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

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O₃ with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R² (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R², 0.93, and performance was weakest for the western mountainous regions (R² of 0.86) and New England (R² of 0.87). For the cross validation by season, our model had the best performance during summer with an R² of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O₃ over space and time, especially in health studies at an intra-urban scale.

An Ensemble Learning Approach for Estimating High Spatiotemporal Resolution of Ground-Level Ozone in the Contiguous United States

In this paper, we integrated multiple types of predictor variables and three types of machine learners (neural network, random forest, and gradient boosting) into a geographically weighted ensemble model to estimate the daily maximum 8 h O₃ with high resolution over both space (at 1 km × 1 km grid cells covering the contiguous United States) and time (daily estimates between 2000 and 2016). We further quantify monthly model uncertainty for our 1 km × 1 km gridded domain. The results demonstrate high overall model performance with an average cross-validated R² (coefficient of determination) against observations of 0.90 and 0.86 for annual averages. Overall, the model performance of the three machine learning algorithms was quite similar. The overall model performance from the ensemble model outperformed those from any single algorithm. The East North Central region of the United States had the highest R², 0.93, and performance was weakest for the western mountainous regions (R² of 0.86) and New England (R² of 0.87). For the cross validation by season, our model had the best performance during summer with an R² of 0.88. This study can be useful for the environmental health community to more accurately estimate the health impacts of O₃ over space and time, especially in health studies at an intra-urban scale.

A kriging-calibrated machine learning method for estimating daily ground-level NO2 in mainland China

It is unclear how to develop a model based on the combined satellite data and ground monitoring data to accurately estimate daily NO₂ levels. Furthermore, the conventional cross-validation (CV) results represent average levels but the model performance may vary greatly from grid to grid. It is an essential issue to evaluate model's prediction ability in different grids and determine the factors affecting model extrapolating ability, which have never been well examined to date. The aim of this study was to compare the ability of three different methods to estimate the daily NO₂ across mainland China during 2014-2016; and to develop a novel two-stage meta-analysis method for exploring the influence of the number and the distribution of nearby sites on grid-level prediction accuracy. For better estimating the daily NO₂ level, we developed and compared three methods, including universal kriging model, satellite-based method (Non-linear exposure-lag-response model & Extreme gradient boosting combined technique) and the kriging-calibrated satellite method. For exploring influencing factors, the two-stage meta-analysis method was purposed. The kriging-calibrated satellite method had an overall CV R-square and root mean square error (RMSE) of 0.85 and 7.87μg/m³, better than the Universal Kriging model and the satellite-based method (CV R² = 0.57 and 0.81). The two-stage meta-analysis method revealed that the model performance did decrease with the sparser distribution of nearby sites. And adding 5 sites within 50 km in the random mode can bring 17.51% improvement in model extrapolating ability. The kriging-calibration can help satellite-based machine learning to provide more accurate NO₂ prediction. Our novel evaluation method can provide the suggestion of adding new sites effectively within a limit budget.

Blood lead level risk factors and reference value derivation in a cross-sectional study of potentially lead-exposed workers in Iran

OBJECTIVES

This exploratory investigation aimed to measure blood lead levels and associated risk factors in exposed workers in Iran, and to derive appropriate reference values for blood lead in this population as a means of epidemiological comparison.

DESIGN

Cross-sectional.

SETTING

Manufacturing plants with potential lead exposure in Southern Khorasan Province, Iran.

PARTICIPANTS

The study included 630 workers, selected through stratified random sampling.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary measures in this exploratory investigation were venous blood lead concentration (BLC) and associated risk factors of age, gender, work experience, cigarette smoking and history of opium use. The secondary measures were symptoms associated with lead toxicity. Data analyses were conducted using Student's t-test, Mann-Whitney U test, one-way analysis of variance, Kruskal-Wallis test, Spearman correlation coefficient and regression analysis.

RESULTS

Mean and median BLCs were 6.5±8.1 μg/dL and 3.9 μg/dL (IQR: 2.9-5.8), respectively. Of the subjects, 85 (13.5%) had BLC ≥10 μg/dL. The derived reference BLC value in this study was 30 μg/dL for men and 14 μg/dL for women. Increasing work experience and age were associated with BLC >10 μg/dL. Radiator manufacturers were up to 12.9 times (95% CI 4.6 to 35, p<0.005) more likely than painters to have BLC >10 μg/dL. Most subjects reported multiple symptoms.

CONCLUSIONS

The mean BLC was above the maximum recommended concentration. There was a significant relationship between higher BLC and age or working in a printing factory or radiator manufacturing. These findings can direct efforts towards reducing occupational lead exposure.

Space-time scan for identification of risk areas for hospitalization of children due to asthma in Mato Grosso, Brazil

INTRODUCTION

Asthma is the result of a complex interaction between genetic, environmental and socioeconomic factors. It represents a serious global public health problem. The goal of this study was to identify geographic areas for priority actions in order to control of asthma in children.

METHOD

Ecological study that space-time statistic Scan was used. Non-elective, short-stay (type 1) paid authorizations of hospitalizations were selected according to hospitalizations year and children place of residence.

RESULTS

In the two periods of the study, the high risk primary cluster was located in the region of Barra do Bugres (relative risk = 8.17, in the first period, and 10.37, in the second). The number of high-risk clusters increased from 8, in the period 2001-2004, to 9, in 2005-2012; while low-risk clusters decreased from 6, in the initial period, to 4, in the latest. The priority geographic areas for attention and intervention for children with asthma are the region around Barra do Bugres, which remained in the two periods with high risk primary clusters and the southwest border of the State that presented increase of the risk. Furthermore, there was an increase of 87% in the number of high risk counties and a reduction of 28% of the counties of protection.

