Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk: An International Journal of Medical Toxicology and Drug Experience

Introduction Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking. Objectives In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk. Methods The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included. Results Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected. Conclusions We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Space-time risk clusters of COVID-19 in a tourism area in the northeastern Brazil: an epidemiological overview after two years of pandemic

The world is facing a serious viral infection caused by the new Severe Acute Respiratory Syndrome Coronavirus 2. We aimed to evaluate and map the high-risk clusters of COVID-19 in the State of Alagoas, a touristic area in northeastern Brazil, after two years of pandemic by a population-based ecological study, using COVID-19 cases reported in the State of Alagoas, between March, 2020 and April, 2022. We performed a descriptive and statistical analysis of epidemiological data. We then map high-risk areas for COVID-19, using spatial analysis, considering the incidence rate by municipality. 297,972 positive cases were registered;56.9% were female and 42.7% aged between 20 and 39 years old. Men (OR = 1.59) and older than 60 years old (OR = 29.64) had a higher risk of death, while the highest incidence rates of the disease occurred in the metropolitan region. Our data demonstrate the impact of COVID-19 in the State of Alagoas, through the two years of pandemic. Although the number of cases were greater among women and young adults, the chance of death was greater among men and older adults. High-risk clusters of the disease initially occur in metropolitan cities and tourist areas.

An analytical approach to evaluate the impact of age demographics in a pandemic.

The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.

Conjoint Analysis based Predictive Analytics to study the employee attitude towards virtual and digital work practices in IoT work environments

Covid-19 has been one of the most disruptive pandemics to date. Among the other aspects of disruption, it also disrupted the way people work in organizations. Many of the organizations surrendered their offices for good. However, there are many ill effects of these unconventional work practices also. This research study aims to explore the perception of the employees towards the adoption of Virtual and flexible work practices. The study uses a conjoint analysis approach on different possible Work Practice Profiles, that specify the nature of work (Virtual, offline, or hybrid), nature of work schedule (flexible, or fixed), nature of ownership (individual, or team), and length of working hours (8.5 hours, or 9.5 hours or 10.5 hours). The study finds that the number of working hours is the most important criterion for the employees followed by mode of work, responsibility, and work schedule. © 2023 IEEE.

Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.

Impacts of Daily Travel by Distances on the Spread of COVID-19: An Artificial Neural Network Model.

The continued spread of COVID-19 poses significant threats to the safety of the community. Since it is still uncertain when the pandemic will end, it is vital to understand the factors contributing to new cases of COVID-19, especially from the transportation perspective. This paper examines the effect of the United States residents' daily trips by distances on the spread of COVID-19 in the community. The artificial neural network method is used to construct and test the predictive model using data collected from two sources: Bureau of Transportation Statistics and the COVID-19 Tracking Project. The dataset uses ten daily travel variables by distances and new tests from March to September 2020, with a sample size of 10,914. The results indicate the importance of daily trips at different distances in predicting the spread of COVID-19. More specifically, trips shorter than 3 mi and trips between 250 and 500 mi contribute most to predicting daily new cases of COVID-19. Additionally, daily new tests and trips between 10 and 25 mi are among the variables with the lowest effects. This study's findings can help governmental authorities evaluate the risk of COVID-19 infection based on residents' daily travel behaviors and form necessary strategies to mitigate the risks. The developed neural network can be used to predict the infection rate and construct various scenarios for risk assessment and control.

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Developing a comorbidity score in cancer patients using healthcare utilization databases during the COVID-19 pandemic: An experience from Italy.

BACKGROUND: A strong relationship has been observed between comorbidities and the risk of severe/fatal COVID-19 manifestations, but no score is available to evaluate their association in cancer patients. To make up for this lacuna, we aimed to develop a comorbidity score for cancer patients, based on the Lombardy Region healthcare databases. METHODS: We used hospital discharge records to identify patients with a new diagnosis of solid cancer between February and December 2019; 61 comorbidities were retrieved within 2 years before cancer diagnosis. This cohort was split into training and validation sets. In the training set, we used a LASSO-logistic model to identify comorbidities associated with the risk of developing a severe/fatal form of COVID-19 during the first pandemic wave (March-May 2020). We used a logistic model to estimate comorbidity score weights and then we divided the score into five classes (<=-1, 0, 1, 2-4, >=5). In the validation set, we assessed score performance by areas under the receiver operating characteristic curve (AUC) and calibration plots. We repeated the process on second pandemic wave (October-December 2020) data. RESULTS: We identified 55,425 patients with an incident solid cancer. We selected 21 comorbidities as independent predictors. The first four score classes showed similar probability of experiencing the outcome (0.2% to 0.5%), while the last showed a probability equal to 5.8%. The score performed well in both the first and second pandemic waves: AUC 0.85 and 0.82, respectively. Our results were robust for major cancer sites too (i.e., colorectal, lung, female breast, and prostate). CONCLUSIONS: We developed a high performance comorbidity score for cancer patients and COVID-19. Being based on administrative databases, this score will be useful for adjusting for comorbidity confounding in epidemiological studies on COVID-19 and cancer impact.

