Number Of Death Cases Research Articles

Burden of gastrointestinal luminal tract cancer (GLC) from 2000 to 2019 in low SDI countries.

10558 Background: In regions with low social and economic development, gastrointestinal luminal cancer (GLC) poses a significant burden, and disparities in care are prevalent, leading to adverse disease outcomes but remain poorly studied. This study aims to assess the epidemiology of GLC in low Socio-demographic Index (SDI) regions. Methods: We analyzed temporal trends in the burden of GLC (Colorectal cancer, Esophageal cancer, Stomach cancer) using data from 2000 to 2019 from the Global Burden of Disease Study 2019. We conducted a comprehensive analysis of annual frequencies, age-standardized rates (ASRs), age-specific death rate (ASDR), and annual percentage change (APC) of luminal GI cancer incidence, death, and disability-adjusted life-years (DALYs) by sex, country, and cancer type. Results: In 2019, the frequency of incident cases due to colon cancer, esophageal cancer, and stomach cancer was 39,045, 28,132, and 43,188 respectively, with a male predominance. The number of death cases and DALYs is highest in stomach cancer, with 43,876 deaths and 1,233,251 DALYs, respectively. During 2000 to 2019, the ASDRs increased significantly for colon cancer (APC: 0.78% (0.71 to 0.84), while they decreased significantly for esophageal cancer (APC: -0.57% (-0.65 to -0.48)) and stomach cancer (APC: -1.1% (-1.26 to -0.96)). The highest ASDRs in colorectal cancer were noted in 2019 in Uganda (11.55 (9.24 - 13.75)), while the highest increase in APC in colorectal cancer was noted in Burkina Faso (APC: 1.27% (1.12 to 1.42)). Conclusions: In the preceding decade, the epidemiological landscape of GLC in low SDI countries has undergone dynamic shifts, demonstrating a heavier shift towards colorectal cancer. These findings warrant heightened awareness and strategic interventions to combat the disease.

Mortality trend of cutaneous melanoma in Montenegro from 1990-2018.

Every year, melanoma claims over 20,000 lives in Europe. In Montenegro, as in Europe, numerous campaigns have been initiated to raise public awareness about the importance of melanoma prevention and its early detection. Thus, accompanying current diagnostic and therapeutic protocols, new methods of melanoma diagnosis and treatment have been implemented. Studying the trend enables the identification of the groups most burdened by mortality and assesses whether there has been a change in trends based on interventions aiming to reduce mortality. The objective of this study is to evaluate the mortality trend from cutaneous melanoma in Montenegro for the period 1990-2018. We have utilized national data on the causes of death from melanoma, code 179 from the ninth and C43 from the tenth revision of the International Classification of Diseases, categorized by gender and age groups. The study utilized various regression techniques, including Joinpoint regression in the Joinpoint Program, Poisson regression, and linear regression in the SPSS 26th Program, to describe the trend. In Montenegro, during the period from 1990 to 2018, a total of 281 individuals died (51.6% male and 48.4% female). This ranks as the 13th leading cancer in terms of mortality among all cancers. The average age-standardized rate was 1.1 deaths per 100,000 (1.2 for males and 1.0 for females). The number of death cases has been increasing on average by 3.3% annually [average annual percentage change (AAPC) (95% CI) = 3.3 (1.7-4.9); p<0.001] on an overall level and by 5.4% annually among males [AAPC (95% CI) = 5.4 (3.6-7.3); p<0.001] due to the rises in the age groups 55-64 years and 65-74 years with an average annual percent change of respectively 3.2% [AAPC (95% CI) = 3.2 (0.8-5.8); p=0.012] and 5.4% [AAPC (95% CI) = 5.4 (2.7-8.1); p<0.001] overall level, and 4.8% [AAPC (95% CI) = 4.8 (2.4-7.3); p<0.001] and 7.5% [AAPC (95% CI) = 7.5 (4.9-10.2); p<0.001] among males. For females, an increase of 1.1% was recorded, which was not statistically significant [AAPC (95% CI) = 1.1 (-0.8-3.0); p=0.255]. Furthermore, there was a noted increase in the rates at an overall level [β (95% CI) = 0.027 (0.008-0.046); p=0.007] and in the age group 65-74 years [β (95% CI) = 0.249 (0.090-0.407); p=0.003], as well as among males at an overall level [β (95% CI) = 0.052 (0.025-0.079); p<0.001] and for age groups 45-54 years [β (95% CI) = 0.102 (0.011-0.193); p=0.030] and 65-74 [β (95% CI) = 0.410 (0.144-0.676); p=0.004]. In contrast, the rates for females remained constant. The three age groups most burdened by melanoma skin cancer mortality are 65-74 years (23.5%), 55-64 years (21.7%) and 75-84 years (19.2%). The results of regression analyses indicate a significant rise in both the number of death cases and mortality rates overall, specifically among males in Montenegro. In females, however, the increase in the number of death cases and rates is not statistically significant. Preventive campaign activities should be redirected towards the most vulnerable groups in terms of mortality, namely males and the elderly population.

