Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function

Long-term exposure to air pollution and lung function among children in China: Association and effect modification

Background

Children are vulnerable to the respiratory effects of air pollution, and their lung function has been associated with long-term exposure to low air pollution level in developed countries. However, the impact of contemporary air pollution level in developing countries as a result of recent efforts to improve air quality on children's lung function is less understood.

Methods

We obtained a cross-sectional sample of 617 schoolchildren living in three differently polluted areas in Anhui province, China. 2-year average concentrations of air pollutants at the year of spirometry and the previous year (2017-2018) obtained from district-level air monitoring stations were used to characterize long-term exposure. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), and forced expiratory flow between 25 and 75% of FVC (FEF_25-75) were determined under strict quality control. Multivariable regression was employed to evaluate the associations between air pollution level and lung function parameters, overall and by demographic characteristics, lifestyle, and vitamin D that was determined by liquid chromatography tandem mass spectrometry.

Results

Mean concentration of fine particulate matter was 44.7 μg/m³, which is slightly above the interim target 1 standard of the World Health Organization. After adjusting for confounders, FVC, FEV₁, and FEF_25-75 showed inverse trends with increasing air pollution levels, with children in high exposure group exhibiting 87.9 [95% confidence interval (CI): 9.5, 166.4] mL decrement in FEV₁ and 195.3 (95% CI: 30.5, 360.1) mL/s decrement in FEF_25-75 compared with those in low exposure group. Additionally, the above negative associations were more pronounced among those who were younger, girls, not exposed to secondhand smoke, non-overweight, physically inactive, or vitamin D deficient.

Conclusions

Our study suggests that long-term exposure to relatively high air pollution was associated with impaired lung function in children. More stringent pollution control measures and intervention strategies accounting for effect modification are needed for vulnerable populations in China and other developing countries.

Exposure to road traffic noise and behavioral problems in Chinese schoolchildren: A cross-sectional study

BACKGROUND

Transportation noise is second only to air pollution as an environmental contributor to adverse health outcomes in Western countries. However, no studies investigated the association between road traffic noise and behavioral problems among schoolchildren in China.

OBJECTIVE

To investigate the association between exposure to road traffic noise at home and behavioral problems in Chinese schoolchildren.

METHODS

From January to June 2017, we screened 3236 children aged 7-13 years in Guangzhou (Guangdong, China) from the first investigation of an ongoing school-based cohort study with complete information on behavioral problems and residential geolocation. Residential road traffic noise exposure levels were assessed using a validated modeling method in different periods of the day, including daytime (L_day), nighttime (L_night), and weighted 24-hr (L_dn). The annual mean concentration of nitrogen dioxide was also modeled in our study. Behavioral problems were assessed by the parent-rated Strengths and Difficulties Questionnaire (SDQ). Generalized linear mixed models were utilized to estimate the associations between road traffic noise and behavioral problems.

RESULTS

In the main adjusted model, a 10-dB increase in L_dn was associated with 0.33 (95% CI: 0.08, 0.59), 0.08 (95% CI: -0.01, 0.17), and 0.09 (95% CI: 0.01, 0.16) increases in three subscales score for total difficulties score, emotional problems, and conduct problems, respectively. The same increase in L_dn was also associated with the abnormal total difficulties score (OR = 1.25, 95%CI: 1.01, 1.55), emotional problems (OR = 1.19, 95%CI: 0.98, 1.44), and conduct problems (OR = 1.28, 95%CI:1.02, 1.63). Externalising behavior appears more susceptible to traffic noise than internalising behavior. NO₂ had a small impact on the associations between L_dn and behavioral problems. Our estimates were generally robust in several sensitivity analyses.

CONCLUSION

Residential road traffic noise exposure might be related to increased behavioral problems in Chinese schoolchildren, such as emotional symptoms and conduct problems.

MobiliSense cohort study protocol: do air pollution and noise exposure related to transport behaviour have short-term and longer-term health effects in Paris, France?

INTRODUCTION

MobiliSense explores effects of air pollution and noise related to personal transport habits on respiratory and cardiovascular health. Its objectives are to quantify the contribution of personal transport/mobility to air pollution and noise exposures of individuals; to compare exposures in different transport modes; and to investigate whether total and transport-related personal exposures are associated with short-term and longer-term changes in respiratory and cardiovascular health.

METHODS AND ANALYSIS

MobiliSense uses sensors of location, behaviour, environmental nuisances and health in 290 census-sampled participants followed-up after 1/2 years with an identical sensor-based strategy. It addresses knowledge gaps by: (1) assessing transport behaviour over 6 days with GPS receivers and GPS-based mobility surveys; (2) considering personal exposures to both air pollution and noise and improving their characterisation (inhaled doses, noise frequency components, etc); (3) measuring respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function with spirometry, resting blood pressure, ambulatory brachial/central blood pressure, arterial stiffness and heart rate variability) and (4) investigating short-term and longer-term (over 1-2 years) effects of transport.

ETHICS AND DISSEMINATION

The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Our final aim is to determine, for communicating with policy-makers, how scenarios of changes in personal transport behaviour affect individual exposure and health.

Personal exposure to concentrations and inhalation of black carbon according to transport mode use: The MobiliSense sensor-based study

INTRODUCTION

Epidemiological evidence suggests that motorized vehicle users have a higher air pollutant exposure (especially from vehicle exhaust) than active (walking or cycling) transport users. However, studies often relied on insufficiently diverse sample and ignored that minute ventilation has an effect on individuals' inhaled dose. This study examined commuters' breathing zone concentration and inhaled doses of black carbon (BC) when travelling by different transport modes in the Grand Paris region.

