Effects of COVID-19 on Synaptic and Neuronal Degeneration.

Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.

The Impact of Three Communication Channels on the Dissemination of a Serious Game Designed to Enhance COVID-19 Prevention.

Infection prevention interventions can only be effective if they are both well known and easily accessible. A randomized controlled trial showed that a serious game, "Escape COVID-19", was significantly more effective at improving the intention of adopting adequate infection prevention behavior than regular guidelines among long-term care facility employees. However, less than a fifth of all potential participants were finally recruited in this study. To determine whether a specific communication intervention was more effective than another, we carried out a retrospective analysis of account creation data over a six-month period. During the first period (53 days), information about the serious game was disseminated by a part-time worker. The second period (15 days) corresponded to a press release, while the third period (15 days) reflected an official communication from the Swiss Federal Office of Public Health. A total of 3995 accounts were created during the study period. Most accounts were created by health care workers (2699/3995, 67.6%). Median daily account creation was highest during the press release period (25; Q1:Q3 9:172) and lowest during the official communication period (6; Q1:Q3 4:20). The association between communication intervention and account creation was statistically significant both when considering the overall population (p = 0.013) and when only analyzing health care workers (p = 0.036).

A Novel Multicolor-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases using Chest X-Ray Images.

OBJECTIVES: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause Acute Respiratory Distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures. METHODS: Nineteen separately colored layers were created from X-ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. In a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected unclear abnormalities varied slightly. RESULTS: The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infections compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing X-rays into five severity levels. CONCLUSION: By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic's short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic's aftereffects, utilising the notion of the bullwhip phenomenon's short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

Reconstruction of transmission chains of SARS-CoV-2 amidst multiple outbreaks in a geriatric acute-care hospital: a combined retrospective epidemiological and genomic study.

Background: There is ongoing uncertainty regarding transmission chains and the respective roles of healthcare workers (HCWs) and elderly patients in nosocomial outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in geriatric settings. Methods: We performed a retrospective cohort study including patients with nosocomial coronavirus disease 2019 (COVID-19) in four outbreak-affected wards, and all SARS-CoV-2 RT-PCR positive HCWs from a Swiss university-affiliated geriatric acute-care hospital that admitted both Covid-19 and non-Covid-19 patients during the first pandemic wave in Spring 2020. We combined epidemiological and genetic sequencing data using a Bayesian modelling framework, and reconstructed transmission dynamics of SARS-CoV-2 involving patients and HCWs, to determine who infected whom. We evaluated general transmission patterns according to case type (HCWs working in dedicated Covid-19 cohorting wards: HCWcovid; HCWs working in non-Covid-19 wards where outbreaks occurred: HCWoutbreak; patients with nosocomial Covid-19: patientnoso) by deriving the proportion of infections attributed to each case type across all posterior trees and comparing them to random expectations. Results: During the study period (1 March to 7 May 2020), we included 180 SARS-CoV-2 positive cases: 127 HCWs (91 HCWcovid, 36 HCWoutbreak) and 53 patients. The attack rates ranged from 10% to 19% for patients, and 21% for HCWs. We estimated that 16 importation events occurred with high confidence (4 patients, 12 HCWs) that jointly led to up to 41 secondary cases; in six additional cases (5 HCWs, 1 patient), importation was possible with a posterior probability between 10% and 50%. Most patient-to-patient transmission events involved patients having shared a ward (95.2%, 95% credible interval [CrI] 84.2%-100%), in contrast to those having shared a room (19.7%, 95% CrI 6.7%-33.3%). Transmission events tended to cluster by case type: patientnoso were almost twice as likely to be infected by other patientnoso than expected (observed:expected ratio 2.16, 95% CrI 1.17-4.20, p=0.006); similarly, HCWoutbreak were more than twice as likely to be infected by other HCWoutbreak than expected (2.72, 95% CrI 0.87-9.00, p=0.06). The proportion of infectors being HCWcovid was as expected as random. We found a trend towards a greater proportion of high transmitters (≥2 secondary cases) among HCWoutbreak than patientnoso in the late phases (28.6% vs. 11.8%) of the outbreak, although this was not statistically significant. Conclusions: Most importation events were linked to HCW. Unexpectedly, transmission between HCWcovid was more limited than transmission between patients and HCWoutbreak. This finding highlights gaps in infection control and suggests the possible areas of improvements to limit the extent of nosocomial transmission. Funding: This study was supported by a grant from the Swiss National Science Foundation under the NRP78 funding scheme (Grant no. 4078P0_198363).