Decline in Traffic Congestion Increased Crash Severity in the Wake of COVID-19.

Highway fatalities are a leading cause of death in the U.S. and other industrialized countries. Using highly detailed crash, speed, and flow data, we show highway travel and motor vehicle crashes fell substantially in California during the response to the COVID-19 pandemic. However, we also show the frequency of severe crashes increased owing to lower traffic congestion and higher highway speeds. This "speed effect" is largest in counties with high pre-existing levels of congestion, and we show it partially or completely offsets the "VMT effect" of reduced vehicle miles traveled on total fatalities. During the first eleven weeks of the COVID-19 response, highway driving decreased by approximately 22% and total crashes decreased by 49%. While average speeds increased by a modest 2 to 3 mph across the state, they increased between 10 and 15 mph in several counties. The proportion of severe crashes increased nearly 5 percentage points, or 25%. While fatalities decreased initially following restrictions, increased speeds mitigated the effect of lower vehicle miles traveled on fatalities, yielding little to no reduction in fatalities later in the COVID period.

The intention to accept the vaccine against COVID-19 at risk sectors in Ecuador

Introduction: COVID-19 caused healthcare professional workers have faced the pandemic on the frontline at the risk of being infected with the virus. Despite the low mortality rate at present and the low presence of patients with COVID-19 in health care centers, the application of a fourth booster dose has generated different positions among several countries. Background: to determine whether personnel considered being at high risk of vulnerability in the city of Santo Domingo de los Colorados, Ecuador, have favorable intentions for receiving the fourth booster dose of the COVID-19 vaccine. Method: a quantitative study of correlational scope and cross-sectional design was developed. A questionnaire consisting of 16 questions measured the following variables: risk of infection, perceived knowledge of the vaccine, confidence in the vaccine and intention to be vaccinated;this questionnaire was applied to 375 participants. Statistical analyses were developed using the microsoft Excel spreadsheed and Statistical Packagefor Social Sciences 21 (SPSS 21). Results: statistical analyses showed that the risk of infection (beta = 0.178**), perceived knowledge about the vaccine (beta = 0.218**) and confidence about the vaccine (beta = 0.192**) are significantly correlated with the intention to be fully vaccinated, thus showing the need for a fourth booster dose by vulnerable sectors. Conclusion: this is the first research that presents results regarding the intention to vaccinate vulnerable people and highlights the intention to access a fourth booster dose.

Does the Size of the Business Still Matter, or Is Profitability under New Management, by Order of the COVID-19?

Businesses should come up with a strategy, plans, and goals so that their total assets can make a profit during the transformation process. Utilizing various features of a property can generate this income. This comparison provides evidence of profitability. During the global economic downturn, a number of businesses encountered issues that caused their payment situations and profitability to deteriorate. The goal of this article is to ascertain whether particular profitability indicators also revealed the pandemic-related global crisis, particularly in the Visegrad Group countries. This analysis was conducted based on categories of business size. Specifically, 8671 enterprises were analyzed. The evaluation of indicators revealed whether there was a significant change in a negative direction, a significant change in a positive direction, or no significant change. It was possible to make a clear diagram of the companies that took part in the study and to figure out the median values in order to compare the results of the chosen profitability indicators. Correspondence analysis was conducted so that conclusions could be more accurate. According to the findings of this study, indicators of ROA, ROE, and ROS did not change significantly across enterprise size categories in the years preceding, during, and after the pandemic. Since the government regulations of the V4 countries had a significant impact on these businesses, the change was most obvious in the case of small businesses within the ROS indicator. The added value of the article is derived from its analysis of selected profitability indicators in the largest group of Central European nations and its relevance.

Measurement of Phthalates in Settled Dust in University Dormitories and Its Implications for Exposure Assessment

Six phthalates: dimethyl phthalate (DMP), diethyl phthalate (DEP), di(n-butyl) phthalate (DnBP), butyl benzyl phthalate (BBzP), di(2-ethylhexyl) phthalate (DEHP), and di(n-octyl) phthalate (DOP) in settled dust on different indoor surfaces were measured in 30 university dormitories. A Monte Carlo simulation was used to estimate college students' exposure via inhalation, non-dietary ingestion, and dermal absorption based on measured concentrations. The detection frequencies for targeted phthalates were more than 80% except for DEP (roughly 70%). DEHP was the most prevalent compound in the dust samples, followed by DnBP, DOP, and BBzP. Statistical analysis suggested that phthalate levels were higher in bedside dust than that collected from table surfaces, indicating a nonuniform distribution of dust-phase phthalates in the sleep environment. The simulation showed that the median DMP daily intake was 0.81 μg/kg/day, which was the greatest of the targeted phthalates. For the total exposures to all phthalates, the mean contribution of exposures during the daytime and sleeping time was 54% and 46%, respectively.

