Spatial analysis for environmental health research: concepts, methods, and examples

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE

This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States.

METHODS

We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence.

RESULTS

The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties.

CONCLUSIONS

Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Decrease in Ambient Polycyclic Aromatic Hydrocarbon Concentrations in California's San Joaquin Valley 2000-2019

As part of our ongoing research to understand the impact of polycyclic aromatic hydrocarbon (PAH) exposures on health in the San Joaquin Valley, we evaluated airborne PAH concentration data collected over 19 years (2000-2019) at the central air monitoring site in Fresno, California. We found a dramatic decline in outdoor airborne PAH concentrations between 2000 and 2004 that has been maintained through 2019. This decline was present in both the continuous particle-bound PAHs and the filter-based individual PAHs. The decline was more extreme when restricted to winter concentrations. Annual mean PAHs concentrations in 2017- 2018 of particle-bound PAHs were 6.8 ng/m3 or 62% lower than 2000 - 2001. The decline for winter concentrations of continuous particle-bound PAHs between winter 2019 and winter 2001 was 17.2 ng/m3, a drop of 70%. The 2001 to 2018 decline in average wintertime concentrations for filter-based individual PAHs was 82%. We examined industrial emissions, on-road vehicle emissions, residential wood burning, and agricultural and biomass waste burning as possible explanations. The major decline in PAHs from 2000-2004 was coincident with and most likely due to a similar decline in the amount of agricultural and biomass waste burned in Fresno and Madera Counties. On-road vehicle emissions and residential wood burning did not decline until after 2005. Industrial emissions were too low (2% of total) to explain such large decreases in PAH concentrations.

Oncomelania hupensis quadrasi: Snail intermediate host of Schistosoma japonicum in the Philippines

Oncomelania hupensis quadrasi is the snail intermediate host of Schistosoma japonicum in the Philippines. It was discovered by Dr. Marcos Tubangui in 1932 more than two decades after the discovery of the disease in the country in 1906. This review, the first for O. h. quadrasi, presents past and present works on the taxonomy, biology, ecology, control, possible paleogeographic origin of the snail intermediate host and future in research, control and surveillance of the snail. Extensive references are made of other subspecies of O. hupensis such as the subspecies in China for which majority of the advances has been accomplished. Contrasting views on whether the snail is to be considered an independent species of Oncomelania or as one of several subspecies of Oncomelania hupensis are presented. Snail control methods such as chemical methods using synthetic and botanical molluscicides, environmental manipulation and biological control are reviewed. Use of technologies such as Remote Sensing, Geographical Information System and landscape genetics is stressed for snail surveillance. Control and prevention efforts in the Philippines have consistently focused on mass drug administration which has proved inadequate in elimination of the disease. An integrated approach that includes snail control, environmental sanitation and health education has been proposed. Population movement such as migration for employment and economic opportunities and ecotourism and global climate change resulting in heavy rains and flooding challenge the gains of control and elimination efforts. Concern for possible migration of snails to non-endemic areas is expressed given the various changes both natural and mostly man-made favoring habitat expansion.

Current State of Geospatial Methodologic Approaches in Canadian Population Oncology Research

Geospatial analyses are increasingly used in population oncology. We provide a first review of geospatial analysis in Canadian population oncology research, compare to international peers, and identify future directions. Geospatial-focused peer-reviewed publications from 1992-2020 were compiled using PubMed, MEDLINE, Web of Science, and Google Scholar. Abstracts were screened for data derived from a Canadian cancer registry and use of geographic information systems. Studies were classified by geospatial methodology, geospatial unit, location, cancer site, and study year. Common limitations were documented from article discussion sections. Our search identified 71 publications using data from all provincial and national cancer registries. Thirty-nine percent (N = 28) were published in the most recent 5-year period (2016-2020). Geospatial methodologies included exposure assessment (32.4%), identifying spatial associations (21.1%), proximity analysis (16.9%), cluster detection (15.5%), and descriptive mapping (14.1%). Common limitations included confounding, ecologic fallacy, not accounting for residential mobility, and small case/population sizes. Geospatial analyses are increasingly used in Canadian population oncology; however, efforts are concentrated among a few provinces and common cancer sites, and data are over a decade old. Limitations were similar to those documented internationally, and more work is needed to address them. Organized efforts are needed to identify common challenges, develop leading practices, and identify shared priorities.

