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The rapid transmissibility of the severe acute respiratory syndrome-coronavirus-2 causing coronavirus disease-2019, requires timely dissemination of information and public health responses, with all 47 countries of the WHO African Region simultaneously facing significant risk, in contrast to the usual highly localised infectious disease outbreaks. This demanded a different approach to information management and an adaptive information strategy was implemented, focusing on data collection and management, reporting and analysis at the national and regional levels. This approach used frugal innovation, building on tools and technologies that are commonly used, and well understood; as well as developing simple, practical, highly functional and agile solutions that could be rapidly and remotely implemented, and flexible enough to be recalibrated and adapted as required. While the approach was successful in its aim of allowing the WHO Regional Office for Africa (WHO AFRO) to gather surveillance and epidemiological data, several challenges were encountered that affected timeliness and quality of data captured and reported by the member states, showing that strengthening data systems and digital capacity, and encouraging openness and data sharing are an important component of health system strengthening.
Markedly elevated adverse mental health symptoms were widely observed early in the coronavirus disease-2019 (COVID-19) pandemic. Unlike the U.S., where cross-sectional data indicate anxiety and depression symptoms have remained elevated, such symptoms reportedly declined in the U.K., according to analysis of repeated measures from a large-scale longitudinal study. However, nearly 40% of U.K. respondents (those who did not complete multiple follow-up surveys) were excluded from analysis, suggesting that survivorship bias might partially explain this discrepancy. We therefore sought to assess survivorship bias among participants in our longitudinal survey study as part of The COVID-19 Outbreak Public Evaluation (COPE) Initiative.
Methods
Survivorship bias was assessed in 4039 U.S. respondents who completed surveys including the assessment of mental health as part of The COPE Initiative in April 2020 and were invited to complete follow-up surveys. Participants completed validated screening instruments for symptoms of anxiety, depression and insomnia. Survivorship bias was assessed for (1) demographic differences in follow-up survey participation, (2) differences in initial adverse mental health symptom prevalence adjusted for demographic factors and (3) differences in follow-up survey participation based on mental health experiences adjusted for demographic factors.
Results
Adjusting for demographics, individuals who completed only one or two out of four surveys had significantly higher prevalence of anxiety and depression symptoms in April 2020 (e.g. one-survey v. four-survey, anxiety symptoms, adjusted prevalence ratio [aPR]: 1.30, 95% confidence interval [CI]: 1.08–1.55, p = 0.0045; depression symptoms, aPR: 1.43, 95% CI: 1.17–1.75, p = 0.00052). Moreover, individuals who experienced incident anxiety or depression symptoms had significantly higher adjusted odds of not completing follow-up surveys (adjusted odds ratio [aOR]: 1.68, 95% CI: 1.22–2.31, p = 0.0015, aOR: 1.56, 95% CI: 1.15–2.12, p = 0.0046, respectively).
Conclusions
Our findings reveal significant survivorship bias among longitudinal survey respondents, indicating that restricting analytic samples to only respondents who provide repeated assessments in longitudinal survey studies could lead to overly optimistic interpretations of mental health trends over time. Cross-sectional or planned missing data designs may provide more accurate estimates of population-level adverse mental health symptom prevalence than longitudinal surveys.
There is a paucity of literature on the relationship between pre-existing mental health conditions and coronavirus disease-2019 (COVID-19) outcomes. The aim was to examine the association between pre-existing mental health diagnosis and COVID-19 outcomes (positive screen, hospitalization, mortality).
Methods
Electronic medical record data for 30 976 adults tested for COVID-19 between March 2020 and 10th July 2020 was analyzed. COVID-19 outcomes included positive screen, hospitalization among screened positive, and mortality among screened positive and hospitalized. Primary independent variable, mental health disorders, was based on ICD-10 codes categorized as bipolar, internalizing, externalizing, and psychoses. Descriptive statistics were calculated, unadjusted and adjusted logistic regression and Cox proportional hazard models were used to investigate the relationship between each mental health disorder and COVID-19 outcomes.
