Differences in non-positive intention to accept the COVID-19 booster vaccine between three countries in the cross-border region Meuse-Rhine Euroregion: The Netherlands, Belgium, and Germany.

COVID-19 booster vaccination has shown to add to the protection against infection with SARS-CoV2 and subsequent severe disease. This longitudinal cross-border study aimed to identify factors associated with COVID-19 booster vaccine intentions in an initially vaccinated adult population living in the Meuse-Rhine Euroregion (EMR; including the Netherlands, Belgium, and Germany) and differences between countries. Data collection took place in autumn of 2021 and consisted of online questionnaires sent to a random sample of the population based on governmental registries. Data from 3,319 fully and partially vaccinated adults were used to examine determinants of non-positive intention for a booster vaccination (i.e., uncertain or do not want), using multivariable logistic regression analyses weighted by age group, sex, and country. Compared to German residents, Dutch residents (OR = 2.4) and Belgian residents (OR = 1.4) were more likely to be uncertain or not want to receive a booster vaccine in September-October 2021. Factors independently associated with non-positive intention were female sex (OR = 1.6), absence of comorbidities (OR = 1.3), time since last vaccination less than 3 months ago for those fully vaccinated (OR = 1.6), being partially vaccinated (OR = 3.6), a negative experience with communication of COVID-19 measures (OR = 2.2), and regarding measures as ineffective (OR = 1.1). Results indicate that booster vaccine intentions differ between countries in the cross border Meuse-Rhine Euroregion. Non-positive intention for the booster vaccine is prevalent in all three countries of the EMR, but to a different extent, as shown in this study. Cross-border collaboration and sharing information and knowledge about vaccination strategies could play a role in limiting the impact of COVID-19.

COVID-19 Vaccine Hesitancy among the Population of Quetta-Associated Beliefs and Barriers

Objectives: This study aimed to determine the prevalence of hesitancy towards COVID-19 vaccination among the adult population of Quetta and the likelihood predictors of hesitancy towards COVID-19 vaccination. Study Design: Analytical cross-sectional study. Place and Duration of Study: Study was conducted at Quetta, from Nov 2021 to May 2022. Methodology: A total of 396 individuals participated in the study. Along with socio-demographic details, participants responded to the COVID-VAX scale and questions from WHO determinants of vaccine-hesitancy. A relationship was modelled between WHO determinants of vaccine-hesitancy, socio-demographic characteristics and vaccine-hesitancy using logistic regression. Results: Results showed that majority of individuals were vaccine-hesitant (67.2%). The final model obtained significant predictors among contextual influences as demonstrated past bad experiences on vaccination, non-belief in risking their own and family's health for being non-vaccinated and negative influences by an influential person. Among individual influences, knowledge about someone having bad experiences to vaccines, confusion about scheduling of vaccines, lack of trust in the healthcare system and provider, disbelieving in immunization as a social norm and concerns regarding vaccine. Among factors directly related to vaccine/vaccination, difficult vaccination schedule, fear of pain/needles, and non-willingness to pay for vaccination were found as significant predictors of the COVID-19 vaccine-hesitancy (p-value <0.05). Conclusions: More than half of the participants showed hesitancy towards COVID-19 vaccine. Factors contributing to vaccine-hesitancy must be addressed to build confidence regarding COVID-19 vaccines among the people to attain the goal of herd immunization against COVID-19. © 2022, Army Medical College. All rights reserved.

COVID-19 Vaccine Hesitancy among the Population of Quetta-Associated Beliefs and Barriers

ABSTRACT Objectives: This study aimed to determine the prevalence of hesitancy towards COVID-19 vaccination among the adult population of Quetta and the likelihood predictors of hesitancy towards COVID-19 vaccination. Study Design: Analytical cross-sectional study. Place and Duration of Study: Study was conducted at Quetta, from Nov 2021 to May 2022. Methodology: A total of 396 individuals participated in the study. Along with socio-demographic details, participants responded to the COVID-VAX scale and questions from WHO determinants of vaccine-hesitancy. A relationship was modelled between WHO determinants of vaccine-hesitancy, socio-demographic characteristics and vaccine-hesitancy using logistic regression. Results: Results showed that majority of individuals were vaccine-hesitant (67.2%). The final model obtained significant predictors among contextual influences as demonstrated past bad experiences on vaccination, non-belief in risking their own and family's health for being non-vaccinated and negative influences by an influential person. Among individual influences, knowledge about someone having bad experiences to vaccines, confusion about scheduling of vaccines, lack of trust in the healthcare system and provider, disbelieving in immunization as a social norm and concerns regarding vaccine. Among factors directly related to vaccine/vaccination, difficult vaccination schedule, fear of pain/needles, and non-willingness to pay for vaccination were found as significant predictors of the COVID-19 vaccine-hesitancy (p-value <0.05). Conclusions: More than half of the participants showed hesitancy towards COVID-19 vaccine. Factors contributing to vaccine-hesitancy must be addressed to build confidence regarding COVID-19 vaccines among the people to attain the goal of herd immunization against COVID-19.

