The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, has been rapidly increasing in the United States. Boroughs of New York City, including Queens county, turn out to be the epicenters of this infection. According to the data provided by the New York State Department of Health, most of the cases of new COVID-19 infections in New York City have been found in the Queens county where 42,023 people have tested positive, and 3221 people have died as of 20 April 2020. Person-to-person transmission and travels were implicated in the initial spread of the outbreaks, but factors related to the late phase of rapidly spreading outbreaks in March and April are still uncertain. A few previous studies have explored the links between air pollution and COVID-19 infections, but more data is needed to understand the effects of short-term exposures of air pollutants and meteorological factors on the spread of COVID-19 infections, particularly in the U.S. disease epicenters. In this study, we have focused on ozone and PM_2.5, two major air pollutants in New York City, which were previously found to be associated with respiratory viral infections. The aim of our regression modeling was to explore the associations among ozone, PM_2.5, daily meteorological variables (wind speed, temperature, relative humidity, absolute humidity, cloud percentages, and precipitation levels), and COVID-19 confirmed new cases and new deaths in Queens county, New York during March and April 2020. The results from these analyses showed that daily average temperature, daily maximum eight-hour ozone concentration, average relative humidity, and cloud percentages were significantly and positively associated with new confirmed cases related to COVID-19; none of these variables showed significant associations with new deaths related to COVID-19. The findings indicate that short-term exposures to ozone and other meteorological factors can influence COVID-19 transmission and initiation of the disease, but disease aggravation and mortality depend on other factors.

The Permanente Journal is a peer-reviewed journal of medical science, social science in medicine, and medical humanities published quarterly by The Permanente Press.

Wastewater workers are exposed to different occupational hazards such as chemicals, gases, viruses, and bacteria. Personal protective equipment (PPE) is a significant factor that can reduce or decrease the probability of an accident from hazardous exposures to chemicals and microbial contaminants. The purpose of this study was to examine wastewater worker’s beliefs and practices on wearing PPE through the integration of the Health Belief Model (HBM), identify the impact that management has on wastewater workers wearing PPE, and determine the predictors of PPE compliance among workers in the wastewater industry. Data was collected from 272 wastewater workers located at 33 wastewater facilities across the southeast region of the United States. Descriptive statistical analysis was conducted to present frequency distributions of participants’ knowledge and compliance with wearing PPE. Univariate and multiple linear regression models were applied to determine the association of predictors of interest with PPE compliance. Wastewater workers were knowledgeable of occupational exposures and PPE requirements at their facility. Positive predictors of PPE compliance were perceived susceptibility and perceived severity of contracting an occupational illness (p < 0.05). A negative association was identified between managers setting the example of wearing PPE sometimes and PPE compliance (p < 0.05). Utilizing perceived susceptibility and severity for safety programs and interventions may improve PPE compliance among wastewater workers.

Background and Objectives: Public health workforce recruitment and retention continue to challenge public health agencies. This study aims to describe the trends in intention to leave and retire and analyze factors associated with intentions to leave and intentions to stay. Design: Using national-level data from the 2017 and 2021 Public Health Workforce Interests and Needs Surveys, bivariate analyses of intent to leave were conducted using a Rao-Scott adjusted chi-square and multivariate analysis using logistic regression models. Results: In 2021, 20% of employees planned to retire and 30% were considering leaving. In contrast, 23% of employees planned to retire and 28% considered leaving in 2017. The factors associated with intentions to leave included job dissatisfaction, with adjusted odds ratio (AOR) of 3.8 (95% CI, 3.52-4.22) for individuals who were very dissatisfied or dissatisfied. Odds of intending to leave were significantly high for employees with pay dissatisfaction (AOR = 1.83; 95% CI, 1.59-2.11), those younger than 36 years (AOR = 1.58; 95% CI, 1.44-1.73) or 65+ years of age (AOR = 2.80; 95% CI, 2.36-3.33), those with a graduate degree (AOR = 1.14; 95% CI, 1.03-1.26), those hired for COVID-19 response (AOR = 1.74; 95% CI, 1.49-2.03), and for the BIPOC (Black, Indigenous, and people of color) (vs White) staff (AOR = 1.07; 95% CI, 1.01-1.15). The leading reasons for employees' intention to stay included benefits such as retirement, job stability, flexibility (eg, flex hours/telework), and satisfaction with one's supervisor. Conclusions: Given the cost of employee recruitment, training, and retention of competent employees, government public health agencies need to address factors such as job satisfaction, job skill development, and other predictors of employee retention and turnover. Implications: Public health agencies may consider activities for improving retention by prioritizing improvements in the work environment, job and pay satisfaction, and understanding the needs of subgroups of employees such as those in younger and older age groups, those with cultural differences, and those with skills that are highly sought-after by other industries.