describe cancer-mortality patterns, and Wartenberg and Greenberg (1990) investigated the use of spatial correlations for the study of disease clusters. Cook and Pocock (1983) modeled the spatial correlation of errors in a multiple-regression analysis of mortality patterns. Diggle et al. (1990) used kernel-smoothing methods to derive expected values for assessment of the spatial distribution of cases of laryngeal cancer near a hazardous-waste incinerator. Additional applications include assessment of the spatial pattern of soil contamination near lead smelters (Simpson, 1985).

Modeling Exposure with Additional Exposure Data

Cost often limits the collection of detailed exposure data. A protocol for collection of additional information might allow for the development of a better predictor of exposure than could be derived from the least-costly data available for all subjects.

An example of such an improvement is the use of diaries to record activity patterns. Ostro et al. (1991) assessed the impact of air pollution on persons with asthma living in Denver. They constructed an estimate of exposure by using outdoor monitoring, diary data on time spent outdoors, and a crude estimate of indoor/outdoor ratios of air pollutants. A stronger association was found with this measure than when data from the outdoor monitor alone were used as the exposure measure.

More-complicated models are possible. For example, Hasabelnaby et al. (1989) used indoor fine-particle measurements in a sub-sample of homes to estimate passive smoking exposure. These measurements were regressed against questionnaire data on maternal and paternal smoking, amount of smoking in home, housing characteristics, etc. This yielded a predictive model, whose independent variables were available for all subjects. Using the predicted exposure for all individuals improved model fit over that found using only questionnaire data on passive smoke exposure.

An important caveat related to the use of these methods is that the exposure metric is altered by the decision to use a modeled personal exposure instead of measured outdoor exposure. The regression coefficients from these approaches cannot be applied directly to exposure data from, e.g., central monitoring sites, to forecast effects. Thus, while these methods generally increase the power to detect an effect in the epidemiology study, they may complicate risk assessment. Reliance on data from central monitoring sites, in contrast, simplifies the risk-assessment process.

Advances in Study Design

The principal observational designs for assessing the effects of environmental agents include the cross-sectional, cohort, and case-control



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