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Data literacy is a growing area of focus across multiple disciplines in higher education. The dominant forms of introduction focus on computational toolchains and statistical ways of knowing. As data driven decision-making becomes more central to democratic processes, a larger group of learners must be engaged in order to ensure they have a seat at the table in civic settings. This requires a rethinking to support many paths into data literacy. In this paper we introduce "data theatre," a set of activities designed for data novices that may have limited experience or comfort with spreadsheets, math, and other quantitative operations. Through iterative co-design over three workshops, we tested and produced two activity guides for educators, building on long-standing practices in participatory theatre that center social justice and liberation. Our initial findings provide very early evidence that this approach can help these learners overcome hesitations to working with information, begin building a critical perspective when viewing data, and create emotionally impactful data stories told through theatrical performance. This prototype work suggests to us that the concept of "Data theatre" warrants further study to build a more robust understanding of its affordances and limitations.

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.

BackgroundEmerging evidence about the effects of endocrine disruptors on asthma symptoms suggests new opportunities to reduce asthma by changing personal environments. Right-to-know ethics supports returning personal results for these chemicals to participants, so they can make decisions to reduce exposures. Yet researchers and institutional review boards have been reluctant to approve results reports in low-income communities, which are disproportionately affected by asthma. Concerns include limited literacy, lack of resources to reduce exposures, co-occurring stressors, and lack of models for effective reporting. To better understand the ethical and public health implications of returning personal results in low-income communities, we investigated parents' experiences of learning their children's environmental chemical and biomonitoring results in the Green Housing Study of asthma.MethodsThe Green Housing Study measured indoor chemical exposures, allergens, and children's asthma symptoms in "green"-renovated public housing and control sites in metro-Boston and Cincinnati in 2011-2013. We developed reports for parents of children in the study, including results for their child and community. We observed community meetings where results were reported, and metro-Boston residents participated in semi-structured interviews in 2015 about their report-back experience. Interviews were systematically coded and analyzed.ResultsReport-back was positively received, contributed to greater understanding, built trust between researchers and participants, and facilitated action to improve health. Sampling visits and community meetings also contributed to creating a positive study experience for participants. Participants were able to make changes in their homes, such as altering product use and habits that may reduce asthma symptoms, though some faced roadblocks from family members. Participants also gained access to medical resources, though some felt that clinicians were not responsive. Participants wanted larger scale change from government or industry and wanted researchers to leverage study results to achieve change.ConclusionsReport-back on environmental chemical exposures in low-income communities can enhance research benefits by engaging residents with personally relevant information that informs and motivates actions to reduce exposure to asthma triggers. Ethical practices in research should support deliberative report-back in vulnerable communities.

Phosphate flame retardants (PFRs) are abundant and found at the highest concentrations relative to other flame retardant chemicals in house dust; however, little is known about the biological levels of PFRs and their relationship with house dust concentrations. These relationships provide insight into major exposure pathways and potential health risks. We analyzed urine samples from 16 California residents in 2011 for 6 chlorinated and nonchlorinated dialkyl or diaryl phosphates (DAPs), the expected major metabolites of the most prominent PFRs, and qualitatively screened for 18 other metabolites predicted from in vitro studies. We detected all 6 DAPs within the range of previously reported levels, although very few comparisons are available. We found weakly positive nonsignificant correlations between urine and dust concentrations and maxima urine corresponding to maxima dust for the pairs bis(1,3-dichloro-2-propyl) phosphate (BDCIPP)-tris(1,3-dichloro-isopropyl) phosphate (TDCIPP) and bis(2-chloroethyl) phosphate (BCEP)-tris(2-chloroethyl) phosphate (TCEP). Metabolite levels of PFRs were correlated for many PFR combinations, suggesting they commonly co-occur. As far as we know, this is the first study to measure these 6 DAP metabolites simultaneously and to detect other PFR metabolites in US urine samples. We recommend biomonitoring studies include these 6 DAPs as well as several additional compounds detected through qualitative screening and previous ADME studies. PFRs represent a class of poorly studied commercial chemicals with widespread exposure and raise concerns for health effects including carcinogenicity and neurotoxicity.

