Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression
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Purpose of Review
The inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the “bad actors” most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures.
One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information.
In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.
KeywordsBayesian profile regression Multi-pollutant modeling Health effects Susceptible subpopulations Health policy
Compliance with Ethical Standards
Conflict of Interest
Eric Coker reports grants from Health Resources and Services Administration (HRSA) and grants from Fogarty International Center, Global Health Equity Scholar. John Molitor, Silvia Liverani, and Jason G. Su declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
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