CONCLUSION

In conclusion, the surroundings areas of Barra do Bugres and Porto Estrela and the east and northeast border of the state are priority for health care, once there was an increased risk of hospitalization of children due to asthma.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Associations of greenness, greyness and air pollution exposure with children's health: a cross-sectional study in Southern Italy

BACKGROUND

Due to the complex interplay among different urban-related exposures, a comprehensive approach is advisable to estimate the health effects. We simultaneously assessed the effect of "green", "grey" and air pollution exposure on respiratory/allergic conditions and general symptoms in schoolchildren.

METHODS

This study involved 219 schoolchildren (8-10 years) of the Municipality of Palermo, Italy. Data were collected through questionnaires self-administered by parents and children. Exposures to greenness and greyness at the home addresses were measured using the normalized difference vegetation index (NDVI), residential surrounding greyness (RSG) and the CORINE land-cover classes (CLC). RSG was defined as the percentage of buffer covered by either industrial, commercial and transport units, or dump and construction sites, or urban fabric related features. Two specific categories of CLC, namely "discontinuous urban fabric - DUF" - and "continuous urban fabric - CUF" - areas were found. Exposure to traffic-related nitrogen dioxide (NO₂) was assessed using a Land-Use Regression model. A symptom score ranging from 0 to 22 was built by summing affirmative answers to twenty-two questions on symptoms. To avoid multicollinearity, multiple Logistic and Poisson ridge regression models were applied to assess the relationships between environmental factors and self-reported symptoms.

RESULTS

A very low exposure to NDVI ≤0.15 (1st quartile) had a higher odds of nasal symptoms (OR = 1.47, 95% CI [1.07-2.03]). Children living in CUF areas had higher odds of ocular symptoms (OR = 1.49, 95% CI [1.10-2.03]) and general symptoms (OR = 1.18, 95% CI [1.00-1.48]) than children living in DUF areas. Children living in proximity (≤200 m) to High Traffic Roads (HTRs) had increased odds of ocular (OR = 1.68, 95% CI [1.31-2.17]) and nasal symptoms (OR = 1.49, 95% CI [1.12-1.98]). A very high exposure to NO₂ ≥ 60 μg/m³ (4th quartile) was associated with a higher odds of general symptoms (OR = 1.28, 95% CI [1.10-1.48]). No associations were found with RGS. A Poisson ridge regression model on the symptom score showed that children living in proximity to HTRs (≤200 m) had a higher symptoms score (RR = 1.09, 95% CI [1.02-1.17]) than children living > 200 m from HTRs. Children living in CUF areas had a higher symptoms score (RR = 1.11, 95% CI [1.03-1.19]) than children living in DUF areas.

CONCLUSIONS

Multiple exposures related to greenness, greyness (measured by CORINE) and air pollution within the urban environment are associated with respiratory/allergic and general symptoms in schoolchildren. No associations were found when considering the individual exposure to greyness measured using the RSG indicator.

Development and Comparison of Air Pollution Exposure Surfaces Derived from On-Road Mobile Monitoring and Short-Term Stationary Sidewalk Measurements

Land-use regression (LUR) models of air pollutants are frequently developed on the basis of short-term stationary or mobile monitoring approaches, which raises the question of whether these two data collection protocols lead to similar exposure surfaces. In this study, we measured ultrafine particles (UFP) and black carbon (BC) concentrations in Toronto during summer 2016, using two short-term data collection approaches: mobile, involving 3023 road segments sampled on bicycles, and stationary, involving 92 sidewalk locations. We developed four LUR models and exposure surfaces, for the two pollutants and measurement protocols. Coefficients of determination ( R²) varied from 0.434 to 0.525. Various small-scale traffic variables were included in the mobile LUR. Pearson correlation coefficients between the mobile and stationary surfaces were 0.23 for UFP and 0.49 for BC. We also compared the two surfaces using personal exposures from a panel study in Toronto conducted during the same period. The personal exposures differed from the outdoor exposures derived from the combination of GPS information and exposure surfaces. For UFP, the median for personal outdoor exposure was 26 344 part/cm³, while the cycling and stationary surfaces predicted medians of 31 201 and 19 057 part/cm³. Similar trends were observed for BC, with median exposures of 1764 (personal), 1799 (cycling), and 1469 ng/m³ (stationary).

Association of PM2.5 with diabetes, asthma, and high blood pressure incidence in Canada: A spatiotemporal analysis of the impacts of the energy generation and fuel sales

Numerous studies have reported an association between fine particulate matter (PM_2.5) and human health. Often these relationships are influenced by environmental factor that varies spatially and/or temporally. To our knowledge, there are no studies in Canada that have considered energy generation and fuel sales as PM_2.5 effects modifiers. Determining exposure and disease-specific risk factors over space and time is crucial for disease prevention and control. In this study, we evaluated the association of PM_2.5 with diabetes, asthma, and High Blood Pressure (HBP) incidence in Canada. Then we explored the impact of the energy generation and fuel sales on association changes. We fit an age-period-cohort as the study design, and we applied an over-dispersed Poisson regression model to estimate the risk. We conducted a sensitivity analysis to explore the impact of variation in clean energy rates and fuel sales on outcomes changes. The study included 117 health regions in Canada between 2007 and 2014. Our findings showed strong association of PM_2.5 with diabetes, asthma, and HBP incidence. A two-year increase of 10μg/m³ in PM_2.5 was associated with an increased risk of 5.34% (95% CI: 2.28%; 12.53%) in diabetes incidence, 2.24% (95% CI: 0.93%; 5.38%) in asthma incidence, and 8.29% (95% CI: 3.44%; 19.98%) in HBP incidence. Our sensitivity analysis findings suggest higher risks of diabetes, asthma and HBP incidence when there is low clean energy generation. On the other hand, we found lower risk when we considered high rate of clean energy generation. For example, considering only diabetes incidence, we found that the risk in health regions with low rates of clean electricity is approximately 700% higher than the risk in health regions with high rates of clean electricity. Furthermore, our analysis suggested that the risk in regions with low fuel sales is 66% lower than the risk is health regions with low rates of clean electricity. Our study provides support for the creation of effective environmental health public policies that take into account the risk factors present in Canadians health regions.