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

BACKGROUND: Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. METHODS: We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. FINDINGS: The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]). INTERPRETATION: Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. FUNDING: Medical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Validation of ALOS/PALSAR subsurface penetration depth in arid regions based on field ground-penetrating radar measurements (preprint)

In the last few years, ALOS/PALSAR (L-band) (HH, HV, VH, and VV) images have been widely used due to their ability to penetrate the surface in certain conditions, such as low moisture or dry friable sandy soil. Images from the ALOS-1 sensor were used to delineate subsurface structures, and optical images such as Landsat-7 ETM + data were used to discriminate between scatterings from the Earth’s surface and subsurface materials. Thus, the Farafra Desert is an optimal geological environment for L-band microwave penetration, as its geology is characterized by friable sand sheets covering limestone (Tarawan Formation). Speckle noise is found in radar images for many reasons, such as when an object strongly reflected between itself and the spacecraft causes noise. Refined LEE filter (RLF) is applied for speckle noise reduction; moreover, full polarimetric ALOS/PALSAR images (PLR) are transformed into circular polarization by changing both angles into orientation angle ψ = 0° and elliptical angle χ = 45°. The validation of ALOS/PALSAR outputs was carried out using ground penetrating radar (GPR) measurements. Three GPR long profiles using a 200 MHz antenna were scanned along with areas that were annotated according to ALOS/PALSAR results (high backscattering coefficient). The GPR system operated by a low-frequency antenna with a frequency of 200 MHz was capable of detecting the annotated geological structures beneath the sand sheets. Furthermore, statistical comparison of L-band SAR and GPR data illustrated a correlation that can reveal identical regions to delineate subsurface structures. These results prove that the integration of synthetic aperture radar SAR (L-band) and on-site low-frequency radar systems can be vital to detect soil structures down to several meters, ultimately innovating Earth observation systems for geological and hydrogeological mapping in arid regions.

The Potential Impact of COVID-19 Virus on the Heart and the Circulatory System.

Heart attacks, arrhythmias, and cardiomyopathy are all linked to the 2019 coronavirus disease (COVID-19), which has been identified as a risk factor for cardiovascular disease. Nothing can be held accountable in the current state of affairs. Undiagnosed chronic systolic heart failure (CSHF) develops when the heart's second half of the cardiac cycle does not function properly. As a result, the heart's blood pumping function is interrupted. Stress-induced cardiomyopathy may be caused by a variety of factors inside the body (SICM). Cytokine storm and microvascular dysfunction are among the issues. There is inflammation in the heart muscle, which may lead to stress-induced cardiomyopathy. A major part of our study is going to be devoted to understanding the effects of coronavirus on the cardiovascular system and blood vessels. A lot of time and effort has been put into figuring out the health effects of radiation exposure. The heart and circulatory system are shown to be affected by the coronavirus in this research. COVID-19 is shown to influence persons with heart disease, heart failure, arrhythmias, microvascular angiopathy, and cardiac damage in this study.

Occupational and community risk of SARS-CoV-2 infection among employees of a long-term care facility: an observational study.

BACKGROUND: We investigated the contribution of both occupational and community exposure for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among employees of a university-affiliated long-term care facility (LTCF), during the 1st pandemic wave in Switzerland (March-June 2020). METHODS: We performed a nested analysis of a seroprevalence study among all volunteering LTCF staff to determine community and nosocomial risk factors for SARS-CoV-2 seropositivity using modified Poison regression. We also combined epidemiological and genetic sequencing data from a coronavirus disease 2019 (COVID-19) outbreak investigation in a LTCF ward to infer transmission dynamics and acquisition routes of SARS-CoV-2, and evaluated strain relatedness using a maximum likelihood phylogenetic tree. RESULTS: Among 285 LTCF employees, 176 participated in the seroprevalence study, of whom 30 (17%) were seropositive for SARS-CoV-2. Most (141/176, 80%) were healthcare workers (HCWs). Risk factors for seropositivity included exposure to a COVID-19 inpatient (adjusted prevalence ratio [aPR] 2.6; 95% CI 0.9-8.1) and community contact with a COVID-19 case (aPR 1.7; 95% CI 0.8-3.5). Among 18 employees included in the outbreak investigation, the outbreak reconstruction suggests 4 likely importation events by HCWs with secondary transmissions to other HCWs and patients. CONCLUSIONS: These two complementary epidemiologic and molecular approaches suggest a substantial contribution of both occupational and community exposures to COVID-19 risk among HCWs in LTCFs. These data may help to better assess the importance of occupational health hazards and related legal implications during the COVID-19 pandemic.