METHODS

Personal exposure to BC was continuously measured with MicroAethalometer (MicroAeth AE51) portable monitors strapped on participants' shoulder with tube inlet at the level of the neck (breathing zone), and inhaled doses were derived from several methods estimating ventilation [based on metabolic equivalents from accelerometry [METs], heart rate, and breathing rate]. Trip stages and transport modes were assessed from GPS and mobility survey data. Breathing zone concentrations and inhaled doses of BC were compared across transport modes at the trip stage level (n = 7495 for 283 participants) using linear mixed effect models with a random intercept at individual level.

RESULTS

Trip stages involving public transport and private motorized transport were associated with a 2.20 µg/m³ (95% CI: 1.99, 2.41) and 2.29 µg/m³ (95% CI: 2.10, 2.48) higher breathing zone concentration to BC than walking, respectively. Trip stages with other active modes had a 0.41 µg (95% CI: 0.25, 0.57) higher inhaled dose, while those involving public transport and private motorized transport had a 0.25 µg (95% CI: -0.35, -0.15) and 0.19 µg (95 %CI: -0.28, -0.10) lower inhaled dose of BC per 30 min than walking.

CONCLUSION

The ranking of transport modes in terms of personal exposure was markedly different when breathing zone concentrations and inhaled doses were considered. Future studies should take both into account to explore the relationship of air pollutants in transport microenvironments with physiological response.

A review on the role of dispersion and receptor models in asthma research

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

Uncertainty in geospatial health: challenges and opportunities ahead

PURPOSE

Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.

METHODS

We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis.

RESULTS

We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health.

CONCLUSIONS

Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

Social Susceptibility to Multiple Air Pollutants in Cardiovascular Disease

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of death in the United States, and substantial research has linked ambient air pollution to elevated rates of CVD etiology and events. Much of this research identified increased effects of air pollution in lower socioeconomic position (SEP) communities, where pollution exposures are also often higher. The complex spatial confounding between air pollution and SEP makes it very challenging, however, to disentangle the impacts of these very different exposure types and to accurately assess their interactions. The specific causal components (i.e., specific social stressors) underlying this SEP-related susceptibility remain unknown, because there are myriad pathways through which poverty and/or lower-SEP conditions may influence pollution susceptibility - including diet, smoking, coexposures in the home and occupational environments, health behaviors, and healthcare access. Growing evidence suggests that a substantial portion of SEP-related susceptibility may be due to chronic psychosocial stress - given the known wide-ranging impacts of chronic stress on immune, endocrine, and metabolic function - and to a higher prevalence of unpredictable chronic stressors in many lower-SEP communities, including violence, job insecurity, and housing instability. As such, elucidating susceptibility to pollution in the etiology of CVD, and in the risk of CVD events, has been identified as a research priority. This interplay among social and environmental conditions may be particularly relevant for CVD, because pollution and chronic stress both impact inflammation, metabolic function, oxidative stress, hypertension, atherosclerosis, and other processes relevant to CVD etiology. Because pollution exposures are often spatially patterned by SEP, disentangling their effects - and quantifying any interplay - is especially challenging. Doing so, however, would help to improve our ability to identify and characterize susceptible populations and to improve our understanding of how community stressors may alter responses to multiple air pollutants. More clearly characterizing susceptible populations will improve our ability to design and target interventions more effectively (and cost-effectively) and may reveal greater benefits of pollution reduction in susceptible communities, strengthening cost-benefit and accountability analyses, ultimately reducing the disproportionate burden of CVD and reducing health disparities.

METHODS

In the current study, we aimed to quantify combined effects of multiple pollutants and stressor exposures on CVD events, using a number of unique datasets we have compiled and verified, including the following. 1. Poverty metrics, violent crime rates, a composite socioeconomic deprivation index (SDI), an index of racial and economic segregation, noise disturbance metrics, and three composite spatial factors produced from a factor analysis of 27 community stressors. All indicators have citywide coverage and were verified against individual reports of stress and stressor exposure, in citywide focus groups and surveys. 2. Spatial surfaces for multiple pollutants from the New York City (NYC) Community Air Survey (NYCCAS), which monitored multiple pollutants year-round at 150 sites and used land use regression (LUR) modeling to estimate fine-scale (100-m) intra-urban spatial variance in fine particles (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). 3. Daily data and time-trends derived from all U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) monitors in NYC for 2005-2011, which we combined with NYCCAS surfaces to create residence- and day-specific spatiotemporal exposure estimates. 4. Complete data on in- and out-patient unscheduled CVD events presented in NYC hospitals for 2005-2011 (n = 1,113,185) from the New York State (NYS) Department of Health's Statewide Planning and Research Cooperative System (SPARCS). In the study, we quantified relationships between multiple pollutant exposures and both community CVD event rates and individual risk of CVD events in NYC and tested whether pollution-CVD associations varied by community SEP and social stressor exposures. We hypothesized (1) that greater chronic community-level SEP, stressor, and pollution exposures would be associated with higher community CVD rates; (2) that spatiotemporal variations in multiple pollutants would be associated with excess risk of CVD events; and (3) that pollution-CVD associations would be stronger in communities of lower SEP or higher stressor exposures.

