ABSTRACT
BACKGROUND: A prospective study was conducted during the second phase of the coronavirus disease 2019 (COVID-19) pandemic in India to assess the prevalence of anxiety and depressive symptoms among healthcare workers (HCWs) and factors that influence the outcome. METHODS: A self-administered questionnaire was completed by 1124 HCWs during the COVID-19 pandemic (March 30, 2020, to April 2, 2020). Demographic data, questions on COVID-19 and scores of the Hospital Anxiety and Depression Scale were analysed using the chi-square test (Bonferroni correction) and binary logistic regression. RESULTS: The study consists of 1124 HCWs, including 749 doctors, 207 nurses, 135 paramedics, 23 administrators and ten supporting staff members. The prevalence of anxiety and depressive symptoms were reported as 37.2% and 31.4%, respectively. The risk factors for anxiety were female gender (30.6% vs 45.5%), age group (20-35 years) (50.4% vs 61.2%), unmarried (21.2% vs 30.6%) and job profile (nurse) (14.7% vs 26.4%). The protective factor was having service of more than 20 years (23.4% vs 14.8%). The risk factors for depression were age group (20-35 years) (51.3% vs 61.3%) and employed at a primary care hospital (16.2% vs 23.4%). The protective factors were job profile (doctor) (69.9% vs 59.6%) and having service of more than 20 years (22.3% vs 15.5%). CONCLUSION: Approximately one-third of the HCWs reported anxiety and depressive symptoms. The risk factors for anxiety symptoms were female gender, younger age and job profile (nurse) and for depressive symptoms were younger age and working at a primary care hospital. Future research studies should identify strategies for providing a safer and supportive work environment for HCWs to face epidemics/pandemics.
ABSTRACT
The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score.
ABSTRACT
The world is presently struggling with coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A patient with COVID-19 typically presents with fever, non-productive cough, dyspnea, and myalgia. A 49-year-old female presented with complaints of subacute onset and progressive symmetrical proximal muscle weakness of both upper limbs and lower limbs with no sensory, cranial nerve deficit. She had elevated creatine phosphokinase levels of 906 U/L, an aspartate aminotransferase level of 126 IU/L, a lactate dehydrogenase level of 354 U/L, and an erythrocyte sedimentation rate of 68 mm/1 hr, and magnetic resonance imaging of the pelvis and thigh revealed muscle edema suggestive of myositis. Her reverse transcriptase-polymerase chain reaction result for SARS-CoV-2 was positive. Her evaluation for other causes of myositis was negative. She was managed with intravenous immunoglobulins and supportive care. She showed rapid improvement in symptoms and motor weakness. To our knowledge, this is the first reported case of COVID-19 related disabling myositis in India.
ABSTRACT
BACKGROUND: A pandemic poses a significant challenge to the healthcare staff and infrastructure. We studied the prevalence of anxiety and depressive symptoms among armed forces doctors in India during the COVID-19 pandemic and the factors that contribute to these symptoms. METHODS: The study was conducted from March 30, 2020, to April 2, 2020, using a self-administered questionnaire questionnaire using the hospital anxiety and depression scale (HADS), which was sent through Google Forms. Responses were received from 769 respondents. Data were analyzed for demographic details and HADS scores using the chi-square test and backward logistic regression. RESULTS: Anxiety and depressive symptoms were seen in 35.2% and 28.2% of the doctors, respectively. In doctors with anxiety symptoms, significant associations were observed with age (20-35 years, 39.4%, P = 0.01), gender (females, 44.6%, P < 0.001), duration of service (0-10 years, 38%, P = 0.03), and clinical versus non-clinical specialties (non-clinical, 41.3%, P < 0.001) as opposed to marital status, education level, and current department of work.In doctors with depressive symptoms, significant associations were observed with age (P = 0.04), clinical versus non-clinical specialties (P < 0.001), duration of service (0-10 years, 30.1%, P = 0.03), and doctoral degree (P = 0.04) as opposed to gender, marital status, education level, and current working department. CONCLUSION: The study revealed a high prevalence of anxiety and depressive symptoms among armed forces doctors. The main contributing factors are female gender, young age group, non-clinical specialties, and having a doctoral degree.