.
Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors.
Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164).
Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data.
Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566
Although many studies have linked elevations in tropospheric ozone to adverse health outcomes, the effect of long-term exposure to ozone on air pollution–related mortality remains uncertain. We examined the potential contribution of exposure to ozone to the risk of death from cardiopulmonary causes and specifically to death from respiratory causes.
Data from the study cohort of the American Cancer Society Cancer Prevention Study II were correlated with air-pollution data from 96 metropolitan statistical areas in the United States. Data were analyzed from 448,850 subjects, with 118,777 deaths in an 18-year follow-up period. Data on daily maximum ozone concentrations were obtained from April 1 to September 30 for the years 1977 through 2000. Data on concentrations of fine particulate matter (particles that are ≤2.5 μm in aerodynamic diameter [PM2.5]) were obtained for the years 1999 and 2000. Associations between ozone concentrations and the risk of death were evaluated with the use of standard and multilevel Cox regression models.
In single-pollutant models, increased concentrations of either PM2.5 or ozone were significantly associated with an increased risk of death from cardiopulmonary causes. In two-pollutant models, PM2.5 was associated with the risk of death from cardiovascular causes, whereas ozone was associated with the risk of death from respiratory causes. The estimated relative risk of death from respiratory causes that was associated with an increment in ozone concentration of 10 ppb was 1.040 (95% confidence interval, 1.010 to 1.067). The association of ozone with the risk of death from respiratory causes was insensitive to adjustment for confounders and to the type of statistical model used.
In this large study, we were not able to detect an effect of ozone on the risk of death from cardiovascular causes when the concentration of PM2.5 was taken into account. We did, however, demonstrate a significant increase in the risk of death from respiratory causes in association with an increase in ozone concentration.
Spatial variation in childhood asthma and a recent increase in prevalence indicate that environmental factors play a significant role in the etiology of this important disease. Socioeconomic position (SEP) has been associated inversely and positively with childhood asthma. These contradictory results indicate a need for systematic research about SEP and asthma. Pathways have been suggested for effects of SEP on asthma at both the individual and community level. We examined the relationship of prevalent asthma to community-level indicators of SEP among 5762 children in 12 Southern California, using a multilevel random effects model. Estimates of community-level SEP were derived by summarizing census block group-level data using a novel method of weighting by the proportion of the block groups included in a community-specific bounding rectangle that contained 95% of local study subjects. Community characteristics included measures of male unemployment, household income, low education (i.e. no high school diploma), and poverty. There was a consistent inverse association between male unemployment and asthma across the inter-quartile range of community unemployment rates, indicating that asthma rates increase as community SEP increases. The results were robust to individual-level confounding, methods for summarizing census block group data to the community level, scale of analysis (i.e. community-level vs. neighborhood-level) and the modeling algorithm. The positive association between SEP and prevalent childhood asthma might be explained by differential access to medical care that remains unmeasured, by the hygiene hypothesis (e.g. lower SES may associate with higher protective exposures to endotoxin in early life), or by SEP acting as a proxy for unmeasured neighborhood characteristics.
USA; neighborhood; childhood asthma; multi-level modeling; socioeconomic position; contextual factors
Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created an model to predict ambient particulate matter less than 2.5 microns in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 dataset included 104,172 monthly observations at 1,464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R2 values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R2 were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.
In this report we review the health effects of three short-lived greenhouse pollutants—black carbon, ozone, and sulphates. We undertook new meta-analyses of existing time-series studies and an analysis of a cohort of 352 000 people in 66 US cities during 18 years of follow-up. This cohort study provides estimates of mortality effects from long-term exposure to elemental carbon, an indicator of black carbon mass, and evidence that ozone exerts an independent risk of mortality. Associations among these pollutants make drawing conclusions about their individual health effects difficult at present, but sulphate seems to have the most robust effects in multiple-pollutant models. Generally, the toxicology of the pure compounds and their epidemiology diverge because atmospheric black carbon, ozone, and sulphate are associated and could interact with related toxic species. Although sulphate is a cooling agent, black carbon and ozone could together exert nearly half as much global warming as carbon dioxide. The complexity of these health and climate effects needs to be recognised in mitigation policies.
Biologically plausible mechanisms link traffic-related air pollution to metabolic disorders and potentially to obesity. Here we sought to determine whether traffic density and traffic-related air pollution were positively associated with growth in body mass index (BMI = kg/m2) in children aged 5–11 years.