Hospital outcomes of community-acquired COVID-19 versus influenza: Insights from the Swiss hospital-based surveillance of influenza and COVID-19.

BackgroundSince the onset of the COVID-19 pandemic, the disease has frequently been compared with seasonal influenza, but this comparison is based on little empirical data.AimThis study compares in-hospital outcomes for patients with community-acquired COVID-19 and patients with community-acquired influenza in Switzerland.MethodsThis retrospective multi-centre cohort study includes patients > 18 years admitted for COVID-19 or influenza A/B infection determined by RT-PCR. Primary and secondary outcomes were in-hospital mortality and intensive care unit (ICU) admission for patients with COVID-19 or influenza. We used Cox regression (cause-specific and Fine-Gray subdistribution hazard models) to account for time-dependency and competing events with inverse probability weighting to adjust for confounders.ResultsIn 2020, 2,843 patients with COVID-19 from 14 centres were included. Between 2018 and 2020, 1,381 patients with influenza from seven centres were included; 1,722 (61%) of the patients with COVID-19 and 666 (48%) of the patients with influenza were male (p < 0.001). The patients with COVID-19 were younger (median 67 years; interquartile range (IQR): 54-78) than the patients with influenza (median 74 years; IQR: 61-84) (p < 0.001). A larger percentage of patients with COVID-19 (12.8%) than patients with influenza (4.4%) died in hospital (p < 0.001). The final adjusted subdistribution hazard ratio for mortality was 3.01 (95% CI: 2.22-4.09; p < 0.001) for COVID-19 compared with influenza and 2.44 (95% CI: 2.00-3.00, p < 0.001) for ICU admission.ConclusionCommunity-acquired COVID-19 was associated with worse outcomes compared with community-acquired influenza, as the hazards of ICU admission and in-hospital death were about two-fold to three-fold higher.

The Potential Contribution of Biopolymeric Particles in Lung Tissue Regeneration of COVID-19 Patients.

The lung is a vital organ that houses the alveoli, which is where gas exchange takes place. The COVID-19 illness attacks lung cells directly, creating significant inflammation and resulting in their inability to function. To return to the nature of their job, it may be essential to rejuvenate the afflicted lung cells. This is difficult because lung cells need a long time to rebuild and resume their function. Biopolymeric particles are the most effective means to transfer developing treatments to airway epithelial cells and then regenerate infected lung cells, which is one of the most significant symptoms connected with COVID-19. Delivering biocompatible and degradable natural biological materials, chemotherapeutic drugs, vaccines, proteins, antibodies, nucleic acids, and diagnostic agents are all examples of these molecules' usage. Furthermore, they are created by using several structural components, which allows them to effectively connect with these cells. We highlight their most recent uses in lung tissue regeneration in this review. These particles are classified into three groups: biopolymeric nanoparticles, biopolymeric stem cell materials, and biopolymeric scaffolds. The techniques and processes for regenerating lung tissue will be thoroughly explored.

Nationwide Deployment of a Serious Game Designed to Improve COVID-19 Infection Prevention Practices in Switzerland: Prospective Web-Based Study.

BACKGROUND: Lassitude and a rather high degree of mistrust toward the authorities can make regular or overly constraining COVID-19 infection prevention and control campaigns inefficient and even counterproductive. Serious games provide an original, engaging, and potentially effective way of disseminating COVID-19 infection prevention and control guidelines. Escape COVID-19 is a serious game for teaching COVID-19 infection prevention and control practices that has previously been validated in a population of nursing home personnel. OBJECTIVE: We aimed to identify factors learned from playing the serious game Escape COVID-19 that facilitate or impede intentions of changing infection prevention and control behavior in a large and heterogeneous Swiss population. METHODS: This fully automated, prospective web-based study, compliant with the Checklist for Reporting Results of Internet E-Surveys (CHERRIES), was conducted in all 3 main language regions of Switzerland. After creating an account on the platform, participants were asked to complete a short demographic questionnaire before accessing the serious game. The only incentive given to the potential participants was a course completion certificate, which participants obtained after completing the postgame questionnaire. The primary outcome was the proportion of participants who reported that they were willing to change their infection prevention and control behavior. Secondary outcomes were the infection prevention and control areas affected by this willingness and the presumed evolution in the use of specific personal protective equipment items. The elements associated with intention to change infection prevention and control behavior, or lack thereof, were also assessed. Other secondary outcomes were the subjective perceptions regarding length, difficulty, meaningfulness, and usefulness of the serious game; impression of engagement and boredom while playing the serious game; and willingness to recommend its use to friends or colleagues. RESULTS: From March 9 to June 9, 2021, a total of 3227 accounts were created on the platform, and 1104 participants (34.2%) completed the postgame questionnaire. Of the 1104 respondents, 509 respondents (46.1%) answered that they intended to change their infection prevention and control behavior after playing the game. Among the respondents who answered that they did not intend to change their behavior, 86.1% (512/595) answered that they already apply these guidelines. Participants who followed the German version were less likely to intend to change their infection prevention and control behavior (odds ratio [OR] 0.48, 95% CI 0.24-0.96; P=.04) and found the game less engaging (P<.001). Conversely, participants aged 53 years or older had stronger intentions of changing infection prevention and control behavior (OR 2.07, 95% CI 1.44-2.97; P<.001). CONCLUSIONS: Escape COVID-19 is a useful tool to enhance correct infection prevention and control measures on a national scale, even after 2 COVID-19 pandemic waves; however, the serious game's impact was affected by language, age category, and previous educational training, and the game should be adapted to enhance its impact on specific populations.