Fatal Occupational Accidents in Turkey from a City and Country Perpective

Occupational accidents and death cases resulting from occupational accidents are among the most critical factors threatening work peace. Occupational accidents cause injury, disability and even death for many employees. In 2021, 1394 employees died due to occupational accidents in Turkey, while 3123 became permanently incapacitated, that is, disabled for life. In addition, tens of thousands of employees were injured in occupational accidents, resulting in temporary incapacitation and exposing them and their families to emotional distress. The fact that occupational accidents are preventable events to a large extent makes the incidents caused by occupational accidents, such as death, loss of limb, and severe injury due to occupational accidents, even more tragic. Especially multiple-fatality occupational accidents are accepted as an indicator of the importance countries place on human life. Human life is above any economic value. Turkey is the country with the worst performance in Europe in terms of fatal occupational accident incidence rates. For all these reasons, in this study, fatal occupational accidents in Turkey in 2012-2022 were analyzed with the Health and Safety Labour Watch-Turkey (HESA Labour Watch) data. In addition, Social Security Institution (SSI) data for 2012-2021 was compared with the HESA Labour Watch data, and it was examined whether fatal occupational accidents were officially recorded. Finally, using official SSI data, Turkey and EU-27 countries were compared regarding the number of death cases resulting from occupational accidents and fatal occupational accident incidence rates. The study revealed the following results; Some of the fatal occupational accidents cannot be recorded in Turkey, After the OSH law, the number of death cases as a result of occupational accidents increased, compared to the EU-27 countries, Turkey has the worst performance in terms of both the number of death cases as a result of occupational accidents and the fatal accidents incidence rate.

Modelling the Count of Diphtheria in West Java in 2022 using Geographically Weighted Zero-Inflated Poisson Regression

The number of death cases caused by infectious diseases, that is to say diphtheria, especially in West Java so that it is categorized as an Extraordinary Event (KLB) generally attacks from the age of toddlers to the elderly. To model diphtheria cases in West Java in 2022 containing over dispersion using Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR). Parameter estimation uses Expectation Maximization (EM) algorithm with spatial weight using Adaptive Gaussian Kernel. The resulting models include a Poisson state model that shows the chance of diphtheria cases occurring with relevant factors of the percentage within the population who apply Hygienic and Healthful Lifestyle (PHBS), who have been vaccinated against DPT-HB3, the number of health workers at Puskesmas, and the percentage of active posyandu and a Zero state model that shows the chance of no diphtheria cases occurring with relevant factors of the percentage who have been vaccinated against DPT-HB3, the number of health workers at Puskesmas, and the percentage of active posyandu.

