Environmental exposure levels are often sufficiently disparate between populations such that there is little or no overlap, complicating our ability to ascertain the full dose-response curve and as such create informed regulatory policy.I reviewed the literature on methods available to address non- and partially-overlapping exposure distributions, drawing from both epidemiology as well as other relevant disciplines to describe the universe of proposed solutions. I also used the case study of maternal PCB-153 exposure and birthweight, utilizing real-world and simulated data to explore our ability to ascertain “true” dose-response curves from observational data given the limited cohort-specific exposure ranges. I investigated the importance of controlled and uncontrolled confounding as well as the impact of sample size on our ability to ascertain a “true” underlying dose-response curve. Pooling and meta-analysis were useful to increase the heterogeneity of exposure distributions despite imperfect confounding control and heterogenous confounding structures across cohorts. The analyses also serve as continued evidence of the challenges of making population-wide inferences from study samples with restricted exposure ranges as well as the danger of pooling multisite data without sufficiently accounting for heterogeneity in both exposure level and distribution of confounders. These results highlight the limitations of using both individual studies and systematic reviews of environmental chemicals, and emphasize the need for pooling and meta-analysis to widen exposure distributions that in turn permit us to accurately capture the negative effects of these environmental chemicals.