Wave Of COVID-19 Research Articles

Post COVID rhino-cerebral mucormycosis at a tertiary hospital in Central India: a retrospective cohort study

BackgroundUsually non-pathogenic in immunocompetent individuals, mucormycosis is an aggressive, angio-invasive fungus. It is a rare, opportunistic infection belonging to the zygomycete family. The second wave of COVID-19 saw the emergence of a more dangerous and a potentially deadly invasive fungal sinusitis evolving in rhino-cerebral mucormycosis thus compounding and complicating the pandemic. Alarming and exponential rise in the number of cases of rhino-cerebral mucormycosis in India versus the rest of the world when the treatment protocols essentially remained the same needs to be probed scientifically along with its prompt management. Here an attempt has been made to explore this in the study of 50 cases of rhino-cerebral mucormycosis. This study aims to explore the cause and extension of the disease along with its treatment and outcome in 50 cases of post COVID rhino-cerebral mucormycosis.ResultsThis is a retrospective cohort study conducted on 50 patients with post COVID-19 invasive mucormycosis of the paranasal sinuses with extension to surrounding structure visiting Otorhinolaryngology department at Acharya Vinoba Bhave Rural Hospital, Wardha. Among 50 patients, 86% males in 4th to 7th decade with mean age of 51.08 years. Large majority had mild–moderate severity on high-resolution computed tomography thorax with 84% having uncontrolled diabetes and 18% newly diagnosed diabetic cases. Seventy-two percent had history of steroid intake for COVID-19 management. Maxillary sinus was most commonly involved sinus in 94% cases while 32% had commonest extra nasal spread hard palate. Seventy-eight percent of the post operated mucormycosis cases are alive with no recurrence, 8% are alive and had recurrence of the disease while 14% resulted in death.ConclusionEffective management and control of the infection can be achieved with the use of strong antibiotics, oral or systemic antifungal drugs, extensive surgical excision of diseased tissue, and addressing underlying risk factors can effectively manage and control the infection. This approach results in positive outcomes and reduced morbidity compared to the time prior to the COVID-19 pandemic including cases with extensive disease.

Effect of the second and third COVID-19 pandemic waves on routine outpatient malaria indicators and case management practices in Uganda: an interrupted time series analysis

BackgroundReports on the impact of COVID-19 pandemic on the quality of malaria care and burden in sub Saharan Africa have provided a mixed picture to date. The impact of the 2nd (Delta) and 3rd (Omicron) COVID-19 waves on outpatient malaria indicators and case management practices was assessed at three public health facilities with varying malaria transmission intensities in Uganda.MethodsIndividual level data from all patients presenting to the out-patient departments (OPD) of the three facilities (Kasambya, Walukuba and Lumino) between January 2019 and February 2022 were included in the analysis. Outcomes of interest included total number of outpatient (OPD) visits, proportion of patients suspected to have malaria, proportion of suspected malaria cases tested with a malaria diagnostic test, test positivity rates (TPR) and proportion of malaria cases prescribed artemether-lumefantrine (AL). Using the pre-COVID-19 trends between January 2019 and February 2020, interrupted time series analysis was used to predict the expected trends for these study outcomes during the 2nd wave (May 2021–August 2021) and 3rd wave (November 2021–February 2022). The observed trends of the study outcomes were compared with the expected trends.ResultsThere were no significant differences between the observed versus expected overall outpatient visits in the 2nd wave, however, a significant decline in OPD attendance was observed during the 3rd wave (15,101 vs 31,154; incidence rate ratio (IRR) = 0.48 [0.41–0.56]). No significant differences in the overall observed versus expected proportions of suspected malaria cases and test positivity rates in both COVID waves. However, a significant decrease in the overall proportion of suspected malaria cases tested with a malaria diagnostic test was observed during the 3rd wave (99.86% vs 99.99%; relative percent ratio [RPR] = 0.99 [0.99–0.99]). Finally, a significant decline in the overall proportion of malaria cases prescribed AL was observed during the 2nd wave (94.99% vs 99.85%; RPR = 0.95 [0.92–0.98]) but not the 3rd wave.ConclusionSignificant declines in OPD attendance and suspected malaria cases tested with malaria diagnostic test were observed during the 3rd COVID-19 wave, while AL prescription significantly reduced during the 2nd COVID-19 wave. These findings add to the body of knowledge highlighting the adverse impact of COVID-19 pandemic on the malaria which could explain the increase in the malaria burden observed during this period.

