The state of health in Pakistan and its provinces and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding health trends and estimating the burden of disease at the national and subnational levels helps policy makers track progress and identify disparities in overall health performance. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides comprehensive estimates for Pakistan. Comparison of health indicators since 1990 provides valuable insights about Pakistan's ability to strengthen its health-care system, reduce inequalities, improve female and child health outcomes, achieve universal health coverage, and meet the UN Sustainable Development Goals. We present estimates of the burden of disease, injuries, and risk factors for Pakistan provinces and territories from 1990 to 2019 based on GBD 2019 to improve health and health outcomes in the country. Methods We used methods and data inputs from GBD 2019 to estimate socio-demographic index, total fertility rate, cause-specific deaths, years of life lost, years lived with disability, disability-adjusted life-years, healthy life expectancy, and risk factors for 286 causes of death and 369 causes of non-fatal health loss in Pakistan and its four provinces and three territories from 1990 to 2019. To generate estimates for Pakistan at the national and subnational levels, we used 68 location-years of data to estimate Pakistan-specific demographic indicators, 316 location-years of data for Pakistan-specific causes of death, 579 location-years of data for Pakistan-specific non-fatal outcomes, 296 location-years of data for Pakistan-specific risk factors, and 3089 location-years of data for Pakistan-specific covariates. Findings Life expectancy for both sexes in Pakistan increased nationally from 61 center dot 1 (95% uncertainty interval [UI] 60 center dot 0-62 center dot 1) years in 1990 to 65 center dot 9 (63 center dot 8-67 center dot 8) years in 2019;however, these gains were not uniform across the provinces and federal territories. Pakistan saw a narrowing of the difference in healthy life expectancy between the sexes from 1990 to 2019, as health gains for women occurred at faster rates than for men. For women, life expectancy increased by 8 center dot 2% (95% UI 6middot3-13middot8) between 1990 and 2019, whereas the male life expectancy increased by 7 center dot 6% (3 center dot 5-11 center dot 8). Neonatal disorders, followed by ischaemic heart disease, stroke, diarrhoeal diseases, and lower respiratory infections were the leading causes of all-age premature mortality in 2019. Child and maternal malnutrition, air pollution, high systolic blood pressure, dietary risks, and tobacco consumption were the leading all-age risk factors for death and disability-adjusted life-years at the national level in 2019. Five non-communicable diseases-ischaemic heart disease, stroke, congenital defects, cirrhosis, and chronic kidney disease-were among the ten leading causes of years of life lost in Pakistan. Burden varied by socio-demographic index. Notably, Balochistan and Khyber Pakhtunkhwa had the lowest observed gains in life expectancy. Dietary iron deficiency was the leading cause of years lived with disability for both men and women in 1990 and 2019. Low birthweight and short gestation and particulate matter pollution were the leading contributors to overall disease burden in both 1990 and 2019 despite moderate improvements, with a 23 center dot 5% (95% UI 3 center dot 8-39 center dot 2) and 27 center dot 6% (14 center dot 3-38 center dot 6) reduction in age-standardised attributable DALY rates during the study period. Interpretation Our study shows that progress has been made on reducing Pakistan's disease burden since 1990, but geographical, age, and sex disparities persist. Equitable investment in the health system, as well as the prioritisation of high-impact policy interventions and programmes, are needed to save lives and improve health outcomes. Pakistan is facing several domestic and foreign challenges-the Taliban's return to power in Afghanistan, political turmoil, catastrophic flooding, the COVID-19 pandemic-that will shape the trajectory of the country's health and development. Pakistan must address the burden of infectious disease and curb rising rates of non-communicable diseases. Prioritising these three areas will enhance Pakistan's ability to achieve universal health coverage, meet its Sustainable Development Goals, and improve the overall health outcomes.

BADR: A Talking Face To Teach Arabic Vocabulary Under COVID-19

We propose an Arabic talking face called Badr to teach the language vocabulary for young students under COVID-19 pandemic. Badr is built on the previous talking face Baldi (e.g., an American English talking face) with many enhancements in interaction and words pronunciation. It can complement the work of instructors who can get tired and bored when teaching online for long hours. It can pronounce standard Arabic vocabulary accurately and interact with learners in natural manner. Listening and observing Badr improve the learners' ability to understand speech in a noisy background. We tested Badr with Qatar University students, and we show its effectiveness and usefulness in introducing smoothly new vocabulary. Badr can act as an independent tutor for different categories of learners including those with learning difficulties, slow learners, and non-Arabic speaking learners. © 2022 IEEE.

