Search

In recent years, many spatial epidemiological studies that use proximity of subjects to putative sources as a surrogate for exposure have been published and are increasingly cited as evidence of environmental problems requiring public health interventions. In these studies, the simple finding of a significant, positive association between proximity and disease incidence has been interpreted as evidence of causality. However, numerous authors have pointed out limitations to such interpretations. This, the first of two companion studies, examines the effects of analyzing (real and simulated) spatial data using logistic regression. Simulation is also employed to explore the statistical power of such analyses to detect true effects, quantify the probabilities of Type I and Type II errors, and to evaluate a proposed mechanism that explains the observed effects. Results indicate that, even when the odds ratios of cases and controls are regressed against random or nonsense sources, significant, positive associations are observed at frequencies substantially greater than chance. These frequencies increase when targets are highly non-uniformly distributed such that, for example, false-positive associations are more likely than not when odds ratios are regressed against the actual distribution of ultramafic rocks in California. The coefficients of true, causal associations are substantially attenuated under realistic conditions so that, absent corroborating analyses, there is no non-arbitrary means of distinguishing causal from spurious or real but non-causal associations. Factors affecting where people choose to live act as powerful confounders, creating spurious or real but non-causal associations between exposure and response variables (as well as between other pairs of variables). Consequently, future epidemiological studies that use proximity as a surrogate for exposure should be required to include adequate negative control analyses and/or other kinds of corroborating analyses before they are accepted for publication.

Aggregate exposure metrics based on sums or weighted averages of component exposures are widely used in risk assessments of complex mixtures, such as asbestos-associated dusts and fibers. Allowed exposure levels based on total particle or fiber counts and estimated ambient concentrations of such mixtures may be used to make costly risk-management decisions intended to protect human health and to remediate hazardous environments. We show that, in general, aggregate exposure information alone may be inherently unable to guide rational risk-management decisions when the components of the mixture differ significantly in potency and when the percentage compositions of the mixture exposures differ significantly across locations. Under these conditions, which are not uncommon in practice, aggregate exposure metrics may be "worse than useless," in that risk-management decisions based on them are less effective than decisions that ignore the aggregate exposure information and select risk-management actions at random. The potential practical significance of these results is illustrated by a case study of 27 exposure scenarios in El Dorado Hills, California, where applying an aggregate unit risk factor (from EPA's IRIS database) to aggregate exposure metrics produces average risk estimates about 25 times greater - and of uncertain predictive validity - compared to risk estimates based on specific components of the mixture that have been hypothesized to pose risks of human lung cancer and mesothelioma.