Global burden of type 2 diabetes attributable to non-high body mass index from 1990 to 2019

BackgroundThe prevalence of type 2 diabetes mellitus (T2DM) currently was increased in some countries of the world like China. However, the epidemiological trends of T2DM attributable to non-high body mass index (BMI) remain unclear. Thus, we aimed to describe the burden of T2DM attributable to non-high BMI.MethodsTo estimate the burden of T2DM attributable to non-high BMI, data from the Global Burden of Disease Study 2019 were used to calculate the deaths and disability-adjusted life years (DALYs) by age, sex, year, and location. The estimated annual percentage change (EAPC) was applied in the analysis of temporal trends in T2DM from 1990 to 2019.ResultsGlobally in 2019, the number of death cases and DALYs of T2DM attributable to non-high BMI accounted for 57.9% and 48.1% of T2DM-death from all risks, respectively. Asia accounted for 59.5% and 63.6% of the global non-high-BMI-related death cases and DALYs of T2DM in 2019, respectively. From 1990 to 2019, regions in the low-income experienced a rise in DALYs attributable to non-high BMI. As compared to other age groups, older participants had higher deaths and DALYs of T2DM attributable to non-high BMI. The death and DALY rates of T2DM due to non-high BMI were higher in males and people in regions with low socio-demographic index (SDI) countries.ConclusionsThe burden of T2DM attributable to non-high BMI is higher in the elderly and in people in regions with low- and middle-SDI, resulting in a substantial burden on human health and the social cost of healthcare.

Geographic Medical Overview of Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory of the AP Vojvodina (Northern Serbia).

The objective of this study was a geographic medical analysis of noncommunicable diseases (cardiovascular diseases from 2010 to 2020 and diabetes from 2010 to 2019) in the AP Vojvodina (northern Serbia) in order to identify the most and least burdened counties as well as to present trends in the mentioned diseases. The Mann-Kendall trend test, a cluster analysis, and Getis-Ord Gi* method for hot spot analysis were applied in this analysis. Regarding acute coronary syndrome and myocardial infarction, the North Backa County had a lower mortality rate although the number of newly reported cases was above average. The largest number of new cases of unstable angina pectoris was in the North Backa, North Banat, and Middle Banat Counties, while the West Backa County was identified as a county with a higher mortality rate. The cluster analysis showed that the number of death cases from diabetes in the Srem County is significantly higher than that in the other counties. Likewise, the West Backa County had a high number of new diabetes patients, but also a much lower mortality rate. Chronic noncommunicable diseases are predominant in newly diagnosed incidences and death cases in the AP Vojvodina. Studies of this kind promote public health and healthcare systems in the researched area and in the Republic of Serbia, as well as in other countries.

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning.

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

Insight into genomic organization of pathogenic coronaviruses, SARS-CoV-2: Implication for emergence of new variants, laboratory diagnosis and treatment options.

SARS-CoV-2 is a novel zoonotic positive-sense RNA virus (ssRNA+) belonging to the genus beta coronaviruses (CoVs) in the Coronaviridae family. It is the causative agent for the outbreak of the disease, COVID-19. It is the third CoV causing pneumonia around the world in the past 2decades. To date, it has caused significant deaths worldwide. Notably, the emergence of new genetic variants conferring efficient transmission and immune evasion remained a challenge, despite the reduction in the number of death cases, owing to effective vaccination regimen (boosting) and safety protocols. Thus, information harnessed from SARS-CoV-2 genomic organization is indispensable for seeking laboratory diagnosis and treatment options. Here in, we review previously circulating variants of SARS-CoV-2 designated variant of concern (VOC) including the Alpha (United Kingdom), Beta (South Africa), Gamma (Brazil), Delta (India), and recently circulating VOC, Omicron (South Africa) and its divergent subvariants (BA.1, BA.2, BA.3, BA.2.12.1, BA.4 and BA.5) with BA.5 currently becoming dominant and prolonging the COVID pandemic. In addition, we address the role of computational models for mutagenesis analysis which can predict important residues that contribute to transmissibility, virulence, immune evasion, and molecular detections of SARS-CoV-2. Concomitantly, the importance of harnessing the immunobiology of SARS-CoV-2 and host interaction for therapeutic purpose; and use of an in slilico based biocomputational approaches to achieve this purpose via predicting novel therapeutic agents targeting PRR such as toll like receptor, design of universal vaccine and chimeric antibodies tailored to the emergent variant have been highlighted.

Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany

The Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany’s 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spillover effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses.

Characteristics and Comparison of Adverse Events of Coronavirus Disease 2019 Vaccines Reported to the United States Vaccine Adverse Event Reporting System Between 14 December 2020 and 8 October 2021.

BackgroundThis study assessed and compared the frequency and type of adverse events (AEs) of the Pfizer-BioNTech, Moderna, and Janssen coronavirus disease 2019 (COVID-19) vaccines reported in the Vaccine Adverse Event Reporting System (VAERS).MethodsA retrospective analysis examined VAERS reports between 14 December 2020 and 8 October 2021 and focused on AE reports related to COVID-19 vaccines and AE outcomes [e.g., emergency room (ER) visits after being vaccinated, hospitalization, prolongation of existing hospitalization, life-threatening events, disability, birth defect, and death]. Reporting odds ratios (RORs) and Breslow-Day statistics were used to compare AE reporting between COVID-19 and non-COVID vaccines and between individual COVID-19 vaccines.ResultsA total of 604,157 AEs of COVID-19 vaccines were reported, including 43.51% for the Pfizer-BioNTech vaccine, 47.13% for the Moderna vaccine, and 9.12% for the Janssen COVID-19 vaccine. About 12.56% of patients visited ER after being vaccinated, 5.96% reported hospitalization, and 1.52% reported life-threatening events. Among the number of death cases (n = 7,674; mean age = 73), 2,025 patients (26.39%) had hypertension and 1,237 (16.12%) patients had cancer. RORs between COVID-19 vaccines and non-COVID vaccines identified increased ROR in ER visits, hospitalization, and life-threatening events. The results of the Breslow-Day statistics indicated heterogeneities between the disproportionality of reports across the four serious AE outcomes (i.e., ER visits, hospitalization, life-threatening events, and disability) between individual COVID-19 vaccines.ConclusionMost current VAERS reports showed that the most commonly reported AEs of COVID-19 vaccines were mild. Cases with a mortality outcome tended to occur in older adults with underneath conditions. Close ongoing surveillance in the safety of COVID-19 vaccines is critical and will inform the use of individual COVID-19 vaccines. Given the known limitations associated with the passive spontaneous reporting system, such as VAERS, our findings need to be further assessed and verified through longitudinal, large healthcare data systems.

Sociodemographic factors associated with Covid-19 in Canada

BACKGROUND: Coronavirus disease or Covid-19 has caused more than 30 million documented infections and 1 million deaths worldwide as of Oct 2020. It was shown that several sociodemographic factors play a significant role in shaping the Covid-19 outcome and associated death rates across the globe. Thus the present study aims to study the sociodemographic parameters associated with the Covid-19 cases in Canada.&#x0D; MATERIAL AND METHODS: In this retrospective study, the data was collected from the Official data repository present in Canada. The patients' data were evaluated and sociodemographic parameters were checked and recorded. After the data was recorded they are categorized based on the different states and statistical analysis was done.&#x0D; RESULT:The present study reported that in Canada total cases as reported in the repository are 1,253,519 cases. This result indicates that maximum of the patients suffering from Covid-19 belonged to the younger age category. Compared to the males, females were more to suffer from Covid-19. Most of the patients who required hospitalization were in the 80+ year age group (28.5%). Only 1.7% of patients in the age group below 19 years are required to be hospitalized. The regional data variation showed that in Alberta female patients were more in all the age groups compared with the male patients. Saskatchewan also reported a higher number of death cases in older people. In Manitoba, in the younger age category (0-29 years) less number of female patients suffered Covid-19. Interestingly, this number reversed as the age group increased. In Ontario, 72.1% of people reported being admitted to ICU and required a ventilator. In British Columbia, the gender distribution showed no such difference among all the Covid-19 positive cases. In Quebec among the covid-19 positive cases, 47.2% were male and 52.8% were females.&#x0D; CONCLUSION: Age is a significant predictor of Covid-19 mortality and patients from both genders aged more than 75 years and more need to provide more care and increased medical supervision to decrease the Covid-19 casualty.