Groundwater Contamination Monitoring for Pollution Measurement and Transmission Applying WQI Approaches from a Region of the Erode District, Tamilnadu, India

The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.

MMSports'22: 5th International ACM Workshop on Multimedia Content Analysis in Sports

The fifth ACM International Workshop on Multimedia Content Analysis in Sports (ACM MMSports'22) is part of the ACM International Conference on Multimedia 2022 (ACM Multimedia 2022). After two years of pure virtual MMSports workshops due to COVID-19, MMSports'22 is held on-site again. The goal of this workshop is to bring together researchers and practitioners from academia and industry to address challenges and report progress in mining, analyzing, understanding, and visualizing multimedia/multimodal data in sports, sports broadcasts, sports games and sports medicine. The combination of sports and modern technology offers a novel and intriguing field of research with promising approaches for visual broadcast augmentation and understanding, for statistical analysis and evaluation, and for sensor fusion during workouts as well as competitions. There is a lack of research communities focusing on the fusion of multiple modalities. We are helping to close this research gap with this workshop series on multimedia content analysis in sports. Related Workshop Proceedings are available in the ACM DL at: https://dl.acm.org/doi/proceedings/10.1145/3552437. © 2022 Owner/Author.

A Tale of Two Cities: COVID-19 Vaccine Hesitancy as a Result of Racial, Socioeconomic, Digital, and Partisan Divides

The unprecedented COVID-19 pandemic has drawn great attention to the issue of vaccine hesitancy, as the acceptance of the innovative RNA vaccine is relatively low. Studies have addressed multiple factors, such as socioeconomic, political, and racial backgrounds. These studies, however, rely on survey data from participants as part of the population. This study utilizes the actual data from the U.S. Census Bureau as well as actual 2020 U.S. presidential election results to generate four major category of factors that divide the population: socioeconomic status, race and ethnicity, access to technology, and political identification. This study then selects a region in a traditionally democratic state (Capital Region in New York) and a region in a traditionally republican state (Houston metropolitan area in Texas). Statistical analyses such as correlation and geographically weighted regression reveal that factors such as political identification, education attainment, and non-White Hispanic ethnicity in both regions all impact vaccine acceptance significantly. Other factors, such as poverty and particular minority races, have different influences in each region. These results also highlight the necessity of addressing additional factors to further shed light on vaccine hesitancy and potential solutions according to identified factors.

Covid-19 Economic Vulnerability Index: EU Evidence.

The COVID-19 pandemic outbreak caused many negative effects on both the global and national economies. To implement effective policies to mitigate the negative impact of a pandemic, it is necessary to identify particularly vulnerable areas. The objective of this paper is to rank the EU countries in terms of the level of vulnerability of their economies to the impact of the pandemic. For this purpose, the COVID-19 Economic Vulnerability Index (CEVI) was constructed. It replaces the 15-dimensional set of characteristics of the countries with one aggregate, synthetic indicator estimated for 27 EU member states. In the study multivariate statistical methods, including agglomerative clustering and multi-attribute methods of object assessment were used to analyse the effects of the pandemic. The research shows that EU countries have different levels of economic vulnerability to the impact of the COVID-19 pandemic. The southern European countries (Spain, Croatia, Greece and Italy), where the tourism sector plays an important role in GDP composition, are the most fragile. Germany and the Scandinavian countries proved to be the least sensitive to the negative impact of the pandemic. The CEVI can be an important part of the decision support system. It enables the identification of countries that show greater vulnerability to the economic impact of the COVID-19 pandemic and may help support countries that need help the most. The proposed index also indicates certain areas in the country's economy that make it more vulnerable. The CEVI in combination with other instruments can be a very useful tool to improve the economy's resilience and help it recover faster in the event of a pandemic shock.