RESULTS

To first understand the separate and combined associations with CVD for both stressors and pollutants measured at the same spatial and temporal scale of resolution, we used ecological cross-sectional models to examine spatial relationships between multiple chronic pollutant and stressor exposures and age-adjusted community CVD rates. Using census-tract-level annual averages (n = 2,167), we compared associations with CVD rates for multiple pollutant concentrations and social stressors. We found that associations with community CVD rates were consistently stronger for social stressors than for pollutants, in terms of both magnitude and significance. We note, however, that this result may be driven by the relatively greater variation (on a proportional basis) for stressors than for pollutants in NYC. We also tested effect modification of pollutant-CVD associations by each social stressor and found evidence of stronger associations for NO2, PM2.5, and wintertime SO2 with CVD rates, particularly across quintiles of increasing community violence or assault rates (P trend < 0.0001). To examine individual-level associations between spatiotemporal exposures to multiple pollutants and the risk of CVD events, across multiple lag days, we examined the combined effects of multiple pollutant exposures, using spatiotemporal (day- and residence-specific) pollution exposure estimates and hospital data on individual CVD events in case-crossover models, which inherently adjust for nontime-varying individual confounders (e.g., sex and race) and comorbidities. We found consistent significant relationships only for same-day pollutant exposures and the risk of CVD events, suggesting very acute impacts of pollution on CVD risk. Associations with CVD were positive for NO2, PM2.5, and SO2, as hypothesized, and we found inverse associations for O3 (a secondary pollutant chemically decreased ["scavenged"] by fresh emissions that, in NYC, displays spatial and temporal patterns opposite those of NO2). Finally, to test effect modification by chronic community social stressors on the relationships between spatiotemporal pollution measures and the risk of CVD events, we used individual-level case-crossover models, adding interaction terms with categorical versions of each social stressor. We found that associations between NO2 and the risk of CVD events were significantly elevated only in communities with the highest exposures to social stressors (i.e., in the highest quintiles of poverty, socioeconomic deprivation, violence, or assault). The largest positive associations for PM2.5 and winter SO2 were generally found in the highest-stressor communities but were not significant in any quintile. We again found inverse associations for O3, which were likewise stronger for individuals living in communities with greater stressor exposures.

CONCLUSIONS

In ecological models, we found stronger relationships with community CVD rates for social stressors than for pollutant exposures. In case-crossover analyses, higher exposures to NO2, PM2.5, and SO2 were associated with greater excess risk of CVD events but only on the case day (there were no consistent significant lagged-day effects). In effect-modification analyses at both the community and individual level, we found evidence of stronger pollution-CVD associations in communities with higher stressor exposures. Given substantial spatial confounding across multiple social stressors, further research is needed to disentangle these effects in order to identify the predominant social stressors driving this observed differential susceptibility.

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km²), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

Residential exposure to outdoor air pollution and adult lung function, with focus on small airway obstruction

Although a growing body of evidence suggests that chronic exposure to outdoor air pollution is linked to a decline in lung function, data on flow at low lung volumes that may be more specific of small airway obstruction are still scarce. We aimed to study the associations between residential exposure to air pollution and lung function, with specific focus on small airways obstruction. We assessed 2995 French participants (aged between 40 and 65) in the ELISABET cross-sectional survey. Residential exposures to nitrogen dioxide (NO₂), particulate matter with a diameter <10 μm (PM₁₀) and sulphur dioxide (SO₂) were assessed. The spirometric parameters were forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and forced expiratory flow between 25% and 75% of FVC (FEF25-75) and at 75% of FVC (FEF75). Coefficients in linear regression models were expressed as the z-score [95% confidence interval] for an increment of 5 μg/m³ in NO₂ and 2 μg/m³ in PM₁₀ and SO₂. NO₂ was associated with significantly lower values of FEV1 (-0.10 [-0.15;-0.05]), FVC (-0.06 [-0.11;-0.02]), FEV1/FVC (-0.07 [-0.11;-0.03]), FEF25-75 (-0.09 [-0.14;-0.05]) and FEF75 (-0.08 [-0.12;-0.04]). PM₁₀ was associated with significantly lower values of FEV1 (-0.10 [-0.15;-0.04]), FVC (-0.06 [-0.11;-0.01]), FEV1/FVC (-0.06 [‒0.11;-0.01]), FEF25-75 (-0.08 [-0.13;-0.03]) and FEF75 (-0.08 [-0.12;-0.04]). SO₂ was associated with significantly lower values of FEV1 (-0.09 [-0.16;-0.02]), FEV1/FVC (-0.07 [-0.13;-0.01]), FEF25-75 (-0.09 [-0.15;-0.02]) and FEF75 (-0.08 [-0.14;-0.03]) but not FVC (-0.05 [-0.11; 0.009]). Even though spatial variations in pollutant levels were low, residential exposure to outdoor air pollution was associated with lower lung function, including lower FEF25-75 and FEF75 suggesting small airway obstruction.

Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM_2.5) and respiratory hospitalizations in New York State (2000-2005). We observed non-significantly higher PM_2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (-0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.

Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse

OBJECTIVE

Clinical research data warehouses are largely populated from information extracted from electronic health records (EHRs). While these data provide information about a patient's medications, laboratory results, diagnoses, and history, her social, economic, and environmental determinants of health are also major contributing factors in readmission, morbidity, and mortality and are often absent or unstructured in the EHR. Details about a patient's socioeconomic status may be found in the U.S. census. To facilitate researching the impacts of socioeconomic status on health outcomes, clinical and socioeconomic data must be linked in a repository in a fashion that supports seamless interrogation of these diverse data elements. This study demonstrates a method for linking clinical and location-based data and querying these data in a de-identified data warehouse using Informatics for Integrating Biology and the Bedside.

MATERIALS AND METHODS

Patient data were extracted from the EHR at Nebraska Medicine. Socioeconomic variables originated from the 2011-2015 five-year block group estimates from the American Community Survey. Data querying was performed using Informatics for Integrating Biology and the Bedside. All location-based data were truncated to prevent identification of a location with a population <20 000 individuals.

RESULTS

We successfully linked location-based and clinical data in a de-identified data warehouse and demonstrated its utility with a sample use case.

DISCUSSION

With location-based data available for querying, research investigating the impact of socioeconomic context on health outcomes is possible. Efforts to improve geocoding can readily be incorporated into this model.

CONCLUSION

This study demonstrates a means for incorporating and querying census data in a de-identified clinical data warehouse.

Error in air pollution exposure model determinants and bias in health estimates

BACKGROUND

Land use regression (LUR) models are commonly used in environmental epidemiology to assign spatially resolved estimates of air pollution to study participants. In this setting, estimated LUR model parameters are assumed to be transportable to a main study (the ''transportability assumption''). We provide an empirical illustration of how violation of this assumption can affect exposure predictions and bias health-effect estimates.

METHODS

We based our simulation on two existing LUR models, one for nitrogen dioxide, the other for particulate matter with aerodynamic diameter <2.5 μm. We assessed the impact of error in exposure determinants used in the LUR models on resultant air pollution predictions and on bias in an exposure-health-effect estimate assessed in a hypothetical cohort. We assigned error to predictors at monitoring sites (sites used to develop the LUR model) and at prediction sites (sites for which exposure predictions were needed), allowing for different error levels between site types.

RESULTS

Realistic error in the exposure determinants of the selected LUR models did not induce large additional error in exposure predictions and resulted in only minor (<1%) bias in health-effect estimates. Bias in the health-effect estimates strongly increased (up to 13.6%) when exposure determinant errors were different for monitoring sites than for prediction sites.

CONCLUSIONS

These results suggest that only modest reductions in bias in estimated exposure health-effects are to be expected from reducing error in exposure determinants. It is important to avoid heterogeneous errors in exposure determinants between monitoring sites and prediction sites to satisfy the transportability assumption and avoid bias in estimated exposure health-effects.

Development and performance evaluation of a GIS-based metric to assess exposure to airborne pollutant emissions from industrial sources

BACKGROUND

Dioxins are environmental and persistent organic carcinogens with endocrine disrupting properties. A positive association with several cancers, including risk of breast cancer has been suggested.

OBJECTIVES

This study aimed to develop and assess performances of an exposure metric based on a Geographic Information System (GIS) through comparison with a validated dispersion model to estimate historical industrial dioxin exposure for its use in a case-control study nested within a prospective cohort.

METHODS

Industrial dioxin sources were inventoried over the whole French territory (n > 2500) and annual average releases were estimated between 1990 and 2008. In three selected areas (rural, urban and urban-costal), dioxin dispersion was modelled using SIRANE, an urban Gaussian model and exposure of the French E3N cohort participants was estimated. The GIS-based metric was developed, calibrated and compared to SIRANE results using a set of parameters (local meteorological data, characteristics of industrial sources, e.g. emission intensity and stack height), by calculating weighted kappa statistics (wκ) and coefficient of determination (R2). Furthermore, as performance evaluation, the final GIS-based metric was tested to assess atmospheric exposure to cadmium.

RESULTS

The concordance between the GIS-based metric and the dispersion model for dioxin exposure estimate was strong (wκ median = 0.78 (1st quintile = 0.72, 3rd quintile =0.82) and R2 median = 0.82 (1st quintile = 0.71, 3rd quintile = 0.87)). We observed similar performance for cadmium.

CONCLUSIONS

Our study demonstrated the ability of the GIS-based metric to reliably characterize long-term environmental dioxin and cadmium exposures as well as the pertinence of using dispersion modelling to construct and calibrate the GIS-based metric.

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.

Evaluation of geoimputation strategies in a large case study

BACKGROUND

Health data usually has missing or incomplete location information, which impacts the quality of research. Geoimputation methods are used by health professionals to increase the spatial resolution of address information for more accurate analyses. The objective of this study was to evaluate geo-imputation methods with respect to the demographic and spatial characteristics of the data.

METHODS

We evaluated four geoimputation methods for increasing spatial resolution of records with known locational information at a coarse level. In order to test and rigorously evaluate two stochastic and two deterministic strategies, we used the Texas Sex Offender registry database with over 50,000 records with known demographic and coordinate information. We reduced the spatial resolution of each record to a census block group and attempted to recover coordinate information using the four strategies. We rigorously evaluated the results in terms of the error distance between the original coordinates and recovered coordinates by studying the results by demographic sub groups and the characteristics of the underlying geography.

