Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States

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.

Exposure to traffic-related air pollution during pregnancy and term low birth weight: estimation of causal associations in a semiparametric model

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.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

Organized physical activity in young school children and subsequent 4-year change in body mass index

OBJECTIVE

To determine whether participation in organized outdoor team sports and structured indoor nonschool activity programs in kindergarten and first grade predicted subsequent 4-year change in body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) during the adiposity rebound period of childhood.

DESIGN

Longitudinal cohort study.

SETTING

Forty-five schools in 13 communities across Southern California.

PARTICIPANTS

Largely Hispanic and non-Hispanic white children (N = 4550) with a mean (SD) age at study entry of 6.60 (0.65) years.

MAIN EXPOSURES

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.

MAIN OUTCOME MEASURES

Each child's height and weight were measured annually during 4 years of follow-up.

RESULTS

After adjusting for several confounders, BMI increased at a rate 0.05 unit/year slower for children who participated in outdoor organized team sports at least twice per week compared with children who did not. For participation in each additional indoor nonschool structured activity class, lesson, and program, BMI increased at a rate 0.05 unit/year slower, and the attained BMI level at age 10 years was 0.48 units lower.

CONCLUSION

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.

Assessing the influence of traffic-related air pollution on risk of term low birth weight on the basis of land-use-based regression models and measures of air toxics

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.

Confounding and exposure measurement error in air pollution epidemiology

Studies in air pollution epidemiology may suffer from some specific forms of confounding and exposure measurement error. This contribution discusses these, mostly in the framework of cohort studies. Evaluation of potential confounding is critical in studies of the health effects of air pollution. The association between long-term exposure to ambient air pollution and mortality has been investigated using cohort studies in which subjects are followed over time with respect to their vital status. In such studies, control for individual-level confounders such as smoking is important, as is control for area-level confounders such as neighborhood socio-economic status. In addition, there may be spatial dependencies in the survival data that need to be addressed. These issues are illustrated using the American Cancer Society Cancer Prevention II cohort. Exposure measurement error is a challenge in epidemiology because inference about health effects can be incorrect when the measured or predicted exposure used in the analysis is different from the underlying true exposure. Air pollution epidemiology rarely if ever uses personal measurements of exposure for reasons of cost and feasibility. Exposure measurement error in air pollution epidemiology comes in various dominant forms, which are different for time-series and cohort studies. The challenges are reviewed and a number of suggested solutions are discussed for both study domains.

A cohort study relating urban green space with mortality in Ontario, Canada

Parks and green space areas are important to human health for psychological and physiological reasons. There have been few evaluations of access to green space on mortality. This paper describes a cohort study of approximately 575,000 adults, 35 years of age and older, who resided in 10 urban areas in Ontario, Canada, between 1982 and 1986. Individuals were identified from income tax filings, and vital status was determined up to December 31, 2004 through record linkage to the Canadian Mortality Data Base. Place of residence was defined by postal code data that were extracted from income tax filings. Urban green space was defined by Landsat satellite retrievals with the Normalized Difference Vegetation Index and this was assigned to individuals' place of residence at inception into the cohort using both a 30 m grid cell and a 500 m buffer. The proportional hazards model was used to estimate rate ratios (RRs) and their corresponding 95% confidence intervals (CI) for selected underlying causes of death. The rate ratios were adjusted for income, marital status, ambient air pollution, and contextual neighborhood characteristics. About 187,000 subjects died during follow-up. An increase in the interquartile range of green space, using a 500 m buffer, was associated with reduced non-accidental mortality (RR=0.95, 95% CI=0.94-0.96). Reductions in mortality with increased residential green space were observed for each underlying cause of death; the strongest association was found for respiratory disease mortality (RR=0.91, 95% CI=0.89-0.93). Risk estimates were essentially unchanged after adjusting for ambient air pollution. Our study suggests that green space in urban environments was associated with long-term reduction in mortality although this finding should be interpreted cautiously as this association may be influenced by residual confounding of sociodemographic and lifestyle factors. Further research is needed to: confirm these findings, better understand the relationships between access to green space and behavioral risk factors for mortality, and identify what green space characteristics may confer the greatest health benefit.

Risk of nonaccidental and cardiovascular mortality in relation to long-term exposure to low concentrations of fine particulate matter: a Canadian national-level cohort study

BACKGROUND

Few cohort studies have evaluated the risk of mortality associated with long-term exposure to fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM(2.5))]. This is the first national-level cohort study to investigate these risks in Canada.

OBJECTIVE

We investigated the association between long-term exposure to ambient PM(2.5) and cardiovascular mortality in nonimmigrant Canadian adults.

METHODS

We assigned estimates of exposure to ambient PM(2.5) derived from satellite observations to a cohort of 2.1 million Canadian adults who in 1991 were among the 20% of the population mandated to provide detailed census data. We identified deaths occurring between 1991 and 2001 through record linkage. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) adjusted for available individual-level and contextual covariates using both standard Cox proportional survival models and nested, spatial random-effects survival models.

RESULTS

Using standard Cox models, we calculated HRs of 1.15 (95% CI: 1.13, 1.16) from nonaccidental causes and 1.31 (95% CI: 1.27, 1.35) from ischemic heart disease for each 10-μg/m(3) increase in concentrations of PM(2.5). Using spatial random-effects models controlling for the same variables, we calculated HRs of 1.10 (95% CI: 1.05, 1.15) and 1.30 (95% CI: 1.18, 1.43), respectively. We found similar associations between nonaccidental mortality and PM2.5 based on satellite-derived estimates and ground-based measurements in a subanalysis of subjects in 11 cities.

