Alexander Keil, Ph.D., M.S.P.H.
NCI Shady Grove | Room 6E614
Dr. Alex Keil joined DCEG as an Earl Stadtman Investigator in the Occupational and Environmental Epidemiology Branch (OEEB) in May 2022. He earned an M.S.P.H. in epidemiology in 2010 and a Ph.D. in epidemiology in 2014, both from the University of North Carolina (UNC) at Chapel Hill. Prior to joining DCEG, Dr. Keil served at UNC as a postdoctoral fellow until 2016, then as a Research Assistant Professor until May 2022. He has received numerous awards for his work, including the EPICOH Young Investigator Award, a Rising Star award from the UNC Center for Environmental Health and Susceptibility, and a Paper of the Year award from the American Journal of Epidemiology.
Dr. Keil's research is focused on improving the link between epidemiologic studies and policy choices through the application of state-of-the art statistical methods and theory. His primary interest is in providing a rational background for policies on occupational and environmental exposures that takes into account exposure across the life course and the complex interactions between exposures in bringing about changes in health. Dr. Keil’s program of research includes methods to deal with bias in occupational studies, such as healthy worker survivor bias, and methodological approaches to estimating human health effects of exposure mixtures.
Healthy Worker Survivor Bias
Especially in manual trades, workers who can tolerate difficult work environments or irritants in workplaces can stay longer in jobs than other workers, which can lead to accumulation of more exposure over time for these workers. Paradoxically, when we study data from those worksites, these healthy, higher-exposed workers may appear to be at lower risk of diseases, including cancer, than lower-exposed workers. These data can severely understate the harm caused by workplace exposure—a phenomenon known as healthy worker survivor bias. This bias in data analysis can ultimately lead to workplace exposure standards that fail to protect workers.
Using data from workers in an array of industries with exposure to arsenic, radon, silica, and acrylonitrile, Dr. Keil is leading an effort to apply and expand modern causal inference approaches to estimate the effects of workplace exposure standards while reducing or controlling healthy worker survivor bias. He is also conducting work to develop methods and tools that enable occupational health researchers to evaluate, easily and quickly, potential impacts of new occupational exposure standards.
Health Effects of Exposure Mixtures
All exposures occur in a mixture of other exposures. For example, arsenic exposures in well water nearly always coincide with exposure to other metals, metalloids, and non-metallic contaminants, each of which may or may not confer some risk. The inextricability of individual exposures within such a mixture makes it difficult for researchers to identify bad actors within a mixture. More crucially, it is difficult to predict the human health impact of interventions on exposures within a mixture; it is difficult, after all, to filter out arsenic from water without also removing essential elements like calcium.
Using exposure mixtures from air, water, and biomarkers, Dr. Keil’s research program has a strong focus on developing methodologies that tie the remarkable statistical and computational advances from recent years to the equally remarkable advances in causal inference. This confluence of fields provides a rich set of tools to identify bad actors within a mixture as well as to study the potential effects of implementing interventions that act on exposure mixtures. Dr. Keil’s work in this area is leading to the development of new tools and new insights around how best to use data on exposure mixtures to drive improvements in public health.
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