LAUSR

Humans are exposed to myriad chemicals simultaneously. Based on their sources, many of these chemicals co-occur, leading to high correlations between certain chemical concentrations. For example, individuals who live in urban areas may have higher exposures to air pollutants relative to residents of rural areas, and those who eat fish and red meat may have elevated levels of PFAS and mercury relative to those who eat these foods less frequently.2–4 Identifying behavioral patterns that reflect unique groups of multipollutant exposures may be useful as an intervention aimed at exposure reduction because regulations and policy changes to remove specific chemicals from commence take years. Ascertaining upstream sources of chemical exposures is also useful for risk assessment because this may allow us to identify the most vulnerable populations. To date, the vast majority of environmental epidemiology studies have assessed the effects of single chemicals one at a time, and regulatory guidelines set by the U.S. Environmental Protection Agency generally focus on a few specific chemicals, as opposed to considering chemical classes. A problem with these approaches is that they fail to consider coexposure to other pollutants, which may produce additive or synergistic health effects. To account for highly correlated coexposures, environmental epidemiologists have developed environmental mixture methods,7–9 which allow us to estimate the impact of exposure to a group of pollutants on adverse health outcomes. Although exposure assessment and mixture methods have provided novel insights, challenges remain. Data obtained from laboratory-based exposure assessment often include observations below the limit of detection (LOD), leading to incomplete and missing data, which pose a challenge from a statistical standpoint because researchers are left with little to no information regarding the concentration. Given that the actual value lies somewhere between 0 and just below the LOD and that most current mixture methods cannot account for missing data, researchers commonly impute values below the LOD with LOD= ffiffiffi