Investigating the impact of CO2emissions on the COVID-19 pandemic by generalized linear mixed model approach with inverse Gaussian and gamma distributions

Carbon dioxide (CO2) rate within the atmosphere has been rising for decades due to human activities especially due to usage of fuel types such as coal, cement, flaring, gas, oil, etc. Especially in 2020, COVID-19 pandemic caused major economic, production, and energy crises all around the world. As a result of this situation, there was a sharp decrease in the global CO2 emissions depending on the fuel types used during this pandemic. The aim of this study was to explore the effects of "CO2 emissions due to the fuel types"on "percentage of deaths in total cases"attributed to the COVID-19 pandemic using generalized linear model and generalized linear mixed model (GLMM) approaches with inverse Gaussian and gamma distributions, and also to obtain global statistical inferences about 169 World Health Organization member countries that will disclose the impact of the CO2 emissions due to the fuel types during this pandemic. The response variable is taken as "percentage of deaths in total cases attributed to the COVID-19 pandemic"calculated as "(total deaths/total confirmed cases attributed to the COVID-19 pandemic until December 31, 2020)∗100."The explanatory variables are taken as "production-based emissions of CO2 from different fuel types,"measured in tonnes per person, which are "coal, cement, flaring, gas, and oil."As a result of this study, according to the goodness-of-fit test statistics, "GLMM approach with gamma distribution"called "gamma mixed regression model"is determined as the most appropriate statistical model for investigating the impact of CO2 emissions on the COVID-19 pandemic. As the main findings of this study, 1 t CO2 emissions belonging to the fuel types "cement, coal, flaring, gas, and oil"per person cause increase in deaths in total cases attributed to the COVID-19 pandemic by 2.8919, 2.6151, 2.5116, 2.5774, and 2.5640%, respectively. © 2023 the author(s), published by De Gruyter.

A linear mixed model to estimate COVID-19-induced excess mortality.

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

The Factors and Relationships Influencing Forest Hiking Exercise Characteristics after COVID-19 Occurrence: At Seoul Metropolitan Area and in Hikers' 20s and 30s.

The growing interest and usage of green space during the pandemic relates to why greenery increases enjoyment of daily life and alleviates troubles arising from infectious periods. However, it is necessary to understand what reactions to the degree of COVID-19's spread were expressed in hiking exercise characteristics (hiking frequency, actual movement distance, average speed, total exercise time). To understand the progress of forest usage behavior during the pandemic, we analyzed factors influencing hiking exercise characteristics and relationships between those factors and hiking exercise characteristics. Hiking record data were obtained (2019-2020) from the workout app "Tranggle" pertaining to hikers in their 20s and 30s who visited the 13 mountains in the Seoul Metropolitan Area most frequently. The differences in hiking exercise characteristics (2019 data subtracted from 2020) were linked with factor data that could be related to them, including the degree of COVID-19's spread (Rt). To explore relationships between hiking exercise characteristics and factors, we developed four models with a linear mixed model. We found that Rt, week, weekdays/weekends, and PM10 contributed to explaining the hiking exercise characteristics' differences. As a result of this analysis, the degree of COVID-19's spread (Rt) affected hiking frequency; the week affected hiking frequency and actual movement distance; weekdays or weekends affected hiking frequency, actual movement distance, average speed, and total exercise time; and PM10 affected hiking frequency. These findings indicate that hiking was an alternative way for those looking for a new strategy to replace lost opportunities for physical activity. Therefore, we conclude that it is necessary to induce the usage of green space so that many people can take advantage of the functions and benefits of greenery, which stood out during the pandemic era.

Disentangling crucial factors of the pandemic in Brazil: Effect of lockdown restrictions on mental health.

Quarantine and social distance restrictions have been enforced worldwide to reduce the spread of coronavirus disease 2019 (COVID-19). The effects of these measures on mental health are recognised, but remaining unclear, is whether these effects are a consequence of the virus itself or policies that are enforced to prevent it. The present study investigated the impact of lockdown restrictions on anxiety and depression at two different times in 2020. Data were collected from 118 participants from all regions of Brazil. After easing quarantine restrictions in the second half of 2020, two natural groups were formed. One group included participants who voluntarily remained at home (n = 73). The other group consisted of those who decided to leave home (n = 45). A linear mixed model was used to determine the effects of group and time and their interaction. The McNemar test was used to determine intragroup differences in perceptions and concerns about COVID-19. Logistic regression identified predictors of high and stable depression and anxiety. None of the factors or their interactions was significant. Indicators of depression and anxiety remained stable over time, regardless of whether the participants left home or remained at home. Significantly, a strong and stable agreement with quarantine was found. The participants agreed that COVID-19 was a threat to public health. Political orientation was a predictor of high and stable levels of depression but not anxiety. Participants who self-identified as liberal politically were at a greater risk of developing depression. The results suggest that the lockdown policy did not contribute to disruptions of mental health, which instead was a consequence of the pandemic and virus itself. We also found wide and strong support amongst the participants for lockdown and mandatory stay-at-home policies.