Results
Adults with externalizing (odds ratio (OR) 0.67, 95%CI 0.57–0.79) and internalizing disorders (OR 0.78, 95% CI 0.70–0.88) had lower odds of having a positive COVID-19 test in fully adjusted models. Adults with bipolar disorder had significantly higher odds of hospitalization in fully adjusted models (OR 4.27, 95% CI 2.06–8.86), and odds of hospitalization were significantly higher among those with externalizing disorders after adjusting for demographics (OR 1.71, 95% CI 1.23–2.38). Mortality was significantly higher in the fully adjusted model for patients with bipolar disorder (hazard ratio 2.67, 95% CI 1.07–6.67).
Conclusions
Adults with mental health disorders, while less likely to test positive for COVID-19, were more likely to be hospitalized and to die in the hospital. Study results suggest the importance of developing interventions that incorporate elements designed to address smoking cessation, nutrition and physical activity counseling and other needs specific to this population to improve COVID-19 outcomes.
En mars 2020, le premier ministre Legault a fait appel aux influenceur.euses et aux célébrités québécoises dans le cadre de la campagne #Propage l'info, pas le virus afin de sensibiliser les jeunes au respect des consignes sanitaires liées à la COVID-19. Cet article offre un éclairage inédit sur les différentes manières dont ces personnes renommées ont répondu à l'appel ainsi que sur les formes de leurs réponses à l'aide d'une analyse de contenu de leur vidéo partagée sur les réseaux sociaux. Le codage des vidéos s'est fait à partir d'une grille d'analyse qualitative de contenu, inspirée de celle de Fields (1988). Il ressort des analyses que différents moyens ont permis d'accentuer le sentiment de proximité entre la célébrité et son public, dans le but d'augmenter l'adhésion au message. L'utilisation du pronom « On », l'emploi de formules narratives et l'intimité qui se dégage des vidéos informatives vont en ce sens.
The coronavirus disease 2019 (COVID-19) pandemic has resulted in shortages of personal protective equipment (PPE), underscoring the urgent need for simple, efficient, and inexpensive methods to decontaminate masks and respirators exposed to severe acute respiratory coronavirus virus 2 (SARS-CoV-2). We hypothesized that methylene blue (MB) photochemical treatment, which has various clinical applications, could decontaminate PPE contaminated with coronavirus.
Design:
The 2 arms of the study included (1) PPE inoculation with coronaviruses followed by MB with light (MBL) decontamination treatment and (2) PPE treatment with MBL for 5 cycles of decontamination to determine maintenance of PPE performance.
Methods:
MBL treatment was used to inactivate coronaviruses on 3 N95 filtering facepiece respirator (FFR) and 2 medical mask models. We inoculated FFR and medical mask materials with 3 coronaviruses, including SARS-CoV-2, and we treated them with 10 µM MB and exposed them to 50,000 lux of white light or 12,500 lux of red light for 30 minutes. In parallel, integrity was assessed after 5 cycles of decontamination using multiple US and international test methods, and the process was compared with the FDA-authorized vaporized hydrogen peroxide plus ozone (VHP+O3) decontamination method.
Results:
Overall, MBL robustly and consistently inactivated all 3 coronaviruses with 99.8% to >99.9% virus inactivation across all FFRs and medical masks tested. FFR and medical mask integrity was maintained after 5 cycles of MBL treatment, whereas 1 FFR model failed after 5 cycles of VHP+O3.
Conclusions:
MBL treatment decontaminated respirators and masks by inactivating 3 tested coronaviruses without compromising integrity through 5 cycles of decontamination. MBL decontamination is effective, is low cost, and does not require specialized equipment, making it applicable in low- to high-resource settings.