Association Between Organizational Context and Resident Pain in Assisted Living: A Repeated Cross-Sectional Study.

Resident pain has been a common quality challenge in congregate care for older adults, and organizational context may explain variations in resident pain beyond resident-level factors. Most studies have focused on nursing homes (NHs), largely neglecting assisted living (AL). AL residents are at similar risk for pain as NH resident, but with AL providing fewer services and staffing resources. Our objective was to examine whether organizational context in AL was associated with resident pain during the first two waves of the COVID-19 pandemic. This repeated cross-sectional study linked AL home surveys, collected in COVID-19 waves 1 (March-June 2020) and 2 (October 2020-February 2021) from a key contact, to administrative Resident Assessment Instrument-Home Care (RAI-HC) records in these homes. Surveys assessed preparedness for COVID-19 outbreaks, availability of a registered nurse or nurse practitioner, direct care staff shortages, decreased staff morale, COVID-19 outbreaks, confinement of residents to their rooms, supporting video calls with physicians, facilitating caregiver involvement. The dependent variable (moderate daily pain or pain of a severe intensity) and resident covariates came from the RAI-HC. Using general estimating equations, adjusted for repeated resident assessments and covariates, we assessed whether AL organizational context was associated with resident pain during the pandemic. We included 985 residents in 41 facilities (wave 1), and 1134 residents in 42 facilities (wave 2). Pain prevalence [95% confidence interval] decreased non-significantly from 20.6% [18.6%-23.2%] (March-June 2019) to 19.1% [16.9%-21.6%] (October 2020-February 2021). Better preparedness (odds ratio=1.383 [1.025-1.866]), confinement of residents to their rooms (OR=1.616 [1.212-2.155]), availability of a nurse practitioner (OR=0.761 [0.591-0.981]), and staff shortages (OR=0.684 [0.527-0.888]) were associated with resident pain. AL organizational context factors were associated with resident pain. Policy and management interventions can and must address such factors, providing potentially powerful levers for improving AL resident quality of care.

The effect of 2 years COVID-19 pandemic on myocardial infarction and stroke admissions: nationwide registry study, sex differences and mortality

Abstract Background Throughout the COVID-19 pandemic many countries reported a decline in myocardial infarction (MI) and stroke admissions. Factors leading to the decline remain uncertain. Purpose In this nationwide study spanning the first two years of the COVID-19 pandemic we evaluated the changes in MI and stroke admissions as the effect of pandemic intensity and nationwide lockdown. We also investigated sex and age differences. We benefited from the fact that the Czech Republic had both, the lowest and one of the highest COVID-19 incidence. Methods We compared the first (March 1, 2020, to February 28, 2021) and the second (March 1, 2021, to February 28, 2022) pandemic years overall and during the COVID-19 waves with the preceding year (March 1, 2019, to February 28, 2020). The occurrence of MI and stroke, patient characteristics, and mortality data were obtained from two nationwide registers covering every hospitalization and every death in the Czech Republic. Results During the first and second pandemic year as compared to the preceding year there were 13,856 and 14,123 vs 15,499 MI admissions, representing a -10,6% (p<0.001) and -8,9% (p<0.001) decline; and 23,194 and 23,231 vs 25,046 stroke admissions, representing a -7.4% (p<0.001) and -7.2% (p<0.001) decline respectively. The significant decline appeared in both MI and stroke in all COVID-19 waves (spring 2020, spring 2021, autumn 2020/winter 2021, -autumn 2021/winter 2022). The greatest MI hospitalization decline (-16%) occurred during the first strict national lock down, during the first COVID-19 wave (spring 2020), the pandemic wave with the lowest COVID-19 incidence. Both, MI and stroke admissions reversed to pre-pandemic numbers during the summer (2020, 2021) when most lockdown restrictions were lifted. The decline in MI was greater in women than in men during both the first (-11.8%, -10%; p<0.001) and second (-10.8%, -7.9%; p<0.001) pandemic years, also for stroke in women greater than in men during the first (-9%, -5.9%; p<0.001) and second (-9.7%, -4.8%; p<0.001) pandemic years. The 30-day all-cause mortality in patients with MI was higher during the first (10.6%; p<0.001) but not the second (9.6%; p=0.34) pandemic year as compared to the preceding year (9.3%), whereas the 30-day mortality in patients with stroke remained unchanged. Conclusions There was a decline in both MI and stroke admissions during the first two years of the pandemic, but such decline was not related to COVID-19 incidence. The greatest reduction in both, MI and stroke admissions, appeared during the first lockdown in the first COVID-19 wave (spring 2020), the period of the lowest COVID-19 incidence. Moreover, the decline in MI and stroke admissions was greater in women. This highlights potential sex differences in health effect. Health authorities should be aware of these findings, in order to adapt their message and restriction measures in case of future major epidemics.Myocardial infarction casesStroke cases

