Changes in lifestyle and physical and mental health related to long-confinement due COVID-19: a study during the first and second pandemic waves in Mexico and Chile.

We investigate changes in lifestyle, physical, and mental health during the confinement period of the first and second waves of COVID-19, as well as their relation to sociodemographic parameters and confinement status. Sociodemographic data and information regarding changes in their lifestyle behavior and changes in body weight and physical activity (PA) were collected. The SF-36 questionnaire was implemented for measuring the domains related to physical health (PH) and mental health (MH). The growth frequency of weight gain in the Mexican (4.8%) and Chilean (10.9%) populations was observed during the second wave. The MH component decreased in the Mexican and the Chilean population (p < 0.05). Moreover, the MH decreased significantly according to the degree of confinement (p < 0.01). Although some sociodemographic factors were related to the presence of a very low score (<50 scores) for the MH component during the first wave, it is perceived as a higher relative risk during the second wave in both populations. The long confinement due to COVID-19 is associated to negative changes in nutritional and physical lifestyle behavior, affecting mainly the MH component.

Comparison of clinical characteristics, hospital treatment and outcomes in all four waves of Covid-19 patients at RTEH Muzaffargarh, Pakistan.

Background and Objective: The coronavirus pandemic followed a succession of COVID-19 waves globally, and had a varying pattern of frequency of cases and disease spectrum as each wave came with its distinct viral characteristics. The objective of this study was to compare clinical characteristics, treatments and outcomes of patients admitted with severe COVID -19 pneumonia in all four waves at Recep Tayyip Erdogan Hospital (RTEH). Methods: A cross sectional retrospective study was conducted at the COVID unit of Recep Tayyip Erdogan Hospital (RTEH), Muzaffargarh, from April 2020 to December 2021. Retrospective data was taken from Electronic Medical Records of patients of Covid pneumonia and divided into four groups according to four waves of Covid pandemic. The main objective was to compare disease spectrum, treatments and outcomes of patients admitted with severe COVID-19 pneumonia in all four waves at RTEH. Demographic characteristics, inflammatory markers such as C reactive protein (CRP), serum lactate dehydrogenase (LDH), serum ferratin and absolute lymphocyte counts, mortality, length of hospital and ICU stay and event of mechanical ventilation were compared between groups. The Kolmogorov-Smirnov test was applied to check the normality. P-value <0.05 was considered significance. Results: Of a total of 903 patients with covid pneumonia, 521(57.7%) were males and 382 (42.3%) females. Their mean age was 55.56±15.06 years. The mean length of stay (LOS) at the hospital was higher in first wave and least in fourth wave, 9.06±6.46 days and 6.56±5.34 days, respectively, (p<0.010). In first wave, LOS was generally >10 days with 21 (22.6%) while 33(26.8%) patients were shifted to ICU in first and second waves, respectively. Whereas, 35(8.2%) patients shifted to ICU in fourth wave (p<0.010). The use of mechanical ventilation was most common in first and second wave, 14 (15.1%) and 18 (14.6%), respectively. Mortality rate was highest in the third wave, 102 (38.9%, p<0.010) compared to the rest of the waves. Conclusion: Comparison of COVID-19 pneumonia patients across pandemic waves has revealed dynamic trends in patient outcomes. The initial waves had higher ICU admissions and mortality rate, suggesting a need for improved early response and resource allocation. Continuous adaptability in healthcare strategies was paramount for enhancing patient care during the ever-changing pandemic landscape.

A multiscale modeling framework to study the interdependence of brain, behavior, and pandemic.

A major constraint of the behavioral epidemiological models is the assumption that human behavior is static; however, it is highly dynamic, especially in uncertain circumstances during a pandemic. To incorporate the dynamicity of human nature in the existing epidemiological models, we propose a population-wide multi-time-scale theoretical framework that assimilates neuronal plasticity as the basis of altering human emotions and behavior. For that, variable connection weights between different brain regions and their firing frequencies are coupled with a compartmental susceptible-infected-recovered model to incorporate the intrinsic dynamicity in the contact transmission rate ( ß ). As an illustration, a model of fear conditioning in conjunction with awareness campaigns is developed and simulated. Results indicate that in the presence of fear conditioning, there exists an optimum duration of daily broadcast time during which awareness campaigns are most effective in mitigating the pandemic. Further, global sensitivity analysis using the Morris method highlighted that the learning rate and firing frequency of the unconditioned circuit are crucial regulators in modulating the emergent pandemic waves. The present study makes a case for incorporating neuronal dynamics as a basis of behavioral immune response and has further implications in designing awareness campaigns.