CONCLUSIONS

In this large national cohort of nonimmigrant Canadians, mortality was associated with long-term exposure to PM(2.5). Associations were observed with exposures to PM(2.5) at concentrations that were predominantly lower (mean, 8.7 μg/m(3); interquartile range, 6.2 μg/m(3)) than those reported previously.

Green space, health inequality and pregnancy

Green spaces have been suggested to improve physical and mental health and well-being by increasing physical activity, reducing air pollution, noise, and ambient temperature, increasing social contacts and relieving psychophysiological stress. Although these mechanisms also suggest potential beneficial effects of green spaces on pregnancy outcomes, to our knowledge there is no available epidemiological evidence on this impact. We investigated the effects of surrounding greenness and proximity to major green spaces on birth weight and gestational age at delivery and described the effect of socioeconomic position (SEP) on these relationships. This study was based on a cohort of births (N=8246) that occurred in a major university hospital in Barcelona, Spain, during 2001-2005. We determined surrounding greenness from satellite retrievals as the average of Normalized Difference Vegetation Index (NDVI) in a buffer of 100 m around each maternal place of residence. To address proximity to major green spaces, a binary variable was used to indicate whether maternal residential address is situated within a buffer of 500 m from boundaries of a major green space. For each indicator of green exposure, linear regression models were constructed to estimate change in outcomes adjusted for relevant covariates including individual and area level SEP. None of the indicators of green exposure was associated with birth weight and gestational age. After assessing effect modification based on the level of maternal education, we detected an increase in birth weight (grams) among the lowest education level group (N=164) who had higher surrounding NDVI (Regression coefficient (95% confidence interval (CI) of 436.3 (43.1, 829.5)) or lived close to a major green space (Regression coefficient (95% CI)) of 189.8 (23.9, 355.7)). Our findings suggest a beneficial effect of exposure to green spaces on birth weight only in the lowest SEP group.

Inequalities in cumulative environmental burdens among three urbanized counties in California

Low-income communities and communities of color often suffer from multiple environmental hazards that pose risks to their health. Here we extended a cumulative environmental hazard inequality index (CEHII) - developed to assess inequalities in air pollution hazards - to compare the inequality among three urban counties in California: Alameda, San Diego, and Los Angeles. We included a metric for heat stress to the analysis because exposure to excessively hot weather is increasingly recognized as a threat to human health and well-being. We determined if inequalities from heat stress differed between the three regions and if this added factor modified the metric for inequality from cumulative exposure to air pollution. This analysis indicated that of the three air pollutants considered, diesel particulate matter had the greatest inequality, followed by nitrogen dioxide (NO(2)) and fine particulate matter (PM(2.5)). As measured by our index, the inequalities from cumulative exposure to air pollution were greater than those of single pollutants. Inequalities were significantly different among single air pollutant hazards within each region and between regions; however, inequalities from the cumulative burdens did not differ significantly between any two regions. Modeled absolute and relative heat stress inequalities were small except for relative heat stress in San Diego which had the second highest inequality. Our analysis, techniques, and results provide useful insights for policy makers to assess inequalities between regions and address factors that contribute to overall environmental inequality within each region.

Air pollution and incidence of hypertension and diabetes mellitus in black women living in Los Angeles

BACKGROUND

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 the cumulative adverse effects that develop over longer durations lead to the genesis of chronic disease. Preliminary epidemiological and clinical evidence suggests that air pollution may contribute to the development of hypertension and type 2 diabetes mellitus.

METHODS AND RESULTS

We used Cox proportional hazards models to assess incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for incident hypertension and diabetes mellitus associated with exposure to fine particulate matter (PM(2.5)) and nitrogen oxides in a cohort of black women living in Los Angeles. Pollutant levels were estimated at participants' residential addresses with land use regression models (nitrogen oxides) and interpolation from monitoring station measurements (PM(2.5)). Over follow-up from 1995 to 2005, 531 incident cases of hypertension and 183 incident cases of diabetes mellitus occurred. When pollutants were analyzed separately, the IRR for hypertension for a 10-μg/m(3) increase in PM(2.5) was 1.48 (95% CI, 0.95-2.31), and the IRR for the interquartile range (12.4 parts per billion) of nitrogen oxides was 1.14 (95% CI, 1.03-1.25). The corresponding IRRs for diabetes mellitus 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 PM(2.5) were attenuated and the IRRs for nitrogen oxides were essentially unchanged for both outcomes.

CONCLUSION

Our results suggest that exposure to air pollutants, especially traffic-related pollutants, may increase the risk of type 2 diabetes mellitus and possibly of hypertension.

A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data

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-s 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 during school-hours, 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). An individual-level analysis found children who experienced >20 min 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).

Traffic-related air toxics and term low birth weight in Los Angeles County, California

BACKGROUND

Numerous studies have linked criteria air pollutants with adverse birth outcomes, but there is less information on the importance of specific emission sources, such as traffic, and air toxics.

OBJECTIVES

We used three exposure data sources to examine odds of term low birth weight (LBW) in Los Angeles, California, women when exposed to high levels of traffic-related air pollutants during pregnancy.

METHODS

We identified term births during 1 June 2004 to 30 March 2006 to women residing within 5 miles of a South Coast Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. Pregnancy period average exposures were estimated for air toxics, including polycyclic aromatic hydrocarbons (PAHs), source-specific particulate matter < 2.5 μm in aerodynamic diameter (PM2.5) based on a chemical mass balance model, criteria air pollutants from government monitoring data, and land use regression (LUR) model estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of term LBW (< 2,500 g) were examined using logistic regression.

RESULTS

Odds of term LBW increased approximately 5% per interquartile range increase in entire pregnancy exposures to several correlated traffic pollutants: LUR measures of NO, NO2, and NOx, elemental carbon, and PM2.5 from diesel and gasoline combustion and paved road dust (geological PM2.5).