The association between air pollution and weather conditions with increase in the number of admissions of asthmatic patients in emergency wards: a case study in Kermanshah

BACKGROUND

Industrialization and urbanization had a devastating impact on public health and caused an increase in health related morbidity and mortality. In fact, asthma is a chronic condition which is considered as one of the significant challenges of public health. In this study, we investigated the association of air pollution and weather conditions with excess emergency ward admissions of asthmatic patients in Kermanshah hospitals.

METHODS

This was an ecological study. The total number of hospital admissions to emergency wards from all related and major hospitals of Kermanshah was collected from September 2008 through August 2009. In addition, data on air pollution as well as meteorological data were collected from the Environmental Protection Agency and Meteorological Organization of Kermanshah. To determine the association between the number of hospitalization due to asthma with those parameters, Poisson regression was used.

RESULTS

The results of Poisson regression revealed a significant association between carbon monoxide, ozone, nitrogen dioxide and temperature with emergency room visits due to asthma in Kermanshah. No associations were found for sulfur dioxide or for particulate matter.

CONCLUSION

This study provides further evidence for the significant effect of monoxide carbon on asthma; and it suggests that temperature may have a role in the exacerbation of asthma. However, due to the multi-factorial nature of asthma, other factors also play a major role in the development and exacerbation of this illness.

The effects of outdoor air pollution on the respiratory health of Canadian children: A systematic review of epidemiological studies

BACKGROUND

Outdoor air pollution is a global problem with serious effects on human health, and children are considered to be highly susceptible to the effects of air pollution.

OBJECTIVE

To conduct a comprehensive and updated systematic review of the literature reporting the effects of outdoor air pollution on the respiratory health of children in Canada.

METHODS

Searches of four electronic databases between January 2004 and November 2014 were conducted to identify epidemiological studies evaluating the effect of exposure to outdoor air pollutants on respiratory symptoms, lung function measurements and the use of health services due to respiratory conditions in Canadian children. The selection process and quality assessment, using the Newcastle-Ottawa Scale, were conducted independently by two reviewers.

RESULTS

Twenty-seven studies that were heterogeneous with regard to study design, population, respiratory outcome and air pollution exposure were identified. Overall, the included studies reported adverse effects of outdoor air pollution at concentrations that were below Canadian and United States standards. Heterogeneous effects of air pollutants were reported according to city, sex, socioeconomic status and seasonality. The present review also describes trends in research related to the effect of air pollution on Canadian children over the past 25 years.

CONCLUSION

The present study reconfirms the adverse effects of outdoor air pollution on the respiratory health of children in Canada. It will help researchers, clinicians and environmental health authorities identify the available evidence of the adverse effect of outdoor air pollution, research gaps and the limitations for further research.

Does exposure prediction bias health-effect estimation?: The relationship between confounding adjustment and exposure prediction

In environmental epidemiology, we are often faced with 2 challenges. First, an exposure prediction model is needed to estimate the exposure to an agent of interest, ideally at the individual level. Second, when estimating the health effect associated with the exposure, confounding adjustment is needed in the health-effects regression model. The current literature addresses these 2 challenges separately. That is, methods that account for measurement error in the predicted exposure often fail to acknowledge the possibility of confounding, whereas methods designed to control confounding often fail to acknowledge that the exposure has been predicted. In this article, we consider exposure prediction and confounding adjustment in a health-effects regression model simultaneously. Using theoretical arguments and simulation studies, we show that the bias of a health-effect estimate is influenced by the exposure prediction model, the type of confounding adjustment used in the health-effects regression model, and the relationship between these 2. Moreover, we argue that even with a health-effects regression model that properly adjusts for confounding, the use of a predicted exposure can bias the health-effect estimate unless all confounders included in the health-effects regression model are also included in the exposure prediction model. While these results of this article were motivated by studies of environmental contaminants, they apply more broadly to any context where an exposure needs to be predicted.

A land use regression model for estimating the NO2 concentration in Shanghai, China

Limited by data accessibility, few exposure assessment studies of air pollutants have been conducted in China. There is an urgent need to develop models for assessing the intra-urban concentration of key air pollutants in Chinese cities. In this study, a land use regression (LUR) model was established to estimate NO2 during 2008-2011 in Shanghai. Four predictor variables were left in the final LUR model: the length of major road within the 2-km buffer around monitoring sites, the number of industrial sources (excluding power plants) within a 10-km buffer, the agricultural land area within a 5-km buffer, and the population counts. The model R(2) and the leave-one-out-cross-validation (LOOCV) R(2) of the NO2 LUR models were 0.82 and 0.75, respectively. The prediction surface of the NO2 concentration based on the LUR model was of high spatial resolution. The 1-year predicted concentration based on the ratio and the difference methods fitted well with the measured NO2 concentration. The LUR model of NO2 outperformed the kriging and inverse distance weighed (IDW) interpolation methods in Shanghai. Our findings suggest that the LUR model may provide a cost-effective method of air pollution exposure assessment in a developing country.

Environment and Health in Children Day Care Centres (ENVIRH) - Study rationale and protocol

BACKGROUND

Indoor air quality (IAQ) is considered an important determinant of human health. The association between exposure to volatile organic compounds, particulate matter, house dust mite, molds and bacteria in day care centers (DCC) is not completely clear. The aim of this project was to study these effects.

METHODS – STUDY DESIGN

This study comprised two phases. Phase I included an evaluation of 45 DCCs (25 from Lisbon and 20 from Oporto, targeting 5161 children). In this phase, building characteristics, indoor CO2 and air temperature/relative humidity, were assessed. A children's respiratory health questionnaire derived from the ISAAC (International Study on Asthma and Allergies in Children) was also distributed. Phase II encompassed two evaluations and included 20 DCCs selected from phase I after a cluster analysis (11 from Lisbon and 9 from Oporto, targeting 2287 children). In this phase, data on ventilation, IAQ, thermal comfort parameters, respiratory and allergic health, airway inflammation biomarkers, respiratory virus infection patterns and parental and child stress were collected.