Participants were drawn from a prospective cohort of children who lived in 13 communities across Southern California (N = 4550). Children were enrolled while attending kindergarten and first grade and followed for 4 years, with height and weight measured annually. Dispersion models were used to estimate exposure to traffic-related air pollution. Multilevel models were used to estimate and test traffic density and traffic pollution related to BMI growth. Data were collected between 2002–2010 and analyzed in 2011–12.
Traffic pollution was positively associated with growth in BMI and was robust to adjustment for many confounders. The effect size in the adjusted model indicated about a 13.6% increase in annual BMI growth when comparing the lowest to the highest tenth percentile of air pollution exposure, which resulted in an increase of nearly 0.4 BMI units on attained BMI at age 10. Traffic density also had a positive association with BMI growth, but this effect was less robust in multivariate models.
Traffic pollution was positively associated with growth in BMI in children aged 5–11 years. Traffic pollution may be controlled via emission restrictions; changes in land use that promote jobs-housing balance and use of public transit and hence reduce vehicle miles traveled; promotion of zero emissions vehicles; transit and car-sharing programs; or by limiting high pollution traffic, such as diesel trucks, from residential areas or places where children play outdoors, such as schools and parks. These measures may have beneficial effects in terms of reduced obesity formation in children.
Childhood obesity; Air pollution; Traffic; California
Traditional methods of exposure assessment in epidemiological studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power or both in health effects estimates. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technology to track person-level time, geographic location, and physical activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free living-population of 36 subjects in Barcelona, Spain. Information obtained on physical activity and geographic location was linked to space-time air pollution mapping. For instance, we found on average travel activities accounted for 6% of people’s time and 24% of their daily inhaled NO2. Due to the large number of mobile phone users, this technology potentially provides an unobtrusive means of collecting epidemiologic exposure data at low cost.
smart phone; activity patterns; exposure; inhalation; air pollution; Global Positioning System
Growing evidence suggests that close contact with nature brings benefits to human health and well-being, but the proposed mechanisms are still not well understood and the associations with health remain uncertain. The Positive Health Effects of the Natural Outdoor environment in Typical Populations in different regions in Europe (PHENOTYPE) project investigates the interconnections between natural outdoor environments and better human health and well-being.
Aims and methods
The PHENOTYPE project explores the proposed underlying mechanisms at work (stress reduction/restorative function, physical activity, social interaction, exposure to environmental hazards) and examines the associations with health outcomes for different population groups. It implements conventional and new innovative high-tech methods to characterise the natural environment in terms of quality and quantity. Preventive as well as therapeutic effects of contact with the natural environment are being covered. PHENOTYPE further addresses implications for land-use planning and green space management. The main innovative part of the study is the evaluation of possible short-term and long-term associations of green space and health and the possible underlying mechanisms in four different countries (each with quite a different type of green space and a different use), using the same methodology, in one research programme. This type of holistic approach has not been undertaken before. Furthermore there are technological innovations such as the use of remote sensing and smartphones in the assessment of green space.
The project will produce a more robust evidence base on links between exposure to natural outdoor environment and human health and well-being, in addition to a better integration of human health needs into land-use planning and green space management in rural as well as urban areas.
Green space; Blue space; Health; Well being; Physical activity
Increasing rates of childhood obesity in the U.S. and other Western countries are cause for serious public health concern. Neighborhood and community environments are thought to play a contributing role in the development of obesity among youth, but it is not well understood which types of physical environmental characteristics have the most potential to influence obesity outcomes. This paper reports the results of a systematic review of quantitative research examining built and biophysical environmental variables associated with obesity in children and adolescents through physical activity. Literature searches in PubMed, PsychInfo, and Geobase were conducted. Fifteen quantitative studies met the inclusion criteria for this systematic review. The majority of studies were cross-sectional and published after 2005. Overall, few consistent findings emerged. For children, associations between physical environmental variables and obesity differed by gender, age, socioeconomic status, population density, and whether reports were made by the parent or child. Access to equipment and facilities, neighborhood pattern (e.g., rural, exurban, suburban), and urban sprawl were associated with obesity outcomes in adolescents. For most environmental variables considered, strong empirical evidence is not yet available. Conceptual gaps, methodological limitations, and future research directions are discussed.
Environment; Overweight; Obesity; Children; Adolescents
Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000–2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants.
air pollution; confounding factors (epidemiology); infant, low birth weight; pregnancy
To determine whether participation in organized outdoor team sports and structured indoor non-school activity programs in kindergarten and first grade predicted subsequent 4-year change in Body Mass Index (BMI) across the adiposity rebound period of childhood.