CONCLUSIONS

These analyses provide additional evidence of the potential impact of traffic-related air pollution on fetal growth. Particles from traffic sources should be a focus of future studies.

The association between chronic exposure to traffic-related air pollution and ischemic heart disease

Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO₂], fine particulate matter [PM₂.₅], and ozone [O₃]) in a population of susceptible subjects in Toronto. Local (NO₂) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM₂.₅, O₃) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412-414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO₂ was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM₂.₅ and O₃ were not associated with risk of IHD.

Long-term exposure to air pollution and cardiorespiratory disease in the California teachers study cohort

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.

Lung cancer and cardiovascular disease mortality associated with ambient air pollution and cigarette smoke: shape of the exposure-response relationships

BACKGROUND

Lung cancer and cardiovascular disease (CVD) mortality risks increase with smoking, secondhand smoke (SHS), and exposure to fine particulate matter < 2.5 μm in diameter (PM₂.₅) from ambient air pollution. Recent research indicates that the exposure-response relationship for CVD is nonlinear, with a steep increase in risk at low exposures and flattening out at higher exposures. Comparable estimates of the exposure-response relationship for lung cancer are required for disease burden estimates and related public health policy assessments.

OBJECTIVES

We compared exposure-response relationships of PM₂.₅ with lung cancer and cardiovascular mortality and considered the implications of the observed differences for efforts to estimate the disease burden of PM2.5.

METHODS

Prospective cohort data for 1.2 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II. We estimated relative risks (RRs) for increments of cigarette smoking, adjusting for various individual risk factors. RRs were plotted against estimated daily dose of PM₂.₅ from smoking along with comparison estimates for ambient air pollution and SHS.

RESULTS

For lung cancer mortality, excess risk rose nearly linearly, reaching maximum RRs > 40 among long-term heavy smokers. Excess risks for CVD mortality increased steeply at low exposure levels and leveled off at higher exposures, reaching RRs of approximately 2-3 for cigarette smoking.

CONCLUSIONS

The exposure-response relationship associated with PM₂.₅ is qualitatively different for lung cancer versus cardiovascular mortality. At low exposure levels, cardiovascular deaths are projected to account for most of the burden of disease, whereas at high levels of PM₂.₅, lung cancer becomes proportionately more important.

Lung cancer and cardiovascular disease mortality associated with ambient air pollution and cigarette smoke: shape of the exposure-response relationships

BACKGROUND

Lung cancer and cardiovascular disease (CVD) mortality risks increase with smoking, secondhand smoke (SHS), and exposure to fine particulate matter < 2.5 μm in diameter (PM₂.₅) from ambient air pollution. Recent research indicates that the exposure-response relationship for CVD is nonlinear, with a steep increase in risk at low exposures and flattening out at higher exposures. Comparable estimates of the exposure-response relationship for lung cancer are required for disease burden estimates and related public health policy assessments.

OBJECTIVES

We compared exposure-response relationships of PM₂.₅ with lung cancer and cardiovascular mortality and considered the implications of the observed differences for efforts to estimate the disease burden of PM2.5.

METHODS

Prospective cohort data for 1.2 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II. We estimated relative risks (RRs) for increments of cigarette smoking, adjusting for various individual risk factors. RRs were plotted against estimated daily dose of PM₂.₅ from smoking along with comparison estimates for ambient air pollution and SHS.

RESULTS

For lung cancer mortality, excess risk rose nearly linearly, reaching maximum RRs > 40 among long-term heavy smokers. Excess risks for CVD mortality increased steeply at low exposure levels and leveled off at higher exposures, reaching RRs of approximately 2-3 for cigarette smoking.

CONCLUSIONS

The exposure-response relationship associated with PM₂.₅ is qualitatively different for lung cancer versus cardiovascular mortality. At low exposure levels, cardiovascular deaths are projected to account for most of the burden of disease, whereas at high levels of PM₂.₅, lung cancer becomes proportionately more important.

Understanding the cumulative impacts of inequalities in environmental health: implications for policy

Racial or ethnic minority groups and low-income communities have poorer health outcomes than others. They are more frequently exposed to multiple environmental hazards and social stressors, including poverty, poor housing quality, and social inequality. Researchers are grappling with how best to characterize the cumulative effects of these hazards and stressors in order to help regulators and decision makers craft more-effective policies to address health and environmental disparities. In this article we synthesize the existing scientific evidence regarding the cumulative health implications of higher rates of exposure to environmental hazards, along with individual biological susceptibility and social vulnerability. We conclude that current environmental policy, which is focused narrowly on pollutants and their sources, should be broadened to take into account the cumulative impact of exposures and vulnerabilities encountered by people who live in neighborhoods consisting largely of racial or ethnic minorities or people of low socioeconomic status.

Traffic-related air toxics and preterm birth: a population-based case-control study in Los Angeles County, California

BACKGROUND

Numerous studies have associated air pollutant exposures with adverse birth outcomes, but there is still relatively little information to attribute effects to specific emission sources or air toxics. We used three exposure data sources to examine risks of preterm birth in Los Angeles women when exposed to high levels of traffic-related air pollutants--including specific toxics--during pregnancy.

METHODS

We identified births during 6/1/04-3/31/06 to women residing within five miles of a Southern California Air Quality Management District (SCAQMD) Multiple Air Toxics Exposure Study (MATES III) monitoring station. We identified preterm cases and, using a risk set approach, matched cases to controls based on gestational age at birth. Pregnancy period exposure averages were estimated for a number of air toxics including polycyclic aromatic hydrocarbons (PAHs), source-specific PM2.5 (fine particulates with aerodynamic diameter less than 2.5 μm) based on a Chemical Mass Balance model, criteria air pollutants based on government monitoring data, and land use regression (LUR) estimates of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOx). Associations between these metrics and odds of preterm birth were estimated using conditional logistic regression.