Spatial modeling of daily concentrations of ground-level ozone in Montreal, Canada: A comparison of geostatistical approaches

Ground-level ozone (O₃) is a powerful oxidizing agent and a harmful pollutant affecting human health, forests and crops. Estimating O₃ exposure is a challenge because it exhibits complex spatiotemporal patterns. The aim in this study was to provide high-resolution maps (100 × 100 m) of O₃ for the metropolitan area of Montreal, Canada. We assessed the kriging with external drift (KED) model to estimate O₃ concentration by synoptic weather classes for 2010. We compared these results with ordinary kriging (OK), and a simple average of 12 monitoring stations. We also compared the estimates obtained for the 2010 summer with those from a Bayesian maximum entropy (BME) model reported in the literature (Adam-Poupart et al., 2014). The KED model with road and vegetation density as covariates showed good performance for all six synoptic classes (daily R² estimates ranging from 0.77 to 0.92 and RMSE from 2.79 to 3.37 ppb). For the summer of 2010, the model using KED demonstrated the best results (R² = 0.92; RMSE = 3.14 ppb), followed by the OK model (R² = 0.85, RMSE = 4 ppb). Our results showed that errors appear to be substantially reduced with the KED model. This may increase our capacity of linking O₃ levels to health problems by means of improved assessments of ambient exposures. However, future work integrating the temporal dependency in the data is needed to not overstate the performance of the KED model.

Spatio-temporal models to estimate daily concentrations of fine particulate matter in Montreal: Kriging with external drift and inverse distance-weighted approaches

Air pollution is a major environmental and health problem, especially in urban agglomerations. Estimating personal exposure to fine particulate matter (PM2.5) remains a great challenge because it requires numerous point measurements to explain the daily spatial variation in pollutant levels. Furthermore, meteorological variables have considerable effects on the dispersion and distribution of pollutants, which also depends on spatio-temporal emission patterns. In this study we developed a hybrid interpolation technique that combined the inverse distance-weighted (IDW) method with Kriging with external drift (KED), and applied it to daily PM2.5 levels observed at 10 monitoring stations. This provided us with downscaled high-resolution maps of PM2.5 for the Island of Montreal. For the KED interpolation, we used spatio-temporal daily meteorological estimates and spatial covariates as land use and vegetation density. Different KED and IDW daily estimation models for the year 2010 were developed for each of the six synoptic weather classes. These clusters were developed using principal component analysis and unsupervised hierarchical classification. The results of the interpolation models were assessed with a leave-one-station-out cross-validation. The performance of the hybrid model was better than that of the KED or the IDW alone for all six synoptic weather classes (the daily estimate for R(2) was 0.66-0.93 and for root mean square error (RMSE) 2.54-1.89 μg/m(3)).

Mapping suicide in London: a brief methodological case study on the application of the smoothing technique

BACKGROUND

When one intends to globally smooth unstable rates, e.g., suicide rates in a region, one needs to consider whether it is better to smooth the rates toward the global mean of the country or toward the global mean of the same region.

AIMS

The present study aims to provide a methodological framework to answer this question by smoothing suicide rates within London boroughs.

METHODS

Based on the results of the spatial autocorrelation statistics, the noniterative empirical Bayes method of moments was chosen to globally smooth the suicide rate of each borough, first toward the global mean of England and Wales, and second toward the mean of the London region.

RESULTS

The results revealed that smoothing the suicide rates of the boroughs toward the global mean of England and Wales had a stronger influence in reducing the variability of suicide rates than smoothing toward the global mean of the London region.

CONCLUSIONS

Smoothing the rates toward the mean of a region within a country acts somewhat between global and local smoothing.

Using spatial analysis to demonstrate the heterogeneity of the cardiovascular drug-prescribing pattern in Taiwan

BACKGROUND

Geographic Information Systems (GIS) combined with spatial analytical methods could be helpful in examining patterns of drug use. Little attention has been paid to geographic variation of cardiovascular prescription use in Taiwan. The main objective was to use local spatial association statistics to test whether or not the cardiovascular medication-prescribing pattern is homogenous across 352 townships in Taiwan.

