Background. The GuLF STUDY is a cohort study investigating the health of workers who responded to the Deepwater Horizon oil spill in the Gulf of Mexico in 2010. Objectives. To develop an ordinal job-exposure matrix (JEM) of airborne total hydrocarbons (THC), dispersants and particulates to estimate study participants’ exposures. Methods. Information was collected on participants’ spill-related tasks. A JEM of exposure groups (EGs) was developed from tasks and THC air measurements taken during and after the spill, using relevant exposure determinants. THC arithmetic means were developed for the EG, assigned an ordinal value, and linked to the participants using determinants from the questionnaire. Different approaches were taken for combining exposures across EGs. EGs for dispersants and particulates were based on questionnaire responses. Results. Considerable differences in THC exposure levels were found among EGs. Based on the maximum THC level participants experienced across any job held, about 14% of the subjects were identified in the highest exposure category. Approximately 10% of the cohort was exposed to dispersants or particulates. Conclusions. Considerable differences were found across exposures of the various EGs, which facilitates investigation of exposure-response relationships. The JEM is flexible to allow for different assumptions about several possibly relevant exposure metrics.

Objective: We investigated whether patterns of work during COVID-19 pandemic altered by effort to contain the outbreak affected anxiety and depression. Methods: We conducted a cross-sectional online survey of 911 residents of Philadelphia, inquiring about their working lives during early months of the epidemic, symptoms of anxiety and depression, plus demographics, perceived sources of support, and general health. Results: Occupational contact with suspected COVID-19 cases was associated with anxiety. Concerns about return to work, childcare, lack of sick leave, and loss/reduction in work correlated with anxiety and depression, even when there was no evidence of occupational contact with infected persons; patterns differed by sex. Conclusions: Heightened anxiety and depression during COVID-19 pandemic can be due to widespread disruption of working lives, especially in “non-essential” low-income industries, on par with experience in healthcare.

Classical statistical methods for analyzing exposure data with values below the detection limits are well described in the occupational hygiene literature, but an evaluation of a Bayesian approach for handling such data is currently lacking. Here, we first describe a Bayesian framework for analyzing censored data. We then present the results of a simulation study conducted to compare the β-substitution method with a Bayesian method for exposure datasets drawn from lognormal distributions and mixed lognormal distributions with varying sample sizes, geometric standard deviations (GSDs), and censoring for single and multiple limits of detection. For each set of factors, estimates for the arithmetic mean (AM), geometric mean, GSD, and the 95th percentile (X 0.95 ) of the exposure distribution were obtained. We evaluated the performance of each method using relative bias, the root mean squared error (rMSE), and coverage (the proportion of the computed 95% uncertainty intervals containing the true value). The Bayesian method using non-informative priors and the β-substitution method were generally comparable in bias and rMSE when estimating the AM and GM. For the GSD and the 95th percentile, the Bayesian method with non-informative priors was more biased and had a higher rMSE than the β-substitution method, but use of more informative priors generally improved the Bayesian method's performance, making both the bias and the rMSE more comparable to the β-substitution method. An advantage of the Bayesian method is that it provided estimates of uncertainty for these parameters of interest and good coverage, whereas the β-substitution method only provided estimates of uncertainty for the AM, and coverage was not as consistent. Selection of one or the other method depends on the needs of the practitioner, the availability of prior information, and the distribution characteristics of the • 56Ann. Occup. Hyg., 2016, Vol. 60, No. 1, 56-73 doi:10.1093/annhyg/mev049 Advance Access publication 24 July 2015 measurement data. We suggest the use of Bayesian methods if the practitioner has the computational resources and prior information, as the method would generally provide accurate estimates and also provides the distributions of all of the parameters, which could be useful for making decisions in some applications.

Objective This qualitative study explored factors that influence health and safety practices among Vietnamese nail salon technicians and owners. Methods We conducted semi‐structured focus group discussions and individual interviews with a sample of 17 Vietnamese nail salon technicians and owners in the Philadelphia metropolitan area, Pennsylvania. Results Analysis of transcripts revealed perceived health benefits/concerns, knowledge about work‐related hazards, salon's management and policies attributed to owners, client influence, external policies/regulations, and protective equipment‐specific challenges were among factors affecting workplace health promotion practices at the salons. Conclusions The study highlighted a complex interplay among the various stakeholders including nail technicians, owners, clients, policy makers, and enforcers. Interventions addressing barriers at the personal and organizational levels, as well as public policy change and enforcement are needed to create sustainable behavioral and organizational change in nail salons.

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