RESULTS

Odds of preterm birth increased 6-21% per inter-quartile range increase in entire pregnancy exposures to organic carbon (OC), elemental carbon (EC), benzene, and diesel, biomass burning and ammonium nitrate PM2.5, and 30% per inter-quartile increase in PAHs; these pollutants were positively correlated and clustered together in a factor analysis. Associations with LUR exposure metrics were weaker (3-4% per inter-quartile range increase).

CONCLUSIONS

These latest analyses provide additional evidence of traffic-related air pollution's impact on preterm birth for women living in Southern California and indicate PAHs as a pollutant of concern that should be a focus of future studies. Other PAH sources besides traffic were also associated with higher odds of preterm birth, as was ammonium nitrate PM2.5, the latter suggesting potential importance of secondary pollutants. Future studies should focus on accurate modeling of both local and regional spatial and temporal distributions, and incorporation of source information.

Social environment and asthma: associations with crime and No Child Left Behind programmes

BACKGROUND

The relationship between asthma and socio-economic status remains unclear. The authors investigated how neighbourhood, school and community social environments were associated with incident asthma in Southern California schoolchildren.

METHODS

New-onset asthma was measured over 3 years of follow-up in the Children's Health Study cohort. Multilevel random-effects models assessed associations between social environments and asthma, adjusted for individual risk factors. At baseline, subjects resided in 274 census tracts (ie, neighbourhoods) and attended kindergarten or first grade in one of 45 schools distributed in 13 communities throughout Southern California. Neighbourhoods and communities were characterised by measures of deprivation, income inequality and racial segregation. Communities were further described by crime rates. Information on schools included whether a school received funding related to the Title 1 No Child Left Behind programme, which aims to reduce academic underachievement in disadvantaged populations.

RESULTS

Increased risk for asthma was observed in subjects attending schools receiving Title I funds compared with those from schools without funding (adjusted HR 1.71, 95% CI 1.14 to 2.58), and residing in communities with higher rates of larceny crime (adjusted HR 2.02, 95% CI 1.08 to 3.02 across the range of 1827 incidents per 100,000 population).

CONCLUSIONS

Risk for asthma was higher in areas of low socio-economic status, possibly due to unmeasured risk factors or chronic stress.

Long-term exposure to air pollution and cardiorespiratory disease in the California teachers study cohort

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.

Identifying vulnerable populations through an examination of the association between multipollutant profiles and poverty

Recently, concerns have centered on how to expand knowledge on the limited science related to the cumulative impact of multiple air pollution exposures and the potential vulnerability of poor communities to their toxic effects. The highly intercorrelated nature of exposures makes application of standard regression-based methods to these questions problematic due to well-known issues related to multicollinearity. Our paper addresses these problems by using, as its basic unit of inference, a profile consisting of a pattern of exposure values. These profiles are grouped into clusters and associated with a deprivation outcome. Specifically, we examine how profiles of NO(2)-, PM(2.5)-, and diesel- (road and off-road) based exposures are associated with the number of individuals living under poverty in census tracts (CT's) in Los Angeles County. Results indicate that higher levels of pollutants are generally associated with higher poverty counts, though the association is complex and nonlinear. Our approach is set in the Bayesian framework, and as such the entire model can be fit as a unit using modern Bayesian multilevel modeling techniques via the freely available WinBUGS software package, (1) though we have used custom-written C++ code (validated with WinBUGS) to improve computational speed. The modeling approach proposed thus goes beyond single-pollutant models in that it allows us to determine the association between entire multipollutant profiles of exposures with poverty levels in small geographic areas in Los Angeles County.

Creating national air pollution models for population exposure assessment in Canada

BACKGROUND

Population exposure assessment methods that capture local-scale pollutant variability are needed for large-scale epidemiological studies and surveillance, policy, and regulatory purposes. Currently, such exposure methods are limited.

METHODS

We created 2006 national pollutant models for fine particulate matter [PM with aerodynamic diameter ≤ 2.5 μm (PM2.5)], nitrogen dioxide (NO2), benzene, ethylbenzene, and 1,3-butadiene from routinely collected fixed-site monitoring data in Canada. In multiple regression models, we incorporated satellite estimates and geographic predictor variables to capture background and regional pollutant variation and used deterministic gradients to capture local-scale variation. The national NO2 and benzene models are evaluated with independent measurements from previous land use regression models that were conducted in seven Canadian cities. National models are applied to census block-face points, each of which represents the location of approximately 89 individuals, to produce estimates of population exposure.

RESULTS

The national NO2 model explained 73% of the variability in fixed-site monitor concentrations, PM2.5 46%, benzene 62%, ethylbenzene 67%, and 1,3-butadiene 68%. The NO2 model predicted, on average, 43% of the within-city variability in the independent NO2 data compared with 18% when using inverse distance weighting of fixed-site monitoring data. Benzene models performed poorly in predicting within-city benzene variability. Based on our national models, we estimated Canadian ambient annual average population-weighted exposures (in micrograms per cubic meter) of 8.39 for PM2.5, 23.37 for NO2, 1.04 for benzene, 0.63 for ethylbenzene, and 0.09 for 1,3-butadiene.

CONCLUSIONS

The national pollutant models created here improve exposure assessment compared with traditional monitor-based approaches by capturing both regional and local-scale pollution variation. Applying national models to routinely collected population location data can extend land use modeling techniques to population exposure assessment and to informing surveillance, policy, and regulation.