METHODS

The statistical methods used were the global measures of Moran's I and Local Indicators of Spatial Association (LISA). While Moran's I provides information on the overall spatial distribution of the data, LISA provides information on types of spatial association at the local level. LISA statistics can also be used to identify influential locations in spatial association analysis. The major classes of prescription cardiovascular drugs were taken from Taiwan's National Health Insurance Research Database (NHIRD), which has a coverage rate of over 97%. The dosage of each prescription was converted into defined daily doses to measure the consumption of each class of drugs. Data were analyzed with ArcGIS and GeoDa at the township level.

RESULTS

The LISA statistics showed an unusual use of cardiovascular medications in the southern townships with high local variation. Patterns of drug use also showed more low-low spatial clusters (cold spots) than high-high spatial clusters (hot spots), and those low-low associations were clustered in the rural areas.

CONCLUSIONS

The cardiovascular drug prescribing patterns were heterogeneous across Taiwan. In particular, a clear pattern of north-south disparity exists. Such spatial clustering helps prioritize the target areas that require better education concerning drug use.

Spatiotemporal trends in oral cancer mortality and potential risks associated with heavy metal content in Taiwan soil

Central and Eastern Taiwan have alarmingly high oral cancer (OC) mortality rates, however, the effect of lifestyle factors such as betel chewing cannot fully explain the observed high-risk. Elevated concentrations of heavy metals in the soil reflect somewhat the levels of exposure to the human body, which may promote cancer development in local residents. This study assesses the space-time distribution of OC mortality in Taiwan, and its association with prime factors leading to soil heavy metal content. The current research obtained OC mortality data from the Atlas of Cancer Mortality in Taiwan, 1972-2001, and derived soil heavy metals content data from a nationwide survey carried out by ROCEPA in 1985. The exploratory data analyses showed that OC mortality rates in both genders had high spatial autocorrelation (Moran's I = 0.6716 and 0.6318 for males and females). Factor analyses revealed three common factors (CFs) representing the major pattern of soil pollution in Taiwan. The results for Spatial Lag Models (SLM) showed that CF1 (Cr, Cu, Ni, and Zn) was most spatially related to male OC mortality which implicates that some metals in CF1 might play as promoters in OC etiology.

Spatial modeling in environmental and public health research

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

[Determinants of geographic variations in asthma medication in Quebec]

BACKGROUND

The study of geographic variations in the use of medication can constitute an interesting and useful option in public health surveillance for the identification of population needs and for the fair partition of resources. Many studies report variations in the use of medication that are not always understood. Considering the importance of medication, particularly for chronic diseases like asthma, it is important to identify factors that can explain geographic differences in the utilization of medications in order to determine optimal prescription practices.

METHODS

We used a multilevel analysis to explore determinants of variation among welfare recipients and subscribers covered by the Quebec public drug plan, aged five to 44, who claimed at least one prescription for an asthmatic medication in 2003. The analysis was based on 73 small areas in Quebec's 15 health regions.

RESULTS

Prevalence of usage and hospitalization rates are the main predictors of variation in medication intensity. On the other hand, prevalence of usage is related to the area of residence, proportion of welfare recipients and prevalence of smokers. The quality of medication is related to practitioners' specialty.

CONCLUSION

Different factors contribute to the understanding of geographic variations in asthma medication but it remains important to control for severity. Small area analysis makes it possible to identify specific areas facing more important problems in the prevalence and use of medication for asthma and thus leads to better understanding of local needs for more targeted interventions.

Traffic-related air pollution and socioeconomic status: a spatial autocorrelation study to assess environmental equity on a small-area scale

BACKGROUND

: Most ecologic studies of environmental equity show that groups with lower socioeconomic status (SES) are more likely to be exposed to higher air pollution levels than groups of higher SES. However, these studies rarely consider spatial autocorrelation in the data. We investigated the associations between traffic-related air pollution and SES on a small-area level in Strasbourg (France) and assessed the impact of spatial autocorrelation on the results.

METHODS

: We used a deprivation index, constructed from census data, to estimate SES at the block level. Average ambient nitrogen dioxide (NO2) levels during year 2000, modeled at the block level by a dispersion model, served as a marker of traffic exhaust. We estimated the association between exposure to NO2 and the deprivation index by using an ordinary least squares model and a simultaneous autoregressive model that controls for the spatial autocorrelation of data.