A Novel Computational Model for Detecting the Severity of Inflammation in Confirmed COVID-19 Patients Using Chest X-ray Images.

Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread all over the world. The disease is highly contagious, and it may lead to acute respiratory distress (ARD). Medical imaging can play an important role in classifying, detecting, and measuring the severity of the virus. This study aims to provide a novel auto-detection tool that can detect abnormal changes in conventional X-ray images for confirmed COVID-19 cases. X-ray images from patients diagnosed with COVID-19 were converted into 19 different colored layers. Each layer represented objects with similar contrast that could be defined as a specific color. The objects with similar contrasts were formed in a single layer. All the objects from all the layers were extracted as a single-color image. Based on the differentiation of colors, the prototype model was able to recognize a wide spectrum of abnormal changes in the image texture. This was true even if there was minimal variation of the contrast values of the detected uncleared abnormalities. The results indicate that the proposed novel method can detect and determine the degree of lung infection from COVID-19 with an accuracy of 91%, compared to the opinions of three experienced radiologists. The method can also efficiently determine the sites of infection and the severity of the disease by classifying the X-rays into five levels of severity. Thus, the proposed COVID-19 autodetection method can identify locations and indicate the degree of severity of the disease by comparing affected tissue with healthy tissue, and it can predict where the disease may spread.

Severe acute respiratory coronavirus virus 2 (SARS-CoV-2) seroconversion and occupational exposure of employees at a Swiss university hospital: A large longitudinal cohort study.

BACKGROUND: The dynamics of coronavirus disease 2019 (COVID-19) seroconversion of hospital employees are understudied. We measured the proportion of seroconverted employees and evaluated risk factors for seroconversion during the first pandemic wave. METHODS: In this prospective cohort study, we recruited Geneva University Hospitals employees and sampled them 3 times, every 3 weeks from March 30 to June 12, 2020. We measured the proportion of seroconverted employees and determined prevalence ratios of risk factors for seroconversion using multivariate mixed-effects Poisson regression models. RESULTS: Overall, 3,421 participants (29% of all employees) were included, with 92% follow-up. The proportion of seroconverted employees increased from 4.4% (95% confidence interval [CI], 3.7%-5.1%) at baseline to 8.5% [(95% CI, 7.6%-9.5%) at the last visit. The proportions of seroconverted employees working in COVID-19 geriatrics and rehabilitation (G&R) wards (32.3%) and non-COVID-19 G&R wards (12.3%) were higher compared to office workers (4.9%) at the last visit. Only nursing assistants had a significantly higher risk of seroconversion compared to office workers (11.7% vs 4.9%; P = .006). Significant risk factors for seroconversion included the use of public transportation (adjusted prevalence ratio, 1.59; 95% CI, 1.25-2.03), known community exposure to severe acute respiratory coronavirus virus 2 (2.80; 95% CI, 2.22-3.54), working in a ward with a nosocomial COVID outbreak (2.93; 95% CI, 2.27-3.79), and working in a COVID-19 G&R ward (3.47; 95% CI, 2.45-4.91) or a non-COVID-19 G&R ward (1.96; 95% CI, 1.46-2.63). We observed an association between reported use of respirators and lower risk of seroconversion (0.73; 95% CI, 0.55-0.96). CONCLUSION: Additional preventive measures should be implemented to protect employees in G&R wards. Randomized trials on the protective effect of respirators are urgently needed.