Increased feelings of nervousness among older Europeans post-pandemic

Abstract Background Feelings of nervousness are typically associated with situational factors and are temporary, less intense, and manageable with self-care. Study Aim This study examines which situational conditions are significant for feeling nervous among European older adults during the second wave of COVID-19. Methods Using data from Wave 9, COVID-19 Survey 2 (Summer, 2021) of the cross-national panel dataset the Survey of Health, Aging, and Retirement in Europe (SHARE), this study utilizes a sample of 49,253 adults over 50 years old from 27 countries in Europe. Analysis: Binary logistic regression indicates all variables are significant predictors of feeling nervous: Gender [Wald= 498.7, p = 0.001 (<0.05)]; chronic disease [Wald= 1635.002 p = 0.001 (<0.05)], employment [Wald= 164.544, p = 0.001 (<0.05)]; home care [Wald= 138.4, p = 0.001 (<0.05)], technology [Wald= 20.66, p = 0.001 (<0.05)]; social support [Wald= 106.66, p = 0.001 (<0.05)], and financial support [Wald=35.5, p = 0.001 (<0.05)] [Chi-Square=3216.401, df = 6 and p = 0.001 (p < 0.05)]. Findings The odds ratio (OR) for gender is .627 (95% CI .601- .653), for chronic disease, is 2.659 (95% CI: 1.716 - 4.119) and employment is .675 (95% CI: .635-.717) and homecare is (OR)3.47(95% CI: 2.83-4.28); OR for social support is 855(95% CI:.830-.881), financial support 1.779 (95% CI: 1.47-2.15), and technology (OR).956 (95% CI: 938-.975). The model correctly predicted 94% of cases without feeling nervous and 16% of cases where people did feel nervous, giving an overall percentage correct prediction rate of 68%. Implications for Policy and Practice: Knowledge from this study can inform the introduction of public health interventions for older adults targeted toward specific situational circumstances that reduce nervousness. Key messages • While feelings of nervousness can be situational, the impact on mental health can go beyond the contextual environment. • Public health policies can support targeted interventions aimed at developing the coping skills necessary to better respond in times of crisis.

An Artificial Intelligence approach to monitor infectious diseases: lessons learned from COVID-19

Abstract Background The World Health Organization declared the start (March 11th 2020) and the end (May 5th 2023) of COVID19 pandemic, while in Italy it has been present since February 2020. Indicators such as number of new positive cases, deaths and hospitalizations are used to monitor epidemiological trends, but they suffer from biases limiting their effectiveness. Methods We used data from the Emergency Medical Services Activities as an alternative and studied three types of contributions: COVID19, flu and baseline during three time frames: -period 1 (July 1st-Oct 11th 2016; Feb 10th-May 20th 2017) used to model the baseline, when flu contribution should be negligible. -Period 2: (Dec 15th-Feb 14th 2017), when flu in 2017 is very evident. -Period 3: (March 11th-31st 2020), when the first COVID19 wave is dominant. To extract the pure contribution from flu and COVID19, the baseline contribution was properly subtracted. Results From these data we developed a machine learning approach (MLA) that offers a simple and powerful tool to monitor COVID19 or future infectious diseases. To maximize the identification power of COVID19, we used the Toolkit for multivariate analysis package. An artificial neural network, multilayer perceptron, deep neural network and boosted decision tree methods have been trained, considering the events in period 3 as signal and period 1 as background. To avoid overtraining, the samples were divided in two: one half to train the algorithm, and the other to check the performance. The results are stable over time and able to efficiently discriminate COVID19. With 50% efficiency of accepting COVID19 patients, roughly 95% of baseline patients can be rejected. Conclusions MLA can be used to early assign a probability of COVID19 without specific test, only relying on standard triage and emergency call details. This tool could be very useful to early detect the presence of new pandemics and tag positive patients before the official healthcare reporting system. Key messages • AI model using data from Emergency Medical Services Activities can be a useful tool to early detect new pandemics and tag positive patients before the official healthcare reporting system. • Covid19 pandemic gave us the possibility to develop new digital tools to use in the analysis of epidemiological trends.