Interest in the health effects of potential endocrine-disrupting compounds (EDCs) that are high production volume chemicals used in consumer products has made exposure assessment and source identification a priority. We collected paired indoor and outdoor air samples in 40 nonsmoking homes in urban, industrial Richmond, CA, and 10 in rural Bolinas, CA. Samples were analyzed by GC-MS for 104 analytes, including phthalates (11), alkylphenols (3), parabens (3), polybrominated diphenyl ether (PBDE) flame retardants (3), polychlorinated biphenyls (PCBs) (3), polycyclic aromatic hydrocarbons (PAHs) (24), pesticides (38), and phenolic compounds (19). We detected 39 analytes in outdoor air and 63 in indoor air. For many of the phenolic compounds, alkylphenols, phthalates, and PBDEs, these represent some of the first outdoor measures and the first analysis of the relative importance of indoor and outdoor sources in paired samples. Data demonstrate higher indoor concentrations for 32 analytes, suggesting primarily indoor sources, as compared with only 2 that were higher outdoors. Outdoor air concentrations were higher in Richmond than Bolinas for 3 phthalates, 10 PAHs, and o-phenylphenol, while indoor air levels were more similar between communities, except that differences observed outdoors were also seen indoors. Indoor concentrations of the most ubiquitous chemicals were generally correlated with each other (4-t-butylphenol, o-phenylphenol, nonylphenol, several phthalates, and methyl phenanthrenes; Kendall correlation coefficients 0.2-0.6, p

Background: Sharing research data uses resources effectively; enables large, diverse data sets; and supports rigor and reproducibility. However, sharing such data increases privacy risks for participants who may be re-identified by linking study data to outside data sets. These risks have been investigated for genetic and medical records but rarely for environmental data. Objectives: We evaluated how data in environmental health (EH) studies may be vulnerable to linkage and we investigated, in a case study, whether environmental measurements could contribute to inferring latent categories (e.g., geographic location), which increases privacy risks. Methods: We identified 12 prominent EH studies, reviewed the data types collected, and evaluated the availability of outside data sets that overlap with study data. With data from the Household Exposure Study in California and Massachusetts and the Green Housing Study in Boston, Massachusetts, and Cincinnati, Ohio, we used k-means clustering and principal component analysis to investigate whether participants' region of residence could be inferred from measurements of chemicals in household air and dust. Results: All 12 studies included at least two of five data types that overlap with outside data sets: geographic location (9 studies), medical data (9 studies), occupation (10 studies), housing characteristics (10 studies), and genetic data (7 studies). In our cluster analysis, participants' region of residence could be inferred with 80%-98% accuracy using environmental measurements with original laboratory reporting limits. Discussion: EH studies frequently include data that are vulnerable to linkage with voter lists, tax and real estate data, professional licensing lists, and ancestry websites, and exposure measurements may be used to identify subgroup membership, increasing likelihood of linkage. Thus, unsupervised sharing of EH research data potentially raises substantial privacy risks. Empirical research can help characterize risks and evaluate technical solutions. Our findings reinforce the need for legal and policy protections to shield participants from potential harms of re-identification from data sharing. https://doi.org/10.1289/EHP4817, Introduction The trade-off between sharing personal data and the risks to privacy has become an everyday concern for consumers as social networks, search engines, ride-sharing apps, credit cards, and smart [...]

Researchers are increasingly asked to share research data as part of publication and funding processes and to maximize the benefits of publicly funded research. The Safe Harbor provision of the U.S. Health Information Portability and Accountability Act (HIPAA) offers guidance to researchers by prescribing how to redact data for public sharing. For example, the provision requires removing explicit identifiers (such as name, address and other personally identifiable information), reporting dates in years, and reducing some or all digits of a postal (or ZIP) code. Is this sufficient? Can research participants still be re-identified in research data that adhere to the HIPAA Safe Harbor standard? In 2006, researchers collected air and dust samples and interviewed residents of 50 homes from Bolinas and Richmond (Atchison Village and Liberty Village), California, to analyze the residents' exposure to pollutants. The study, known as the Northern California Household Exposure Study [1], led to publications that have been cited hundreds of times. We conducted experiments with separate "attacker" and "scorer" teams to see whether we could identify study participants from two versions of the data redacted beyond the HIPAA standard, one in which all dates were reported in ranges of 10 or 20 years and another in which a study participant's birth year was reported exactly. The attackers were blinded to the names and addresses of the participants, and the scorers were blinded to the strategy.