You are here:
Identifying Robust Co-Occurrence Patterns in Personal Care Product Purchases
Citation:
Tornero-Velez, R., K. Isaacs, M. Nye, P. Price, AND T. Buckley. Identifying Robust Co-Occurrence Patterns in Personal Care Product Purchases. 2016 International Society of Exposure Science, Utrecht, NETHERLANDS, October 09 - 13, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Personal care products (PCPs) are used for beautification and personal hygiene, and because they are applied to the skin, hair, and mouth, they provide an efficient delivery vehicle for chemicals into our bodies. Although efforts have been made to enumerate the chemicals in individual PCPs and understand their health risks, little is known on the combined chemical exposures and risks that occur from the co-use of PCPs. To address this need, we employed association rules mining, a method with origins in market basket analysis, to assess patterns of co-occurrence in PCP purchases. We applied this method to an anonymized database of consumer product transactions for sixty thousand households collected over one year, provided by a major market research firm. PCP categories included hair care, oral hygiene, cosmetics, soap and bath products, fragrances, and toiletries. To identify co-occurring sets of products, we applied a mathematical technique, the Apriori algorithm, which finds nested combinations of increasing order as long as all satisfy a minimum ‘support’ (occurrence) threshold. We further examined robustness of co-occurrence patterns by demographic variables. Identifying assemblages of products is a prerequisite to assessing risks from the chemicals in multiple products. We demonstrate that this approach is an efficient framework to consider consumer product co-occurrence to inform chemical exposure and risk assessment. This abstract does not necessarily reflect U.S. EPA policy.