Characterizing pandemic waves: A latent class analysis of COVID-19 spread across US counties.

The spread of the COVID-19 pandemic is observed to follow the shape of "waves" (i.e., the rise and fall of population-adjusted daily new infection cases with time). Different geographic regions of the world have experienced different position and span of these waves over time. The presence and strength of these waves broadly characterize the dynamics of the pandemic spread in a given area, so their characterization is important to draw meaningful intervention and mitigation plans tailored for that area. In this paper, we propose a novel technique to represent the trend of COVID-19 spread as a sequence of a fixed-length text string defined on three symbols: R (rise), S (Steady), and F (fall). These strings, termed as trend strings, enabled us searching for specific patterns in them (such as for waves). After analyzing county-level infection data, we observe that, US counties-despite their wide variation in trend strings-can be grouped into a number of heterogeneous classes each of which might have a representative COVID spread pattern over time (in terms of presence and propensity of waves). To this end, we conduct a latent class analysis to cluster 3142 US counties into four distinct classes based on their wave characteristics for one year pandemic data (January 2020 to January 2021). We observe that counties in each class have distinct socio-demographics, location, and human mobility characteristics. In short summary, counties have differing number of waves (class 1 counties have only one wave and class 3 counties have three) and their positions also vary (class 1 had the wave later in the year whereas class 3 had waves throughout the year). We believe that this way of characterizing pandemic waves would provide better insights in understanding the complex dynamics of COVID-19 spread and its future evolution, and would, therefore, help in taking class-specific policy interventions.

Effect of Covid-19 pandemic on gender associated with risk factors: a retrospective data analysis, Thailand.

Background: The COVID-19 pandemic is having a serious impact around the world. Many countries have experienced a two or three wave pattern in reported cases. The virus's spread in Thailand was a cluster event distributed over multiple locations, multi-spender, and multiple waves of outbreaks. Objective: This study aims to study gender associated with age, risk factors, and nationality during coronavirus pandemic in Thailand. Material and methods: A retrospective cohort study was conducted from January 2020 to May 2021 (17 months) to determine the number of confirmed cases and identify gender associated with, age, various risk factors and nationality were analyzed by chi square test and binary logistic regression analyses. Results: The results show that the number of cases increased by over 100,000 over the course of three waves of outbreaks. The logistic regression analysis revealed that genders were significantly related with age, various risk factors, and nationality across different waves (p < 0.01). Across the primary risk factors were community risk, community cluster and close contact with a previously confirmed patient on confirmed cases during COVID-19 pandemic. Conclusion: Significant differences between genders were significantly associated with age, various risk factors, and nationality may be due to weak social distancing policies and the lack of public health interventions. A COVID-19 vaccination plan is needed for people who are at risk of suffering severe symptoms as well as the general population in outbreak areas to increase immunity.

Sleeping under the waves: A longitudinal study across the contagion peaks of the COVID-19 pandemic in Italy.

After the March-April 2020 coronavirus disease 2019 (COVID-19) outbreak, a second contagion wave afflicted Europe in the autumn. The present study aimed to evaluate sleep health/patterns of Italians during this further challenging situation. A total of 2,013 Italians longitudinally participated in a web-based survey during the two contagion peaks of the COVID-19 outbreak. We investigated the risk factors for sleep disturbances during the second wave, and we compared sleep quality and psychological well-being between the two assessments (March-April and November-December 2020). Female gender, low education, evening chronotype, being a high-risk person for COVID-19 infection, reporting negative social or economic impact, and evening smartphone overuse predicted a higher risk of poor sleep and insomnia symptoms during the second wave. Advanced age, living with a high-risk person for COVID-19 infection, and having a relative/friend infected with COVID-19 before the prior 2 weeks were risk categories for poor sleep quality. Living with children, having contracted COVID-19 before the prior 2 weeks, being pessimistic about the vaccine and working in healthcare, were risk factors for insomnia symptoms. The follow-up assessment highlighted reduced insomnia symptoms and anxiety. Nevertheless, we found reduced sleep duration, higher daytime dysfunction, advanced bedtime and wake-up time, and a shift to morningness, confirming the alarming prevalence of poor sleepers (~60%) and severe depression (~20%) in a context of increased perceived stress. The present study showed a persistent impact of the COVID-19 pandemic on sleep and mental health. Large-scale interventions to counteract the chronicity and exacerbation of sleep and psychological disturbances are necessary, especially for the at-risk categories.