Psychological Distress Changes During COVID-19: A Belgian Cohort Study On Psychosocial Factors

Abstract Longitudinal studies have identified an increase in psychological distress across the general population during the COVID-19 pandemic. Nevertheless, the trajectories of mental health outcomes exhibit variations, suggesting potential associations with psychosocial individual factors. This paper identifies the factors of this individual trajectory of psychological distress during the COVID-19 pandemic. Five waves of a prospective cohort survey were conducted with a convenience sample of the general population in Belgium between March 2020 and November 2021 (n = 4,550). Psychological distress was measured using the GHQ-12. Individual covariates included socioeconomic factors (age, gender, level of education), psychological factors (loneliness, social support, and social activities), and factors related to the virus and the lockdown measures (exposure to COVID-19 and survey wave). Multilevel models were used for analysis. Women and young people experienced more pronounced fluctuations in their risk of psychological distress across study waves, experiencing both increases and decreases. We found that individual variance in psychological distress breaks down into two components, respectively 43% for psychosocial factors (time-invariant) and 57% for the survey waves (time-variant) variation. A significant share of the time-invariant difference in psychological distress over COVID-19 is associated with loneliness, social support, and social activities. Loneliness emerged as the most important interpersonal factor associated with psychological distress. The change in psychological distress was mainly associated with psychosocial factors rather than with pandemic-related dynamics (e.g. survey waves). These findings suggest that mitigation policies aiming at controlling the pandemic should focus more on addressing specific individual vulnerabilities rather than solely responding to the fluctuations within pandemic waves to decrease their detrimental impact on mental health.

Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis.

By the end of 2021, the new wave of COVID-19 sparked by the Omicron variant spread rapidly due to its highly contagious nature, affecting more than 170 countries worldwide. Nucleic acid testing became the gold standard for diagnosing novel coronavirus infections. As of July 2022, numerous cities and regions in China have implemented regular nucleic acid testing policies, which have had a significant impact on socioeconomics and people's lives. This policy has garnered widespread attention on social media platforms. This study took the newly issued regular nucleic acid testing policy during the COVID-19 pandemic as an example to explore the sentiment responses and fluctuations of netizens toward new policies during public health emergencies. It aimed to propose strategies for managing public opinion on the internet and provide recommendations for policy making and public opinion control. We collected blog posts related to nucleic acid testing on Weibo from April 1, 2022, to July 31, 2022. We used the topic modeling technique latent Dirichlet allocation (LDA) to identify the most common topics posted by users. We used Bidirectional Encoder Representations from Transformers (BERT) to calculate the sentiment score of each post. We used an autoregressive integrated moving average (ARIMA) model to examine the relationship between sentiment scores and changes over time. We compared the differences in sentiment scores across various topics, as well as the changes in sentiment before and after the announcement of the nucleic acid price reduction policy (May 22) and the lifting of the lockdown policy in Shanghai (June 1). We collected a total of 463,566 Weibo posts, with an average of 3799.72 (SD 1296.06) posts published daily. The LDA topic extraction identified 8 topics, with the most numerous being the Shanghai outbreak, nucleic acid testing price, and transportation. The average sentiment score of the posts was 0.64 (SD 0.31), indicating a predominance of positive sentiment. For all topics, posts with positive sentiment consistently outnumbered those with negative sentiment (χ27=24,844.4, P<.001). The sentiment scores of posts related to "nucleic acid testing price" decreased after May 22 compared with before (t120=3.882, P<.001). Similarly, the sentiment scores of posts related to the "Shanghai outbreak" decreased after June 1 compared with before (t120=11.943, P<.001). During public health emergencies, the topics of public concern were diverse. Public sentiment toward the regular nucleic acid testing policy was generally positive, but fluctuations occurred following the announcement of key policies. To understand the primary concerns of the public, the government needs to monitor social media posts by citizens. By promptly sharing information on media platforms and engaging in effective communication, the government can bridge the information gap between the public and government agencies, fostering a positive public opinion environment.