Longitudinal cohort study.
Forty-five schools in 13 communities across Southern California.
Largely Hispanic and non-Hispanic white children (N = 4,550; average age at study entry 6.60 years, standard deviation 0.65).
Parents completed questionnaires assessing physical activity, demographic characteristics and other relevant covariates at baseline. Data on built and social environmental variables were linked to the neighborhood around children’s homes using geographical information systems (GIS).
Main Outcome Measures
Each child’s height and weight were measured annually during 4-years of follow-up.
After adjusting for several confounders, BMI increased at a 0.05 unit per year slower rate for children who participated in outdoor organized team sports at least twice per week as compared to children who did not. For participation in each additional indoor non-school structured activity classes, lessons, and program, BMI increased at a 0.05 unit per year slower rate, and the attained BMI level at age 10 was 0.48 units lower.
Engagement in organized sports and activity programs as early as kindergarten and the first grade may result in smaller increases in BMI during the adiposity rebound period of childhood.
Background: Laboratory studies suggest that fine particulate matter (≤ 2.5 µm in diameter; PM2.5) can activate pathophysiological responses that may induce insulin resistance and type 2 diabetes. However, epidemiological evidence relating PM2.5 and diabetes is sparse, particularly for incident diabetes.
Objectives: We conducted a population-based cohort study to determine whether long-term exposure to ambient PM2.5 is associated with incident diabetes.
Methods: We assembled a cohort of 62,012 nondiabetic adults who lived in Ontario, Canada, and completed one of five population-based health surveys between 1996 and 2005. Follow-up extended until 31 December 2010. Incident diabetes diagnosed between 1996 and 2010 was ascertained using the Ontario Diabetes Database, a validated registry of persons diagnosed with diabetes (sensitivity = 86%, specificity = 97%). Six-year average concentrations of PM2.5 at the postal codes of baseline residences were derived from satellite observations. We used Cox proportional hazards models to estimate the associations, adjusting for various individual-level risk factors and contextual covariates such as smoking, body mass index, physical activity, and neighborhood-level household income. We also conducted multiple sensitivity analyses. In addition, we examined effect modification for selected comorbidities and sociodemographic characteristics.
Results: There were 6,310 incident cases of diabetes over 484,644 total person-years of follow-up. The adjusted hazard ratio for a 10-µg/m3 increase in PM2.5 was 1.11 (95% CI: 1.02, 1.21). Estimated associations were comparable among all sensitivity analyses. We did not find strong evidence of effect modification by comorbidities or sociodemographic covariates.
Conclusions: This study suggests that long-term exposure to PM2.5 may contribute to the development of diabetes.
cohort study; diabetes; particulate air pollution
Few studies have examined associations of birth outcomes with toxic air pollutants (air toxics) in traffic exhaust. This study included 8,181 term low birth weight (LBW) children and 370,922 term normal-weight children born between January 1, 1995, and December 31, 2006, to women residing within 5 miles (8 km) of an air toxics monitoring station in Los Angeles County, California. Additionally, land-use-based regression (LUR)-modeled estimates of levels of nitric oxide, nitrogen dioxide, and nitrogen oxides were used to assess the influence of small-area variations in traffic pollution. The authors examined associations with term LBW (≥37 weeks’ completed gestation and birth weight <2,500 g) using logistic regression adjusted for maternal age, race/ethnicity, education, parity, infant gestational age, and gestational age squared. Odds of term LBW increased 2%–5% (95% confidence intervals ranged from 1.00 to 1.09) per interquartile-range increase in LUR-modeled estimates and monitoring-based air toxics exposure estimates in the entire pregnancy, the third trimester, and the last month of pregnancy. Models stratified by monitoring station (to investigate air toxics associations based solely on temporal variations) resulted in 2%–5% increased odds per interquartile-range increase in third-trimester benzene, toluene, ethyl benzene, and xylene exposures, with some confidence intervals containing the null value. This analysis highlights the importance of both spatial and temporal contributions to air pollution in epidemiologic birth outcome studies.
air pollution; benzene; fetal growth retardation; hydrocarbons, aromatic; infant, low birth weight; pregnancy
Epidemiological studies on physical activity often lack inexpensive, objective, valid, and reproducible tools for measuring physical activity levels of participants. Novel sensing technologies built into smartphones offer the potential to fill this gap.
We sought to validate estimates of physical activity and determine the usability for large population-based studies of the smartphone-based CalFit software.