Clinical profile of hospitalized COVID-19 patients in first & second wave of the pandemic: Insights from an Indian registry based observational study.

Background & objectives: India witnessed a massive second surge of COVID-19 cases since March 2021 after a period of decline from September 2020. Data collected under the National Clinical Registry for COVID-19 (NCRC) were analysed to describe the differences in demographic and clinical features of COVID-19 patients recruited during these two successive waves. Methods: The NCRC, launched in September 2020, is an ongoing multicentre observational initiative, which provided the platform for the current investigation. Demographic, clinical, treatment and outcome data of hospitalized, confirmed COVID-19 patients were captured in an electronic data portal from 41 hospitals across India. Patients enrolled during September 1, 2020 to January 31, 2021 and February 1 to May 11, 2021 constituted participants of the two successive waves, respectively. Results: As on May 11, 2021, 18961 individuals were recruited in the registry, 12059 and 6903 reflecting in-patients from the first and second waves, respectively. Mean age of the patients was significantly lower in the second wave [48.7 (18.1) yr vs. 50.7 (18.0) yr, P<0.001] with higher proportion of patients in the younger age group intervals of <20, and 20-39 yr. Approximately 70 per cent of the admitted patients were ≥ 40 yr of age in both waves of the pandemic. The proportion of males were slightly lower in second wave as compared to the first [4400 (63.7%) vs. 7886 (65.4%), P=0.02]. Commonest presenting symptom was fever in both waves. In the second wave, a significantly higher proportion [2625 (48.6%) vs. 4420 (42.8%), P<0.003] complained of shortness of breath, developed ARDS [422(13%) vs. 880 (7.9%), P<0.001], required supplemental oxygen [1637 (50.3%) vs. 4771 (42.7%), P<0.001], and mechanical ventilation [260 (15.9%) vs. 530 (11.1%), P<0.001]. Mortality also significantly increased in the second wave [OR: 1.35 (95% CI: 1.19, 1.52)] in all age groups except in <20 yr. Interpretation & conclusions: The second wave of COVID-19 in India was slightly different in presentation than the first wave, with a younger demography, lesser comorbidities, and presentation with breathlessness in greater frequency.

Quickest Detection of COVID-19 Pandemic Onset.

This paper develops an easily-implementable version of Page's CUSUM quickest-detection test, designed to work in certain composite hypothesis scenarios with time-varying data statistics. The decision statistic can be cast in a recursive form and is particularly suited for on-line analysis. By back-testing our approach on publicly-available COVID-19 data we find reliable early warning of infection flare-ups, in fact sufficiently early that the tool may be of use to decision-makers on the timing of restrictive measures that may in the future need to be taken.

Neurological Manifestations of Acute SARS-CoV-2 Infection in Pediatric Patients: A 3-Year Study on Differences between Pandemic Waves.

This study analyzed the neurological manifestation profiles of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across pandemic waves in pediatric patients. The study collected data on patients aged between 0 and 18 years, diagnosed with acute SARS-CoV-2 infection, admitted to a pediatric tertiary hospital between 1 March 2020 and 28 February 2023. This study included 1677 patients. Neurological manifestations were noted in 10% (n = 168) of patients with a median age of 3.2 years (interquartile range: 1-11.92). Neurological manifestations were significantly associated with the pandemic waves (p = 0.006) and age groups (p < 0.001). Seizures were noted in 4.2% of cases and reached an increasing frequency over time (p = 0.001), but were not associated with age groups. Febrile seizures accounted for the majority of seizures. Headache was reported in 2.6% of cases and had similar frequencies across the pandemic waves and age groups. Muscular involvement was noted in 2% of cases, reached a decreasing frequency over time (p < 0.001), and showed different frequencies among the age groups. Neurological manifestations of acute SARS-CoV-2 infection exhibit distinct patterns, depending on the pandemic wave and patient age group. The Wuhan and Omicron waves involved the nervous system more often than the other waves.