Telephonic Follow-up and the Risk of Death in Ambulatory Patients with COVID-19.

BACKGROUND: Primary care level close monitoring of mild COVID-19 patients has shown to provide a risk reduction in hospitalization and death. We aimed to compare the risk of all-cause death among COVID-19 ambulatory patients who received and did not receive telephonic follow-up in primary health care settings. METHODS: A secondary database analysis, 2-group comparative study, was conducted with data from the medical information systems of the Mexican Institute of Social Security. A total of 1,498,808 ambulatory patients aged 20 years old and over and with laboratory confirmed SARS-CoV-2 by PCR or rapid antigen test were analyzed. Of them, 535,898 (35.8%) where followed by telephonic calls. The cases were attended from October 14, 2020, to April 10, 2022. Death incidence was evaluated. To assess the association between death and telephonic follow-up we calculated risk ratio using a multivariate logistic model. RESULTS: Case fatality rate was 1.29% in the patients who received telephonic follow-up and 2.95% in the cases who did not receive phone calls. Medical history of chronic kidney disease, COPD, cardiovascular disease, tobacco consumption and diabetes were associated with increased risk of death. In the multivariate model, telephonic follow-up was associated with lower risk of all-cause death, with an adjusted risk ratio of 0.61 (95% confidence interval from 0.59, 0.64). CONCLUSION: Our data suggest that telephonic follow-up is associated with a risk of death reduction in adult outpatients with mild COVID-19, in the context of a multimodal strategy in the primary health care settings.

Priority Nursing Populations for Mental Health Support Before and During COVID-19: A Survey Study of Individual and Workplace Characteristics.

BACKGROUND: Nursing is a high-risk profession and nurses' exposure to workplace risk factors such as heavy workloads and inadequate staffing is well documented. The COVID-19 pandemic has exacerbated nurses' exposure to workplace risk factors, further deteriorating their mental health. Therefore, it is both timely and important to determine nursing groups in greatest need of mental health interventions and supports. PURPOSE: The purpose of this study is to provide a granular examination of the differences in nurse mental health across nurse demographic and workplace characteristics before and after COVID-19 was declared a pandemic. METHODS: This secondary analysis used survey data from two cross-sectional studies with samples (Time 1 study, 5,512 nurses; Time 2, 4,523) recruited from the nursing membership (∼48,000) of the British Columbia nurses' union. Data was analyzed at each timepoint using descriptive statistics and ordinal logistic regression. RESULTS: Several demographic and workplace characteristics were found to predict significant differences in the number of positive screenings on measures of poor mental health. Most importantly, in both survey times younger age was a strong predictor of worse mental health, as was full-time employment. Nurse workplace health authority was also a significant predictor of worse mental health. CONCLUSIONS: Structural and psychological strategies must be in place, proactively and preventively, to buffer nurses against workplace challenges that are likely to increase during the COVID-19 crisis.

American Twitter users revealed social determinant-related oral health disparities amid the COVID-19 pandemic.

