Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.

Background: A growing body of evidence has associated maternal exposure to air pollution with adverse effects on fetal growth; however, the existing literature is inconsistent.Objectives: We aimed to quantify the association between maternal exposure to particulate air pollution and term birth weight and low birth weight (LBW) across 14 centers from 9 countries, and to explore the influence of site characteristics and exposure assessment methods on between-center heterogeneity in this association.Methods: Using a common analytical protocol, International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) centers generated effect estimates for term LBW and continuous birth weight associated with PM10 and PM2.5 (particulate matter ≤ 10 and 2.5 µm). We used meta-analysis to combine the estimates of effect across centers (~ 3 million births) and used meta-regression to evaluate the influence of center characteristics and exposure assessment methods on between-center heterogeneity in reported effect estimates.Results: In random-effects meta-analyses, term LBW was positively associated with a 10-μg/m3 increase in PM10 [odds ratio (OR) = 1.03; 95% CI: 1.01, 1.05] and PM2.5 (OR = 1.10; 95% CI: 1.03, 1.18) exposure during the entire pregnancy, adjusted for maternal socioeconomic status. A 10-μg/m3 increase in PM10 exposure was also negatively associated with term birth weight as a continuous outcome in the fully adjusted random-effects meta-analyses (–8.9 g; 95% CI: –13.2, –4.6 g). Meta-regressions revealed that centers with higher median PM2.5 levels and PM2.5:PM10 ratios, and centers that used a temporal exposure assessment (compared with spatiotemporal), tended to report stronger associations.Conclusion: Maternal exposure to particulate pollution was associated with LBW at term across study populations. We detected three site characteristics and aspects of exposure assessment methodology that appeared to contribute to the variation in associations reported by centers.

Trichloroethylene is an organic chemical that has been used in dry cleaning, for metal degreasing, and as a solvent for oils and resins. It has been shown to cause liver and kidney cancer in experimental animals. This article reviews over 80 published papers and letters on the cancer epidemiology of people exposed to trichloroethylene. Evidence of excess cancer incidence among occupational cohorts with the most rigorous exposure assessment is found for kidney cancer (relative risk lRR] = 1.7, 95% confidence interval [Cl] 1.1-2.7), liver cancer (RR = 1.9, 95% Cl 1.0-3.4), and non-Hodgkin's lymphoma (RR = 1.5, 95% Cl 0.9-2.3) as well as for cervical cancer, Hodgkin's disease, and multiple myeloma. However, since few studies isolate trichloroethylene exposure, results are likely confounded by exposure to other solvents and other risk factors. Although we believe that solvent exposure causes cancer in humans and that trichloroethylene likely is one of the active agents, we recommend further study to better specify the specific agents that confer this risk and to estimate the magnitude of that risk.

In this paper, I develop a multivariate extension of the univariate method of spatial autocorrelation analysis that I call multivariate spatial correlation (MSC). By accounting for the spatial dependence of data observations and their multivariate covariance simultaneously, complex interactions among many variables in a geographic context are analyzed. Using a methodological scheme borrowed from the techniques of principal components analysis (PCA) and factor analysis, a strategy for the exploratory analysis of spatial pattern in the multivariate domain is developed.Spatial autocorrelation analysis is a statistical approach for quantifying the spatial reIations among a set of univariate data observations. Since many processes occur in a geographic context, allowance for spatial dependence is essential in the analysis of geographically distributed data (Griffith 1978; Cliff and Ord 1981). Multivariate analysis is an array of statistical methods for quantifying the relations among many variables in a set of observations. Since many processes involve more than one variable, allowance for their dependence on each other is essential in modeling and in understanding their covariance (Momson 1976). This paper is an attempt to define an analytical technique that accommodates both of these considerations simultaneously, and examines the spatial dependence of multivariate observations. METHODSSpatial autocorrelation is defined in terms of univariate data observations. Moran's coefficient Z (Moran 1948(Moran , 1950Cliff and Ord 1981), for example, is the weighted sum of the product of separate data observations, centered to the expected value of the observations, standardized to adjust for the variance of the observations, and normalized for the total sum of the weights. The following If, instead of using univariate data, we define each observation as a vector of individual observations of m variables, we can similarly define a matrix of coefficients, M:where M is an m by rn, variable by variable, spatial correlation matrix Z is an n by rn, location by variable, standardized and centered (by variable) Z t is an m by n, variable by location, standardized and centered (by variable) W is an n by n, locality by locality, weight matrix. data matrix data matrix, the transpose of Z Each coefficient in the matrix M is a Mantel-type coefficient (Mantel 1967). That is, each coefficient is a general cross-product statistic among elements of two matrices in which these elements are distances (or similarities) among pairs of objects (Hubert, Golledge, and Costanzo 1981). The distributional properties of each diagonal element of M are the same as for univariate autocorrelation values.Indeed, the diagonal values are themselves Moran's I coefficients. Each off-diagonal element is, by analogy, a bivariate crosscorrelation coefficient, the spatial correlation of one variable with another variable calculated by summing the values over all pairs of localities, and weighted as in the autocorrelations. One such coefficient exists for each pai...

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