Clinical and Virological Features of SARS-CoV-2 Variants during the Four Waves of the Pandemic in the Mexican Southeast.

We conducted a retrospective study using a population of patients who were hospitalized at Dr. Juan Graham Casasus Hospital in Villahermosa (Tabasco, Mexico) and had a positive RT-PCR test for SARS-CoV-2 between June 2020 and January 2022. We analyzed all medical records, including demographic data, SARS-CoV-2 exposure history, underlying comorbidities, symptoms, signs at admission, laboratory findings during the hospital stay, outcome, and whole-genome sequencing data. Finally, the data were analyzed in different sub-groups according to distribution during waves of the COVID-19 pandemic regarding Mexican reports from June 2020 to January 2022. Of the 200 patients who tested positive via PCR for SARS-CoV-2, only 197 had samples that could be sequenced. Of the samples, 58.9% (n = 116) were males and 41.1% (n = 81) females, with a median age of 61.7 ± 17.0 years. Comparisons between the waves of the pandemic revealed there were significant differences in the fourth wave: the age of patients was higher (p = 0.002); comorbidities such as obesity were lower (p = 0.000), while CKD was higher (p = 0.011); and hospital stays were shorter (p = 0.003). The SARS-CoV-2 sequences revealed the presence of 11 clades in the study population. Overall, we found that adult patients admitted to a third-level Mexican hospital had a wide range of clinical presentations. The current study provides evidence for the simultaneous circulation of SARS-CoV-2 variants during the four pandemic waves.

Comparative Study Between the First and Second Wave of COVID-19 Deaths in India: A Single Center Study.

Introduction  The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving, and many mutant variants of the virus are circulating in the world. Recurrent waves of COVID-19 have caused enormous mortality all across the globe. Considering the novelty of the virus, it becomes crucial for healthcare experts and policymakers to understand the demographic and clinical attributes of inpatient deaths in the first and second waves of COVID-19. Methods This hospital record-based comparative study was conducted at a tertiary care hospital in Uttarakhand, India. The study included all COVID-19 RT PCR-positive patients admitted to the hospital during the first wave, from 1st April 2020 to 31st January 2021, and the second wave from 1st March 2021 to 30th June 2021. Comparisons were made with respect to demographic, clinical, laboratory parameters, and course of hospital stay. Results The study exhibited 11.34% more casualties in the second wave, with the number of deaths being 424 and 475 for the first and second waves, respectively. A male preponderance of mortality was evident in both waves with significant differences (p=0.004). There was no significant difference in age between the two waves (p=0.809). The significantly different comorbidities were hypertension (p=0.003) and coronary artery disease (p=0.014). The clinical manifestations demonstrating a significant difference were cough (p=0.000), sore throat (p=0.002), altered mental status (p=0.002), headache (p=0.025), loss of taste and smell (p=0.001), and tachypnea (p=0.000). The lab parameters with a significant difference across both waves were lymphopenia (p=0.000), elevated aspartate aminotransferase (p=0.004), leukocytosis (p=0.008), and thrombocytopenia (p=0.004). During the hospital course of the second wave, in terms of intensive care unit stay, the need for non-invasive ventilation and inotrope support was higher. The complications manifesting in the form of acute respiratory distress syndrome and sepsis were observed more in the second wave. A significant difference was discerned in the median duration of hospital stay in both waves (p=0.000). Conclusion Despite being of shorter duration, the second wave of COVID-19 culminated in more deaths. The study demonstrated that most of the baseline demographic and clinical characteristics attributed to mortality were more common during the second wave of COVID-19, including lab parameters, complications, and duration of hospital stays. The unpredictable nature of COVID-19 waves calls for instituting a well-planned surveillance mechanism in place to identify the surge in cases at the earliest possible time and prompt response, along with developing infrastructure and capacity to manage complications.