OBJECTIVES: To assess self-reported population oral health conditions amid the COVID-19 pandemic using user reports on Twitter. METHOD AND MATERIALS: Oral health-related tweets during the COVID-19 pandemic were collected from 9,104 Twitter users across 26 states (with sufficient samples) in the United States between 12 November 2020 and 14 June 2021. User demographics were inferred by leveraging the visual information from the user profile images. Other characteristics including income, population density, poverty rate, health insurance coverage rate, community water fluoridation rate, and relative change in the number of daily confirmed COVID-19 cases were acquired or inferred based on retrieved information from user profiles. Logistic regression was performed to examine whether discussions vary across user characteristics. RESULTS: Overall, 26.70% of the Twitter users discussed "Wisdom tooth pain/jaw hurt," 23.86% tweeted about "Dental service/cavity," 18.97% discussed "Chipped tooth/tooth break," 16.23% talked about "Dental pain," and the rest tweeted about "Tooth decay/gum bleeding." Women and younger adults (19 to 29 years) were more likely to talk about oral health problems. Health insurance coverage rate was the most significant predictor in logistic regression for topic prediction. CONCLUSION: Tweets inform social disparities in oral health during the pandemic. For instance, people from counties at a higher risk of COVID-19 talked more about "Tooth decay/gum bleeding" and "Chipped tooth/tooth break." Older adults, who are vulnerable to COVID-19, were more likely to discuss "Dental pain." Topics of interest varied across user characteristics. Through the lens of social media, these findings may provide insights for oral health practitioners and policy makers.

Clinical profile, treatment, and outcomes of patients with COVID-19 in a tertiary referral hospital in South Sumatera, Indonesia: A retrospective single-center study

Background: Although there are fewer COVID-19 cases in Indonesia, the pandemic is still ongoing. COVID-19 has a significant death rate in Indonesia, but lack of information on the effect of different clinical and demographic factors on COVID-19-related grimness and mortality in Indonesia.Objective: This study examined the clinical profile, treatment, and outcomes of patients with COVID-19 at Lahat Regency Hospital in South Sumatera, Indonesia, to find relevant markers that might be utilized to predict the prognosis of these patients.Methods: This was a retrospective single-center study of all medical record files of confirmed patients with COVID-19 admitted to Lahat Hospital from September 2020 to August 2021 (n = 285). Descriptive statistics, Chi-square, Mann-Whitney, Multiple Logistic Regression, and Cox's proportional hazards model were used for data analyses. Results: This study included 65 non-hospitalized and 220 hospitalized patients. Hospitalized patients were divided into dead and alive groups. The median age was lower in the non-hospitalized group without gender discrimination, and most hospitalized patients had comorbidities. Vital signs and clinical features were significantly different in hospitalized patients compared to non-hospitalized. The survival patients in the hospitalized group showed lower white blood cell (WBC), neutrophil percentages, and neutrophil-lymphocyte ratio (NLR) but higher lymphocyte and eosinophil. Non-survival patients had elevated alanine aminotransferase (ALT), blood urea nitrogen (BUN), creatinine, blood glucose, and potassium. The use of Favipiravir and Remdesivir was significant between the alive and dead groups. The mean hospital stay for all patients was 9.49 +/- 4.77 days, while the median duration of hospital time was 10.73 +/- 4.33 days in the survival group and 5.39 +/- 3.78 days in the non-survival group. Multiple logistic regression analysis determined respiration rate, WBC, and BUN as predictors of survival.Conclusions: Age and comorbidities are significant elements impacting the seriousness of COVID-19. Abnormal signs in laboratory markers can be used as early warning and prognostic signs to prevent severity and death. Potential biomarkers at various degrees in patients with COVID-19 may also aid healthcare professionals in providing precision medicine and nursing.

Clinical Risk Factors of Need for Intensive Care Unit Ad- mission of COVID-19 Patients, a Cross-sectional Study

Introduction: It could be beneficial to accelerate the hospitalization of patients with the identified clinical risk factors of intensive care unit (ICU) admission, in order to control and reduce COVID-19-related mortality. This study aimed to determine the clinical risk factors associated with ICU hospitalization of COVID-19 patients. Methods: The current research was a cross-sectional study. The study recruited 7182 patients who had positive PCR tests between February 23, 2020, and September 7, 2021 and were admitted to Afzalipour Hospital in Kerman, Iran, for at least 24 hours. Their demo-graphic characteristics, underlying diseases, and clinical parameters were collected. In order to analyze the relationship between the studied variables and ICU admission, multiple logistic regression model, classification tree, and support vector machine were used. Results: It was found that 14.7 percent (1056 patients) of the study participants were admit-ted to ICU. The patients' average age was 51.25 +/- 21 years, and 52.8% of them were male. In the study, some factors such as decreasing oxygen saturation level (OR=0.954, 95%CI: 0.944-0.964), age (OR=1.007, 95%CI: 1.004-1.011), respiratory distress (OR=1.658, 95%CI: 1.410-1.951), reduced level of consciousness (OR=2.487, 95%CI: 1.721-3.596), hypertension (OR=1.249, 95%CI: 1.042-1.496), chronic pulmonary disease (OR=1.250, 95%CI: 1.006-1.554), heart diseases (OR=1.250, 95%CI: 1.009-1.548), chronic kidney disease (OR=1.515, 95%CI: 1.111-2.066), cancer (OR=1.682, 95%CI: 1.130-2.505), seizures (OR=3.428, 95%CI: 1.615-7.274), and gender (OR=1.179, 95%CI: 1.028-1.352) were found to significantly affect ICU admissions. Conclusion: As evidenced by the obtained results, blood oxygen saturation level, the patient's age, and their level of consciousness are crucial for ICU admission.