RESULTS

We observed that in estimating the actual location of a case, the weighted mean method is the most superior for each demographic group followed by the maximum imputation centroid, the random point in matching sub-geographies and the random point in all sub-geographies methods. Higher accuracies were observed for minority populations because minorities tend to cluster in certain neighborhoods, which makes it easier to impute their location. Results are greatly affected by the population density of the underlying geographies. We observed high accuracies in high population density areas, which often exist within smaller census blocks, which makes the search space smaller. Similarly, mapping geoimputation accuracies in a spatially explicit manner reveals that metropolitan areas yield higher accuracy results.

CONCLUSIONS

Based on gains in standard error, reduction in mean error and validation results, we conclude that characteristics of the estimated records such as the demographic profile and population density information provide a measure of certainty of geographic imputation.

Effect of long-term exposure to fine particulate matter on lung function decline and risk of chronic obstructive pulmonary disease in Taiwan: a longitudinal, cohort study

BACKGROUND

Information on the effects of long-term exposure to fine particulate matter with an aerodynamic diameter of 2·5 μm or less (PM_2·5) on lung health is scarce. We aimed to investigate the associations between long-term exposure to PM_2·5, lung function, and chronic obstructive pulmonary disease (COPD) in a large-scale longitudinal cohort.

METHODS

We included 285 046 participants aged 20 years or older from the Taiwan MJ Health Management Institution cohort, who were recruited between 2001 and 2014 and had spirometric tests during the medical examination visit. We used a satellite-based spatiotemporal model to estimate the 2-year average ground concentration of PM_2·5 (for the calendar year of each participant's medical examination and for the previous year) at each participant's address. We used the generalised linear mixed model to examine the associations between PM_2·5 concentrations and lung function and the Cox proportional hazard regression model with time-dependent covariates to investigate the PM_2·5 effects on COPD development.

FINDINGS

Every 5 μg/m³ increment in PM_2·5 was associated with a decrease of 1·18% for forced vital capacity (FVC), 1·46% for forced expiratory volume in 1 s (FEV₁), 1·65% for maximum mid-expiratory flow (MMEF), and 0·21% for FEV₁:FVC ratio. The decrease accelerated over time. Additional annual declines were observed for FVC (0·14%), FEV₁ (0·24%), MMEF (0·44%), and FEV₁:FVC ratio (0·09%). Compared with the participants exposed to the first quartile of PM_2·5, participants exposed to the fourth, third, and second quartiles of PM_2·5 had a hazard ratio of 1·23 (95% CI 1·09-1·39), 1·30 (1·16-1·46), and 1·39 (1·24-1·56) for COPD development, respectively.

INTERPRETATION

Long-term exposure to ambient PM_2·5 is associated with reduced, and faster declines in, lung function. Long-term exposure to ambient PM_2·5 is also associated with an increased risk of the incidence of COPD. This study reinforces the urgency of global strategies to mitigate air pollution for improvement of pulmonary health and prevention of COPD.

FUNDING

Environmental Health Research Fund of the Chinese University of Hong Kong and PhD Studentship of the Chinese University of Hong Kong.

Decentralized and reproducible geocoding and characterization of community and environmental exposures for multisite studies

Objective

Geocoding and characterizing geographic, community, and environmental characteristics of study participants is frequently done in epidemiological studies. However, participant addresses are identifiable protected health information (PHI) and geocoding must be conducted in a Health Insurance Portability and Accountability Act–compliant manner. Our objective was to create a software application for this process that addresses limitations in current approaches.

Materials and Methods

We used a containerization platform to create DeGAUSS (Decentralized Geomarker Assessment for Multi-Site Studies), a software application that facilitates reproducible geocoding and geomarker assessment while maintaining the confidentiality of PHI. To validate the software, 215 350 addresses in Hamilton County, Ohio, were geocoded using DeGAUSS, ArcGIS, Google, and SAS and compared to a gold-standard approach. We distributed the DeGAUSS software to sites in an ongoing multisite study (Electronic Medical Records and Genomics, or eMERGE), and individual sites independently geocoded and assigned median census tract–level income and distance to nearest major roadway to their participants’ addresses, removed associated PHI, and returned deidentified data.

Results

Within a multisite study, 52 244 study participants’ addresses across 5 sites were geocoded with a median distance to roadway of 10 022m and a median census tract income of $57 266, demonstrating the feasibility of DeGAUSS within a multisite study. Compared to other commonly used geocoding platforms, DeGAUSS had similar geocoding and geomarker assessment accuracies.

Conclusion

The open source DeGAUSS software overcomes multiple challenges in the use of address data in multisite studies and also serves as a more general reproducible research tool for geocoding and geomarker assessment.

Accuracy of two geocoding methods for geographic information system-based exposure assessment in epidemiological studies

BACKGROUND

Environmental exposure assessment based on Geographic Information Systems (GIS) and study participants' residential proximity to environmental exposure sources relies on the positional accuracy of subjects' residences to avoid misclassification bias. Our study compared the positional accuracy of two automatic geocoding methods to a manual reference method.

METHODS

We geocoded 4,247 address records representing the residential history (1990-2008) of 1,685 women from the French national E3N cohort living in the Rhône-Alpes region. We compared two automatic geocoding methods, a free-online geocoding service (method A) and an in-house geocoder (method B), to a reference layer created by manually relocating addresses from method A (method R). For each automatic geocoding method, positional accuracy levels were compared according to the urban/rural status of addresses and time-periods (1990-2000, 2001-2008), using Chi Square tests. Kappa statistics were performed to assess agreement of positional accuracy of both methods A and B with the reference method, overall, by time-periods and by urban/rural status of addresses.