RESULTS

In Phase I, building characteristics, occupant behavior and ventilation surrogates were collected from all DCCs. The response rate of the questionnaire was 61.7% (3186 children).Phase II included 1221 children. Association results between DCC characteristics, IAQ and health outcomes will be provided in order to support recommendations on IAQ and children's health. A building ventilation model will also be developed.

DISCUSSION

This paper outlines methods that might be implemented by other investigators conducting studies on the association between respiratory health and indoor air quality at DCC.

Environment and Health in Children Day Care Centres (ENVIRH) - Study rationale and protocol

BACKGROUND

Indoor air quality (IAQ) is considered an important determinant of human health. The association between exposure to volatile organic compounds, particulate matter, house dust mite, molds and bacteria in day care centers (DCC) is not completely clear. The aim of this project was to study these effects.

METHODS - STUDY DESIGN

This study comprised two phases. Phase I included an evaluation of 45 DCCs (25 from Lisbon and 20 from Oporto, targeting 5161 children). In this phase, building characteristics, indoor CO2 and air temperature/relative humidity, were assessed. A children's respiratory health questionnaire derived from the ISAAC (International Study on Asthma and Allergies in Children) was also distributed. Phase II encompassed two evaluations and included 20 DCCs selected from phase I after a cluster analysis (11 from Lisbon and 9 from Oporto, targeting 2287 children). In this phase, data on ventilation, IAQ, thermal comfort parameters, respiratory and allergic health, airway inflammation biomarkers, respiratory virus infection patterns and parental and child stress were collected.

RESULTS

In Phase I, building characteristics, occupant behavior and ventilation surrogates were collected from all DCCs. The response rate of the questionnaire was 61.7% (3186 children). Phase II included 1221 children. Association results between DCC characteristics, IAQ and health outcomes will be provided in order to support recommendations on IAQ and children's health. A building ventilation model will also be developed.

DISCUSSION

This paper outlines methods that might be implemented by other investigators conducting studies on the association between respiratory health and indoor air quality at DCC.

A GIS based approach for assessing the association between air pollution and asthma in New York State, USA

Studies on asthma have shown that air pollution can lead to increased asthma prevalence. The aim of this study is to examine the association between air pollution (fine particulate matter (PM_2.5), sulfur dioxide (SO₂) and ozone (O₃)) and human health (asthma emergency department visit rate (AEVR) and asthma discharge rate (ADR)) among residents of New York, USA during the period 2005 to 2007. Annual rates of asthma were calculated from population estimates for 2005, 2006, and 2007 and number of asthma hospital discharge and emergency department visits. Population data for New York were taken from US Bureau of Census, and asthma data were obtained from New York State Department of Health, National Asthma Survey surveillance report. Data on the concentrations of PM_2.5, SO₂ and ground level ozone were obtained from various air quality monitoring stations distributed in different counties. Annual means of these concentrations were compared to annual variations in asthma prevalence by using Pearson correlation coefficient. We found different associations between the annual mean concentration of PM_2.5, SO₂ and surface ozone and the annual rates of asthma discharge and asthma emergency visit from 2005 to 2007. A positive correlation coefficient was observed between the annual mean concentration of PM_2.5, and SO₂ and the annual rates of asthma discharge and asthma emergency department visit from 2005 to 2007. However, the correlation coefficient between annual mean concentrations of ground ozone and the annual rates of asthma discharge and asthma emergency visit was found to be negative from 2005 to 2007. Our study suggests that the association between elevated concentrations of PM_2.5 and SO₂ and asthma prevalence among residents of New York State in USA is consistent enough to assume concretely a plausible and significant association.

Health effects of ambient air pollution: do different methods for estimating exposure lead to different results?

BACKGROUND

Spatially resolved exposure models are increasingly used in epidemiology. We previously reported that, although exhibiting a moderate correlation, pregnancy nitrogen dioxide (NO2) levels estimated by the nearest air quality monitoring station (AQMS) model and a geostatistical model, showed similar associations with infant birth weight.

OBJECTIVES

We extended this study by comparing a total of four exposure models, including two highly spatially resolved models: a land-use regression (LUR) model and a dispersion model. Comparisons were made in terms of predicted NO2 and particle (aerodynamic diameter<10 μm, PM10) exposure and adjusted association with birth weight.

METHODS

The four exposure models were implemented in two French metropolitan areas where 1026 pregnant women were followed as part of the EDEN mother-child cohort.

RESULTS

Correlations between model predictions were high (≥ 0.70), except for NO2 between the AQMS and both the LUR (r = 0.54) and dispersion models (r = 0.63). Spatial variations as estimated by the AQMS model were greater for NO2 (95%) than for PM10 (22%). The direction of effect estimates of NO2 on birth weight varied according to the exposure model, while PM10 effect estimates were more consistent across exposure models.

CONCLUSIONS

For PM10, highly spatially resolved exposure model agreed with the poor spatial resolution AQMS model in terms of estimated pollutant levels and health effects. For more spatially heterogeneous pollutants like NO2, although predicted levels from spatially resolved models (all but AQMS) agreed with each other, our results suggest that some may disagree with each other as well as with the AQMS regarding the direction of the estimated health effects.

