Journal:Wrangling environmental exposure data: Guidance for getting the best information from your laboratory measurements
| Full article title | Wrangling environmental exposure data: Guidance for getting the best information from your laboratory measurements |
|---|---|
| Journal | Environmental Health |
| Author(s) | Udesky, Julia O.; Dodson, Robin E.; Perovich, Laura J.; Rudel, Ruthann A. |
| Author affiliation(s) | Silent Spring Institute |
| Primary contact | Email: Use journal website to contact |
| Year published | 2019 |
| Volume and issue | 18 |
| Article # | 99 |
| DOI | 10.1186/s12940-019-0537-8 |
| ISSN | 1476-069X |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://ehjournal.biomedcentral.com/articles/10.1186/s12940-019-0537-8 |
| Download | https://ehjournal.biomedcentral.com/track/pdf/10.1186/s12940-019-0537-8 (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Background: Environmental health and exposure researchers can improve the quality and interpretation of their chemical measurement data, avoid spurious results, and improve analytical protocols for new chemicals by closely examining lab and field quality control (QC) data. Reporting QC data along with chemical measurements in biological and environmental samples allows readers to evaluate data quality and appropriate uses of the data (e.g., for comparison to other exposure studies, association with health outcomes, use in regulatory decision-making). However many studies do not adequately describe or interpret QC assessments in publications, leaving readers uncertain about the level of confidence in the reported data. One potential barrier to both QC implementation and reporting is that guidance on how to integrate and interpret QC assessments is often fragmented and difficult to find, with no centralized repository or summary. In addition, existing documents are typically written for regulatory scientists rather than environmental health researchers, who may have little or no experience in analytical chemistry.
Objectives: We discuss approaches for implementing quality assurance/quality control (QA/QC) in environmental exposure measurement projects and describe our process for interpreting QC results and drawing conclusions about data validity.
Discussion: Our methods build upon existing guidance and years of practical experience collecting exposure data and analyzing it in collaboration with contract and university laboratories, as well as the Centers for Disease Control and Prevention. With real examples from our data, we demonstrate problems that would not have come to light had we not engaged with our QC data and incorporated field QC samples in our study design. Our approach focuses on descriptive analyses and data visualizations that have been compatible with diverse exposure studies, with sample sizes ranging from tens to hundreds of samples. Future work could incorporate additional statistically grounded methods for larger datasets with more QC samples.
Conclusions: This guidance, along with example table shells, graphics, and some sample R code, provides a useful set of tools for getting the best information from valuable environmental exposure datasets and enabling valid comparison and synthesis of exposure data across studies.
Keywords: exposure science, environmental epidemiology, environmental chemicals, environmental monitoring, quality assurance/quality control (QA/QC), data validation, exposure measurement, measurement error
References
Notes
This presentation is faithful to the original, with only a few minor changes to presentation, spelling, and grammar. We also added PMCID and DOI when they were missing from the original reference.