Factors Associated with Intention to Vaccinate Children 0-11 Years of Age Against COVID-19.

BACKGROUND: Millions of children have tested positive for SARS-CoV-2, and over 1000 children have died in the US. However, vaccination rates for children 5 to 11 years old are low. METHODS: Starting in August 2020, we conducted a prospective SARS-CoV-2 household surveillance study in Spanish and English-speaking households in New York City and Utah. From October 21 to 25, 2021, we asked caregivers about their likelihood of getting COVID-19 vaccine for their child, and reasons that they might or might not vaccinate that child. We compared intent to vaccinate by site, demographic characteristics, SARS-CoV-2 infection detected by study surveillance, and parents' COVID-19 vaccination status using Chi-square tests and a multivariable logistic regression model, accounting for within-household clustering. RESULTS: Among parents or caregivers of 309 children (0 to 11 years) in 172 households, 87% were very or somewhat likely to intend to vaccinate their child. The most prevalent reasons for intending to vaccinate were to protect family and friends and the community; individual prevention was mentioned less often. The most prevalent reasons for not intending to vaccinate were side effect concerns and wanting to wait and see.In multivariable analysis, parents had much lower odds of intending to vaccinate if someone in the household had tested SARS-CoV-2-positive during the study (adjusted odds ratio = 0.09; 95% confidence interval, 0.03-0.3). CONCLUSION: This study highlighted several themes for clinicians and public health officials to consider including the importance and safety of vaccination for this age-group even if infected previously, and the benefits of vaccination to protect family, friends, and community.

Looking for patterns of change amid pandemic period in students' evaluation of academic teaching.

The COVID-19 pandemic has shaken not only the global economy but every development field, including all levels of the education sector and in every place in the world. The wide spread of this pandemic disaster has undoubtedly changed the education landscape worldwide. Online teaching and learning become the primary instruction method and the global world of schools, colleges and universities were forced to adapt this model. The first concern about online learning is whether this method is effective compared to traditional face-to-face lessons. In this paper, we carried out a quantitative analysis to explore variations in university students' feedback on learning experience in the context of this new challenging situation caused by the COVID-19 pandemic. By adopting an IRT modeling, we compared the appreciation of some aspects of 41 courses taught at the University of Chieti-Pescara (Italy) during the educational emergency with that of the previous year. Overall, from the results of this study, it is arisen that students have given positive feedbacks on their learning experiences and their effectiveness.

Risk score for mortality due to COVID-19: a prospective temporal validation cohort study in the emergency department of a tertiary care hospital

Objective. To validate a previously described hospital emergency department risk model to predict mortality in patients with COVID-19. Methods. Prospective observational noninterventional study. Patients aged over 18 years diagnosed with COVID-19 were included between December 1, 2020, and February 28, 2021. We calculated a risk score for each patient based on age >= 50 years (2 points) plus 1 point each for the presence of the following predictors: Barthel index <90 points, altered level of consciousness, ratio of arterial oxygen saturation to fraction of inspired oxygen <400, abnormal breath sounds, platelet concentration <100 x 10(9)/L, C reactive protein level >= 5 mg/dL, and glomerular filtration rate <45 ml/min. The dependent variable was 30-day mortality. We assessed the score's performance with the area under the receiver operating characteristic curve (AUC). Results. The validation cohort included 1223 patients. After a median follow-up of 80 days, 143 patients had died;901 patients were classified as having low risk (score, <= 4 points), 270 as intermediate risk (5-6 points), and 52 as high risk (>= 7 points). Thirty-day mortality rates at each risk level were 2.8%, 22.5%, and 65.4%, respectively. The AUC for the score was 0.883;for risk categorization, the AUC was 0.818. Conclusion. The risk score described is useful for stratifying risk for mortality in patients with COVID-19 who come to a tertiary-care hospital emergency department.

