Impact of Covid-19 on informal employment: A case study of women domestic workers in Khyber Pakhtunkhwa, Pakistan.

COVID-19 indiscriminately impacted all the segments of the global society. Due to the unstructured job market, women in the informal sector were at high risk to experience the adverse effects of the pandemic. This paper aims to explore the impact of COVID-19 on women domestic workers and their families. Semi structured interviews conducted with fifty-four women domestic workers in three districts of Khyber Pakhtunkhwa, Pakistan were analyzed in five themes: disruption caused by lockdown, loss of livelihood, economic hardships, social support mechanism, and challenges faced during the pandemic. The study underlines that the pandemic left severe impacts on access to basic services, employment, food security at household level and pattern of expenditures. The plummeting economic activities led to sudden drops in earnings that forced families to sell their assets and incur debts. Respondents lamented over the social support system and considered it a necessary but not sufficient condition for uplifting the lives of the poor. Strains in marital relationships led to stress, anxiety and domestic violence among families. The utmost concern was the restoration of economic activities and urgent policy interventions to strengthen social safety measures for the vulnerable segments of society.

Multisystem inflammatory syndrome (MIS-C) in Children on Kawasaki syndrome spectrum: What's new, Local regional experience from Pakistan

Objective: To describe the prevalence, clinical spectrum (Particularly cardiac) and treatment strategies of MIS-C. Study Design: Case Control study (prospective and retrospective surveillance). Setting: Children Hospital and Pediatric Medicine Department DHQ/Allied Hospital Faisalabad, Pakistan. Period: 15th March, 2017, to March 14th, 2020. Material & Methods: All patients with confirmed MIS-C/KD were enrolled and made two groups before and after the pandemic of SARS CoV-2. Results: A total of 6 patients before pandemic (group 1) and 18 patients after the pandemic (group 2) were studied over a period of 3 years. There is female dominance in group 1 and male dominance in group 2. Patients in group 2 presented at higher age (median 8y). Gastrointestinal symptoms (72.2%), Shock (11.2%) and LV dysfunction (11.1%) were more different features of MIS-C as compared to KD. There was more intense hyperinflation response was in MISC as compared to KD (CRP median 15.5 vs 78 and S/Ferritin median 725 vs 1889). Conclusion: MIS-C is on the Kawasaki disease spectrum in many ways both clinically and biochemical markers but the level of inflammation and immune response and severity of cardiac involvement were much bigger than KD. This should be in high consideration in perspective of new waves of SARS CoV-2. [ FROM AUTHOR] Copyright of Professional Medical Journal is the property of Professional Medical Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Methods for Detecting Covid-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures

The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.

Effect of meteorological factors on the COVID-19 cases: a case study related to three major cities of the Kingdom of Saudi Arabia.

The COVID-19 pandemic affected the world through its ability to cause widespread infection. The Middle East including the Kingdom of Saudi Arabia (KSA) has also been hit by the COVID-19 pandemic like the rest of the world. This study aims to examine the relationships between meteorological factors and COVID-19 case counts in three cities of the KSA. The distribution of the COVID-19 case counts was observed for all three cities followed by cross-correlation analysis which was carried out to estimate the lag effects of meteorological factors on COVID-19 case counts. Moreover, the Poisson model and negative binomial (NB) model with their zero-inflated versions (i.e., ZIP and ZINB) were fitted to estimate city-specific impacts of weather variables on confirmed case counts, and the best model is evaluated by comparative analysis for each city. We found significant associations between meteorological factors and COVID-19 case counts in three cities of KSA. We also perceived that the ZINB model was the best fitted for COVID-19 case counts. In this case study, temperature, humidity, and wind speed were the factors that affected COVID-19 case counts. The results can be used to make policies to overcome this pandemic situation in the future such as deploying more resources through testing and tracking in such areas where we observe significantly higher wind speed or higher humidity. Moreover, the selected models can be used for predicting the probability of COVID-19 incidence across various regions.

Healthcare inequalities in antenatal care in the European Region: EBCOG Scientific review.

It is now well recognized that unacceptable inequalities in maternity care that exist due to a woman's ethnicity, socio-economic deprivation, age, residential social status within a country can have adverse effects on the outcomes of their pregnancies. Perceived attitudes can lead to dismissal of concerns, breakdown of trust and can affect interactions with maternity services. Women from black and Asian ethnicity are at 2-4 times higher risk of maternal mortality. Similarly women with underlying mental health issues and those with undocumented status are also at higher risk of adverse outcomes during pregnancy and within the first postnatal year. There is need for research, to understand why these different practices exist and how we can more effectively understand and overcome the barriers and factors which can lead to inequality in access to uniform standard of care. Covid-19 pandemic created challenges for the provision of maternity services. Each country responded by their own creative and pragmatic solutions. It is important that individualized care based on a woman's individual social and medical needs must remain a priority to improve maternity care. It is proposed that EU funding should be made available to set up large scale European wide research to inform future policies.

