Many recent health risk assessments have noted that adverse health outcomes are significantly statistically associated with proximity to suspected sources of health hazard, such as manufacturing plants or point sources of air pollution. Using geographic proximity to sources as surrogates for exposure to (possibly unknown) releases, spatial ecological studies have identified potential adverse health effects based on significant regression coefficients between risk rates and distances from sources in multivariate statistical risk models. Although this procedure has been fruitful in identifying exposure-response associations, it is not always clear whether the resulting regression coefficients have valid causal interpretations. Spurious spatial regression and other threats to valid causal inference may undermine practical efforts to causally link health effects to geographic sources, even when there are clear statistical associations between them. This paper demonstrates the methodological problems by examining statistical associations and regression coefficients between spatially distributed exposure and response variables in a realistic data set for California. We find that distance from "nonsense" sources (such as arbitrary points or lines) are highly statistically significant predictors of cause-specific risks, such as traffic fatalities and incidence of Kaposi's sarcoma. However, the signs of such associations typically depend on the distance scale chosen. This is consistent with theoretical analyses showing that random spatial trends (which tend to fluctuate in sign), rather than true causal relations, can create statistically significant regression coefficients: spatial location itself becomes a confounder for spatially distributed exposure and response variables. Hence, extreme caution and careful application of spatial statistical methods are warranted before interpreting proximity-based exposure-response relations as evidence of a possible or probable causal relation.