Digital Platforms and Real Estate Industry during COVID-19

Real estate is one of the industries that have been most affected by COVID-19. The purpose of this study is to investigate the factors that affect customer use of New Zealand's real estate digital platforms. Trade Me and realestate.co.nz are the two most popular real estate digital platforms with the most significant number of homebuyers in New Zealand. This study uses the reviews on the two platforms from 2018 to 2021. There are a total of 1349 customer reviews on the two platforms, which represent the total population. A sample of 113 reviews from the realestate.co.nz platform and 113 reviews from the Trade Me platform are used for analysis. The findings show that perceived ease of use and usefulness, and information and system quality are the four main factors that affect the willingness of customers to use real estate digital platforms. After the spread of the pandemic, the impact of these factors on customer adoption of the platforms has been reduced. Therefore, the results of this study will be helpful in formulating and developing a digital marketing strategy for the New Zealand real estate industry in the post-pandemic era. © 2022, Global Social Science Institute. All rights reserved.

Digital Platforms and Real Estate Industry during COVID-19

Real estate is one of the industries that have been most affected by COVID-19. The purpose of this study is to investigate the factors that affect customer use of New Zealand's real estate digital platforms. Trade Me and realestate.co.nz are the two most popular real estate digital platforms with the most significant number of homebuyers in New Zealand. This study uses the reviews on the two platforms from 2018 to 2021. There are a total of 1349 customer reviews on the two platforms, which represent the total population. A sample of 113 reviews from the realestate.co.nz platform and 113 reviews from the Trade Me platform are used for analysis. The findings show that perceived ease of use and usefulness, and information and system quality are the four main factors that affect the willingness of customers to use real estate digital platforms. After the spread of the pandemic, the impact of these factors on customer adoption of the platforms has been reduced. Therefore, the results of this study will be helpful in formulating and developing a digital marketing strategy for the New Zealand real estate industry in the post-pandemic era. © 2022, Global Social Science Institute. All rights reserved.

Evaluating the Adverse Events Associated with Three Doses of the COVID-19 Vaccination in Adults in the Western Region of Saudi Arabia: A Cross-Sectional Study.

The Kingdom of Saudi Arabia was one of the countries earliest affected by the coronavirus 2019 (COVID-19) pandemic and had taken precautions including compulsory COVID-19 vaccination. Both the ChAdOx1 nCoV-19 vaccine (Oxford AstraZeneca) and the BNT162b2 vaccine (Pfizer) were approved by the Saudi Ministry of Health, followed by mRNA-1273 (Moderna), all of which were used for population-wide vaccination. This study aimed to assess the short-term side effects following the COVID-19 vaccinations among participants who had received all three doses in the western region of Saudi Arabia. An online survey was distributed to the participants who received either BNT162b2, ChAdOx1 nCoV-19, or mRNA-1273 vaccines, and the type of side effects and their severity were evaluated. Fatigue and headache, pain at the site of the injection and muscle pain were the most common side effects in all three doses. However, the severity depending on the type of vaccination was significant only for the first and second dose, but not the third dose. In contrast, there was a higher percentage of participants who encountered severe side effects from the third dose compared to the first and second. Nevertheless, the majority of participants described all three doses' side effects to be moderately severe. A future evaluation could be made to access the individual types of vaccination and compare between the side effects of the BNT162b2, ChAdOx1 nCoV-19, and mRNA-1273 vaccines specifically for the booster dose.

Teacher Leader Competency Development: A Panacea for a Vuca World?