Patients' Clinical and Psychological Status in Different COVID-19 Waves in Italy: A Quanti-Qualitative Study.

BACKGROUND: COVID-19 waves have been characterized by different clinical manifestations, a decrease of functional abilities, and the presence of psychological symptoms. The aims of this study were to investigate differences in physical and psychological symptoms during the three Italian waves of the coronavirus pandemic. METHODS: Patients undergoing a functional, cardiological and pneumological check-up follow-up at ICS Maugeri Institutes, 2-3 months after recovery from COVID-19 were consecutively recruited to participate in the study, completing a quanti-qualitative questionnaire about anxiety, depression, PTSD symptoms, and personal resources. RESULTS: 104 patients were recruited: 44 and 60 during the first and second/third pandemic waves, respectively. Physical comorbidities were more present in the second/third waves compared to the first one, while no significant differences were found in anxious and depressive symptoms, which were significantly higher than normal during the three waves; PTSD symptoms were reported by 56.3% of patients. Family, social support, and a positive mindset were described as resources to cope with the COVID-19 burden. Negative affects arose during outbreaks (panic) and the disease (fear), while positive affect (joy) characterized only the follow-up period. CONCLUSION: This study shows how psychophysical symptoms might change during the pandemic waves and highlights the importance of protective factors to balance the subjective distress.

[Comparative analysis of clinical characteristics, evolution and mortality of hospitalized patients with COVID-19 during the first and second wave of the pandemic (ECCOVID Study)]. / Análisis comparativo de las características clínicas, evolución y mortalidad de pacientes hospitalizados por COVID-19 durante la primera y segunda ola de la pandemia (Registro ECCOVID).

INTRODUCTION: Clinical features and outcomes of SARSCoV-2 infections may change between different waves of the pandemic. The objective of this study was to compare clinical characteristics and outcomes between two cohorts of patients hospitalized for COVID-19 during the first and second waves in Argentina. METHODS: Multicenter and prospective registry of patients =18 years old with a confirmed diagnosis of COVID-19 admitted to 18 hospitals in Argentina during the first wave (March to October 2020) and second wave (March to July 2021) of the pandemic. Demographics, clinical characteristics, and outcomes of these patients were compared. RESULTS: A total of 1691 patients were included (first wave n = 809, second wave n = 882). Hospitalized patients during the second wave were older (median 53 years vs. 61 years, p < 0.001), had more comorbidities (71% vs. 77%, p=0.007) and required more supplemental oxygen at admission (21% vs 62%, p < 0.001). During hospitalization, patients of the second wave required more supplemental oxygen (49% vs. 85%, p < 0.001), invasive ventilation (12% vs. 22%, p < 0.001) and had higher 30- day mortality (11% vs. 26%, p < 0.001). Comparing only patients who required supplemental oxygen during hospitalization, 30-day mortality was 20% and 30% p < 0.001 for the first and second wave, respectively. CONCLUSION: Compared to patients admitted during the first wave, patients admitted with SARS-CoV2 during the second wave in Argentina were more seriously ill and had a higher mortality.

Introducción: Las características clínicas y evolutivas de los pacientes con diagnóstico de COVID-19 pueden diferir entre las distintas olas de la pandemia. El objetivo de este estudio fue comparar las características clínicas, evolución y mortalidad de pacientes hospitalizados por COVID-19 durante la primera y segunda ola en Argentina. Métodos: Registro multicéntrico y prospectivo de pacientes = 18 años con diagnóstico confirmado de COVID-19 internados en 18 hospitales de Argentina durante la primera (marzo a octubre 2020) y la segunda ola (marzo a julio 2021) de la pandemia. Se compararon variables demográficas, características clínicas, y evolución a 30 días. Resultados: Se incluyeron un total de 1691 pacientes (primera ola n = 809, segunda ola n = 882). Los pacientes hospitalizados durante la segunda ola tenían mayor edad (mediana 53 años vs. 61 años, p < 0.001), comorbilidades (71% vs. 77%, p = 0.007) y requerimiento de oxígeno (21% vs. 62%, p < 0.001). Durante la hospitalización, los pacientes de la segunda ola requirieron más oxigenoterapia (49% vs. 85%, p < 0.001), asistencia mecánica respiratoria (12% vs. 22%, p < 0,001) y presentaron mayor mortalidad (11% vs. 26%, p < 0.001). Comparando únicamente a los que requirieron oxigenoterapia durante la hospitalización, la mortalidad a los 30 días fue de 20% y 30% p < 0.001 en la primera y segunda ola respectivamente. Conclusión: Comparados con los pacientes internados durante la primera ola, los internados durante la segunda ola de SARS-CoV-2 en Argentina presentaron mayor gravedad y mortalidad.