Improving health through policies that promote active travel: a review of evidence to support integrated health impact assessment

BACKGROUND

Substantial policy changes to control obesity, limit chronic disease, and reduce air pollution emissions, including greenhouse gasses, have been recommended. Transportation and planning policies that promote active travel by walking and cycling can contribute to these goals, potentially yielding further co-benefits. Little is known, however, about the interconnections among effects of policies considered, including potential unintended consequences.

OBJECTIVES AND METHODS

We review available literature regarding health impacts from policies that encourage active travel in the context of developing health impact assessment (HIA) models to help decision-makers propose better solutions for healthy environments. We identify important components of HIA models of modal shifts in active travel in response to transport policies and interventions.

RESULTS AND DISCUSSION

Policies that increase active travel are likely to generate large individual health benefits through increases in physical activity for active travelers. Smaller, but population-wide benefits could accrue through reductions in air and noise pollution. Depending on conditions of policy implementations, risk tradeoffs are possible for some individuals who shift to active travel and consequently increase inhalation of air pollutants and exposure to traffic injuries. Well-designed policies may enhance health benefits through indirect outcomes such as improved social capital and diet, but these synergies are not sufficiently well understood to allow quantification at this time.

CONCLUSION

Evaluating impacts of active travel policies is highly complex; however, many associations can be quantified. Identifying health-maximizing policies and conditions requires integrated HIAs.

Does exposure to air pollution in urban parks have socioeconomic, racial or ethnic gradients?

Little is known about the levels of air pollution at public parks where regular exercise takes place or in park-adjacent neighborhoods where people have easy access to parks. In this study we investigated the ambient concentrations of criteria pollutants nitrogen dioxide (NO(2)), fine particulate (PM(2.5)) and ozone (O(3)) at public parks and in park-adjacent neighborhoods for metropolitan Los Angeles. Socioeconomic and racial-ethnic inequalities in exposure to the three criteria pollutants were also investigated using multiple linear regression models. In addition, differences in inhalation doses from breathing the three +criteria pollutants were investigated for the top and bottom quartile racial composition in the parks and neighborhoods. Our research showed that although public parks had on average the lowest pollutant concentrations of NO(2) and PM(2.5), they had relatively high O(3) concentrations. Park-adjacent neighborhoods, by contrast, had the highest NO(2) and PM(2.5) concentrations, but the lowest O(3) concentrations. Higher exposures to NO(2) and PM(2.5) were systematically identified for the lower socioeconomic position or higher minority population neighborhoods. For children and adolescents aged 6-15 engaging in high and moderate intensity activities in and around public parks, those from the top quartile of primarily Hispanic neighborhoods had much higher (63%) inhaled doses of NO(2) compared to the bottom quartile counterpart. PM(2.5) showed a similar but less pronounced pattern of inhalation doses. Evidence of socioeconomic and racial-ethnic gradients was found in air pollution exposure and inhalation doses in and around the urban parks in Los Angeles. This suggests that patterns of exposure inequality found in other environmental justice research are present in exposures in and around urban parks.

Childhood obesity and proximity to urban parks and recreational resources: a longitudinal cohort study

The objective of the research was to assess how proximity to parks and recreational resources affects the development of childhood obesity through a longitudinal study. Data were collected on 3173 children aged 9-10 from 12 communities in Southern California in 1993 and 1996. Children were followed for eight years to collect longitudinal information, including objectively measured body mass index (BMI). Multilevel growth curve models were used to assess associations between attained BMI growth at age 18 and numerous environmental variables, including park space and recreational program access. For park acres within a 500 m distance of children's homes, there were significant inverse associations with attained BMI at age 18. Effect sizes were larger for boys than for girls. Recreation programs within a 10 km buffer of children's homes were significantly and inversely associated with achieved levels in BMI at age 18, with effect sizes for boys also larger than those for girls. We conclude that children with better access to parks and recreational resources are less likely to experience significant increases in attained BMI.

Development, Reliability, and Validity of an Urban Trail Use Survey

Purpose.

To evaluate the psychometric characteristics of the Research on Urban Trail Environments (ROUTES) Trail Use Questionnaire.

Design.

Test-retest reliability was assessed by repeated measures (study 1); validity was assessed by comparing reported trail use to self-reported and objectively measured physical activity (PA) levels (study 2).

Setting.

Study 1: a religious institution situated near a Los Angeles trail. Study 2: 1-mile buffer zones surrounding three urban trails (Chicago, Dallas, and Los Angeles).

Subjects.

Thirty-four adults between 40 and 60 years of age (10 men and 24 women) completed the ROUTES questionnaire twice (study 1). Study 2 participants were 490 adults (48% female and 73% white), mean age 48 years.

Measures.

Trail use for recreation and transportation purposes, time and distance spent on trails, and characteristics of the trail and other trail users. PA was measured using the International Physical Activity Questionnaire and accelerometry.

Analyses.

Pearson correlation coefficients and κ statistics were used for test-retest reliability for continuous and categorical variables, respectively. Generalized linear models were used to evaluate hypotheses on PA comparing trail users and nonusers.

Results.

Test-retest statistics were acceptable (κ = .57, r 5 .66). Validity was supported by correlations between indices of trail use with self-reported PA and accelerometry, and significant group differences between trail users and nonusers in PA levels.

Conclusions.

The ROUTES Trail Use Questionnaire demonstrated good reliability and validity.

Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data

Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.

Childhood incident asthma and traffic-related air pollution at home and school

BACKGROUND

Traffic-related air pollution has been associated with adverse cardiorespiratory effects, including increased asthma prevalence. However, there has been little study of effects of traffic exposure at school on new-onset asthma.

OBJECTIVES

We evaluated the relationship of new-onset asthma with traffic-related pollution near homes and schools.