RESULTS

: The association between the deprivation index and NO2 levels was positive and nonlinear with both regression models; the midlevel deprivation areas were the most exposed. Control of spatial autocorrelation strongly reduced the strength of the association but clearly improved the model's goodness-of-fit; the most pronounced reduction was observed for the midlevel deprivation areas (regression coefficients decreased by 67%).

CONCLUSIONS

: This study confirms the need to take spatial autocorrelation into account in ecologic studies and shows that failure to do so may lead to biased and unreliable estimates and thus to erroneous conclusions. This may be especially important in studying the role of air pollution on social inequalities in health.

Fine particulate matter and mortality: a comparison of the six cities and American Cancer Society cohorts with a medicare cohort

BACKGROUND

The American Cancer Society study and the Harvard Six Cities study are 2 landmark cohort studies for estimating the chronic effects of fine particulate air pollution (PM2.5) on mortality. Using Medicare data, we assessed the association of PM2.5 with mortality for the same locations included in these studies.

METHODS

We estimated the chronic effects of PM2.5 on mortality for the period 2000-2002 using mortality data for cohorts of Medicare participants and average PM2.5 levels from monitors in the same counties included in the 2 studies. We estimated mortality risk associated with air pollution adjusting for individual-level (age and sex) and area-level covariates (education, income level, poverty, and employment). We controlled for potential confounding by cigarette smoking by including standardized mortality ratios for lung cancer and chronic obstructive pulmonary disease.

RESULTS

Using the Medicare data, we estimated that a 10 microg/m increase in the yearly average PM2.5 concentration is associated with 10.9% (95% confidence interval = 9.0-12.8) and with 20.8% (14.8-27.1) increases in all-cause mortality for the American Cancer Society and Harvard Six Cities study counties, respectively. The estimates are somewhat higher than those reported by the original investigators.

CONCLUSION

Although Medicare data lack information on some potential confounding factors, we estimated risks similar to those in the previously published reports, which incorporated more extensive information on individual-level confounders. We propose that the Medicare files can be used to construct on-going cohorts for tracking the risk of air pollution over time.

Panel discussion review: session three--issues involved in interpretation of epidemiologic analyses--statistical modeling

The Clean Air Act mandates that the US Environmental Protection Agency (EPA) develop National Ambient Air Quality Standards for criteria air pollutants and conduct periodic reviews of the standards based on new scientific evidence. In recent reviews, evidence from epidemiologic studies has played a key role. Epidemiologic studies often provide evidence for effects of several air pollutants. Determining whether there are independent effects of the separate pollutants is a challenge. Among the many issues confronting the interpretation of epidemiologic studies of multi-pollutant exposures and health effects are those specifically related to statistical modeling. The EPA convened a workshop on 13 and 14 December 2006 in Chapel Hill, North Carolina, USA, to discuss these and other issues; Session Three of the workshop was devoted specifically to statistical modeling. Prominent statistical modeling issues in epidemiologic studies of air pollution include (1) measurement error across the co-pollutants; (2) correlation and multi-collinearity among the co-pollutants; (3) the timing of the concentration-response function; (4) confounding; and (5) spatial analyses.

Use of a geographic information system in an epidemiological study of measles in Istanbul

A geographic information system was used in an epidemiological study of measles in Istanbul, Turkey. The aim was to obtain more reliable results and to increase the facilities for spatial monitoring in epidemiological studies. The Gaziosmanpasa district of Istanbul was selected as it experiences the highest level of migration, and currently has the highest population growth rate of the city's districts. The epidemic distribution of measles was investigated on the basis of examination of 320 children. The number, geographical distribution, complications and time of measles outbreaks were analysed and represented in thematic maps (a small selection is shown in this paper). The results were evaluated and, in view of these results, new vaccination plans were arranged.

Do hot spots of deprivation predict the rates of suicide within London boroughs?

INTRODUCTION

The ecological associations between suicide rates and different indices of deprivation within London have been investigated at least for half a century. In the present study, the association between rates of suicide with newly developed hot spots of deprivation index within London boroughs have been studied taking into account the results of the spatial dependency between suicide rates in nearby boroughs.