Association between Meteorological Parameters and SARS-CoV-2 Outbreak Trends in Major Divisions of Bangladesh

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has caused the current pandemic situation worldwide. Studies based on different factors affecting the pandemic are useful in order to find effective measures against COVID-19. Therefore, this study aims to explore the association between meteorological parameters and COVID-19 positive cases, along with a number of fatalities. In this study, the number of identified COVID-19 cases along with death figures, daily records of rainfall, temperature, relative humidity, and wind speed were collected from April and May 2020 for eight major divisions in Bangladesh. Significant positive association (r = 0.24 to 0.58) between relative humidity and COVID-19 cases across the cities was found in this study, while for temperature both positive and negative associations (r = -0.23 to 0.72) were observed. Similarly, a positive association is found between humidity ((r = -0.012 to 0.384)) and numbers of COVID-19 death cases. Rainfall and wind speed exhibit positive correlations with COVID-19 positive cases and numbers of death cases. The findings of the study surely help the policymaker for further decision making, conducting new study and initiate the mitigation measures against COVID-19.

Market volatility and illiquidity during the COVID-19 outbreak: Evidence from the Saudi stock exchange through the wavelet coherence approaches

The aim of this paper is to examine the explanatory power of realized volatility on the illiquidity in Saudi stock market during the COVID-19outbreak. To achieve this objective, we consider the Wavelet Coherence approaches as empirical tools to investigate the combined effect of realized volatility and COVID-19 counts on the market illiquidity across frequencies and over time space by taking in account the number of infected cases in Saudi Arabia and over the World, and the number of death cases in Saudi Arabia as well as over the World. Our study reaches two main findings. First, the preliminary results reported by the ARDL bound test as a benchmark model showed significant long-run and short-run effects of the market volatility on illiquidity in contemporaneous and lagged manner. Second, the wavelet coherence analysis tools exhibited important results: (i) the wavelet coherency between illiquidity ratio and realized volatility in Saudi Arabia appear highly pronounced over all time horizons. (ii) PWC plots showed a significant mutual effect between liquidity risk and realized volatility when eliminating the effect of local COVID-19 cases. (iii) MWC plots highlighted that the response of the market illiquidity index to both the amplification in confirmed local cases (resp. international confirmed cases) and the stock market volatility appear significant in the short and middle horizons.

MO33-5 The impact of chemotherapy for cancer patients with COVID-19 on severity and mortality outcomes: A meta-analysis

Until now, the number of death cases from coronavirus disease 2019 (COVID-19) is still increasing. Several meta-analyses have shown that cancer is associated with poor outcomes from COVID-19. However, the decision of whether to continue or not cancer treatment in the current pandemic still give a dilemma. On one side, chemotherapy is meant to halt the progression of cancer and give a better prognosis, but on the other side, the compromised immune response caused by chemotherapy can increase the risk of infection and may worsen the course of infection.

Political regime, data transparency, and COVID-19 death cases

The COVID-19—the worst pandemic since the Spanish flu—has dramatically changed the world, with a significant number of people suffering from and dying of the disease. Some scholars argue that democratic governments are disadvantaged in coping with the current pandemic mainly because they cannot intervene in their citizens' lives as aggressively as their authoritarian counterparts. Other scholars, however, suggest that possible data manipulation may account for the apparent advantage of authoritarian countries. Taking such a possibility seriously, this paper analyzes the relationship between political regimes, data transparency, and COVID-19 deaths using cross-national data for over 108 countries, obtained from Worldometer COVID-19 Data, Polity V Project, Variety of Democracy (V-Dem) Project, HRV Transparency Project among other sources. Regression analyses indicate that authoritarian countries do not necessarily tend to have fewer COVID-19 deaths than their democratic counterparts after controlling for other factors, especially data transparency. The transparency variable itself, on the other hand, is positively correlated with the number of death cases more consistently (P <0.05). Overall, the estimation results point to the possible data manipulation, not the nature of regime characteristics itself, as a more significant source for the seemingly low casualty rates in authoritarian countries.