Provision of antenatal care in Europe-A scientific study commissioned by European Board and College of Obstetrics and Gynaecology (EBCOG).

Differences in the way health care delivery across countries may have important impacts on health outcomes and can result in inequalities. A questionnaire survey of members of national societies through EBCOG and EAPM was carried out in 2021. A total of 53 responses were received from 26 countries. Most countries reported that routine antenatal care is primarily delivered by medical staff, involving obstetric specialists or family doctors mostly in government-run facilities. Women from minority groups are able to access antenatal care easily in most countries. Less than 10% of women did not attend antenatal care throughout the pregnancy. Most booking for antenatal care takes place in the first trimester and the number of visits range from 6 to 10 depending on parity. Most countries provide routine ultrasound with 2-3 reported scans performed by specifically trained health care professionals. Facilities for prenatal screening/diagnosis of malformations in both low- and high-risk cases varied across Europe. While antenatal care is relatively standardized throughout Europe, important differences still exist in care delivery and accessibility to care. Antenatal preventive strategies appear to be variably available throughout Europe.

Covid-19 and its implications for the provision of gynecological services globally.

Covid-19 took the world by surprise and has completely changed the way humans live and work. There is hardly an aspect of life that has not been affected. Whether social, economic, physical, psychological, cultural or religious, this pandemic has revolutionized every aspect of our lives and some of these changes are here to stay for the unforeseeable time. Although much has been written about the negative effects of Covid-19 on our social lives, some technological advances on COVID-19 have profoundly affected various aspects of our lives. These are mostly to do with how we communicate, deliver health services, innovate and investigate new preventative measures and treatments, travel and indeed influenced the carbon footprint of the planet. Although most of gynaecology is elective and was therefore not considered a priority in the early phases of COVI-19, there are considerable consequences of delaying treatment for some of these elective conditions. Of particular importance are infertility, pre-malignant conditions, chronic pelvic pain, sexual disorders and those affecting the psychological and social aspects of women and families. The pandemic forced a rethink of how healthcare is delivered with wide adoption of remote/virtual consultation and triaging of clinical presentations. The rapid development of immunization and drugs against the virus was met with doubts by a large proportion of the population with reluctance to accept these. Consequently, there remains unvaccinated portions of both low and high-risk populations, some of whom may be denied access to gynaecological care. On the other hand, some pregnant women who are frightened of the impact of vaccination on pregnancy put their own lives at risk. While significant progress has been made to combat the pandemic, lessons about healthcare delivery (face-to-face versus virtual), education of the end users and introduction of new technologies into the development of drugs and vaccines must be evaluated and improved moving forward not only during the ongoing epidemic but with future outbreaks.

Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses.

BACKGROUND: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. METHOD: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. RESULTS: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. CONCLUSION: The proposed model is efficient and can minimize the revision complexities of implants.

European board and college of obstetrics and gynaecology position statement to mark "World Patient safety day - Safe maternal and newborn care" on 17th September 2021.

Maternal morbidity and mortality remain stubborn highly in many parts of the world. Similarly Neonatal morbidity, mortality and five years survival in most of the under-resourced countries has not declined significantly over the past decades. Furthermore sexual reproductive health services provision has not met the needs of the women and there remains a huge unmet need for reliable contraception globally. This is the time for a global action plan and for all agencies to work together to achieve meaningful outcomes to improve health of women and their babies. Covid 19 pandemic has led to increase in gender based violence as well which is deplorable. European Board and College of Obstetrics and Gynaecology welcome this initiative and commits to work with all the stakeholders to improve safety and quality of care for women and the newborn.

A National Survey to Assess the COVID-19 Vaccine-Related Conspiracy Beliefs, Acceptability, Preference, and Willingness to Pay among the General Population of Pakistan.

The current study aims to assess the beliefs of the general public in Pakistan towards conspiracy theories, acceptance, willingness to pay, and preference for the COVID-19 vaccine. A cross-sectional study was conducted through an online self-administered questionnaire during January 2021. The Chi-square test or Fisher exact test was utilized for statistical data analysis. A total of 2158 respondents completed the questionnaire, among them 1192 (55.2%) were male with 23.87 (SD: ±6.23) years as mean age. The conspiracy beliefs circulating regarding the COVID-19 vaccine were believed by 9.3% to 28.4% of the study participants. Among them, 1040 (48.2%) agreed to vaccinate on its availability while 934 (43.3%) reported the Chinese vaccine as their preference. The conspiracy beliefs of the participants were significantly associated with acceptance of the COVID-19 vaccine. The existence of conspiracy beliefs and low vaccine acceptance among the general population is a serious threat to successful COVID-19 vaccination.