On Some Stochastic Epidemic Models

In this article, we present some stochastic non-linear epidemic models related to Covid-19. It is very hard to get exact solutions for the non-linear such models, but we have successfully obtained exact solutions of some suitable non-linear stochastic cases. The general stochastic logistic model is solved;and some properties related to functions of Wiener process are studied.

Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases.

BACKGROUND: To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases. METHODS: The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally. RESULTS: 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. CONCLUSIONS: In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.

Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients.

Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77-0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77-0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the "first wave" of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78-0.87]; for full follow-up: 0.82 [95% CI: 0.78-0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.

Acceptance of COVID-19 Vaccination Among Health System Personnel.

INTRODUCTION: One-third of the general public will not accept Coronavirus disease 2019 (COVID-19) vaccination but factors influencing vaccine acceptance among health care personnel (HCP) are not known. We investigated barriers and facilitators to vaccine acceptance within 3 months of regulatory approval (primary outcome) among adult employees and students at a tertiary-care, academic medical center. METHODS: We used a cross-sectional survey design with multivariable logistic regression. Covariates included age, gender, educational attainment, self-reported health status, concern about COVID-19, direct patient interaction, and prior influenza immunization. RESULTS: Of 18,250 eligible persons, 3,347 participated. Two in 5 (40.5%) HCP intend to delay (n = 1020; 30.6%) or forgo (n = 331; 9.9%) vaccination. Male sex (adjusted OR [aOR], 2.43; 95% confidence interval [CI], 2.00-2.95; P < .001), prior influenza vaccination (aOR, 2.35; 95% CI, 1.75-3.18; P < .001), increased concern about COVID-19 (aOR, 2.40; 95% CI, 2.07-2.79; P < .001), and postgraduate education (aOR, 1.41; 95% CI, 1.21-1.65; P < .001) - but not age, direct patient interaction, or self-reported overall health - were associated with vaccine acceptance in multivariable analysis. Barriers to vaccination included concerns about long-term side effects (n = 1197, 57.1%), safety (n = 1152, 55.0%), efficacy (n = 777, 37.1%), risk-to-benefit ratio (n = 650, 31.0%), and cost (n = 255, 12.2%).Subgroup analysis of Black respondents indicates greater hesitancy to accept vaccination (only 24.8% within 3 months; aOR 0.13; 95% CI, 0.08-0.21; P < .001). CONCLUSIONS: Many HCP intend to delay or refuse COVID-19 vaccination. Policymakers should impartially address concerns about safety, efficacy, side effects, risk-to-benefit ratio, and cost. Further research with minority subgroups is urgently needed.

Forecasting the 2020 COVID-19 Epidemic: A Multivariate Quasi-Poisson Regression to Model the Evolution of New Cases in Chile.

Objectives: To understand and forecast the evolution of COVID-19 (Coronavirus disease 2019) in Chile, and analyze alternative simulated scenarios to better predict alternative paths, in order to implement policy solutions to stop the spread and minimize damage. Methods: We have specified a novel multi-parameter generalized logistic growth model, which does not only look at the trend of the data, but also includes explanatory covariates, using a quasi-Poisson regression specification to account for overdispersion of the count data. We fitted our model to data from the onset of the disease (February 28) until September 15. Estimating the parameters from our model, we predicted the growth of the epidemic for the evolution of the disease until the end of October 2020. We also evaluated via simulations different fictional scenarios for the outcome of alternative policies (those analyses are included in the Supplementary Material). Results and Conclusions: The evolution of the disease has not followed an exponential growth, but rather, stabilized and moved downward after July 2020, starting to increase again after the implementation of the Step-by-Step policy. The lockdown policy implemented in the majority of the country has proven effective in stopping the spread, and the lockdown-relaxation policies, however gradual, appear to have caused an upward break in the trend.

Analysis of the prevalence and influencing factors of depression and anxiety among maintenance dialysis patients during the COVID-19 pandemic.