The coronavirus disease 2019 (COVID-19) vaccine was launched in India on 16 January 2021, prioritising health care workers which included medical students. We aimed to assess vaccine hesitancy and factors related to it among medical students in India. An online questionnaire was filled by 1068 medical students across 22 states and union territories of India from 2 February to 7 March 2021. Vaccine hesitancy was found among 10.6%. Concern regarding vaccine safety and efficacy, lack of awareness regarding their eligibility for vaccination and lack of trust in government agencies predicted COVID-19 vaccine hesitancy among medical students. On the other hand, the presence of risk perception regarding themselves being affected with COVID-19 reduced vaccine hesitancy as well as hesitancy in participating in COVID-19 vaccine trials. Vaccine-hesitant students were more likely to derive information from social media and less likely from teachers at their medical colleges. Choosing between the two available vaccines (Covishield and Covaxin) was considered important by medical students both for themselves and for their future patients. Covishield was preferred to Covaxin by students. Majority of those willing to take the COVID-19 vaccine felt that it was important for them to resume their clinical posting, face-to-face classes and get their personal life back on track. Around three-fourths medical students viewed that COVID-19 vaccine should be made mandatory for both health care workers and international travellers. Prior adult vaccination did not have an effect on COVID-19 vaccine hesitancy. Targeted awareness campaigns, regulatory oversight of vaccine trials and public release of safety and efficacy data and trust building activities could further reduce COVID-19 vaccine hesitancy among medical students.
During the first wave of the severe acute respiratory syndrome-coronavirus-2 epidemic in the Netherlands, notifications consisted mostly of patients with relatively severe disease. To enable real-time monitoring of the incidence of mild coronavirus disease 2019 (COVID-19) – for which medical consultation might not be required – the Infectieradar web-based syndromic surveillance system was launched in mid-March 2020. Our aim was to quantify associations between Infectieradar participant characteristics and the incidence of self-reported COVID-19-like illness. Recruitment for this cohort study was via a web announcement. After registering, participants completed weekly questionnaires, reporting the occurrence of a set of symptoms. The incidence rate of COVID-19-like illness was estimated and multivariable Poisson regression used to estimate the relative risks associated with sociodemographic variables, lifestyle factors and pre-existing medical conditions. Between 17 March and 24 May 2020, 25 663 active participants were identified, who reported 7060 episodes of COVID-19-like illness over 131 404 person-weeks of follow-up. The incidence rate declined over the analysis period, consistent with the decline in notified cases. Male sex, age 65+ years and higher education were associated with a significantly lower COVID-19-like illness incidence rate (adjusted rate ratios (RRs) of 0.80 (95% CI 0.76–0.84), 0.77 (0.70–0.85), 0.84 (0.80–0.88), respectively) and the baseline characteristics ever-smoker, asthma, allergies, diabetes, chronic lung disease, cardiovascular disease and children in the household were associated with a higher incidence (RRs of 1.11 (1.04–1.19) to 1.69 (1.50–1.90)). Web-based syndromic surveillance has proven useful for monitoring the temporal trends in, and risk factors associated with, the incidence of mild disease. Increased relative risks observed for several patient factors could reflect a combination of exposure risk, susceptibility to infection and propensity to report symptoms.
There is an ongoing and established need for humanitarian training and professionalization. The coronavirus disease 2019 (COVID-19) pandemic disrupted training programs designed to accomplish this goal, including the Humanitarian Response Intensive Course, which includes a 3-d immersive simulation to prepare humanitarian workers for future field work. To provide program continuity, the 3-d simulation was quickly adapted to a virtual format using a combination of video conferencing, short messaging service, and cloud-based file storage software. Participants were geographically dispersed and participated virtually. Learning objectives were preserved, while some components not amenable to a virtual format were removed.
A virtual humanitarian training simulation is a feasible, acceptable, and affordable alternative to an in-person simulation. Participants were engaged and experienced minimal technological disruptions. The majority of students believed the format met or exceeded expectations. However, feedback also emphasized the importance of providing sufficient time for team collaboration and deliverable preparation in the simulation schedule. The virtual format was more affordable than the traditional in-person simulation, and diverse expert faculty who could not have attended in-person were able to participate. This format could be used to overcome other barriers to in-person simulation training, including geographic, financial, time, or security.