A Comparative Study on Covid-19 Cases in Top 10 States/UTs of India in Using Machine Learning Models

Coronavirus is a dangerous sickness came from a new virus. It has been assumed as an overall pandemic and a very hard circumstance to control the COVID-19 epidemic in India and global and so needed some severe actions to control its rate of increment. This disease causes for cold, dry cough, high fever, sore throat and serious breathing problems. This paper presents analysis of confirmed, cured and deaths cases, age and gender based cases in top 10 States/UTs of India. We analyzed various trends and patterns from various state/UTs units, MHFW of India data sources (up to 16th November 2020). Now a day’s plentiful models are proposed to predict covid-19 cases in India and world countries. The novel COVID-19 datasets are taken from Kaggle and GitHub repositories to analyze the epidemiological cases of the disease in top 10 states/UTs of India. We used various machine learning algorithms like Linear Regression, KNN Regressor, LASSO Regression, Elasticnet Regression and Decision Tree regressor to analyze the number of novel Coronavirus (COVID-19) reported cases in top 10 states/UTs of India. The model analyzes datasets containing the COVID-19 cases (confirmed, cured and death cases) up to 24th November, 2020 using ML models. From the results it is proven that Decision Tree and KNN regressor performs best in analyzing the number of confirmed cases and number of death cases. But for number of cured cases LASSO and linear regression models give the best accuracy results. Unfortunately, Elastic net produced poor accuracy results due to some changes in original datasets. Especially, this work analyzes the calculations based on the exactness rate on a test dataset.

Analyzing and Predicting Covid-19 epidemic using Machine Learning Techniques

The entire world is suffering from a novel disease called covid-19 caused by a coronavirus since 2019. The main reason for the seriousness of the disease is the lack of efficient legitimate medication or vaccine. The World Health Organization (WHO) suggested several precautions to regulate the spread of disease and to reduce the contamination thereby reducing deaths. In this paper, we analysed the covid-19 dataset available in Kaggle. The previous contributions from several authors of similar work focused on covid-19 datasets having a limited number of samples. Our paper used the dataset updated till November 15th 2020. Three different aspects are considered mainly in this paper, namely the number of confirmed cases, number of recovered cases, and number of death cases. All the aspects are analysed in a daily and weekly manner. We applied linear regression, polynomial regression, and holt’s method to predict the future number of confirmed, recovered, and death cases. This analysis is useful for the health sectors and frontline workers to help reduce the contamination caused by this disease.