METHODS

Suicide data were provided by the National Confidential Inquiry into Suicide and Homicide by People with Mental Illness. The hot spots index of deprivation and the population counts were provided by the Department of the Environment, Transport and the Region (DETR) and Office for the National Statistics (ONS), respectively.

RESULTS

The results show that there is no strong spatial dependency between suicide rates in the London boroughs, the 'hot spots' index of deprivation predicts the rates of suicide in males 30-49, better than other age and sex groups. The rate of suicide decreases with decreasing deprivation as indicated by the 'hot spots' index.

CONCLUSION

These findings suggest that at the London boroughs the 'hot spots' index of deprivation (together with other socio-economic and social fragmentation indices) should be considered as a potential explanatory variable to explain the effects of age on rates of suicide in men and women.

The health relevance of ambient particulate matter characteristics: coherence of toxicological and epidemiological inferences

The aim of this article is to review progress toward integration of toxicological and epidemiological research results concerning the role of specific physicochemical properties, and associated sources, in the adverse impact of ambient particulate matter (PM) on public health. Contemporary knowledge about atmospheric aerosols indicates their complex and variable nature. This knowledge has influenced toxicological assessments, pointing to several possible properties of concern, including particle size and specific inorganic and organic chemical constituents. However, results from controlled exposure laboratory studies are difficult to relate to actual community health results because of ambiguities in simulated PM mixtures, inconsistent concentration measurements, and the wide range of different biological endpoints. The use of concentrated ambient particulates (CAPs) coupled with factor analysis has provided an improved understanding of biological effects from more realistic laboratory-based exposure studies. Epidemiological studies have provided information concerning sources of potentially toxic particles or components, adding insight into the significance of exposure to secondary particles, such as sulfate, compared with primary emissions, such as elemental and organic carbon from transportation sources. Recent epidemiological approaches incorporate experimental designs that take advantage of broadened speciation monitoring, multiple monitoring stations, source proximity designs, and emission intervention. However, there continue to be major gaps in knowledge about the relative toxicity of particles from various sources, and the relationship between toxicity and particle physicochemical properties. Advancing knowledge could be facilitated with cooperative toxicological and epidemiological study designs, with the support of findings from atmospheric chemistry.

Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary

We provide a relatively non-technical glossary of terms and a description of the tools used in spatial or geographical epidemiology and associated geographical information systems. Statistical topics included cover adjustment and standardisation to allow for demographic and other background differences, data structures, data smoothing, spatial autocorrelation and spatial regression. We also discuss the rationale for geographical epidemiology and specific techniques such as disease clustering, disease mapping, ecological analyses, geographical information systems and global positioning systems.

Health effects associated with exposure to ambient air pollution

The World Health Organization has identified ambient air pollution as a high public health priority, based on estimates of air pollution related death and disability-adjusted life years derived in its Global Burden of Disease initiative. The NERAM Colloquium Series on Health and Air Quality was initiated to strengthen the linkage between scientists, policymakers, and other stakeholders by reviewing the current state of science, identifying policy-relevant gaps and uncertainties in the scientific evidence, and proposing a path forward for research and policy to improve air quality and public health. The objective of this paper is to review the current state of science addressing the impacts of air pollution on human health. The paper is one of four background papers prepared for the 2003 NERAM/AirNet Conference on Strategies for Clean Air and Health, the third meeting in the international Colloquium Series. The review is based on the framework and findings of the U.S. National Research Committee (NRC) on Research Priorities for Airborne Particulate Matter and addresses key questions underlying air quality risk management policy decisions.

GIS-EpiLink: A Spatial Search Tool for Linking Environmental and Health Data

One inherent characteristic of both environmental data and health data is that they have a location component. This characteristic makes Geographic Information Systems (GIS) an ideal and sometimes indispensable tool for analyzing environmental and health data. Indeed, the past decade witnessed significant efforts in developing GIS tools for supporting epidemiologic research. Despite these efforts, the availability of accessible GIS tools that can be easily used by epidemiologists to link environmental and health data has remained a problem. We present a simple spatial search tool--GIS-EpiLink--that can be used to link environmental and health data when distance between an environmental site and the location of the maternal address of a case or control is used as a proxy for exposure. The tool was used in a research project and it successfully provided the necessary data for epidemiological analyses. This tool should be very useful to epidemiologists in environmental health research.

