Turnover, COVID-19, and Reasons for Leaving and Staying Within Governmental Public Health.

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.

Elevated COVID-19 Case Rates of Government Employees, District of Columbia, 2020-2022.

OBJECTIVES: To estimate the rate ratio (RR) of reported Coronavirus Disease 2019 (COVID-19) cases among governmental employees from seven District of Columbia (D.C.) departments from March 2020 to February 2022. METHODS: Poisson regression models were used to estimate the RR by department, using D.C. residents as the reference and the person-day as the offset. The COVID-19 surveillance data and the full-time equivalent hours for each department were obtained from the D.C. governmental websites. RESULTS: Five of the seven departments had statistically significant higher COVID-19 case rates than D.C. residents. Stratified by four pandemic stages, RR of Fire and Emergency Medical Services (FEMS), Office of Unified Communication (OUC), and Metropolitan Police Department (MPD) were consistently >1: FEMS: 3.34 (95% confidence interval, CI [2.94, 3.77]), 2.39 (95% CI [2.06, 2.75]), 2.48 (95% CI [2.06, 2.95]), and 3.90 (95% CI [3.56, 4.26]), respectively; OUC: 1.47 (95% CI [0.92, 2.18]), 2.72 (95% CI [1.93, 3.69]), 1.85 (95% CI [1.09, 2.92]), and 2.18 (95% CI [1.62, 2.85]), respectively; and MPD: 2.33 (95% CI [2.11, 2.58]), 1.96 (95% CI [1.75, 2.18]), 1.52 (95% CI [1.29, 1.77]), and 1.76 (95% CI [1.60, 1.92]), respectively. CONCLUSIONS: The results suggested higher case rates for emergency responders and frontline personnel than for general population in D.C.

Short-term effects of ambient ozone, PM2.5, and meteorological factors on COVID-19 confirmed cases and deaths in queens, New York

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 PM2.5, 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, PM2.5, 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.

Lag Effects of Ozone, PM2.5, and Meteorological Factors on COVID-19 New Cases at the Disease Epicenter in Queens, New York

The influences of environmental factors on COVID-19 may not be immediate and could be lagged for days to weeks. This study investigated the choice of lag days for calculating cumulative lag effects of ozone, PM2.5, and five meteorological factors (wind speed, temperature, relative humidity, absolute humidity, and cloud percentages) on COVID-19 new cases at the epicenter of Queens County, New York, before the governor’s executive order on wearing of masks in public places (1 March to 11 April 2020). Daily data for selected air pollutants and meteorological factors were collected from the US EPA Air Quality System, weather observation station of the NOAA National Centers for Environmental Information at John F. Kennedy Airport, and World Weather Online. Negative binomial regression models were applied, including the autocorrelations and trend of the time series, as well as the effective reproductive number as confounders. The effects of ozone, PM2.5, and five meteorological factors were significant on COVID-19 new cases with lag9-lag13 days. Incidence rate ratios (IRRs) were consistent for any lag day choice between lag0 and lag14 days and started fluctuating after lag15 days. Considering moving averages >14 days yielded less reliable variables for summarizing the cumulative lag effects of environmental factors on COVID-19 new cases and considering lag days from 9 to 13 would yield significant findings. Future studies should consider this approach of lag day checks concerning the modeling of COVID-19 progression in relation to meteorological factors and ambient air pollutants.