Linear mixed model analysis to evaluate correlations between remdesivir adverse effects with age and gender of patients with mild Covid-19 pneumonia.

We aimed to assess longitudinal changes in clinical indexes of corona disease 2019 (Covid-19) patients with mild pulmonary infection during 5 days of remdesivir therapy and determine the effect of age and gender on remdesivir adverse effects (AE). Patients' clinical data including inflammatory markers, liver and renal function tests, and heart rate (HR) were extracted from medical records. Linear mixed model (LMM) was used to analyze longitudinal changes in patients' clinical indexes. Gender and age were inserted in LMM as covariates to find their correlation with AE and clinical indexes. Of 84 patients, 35 patients met our criteria for the study. There were significant increases in mean levels of white blood cell (WBC; p = 0.005), alanine aminotransferase (ALT; p = 0.001), aspartate aminotransferase (p = 0.001), blood urea nitrogen (BUN; p = 0.001), and creatinine (p = 0.006), whereas mean levels of erythrocyte sedimentation rate (p = 0.005), C-reactive protein (p = 0.001), alkaline phosphatase (p = 0.001), and potassium (p = 0.003) decreased significantly. Estimated glomerular filtration rate (p = 0.001) and HR (p = 0.001) showed a notable decline over the course of treatment. LMM analysis showed that mean changes in WBC (ß = 0.94, p = 0.029), creatinine (ß = 0.12, p = 0.020), and HR (ß = 6.47, p = 0.008) were greater in males than in females. Also, age of patients had a significant effect on the mean changes of WBC (ß = -0.02, p = 0.023), sodium (ß = -0.06, p = 0.010), BUN (ß = 0.23, p = 0.001), and HR (ß = -0.29, p = 0.001). Despite no renal and liver dysfunction, Covid-19 patients with mild pulmonary infection may develop some remdesivir AE and attributed side effects might be affected by gender and age of patients.

An airport level framework for examining the impact of COVID-19 on airline demand.

In this study, we examine the influence of Coronavirus disease 2019 (COVID-19) on airline demand at the disaggregate resolution of airport. The primary focus of our proposed research effort is to develop a framework that provides a blueprint for airline demand recovery as COVID-19 cases evolve over time. Airline monthly demand data is sourced from Bureau of Transportation Statistics for 380 airports for 24 months from January 2019 through December 2020. The demand data is augmented with a host of independent variables including COVID-19 related factors, demographic characteristics and built environment characteristics at the county level, airport specific factors, spatial factors, temporal factors, and adjoining county attributes. The effect of COVID-19 related factors is identified by considering global and local COVID-19 transmission, temporal indicators of pandemic start and progress, and interactions of airline demand predictors with global and local COVID-19 indicators. Finally, we present a blueprint for airline demand recovery where we consider three hypothetical scenarios of COVID-19 transmission rates - expected, pessimistic and optimistic. The results at the airport level from these scenarios are aggregated at the state or regional level by adding the demand from all airports in the corresponding state or region. These trends are presented by State and Region to illustrate potential differences across various scenarios. The results highlight a potentially slow path to airline demand recovery until COVID-19 cases subside.

The Estimation of Excess Mortality during the COVID-19Pandemic in Jakarta, Indonesia

Indonesia is among the countries affected by the coronavirus disease 2019 (COVID-19) pandemic, and DKI Jakarta Province recorded the highest number ofdeaths. This study aimed to analyze the excess mortality across five administrative cities in Jakarta stratified by gender to assess the pandemic impact onmortality. The monthly mortality data from January 2018 to December 2020 was obtained through government sources. This data helped to measure excessmortality by estimating the baseline mortality had the COVID-19 pandemic not occurred. The analysis used a linear mixed model because of its ease and flexibility in forecasting subject-specific mortality. The results showed 13,507 or 35% excess deaths in Jakarta [95% CI: 11,636 to 15,236] between June andDecember 2020. The excess numbers were found relatively higher among men than women. Furthermore, Jakarta has underreported the COVID-19 deathsat least seven times higher than the reported number of confirmed deaths. © 2022 Universitas Indonesia, Faculty of public health. All Rights Reserved.