Mapping health on the internet: a new tool for environmental justice and public health research

This paper examines the prospects for integrating Internet platform GIS or 'web-GIS' into environmental justice and related public health research. Specifically, we document the development of a web-GIS created for investigating relationships between health, air quality and socioeconomic factors in Hamilton, Canada. After development of the web-GIS site, we assembled a focus group of public health professionals to test functionality and render opinions about the potential of the site and geographic information in their program implementation. Results show overwhelming support for the further integration of GIS into public health practice. The results also underscore the potential of web-GIS to alleviate concerns of cost and data availability that often limit the use of GIS in community debates centred on environmental justice issues.

Children's exposure to nitrogen dioxide in Sweden: investigating environmental injustice in an egalitarian country

STUDY OBJECTIVE

Prior studies have shown that children are particularly sensitive to air pollution. This study examined whether children of low socioeconomic status suffered greater exposure to outdoor nitrogen dioxide than more affluent ones, both at their place of residence and at school, in a country with widespread state intervention for social equity.

DESIGN

Local scale data on outdoor nitrogen dioxide obtained from a validated air pollution model were analysed, along with all school children accurately geocoded to their building of residence and school.

PARTICIPANTS

All 29,133 children in grades one through nine (aged 7 to 15 years) residing and attending school in Malmö, Sweden, in 2001.

MAIN RESULTS

Defining the socioeconomic status of children according to the mean income in their residential building, the spatial scan statistic technique allowed the authors to identify eight statistically significant clusters of low socioeconomic status children, all of which were located in the most polluted areas of Malmö. Four clusters of high socioeconomic status children were found, all of them located in the least polluted areas. The neighbourhood socioeconomic status better predicted the nitrogen dioxide exposure of children than the socioeconomic status of their building of residence. Exposure to nitrogen dioxide at the place of residence and school of attendance regularly increased as the socioeconomic status of a child's neighbourhood of residence decreased.

CONCLUSIONS

Evidence of environmental injustice was found, even in a country noted for its egalitarian welfare state. Enforcement of environmental regulations may be necessary to achieve a higher level of environmental equity.

Use of GIS in epidemiology: a case study in Istanbul

In this study, the procedure of using GIS for tracking the distribution of measles in a district of Istanbul was performed. The aim of the study is to present the available questionnaire data, which were collected during the measles epidemic, by means of maps. The designed maps show the relation and the distribution of individual cases on time and spatiality. A database is designed according to the questionnaires. Geo-spatial distribution of measles cases was analyzed. The obtained results were discussed and presented.

Air pollution exposure assessment for epidemiologic studies of pregnant women and children: lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research

The National Children's Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research that may enhance the National Children's Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatial-temporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) "Usual" temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Children's Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period.

Comparison of a spatial perspective with the multilevel analytical approach in neighborhood studies: the case of mental and behavioral disorders due to psychoactive substance use in Malmo, Sweden, 2001

Most studies of neighborhood effects on health have used the multilevel approach. However, since this methodology does not incorporate any notion of space, it may not provide optimal epidemiologic information when modeling variations or when investigating associations between contextual factors and health. Investigating mental disorders due to psychoactive substance use among all 65,830 individuals aged 40-59 years in 2001 in Malmö, Sweden, geolocated at their place of residence, the authors compared a spatial analytical perspective, which builds notions of space into hypotheses and methods, with the multilevel approach. Geoadditive models provided precise cartographic information on spatial variations in prevalence independent of administrative boundaries. The multilevel model showed significant neighborhood variations in the prevalence of substance-related disorders. However, hierarchical geostatistical models provided information on not only the magnitude but also the scale of neighborhood variations, indicating a significant correlation between neighborhoods in close proximity to each other. The prevalence of disorders increased with neighborhood deprivation. Far stronger associations were observed when using indicators measured in spatially adaptive areas, centered on residences of individuals, smaller in size than administrative neighborhoods. In neighborhood studies, building notions of space into analytical procedures may yield more comprehensive information than heretofore has been gathered on the spatial distribution of outcomes.