A sample of 36 participants from Barcelona, Spain, wore a smartphone with CalFit software and an Actigraph GT3X accelerometer for 5 days. The ease of use (usability) and physical activity measures from both devices were compared, including vertical axis counts (VT) and duration and energy expenditure predictions for light, moderate, and vigorous intensity from Freedson’s algorithm. Statistical analyses included (1) Kruskal-Wallis rank sum test for usability measures, (2) Spearman correlation and linear regression for VT counts, (3) concordance correlation coefficient (CCC), and (4) Bland-Altman plots for duration and energy expenditure measures.
Approximately 64% (23/36) of participants were women. Mean age was 31 years (SD 8) and mean body mass index was 22 kg/m2 (SD 2). In total, 25/36 (69%) participants recorded at least 3 days with at least 10 recorded hours of physical activity using CalFit. The linear association and correlations for VT counts were high (adjusted R
2=0.85; correlation coefficient .932, 95% CI 0.931-0.933). CCCs showed high agreement for duration and energy expenditure measures (from 0.83 to 0.91).
The CalFit system had lower usability than the Actigraph GT3X because the application lacked a means to turn itself on each time the smartphone was powered on. The CalFit system may provide valid estimates to quantify and classify physical activity. CalFit may prove to be more cost-effective and easily deployed for large-scale population health studies than other specialized instruments because cell phones are already carried by many people.
cellular phone; accelerometry; global positioning systems; motor activity; monitoring; physiologic
Land use regression (LUR) has emerged as an effective means of estimating exposure to air pollution in epidemiological studies. We created the first LUR models of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx) for the complex megalopolis of Los Angeles (LA), California. Two-hundred and one sampling sites (the largest sampling design to date for LUR estimation) for two seasons were selected using a location-allocation algorithm that maximized the potential variability in measured pollutant concentrations and represented populations in the health study. Traffic volumes, truck routes and road networks, land use data, satellite-derived vegetation greenness and soil brightness, and truck route slope gradients were used for predicting NOx concentrations. A novel model selection strategy known as “ADDRESS” (A Distance Decay REgression Selection Strategy) was used to select optimized buffer distances for potential predictor variables and maximize model performance.
Final regression models explained 81%, 86% and 85% of the variance in measured NO, NO2 and NOx concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87–91%. Remote sensing-derived variables were significantly correlated with NOx concentrations, suggesting these data are useful surrogates for modeling traffic-related pollution when certain land use data are unavailable. Our study also demonstrated that reactive pollutants such as NO and NO2 could have high spatial extents of influence (e.g., > 5000 m from expressway) and high background concentrations in certain geographic areas. This paper represents the first attempt to model traffic-related air pollutants at a fine scale within such a complex and large urban region.
Nitrogen oxides; Air pollution; Traffic; Land use regression; GIS; Remote sensing; Los Angeles
Evidence suggests that longer-term exposure to air pollutants over years confers higher risks of cardiovascular morbidity and mortality than shorter term exposure. One explanation is that cumulative adverse effects that develop over longer durations lead to the genesis of chronic disease. Preliminary epidemiological and clinical evidence suggest that air pollution may contribute to the development hypertension and type 2 diabetes.
Methods and Results
We used Cox proportional hazards models to assess incidence rate ratios (IRRs) and 95% confidence intervals (CI) for incident hypertension and diabetes associated with exposure to fine particulate matter (PM2.5) and nitrogen oxides (NOx) in a cohort of African American women living in Los Angeles. Pollutant levels were estimated at participant residential addresses with land use regression models (NOx) and interpolation from monitoring station measurements (PM2.5). Over follow-up from 1995-2005, 531 incident cases of hypertension and 183 incident cases of diabetes occurred. When pollutants were analyzed separately, the IRR for hypertension for a 10 μg/m3 increase in PM2.5 was 1.48 (95% CI 0.95-2.31) and the IRR for the interquartile range (12.4 parts per billion) of NOx was 1.14 (95% CI 1.03-1.25). The corresponding IRRs for diabetes were 1.63 (95% CI 0.78-3.44) and 1.25 (95% CI 1.07-1.46). When both pollutants were included in the same model, the IRRs for PM2.5 were attenuated and the IRRs for NOx were essentially unchanged for both outcomes.