The global coronavirus disease 2019 (COVID-19) pandemic has altered entire nations and their health systems. The greatest impact of the pandemic has been seen among vulnerable populations, such as those with comorbidities like heart diseases, kidney failure, obesity, or those with worse health determinants such as unemployment and poverty. In the current study, we are proposing previous exposure to fine-grained volcanic ashes as a risk factor for developing COVID-19. Based on several previous studies it has been known since the mid 1980s of the past century that volcanic ash is most likely an accelerating factor to suffer from different types of cancer, including lung or thyroid cancer. Our study postulates, that people who are most likely to be infected during a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) widespread wave will be those with comorbidities that are related to previous exposure to volcanic ashes. We have explored 8703 satellite images from the past 21 y of available data from the National Oceanic and Atmospheric Administration (NOAA) database and correlated them with the data from the national institute of health statistics in Ecuador. Additionally, we provide more realistic numbers of fatalities due to the virus based on excess mortality data of 2020-2021, when compared with previous years. This study would be a very first of its kind combining social and spatial distribution of COVID-19 infections and volcanic ash distribution. The results and implications of our study will also help countries to identify such aforementioned vulnerable parts of the society, if the given geodynamic and volcanic settings are similar.
Our objective is to forecast the number of coronavirus disease 2019 (COVID-19) cases in the state of Maryland, United States, using transfer function time series (TS) models based on a Social Distancing Index (SDI) and determine how their parameters relate to the pandemic mechanics.
Methods:
A moving window of 2 mo was used to train the transfer function TS model that was then tested on the next week data. After accounting for a secular trend and weekly cycle of the SDI, a high correlation was documented between it and the daily caseload 9 days later. Similar patterns were also observed on the daily COVID-19 cases and incorporated in our models.
Results:
In most cases, the proposed models provide a reasonable performance that was, on average, moderately better than that delivered by TS models based only on previous observations. The model coefficients associated with the SDI were statistically significant for most of the training/test sets.
Conclusions:
Our proposed models that incorporate SDI can forecast the number of COVID-19 cases in a region. Their parameters have real-life interpretations and, hence, can help understand the inner workings of the epidemic. The methods detailed here can help local health governments and other agencies adjust their response to the epidemic.
Response to the unprecedented coronavirus disease 2019 (COVID-19) outbreak needs to be augmented in Texas, United States, where the first 5 cases were reported on March 6, 2020, and were rapidly followed by an exponential rise within the next few weeks. This study aimed to determine the ongoing trend and upcoming infection status of COVID-19 in county levels of Texas.
Methods:
Data were extracted from the following sources: published literature, surveillance, unpublished reports, and websites of Texas Department of State Health Services (DSHS), Natality report of Texas, and WHO Coronavirus Disease (COVID-19) Dashboard. The 4-compartment Susceptible-Exposed-Infectious-Removal (SEIR) mathematical model was used to estimate the current trend and future prediction of basic reproduction number and infection cases in Texas. Because the basic reproduction number is not sufficient to predict the outbreak, we applied the Continuous-Time Markov Chain (CTMC) model to calculate the probability of the COVID-19 outbreak.
Results:
The estimated mean basic reproduction number of COVID-19 in Texas is predicted to be 2.65 by January 31, 2021. Our model indicated that the third wave might occur at the beginning of May 2021, which will peak at the end of June 2021. This prediction may come true if the current spreading situation/level persists, i.e., no clinically effective vaccine is available, or this vaccination program fails for some reason in this area.
Conclusion:
Our analysis indicates an alarming ongoing and upcoming infection rate of COVID-19 at county levels in Texas, thereby emphasizing the promotion of more coordinated and disciplined actions by policy-makers and the population to contain its devastating impact.
Population-based seroprevalence studies on coronavirus disease 2019 (COVID-19) in low- and middle-income countries are lacking. We investigated the seroprevalence of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) antibodies in Sergipe state, Northeast Brazil, using rapid IgM−IgG antibody test and fluorescence immunoassay. The seroprevalence was 9.3% (95% CI 8.5–10.1), 10.2% (95% CI 9.2–11.3) for women and 7.9% (IC 95% 6.8–9.1) for men (P = 0.004). We found a decline in the prevalence of SARS-CoV-2 antibodies according to age, but the differences were not statistically significant: 0–19 years (9.9%; 95% CI 7.8–12.5), 20–59 years (9.3%; 95% CI 8.4–10.3) and ≥60 years (9.0%; 95% CI 7.5–10.8) (P = 0.517). The metropolitan area had a higher seroprevalence (11.7%, 95% CI 10.3–13.2) than outside municipalities (8.0%, 95% CI 7.2–8.9) (P < 0.001). These findings highlight the importance of serosurveillance to estimate the real impact of the COVID-19 outbreak and thereby provide data to better understand the spread of the virus, as well as providing information to guide stay-at-home measures and other policies. In addition, these results may be useful as basic data to follow the progress of COVID-19 outbreak as social restriction initiatives start to be relaxed in Brazil.