RESULTS

Respectively 81.4% and 84.4% of addresses were geocoded to the exact address (65.1% and 61.4%) or to the street segment (16.3% and 23.0%) with methods A and B. In the reference layer, geocoding accuracy was higher in urban areas compared to rural areas (74.4% vs. 10.5% addresses geocoded to the address or interpolated address level, p < 0.0001); no difference was observed according to the period of residence. Compared to the reference method, median positional errors were 0.0 m (IQR = 0.0-37.2 m) and 26.5 m (8.0-134.8 m), with positional errors <100 m for 82.5% and 71.3% of addresses, for method A and method B respectively. Positional agreement of method A and method B with method R was 'substantial' for both methods, with kappa coefficients of 0.60 and 0.61 for methods A and B, respectively.

CONCLUSION

Our study demonstrates the feasibility of geocoding residential addresses in epidemiological studies not initially recorded for environmental exposure assessment, for both recent addresses and residence locations more than 20 years ago. Accuracy of the two automatic geocoding methods was comparable. The in-house method (B) allowed a better control of the geocoding process and was less time consuming.

Chronic effects of air pollution on lung function after lung transplantation in the Systems prediction of Chronic Lung Allograft Dysfunction (SysCLAD) study

An irreversible loss in lung function limits the long-term success in lung transplantation. We evaluated the role of chronic exposure to ambient air pollution on lung function levels in lung transplant recipients (LTRs).The lung function of 520 LTRs from the Cohort in Lung Transplantation (COLT) study was measured every 6 months. The levels of air pollutants (nitrogen dioxide (NO₂), particulate matter with an aerodynamic cut-off diameter of x µm (PM_x) and ozone (O₃)) at the patients' home address were averaged in the 12 months before each spirometry test. The effects of air pollutants on forced expiratory volume in 1 s (FEV₁) and forced vital capacity (FVC) in % predicted were estimated using mixed linear regressions. We assessed the effect modification of macrolide antibiotics in this relationship.Increased 12-month levels of pollutants were associated with lower levels of FVC % pred (-2.56%, 95% CI -3.86--1.25 for 5 µg·m^-3 of PM₁₀; -0.75%, 95% CI -1.38--0.12 for 2 µg·m^-3 of PM_2.5 and -2.58%, 95% CI -4.63--0.53 for 10 µg·m^-3 of NO₂). In patients not taking macrolides, the deleterious association between PM and FVC tended to be stronger and PM₁₀ was associated with lower FEV₁Our study suggests a deleterious effect of chronic exposure to air pollutants on lung function levels in LTRs, which might be modified with macrolides.

Fine Particulate Matter in Urban Environments: A Trigger of Respiratory Symptoms in Sensitive Children

The overall objective of this research was to study children’s respiratory illness levels in Targoviste (Romania) in relationship to the outdoor concentrations of airborne particulate matter with an aerodynamic diameter below 2.5 µm (PM_2.5). We monitored and analysed the PM_2.5 concentrations according to a complex experimental protocol. The health trial was conducted over three months (October–December 2015) and required the active cooperation of the children’s parents to monitor carefully the respiratory symptoms of the child, i.e., coughing, rhinorrhoea, wheezing, and fever, as well as their outdoor program. We selected the most sensitive children (n = 25; age: 2–10 years) with perturbed respiratory health, i.e., wheezing, asthma, and associated symptoms. The estimated average PM_2.5 doses were 0.8–14.5 µg·day⁻¹ for weekdays, and 0.4–6.6 µg·day⁻¹ for the weekend. The frequency and duration of the symptoms decreased with increasing age. The 4- to 5-year old children recorded the longest duration of symptoms, except for rhinorrhoea, which suggested that this age interval is the most vulnerable to exogenous trigger agents (p < 0.01) compared to the other age groups. PM_2.5 air pollution was found to have a direct positive correlation with the number of wheezing episodes (r = 0.87; p < 0.01) in November 2015. Monitoring of wheezing occurrences in the absence of fever can provide a reliable assessment of the air pollution effect on the exacerbation of asthma and respiratory disorders in sensitive children.

Spatiotemporal analysis for the effect of ambient particulate matter on cause-specific respiratory mortality in Beijing, China

This study explored the association between particulate matter with an aerodynamic diameter of less than 10 μm (PM10) and the cause-specific respiratory mortality. We used the ordinary kriging method to estimate the spatial characteristics of ambient PM10 at 1-km × 1-km resolution across Beijing during 2008-2009 and subsequently fit the exposure-response relationship between the estimated PM10 and the mortality due to total respiratory disease, chronic lower respiratory disease, chronic obstructive pulmonary disease (COPD), and pneumonia at the street or township area levels using the generalized additive mixed model (GAMM). We also examined the effects of age, gender, and season in the stratified analysis. The effects of ambient PM10 on the cause-specific respiratory mortality were the strongest at lag0-5 except for pneumonia, and an inter-quantile range increase in PM10 was associated with an 8.04 % (95 % CI 4.00, 12.63) increase in mortality for total respiratory disease, a 6.63 % (95 % CI 1.65, 11.86) increase for chronic lower respiratory disease, and a 5.68 % (95 % CI 0.54, 11.09) increase for COPD, respectively. Higher risks due to the PM10 exposure were observed for females and elderly individuals. Seasonal stratification analysis showed that the effects of PM10 on mortality due to pneumonia were stronger during spring and autumn. While for COPD, the effect of PM10 in winter was statistically significant (15.54 %, 95 % CI 5.64, 26.35) and the greatest among the seasons. The GAMM model evaluated stronger associations between concentration of PM10. There were significant associations between PM10 and mortality due to respiratory disease at the street or township area levels. The GAMM model using high-resolution PM10 could better capture the association between PM10 and respiratory mortality. Gender, age, and season also acted as effect modifiers for the relationship between PM10 and respiratory mortality.

Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: Does it make a difference?

Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.

Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.

Air Pollution by Hydrothermal Volcanism and Human Pulmonary Function

The aim of this study was to assess whether chronic exposure to volcanogenic air pollution by hydrothermal soil diffuse degassing is associated with respiratory defects in humans. This study was carried in the archipelago of the Azores, an area with active volcanism located in the Atlantic Ocean where Eurasian, African, and American lithospheric plates meet. A cross-sectional study was performed on a study group of 146 individuals inhabiting an area where volcanic activity is marked by active fumarolic fields and soil degassing (hydrothermal area) and a reference group of 359 individuals inhabiting an area without these secondary manifestations of volcanism (nonhydrothermal area). Odds ratio (OR) and 95% confidence intervals (CIs) were adjusted for age, gender, fatigue, asthma, and smoking. The OR for restrictive defects and for exacerbation of obstructive defects (COPD) in the hydrothermal area was 4.4 (95% CI 1.78-10.69) and 3.2 (95% CI 1.82-5.58), respectively. Increased prevalence of restrictions and all COPD severity ranks (mild, moderate, and severe) was observed in the population from the hydrothermal area. These findings may assist health officials in advising and keeping up with these populations to prevent and minimize the risk of respiratory diseases.

Air Pollution and Lung Function in Dutch Children: A Comparison of Exposure Estimates and Associations Based on Land Use Regression and Dispersion Exposure Modeling Approaches

BACKGROUND

There is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model.

OBJECTIVES

We aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models] and, in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques.

METHODS

We used data of 1,058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF) measurements at 8 years of age. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters ≤ 2.5 and ≤ 10 μm (PM2.5, PM10), and PM2.5 soot] was estimated for the current addresses of the participants by a dispersion and a LUR model. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment.

RESULTS

Correlations between LUR- and dispersion-modeled pollution concentrations were high for NO2, PM2.5, and PM2.5 soot (R = 0.86-0.90) but low for PM10 (R = 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, except for associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model.

CONCLUSIONS

Predictions from LUR and dispersion models correlated very well for PM2.5, NO2, and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children.

Long-term air pollution exposure and lung function in 15 year-old adolescents living in an urban and rural area in Germany: The GINIplus and LISAplus cohorts

INTRODUCTION

The impact of outdoor air pollution exposure on long-term lung development and potential periods of increased lung susceptibility remain unknown. This study assessed associations between early-life and current residential exposure to air pollution and lung function at 15-years of age in two German birth cohorts.

METHODS

Fifteen year-old participants living in an urban and rural area in Germany underwent spirometry before and after bronchodilation (N=2266). Annual average (long-term) exposure to nitrogen dioxide (NO(2)), particles with aerodynamic diameters less than 2.5 μg/m(3) (PM2.5) mass and less than 10 μg/m(3) (PM(10)) mass, PM(2.5) absorbance and ozone were estimated to each participant's birth-, 10- and 15-year home address using land-use regression and kriging (ozone only) modelling. Associations between lung function variables and long-term pollutant concentrations were assessed using linear regression models adjusted for host and environmental covariates and recent short-term air pollution exposures.

RESULTS

Long-term air pollution concentrations assessed to the birth-, 10- and 15-year home addresses were not associated with lung function variables, before and after bronchodilation, in the complete or study area specific populations. However, several lung function variables were negatively associated with long-term NO2 concentrations among asthmatics. For example, NO(2) estimated to the 15-year home address was associated with the ratio of forced expiratory volume in one second to forced vital capacity (FEV(1)/FVC) and the mean flow rate between 25% and 75% of FVC (-3.5%, 95% confidence interval [-6.0, -1.0] and -297.4 ml/s [-592.6, -2.1] per 5.9 μg/m(3) increase in NO(2), respectively). Nearly all effect estimates for the associations between the short-term PM(2.5) mass, PM(10) mass and ozone concentrations and the lung function variables were negative in the complete population.

CONCLUSIONS

Early-life and current long-term air pollution exposures and lung function at the age of 15 years were not associated in the complete study population. Asthmatics may represent a vulnerable group.

[Genetic and environmental factors of asthma and allergy: Results of the EGEA study]

INTRODUCTION AND METHODS

The EGEA study (epidemiological study on the genetics and environment of asthma, bronchial hyperresponsiveness and atopy), which combines a case-control and a family-based study of asthma case (n=2120 subjects) with three surveys over 20 years, aims to identify environmental and genetic factors associated with asthma and asthma-related phenotypes. We summarize the results of the phenotypic characterization and the investigation of environmental and genetic factors of asthma and asthma-related phenotypes obtained since 2007 in the EGEA study (42 articles).

RESULTS

Both epidemiological and genetic results confirm the heterogeneity of asthma. These results strengthen the role of the age of disease onset, the allergic status and the level of disease activity in the identification of the different phenotypes of asthma. The deleterious role of active smoking, exposure to air pollution, occupational asthmogenic agents and cleaning products on the prevalence and/or activity of asthma has been confirmed. Accounting for gene-environment interactions allowed the identification of new genetic factors underlying asthma and asthma-related traits and better understanding of their mode of action.