METHODS

Parent-reported physician diagnosis of new-onset asthma (n = 120) was identified during 3 years of follow-up of a cohort of 2,497 kindergarten and first-grade children who were asthma- and wheezing-free at study entry into the Southern California Children's Health Study. We assessed traffic-related pollution exposure based on a line source dispersion model of traffic volume, distance from home and school, and local meteorology. Regional ambient ozone, nitrogen dioxide (NO(2)), and particulate matter were measured continuously at one central site monitor in each of 13 study communities. Hazard ratios (HRs) for new-onset asthma were scaled to the range of ambient central site pollutants and to the residential interquartile range for each traffic exposure metric.

RESULTS

Asthma risk increased with modeled traffic-related pollution exposure from roadways near homes [HR 1.51; 95% confidence interval (CI), 1.25-1.82] and near schools (HR 1.45; 95% CI, 1.06-1.98). Ambient NO(2) measured at a central site in each community was also associated with increased risk (HR 2.18; 95% CI, 1.18-4.01). In models with both NO(2) and modeled traffic exposures, there were independent associations of asthma with traffic-related pollution at school and home, whereas the estimate for NO(2) was attenuated (HR 1.37; 95% CI, 0.69-2.71).

CONCLUSIONS

Traffic-related pollution exposure at school and homes may both contribute to the development of asthma.

Spatial modeling in environmental and public health research

THIS PAPER HAS TWO AIMS: (1) to summarize various geographic information science methods; and (2) to provide a review of studies that have employed such methods. Though not meant to be a comprehensive review, this paper explains when certain methods are useful in epidemiological studies and also serves as an overview of the growing field of spatial epidemiology.

Bayesian profile regression with an application to the National Survey of Children's Health

Standard regression analyses are often plagued with problems encountered when one tries to make inference going beyond main effects using data sets that contain dozens of variables that are potentially correlated. This situation arises, for example, in epidemiology where surveys or study questionnaires consisting of a large number of questions yield a potentially unwieldy set of interrelated data from which teasing out the effect of multiple covariates is difficult. We propose a method that addresses these problems for categorical covariates by using, as its basic unit of inference, a profile formed from a sequence of covariate values. These covariate profiles are clustered into groups and associated via a regression model to a relevant outcome. The Bayesian clustering aspect of the proposed modeling framework has a number of advantages over traditional clustering approaches in that it allows the number of groups to vary, uncovers subgroups and examines their association with an outcome of interest, and fits the model as a unit, allowing an individual's outcome potentially to influence cluster membership. The method is demonstrated with an analysis of survey data obtained from the National Survey of Children's Health. The approach has been implemented using the standard Bayesian modeling software, WinBUGS, with code provided in the supplementary material available at Biostatistics online. Further, interpretation of partitions of the data is helped by a number of postprocessing tools that we have developed.

Ambient air pollution and the progression of atherosclerosis in adults

BACKGROUND

Cross-sectional studies suggest an association between exposure to ambient air pollution and atherosclerosis. We investigated the association between outdoor air quality and progression of subclinical atherosclerosis (common carotid artery intima-media thickness, CIMT).

METHODOLOGY/PRINCIPAL FINDINGS

We examined data from five double-blind randomized trials that assessed effects of various treatments on the change in CIMT. The trials were conducted in the Los Angeles area. Spatial models and land-use data were used to estimate the home outdoor mean concentration of particulate matter up to 2.5 micrometer in diameter (PM2.5), and to classify residence by proximity to traffic-related pollution (within 100 m of highways). PM2.5 and traffic proximity were positively associated with CIMT progression. Adjusted coefficients were larger than crude associations, not sensitive to modelling specifications, and statistically significant for highway proximity while of borderline significance for PM2.5 (P = 0.08). Annual CIMT progression among those living within 100 m of a highway was accelerated (5.5 micrometers/yr [95%CI: 0.13-10.79; p = 0.04]) or more than twice the population mean progression. For PM2.5, coefficients were positive as well, reaching statistical significance in the socially disadvantaged; in subjects reporting lipid lowering treatment at baseline; among participants receiving on-trial treatments; and among the pool of four out of the five trials.

CONCLUSION

Consistent with cross-sectional findings and animal studies, this is the first study to report an association between exposure to air pollution and the progression of atherosclerosis--indicated with CIMT change--in humans. Ostensibly, our results suggest that air pollution may contribute to the acceleration of cardiovascular disease development--the main causes of morbidity and mortality in many countries. However, the heterogeneity of the volunteering populations across the five trials, the limited sample size within trials and other relevant subgroups, and the fact that some key findings reached statistical significance in subgroups rather than the sample precludes generalizations to the general population.

Automobile traffic around the home and attained body mass index: a longitudinal cohort study of children aged 10-18 years

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Public health benefits of strategies to reduce greenhouse-gas emissions: health implications of short-lived greenhouse pollutants

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.

The active city? Disparities in provision of urban public recreation resources

Recreation is critical to active living yet few studies have focused on disparities in the provision of recreational programs. We investigate the spatial distribution of public recreational programs in southern California. Findings indicate that more than half of all recreation programs take place away from a formal park site. Multivariate modeling results suggest that cities characterized by low household incomes, low fiscal capacity, minority populations, and multi-family housing are disadvantaged with respect to recreation provisions. Such disparities may increase health risks among populations in such communities. Urban planners and public health advocates should enhance recreation programs in lower-income non-white communities.

