Epidemiology
The fundamental guidelines for the design of an environmental epidemiology study are relevant whether or not GIS technology is being used for exposure assessment. A well-designed epidemiologic study takes into account potential confounding factors, including other exposures that may co-occur with the exposure of interest. The study should be designed to have adequate power to detect an association between the exposure and health outcome and to evaluate exposure–response relationships. For many environmental exposures the anticipated magnitude of the association with disease is likely to be modest, therefore a careful evaluation of the expected prevalence of exposure is critical to determining adequate study power.
A GIS can be used to evaluate the population potentially exposed and to determine if there is likely to be adequate variation in exposure across a study area. Wartenberg et al. (1993) used a GIS to develop an automated method for identifying populations living near high-voltage lines for the purpose of evaluating childhood leukemia and electromagnetic radiation. Another example is the use of a GIS to link disease registry information with public water supply monitoring and location data to determine potential study areas for evaluating the relation between disinfection byproducts exposure and adverse reproductive outcomes and cancer (Raucher et al. 2000).
The epidemiologic study should have the capability to evaluate the exposure in relation to an appropriate latency for the disease and to evaluate critical time windows of exposure. One limitation of a GIS is that mapped data often represent only one snapshot in time. However, several recent efforts have used GIS to reconstruct historical exposure to pesticides (Brody et al. 2002) and drinking water contaminants (Swartz et al. 2003) over a period of decades for a study of breast cancer on Cape Cod, Massachusetts. A study of fetal death in California (Bell et al. 2001) used an exposure metric based on agricultural pesticide use near the mother’s residence during specific time periods during the pregnancy.
Misclassification of exposure is of particular concern in environmental epidemiology studies because of the challenges in estimating exposure to environmental contaminants, which can occur across multiple locations and often at low levels. Exposure errors in time–series studies can occur as a continuum of measurement errors between classic-type errors and Berkson errors, as has been presented in detail by Zeger et al. (2000) regarding air pollution and health. Each type of error has different effects on the estimation of risk. Berkson error occurs when the exposure metric is at the population level, and individual exposures vary because of different activity patterns. An example of a population-level or aggregate exposure metric is the assignment of air pollutant levels from a stationary air monitor to the population living in the vicinity of the monitor. Berkson error does not lead to bias in the risk estimate although the variance of the risk estimate is increased (Zeger et al. 2000).
In a classic error model the exposure metric used in an epidemiologic study is measured with error and is an imperfect surrogate for the true exposure. If misclassification of exposure is nondifferential in terms of the health outcome, the effect is generally to bias risk estimates toward the null, thus potentially missing true associations (Copeland et al. 1977; Flegal et al. 1986). To evaluate the degree of misclassification that may occur in an epidemiologic study, it is important to consider the sensitivity and specificity of the exposure metric employed. Sensitivity is the ability of an exposure metric to correctly classify as exposed those who are truly exposed. Specificity is the ability of the metric to correctly classify as unexposed those who are unexposed. Most epidemiologists do not formally assess the validity of their exposure metric before a study is launched; however, small reductions in sensitivity and/or specificity of the exposure metric can have substantial effects on the estimates of risk. When the true prevalence of exposure is low (e.g., less than 10%) small reductions in specificity cause substantial reductions in the risk estimates, whereas reductions in sensitivity have smaller effects. When the exposure is common in the study population, the sensitivity of the exposure metric becomes more important (Stewart and Correa-Villasenor 1991).
A common metric used in studies employing GIS is the proximity between a pollutant source and a residence. Simple proximity metrics are likely to overestimate the population truly exposed (high sensitivity but low specificity). If those truly exposed represent only a small percent of the study population, there will be substantial attenuation of the risk estimate if a true risk exists. Rull and Ritz (2003) compared several methods of classifying a study population in California on the basis of agricultural pesticide use reported by the California Pesticide Use Reporting (CPUR) database (http://www.cdpr.ca.gov/). The prevalence of exposure differed substantially depending on the metric used. They assumed that a metric that accounted for the location of crop fields more accurately represented true exposures and this metric resulted in lower exposure prevalence compared with a metric based on the CPUR database alone. In a simulation study they demonstrated that the reduced specificity of the CPUR metric resulted in substantial attenuation of risk estimates.