Presence of other allergic disease modifies the effect of early childhood traffic-related air pollution exposure on asthma prevalence

Nitrogen dioxide (NO2), a surrogate measure of traffic-related air pollution (TRAP), has been associated with incident childhood asthma. Timing of exposure and atopic status may be important effect modifiers. We collected cross-sectional data on asthma outcomes from Toronto school children aged 5-9years in 2006. Lifetime home, school and daycare addresses were obtained to derive birth and cumulative NO2 exposures for a nested case-control subset of 1497 children. Presence of other allergic disease (a proxy for atopy) was defined as self-report of one or more of doctor-diagnosed rhinitis, eczema, or food allergy. Generalized estimating equations were used to adjust for potential confounders, and examine hypothesized effect modifiers while accounting for clustering by school. In children with other allergic disease, birth, cumulative and 2006 NO2 were associated with lifetime asthma (OR 1.46, 95% CI 1.08-1.98; 1.37, 95% CI 1.00-1.86; and 1.60, 95% CI 1.09-2.36 respectively per interquartile range increase) and wheeze (OR 1.44, 95% CI 1.10-1.89; 1.31, 95% CI 1.02-1.67; and 1.60, 95% CI 1.16-2.21). No or weaker effects were seen in those without allergic disease, and effect modification was amplified when a more restrictive algorithm was used to define other allergic disease (at least 2 of doctor diagnosed allergic rhinitis, eczema or food allergy). The effects of modest NO2 levels on childhood asthma were modified by the presence of other allergic disease, suggesting a probable role for allergic sensitization in the pathogenesis of TRAP initiated asthma.

Refined ambient PM2.5 exposure surrogates and the risk of myocardial infarction

Using a case-crossover study design and conditional logistic regression, we compared the relative odds of transmural (full-wall) myocardial infarction (MI) calculated using exposure surrogates that account for human activity patterns and the indoor transport of ambient PM(2.5) with those calculated using central-site PM(2.5) concentrations to estimate exposure to PM(2.5) of outdoor origin (exposure to ambient PM(2.5)). Because variability in human activity and indoor PM(2.5) transport contributes exposure error in epidemiologic analyses when central-site concentrations are used as exposure surrogates, we refer to surrogates that account for this variability as "refined" surrogates. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM(2.5) is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM(2.5) that penetrates and persists indoors. Use of refined exposure surrogates did not result in larger health effect estimates (ORs=1.10-1.11 with each interquartile range (IQR) increase), narrower confidence intervals, or better model fits compared with the analysis that used central-site PM(2.5). We did observe evidence for heterogeneity in the relative odds of transmural MI with residential AER (effect-modification), with residents of homes with higher AERs having larger ORs than homes in lower AER tertiles. For the level of exposure-estimate refinement considered here, our findings add support to the use of central-site PM(2.5) concentrations for epidemiological studies that use similar case-crossover study designs. In such designs, each subject serves as his or her own matched control. Thus, exposure error related to factors that vary spatially or across subjects should only minimally impact effect estimates. These findings also illustrate that variability in factors that influence the fraction of ambient PM(2.5) in indoor air (e.g., AER) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.

Spatial vulnerability under extreme events: a case of Asian dust storm's effects on children's respiratory health

Asian dust storm (ADS) events have raised concerns regarding their adverse impact on human health. Whether ADS events can result in the heterogeneity of health impacts on children across space and time has not been studied. The goal of this study is to examine the spatial vulnerability impact of ADS events on children's respiratory health geographically and to analyze any patterns related to ADS episodes. From 1998 to 2007, data from both preschool children's and schoolchildren's daily respiratory clinic visits, gathered from patients located in 41 districts of Taipei City and New Taipei City, are analyzed in a Bayesian spatiotemporal model in order to investigate the interaction between spatial effects and ADS episodes. When adjusting for the temporal effect, air pollutants, and temperature, the spatial pattern explicitly varies during defined study periods: non-ADS periods, ADS periods, and post-ADS periods. Compared to non-ADS periods, the relative rate of children's respiratory clinic visits significantly reduced 0.74 to 0.99 times in most districts during ADS periods, while the relative rate rose from 1.01 to 1.11 times in more than half of districts during post-ADS periods, especially in schoolchildren. This spatial vulnerability denotes that the significantly increased relative rate of respiratory clinic visits during post-ADS periods is primarily located in highly urbanized areas for both children's populations. Hence, the results of this study suggest that schoolchildren are particularly more vulnerable to the health impacts of ADS exposure in terms of higher excessive risks over a larger spatial extent than preschool children, especially during post-ADS periods.

Quantile-based Bayesian maximum entropy approach for spatiotemporal modeling of ambient air quality levels

Understanding the daily changes in ambient air quality concentrations is important to the assessing human exposure and environmental health. However, the fine temporal scales (e.g., hourly) involved in this assessment often lead to high variability in air quality concentrations. This is because of the complex short-term physical and chemical mechanisms among the pollutants. Consequently, high heterogeneity is usually present in not only the averaged pollution levels, but also the intraday variance levels of the daily observations of ambient concentration across space and time. This characteristic decreases the estimation performance of common techniques. This study proposes a novel quantile-based Bayesian maximum entropy (QBME) method to account for the nonstationary and nonhomogeneous characteristics of ambient air pollution dynamics. The QBME method characterizes the spatiotemporal dependence among the ambient air quality levels based on their location-specific quantiles and accounts for spatiotemporal variations using a local weighted smoothing technique. The epistemic framework of the QBME method can allow researchers to further consider the uncertainty of space-time observations. This study presents the spatiotemporal modeling of daily CO and PM10 concentrations across Taiwan from 1998 to 2009 using the QBME method. Results show that the QBME method can effectively improve estimation accuracy in terms of lower mean absolute errors and standard deviations over space and time, especially for pollutants with strong nonhomogeneous variances across space. In addition, the epistemic framework can allow researchers to assimilate the site-specific secondary information where the observations are absent because of the common preferential sampling issues of environmental data. The proposed QBME method provides a practical and powerful framework for the spatiotemporal modeling of ambient pollutants.