The rate of change today, be it in political, economic, social or technological realm that impacts and disrupts many of today's organization, is to say the least, a daunting challenge. The chain of unprecedented events such as pandemic Covid-19, massive floods and other environmental disasters happening both within local and global contexts, are some of the lists of difficult challenges that characterized a VUCA world. As the challenges seem mountainous to school leadership, from navigating the quarantine guidelines, school closure and re-opening, remote learning, student engagement, and constant change, many are of the belief that teachers need to be empowered as leaders in order to move from the survival mode to a more thriving school environment. As such, there is a need to rethink on the development of teacher leaders' competencies against the background of a VUCA world by first, identifying the crucial competencies that need to be developed among teacher leaders. Thus, this paper aims to promote teacher leadership competency model that suits a VUCA context by integrating multiple perspectives and researches on teacher leadership, leadership development, the VUCA world and the teacher leadership competency model. The belief is that through a proper teacher leader development it will help to inculcate VUCA-readiness and responsive leadership among teacher leaders which consequentially enhance school effectiveness. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Detecting Users Prone to Spread Fake News on Arabic Twitter

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users' recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task. © European Language Resources Association (ELRA).

Cardiovascular Computed Tomography And FFRCTIn The United States Medicare Population From 2013-2020

Introduction: Over the past decade, through numerous technical advances and clinical studies, cardiovascular computed tomography (CCT) has gained increasing acceptance;recently evidenced by receiving multiple class 1, level A recommendations in the 2021 AHA/ACC Chest Pain Guidelines. We aimed to evaluate recent CCT practice and practitioner trends in the US Medicare population with the motivation of guiding practice, training, and advocacy. Methods: A retrospective cross-sectional analysis of Medicare Part B pay-for-service physician payments was performed between 2013-2020. CCT/FFRCT exams and providers were identified by unique HCPCS codes. Providers, exams, cost, and payment denials were analyzed. Medical specialty, gender, and geo-location of providers were summarized. Results: From 2013 to 2019, the number of providers of CCT exams and the number of exams increased significantly. Providers of CAC scoring increased >210%. Providers of coronary CTA in the hospital setting increased 36% and in independent testing facilities by 9%. CAC scoring exams increased 724% and coronary CTA exams increased 126% (see Figure). In the first year of the COVID-19 pandemic (2020), CAC scoring usage decreased by -9.3% and coronary CTA by -3.3%. Since initial reimbursement in 2018, FFRCT usage has increased by 654% but was applied in only 4% of coronary CTA exams. In 2020, contrary to a moderate CCT exam decline, FFRCT analysis increased by 376% compared to the previous year. Medicare insurance acceptance of cardiac CT became more favorable into 2020 (see Figure). CAC scoring denials decreased from 61.6% to 33.2% and coronary CTA denials decreased slightly from 7.3% to 6.4%. FFRCT denials decreased significantly from 64% to 6%. In 2019, 30.5% of CCT providers were cardiologists with the remainder being predominantly radiologists. On the other hand, 76.2% of FFRCT providers were cardiologists. A slightly lower percentage of FFRCT billing physicians were female compared to CCT billing physicians (14.2% vs 17.9%). CA, NY, MN, TX, and PA had the highest FFRCT utilization. Conclusions: In general, both CAC scoring and coronary CTA utilization have increased, along with a large increase in the utilization of FFRCT over the study time period. This increase in utilization was accompanied by a significant increase in providers and a decrease in reimbursement denials. In the first year of the COVID-19 pandemic, CCT usage was robust and only decreased moderately. [Formula presented]

A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model

Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situa-tion: one of the most common and effective methods is to examine the X-radiation (X-rays) and computed tomography (CT) images for detection of Covid-19. How-ever, this method burdens the radiologist to examine each report. Therefore, to reduce the burden on the radiologist, an effective, robust and reliable detection system has been developed, which may assist the radiologist and medical specia-list in effective detecting of COVID. We proposed a deep learning approach that uses readily available chest radio-graphs (chest X-rays) to diagnose COVID-19 cases. The proposed approach applied transfer learning to the Deep Convolutional Neural Network (DCNN) model, Inception-v4, for the automatic detection of COVID-19 infection from chest X-rays images. The dataset used in this study contains 1504 chest X-ray images, 504 images of COVID-19 infection, and 1000 normal images obtained from publicly available medical repositories. The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%. © 2023, Tech Science Press. All rights reserved.