The Effectiveness of a Centering Meditation Intervention on College Stress and Mindfulness: A Randomized Controlled Trial.

Background: Mental health concerns are climbing steadily on college campuses, and universities do not have the staffing and financial resources to address the overwhelming needs of students seeking counseling services. College counselors generally must place students on waitlists or refer them to external resources. Further, during the COVID-19 pandemic, university counselors have been working tirelessly to treat students through online formats. Alternative, online, evidence-based interventions offer college counselors a significant advantage in effectively treating their students. We seek to expand the empirical evidence for mindfulness interventions through online formats for the college population. We registered the study (ISRCTN13587045) at www.isrctn.com. Objective: We examined the effectiveness of a unique online centering meditation and its impact on stress and trait mindfulness in the college population. Methods: Through a randomized controlled trial, the treatment group participated in a 4-week intervention of centering for 10 min each morning and night. We measured stress and mindfulness in both groups through the Perceived Stress Scale and Cognitive and Affective Mindfulness Scale-Revised at baseline, 2 weeks, and 4 weeks. Results: The centering meditation treatment had a statistically significant positive impact on stress and mindfulness compared to a waitlist control group. The meditation group had an average of 64% adherence rate. Conclusion: The study findings indicate that individuals who participate in a 4-week online centering intervention showed improved levels of stress and trait mindfulness over time. Clinical Trial Registration: WHO International Clinical Registry Platform, identifier: ISRCTN13587045.

Impact of COVID-19 pandemic on glycemic control among outpatients with type 2 diabetes in Japan: A hospital-based survey from a country without lockdown.

AIMS: Some studies have reported changes in glycemic control of patients with diabetes mellitus under lockdown. However, no previous study examined the impact of the pandemic on glycemic control in patients with diabetes in countries that did not introduce a lockdown such as Japan. This study aimed to assess changes in glycemic control during the pandemic in patients with type 2 diabetes treated at a Japanese clinic. METHODS: We conducted a historical cohort study, using electronic medical records of patients with type 2 diabetes who visited our clinic between January 2019 and August 2020. Differences in HbA1c values before and after the outbreak of COVID-19 were the primary outcome, examined using the linear mixed model. RESULTS: HbA1c values significantly increased from 7.45% to 7.53% after the state of emergency was introduced (n = 1,009). Furthermore, a deterioration in HbA1c values was observed in particular among women, patients aged ≥ 65 years, those with body mass index of ≥ 25 kg/m2, and those that were not using insulin. CONCLUSIONS: Glycemic control deteriorated in patients with type 2 diabetes during the pandemic even in a country without a national lockdown.

A Longitudinal Study on Generalized Anxiety Among University Students During the First Wave of the COVID-19 Pandemic in Switzerland.

OBJECTIVE: The COVID-19 pandemic and government measures implemented to counter the spread of the infection may be a major stressor affecting the psychological health of university students. This study aimed to explore how anxiety symptoms changed during the pandemic. METHODS: 676 students (76% females) at Zurich University of Applied Sciences participated in the first (T0) and second (T1) survey waves. Anxiety symptoms were assessed using the Generalized Anxiety Disorder-Scale-7 (GAD-7). Risk and protective factors (e.g., COVID-19-related variables) were examined. RESULTS: GAD-7 scores decreased significantly from T0 to T1 (mean change: -0.446, SE = 0.132, 95% CI: -0.706, -0.186, t = -3.371, df = 659, p = 0.001). Participants with moderate-to-severe anxiety score were 20.2 and 15.6% at T0 and T1, respectively. The following positively predicted anxiety: older age, female gender, non-Swiss nationality, loneliness, participants' concern about their own health, and interaction between time and participants' concern about their own health. Resilience and social support negatively predicted anxiety. CONCLUSIONS: Our findings provide information for public health measures and psychological interventions supporting the mental health of university students during the COVID-19 emergency.

An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions.

The availability of intensive care beds during the COVID-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short-term prediction of COVID-19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area-specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave-last-out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.