Analysis on death trend in AIDS patients and related risk factors in China

Objective: To understand the basic characteristics of death cases, analyze the death trends in AIDS patients and the risk factors in China and provide evidence for the development of AIDS prevention and control strategy. Methods: The data were collected from the national basic information system of HIV/AIDS. The information of the cases in AIDS phase were used. The death number and mortality trends in AIDS cases were described, and Cox Proportion Hazards Regression Model was constructed to assess hazard ratios (HR) for independent variables. Results: By the end of 2019, a total of 582 472 AIDS cases, including 168 391 deaths, had been reported in China. Among the death cases, males accounted for 76.8% (129 343/168 391), heterosexual contact was the main transmission route, accounting for 60.9% (102 516/168 391). The proportion of the death cases who had ever received ART was 54.0% (90 888/168 391). The inter-quartile (P25, P75) of first CD4＋T cells counts (CD4) was 34 cells/μl, 240 cells/μl. Up to 43.5% (73 191/168 391) of the deaths occurred within one year after diagnosis. From 2007 to 2019, the annual death number increased from 5 485 to 18 737, the mortality rates decreased form 10.9%% to 4.3%. The average time interval from diagnosis to death ranged from 1.4 year to 4.0 years, showing increase trend by year. The results of Cox regression analysis showed that older age (50- years old: HR=1.50; ≥65 years old: HR=2.00), being male (HR=1.44)、being in minority ethnic group (HR=1.10), having lower first CD4 levels (0- cells/μl, HR=2.73;200- cells/μl, HR=1.33; 350- cells/μl,HR=1.13), heterosexual transmission route (HR=1.64) and injecting drug use (HR=1.79) were the risk factors related to deaths in AIDS patients. The higher educational levels (junior middle school: HR=0.86, senior high school and above: HR=0.59) and receiving antiviral treatrment (HR=0.09) were protective factors. Conclusions: The number of death cases increased, meanwhile the mortality rates decrease year by year in AIDS patients in China during 2007-2019. It is necessary to strengthen the early detection and treatment of AIDS to reduce the mortality.

MODELLING AND FORECASTING DAILY MORTALITY RATE OF COVID-19 IN ECOWAS: EVIDENCE FROM ARIMA MODEL

The Coronavirus Disease 2019 (COVID-19) pandemic which has claimed thousands of lives over the past 6 months and counting, has resulted in great panic by many worldwide, as the number of death cases is now monotonically growing by the day. This paper isaimed at developing a suitable Autoregressive integrated moving average (ARIMA) model, which shall be used to fit a most appropriate model for the daily number of total deaths recorded in ECOWAS that are traceable to the COVID-19 pandemic. We discovered that ARIMA (2, 1, 0) was the most appropriate model for forecasting the number of COVID-19 related deaths recorded in ECOWAS. A forecast of 234 days from 11th May, 2020 to 31th December, 2020, was carried out and we discovered that, the COVID-19 mortality in ECOWAS may likely take a daily upward trend for the next 6 months.

Analytical Methodologies for the Characterization and Analysis of the Parent Compound and Phase I Metabolites of 4F-MDMB-BICA in Human Microsome, Urine, and Blood Samples.

4F-MDMB-BICA is one of the most dangerous new illicit synthetic cannabinoids (SCs) in 2020. Consumption of 4F-MDMB-BICA has been associated with a number of death cases and related serious adverse health effects in Hungary. Therefore, the use of reliable analytical methods to confirm the intake of 4F-MDMB-BICA is an important issue in forensic practice. Besides the detection of the parent compounds of SCs, the screening of their metabolites provides a reliable confirmation of their consumption, in particular, when the parent compound is under the limit of detection. To the best of our knowledge, this is the first report describing the identification of metabolites of 4F-MDMD-BICA after treatment with pooled human liver microsome (pHLM) and in human urine and blood samples using the combination of data obtained by comprehensive ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC–HRMS) and semi-targeted UHPLC–HRMS-MS methods. Finally, our routine UHPLC coupled with triple-quadrupole tandem low-resolution mass spectrometry method for screening urine and blood SCs was improved by adding the parent compound and selected main biomarkers of 4F-MDMD-BICA. From the pHLM assay of 4F-MDMD-BICA, 30 phase I metabolites were characterized and structural information thus obtained provided the basis of further identification of in vivo urine and blood metabolites. Overall, 20 urinary and 13 blood in vivo metabolites of 4F-MDMD-BICA have been identified by the investigation of five authentic urine and two blood samples. The ester hydrolysis metabolite was selected as a reliable primary biomarker in urine and blood. As secondary targets, urinary mono-hydroxylation metabolite and ester hydrolysis + dehydrogenation metabolite in blood were recommended due to their abundance and selectivity. Overall, the main phase I metabolites of 4F-MDMD-BICA were successfully characterized, and our routine analytical method with related sample preparation procedure provided a reliable analytical tool for screening both 4F-MDMD-BICA and its selected metabolites in urine and blood samples.