here is a call to improve disparities in diversity and equity in patient recruitment for dermatologic research. Charrow et al 1 characterized diversity in dermatologic randomized clinical trials (RCTs) published from 2010 to 2015 and found a low level of reporting for race and ethnicity data as well as low Hispanic representation. 1 Notable efforts to support diversity in RCTs have been made by the US Food and Drug Administration including an action plan in 2014 to support industry efforts at improving diversity in clinical trials and publishing concise information about who participated in clinical trials. 2 However, limited data are available to assess whether these efforts have been associated with increased diversity in dermatology research cohorts with respect to sex and race and ethnicity. To follow up on potential out-comes of these diversity efforts, we conducted a systematic review to reassess representation of racial and ethnic minority groups and women in RCTs published from 2015-2020 pertaining to dermatologic conditions that are well-studied, common, and affect patients within varied demographic categories. Methods Literature Search Strategy and Selection CriteriaA systematic literature review was conducted through a PubMed database search for all peer-reviewed Englishlanguage RCTs published between July 1, 2015, and July 1, IMPORTANCE Although there have been increased efforts in dermatologic research to improve representation of patient sex, race, and ethnicity, there are limited data evaluating resulting changes.OBJECTIVE To characterize the diversity of participants in dermatologic clinical trials conducted in the US published from 2015 to 2020 pertaining to common dermatologic conditions affecting all patient demographic categories compared with findings from 2010-2015.EVIDENCE REVIEW A systematic literature review through the PubMed database was conducted for randomized clinical trials published between July 1, 2015, and July 1, 2020, using keywords alopecia areata, acne, atopic dermatitis, lichen planus, psoriasis, seborrheic dermatitis, and vitiligo. Data collected included distribution of participant demographic characteristics, funding source, and journal type. Reflecting US Census data, studies were defined as unrepresentative of race and ethnicity if they included less than 20% ethnically or racially diverse participants or unrepresentative of sex if they included less than 45% women. Python was used for statistical analysis by χ 2 tests or Fisher exact tests.FINDINGS A total of 392 randomized clinical trials were included. In comparison with the period from 2010-2015, the reporting rate for race and ethnicity in US studies has increased from 59.8% to 71.9% (P = .05). However, the proportion of reporting articles including at least 20% non-White representation remains unchanged at 38.1% with 37 of 97 reporting randomized clinical trials in 2010-2015 and 53 of 139 reporting randomized clinical trials in 2015-2020 (P = .99). Psoriasis studies included the least diversity, with 12.1% of stud...

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the “Community-Workplace” model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and physical distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the “Community-Workplace” model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and social distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue.Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.

No abstract