Assessing the severity of COVID-19 waves: beware of surveillance bias

Abstract Background Indicators to assess the severity of epidemic waves during the COVID-19 pandemic were influenced by differences in detection modalities over time, leading to surveillance bias. We compared four indicators across three pandemic periods to assess surveillance bias. Methods We used data from one region of Switzerland. We compared seroprevalence, cases, hospitalizations, and deaths during three periods (period 1: Feb-Oct 2020, including the 1st wave; period 2: Oct 2020-Feb 2021, including the 2nd wave; period 3: Feb-Aug 2021, including the 3rd wave and after the start of the vaccination campaign). Data were retrieved from the Swiss Federal Office of Public Health or population-based studies. We compared each indicator to a reference indicator (seroprevalence during periods 1 and 2 and hospitalizations during period 3). We also assessed the timeliness of the indicators, i.e., the duration from data generation to the availability of the information to decision-makers. Results According to seroprevalence estimates, the severity of the 2nd wave was slightly larger (by a ratio of 1.4) than the severity of the 1st wave. Compared to seroprevalence, cases largely overestimated the 2nd wave severity (2nd vs 1st wave ratio: 6.5) while hospitalizations (ratio: 2.2) and deaths (ratio: 2.9) were more suitable to compare the severity of these waves. According to hospitalizations, the 3rd wave severity was slightly smaller (by ratio of 0.7) than the 2nd wave. Compared to hospitalizations, cases or deaths slightly underestimated the 3rd wave severity (3rd vs 2nd wave ratio for cases: 0.5; for deaths: 0.4) and seroprevalence was very biased due to high vaccination rates. Across all waves, timeliness for cases and hospitalizations was better than for deaths or seroprevalence. Conclusions To assess the severity of pandemic waves accounting for surveillance bias, different types of indicators must be used across time. Key messages • Differences in detection modalities over time can skew the assessment of the severity of COVID-19 pandemic waves, leading to surveillance bias. • The effectiveness of indicators in describing the severity of COVID-19 pandemic waves depends on the type of indicator used and the stage of the pandemic.

Nutritional habits and eating attitude in university students during the last wave of COVID-19 in Spain

Emotional Eating is eating in response to negative emotional states, such as boredom, loneliness, or anxiety. For some young people, both food and alcohol are a source of pleasure in their social life. A cross-sectional study was conducted in 2022-23 in the Health Sciences Faculty. Mediterranean Diet Adherence Test, alcohol use disorders identification test (AUDIT), Emotional Eater Questionnaire (EEQ), Pittsburgh Sleep Quality Index, Yale Food Addiction, Perceived Stress Scale, and Anxiety Questionnaire STAI, were employed. Emotional eater (EE) regarding to no emotional eater (NEE) presented a mild food addiction, a significantly high intake of carbohydrates, fat, and alcohol. Energy intake is dependent on saturated fat, also they are not conscious of their intake of calories, had worse sleep quality, high perceived stress, and worse healthy eating index (HEI) due to their intake of sweets and soft drinks. The diet quality could be affected by the stress of the pandemic. The association of emotional eating with the intake of sweet foods and alcoholic drinks maybe is a remaining situation of COVID-19. We suppose that maladaptive eating behaviors have improved over time during this pandemic. Even though it is necessary to promote healthier eating among university students, it is also necessary to improve healthy habits.