Comparison of the epidemiological features of COVID-19 patients in the first and second waves: A study from South India.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has seen multiple surges globally since its emergence in 2019. The second wave of the pandemic was generally more aggressive than the first, with more cases and deaths. This study compares the epidemiological features of the first and second COVID-19 waves in Kozhikode district of Kerala and identifies the factors associated with this change. Methods: A comparative cross-sectional study was conducted in Kozhikode district. A total of 132,089 cases from each wave were selected for the study using a consecutive sampling method. Data were collected from the District COVID-19 line list using a semistructured proforma and analyzed using Statistical Package for Social Sciences (SPSS) ver. 18. Results: The second wave had a higher proportion of symptomatic cases (17.3%; 20.1%), cases with severe symptoms (0.3%; 0.6%), intensive care unit (ICU) admissions (11.2%; 17.9%), and case fatality rate (0.69%; 0.72%). Significant difference was noted in the age, gender, locality, source of infection, comorbidity profile, symptom, and the pattern of admission in various healthcare settings between the first and second wave. Among the deceased, gender, duration between onset of symptoms and death, comorbidity status, and cause of death were significantly different in both waves. Conclusion: The presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant, as well as changes in human behavior and threat perception as the pandemic progressed, resulted in significant differences in various epidemiological features of the pandemic in both waves, indicating the need for continued vigilance during each COVID-19 wave.

Impact of first and second/third wave of COVID-19 pandemic on post-acute cardiovascular outcomes in Lombardy.

Background: COVID-19 has been associated with a higher risk of post-acute complications. Our aim was to analyze and compare post-acute cardiovascular complications of COVID-19 survivors of the first and second/third pandemic waves in Lombardy, in both hospitalized and non-hospitalized COVID-19 patients. Methods and results: We included adults aged ≥40 years infected during the first and second/third waves of COVID-19 pandemic. The follow-up initiated 30 days after COVID-19 diagnosis and continued up to 9 months. Hazard ratios (HRs) and 95% confidence intervals (CIs) of the post-acute cardiovascular outcomes were calculated against an inverse probability treatment weighted control group. Subgroup analysis were performed by age classes, sex, previous cardiovascular disease and stratified by COVID-19 hospitalization status to explore the impact of COVID-19 severity on outcomes. Compared to the control group, COVID-19 patients had an increased risk of hospitalization for any cardiovascular complications (HR 1st wave 1.53 95% CI: 1.38-1.69; HR 2nd/3rd wave 1.25 95% CI: 1.19-1.31) and for individual cardiovascular outcomes, although HRs were higher in COVID-19 group from the 1st pandemic wave. The results were confirmed in the subgroup analyses. Of note, the risk for any cardiovascular disease was also evident even among individuals who were not hospitalized during the acute phase of the infection. Conclusion: Our results provide evidence that COVID-19 is a risk factor for post-acute cardiovascular complications among different pandemic waves regardless of COVID-19 severity, age, sex and a history of cardiovascular diseases. Care strategies of people with COVID-19 should include cardiac monitoring.

Temporal variation of excess deaths from diabetes during the COVID-19 pandemic in the United States.