Geographies of risk in studies linking chronic air pollution exposure to health outcomes

This article addresses the question of how to incorporate spatial processes into the assessment of chronic health effects from air pollution exposure. An analytic framework is developed around three related concepts: (1) the geography of susceptibility; (2) the geography of exposure; and (3) points of intersection between these two, termed the geography of risk. The article discusses how each concept encompasses many lower level issues such as meteorological dispersion of pollutants, time-space activity patterns, and population distributions of susceptible individuals in time and space. A key premise is that researchers should target studies with high degrees of overlap between geographies of exposure and susceptibility. Instances where the overlap remains incomplete, or systematically biased, usually produce attenuated or unreliable risk estimates, and some of this discordance may find expression in spatially autocorrelated residuals.

Mortality and long-term exposure to ambient air pollution: ongoing analyses based on the American Cancer Society cohort

This article provides an overview of previous analysis and reanalysis of the American Cancer Society (ACS) cohort, along with an indication of current ongoing analyses of the cohort with additional follow-up information through to 2000. Results of the first analysis conducted by Pope et al. (1995) showed that higher average sulfate levels were associated with increased mortality, particularly from cardiopulmonary disease. A reanalysis of the ACS cohort, undertaken by Krewski et al. (2000), found the original risk estimates for fine-particle and sulfate air pollution to be highly robust against alternative statistical techniques and spatial modeling approaches. A detailed investigation of covariate effects found a significant modifying effect of education with risk of mortality associated with fine particles declining with increasing educational attainment. Pope et al. (2002) subsequently reported results of a subsequent study using an additional 10 yr of follow-up of the ACS cohort. This updated analysis included gaseous copollutant and new fine-particle measurements, more comprehensive information on occupational exposures, dietary variables, and the most recent developments in statistical modeling integrating random effects and nonparametric spatial smoothing into the Cox proportional hazards model. Robust associations between ambient fine particulate air pollution and elevated risks of cardiopulmonary and lung cancer mortality were clearly evident, providing the strongest evidence to date that long-term exposure to fine particles is an important health risk. Current ongoing analysis using the extended follow-up information will explore the role of ecologic, economic, and, demographic covariates in the particulate air pollution and mortality association. This analysis will also provide insight into the role of spatial autocorrelation at multiple geographic scales, and whether critical instances in time of exposure to fine particles influence the risk of mortality from cardiopulmonary and lung cancer. Information on the influence of covariates at multiple scales and of critical exposure time windows can assist policymakers in establishing timelines for regulatory interventions that maximize population health benefits.

Ambient air pollution and atherosclerosis in Los Angeles

Associations have been found between long-term exposure to ambient air pollution and cardiovascular morbidity and mortality. The contribution of air pollution to atherosclerosis that underlies many cardiovascular diseases has not been investigated. Animal data suggest that ambient particulate matter (PM) may contribute to atherogenesis. We used data on 798 participants from two clinical trials to investigate the association between atherosclerosis and long-term exposure to ambient PM up to 2.5 microm in aerodynamic diameter (PM2.5). Baseline data included assessment of the carotid intima-media thickness (CIMT), a measure of subclinical atherosclerosis. We geocoded subjects' residential areas to assign annual mean concentrations of ambient PM2.5. Exposure values were assigned from a PM2.5 surface derived from a geostatistical model. Individually assigned annual mean PM2.5 concentrations ranged from 5.2 to 26.9 microg/m3 (mean, 20.3). For a cross-sectional exposure contrast of 10 microg/m3 PM2.5, CIMT increased by 5.9% (95% confidence interval, 1-11%). Adjustment for age reduced the coefficients, but further adjustment for covariates indicated robust estimates in the range of 3.9-4.3% (p-values, 0.05-0.1). Among older subjects (greater than or equal to 60 years of age), women, never smokers, and those reporting lipid-lowering treatment at baseline, the associations of PM2.5 and CIMT were larger with the strongest associations in women 60 years of age (15.7%, 5.7-26.6%). These results represent the first epidemiologic evidence of an association between atherosclerosis and ambient air pollution. Given the leading role of cardiovascular disease as a cause of death and the large populations exposed to ambient PM2.5, these findings may be important and need further confirmation.