Food purchase and consumption behavior during the COVID-19 pandemic lockdown in Bangladesh: association between sociodemographic composition

BackgroundBangladesh suffered a severe COVID-19 wave from June to August 2021, which forced the government to impose emergency nationwide lockdown measures for three months with discontinuities. The sudden lockdown strongly affected the dietary preferences, financial circumstances, and social interactions of citizens.MethodsIn this comprehensive study, we collected sociodemographic information as well as data on food purchase and consumption behaviors from 1,350 adults in Bangladesh during the weeks of enforced confinement. The association between the sociodemographic factors (viz., gender, age, education status, income, occupation, and household size) and food purchase and consumption behaviors were determined by conducting bivariate and multivariate logistic regression models reporting as odds ratios.ResultsThe descriptive result reveals that 49.63% of the participants experienced wage reductions, with many people enduring reductions of up to 75%; 12.22% lost their jobs. Besides, a decline in the frequency of shopping was observed by 35.04%; and 24.52% avoided outdoor shopping during the pandemic period. Additionally, 28.74% of participants purchased less overall, while 19.48% purchased significantly fewer items per trip compared to the pre-pandemic period. The multivariate analysis shows a prominent increase in online grocery shopping (OR: 4.03, 95% CI: 2.38–6.83, p < 0.001) and meal delivery services (OR = 5.19, 95% CI = 3.21–7.17, p < 0.001) among higher educated individuals compared to the individuals having no institutional education. The personnel having formal jobs purchased 2.34 times more from online grocery sites (95% CI = 0.34–4.08, p = 0.003) compared to the unemployed one. In contrast, a notable increase in panic buying and stockpiling was observed, driven by scarcity and escalating prices of essential food items. The frequency of meals with families, active engagement in meal preparation, and the exploration of novel recipes also increased notably.ConclusionsThe consumption of takeaway food and inter-meal snacks increased significantly with the education level of individuals. These findings highlighted that a prolonged pandemic could magnify economic vulnerability in Bangladesh.

The Effects of SSRIs and Antipsychotics on Long COVID Development in a Large Veteran Population

The development of Long COVID is a complex disease process that may be partially driven by neuroinflammation. Antipsychotics have been shown to exert neuroprotective effects under certain conditions. Our study aimed to determine if veterans treated with antipsychotics and/or selective serotonin reuptake inhibitors (SSRIs) for a psychiatric condition had a reduced risk of developing long-term COVID. We conducted a retrospective cohort study with two cohorts of patients based on the COVID-19 wave in which the patient’s initial infection occurred (Cohort 1: alpha/beta waves, and Cohort 2: delta/omicron waves) with stratification by age. A multivariate logistic regression model was used to evaluate the association between the use of antipsychotics and Long COVID diagnosis. In Cohort 1, antipsychotic use was associated with 43% and 34% reductions in the odds of developing Long COVID in patients aged &lt;65 and &gt;65 years, respectively. This association was reduced in the second cohort to 11% in patients aged &lt;65 years and without an association over 65 years of age. SSRIs showed no benefit in either age group or cohort. Our results show that antipsychotic use for the treatment of a mental health condition was associated with a reduction in the risk of developing Long COVID, and the magnitude of this reduction varied between COVID-19 cohorts.

Awareness and preparedness of healthcare workers for the initial wave of COVID-19 in Aotearoa New Zealand.

The TMGH-Global COVID-19 Collaborative was a multinational, multicentre, cross-sectional survey assessing the awareness and preparedness of healthcare workers (HCWs) during the first wave of the pandemic across 57 countries. Here, we report the results from Aotearoa New Zealand. This cross-sectional survey was conducted at Christchurch Hospital between February and May 2020. Data were collected from a convenience sample of HCWs and analysed using descriptive and multivariate regression to determine awareness (out of 40) and preparedness (out of 15) scores and influencing factors. Of the 158 participants (response rate 20.8%), most were women (73%) and doctors (58%) with a median age of 38 years (interquartile range [IQR] 29-49). The median awareness and preparedness scores were 33.6 (IQR 31.1-35.1) and 8 (IQR 6-8), respectively. Mainstream media was the primary source of information on COVID-19 among HCWs. The awareness score was significantly affected by gender and profession, whereas the preparedness score was influenced by age, profession, clinical experience duration and COVID-19 training. Although frontline HCWs had high awareness levels, preparedness was low. Variables influenced awareness and preparedness differently. These findings identified gaps in pandemic readiness and factors that can be leveraged to enhance future pandemic preparedness and response in New Zealand.