BACKGROUND: Although the COVID-19 pandemic has persisted for more than two years with the evident excess mortality from diabetes, few studies have investigated its temporal patterns. This study aims to estimate the excess deaths from diabetes in the United States (US) during the COVID-19 pandemic and evaluate the excess deaths by spatiotemporal pattern, age groups, sex, and race/ethnicity. METHODS: Diabetes as one of multiple causes of death or an underlying cause of death were both considered into analyses. The Poisson log-linear regression model was used to estimate weekly expected counts of deaths during the pandemic with adjustments for long-term trend and seasonality. Excess deaths were measured by the difference between observed and expected death counts, including weekly average excess deaths, excess death rate, and excess risk. We calculated the excess estimates by pandemic wave, US state, and demographic characteristic. RESULTS: From March 2020 to March 2022, deaths that diabetes as one of multiple causes of death and an underlying cause of death were about 47.6 % and 18.4 % higher than the expected. The excess deaths of diabetes had evident temporal patterns with two large percentage increases observed during March 2020, to June 2020, and June 2021 to November 2021. The regional heterogeneity and underlying age and racial/ethnic disparities of the excess deaths were also clearly observed. CONCLUSIONS: This study highlighted the increased risks of diabetes mortality, heterogeneous spatiotemporal patterns, and associated demographic disparities during the pandemic. Practical actions are warranted to monitor disease progression, and lessen health disparities in patients with diabetes during the COVID-19 pandemic.

COVID-19 treatment strategies with drugs centrally procured by the German Federal Ministry of Health in a representative tertiary care hospital: a temporal analysis.

Introduction: To ensure the fastest and earliest possible treatment, the German Federal Ministry of Health (BMG) initiated central procurement and nationwide distribution of new drugs against COVID-19. A single centre was used for a retrospective temporal analysis of this procedure. Methods: A descriptive analysis of hospitalization and treatment of COVID-19 patients with drugs centrally procured by the BMG at St. Georg Hospital, Leipzig, Germany, for the period from 1 March 2020 to 28 February 2023 was employed considering the approval status, evolving guidelines and recommendations of medical societies. Results: In total, 3,412 patients ≥18 years (54.9% men) with PCR-confirmed SARS-CoV-2 infection were admitted. The mean age was 64 years during the reporting period and 66.1/70.6 years during the first and second COVID-19 waves, respectively. 964 patients (28.2%) received COVID-19 therapy with drugs procured centrally by the BMG. Remdesivir was the most commonly used (63%). SARS-CoV-2 neutralizing monoclonal antibodies represented 23% of the therapies. Peroral antivirals (nirmatrelvir/ritonavir and molnupiravir) were used in 14% of COVID-19 patients, with molnupiravir being insignificant (five prescriptions). Conclusions: Specific therapeutic approaches were mainly based on antiviral therapy in the early phase of COVID-19 to prevent severe disease progression in vulnerable patient groups. Most drugs had not been approved at the time of central procurement; therefore, prescriptions were given on a case-by-case basis after careful risk-benefit assessments. All available neutralizing monoclonal SARS-CoV-2 antibodies lost efficacy during the pandemic due to different circulating immune escape variants. Remdesivir and nirmatrelvir/ritonavir remained effective therapies in the early phase of COVID-19.

Evolution of in-hospital patient characteristics and predictors of death in the COVID-19 pandemic across four waves: are they moving targets with implications for patient care?

Objectives: The aim of this work was to study characteristics, outcomes and predictors of all-cause death in inpatients with SARS-CoV-2 infection across the pandemic waves in one large teaching hospital in Italy to optimize disease management. Methods: All patients with SARS-CoV-2 infection admitted to our center from March 2020 to June 2022 were included in this retrospective observational cohort study. Both descriptive and regression tree analyses were applied to identify factors influencing all-cause mortality. Results: 527 patients were included in the study (65.3% with moderate and 34.7% with severe COVID-19). Significant evolutions of patient characteristics were found, and mortality increased in the last wave with respect to the third wave notwithstanding vaccination. Regression tree analysis showed that in-patients with severe COVID-19 had the greatest mortality across all waves, especially the older adults, while prognosis depended on the pandemic waves in patients with moderate COVID-19: during the first wave, dyspnea was the main predictor, while chronic kidney disease emerged as determinant factor afterwards. Conclusion: Patients with severe COVID-19, especially the older adults during all waves, as well as those with moderate COVID-19 and concomitant chronic kidney disease during the most recent waves require more attention for monitoring and care. Therefore, our study drives attention towards the importance of co-morbidities and their clinical impact in patients with COVID-19 admitted to hospital, indicating that the healthcare system should adapt to the evolving features of the epidemic.