Role of initial and follow-up IL-6 (interleukin-6) titre in COVID-19 pneumonia: a single center experience

Background: Robust data of IL-6 is available in bacterial infection, and now it can be utilized in currently ongoing COVID-19 (corona virus disease-19) pneumonia pandemic to guide treatment strategy as marker of inflammation. Methods: Prospective, observational study included 1,000 COVID-19 cases confirmed with RT PCR (reverse transcription polymerase chain reaction). All cases were undergone categorized after clinical details, HRCT (high resolution computerized tomography) thorax, oxygen saturation, IL-6 (interleukin 6) at entry point and follow up. Age, gender, comorbidity and use BIPAP/NIV (bilevel positive airway pressure/non-invasive ventilation), and outcome as with or without lung fibrosis as per HRCT severity were key observations. Statistical analysis is done by using Chi-square test. Results: In study of 1,000 COVID-19 pneumonia cases, age (<50 and >50 years) and gender has significant association with IL-6. HRCT severity score at entry point has significant correlation with IL-6 level (p < 0.00001). IL-6 level has significant association with duration of illness (p < 0.00001). Comorbidities has significant association with IL-6 level (p < 0.00001). IL-6 level has significant association with oxygen saturation (p < 0.00001). BIPAP/NIV requirement has significant association with IL-6 level (p < 0.00001). Timing of BIPAP/NIV requirement during course of hospitalization has significant association with IL-6 level (p < 0.00001). Follow-up IL-6 titer during hospitalization as compared to entry point normal and abnormal IL-6 has significant association with post-COVID-19 lung fibrosis, respectively (p < 0.00001). Conclusion: IL-6 has very crucialrole in COVID-19 pneumonia in predicting severity of illness, progression of illness including 'cytokine storm' and assessing response to treatment during hospitalization and follow-up titers in analyzing post-COVID-19 lung fibrosis.

A hospital based observational study of clinical spectrum and outcome of mucormycosis during the COVID pandemic

Background : Mucormycosis is a life threatening fungal disease caused by the filamentous fungi mucormycetes. Though a known entity for decades, it began to manifest in an unprecedented manner in the COVID scenario specially with the second wave in India. The objectives were to describe the demographic characteristics, clinical presentations, risk factors, therapy and in-hospital mortality of patients with Mucormycosis. Material and Methods : We conducted a retrospective observational study for a period of six months from March 2021 to August 2021. The data was collected for cases of mucormycosis from multiple centres all over West Bengal and analysed. All consecutive individuals with confirmed mucormycosis were enrolled in this study. The data documenting demographic particulars, presentation, predisposing factors and comorbiditieswere recorded in a pre validated case report form Details of investigation recording site and extent of disease, therapeutic intervention and outcome was mentioned . Statistical analysis was done using SPSS 21.0 for MS-Windows. Results : The total number of cases from March to August 2021 was 263 . There were 171 males and 92 females and the mean age of occurrence was 50.8+or-0.4 years .In West Bengal clusters of cases were being reported most commonly from the districts of North 24 Parganas, Kolkata, Jalpaiguri, Darjeeling and Hooghly. Some cases admitted here hailed from outside states like Bihar, Jharkhand, Odisha and Assam. The majority of the cases 74.22% (196)were COVID Associated Mucormycosis (CAM) while only 25.78% were non COVID associated. Diabetes mellitus was associated in 78.7% and history of prolonged steroid therapy in 57.4% of cases. We encountered rhino orbital mucormycosis in 99.24% of cases and cerebral involvement in 47.3%. They were treated with Amphotericin B deoxycholate along with endoscopic debridement. The most common side effects of Amphotericin B Deoxycholate were hypokalemia (93%), hypomagnesemia (32%) and AKI (74%) of the cases . The number of patients discharged was 16.7% and 10 left against medical advice (LAMA) . In hospital deaths were recorded to be 26.7%. Cause of death was commonly -AKI, septic shock and multiorgan failure . Conclusion : Prevention is better than cure of this devastating disease which is difficult todiagnose and treat . Awareness about mucormycosis and careful clinical evaluation of post-COVID patients is mandatory in this era in order to rapidly diagnose and treat mucormycosis.

Covid-19: The Effect on Hospitalization Patient of Ophthalmology Department in "Antonio Cardarelli” Hospital

A pneumonia outbreak of unknown origin was reported in Wuhan, China in late December. This virus, called coronavirus-2, has an impact on the respiratory tract, leading to acute respiratory syndromes. In 2020, this virus was declared a pandemic by the World Health Organization since it caused a high number of deaths worldwide. In addition, this pandemic has had a negative impact on the world economy, focusing the attention of the practitioners on the resource management in health structures. This work was carried out to evaluate the effects of the pandemic on the ordinary hospitalization activities of the Department of Ophthalmology at "A. Cardarelli” based in Naples (Italy). The dataset was evaluated using statistical analysis techniques and logistic regression. The results, for this department, did not show significant differences when comparing the health variables of the pre-pandemic year (2019) with the pandemic year (2020). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.