Our results suggest that exposure to air pollutants, especially traffic-related pollutants, may increase the risk of type 2 diabetes and possibly of hypertension.
air pollution; epidemiology; diabetes mellitus; hypertension
A travel mode shift to active transportation such as bicycling would help reduce traffic volume and related air pollution emissions as well as promote increased physical activity level. Cyclists, however, are at risk for exposure to vehicle-related air pollutants due to their proximity to vehicle traffic and elevated respiratory rates. To promote safe bicycle commuting, the City of Berkeley, California, has designated a network of residential streets as “Bicycle Boulevards.” We hypothesized that cyclist exposure to air pollution would be lower on these Bicycle Boulevards when compared to busier roads and this elevated exposure may result in reduced lung function.
We recruited 15 healthy adults to cycle on two routes – a low-traffic Bicycle Boulevard route and a high-traffic route. Each participant cycled on the low-traffic route once and the high-traffic route once. We mounted pollutant monitors and a global positioning system (GPS) on the bicycles. The monitors were all synced to GPS time so pollutant measurements could be spatially plotted. We measured lung function using spirometry before and after each bike ride.
We found that fine and ultrafine particulate matter, carbon monoxide, and black carbon were all elevated on the high-traffic route compared to the low-traffic route. There were no corresponding changes in the lung function of healthy non-asthmatic study subjects. We also found that wind-speed affected pollution concentrations.
These results suggest that by selecting low-traffic Bicycle Boulevards instead of heavily trafficked roads, cyclists can reduce their exposure to vehicle-related air pollution. The lung function results indicate that elevated pollutant exposure may not have acute negative effects on healthy cyclists, but further research is necessary to determine long-term effects on a more diverse population. This study and broader field of research have the potential to encourage policy-makers and city planners to expand infrastructure to promote safe and healthy bicycle commuting.
Bicycle boulevards; Active transportation; Air pollution; Lung function
This study examined relationships between greenness exposure and free-living physical activity behavior of children in smart growth and conventionally designed communities. Normalized Difference Vegetation Index (NDVI) was used to quantify children’s (n=208) greenness exposure at 30-second epoch accelerometer and GPS data points. A generalized linear mixed model with a kernel density smoothing term for addressing spatial autocorrelation was fit to analyze residential neighborhood activity data. Excluding activity at home and school-time, an epoch-level analysis found momentary greenness exposure was positively associated with the likelihood of contemporaneous moderate-to-vigorous physical activity (MVPA). This association was stronger for smart growth residents who experienced a 39% increase in odds of MVPA for a 10th to 90th percentile increase in exposure to greenness (OR=1.39, 95% CI 1.36–1.44). A subject-level analysis found children who experienced >20 minutes of daily exposure to greener spaces (>90th percentile) engaged in nearly 5 times the daily rate of MVPA of children with nearly zero daily exposure to greener spaces (95% CI 3.09–7.20).
Physical activity; GPS; Built environment; Smart growth; Obesity
Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data.
Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation.
Methods: We developed a space–time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals.
Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates.
Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.
air pollution; chronic exposure; geostatistics; PM2.5; remote sensing
Rationale: Several studies have linked long-term exposure to particulate air pollution with increased cardiopulmonary mortality; only two have also examined incident circulatory disease.
Objectives: To examine associations of individualized long-term exposures to particulate and gaseous air pollution with incident myocardial infarction and stroke, as well as all-cause and cause-specific mortality.
Methods: We estimated long-term residential air pollution exposure for more than 100,000 participants in the California Teachers Study, a prospective cohort of female public school professionals. We linked geocoded residential addresses with inverse distance-weighted monthly pollutant surfaces for two measures of particulate matter and for several gaseous pollutants. We examined associations between exposure to these pollutants and risks of incident myocardial infarction and stroke, and of all-cause and cause-specific mortality, using Cox proportional hazards models.
Measurements and Main Results: We found elevated hazard ratios linking long-term exposure to particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5), scaled to an increment of 10 μg/m3 with mortality from ischemic heart disease (IHD) (1.20; 95% confidence interval [CI], 1.02–1.41) and, particularly among postmenopausal women, incident stroke (1.19; 95% CI, 1.02–1.38). Long-term exposure to particulate matter less than 10 μm in aerodynamic diameter (PM10) was associated with elevated risks for IHD mortality (1.06; 95% CI, 0.99–1.14) and incident stroke (1.06; 95% CI, 1.00–1.13), while exposure to nitrogen oxides was associated with elevated risks for IHD and all cardiovascular mortality.
Conclusions: This study provides evidence linking long-term exposure to PM2.5 and PM10 with increased risks of incident stroke as well as IHD mortality; exposure to nitrogen oxides was also related to death from cardiovascular diseases.
particulate matter; cardiovascular diseases; air pollutants; epidemiology