EBCOG position statement on COVID-19 vaccination for pregnant and breastfeeding women.

Covid 19 pandemic has led to significant mortality and long term morbidity globally. Pregnant women are at increased risk of severe illness from COVID 19 infection. There is an urgent need for all health authorities and Governments to offer vaccination to all pregnant women especially those with high risk pregnancy.

Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database.

Currently, the coronavirus disease 2019 (COVID19) pandemic has killed more than one million people worldwide. In the present outbreak, radiological imaging modalities such as computed tomography (CT) and X-rays are being used to diagnose this disease, particularly in the early stage. However, the assessment of radiographic images includes a subjective evaluation that is time-consuming and requires substantial clinical skills. Nevertheless, the recent evolution in artificial intelligence (AI) has further strengthened the ability of computer-aided diagnosis tools and supported medical professionals in making effective diagnostic decisions. Therefore, in this study, the strength of various AI algorithms was analyzed to diagnose COVID19 infection from large-scale radiographic datasets. Based on this analysis, a light-weighted deep network is proposed, which is the first ensemble design (based on MobileNet, ShuffleNet, and FCNet) in medical domain (particularly for COVID19 diagnosis) that encompasses the reduced number of trainable parameters (a total of 3.16 million parameters) and outperforms the various existing models. Moreover, the addition of a multilevel activation visualization layer in the proposed network further visualizes the lesion patterns as multilevel class activation maps (ML-CAMs) along with the diagnostic result (either COVID19 positive or negative). Such additional output as ML-CAMs provides a visual insight of the computer decision and may assist radiologists in validating it, particularly in uncertain situations Additionally, a novel hierarchical training procedure was adopted to perform the training of the proposed network. It proceeds the network training by the adaptive number of epochs based on the validation dataset rather than using the fixed number of epochs. The quantitative results show the better performance of the proposed training method over the conventional end-to-end training procedure. A large collection of CT-scan and X-ray datasets (based on six publicly available datasets) was used to evaluate the performance of the proposed model and other baseline methods. The experimental results of the proposed network exhibit a promising performance in terms of diagnostic decision. An average F1 score (F1) of 94.60% and 95.94% and area under the curve (AUC) of 97.50% and 97.99% are achieved for the CT-scan and X-ray datasets, respectively. Finally, the detailed comparative analysis reveals that the proposed model outperforms the various state-of-the-art methods in terms of both quantitative and computational performance.

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data.

In the present epidemic of the coronavirus disease 2019 (COVID-19), radiological imaging modalities, such as X-ray and computed tomography (CT), have been identified as effective diagnostic tools. However, the subjective assessment of radiographic examination is a time-consuming task and demands expert radiologists. Recent advancements in artificial intelligence have enhanced the diagnostic power of computer-aided diagnosis (CAD) tools and assisted medical specialists in making efficient diagnostic decisions. In this work, we propose an optimal multilevel deep-aggregated boosted network to recognize COVID-19 infection from heterogeneous radiographic data, including X-ray and CT images. Our method leverages multilevel deep-aggregated features and multistage training via a mutually beneficial approach to maximize the overall CAD performance. To improve the interpretation of CAD predictions, these multilevel deep features are visualized as additional outputs that can assist radiologists in validating the CAD results. A total of six publicly available datasets were fused to build a single large-scale heterogeneous radiographic collection that was used to analyze the performance of the proposed technique and other baseline methods. To preserve generality of our method, we selected different patient data for training, validation, and testing, and consequently, the data of same patient were not included in training, validation, and testing subsets. In addition, fivefold cross-validation was performed in all the experiments for a fair evaluation. Our method exhibits promising performance values of 95.38%, 95.57%, 92.53%, 98.14%, 93.16%, and 98.55% in terms of average accuracy, F-measure, specificity, sensitivity, precision, and area under the curve, respectively and outperforms various state-of-the-art methods.

Psychosocial Barriers of Public Transport Use and Social Exclusion among Older Adults: Empirical Evidence from Lahore, Pakistan.

