Turnover intention and associated factors among public health workers during the fight against COVID-19 in Guangdong Province

Objective To realize the current situation and influencing factors of turnover intention among public health workers fighting against COVID-19 in Guangdong Province, explore the moderating effect of social support, and provide evidence for improving the stability of epidemic prevention team. Methods A self-constructed online questionnaire was used to investigate the personnel of Centres for Disease Control and Prevention and primary health care institutes in Guangdong Province. Hierarchical regression analysis was conducted to examine the associated factors of turnover intention and the moderating role of social support. Results A total of 2 168 participants were collected, of which 632(29.15%) had turnover intention. Anti-epidemic public health workers with senior title, working in CDC, having a fixed establishment, sleeping ≥ 6 h, showing more job satisfaction and reporting higher leadership/colleague/relative support had lower turnover intention, while those working overnight and working overtime on rest days were more likely to report turnover intention. The interaction term "job satisfaction × family support" had a negative moderating effect on the relationship between job satisfaction and turnover intention. Conclusions A relatively high turnover intention is reported among public health workers during the fight against COVID-19 in Guangdong Province. Improving the incentive mechanism, increasing job satisfaction and providing more support to primary health workers may reduce their turnover intention. © 2022, Publication Centre of Anhui Medical University. All rights reserved.

[Epidemiological characteristics of local outbreak of COVID-19 caused by SARS-CoV-2 Delta variant in Liwan district, Guangzhou].

Objective: To analyze the epidemiological characteristics of a local outbreak of COVID-19 caused by SARS-CoV-2 B.1.617.2(Delta) variant in Liwan district, Guangzhou, and provide evidence for the further prevention and control of the Delta variant of COVID-19. Methods: From May 21 to June 18, 2021, the incidence data of COVID-19 caused by Delta variant were obtained from National Notifiable Disease Report System of Chinese Disease Prevention and Control Information System and Liwan District Center for Disease Control and Prevention of Guangzhou.Frequency analysis (proportions), histograms, and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. The incubation period and time-varying reproduction numbers (Rt) estimations were used for the further analysis. Results: By June 18, 2021, a total of 127 COVID-19 cases caused by Delta variant was reported in Liwan district. The youngest case was aged 2 years and the oldest was aged 85 years. There were 18.9% (24/127) aged <18 years, 43.3% (55/127) aged 18-59 years, and 37.8% (48/127) aged ≥60 years, the male to female ratio of the cases was 1∶1.35 (54∶73). The cases were mainly retired people (32.3%, 41/127), the jobless or unemployed (18.1%, 23/127), and students (16.5%, 21/127). The infections mainly occurred in Baihedong (70.1%, 89/127) and Zhongnan street (23.6%, 30/127) communities in the southern area of Liwan district. The median incubation period of the Delta variant infection was 6 days (range: 1-15 days). The clinical classification were mainly common type (64.6%, 82/127). The basic reproduction number (R0) was 5.1, Rt which once increased to 7.3. The transmissions mainly occurred in confined spaces, such as home (26.8%), restaurant (29.1%), neighborhood (3.9%), and market (3.1%), the household clustering was predominant. Close contacts tracing (66.1%) and community screening (33.1%) were the main ways to find the infections. Conclusion: The COVID-19 outbreak caused by Delta variant in Liwan district of Guangzhou was highly contagious, with the obvious characteristics of household clustering and high proportions of cases in adults aged 18-59 years and elderly people aged ≥60 years.

[Estimating the distribution of COVID-19 incubation period by interval-censored data estimation method].

Objectives: The COVID-19 has been the public health issues of global concern, but the incubation period was still under discussion. This study aimed to estimate the incubation period distribution of COVID-19. Methods: The exposure and onset information of COVID-19 cases were collected from the official information platform of provincial or municipal health commissions. The distribution of COVID-19 incubation period was estimated based on the Log- normal, Gamma and Weibull distribution by interval-censored data estimation method. Results: A total of 109 confirmed cases were collected, with an average age of 39.825 years. The median COVID-19 incubation period based on Log-normal, Gamma, and Weibull distribution were 4.958 (P(25)-P(75): 3.472-7.318) days, 5.083 (P(25)-P(75): 3.511-7.314) days, and 5.695 (P(25)-P(75): 3.675-7.674) days, respectively. Gamma distribution had the largest log-likelihood result. Conclusions: The distribution of COVID-19 incubation period followed the Gamma distribution, and the interval-censored data estimation method can be used to estimate the incubation period distribution.

[Using Markov Chain Monte Carlo methods to estimate the age-specific case fatality rate of COVID-19].

Objectives: The COVID-19 epidemic has swept all over the world. Estimates of its case fatality rate were influenced by the existing confirmed cases and the time distribution of onset to death, and the conclusions were still unclear. This study was aimed to estimate the age-specific case fatality rate of COVID-19. Methods: Data on COVID-19 epidemic were collected from the National Health Commission and China CDC. The Gamma distribution was used to fit the time from onset to death. The Markov Chain Monte Carlo simulation was used to estimate age-specific case fatality rate. Results: The median time from onset to death of COVID-19 was M=13.77 (P(25)-P(75): 9.03-21.02) d. The overall case fatality rate of COVID-19 was 4.1% (95%CI: 3.7%-4.4%) and the age-specific case fatality rate were 0.1%, 0.4%, 0.4%, 0.4%,0.8%, 2.3%, 6.4%, 14.0 and 25.8% for 0-, 10-, 20-, 30-, 40-, 50-, 60-, 70- and ≥80 years group, respectively. Conclusions: The Markov Chain Monte Carlo simulation method adjusting censored is suitable for case fatality rate estimation during the epidemic of a new infectious disease. Early identification of the COVID-19 case fatality rate is helpful to the prevention and control of the epidemic.