Exposure to COVID-19 during the First and the Second Wave of the Pandemic and Coronavirus-Related PTSD Risk among University Students from Six Countries—A Repeated Cross-Sectional Study

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Abstract

This study aimed to reveal differences in exposure to coronavirus disease (COVID-19) during the first (W1) and the second (W2) waves of the pandemic in six countries among university students and to show the prevalence and associations between exposure to COVID-19 and coronavirus-related post-traumatic stress syndrome (PTSD) risk during W2. The repeated cross-sectional study was conducted among university students from Germany, Poland, Russia, Slovenia, Turkey, and Ukraine (W1: n = 1684; W2: n = 1741). Eight items measured exposure to COVID-19 (regarding COVID-19 symptoms, testing, hospitalizing quarantine, infected relatives, death of relatives, job loss, and worsening economic status due to the COVID-19 pandemic). Coronavirus-related PTSD risk was evaluated by PCL-S. The exposure to COVID-19 symptoms was higher during W2 than W1 among students from all countries, except Germany, where, in contrast, the increase in testing was the strongest. Students from Poland, Turkey, and the total sample were more frequently hospitalized for COVID-19 in W2. In these countries, and Ukraine, students were more often in quarantine. In all countries, participants were more exposed to infected friends/relatives and the loss of a family member due to COVID-19 in W2 than W1. The increase in job loss due to COVID-19 was only noted in Ukraine. Economic status during W2 only worsened in Poland and improved in Russia. This was due to the significant wave of restrictions in Russia and more stringent restrictions in Poland. The prevalence of coronavirus-related PTSD risk at three cutoff scores (25, 44, and 50) was 78.20%, 32.70%, and 23.10%, respectively. The prediction models for different severity of PTSD risk differed. Female gender, a prior diagnosis of depression, a loss of friends/relatives, job loss, and worsening economic status due to the COVID-19 were positively associated with high and very high coronavirus-related PTSD risk, while female gender, a prior PTSD diagnosis, experiencing COVID-19 symptoms, testing for COVID-19, having infected friends/relatives and worsening economic status were associated with moderate risk.

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G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

Franz Faul, Edgar Erdfelder, Albert-Georg Lang … (2007)

G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.