In Ireland, the coronavirus disease 2019 (COVID-19) pandemic has led to a total of 230,599 cases of infection as on 20 March 2021, and 4323 deaths. Although the Irish hospital network has not been overwhelmed, it has faced pressures, with a total of 13,313 persons hospitalised, including 1402 admitted to the intensive care unit. Out of caution, in spring 2020, in anticipation of possible surges in hospitals in light of international experience, the Irish government reached an agreement with private hospitals to access their capacity for three months to alleviate pressure on the public system, as part of its comprehensive response to the pandemic. This piece analyses the agreement with private hospitals, based on the legally binding Heads of Terms of the agreement, which were signed by the parties, along with publicly reported details from media reports and Oireachtas (parliamentary) committee hearings. We argue that although the new relationship could, in theory, have paved the way to the nationalisation of the whole hospital system, in fact, the experiment is best interpreted as a lost opportunity to integrate and simplify Ireland's hospital system.
Acute epistaxis can be a life-threatening airway emergency, requiring in-patient admission. The coronavirus disease 2019 pandemic placed significant strain on hospital resources, and management has shifted towards an out-patient-centred approach.
Methods
A five-month single-centre retrospective study was undertaken of all epistaxis patients managed by the ENT department. A pre-coronavirus disease 2019 pandemic group was managed with pre-existing guidelines, compared to new guidelines for the coronavirus disease 2019 pandemic group. A telephone survey was performed on out-patients with non-dissolvable packs to assess patient comfort and satisfaction.
Results
A total of 142 patients were seen. The coronavirus disease 2019 pandemic group had significantly more patients aged over 65 years (p = 0.004), an increased use of absorbable dressings and local haemostatic agents (Nasopore and Surgiflo), and fewer admissions (all p < 0.0005). Rates of re-presentation and morbidity, and length of hospital stay were similar. The telephone survey revealed out-patient management to be efficacious and feasible.
Conclusion
The coronavirus disease 2019 pandemic has shifted epistaxis management towards local haemostatic agents and out-patient management; this approach is as safe and effective as previously well-established regimens.
The aim of this study was to present the clinical characteristics and dynamic changes in laboratory parameters of the coronavirus disease 2019 (COVID-19) in Guangzhou, and explore the probable early warning indicators of disease progression.
Method:
We enrolled all the patients diagnosed with COVID-19 in the Guangzhou No. 8 People’s Hospital. The patients’ demographic and epidemiologic data were collected, including chief complaints, lab results, and imaging examination findings.
Results:
The characteristics of the patients in Guangzhou are different from those in Wuhan. The patients were younger in age, predominately female, and their condition was not commonly combined with other diseases. A total of 75% of patients suffered fever on admission, followed by cough occurring in 62% patients. Comparing the mild/normal and severe/critical patients, being male, of older age, combined with hypertension, abnormal blood routine test results, raised creatine kinase, glutamic oxaloacetic transaminase, lactate dehydrogenase, C-reactive protein, procalcitonin, D-dimer, fibrinogen, activated partial thromboplastin time, and positive proteinuria were early warning indicators of severe disease.
Conclusion:
The patients outside epidemic areas showed different characteristics from those in Wuhan. The abnormal laboratory parameters were markedly changed 4 weeks after admission, and also were different between the mild and severe patients. More evidence is needed to confirm highly specific and sensitive potential early warning indicators of severe disease.
We evaluated adverse drug events (ADEs) by chart review in a random national sample of 428 veterans with coronavirus disease 2019 (COVID-19) who received tocilizumab (n = 173 of 428). ADEs (median time, 5 days) occurred in 51 of 173 (29%) and included hepatoxicity (n = 29) and infection (n = 13). Concomitant medication discontinuation occurred in 22% of ADE patients; mortality was 39%.