An index for assessing demographic inequalities in cumulative environmental hazards with application to Los Angeles, California

Researchers in environmental justice contend that low-income communities and communities of color face greater impacts from environmental hazards. This is also of concern for policy makers. In this context, our paper has two principal objectives. First, we propose a method for creating an index capable of summarizing racial-ethnic and socioeconomic inequalities from the impact of cumulative environmental hazards. Second, we apply the index to Los Angeles County to illustrate the potential applications and complexities of its implementation. Individual environmental inequality indices are calculated based on unequal shares of environmental hazards for racial-ethnic groups and socioeconomic positions. The illustrated hazards include ambient concentrations of particulate matter, nitrogen dioxide, and estimates of cancer risk associated with modeled estimates for diesel particulate matter. The cumulative environmental hazard inequality index (CEHII) then combines individual environmental hazards, using either a multiplicative or an additive model. Significant but modest inequalities exist for both individual and cumulative environmental hazards in Los Angeles. The highest level of inequality among racial-ethnic and socioeconomic groups occurs when a multiplicative model is used to estimate cumulative hazard. The CEHII provides a generalized framework that incorporates environmental hazards and socioeconomic characteristics to assess inequalities in cumulative environmental risks.

Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults

RATIONALE

Little is known about the long-term effects of air pollution on pneumonia hospitalization in the elderly.

OBJECTIVES

To assess the effect of long-term exposure to ambient nitrogen dioxide, sulfur dioxide, and fine particulate matter with diameter equal to or smaller than 2.5 microm (PM(2.5)) on hospitalization for community-acquired pneumonia in older adults.

METHODS

We used a population-based case-control study in Hamilton, Ontario, Canada. We enrolled 345 hospitalized patients aged 65 years or more for community-acquired pneumonia and 494 control participants, aged 65 years and more, randomly selected from the same community as cases from July 2003 to April 2005. Health data were collected by personal interview. Annual average levels of nitrogen dioxide, sulfur dioxide, and PM(2.5) before the study period were estimated at the residential addresses of participants by inverse distance weighting, bicubic splined and land use regression methods and merged with participants' health data.

MEASUREMENTS AND MAIN RESULTS

Long-term exposure to higher levels of nitrogen dioxide and PM(2.5) was significantly associated with hospitalization for community-acquired pneumonia (odds ratio [OR], 2.30; 95% confidence interval [CI], 1.25 to 4.21; P = 0.007 and OR, 2.26; 95% CI, 1.20 to 4.24; P = 0.012, respectively, over the 5th-95th percentile range increase of exposure). Sulfur dioxide did not appear to have any association (OR, 0.97; 95% CI, 0.59 to 1.61; P = 0.918). Results were somewhat sensitive to the choice of methods used to estimate air pollutant levels at residential addresses, although all risks from nitrogen dioxide and PM(2.5) exposure were positive and generally significant.

CONCLUSIONS

In older adults, exposure to ambient nitrogen dioxide and PM(2.5) was associated with hospitalization for community-acquired pneumonia.

Cardiovascular mortality and exposure to airborne fine particulate matter and cigarette smoke: shape of the exposure-response relationship

BACKGROUND

Fine particulate matter exposure from both ambient air pollution and secondhand cigarette smoke has been associated with larger risks of cardiovascular mortality than would be expected on the basis of linear extrapolations of the relative risks from active smoking. This study directly assessed the shape of the exposure-response relationship between cardiovascular mortality and fine particulates from cigarette smoke and ambient air pollution.

METHODS AND RESULTS

Prospective cohort data for >1 million adults were collected by the American Cancer Society as part of the Cancer Prevention Study II in 1982. Cox proportional hazards regression models that included variables for increments of cigarette smoking and variables to control for education, marital status, body mass, alcohol consumption, occupational exposures, and diet were used to describe the mortality experience of the cohort. Adjusted relative risks of mortality were plotted against estimated average daily dose of fine particulate matter from cigarette smoke along with comparison estimates for secondhand cigarette smoke and air pollution. There were substantially increased cardiovascular mortality risks at very low levels of active cigarette smoking and smaller but significant excess risks even at the much lower exposure levels associated with secondhand cigarette smoke and ambient air pollution.

CONCLUSIONS

Relatively low levels of fine particulate exposure from either air pollution or secondhand cigarette smoke are sufficient to induce adverse biological responses increasing the risk of cardiovascular disease mortality. The exposure-response relationship between cardiovascular disease mortality and fine particulate matter is relatively steep at low levels of exposure and flattens out at higher exposures.

Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy

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.

Physical environmental correlates of childhood obesity: a systematic review

Increasing rates of childhood obesity in the USA and other Western countries are a 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.

A distance-decay variable selection strategy for land use regression modeling of ambient air pollution exposures

Land use regression (LUR) has emerged as an effective and economical means of estimating air pollution exposures for epidemiological studies. To date, no systematic method has been developed for optimizing the variable selection process. Traditionally, a limited number of buffer distances assumed having the highest correlations with measured pollutant concentrations are used in the manual stepwise selection process or a model transferred from another urban area. In this paper we propose a novel and systematic way of modeling long-term average air pollutant concentrations through "A Distance Decay REgression Selection Strategy" (ADDRESS). The selection process includes multiple steps and, at each step, a full spectrum of correlation coefficients and buffer distance decay curves are used to select a spatial covariate of the highest correlation (compared to other variables) at its optimized buffer distance. At the first step, the series of distance decay curves is constructed using the measured concentrations against the chosen spatial covariates. A variable with the highest correlation to pollutant levels at its optimized buffer distance is chosen as the first predictor of the LUR model from all the distance decay curves. Starting from the second step, the prediction residuals are used to construct new series of distance decay curves and the variable of the highest correlation at its optimized buffer distance is chosen to be added to the model. This process continues until a variable being added does not contribute significantly (p>0.10) to the model performance. The distance decay curve yields a visualization of change and trend of correlation between the spatial covariates and air pollution concentrations or their prediction residuals, providing a transparent and efficient means of selecting optimized buffer distances. Empirical comparisons suggested that the ADDRESS method produced better results than a manual stepwise selection process of limited buffer distances. The method also enables researchers to understand the likely scale of variables that influence pollution levels, which has potentially important ramifications for planning and epidemiological studies.