The Impact of YouTube Pandemic Advertising on People’s Attitudes Towards COVID-19

The main objective of this study is to examine the impact of YouTube pandemic advertising on people’s attitudes towards COVID-19. YouTube, as one of the most well-known social platforms, has performed well in this pandemic situation in terms of transmitting vital information through advertising. A quantitative approach was employed and the data were collected from 205 respondents through an online survey. People’s opinions of pandemic advertisements and the dissemination of information through YouTube are both critical factors in determining the impact of YouTube pandemic advertisements on people’s attitudes towards COVID-19. The findings also reveal that there is an impact of COVID-19 advertising on its viewers. Majority of respondents followed instructions with varied degree such as keeping social distance found in the advertised information and became more willing to pay attention to health issues in future. © 2022 by authors.

An Efficient Deep Learning Framework for Face Mask Detection in Complex Scenes

COVID-19 has caused a global health crisis that has infected millions of people across the globe. Currently, the fourth wave of COVID-19 is about to be declared as Omicron. The new variant of COVID-19 has caused an unprecedented increase in cases. According to World Health Organization, safety measures must be adopted in public places to prevent the spread of the virus. One effective safety measure is to wear face masks in crowded places. To create a safe environment, government agencies adopt strict rules to ensure adherence to safety measures. However, it is difficult to manually analyze the crowded scenes and identify people violating the safety measures. This paper proposed an automated approach based on a deep learning framework that automatically analyses the complex scenes and identifies people with face masks or without facemasks. The proposed framework consists of two sequential parts. In the first part, we generate scale aware proposal to cover scale variations, and in the second part, the framework classifies each proposal. We evaluate the performance of the proposed framework on a challenging benchmark data set. We demonstrate that the proposed framework achieves high performance and outperforms other reference methods by a considerable margin from experimental results. © 2022, IFIP International Federation for Information Processing.

Management of Symptomatic Inguinal Hernia under Local Anesthesia in Covid Pandemic Our Experience

Background: Health care burden has increased since the pandemic of Covid-19 has emerged. The healthcare resources are limited currently and majority surgeries have been postponed because of the current pandemic. Therefore, the main concern of carrying out any surgery at current point is mainly in those patients that are landing in emergency. Symptomatic inguinal hernia being a common presentation in emergency can be dealt by applying local anesthesia. Objective: To evaluate the outcome (in terms of efficacy and safety) of local anesthesia for managing symptomatic inguinal hernia in a tertiary care hospital during current Covid-19 pandemic. Methodology: It was a descriptive study.60 males were enrolled with inguinal hernia of age 30-60 years. IV line was secured and local anesthesia was administered under aseptic measures. Lichtenstein repair, a mesh technique was applied in all patients for treating the inguinal hernia. Patients were evaluated postoperatively after 2 hours and 6 hours for any complications. Results: Mean age of the patients was 38.2±10.542, Mean time to eat was 3.85 ±3.138. Mean time to ambulate was 4.37 ±2.51. Mean pain score was 5.78± 2.131 postoperatively and after 6 hours it was 2.24 ±0.84. Indirect hernia was present in 74% patients whereas direct hernia was present in 26%. 6.67% patients had nausea/vomiting, 3.3% developed hematoma and 1.67% had wound infection. Conclusion: Local anesthesia is effective in all patients who have to undergo inguinal hernia surgery, in terms of efficacy and safety.

Association of Respiratory Tract Infections causing Alterations in Lung Parenchyma and Pulmonary Vasculature with Body Electrolyte Imbalance

Aim: The assessment of serum electrolytes at the time of initial presentation of the patient with respiratory tract infection possibly causing lung parenchyma and pulmonary vasculature damage and serial monitoring during the stay could be beneficial in order to determine when and how to take remedial action when necessary. Methodology: A non-probability sampling was done on 139 subjects with suspected respiratory tract infection. For confirmation, culture, MTB PCR, COVID-19 testing was done to diagnose the nature of infection. Serum electrolytes were tested on chemical analyses Alinity instrument. Results: Most common infections found were COVID-19 and bacterial (n=59) collectively in a co-morbid state. Mycobacterium tuberculosis and fungal infections were also found in (n=8) each. Electrolytes imbalance was markedly observed in high prevalence amongst Tuberculosis and COVID-19 patients but also showed significant association with other respiratory investigated infections. Conclusion: A robust association of electrolyte imbalance was found in all cases presented with upper or lower respiratory tract infections.