The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises.

Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves overthe past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention and control. However, detecting high-risk variants through experimental and epidemiological research is time-consuming and often lags behind the emergence and spread of these variants. In this study, HiRisk-Detector a machine learning algorithm based on haplotype network, is developed for computationally early detecting high-risk SARS-CoV-2 variants. Leveraging over 7.6 million high-quality and complete SARS-CoV-2 genomes and metadata, the effectiveness, robustness, and generalizability of HiRisk-Detector are validated. First, HiRisk-Detector is evaluated on actual empirical data, successfully detecting all 13 high-risk variants, preceding World Health Organization announcements by 27 days on average. Second, its robustness is tested by reducing sequencing intensity to one-fourth, noting only a minimal delay of 3.8 days, demonstrating its effectiveness. Third, HiRisk-Detector is applied to detect risks among SARS-CoV-2 Omicron variant sub-lineages, confirming its broad applicability and high ROC-AUC and PR-AUC performance. Overall, HiRisk-Detector features powerful capacity for early detection of high-risk variants, bearing great utility for any public emergency caused by infectious diseases or viruses.

Outcomes and Healthcare Resource Utilization in Patients with COVID-19 Treated with Nirmatrelvir-Ritonavir: Real-World Data Analysis.

Background: Nirmatrelvir-ritonavir was granted emergency use authorization in Israel in January 2022 to treat high-risk patients with mild-to-moderate COVID-19. The aim of the study was to assess the association between nirmatrelvir-ritonavir treatment and COVID-19-related hospitalization and healthcare resource utilization (HCRU) in a country with a high level of vaccinations compared to patients who were offered treatment and declined. Methods: The Maccabi Healthcare Services dataset was used to identify high-risk SARS-CoV-2-positive adults from January to February 2022 who received nirmatrelvir-ritonavir within 5 days of symptom onset (treatment group) or who were offered nirmatrelvir-ritonavir treatment and declined it (reference group). COVID-19-related hospitalizations and all-cause mortality and HCRU within 30 days were compared between treatment and reference groups using inverse probability of treatment weighting. Results: Treatment and reference groups included 3460 (median age, 68.4 years) and 1654 (70.2 years) patients, respectively. Patients with ≥1 dose of COVID-19 vaccine accounted for 89.5% (treatment group) and 72.1% (reference group) of the total. Treatment was associated with a lower risk of COVID-19-related hospitalization (adjusted OR, 0.59 [95% CI, 0.41,0.83]). Results were similar by age group (18-64/≥65 years) and among patients with/without vaccination in the prior 180 days. There were 11 (0.3%) versus 11 (0.7%) deaths in the treatment and reference groups, respectively. Treated patients had lower inpatient HCRU and greater less intensive outpatient HCRU (e.g., telemedicine and emergency room visits). Conclusions: Nirmatrelvir-ritonavir treatment was associated with a reduced risk of COVID-19-related hospitalization and a shift to less intensive outpatient HCRU. Comparison with a reference group of nirmatrelvir-ritonavir-eligible patients who declined treatment enabled an unbiased outcome assessment. Real-world data gathered during the Omicron BA.1 variant wave of COVID-19 in Israel support the continued use of nirmatrelvir-ritonavir for high-risk adults of all ages, regardless of previous vaccinations.