Wastewater surveillance monitoring of SARS-CoV-2 variants of concern and dynamics of transmission and community burden of COVID-19.

Wastewater-based surveillance is a valuable approach for monitoring COVID-19 at community level. Monitoring SARS-CoV-2 variants of concern (VOC) in wastewater has become increasingly relevant when clinical testing capacity and case-based surveillance are limited. In this study, we ascertained the turnover of six VOC in Alberta wastewater from May 2020 to May 2022. Wastewater samples from nine wastewater treatment plants across Alberta were analysed using VOC-specific RT-qPCR assays. The performance of the RT-qPCR assays in identifying VOC in wastewater was evaluated against next generation sequencing. The relative abundance of each VOC in wastewater was compared to positivity rate in COVID-19 testing. VOC-specific RT-qPCR assays performed comparatively well against next generation sequencing; concordance rates ranged from 89% to 98% for detection of Alpha, Beta, Gamma, Omicron BA.1 and Omicron BA.2, with a slightly lower rate of 85% for Delta (p < 0.01). Elevated relative abundance of Alpha, Delta, Omicron BA.1 and BA.2 were each associated with increased COVID-19 positivity rate. Alpha, Delta and Omicron BA.2 reached 90% relative abundance in wastewater within 80, 111 and 62 days after their initial detection, respectively. Omicron BA.1 increased more rapidly, reaching a 90% relative abundance in wastewater after 35 days. Our results from VOC surveillance in wastewater correspond with clinical observations that Omicron is the VOC with highest disease burden over the shortest period in Alberta to date. The findings suggest that changes in relative abundance of a VOC in wastewater can be used as a supplementary indicator to track and perhaps predict COVID-19 burden in a population.

Understanding Digital Mental Health Needs and Usage With an Artificial Intelligence-Led Mental Health App (Wysa) During the COVID-19 Pandemic: Retrospective Analysis.

BACKGROUND: There has been a surge in mental health concerns during the COVID-19 pandemic, which has prompted the increased use of digital platforms. However, there is little known about the mental health needs and behaviors of the global population during the pandemic. This study aims to fill this knowledge gap through the analysis of real-world data collected from users of a digital mental health app (Wysa) regarding their engagement patterns and behaviors, as shown by their usage of the service. OBJECTIVE: This study aims to (1) examine the relationship between mental health distress, digital health uptake, and COVID-19 case numbers; (2) evaluate engagement patterns with the app during the study period; and (3) examine the efficacy of the app in improving mental health outcomes for its users during the pandemic. METHODS: This study used a retrospective observational design. During the COVID-19 pandemic, the app's installations and emotional utterances were measured from March 2020 to October 2021 for the United Kingdom, the United States of America, and India and were mapped against COVID-19 case numbers and their peaks. The engagement of the users from this period (N=4541) with the Wysa app was compared to that of equivalent samples of users from a pre-COVID-19 period (1000 iterations). The efficacy was assessed for users who completed pre-post assessments for symptoms of depression (n=2061) and anxiety (n=1995) on the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) test measures, respectively. RESULTS: Our findings demonstrate a significant positive correlation between the increase in the number of installs of the Wysa mental health app and the peaks of COVID-19 case numbers in the United Kingdom (P=.02) and India (P<.001). Findings indicate that users (N=4541) during the COVID period had a significantly higher engagement than the samples from the pre-COVID period, with a medium to large effect size for 80% of these 1000 iterative samples, as observed on the Mann-Whitney test. The PHQ-9 and GAD-7 pre-post assessments indicated statistically significant improvement with a medium effect size (PHQ-9: P=.57; GAD-7: P=.56). CONCLUSIONS: This study demonstrates that emotional distress increased substantially during the pandemic, prompting the increased uptake of an artificial intelligence-led mental health app (Wysa), and also offers evidence that the Wysa app could support its users and its usage could result in a significant reduction in symptoms of anxiety and depression. This study also highlights the importance of contextualizing interventions and suggests that digital health interventions can provide large populations with scalable and evidence-based support for mental health care.