Quality of Nursing Care in Respiratory Isolation Units of Coronavirus Disease

Background: COVID-19 has had a significant impact and challenge, especially for nursing practices, and since the quality of nursing care is one of the most important priorities in the field of health care, it has had its share of these challenges and impacts due to the epidemic. The information available about quality of nursing care in healthcare facilities during the COVID-19 pandemic is very limited. Objective: This study aimed to fill this gap by assessing quality of nursing care in respiratory isolation units of coronavirus disease. Methodology: A descriptive cross-sectional design was carried out in four hospitals at isolation units of coronavirus disease from the period (21thDecember, 2021 to 27thJanuary, 2022). A non-probability (convenience) sampling method consists of (112) patient were selected convenience based on the study criteria. The tool used to measure the quality of nursing care is the Patient Satisfaction with Nursing Care Quality Questionnaire-Arabic (PSNCQQ-Ar). Results of the study: presented that patients' satisfaction with quality of nursing care were within high score in all items because the mean more than (3.18), except for item (19) which presented that patient reflected they have low level of health (Mean = 2.8). Conclusion: The level of quality of nursing care provided to patients in the respiratory isolation units for Coronavirus disease was not affected, as the level of quality of care remained high.

The effectiveness of electronic word-of-mouth (eWOM) in the recreational vehicle tourism industry in New Zealand

This research aimed to investigate the impact of electronic word-of-mouth (eWOM) in measuring the service quality of the recreational vehicle tourism industry in New Zealand. SERVQUAL model was employed to measure the service quality among RV rental service providers (Maui and Mighty) in New Zealand. Data was collected from the websites of Maui and Mighty which belong to Tourism Holdings Ltd (thl). A quantitative research approach was adopted to analyze the demographic features of the customers. The study analyzed 461 online customer reviews using qualitative method to measure RV tourism service quality in New Zealand. Findings showed that reviews were more about the tangible factors of the services and were most frequently coded and represented 60.4 percent of the total. Nearly 70 percent of reviews were positive about the services of RV tourism industry. This study revealed that eWOM could conclusively indicate the service quality from direct customer feedback. eWOM would benefit customers, business managers, and tourism industry. This study was conducted in February 2020 just before the COVID-19 pandemic so this gave us an opportunity to analyze the customers' reviews during lockdown period. Further research is necessary to ascertain the impact of eWOM on recreational vehicle tourism industry after COVID-19.

Investigating the Intrinsic Motivation among Second Language Learners in Using Digital Learning Platforms during the Covid-19 Pandemic

The study aims to investigate the intrinsic motivation among L2 in their uses of digital learning platforms during the Covid-19 pandemic. In particular, the central questions in the study are to identify the profiles of intrinsic motivation among L2 learners using digital learning platforms, determine gender differences using digital learning platforms, investigate the difference in motivation according to students' performances, and describe the perceived enjoyment of using digital learning platforms. The study that used a questionnaire to collect data among 72 engineering students found positive intrinsic motivation utilizing the medium to learn English. However, there was insufficient evidence to show a difference between gender using the digital learning platforms with intrinsic motivation. There was also insufficient evidence to prove that grades are associated with students' intrinsic motivation when the teacher assigned synchronous or asynchronous classroom tasks. Yet, the present study found that publishing works in Canva and writing posts on LinkedIn were among the students' most preferred digital learning platforms activities. The study is significant to teachers since it assists them in managing classes and discovering the uses of digital learning platforms for a conducive learning atmosphere during the pandemic. It is recommended that teachers need to be creative in using digital learning platforms to promote collaborative learning among learners.

Spatial Models and Masks in Indoor Analysis for the Spread of COVID-19

Face masks have been shown to slow or stop the spread of airborne COVID-19 droplets and aerosols. There is an apparent lack of research examining the effect of different types of masks used at the same time, and their impact on the spread of viral particles in a spatial sense. We introduce a rapid prototype model to overcome the issues in the available research using the Cell-DEVS formalism. We also build scenarios for the model to examine the effectiveness of all types of masks and respirators recommended by the World Health Organization on the spread of viral particles in an indoor environment. © 2021 IEEE.