The role of spatial representation in the development of a LUR model for Ottawa, Canada

A land use regression (LUR) model for the mapping of NO(2) concentrations in Ottawa, Canada was created based on data from 29 passive air quality samplers from the City of Ottawa's National Capital Air Quality Mapping Project and two permanent stations. Model sensitivity was assessed against three spatial representations of population: population at the dissemination area level, population at the dissemination block level and a dasymetrically derived population representation. A spatial database with land use, roads, population, zoning, greenspaces and elevation was created. Polycategorical zoning data were used in dasymetric mapping to spatially focus population data derived from the dissemination blocks to a sub-block level for comparison purposes. Dasymetric population mapping provided no significant LUR model improvement in explained variance when compared to block level population; however, both the former were significantly better than the dissemination area level population representations. However, where block level population is not available or too costly to acquire, our method using polycategorical zoning data provides a viable alternative in LUR modelling endeavours.

Motor vehicle air pollution and asthma in children: a meta-analysis

BACKGROUND

Asthma affects more than 17 million people in the United States;1/3 of these are children. Children are particularly vulnerable to airborne pollution because of their narrower airways and because they generally breathe more air per pound of body weight than adults, increasing their exposure to air pollutants. However, the results from previous studies on the association between motor vehicle emissions and the development of childhood wheeze and asthma are conflicting. Therefore, we conducted a meta-analysis to clarify their potential relationship.

METHODS

MEDLINE, Highwire, and The Cochrane Library databases were searched for relevant studies. Adjusted odds ratio (OR) with corresponding 95% confidence interval (CI) for the association between traffic air pollutants and wheeze or asthma were retrieved from individual studies and pooled to generate summary effect estimates (meta-OR) in STATA 11.1.

RESULTS

Nineteen studies were included in the meta-analysis. Exposure to nitrogen dioxide (meta-OR: 1.05, 95% CI: 1.00-1.11), nitrous oxide (meta-OR: 1.02, 95% CI: 1.00-1.04), and carbon monoxide (meta-OR: 1.06, 95% CI: 1.01-1.12) were positively associated with a higher prevalence of childhood asthma. Exposure to sulfur dioxide (meta-OR: 1.04, 95% CI: 1.01-1.07) was positively associated with a higher prevalence of wheeze in children. Exposure to nitrogen dioxide was positively associated with a higher incidence of childhood asthma (meta-OR: 1.14, 95% CI: 1.06-1.24), and exposures to particulate matter was positively associated with a higher incidence of wheeze in children (meta-OR: 1.05, 95% CI: 1.04-1.07).

CONCLUSIONS

Living or attending schools near high traffic density roads exposes children to higher levels of motor vehicle air pollutants, and increases the incidence and prevalence of childhood asthma and wheeze.

Socioeconomic status and the health of youth: a multilevel, multidomain approach to conceptualizing pathways

Previous research has clearly established associations between low socioeconomic status (SES) and poor youth physical health outcomes. This article provides an overview of the main pathways through which low SES environments come to influence youth health. We focus on 2 prevalent chronic health problems in youth today, asthma and obesity. We review and propose a model that encompasses (a) multiple levels of influence, including the neighborhood, family and person level; (b) both social and physical domains in the environment; and finally (c) dynamic relationships between these factors. A synthesis of existing research and our proposed model draw attention to the notion of adverse physical and social exposures in youth's neighborhood environments altering family characteristics and youth psychosocial and behavioral profiles, thereby increasing youth's risk for health problems. We also note the importance of acknowledging reciprocal influences across levels and domains (e.g., between family and child) that create self-perpetuating patterns of influence that further accentuate the impact of these factors on youth health. Finally, we document that factors across levels can interact (e.g., environmental pollution levels with child stress) to create unique, synergistic effects on youth health. Our model stresses the importance of evaluating influences on youth's physical health not in isolation but in the context of the broader social and physical environments in which youth live. Understanding the complex relationships between the factors that link low SES to youth's long-term health trajectories is necessary for the creation and implementation of successful interventions and policies to ultimately reduce health disparities.

A spatial analysis of asthma prevalence in Ontario

OBJECTIVE

The objective of this paper is to examine spatial patterns of asthma prevalence in the province of Ontario by age and sex between 2002 and 2006.

METHODS

We conducted a population-based, ecological-level study using the Ontario Asthma Surveillance Information System Database (OASIS), a validated registry of all Ontario residents with asthma. Data were mapped and analyzed at the sub-Local Health Integration Network (subLHIN) level (n=141). Comparative morbidity figures (CMFs) were calculated and analyzed for local clusters of high and low values ("hot spots" and "cold spots").

RESULTS

There were 1,601,353 individuals identified as having asthma over the study period, representing an overall prevalence rate of 12.93%. Results demonstrate distinct spatial patterns of asthma prevalence across the province which are age- and sex-specific. There was little overlap between asthma hot spots by age group, suggesting that different spatial processes are at play. Patterns of cold spots are consistently seen in the urban and suburban subLHINs in and around Toronto and Hamilton as well as in several of the highly rural northern subLHINs.

CONCLUSIONS

Findings illustrate the need for more geographically focused public health and health care planning and resource allocation, and highlight the need for research aimed at understanding the factors that may explain the spatial patterns identified here.

A spatial analysis of asthma prevalence in Ontario

OBJECTIVE

The objective of this paper is to examine spatial patterns of asthma prevalence in the province of Ontario by age and sex between 2002 and 2006.

METHODS

We conducted a population-based, ecological-level study using the Ontario Asthma Surveillance Information System Database (OASIS), a validated registry of all Ontario residents with asthma. Data were mapped and analyzed at the sub-Local Health Integration Network (subLHIN) level (n=141). Comparative morbidity figures (CMFs) were calculated and analyzed for local clusters of high and low values ("hot spots" and "cold spots").

RESULTS

There were 1,601,353 individuals identified as having asthma over the study period, representing an overall prevalence rate of 12.93%. Results demonstrate distinct spatial patterns of asthma prevalence across the province which are age- and sex-specific. There was little overlap between asthma hot spots by age group, suggesting that different spatial processes are at play. Patterns of cold spots are consistently seen in the urban and suburban subLHINs in and around Toronto and Hamilton as well as in several of the highly rural northern subLHINs.