CONCLUSION

The EGEA study is contributing to the advances in respiratory research at the international level. The new phenotypic, environmental and biological data available in EGEA study will help characterizing the long-term evolution of asthma and the factors associated to this evolution.

Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities

Noise prediction models and noise maps are used to estimate the exposure to road traffic noise, but their availability and the quality of the noise estimates is sometimes limited. This paper explores the application of land use regression (LUR) modelling to assess the long-term intraurban spatial variability of road traffic noise in three European cities. Short-term measurements of road traffic noise taken in Basel, Switzerland (n=60), Girona, Spain (n=40), and Grenoble, France (n=41), were used to develop two LUR models: (a) a "GIS-only" model, which considered only predictor variables derived with Geographic Information Systems; and (b) a "Best" model, which in addition considered the variables collected while visiting the measurement sites. Both noise measurements and noise estimates from LUR models were compared with noise estimates from standard noise models developed for each city by the local authorities. Model performance (adjusted R(2)) was 0.66-0.87 for "GIS-only" models, and 0.70-0.89 for "Best" models. Short-term noise measurements showed a high correlation (r=0.62-0.78) with noise estimates from the standard noise models. LUR noise estimates did not show any systematic differences in the spatial patterns when compared with those from standard noise models. LUR modelling with accurate GIS source data can be a promising tool for noise exposure assessment with applications in epidemiological studies.

Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations

BACKGROUND

With the increased availability of spatial data and computing power, spatial prediction approaches have become a standard tool for exposure assessment in environmental epidemiology. However, such models are largely dependent on accurate input data. Uncertainties in the input data can therefore have a large effect on model predictions, but are rarely quantified.

METHODS

With Monte Carlo simulation we assessed the effect of input uncertainty on the prediction of radio-frequency electromagnetic fields (RF-EMF) from mobile phone base stations at 252 receptor sites in Amsterdam, The Netherlands. The impact on ranking and classification was determined by computing the Spearman correlations and weighted Cohen's Kappas (based on tertiles of the RF-EMF exposure distribution) between modelled values and RF-EMF measurements performed at the receptor sites.

RESULTS

The uncertainty in modelled RF-EMF levels was large with a median coefficient of variation of 1.5. Uncertainty in receptor site height, building damping and building height contributed most to model output uncertainty. For exposure ranking and classification, the heights of buildings and receptor sites were the most important sources of uncertainty, followed by building damping, antenna- and site location. Uncertainty in antenna power, tilt, height and direction had a smaller impact on model performance.

CONCLUSIONS

We quantified the effect of input data uncertainty on the prediction accuracy of an RF-EMF environmental exposure model, thereby identifying the most important sources of uncertainty and estimating the total uncertainty stemming from potential errors in the input data. This approach can be used to optimize the model and better interpret model output.

Long-term effects of traffic particles on lung function decline in the elderly

RATIONALE

Few studies have been performed on air pollution effects on lung function in the elderly, a vulnerable population with low reserve capacity, and even fewer have looked at changes in the rate of lung function decline.

OBJECTIVES

We evaluated the effect of long-term exposure to black carbon on levels and rates of decline in lung function in the elderly.

METHODS

FVC and FEV1 were measured one to six times during the period 1995-2011 in 858 men participating in the Normative Aging Study. Exposure to black carbon, a tracer of traffic emissions, was estimated by a spatiotemporal land use regression model. We investigated the effects of moving averages of black carbon of 1-5 years before the lung function measurement using linear mixed models.

MEASUREMENTS AND MAIN RESULTS

A 0.5 μg/m(3) increase in long-term exposure to black carbon was associated with an additional rate of decline in FVC and FEV1 of between 0.5% and 0.9% per year, respectively, depending on the averaging time. In addition, black carbon exposure before the baseline visit was associated with lower levels of both FVC and FEV1, with effect estimates increasing up to 6-7% with a 5-year average exposure.

CONCLUSIONS

Our results support adverse effects of long-term exposure to traffic particles on lung function level and rate of decline in the elderly and suggest that functionally significant differences in health and risk of disability occur below the annual Environmental Protection Agency National Air Quality Standards.

Ensuring Confidentiality of Geocoded Health Data: Assessing Geographic Masking Strategies for Individual-Level Data

Public health datasets increasingly use geographic identifiers such as an individual's address. Geocoding these addresses often provides new insights since it becomes possible to examine spatial patterns and associations. Address information is typically considered confidential and is therefore not released or shared with others. Publishing maps with the locations of individuals, however, may also breach confidentiality since addresses and associated identities can be discovered through reverse geocoding. One commonly used technique to protect confidentiality when releasing individual-level geocoded data is geographic masking. This typically consists of applying a certain amount of random perturbation in a systematic manner to reduce the risk of reidentification. A number of geographic masking techniques have been developed as well as methods to quantity the risk of reidentification associated with a particular masking method. This paper presents a review of the current state-of-the-art in geographic masking, summarizing the various methods and their strengths and weaknesses. Despite recent progress, no universally accepted or endorsed geographic masking technique has emerged. Researchers on the other hand are publishing maps using geographic masking of confidential locations. Any researcher publishing such maps is advised to become familiar with the different masking techniques available and their associated reidentification risks.