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Reporting Individualized Results on Environmental Contaminants to Study Participants

This two-phase Silent Spring Institute project explores how to present data to people in an engaging manner suited to varied interests and levels of scientific knowledge. The goal of the first phase is to design static paper data packets to report individualized findings to research study participants.

All people are exposed to chemicals present in the air and dust in homes. Exposure scientists often measure concentrations of these chemicals, but rarely complete the time consuming process of reporting individualized results to study participants. In addition to the substantial resource barrier, the exact sources and health effects of these emerging contaminants are not well-established, so it can be difficult to present this information to people in a way that is accurate, meaningful, and empowering. Yet providing this information to individuals benefits both scientists and communities.


We designed information packets including high quality individualized graphics and supporting materials for each study participant on the chemicals found in their homes and communities. The plots provided comprehensive information and put the data in the context of other research. Data packets were pilot tested and designed to be easy to read, convenient, and self-contained while also providing further information and resources to interested individuals. Participants were interviewed upon completion of the study to illicit report-back experiences and technical misunderstandings.

The second phase of the project builds from this work to create a digital report-back tool that increases the flexibility of the report-back experience.

Role: Collaborating with experts in toxicology, sociology, psychology, and environmental science, design individualized plots and supporting materials for first project phase. Write code in R to manage, process, and analyze raw data from the lab in preparation for result reporting. Refine existing R code to create individualized participant graphs.