CONCLUSIONS

Findings illustrate the need for more geographically focused public health and health care planning and resource allocation, and highlight the need for research aimed at understanding the factors that may explain the spatial patterns identified here.

Urban air pollutants are significant risk factors for asthma and pneumonia in children: the influence of location on the measurement of pollutants

BACKGROUND

Air pollution is associated with a substantial burden on human health; however, the most important pollutants may vary with location. Proper monitoring is necessary to determine the effect of these pollutants on respiratory health.

OBJECTIVES

This study was designed to evaluate the role of outdoor, indoor and personal exposure to combustion-related pollutants NO(2) and O(3) on respiratory health of children in a non-affluent urban area of São Paulo, Brazil.

METHODS

Levels of NO(2) and O(3) were continuously measured in outdoor and indoor air, as well as personal exposure, for 30 days using passive measurement monitors. Respiratory health was assessed with a Brazilian version of the ISAAC questionnaire.

RESULTS

Complete data were available from 64 children, aged 6-10 years. Respiratory morbidity was high, with 43 (67.2%) reporting having had wheezing at any time, 27 (42.2%) wheezing in the last month, 17 (26.6%) asthma at any time and 21 (32.8%) pneumonia at any time. Correlations between levels of NO(2) and O(3) measured in the three locations evaluated were poor. Levels of NO(2) in indoor air and personal exposure to O(3) were independently associated with asthma (both cases P=.02), pneumonia (O(3), P=.02) and wheezing at any time (both cases P<.01). No associations were seen between outdoor NO(2) and O(3) and respiratory health.

CONCLUSIONS

Exposure to higher levels of NO(2) and O(3) was associated with increased risk for asthma and pneumonia in children. Nonetheless, the place where the pollutants are measured influences the results. The measurements taken in indoor and personal exposure were the most accurate.

The modifiable areal unit problem (MAUP) in the relationship between exposure to NO2 and respiratory health

Background

Many Canadian population health studies, including those focusing on the relationship between exposure to air pollution and health, have operationalized neighbourhoods at the census tract scale. At the same time, the conceptualization of place at the local scale is one of the weakest theoretical aspects in health geography. The modifiable areal unit problem (MAUP) raises issues when census tracts are used as neighbourhood proxies, and no other alternate spatial structure is used for sensitivity analysis. In the literature, conclusions on the relationship between NO₂ and health outcomes are divided, and this situation may in part be due to the selection of an inappropriate spatial structure for analysis. Here, we undertake an analysis of NO₂ and respiratory health in Ottawa, Canada using three different spatial structures in order to elucidate the effects that the spatial unit of analysis can have on analytical results.

Results

Using three different spatial structures to examine and quantify the relationship between NO₂ and respiratory morbidity, we offer three main conclusions: 1) exploratory spatial analytical methods can serve as an indication of the potential effect of the MAUP; 2) OLS regression results differ significantly using different spatial representations, and this could be a contributing factor to the lack of consensus in studies that focus on the relation between NO₂ and respiratory health at the area-level; and 3) the use of three spatial representations confirms no measured effect of NO₂ exposure on respiratory health in Ottawa.

Conclusions

Area units used in population health studies should be delineated so as to represent the a priori scale of the expected scale interaction between neighbourhood processes and health. A thorough understanding of the role of the MAUP in the study of the relationship between NO₂ and respiratory health is necessary for research into disease pathways based on statistical models, and for decision-makers to assess the scale at which interventions will have maximum benefit. In general, more research on the role of spatial representation in health studies is needed.

Heavy vehicle traffic is related to wheeze among schoolchildren: a population-based study in an area with low traffic flows

BACKGROUND

An association between traffic air pollution and respiratory symptoms among children has been reported. However, the effects of traffic air pollution on asthma and wheeze have been very sparsely studied in areas with low traffic intensity in cold climate with poor dispersion. We evaluated the impact of vehicle traffic on childhood asthma and wheeze by objective exposure assessment.

METHODS

As a part of the Obstructive Lung Disease in Northern Sweden (OLIN) studies, a questionnaire was sent to the families of all children attending first or second grade in Luleå (72,000 inhabitants) in Northern Sweden in 2006. The age of the children was 7-8 years and the participation rate was 98% (n = 1357). Skin prick tests were performed in 1224 (89%) children. The home addresses were given geographical coordinates and traffic counts were obtained from the local traffic authorities. A proximity model of average daily traffic and average daily heavy vehicle traffic within 200 meters from each participant's home address was used. The associations between traffic exposure and asthma and wheeze, respectively, were analysed in an adjusted multiple logistic regression model.

RESULTS

Exposure to high traffic flows was uncommon in the study area; only 15% of the children lived within 200 meters from a road with a traffic flow of ≥8000 vehicles per day. Living closer than 200 meters from a road with ≥500 heavy vehicles daily was associated with current wheeze, odds ratio 1.7 (confidence interval 1.0-2.7). A dose-response relation was indicated. An increased risk of asthma was also seen, however not significant, odds ratio 1.5 (confidence interval 0.8-2.9). Stratified analyses revealed that the effect of traffic exposure was restricted to the non-sensitized phenotype of asthma and wheeze. The agreement between self-reported traffic exposure and objective measurements of exposure was moderate.

CONCLUSIONS

This study showed that already at low levels of exposure, vehicle traffic is related to an increased risk of wheeze among children. Thus, the global burden of traffic air pollution may be underestimated.

Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods

Fine airborne particulate matter (PM_2.5) has adverse effects on human health. Assessing the long-term effects of PM_2.5 exposure on human health and ecology is often limited by a lack of reliable PM_2.5 measurements. In Taipei, PM_2.5 levels were not systematically measured until August, 2005. Due to the popularity of geographic information systems (GIS), the landuse regression method has been widely used in the spatial estimation of PM concentrations. This method accounts for the potential contributing factors of the local environment, such as traffic volume. Geostatistical methods, on other hand, account for the spatiotemporal dependence among the observations of ambient pollutants. This study assesses the performance of the landuse regression model for the spatiotemporal estimation of PM_2.5 in the Taipei area. Specifically, this study integrates the landuse regression model with the geostatistical approach within the framework of the Bayesian maximum entropy (BME) method. The resulting epistemic framework can assimilate knowledge bases including: (a) empirical-based spatial trends of PM concentration based on landuse regression, (b) the spatio-temporal dependence among PM observation information, and (c) site-specific PM observations. The proposed approach performs the spatiotemporal estimation of PM_2.5 levels in the Taipei area (Taiwan) from 2005–2007.

