Objective: To conduct a series of multi-level regression models to identify any association between wind turbines and cancer incidence between counties, within counties over time, and between cancer types. Background: In a recent campaign speech, President Trump declared that the noise from wind turbines may cause cancer. This statement resulted in tremendous outcry from political opponents and wind energy advocates. But this question does not lack merit from a scientific perspective. In fact, public health researchers have begun to use large-scale population data to evaluate the effect of wind turbine noise on a variety of health issues. More so, biological studies have identified noise as a potential mechanism driving certain subtypes of breast cancer. Significance: Largely due to confounding political factors and a lack of comprehensive data on wind turbine development, no previous research has directly investigated the effect of long-term exposure to wind turbines on cancer incidence. However, multi-level model analyses have been shown to mitigate bias against possible inference by parsing out inherent county and state-level characteristics. This framework can be utilized to remove endogenous factors related to cancer and wind turbine development. A hierarchical analysis linking SEER data on cancer incidence with a newly released wind turbine database creates an opportunity for econometric research to further inform public health policy in the highly partisan energy sector. Methods: Age-adjusted cancer incidence data were obtained from the Surveillance Epidemiology End Results Program (SEER). Wind turbine count and characteristic data were accessed from the U.S. Wind Turbine Database created by the U.S. Geological Survey Program. In all, four hierarchical models were constructed: one cross-sectional study nesting counties within states; one three-level longitudinal model nesting repeated measures within counties, within states; another longitudinal model nesting repeated measures within counties; and a final cross clustered model nesting repeated measures within counties and cancer sites. Independent variables included county and state-level demographic controls associated with cancer incidence, as well as wind turbine count and capacity measures. To estimate the quasi-intervention effect of wind turbine development at the county-level, each longitudinal model incorporated a Difference-in-Difference analysis (DID). Results: The cross-sectional analysis revealed that counties with wind turbines had higher cancer incidence than counties without (FE = 4.3, p = .06). However, for counties with turbines, as exposure to higher quantities per square mile increased, cancer incidence decreased (FE = -26.7, p = .09). In each longitudinal analysis, the effect of exposure completely disappeared. Exposure to higher burden of wind turbines was shown to increase cancer incidence, but this effect was marginal and only found in one test (FE = 3.1, p = .06). Each of the quasi-experimental designs brought similar results, that the effect of early implementation of a wind turbines program was protective against rising cancer incidence, but with the onset of later development, the overall effect became marginally significant (DID = -16., p > .5). Finally, stratifying by cancer site revealed breast cancer’s significant fixed and random effects from the natural experiment (FE = 20.7, p = .002). Conclusion: Greater exposure to wind turbines does not appear to increase cancer incidence. And while the effect of turbine development on cancer incidence varies in non-geographic ways, possibly by cancer type, these findings remain limited by the dearth of biological evidence suggesting any causal effect from wind turbine exposure. Implications: Without concrete, consensus support from the fields of basic science and epidemiology, health policy analysis lacks any ability to confront assumptions needed for confident natural-experimentation. The causes of cancer heterogeneity will remain a critical public health concern until political platforms no longer hold greater legitimacy than scientific investigation and public health priorities. Acknowledgments: This research was part of the graduate course, "Applied Hierarchical Linear Models" taught by Dr. Jean Marshall. All analyses were completed using HLM7. Access to this beta statistical software was generously provided by Dr. Steve Raudenbush. A special thanks to Dr. Marshall for the support throughout this project.