A cohort study of traffic-related air pollution and mortality in Toronto, Ontario, Canada

BACKGROUND

Chronic exposure to traffic-related air pollution (TRAP) may contribute to premature mortality, but few studies to date have addressed this topic.

OBJECTIVES

In this study we assessed the association between TRAP and mortality in Toronto, Ontario, Canada.

METHODS

We collected nitrogen dioxide samples over two seasons using duplicate two-sided Ogawa passive diffusion samplers at 143 locations across Toronto. We calibrated land use regressions to predict NO2 exposure on a fine scale within Toronto. We used interpolations to predict levels of particulate matter with aerodynamic diameter < or = 2.5 microm (PM(2.5)) and ozone levels. We assigned predicted pollution exposures to 2,360 subjects from a respiratory clinic, and abstracted health data on these subjects from medical billings, lung function tests, and diagnoses by pulmonologists. We tracked mortality between 1992 and 2002. We used standard and multilevel Cox proportional hazard models to test associations between air pollution and mortality.

RESULTS

After controlling for age, sex, lung function, obesity, smoking, and neighborhood deprivation, we observed a 17% increase in all-cause mortality and a 40% increase in circulatory mortality from an exposure contrast across the interquartile range of 4 ppb NO2. We observed no significant associations with other pollutants.

CONCLUSIONS

Exposure to TRAP was significantly associated with increased all-cause and circulatory mortality in this cohort. A high prevalence of cardiopulmonary disease in the cohort probably limits inference of the findings to populations with a substantial proportion of susceptible individuals.

Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

Spatial analysis of air pollution and childhood asthma in Hamilton, Canada: comparing exposure methods in sensitive subgroups

BACKGROUND

Variations in air pollution exposure within a community may be associated with asthma prevalence. However, studies conducted to date have produced inconsistent results, possibly due to errors in measurement of the exposures.

METHODS

A standardized asthma survey was administered to children in grades one and eight in Hamilton, Canada, in 1994-95 (N approximately 1467). Exposure to air pollution was estimated in four ways: (1) distance from roadways; (2) interpolated surfaces for ozone, sulfur dioxide, particulate matter and nitrous oxides from seven to nine governmental monitoring stations; (3) a kriged nitrogen dioxide (NO2) surface based on a network of 100 passive NO2 monitors; and (4) a land use regression (LUR) model derived from the same monitoring network. Logistic regressions were used to test associations between asthma and air pollution, controlling for variables including neighbourhood income, dwelling value, state of housing, a deprivation index and smoking.

RESULTS

There were no significant associations between any of the exposure estimates and asthma in the whole population, but large effects were detected the subgroup of children without hayfever (predominately in girls). The most robust effects were observed for the association of asthma without hayfever and NO2LUR OR = 1.86 (95%CI, 1.59-2.16) in all girls and OR = 2.98 (95%CI, 0.98-9.06) for older girls, over an interquartile range increase and controlling for confounders.

CONCLUSION

Our findings indicate that traffic-related pollutants, such as NO2, are associated with asthma without overt evidence of other atopic disorders among female children living in a medium-sized Canadian city. The effects were sensitive to the method of exposure estimation. More refined exposure models produced the most robust associations.

BME estimation of residential exposure to ambient PM10 and ozone at multiple time scales

BACKGROUND

Long-term human exposure to ambient pollutants can be an important contributing or etiologic factor of many chronic diseases. Spatiotemporal estimation (mapping) of long-term exposure at residential areas based on field observations recorded in the U.S. Environmental Protection Agency's Air Quality System often suffer from missing data issues due to the scarce monitoring network across space and the inconsistent recording periods at different monitors.

OBJECTIVE

We developed and compared two upscaling methods: UM1 (data aggregation followed by exposure estimation) and UM2 (exposure estimation followed by data aggregation) for the long-term PM10 (particulate matter with aerodynamic diameter < or = 10 microm) and ozone exposure estimations and applied them in multiple time scales to estimate PM and ozone exposures for the residential areas of the Health Effects of Air Pollution on Lupus (HEAPL) study.

METHOD

We used Bayesian maximum entropy (BME) analysis for the two upscaling methods. We performed spatiotemporal cross-validations at multiple time scales by UM1 and UM2 to assess the estimation accuracy across space and time.

RESULTS

Compared with the kriging method, the integration of soft information by the BME method can effectively increase the estimation accuracy for both pollutants. The spatiotemporal distributions of estimation errors from UM1 and UM2 were similar. The cross-validation results indicated that UM2 is generally better than UM1 in exposure estimations at multiple time scales in terms of predictive accuracy and lack of bias. For yearly PM10 estimations, both approaches have comparable performance, but the implementation of UM1 is associated with much lower computation burden.

CONCLUSION

BME-based upscaling methods UM1 and UM2 can assimilate core and site-specific knowledge bases of different formats for long-term exposure estimation. This study shows that UM1 can perform reasonably well when the aggregation process does not alter the spatiotemporal structure of the original data set; otherwise, UM2 is preferable.

Long-term ozone exposure and mortality

BACKGROUND

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.

METHODS

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 < or = 2.5 microm in aerodynamic diameter [PM(2.5)]) 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.

RESULTS

In single-pollutant models, increased concentrations of either PM(2.5) or ozone were significantly associated with an increased risk of death from cardiopulmonary causes. In two-pollutant models, PM(2.5) 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.

CONCLUSIONS

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 PM(2.5) 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.