Outdoor air pollution and asthma in children

BACKGROUND

Asthma, a chronic inflammatory disease of the airways, is associated with reversible airway obstruction and hyperresponsiveness to triggers; clinical symptoms include wheezing, episodic cough, shortness of breath, and increased mucous production. Ambient or outdoor environmental exposure to ozone, particulate matter, sulfur dioxide, and nitrogen oxides has been well documented to exacerbate asthma. Children appear to be most vulnerable to the harmful effects of ambient air pollutants. As their lungs are not completely developed, children may experience greater exposure to environmental pollutants than adults and the higher doses of varied composition may remain in their lungs for a greater duration. Altogether, the negative effects of air pollutants on pulmonary function place children at a greater risk of air pollutant-induced exacerbation of asthma for the duration of their lives.

OBJECTIVES

The aim of this review was to assess recently published literature regarding the influence of air pollution on asthma in children.

METHODS

For this work, we reviewed articles found in PubMed using the key words "outdoor air pollution, asthma, and children" which were published between 2006 and 2009. Only those articles that had a full version available in PubMed were analyzed.

RESULTS

We reviewed studies published between 2006 and 2009 examining the effect of outdoor air pollution on asthma in children. In total, we evaluated 25 articles; of these, 9 were published in 2006, 3 in 2007, 8 in 2008, and 5 in 2009. Of these 25 studies, 1 was a clinical trial, 6 were cross-sectional, 4 were case-control (2 with a case-crossover design), 12 were cohort prospective, and 2 were cohort retrospective studies with varied follow-up times ranging from 10 days to 7 years. The ages of children also differed, ranging from birth to 18 years of age.

CONCLUSIONS

All studies reviewed in this work indicate that outdoor air pollution affects the appearance and exacerbation of asthma in children. Although these findings are of great interest, the limitations of noted works make future investigations of the effect of air pollution on asthma in children essential.

Individual exposure to air pollution and lung function in Korea: spatial analysis using multiple exposure approaches

Interpolation methods can estimate individual-level exposures to air pollution from ambient monitors; however, few studies have evaluated how different approaches may affect health risk estimates. We applied multiple methods of estimating exposure for several air pollutants. We investigated how different methods of estimating exposure may influence health effect estimates in a case study of lung function data, forced expiratory volume in 1s (FEV1), and forced vital capacity (FVC), for 2102 cohort subjects in Ulsan, Korea, for 2003-2007. Measurements from 13 monitors for particulate matter <10 μm (PM(10)), ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide were used to estimate individual-level exposures by averaging across values from all monitors, selecting the value from the nearest monitor, inverse distance weighting, and kriging. We assessed associations between pollutants and lung function in linear regression models, controlling for age, sex, and body mass index. Cross-validation indicated that kriging provided the most accurate estimated exposures. FVC was associated with all air pollutants under all methods of estimating exposure. Only ozone was associated with FEV1. An 11 ppb increase in lag-0-2 8-h maximum ozone was associated with a 6.1% (95% confidence interval 5.0, 7.3%) decrease in FVC and a 0.50% (95% confidence interval 0.03, 0.96%) decrease in FEV1, based on kriged exposures. Central health effect estimates were generally higher using exposures based on averaging across all monitors or kriging. Results based on the nearest monitor approach had the lowest variance. Findings suggest that spatial interpolation methods may provide better estimates than monitoring values alone by reflecting the spatial variability of individual-level exposures and generating estimates for locations without monitors.

Who is affected more by air pollution-sick or healthy? Some evidence from a health survey of schoolchildren living in the vicinity of a coal-fired power plant in Northern Israel

OBJECTIVE

To evaluate the effects of exposure to air pollution by NO(x) and SO(2) on the development of pulmonary function of children, characterized by different health status.

METHODS

A cohort of 1181 schoolchildren from the 2nd to 5th grades, residing near a major coal-fired power plant in the Hadera district of Israel, were subdivided into three health status groups, according to the diagnosis given by a physician at the beginning of the study period in 1996: (a) healthy children; (b) children experiencing chest symptoms, and (c) children with asthma or spastic bronchitis. Pulmonary Function Tests (PFTs) were performed twice (in 1996 and 1999) and analyzed in conjunction with air pollution estimates at the children's places of residence and several potential confounders-height, age, gender, parental education, passive smoking, housing density, length of residence in the study area and proximity to the main road.

RESULTS

A significant negative association was found between changes in PFT results and individual exposure estimates to air pollution, controlled for socio-demographic characteristics of children and their living conditions. A sensitivity analysis revealed a decrease in the Forced Expiratory Volume during the First Second (FEV(1)) of about 19.6% for children with chest symptoms, 11.8% for healthy children, and approximately 7.9% for children diagnosed with asthma. Results of a sensitivity test for the Forced Vital Capacity (FVC) were found to be similar.

CONCLUSION

Exposure to air pollution appeared to have had the greatest effect on children with chest symptoms. This phenomenon may be explained by the fact that this untreated symptomatic group might experience the most severe insult on their respiratory system as a result of exposure to ambient air pollution, which is reflected by a considerable reduction in their FEV(1) and FVC. Since asthmatic children have lower baseline and slower growth rates, their PFT change may be affected less by exposure to air pollution, reflecting a well known relationship between pulmonary function change and height growth, according to which age-specific height is very similar for preadolescent children, but shifts upward with age during the growth spurt.