Impact of a Serious Game (Escape COVID-19) on the Intention to Change COVID-19 Control Practices Among Employees of Long-term Care Facilities: Web-Based Randomized Controlled Trial.

BACKGROUND: Most residents of long-term care facilities (LTCFs) are at high risk of complications and death following SARS-CoV-2 infection. In these facilities, viral transmission can be facilitated by shortages of human and material resources, which can lead to suboptimal application of infection prevention and control (IPC) procedures. To improve the dissemination of COVID-19 IPC guidelines, we developed a serious game called "Escape COVID-19" using Nicholson's RECIPE for meaningful gamification, as engaging serious games have the potential to induce behavioral change. OBJECTIVE: As the probability of executing an action is strongly linked to the intention of performing it, the objective of this study was to determine whether LTCF employees were willing to change their IPC practices after playing "Escape COVID-19." METHODS: This was a web-based, triple-blind, randomized controlled trial, which took place between November 5 and December 4, 2020. The health authorities of Geneva, Switzerland, asked the managers of all LTCFs under their jurisdiction to forward information regarding the study to all their employees, regardless of professional status. Participants were unaware that they would be randomly allocated to one of two different study paths upon registration. In the control group, participants filled in a first questionnaire designed to gather demographic data and assess baseline knowledge before accessing regular online IPC guidelines. They then answered a second questionnaire, which assessed their willingness to change their IPC practices and identified the reasons underlying their decision. They were then granted access to the serious game. Conversely, the serious game group played "Escape COVID-19" after answering the first questionnaire but before answering the second one. This group accessed the control material after answering the second set of questions. There was no time limit. The primary outcome was the proportion of LTCF employees willing to change their IPC practices. Secondary outcomes included the factors underlying participants' decisions, the domains these changes would affect, changes in the use of protective equipment items, and attrition at each stage of the study. RESULTS: A total of 295 answer sets were analyzed. Willingness to change behavior was higher in the serious game group (82% [119/145] versus 56% [84/150]; P<.001), with an odds ratio of 3.86 (95% CI 2.18-6.81; P<.001) after adjusting for professional category and baseline knowledge, using a mixed effects logistic regression model with LTCF as a random effect. For more than two-thirds (142/203) of the participants, the feeling of playing an important role against the epidemic was the most important factor explaining their willingness to change behavior. Most of the participants unwilling to change their behavior answered that they were already applying all the guidelines. CONCLUSIONS: The serious game "Escape COVID-19" was more successful than standard IPC material in convincing LTCF employees to adopt COVID-19-safe IPC behavior. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/25595.

Impact of an e-learning module on personal protective equipment knowledge in student paramedics: a randomized controlled trial.

BACKGROUND: Prehospital professionals such as emergency physicians or paramedics must be able to choose and adequately don and doff personal protective equipment (PPE) in order to avoid COVID-19 infection. Our aim was to evaluate the impact of a gamified e-learning module on adequacy of PPE in student paramedics. METHODS: This was a web-based, randomized 1:1, parallel-group, triple-blind controlled trial. Student paramedics from three Swiss schools were invited to participate. They were informed they would be presented with both an e-learning module and an abridged version of the current regional prehospital COVID-19 guidelines, albeit not in which order. After a set of 22 questions designed to assess baseline knowledge, the control group was shown the guidelines before answering a set of 14 post-intervention questions. The e-learning group was shown the gamified e-learning module right after the guidelines, and before answering post-intervention questions. The primary outcome was the difference in the percentage of adequate choices of PPE before and after the intervention. RESULTS: The participation rate was of 71% (98/138). A total of 90 answer sets was analyzed. Adequate choice of PPE increased significantly both in the control (50% [33;83] vs 25% [25;50], P = .013) and in the e-learning group (67% [50;83] vs 25% [25;50], P = .001) following the intervention. Though the median of the difference was higher in the e-learning group, there was no statistically significant superiority over the control (33% [0;58] vs 17% [- 17;42], P = .087). The e-learning module was of greatest benefit in the subgroup of student paramedics who were actively working in an ambulance company (42% [8;58] vs 25% [- 17;42], P = 0.021). There was no significant effect in student paramedics who were not actively working in an ambulance service (0% [- 25;33] vs 17% [- 8;50], P = .584). CONCLUSIONS: The use of a gamified e-learning module increases the rate of adequate choice of PPE only among student paramedics actively working in an ambulance service. In this subgroup, combining this teaching modality with other interventions might help spare PPE and efficiently protect against COVID-19 infection.