A cautionary tale: The characteristics of two-dimensional distributions and their effects on epidemiological studies employing an ecological design

Abstract

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.

• Agent-based models
• asbestos
• causality
• clustering
• demographics
• disease-clustering
• environmental exposure
• exposure-response modeling
• geostatistics
• mesothelioma
• proximity-based exposure
• regression
• risk assessment
• spatial epidemiology

Acknowledgements

We would like to thank the anonymous reviewers for their helpful suggestions, which served to better focus and substantially strengthen this manuscript.