Center for Appalachian Research in Environmental Sciences: Career Development Award for W. Jay Chrisitan - Using Commercially Available Residential Histories for Exposure Assessment in Environmental

While there are known health risks associated with an individual’s occupational exposure to agents generated by coal mining activities (e.g., pneumoconiosis), researchers over the past several years have also explored potential environmental exposures relevant to population health. Multiple ecological and cross-sectional studies have demonstrated, for example, that the risk of lung cancer is elevated among populations in coal mining regions, even after accounting for rates of cigarette smoking, the foremost cause of this disease (1-3). Rigorous individual-level studies of lung cancer risk and environmental exposures derived from coal mining activities have not been reported, however, despite the population-based evidence. Case-control study designs in particular could provide stronger evidence of a relationship, if one truly exists. Latency is a major challenge for case-control studies of environmental exposures and cancer. Due to the decades-long latency of lung cancer, estimating exposures occurring over decades is sometimes necessary to accurately calculate risk. Residential histories with sufficient spatial and temporal detail can provide important data for long-term exposure assessment, but these are often hindered by participants’ difficulty with recall of particular details, especially full street addresses including house or building number. Without such precise locational data, it is much more difficult to accurately assess potential exposures among research participants, since most geocoding algorithms will assign individuals to locations, such as ZIP code or county centroids, that might be miles from their actual residence, especially in rural regions. The use of address data from LexisNexis, Inc., a commercial provider of data services, has previously demonstrated promise for robust analysis of cancer risk in relation to residential history. Jacquez and colleagues conducted an analysis of bladder cancer in coordination with the Michigan Cancer Registry, and Hurley and colleagues recently completed a similar analysis of breast cancer using data from the California Teachers Study (CTS) (4, 5). Both studies demonstrated that address data from LexisNexis provided information that further enhanced the residential history survey data already collected, but also presented some notable challenges, including duplicate records and invalid dates of residence. Automated sources such as these have inherent flaws, like surveys, but appear to contribute key information for exposure assessment in environmental epidemiologic studies of cancer. Still, these innovative techniques have not yet been employed in Central Appalachia, a rural region with some of the highest rates of cancer in the U.S. The ultimate goal of our research is to understand the effect of environmental exposures on risk for cancer in Kentucky and Central Appalachia. “Mountaintop removal” mining has been practiced in the region since the 1990s, and is concerning in terms of potential environmental exposures because it requires displacement of large quantities of rock and soil “overburden” to reach thin seams of coal. We will examine the risk of lung cancer in relation to duration of residence in proximity to such mining sites by leveraging survey data from a previous case-control study of lung cancer in the Central Appalachian region and augmenting it with LexisNexis data. Additionally, we will conduct a case-control study of cancers potentially related to Superfund sites using Kentucky Cancer Registry (KCR) data linked to LexisNexis data. We hypothesize that personyears of residential proximity to both sites is positively associated with cancer risk.