Most households and workplaces all over the world possess furnishings and electronics, all of which contain potentially toxic flame retardant chemicals to prevent fire hazards. Indoor dust is a recognized repository of these types of chemicals including polybrominated diphenyl ethers (PBDEs) and non-polybrominated diphenyl ethers (non-PBDEs). However, no previous U.S. studies have differentiated concentrations from elevated surface dust (ESD) and floor dust (FD) within and across microenvironments. We address this information gap by measuring twenty-two flame-retardant chemicals in dust on elevated surfaces (ESD; n=10) and floors (FD; n=10) from rooms on a California campus that contain various concentrations of electronic products. We hypothesized a difference in chemical concentrations in ESD and FD. Secondarily, we examined whether or not this difference persisted: (a) across the studied microenvironments and (b) in rooms with various concentrations of electronics. A Wilcoxon signed-rank test demonstrated that the ESD was statistically significantly higher than FD for BDE-47 (p=0.01), BDE-99 (p=0.01), BDE-100 (p=0.01), BDE-153 (p=0.02), BDE-154 (p=0.02), and 3 non-PBDEs including EH-TBB (p=0.02), BEH-TEBP (p=0.05), and TDCIPP (p=0.03). These results suggest different levels and kinds of exposures to flame-retardant chemicals for individuals spending time in the sampled locations depending on the position of accumulated dust. Therefore, further research is needed to estimate human exposure to flame retardant chemicals based on how much time and where in the room individuals spend their time. Such sub-location estimates will likely differ from assessments that assume continuous unidimensional exposure, with implications for improved understanding of potential health impacts of flame retardant chemicals.

Background Spatial variability of COVID-19 cases may suggest geographic disparities of social determinants of health. Spatial analyses of population-level data may provide insight on factors that may contribute to COVID-19 transmission, hospitalization, and death. Methods Generalized additive models were used to map COVID-19 risk from March 2020 to February 2021 in Orange County (OC), California. We geocoded and analyzed 221,843 cases to OC census tracts within a Poisson framework while smoothing over census tract centroids. Location was randomly permuted 1000 times to test for randomness. We also separated the analyses temporally to observe if risk changed over time. COVID-19 cases, hospitalizations, and deaths were mapped across OC while adjusting for population-level demographic data in crude and adjusted models. Results Risk for COVID-19 cases, hospitalizations, and deaths were statistically significant in northern OC. Adjustment for demographic data substantially decreased spatial risk, but areas remained statistically significant. Inclusion of location within our models considerably decreased the magnitude of risk compared to univariate models. However, percent minority (adjusted RR: 1.06, 95%CI: 1.06, 1.07), average household size (aRR: 1.06, 95%CI: 1.05, 1.07), and percent service industry (aRR: 1.05, 95%CI: 1.04, 1.06) remained significantly associated with COVID-19 risk in adjusted spatial models. In addition, areas of risk did not change between surges and risk ratios were similar for hospitalizations and deaths. Conclusion Significant risk factors and areas of increased risk were identified in OC in our adjusted models and suggests that social and environmental factors contribute to the spread of COVID-19 within communities. Areas in north OC remained significant despite adjustment, but risk substantially decreased. Additional investigation of risk factors may provide insight on how to protect vulnerable populations in future infectious disease outbreaks.

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