Diagnosis from an Epidemiological Point of View. The Example of COVID-19

A diagnostic test is anything that provides data about the patient's health or disease. All such tests can be formally evaluated for their accuracy and precision (sensitivity and specificity). Once a test has been performed, whether positive or negative, sensitivity and specificity are not the priority issues; the physician's dilemma is whether or not the patient has the disease, once the test result is known. This is the positive and negative predictive value. However, the positive predictive value depends on the prevalence: when prevalence is high, the positive predictive value of the test increases and, as a consequence, there are fewer false positives and more false negatives. When prevalence is low, the opposite occurs: the positive predictive value of the test decreases, and there will be more false positives and fewer false negatives. Furthermore, diagnoses are generally not determined and labeled in isolation; rather, diagnoses are applied sequentially (Bayesian inference). Thus, prior knowledge facilitates rapid decision-making that is generally correct by increasing the pre-test diagnostic probability. When tests are performed in series (one after the other), specificity and positive predictive value are maximized, but sensitivity and negative predictive value are reduced. Thus, we have the example of COVID-19. For a low prevalence scenario (such as the current one), assuming a specificity of 98%, a positive test ensures the diagnosis in at least 2/3 of patients. And a negative result practically rules it out. When the incidence of COVID-19 is high. For example, with a prevalence of 30% or in practice when we are in some "COVID-19 wave", a positive result ensures the diagnosis in 94% but a negative result can occur in up to 10% of patients. On the other hand, a second test after the first gives a more reliable result if its results go in the same direction.

Impact of COVID-19 infection on genitourinary cancer management. SOGUG-COVID-19: A spanish, multicenter, observational study.

The COVID-19 pandemic is a great burden worldwide, but its impact on patients with genitourinary cancer (GUC) is poorly characterized. This study aimed to characterize the clinical features and evolution of GUC patients affected by COVID-19 in Spain. SOGUG-COVID-19 was an observational ambispective non-interventional study that recruited patients with SARS-CoV-2 infection who had been treated for GUC in 32 Spanish hospitals. Data were collected from patients' medical records in a short period of time, coinciding with the first waves of COVID-19, when the mortality was also higher in the general population. From November 2020 to April 2021, 408 patients were enrolled in the study. The median age was 70years, and 357 patients (87.5%) were male. Most frequent Cancer Origin was: prostate (40.7%), urothelial (31.4%) and kidney (22.1%). Most patients (71.3%) were diagnosed at the metastatic stage, and 33.3% had poorly differentiated histology. Anticancer treatment during the infection was reported in 58.3% of patients, and 21.3% had received immunotherapy prior to or concurrent with the infection. The most frequent COVID-19 symptoms were pyrexia (49.0%), cough (38.2%) and dyspnea (31.9%). Median age was higher for patients with pneumonia (p < 0.001), patchy infiltrates (p = 0.005), ICU admission (p < 0.001) and death (p < 0.001). Tumor stage was associated with complications (p = 0.006). The fatality rate was 19.9% and the 6-month COVID-19-specific survival rate was 79.7%. Patients with genitourinary cancers seem exceptionally vulnerable to COVID-19 regardless of tumor type or anticancer therapy. Age and tumor stage were the only identified risk factors for severe COVID-19.

Study of noise perception and health effects on population through a questionnaire in Spanish households during the COVID-19 lockdown

During the first wave of COVID-19, an online questionnaire was conducted to gather the Spanish population's perception of noise from households and its relationship with health effects. This research, which was conducted with 582 participants, examined several sources of noise (general noise, noise produced by family members inside the home, noise from neighbors, noise from common areas, facilities, and exterior noise) and various self-declared health effects by the participants such as stress, lack of concentration, sleep disturbances, anxiety, or irritability. A descriptive and statistical analysis was established between variables to observe relational trends for two periods (before and during confinement). The study demonstrates interesting relational trends that allow for both associating different noise sources with potential health effects and contextualizing them against other causes that remained consistent in both periods.

Bayesian State-space modelling of stock Markets in G7 Countries

This work examines the impact of Coronavirus disease (COVID-19) on the stock market of Group of Seven (G7) countries during the first wave of COVID-19 using daily data from March, 1st of 2020 to December, 31st of 2020. Focussing on such period, a Bayesian Structural Time Series Model (BSTSM) was used to capture the effects of first wave of COVID-19 on the stock market performance of these G7 countries by employing a Markov Chain Monte Carlo (MCMC) method. We considered an Autoregressive (AR) model with time-varying parameters and a local linear trend model to know if the stock price of these countries during the period of the first wave of COVID-19 is changing over time. There was a stochastic trend in stock prices of G7 countries during the period of the first wave of COVID-19 while the AR process itself was also changing over time. The stock market of the USA followed by Japan performed better than other G7 countries during the first phase of the COVID-19 pandemic while the stock market of France was affected during the COVID-19 pandemic.