Transport planning and public health have been intertwined historically. The health impact of public transport services, such as social exclusion, is a widely discussed research topic. Social exclusion is a paramount concern for older adults' health in the wake of emerging global challenges. However, there remains a significant research gap on how psychosocial barriers faced by older adults in using public transport services influence the social exclusion behavior. The present research provides empirical evidence and shows the impact of certain psychosocial barriers of public transportation on older adults' social exclusion. A total of 243 Pakistani older adults (aged 60-89 years old) voluntarily participated in this cross-sectional study. The participants provided self-reports on their psychosocial barriers (including perceived norms, attitude, personal ability, habits, neighborhood social constraints, and intention) and the corresponding social exclusion. Partial Least Square Structural Equation Modeling (PLS-SEM) was utilized for the data analysis. The structural path model supported the significant associations between psychosocial barriers and social exclusion. Except for perceived descriptive norms, all other psychosocial barriers predicted older adults' social exclusion. The research portrays the significance of the psychosocial factors to examine social exclusion and offers practical implications for urban and transport planners. The concerned policymakers can use the research findings to develop age-sensitive, socially sustainable, and healthy cities.

EBCOG position statement - Simulation-based training for obstetrics and gynaecology during the COVID-19 pandemic.

The specialty of Obstetrics and Gynaecology has been on the forefront of introducing simulation in post graduate education for the past two decades. Simulation training is known to enhance psychomotor skills and is considered an important step in the transition from classroom learning to clinical practice. Training on simulators allows trainees to acquire basic skills before getting involved in day to day care in real life situations. Clinical circumstances around the COVID 19 pandemic have highlighted the key importance of simulation training in delivering post graduate curriculum.

The pandemic paradox: domestic violence and happiness of women.

BACKGROUND: Across the globe, lockdowns have been enforced as a pandemic response to COVID-19. Such lockdown coupled with school closures and stay-at-home orders made women more vulnerable in terms of higher responsibility and spending more time with an abusive partner, if any. METHODS: This study investigates the situation of women during COVID-19 induced lockdown by focusing on their happiness and inquiring about the incidence of violence. Using the zero-inflated negative binomial model, our findings ascertained that family settings, type of relationship with a spouse, and age significantly affects the positive count of violence during the lockdown. We further estimated the determinants of happiness and found that years of schooling, the role of women in household decision making, and feeling empowered is affecting their happiness. RESULTS: Women having higher education have more odds of zero violence. Unemployed women and women who are not working have higher odds of zero violence as compared to women who are working. During this lockdown after the COVID-19 pandemic, women living in urban areas, having higher education, having an adequate household income to meet the expenditures, having lesser anxiety, not facing violence, feeling empowered when their husband is around, and have higher decision-making power are happier. DISCUSSION AND CONCLUSION: The study is important in the context of happiness and violence inflicted on women during the lockdown and provides the basis to improve the pandemic response policy. The inclusion of women's safety and happiness in pandemic response policy is important to ensure the well-being of women and to devise better health and economic policy. Our estimates suggest higher education results in less incidence of violence which could be argued as desirable outcomes for building healthy, productive, and happy communities. In addition to this, as pandemic induced lock-down is likely to result in higher unemployment across the globe including Pakistan, therefore, in light of our estimates pertaining to the role of unemployment in the incidence of violence, policymakers should deploy more resources to enhance income and to combat the rising unemployment. As a counter-intuitive outcome of these policy interventions, incidence of violence will be dampened, educational attainment and women empowerment will be increased which will certainly increase happiness.

Current perspective on diagnosis, epidemiological assessment, prevention strategies, and potential therapeutic interventions for severe acute respiratory infections caused by 2019 novel coronavirus (SARS-CoV-2).

Coronaviruses are single-stranded RNA viruses that cause severe respiratory, enteric, and systemic infections in a vast range of hosts, including man, fish, mammals, and avian. Scientific interest has heightened on coronaviruses after the emergence of the 2019 novel Coronavirus (SARS-CoV-2). This review provides current perspectives on morphology, genetic diversity, transmission characteristics, replication cycle, diagnostic approaches, epidemiological assessment, and prevention strategies against the SARS-CoV-2. Moreover, different potential biotherapeutics such as small drug molecules, different vaccines, and immunotherapies to control severe acute respiratory infections caused by 2019 novel coronavirus (SARS-CoV-2) are repurposed and discussed with different mechanistic approaches. The current growth trends of the SARS-CoV-2/COVID-19 outbreak globally and preventive measures are briefly discussed. Furthermore, the lessons learned from the COVID-19 outbreak, so far, concluding remarks and future directions for controlling for COVID-19, are also recommended for a safer tomorrow.

How to reduce the potential risk of vertical transmission of SARS-CoV-2 during vaginal delivery?

The risk of vertical transmission during vaginal delivery in COVID-19 pregnant patients is currently a topic of debate. Obstetric norms on vaginal birth assistance to reduce the potential risk of perinatal infection should be promoted by ensuring that the risk of contamination from maternal anus and faecal material is reduced during vaginal delivery.