OBJECTIVE: Currently, the COVID-19 outbreak and its spread around the globe is significantly affecting mental health and health in general, worldwide. During the COVID-19 pandemic, the general medical complications have received the most attention, whereas only a few studies address the potential direct impact of SARS-CoV-2 on mental health. METHODS: A total of 321 maintenance hemodialysis (MHD) patients were selected using random sampling from the hemodialysis center of the second people's Hospital of Yibin. They completed Zung's self-rating anxiety scale (SAS) for anxiety, Zung's self-rating depression scale (SDS) for depression, and the activity of daily living scale (ADL) for the ability of living. Demographic data and laboratory tests were used to analyze the risk factors. RESULTS: The proportions of the prevalence of anxiety and depression symptoms were between 34.89% and 30.02%, respectively, among the MHD patients. The SAS and SDS scores of the 321 patients were 45.42(± 10.99) and 45.23(± 11.59), respectively. The results show that monthly income, medical insurance, vascular access, the duration of dialysis, complication, hemoglobin (HGB), immunoreactive parathyroid hormone (iPTH) and blood phosphorus (P) are factors that influence anxiety among patients (p < 0.05 for all). Vascular access, monthly income, medical insurance, complication, CRP, Alb, are factors that influence depression among patients (p < 0.05 for all). CONCLUSION: Our results suggest that during the pandemic period, the prevalence of anxiety and depression symptoms among MHD patients increased. Some demographic and clinical variables were associated with it. We should, therefore, pay more attention to the patients' psychology, start targeted intervention to alleviate the patients' anxiety and further improve their quality of life.

Influenza Vaccination and Hospitalizations Among COVID-19 Infected Adults.

INTRODUCTION: To date, there are no effective treatments for decreasing hospitalizations in Coronavirus disease 2019 (COVID-19) infections. It has been suggested that the influenza vaccine might attenuate the severity of COVID-19. METHODS: This is a retrospective single-centered cohort review of a de-identified database of 2005 patients over the age of 18 within the University of Florida health care system who tested positive for COVID-19. Comorbidities and influenza vaccination status were examined. The primary outcome was severity of disease as reflected by hospitalization and intensive care unit (ICU) admission. Logistic regression was performed to examine the relationship between influenza status and hospitalization. RESULTS: COVID-19-positive patients who had not received the influenza vaccination within the last year had a 2.44 (95% CI, 1.68, 3.61) greater odds of hospitalization and a 3.29 (95% CI, 1.18, 13.77) greater odds of ICU admission when compared with those who were vaccinated. These results were controlled to account for age, race, gender, hypertension, diabetes, chronic obstructive pulmonary disease, obesity, coronary artery disease, and congestive heart failure. DISCUSSION: Our analysis suggests that the influenza vaccination is potentially protective of moderate and severe cases of COVID-19 infection. This protective effect holds regardless of comorbidity. The literature points to a potential mechanism via natural killer cell activation. Though our data potentially is limited by its generalizability and our vaccination rate is low, it holds significant relevance given the upcoming influenza season. Not only could simply encouraging influenza vaccination decrease morbidity and mortality from the flu, but it might help flatten the curve of the COVID-19 pandemic as well. We encourage further studies into this finding.

Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems.

PURPOSE: Develop and validate simple risk scores based on initial clinical data and no or minimal laboratory testing to predict mortality in hospitalized adults with COVID-19. METHODS: We gathered clinical and initial laboratory variables on consecutive inpatients with COVID-19 who had either died or been discharged alive at 6 US health centers. Logistic regression was used to develop a predictive model using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The models were converted to point scores and their accuracy evaluated in an internal validation group. RESULTS: We identified 1340 adult inpatients with complete data for nonlaboratory parameters and 741 with complete data for white blood cell (WBC) count, differential, c-reactive protein (CRP), and serum creatinine. The COVID-NoLab risk score includes age, respiratory rate, and oxygen saturation and identified risk groups with 0.8%, 11.4%, and 40.4% mortality in the validation group (AUROCC = 0.803). The COVID-SimpleLab score includes age, respiratory rate, oxygen saturation, WBC, CRP, serum creatinine, and comorbid asthma and identified risk groups with 1.0%, 9.1%, and 29.3% mortality in the validation group (AUROCC = 0.833). CONCLUSIONS: Because they use simple, readily available predictors, developed risk scores have potential applicability in the outpatient setting but require prospective validation before use.