COVID-19 vaccination acceptance and adherence among pregnant and lactating high-risk group individuals of Maharashtra State, India.

BACKGROUND: Since the outbreak of the pandemic, the release of the COVID (Corona Virus Disease)-19 vaccine was highly anticipated. Nevertheless, vaccine availability does not symbolize the end of pandemic due to ongoing vaccine hesitancy and anti-vaccination movements. The objective of this study is to evaluate COVID-19 vaccine acceptance among pregnant and lactating individuals in Maharashtra state, India. MATERIALS AND METHODS: A descriptive cross-sectional study was conducted via a hybrid approach using various digital portals such as Whatsapp, telegram, and physical distribution to those who did not have access to smartphone devices. A probability proportional sampling strategy was deployed. A pre-validated structured self-administered questionnaire tool designed by Freeman et al. 2020 (Oxford COVID-19 vaccine hesitancy scale) was used, which consisted of seven close-ended targeted and focused questions. Analysis of several survey items and vaccine acceptance was conducted using the Chi-square test. RESULTS: Analysis revealed that 58% of individuals were willing to take vaccines after government approval and 26% of individuals showed hesitancy toward vaccination. When compared, lactating individuals showed less vaccine hesitancy (22%) than pregnant individuals (27%). The maximum vaccine hesitancy was observed in the third trimester of pregnancy (29.5%). CONCLUSIONS: Trust in vaccines is a crucial factor and is dependent on the ability of the government in promoting vaccines through effective communication; this can be one of the reasons for the high level of acceptance and awareness toward COVID-19 vaccination in this study.

Patients' Satisfaction on Telemedicine Service in Relation to COVID-19 and Non-COVID-19 Problems: A Cross-Sectional Comparative Study.

BACKGROUND: Telemedicine service was historically started for ambulatory and hospice care patients. Since 2020, we have been in the midst of the COVID-19 pandemic. The availability of healthcare facilities became limited due to repeated locked down during the COVID-19 pandemic. Thus, telemedicine service has gained tremendous popularity among healthcare services. Telemedicine service was started at All India Institute of Medical Sciences (AIIMS), Deoghar, as a COVID Helpline facility to provide guidance and care to the home isolated COVID-19 patients during the second wave of COVID-19. But we observed that more than 40% of calls were due to non-COVID-19-related problems, but we managed the non-COVID-19-related calls by discussing with a specialist in conference calls or WhatsApp consultation. Therefore, we planned to compare individual satisfaction with telemedicine services in patients with COVID-19 and non-COVID-19-related problems. METHODS AND MATERIALS: This study was a cross-sectional retrospective analysis of the register of telemedicine maintained in AIIMS, Deoghar, callers were grouped into two- COVID-19 and non-COVID-19-related problems. We obtained feedback from the patients and recorded it in a google form, collected data were analyzed in both groups. Telephonic consent was taken for participating in the study. The sample size was calculated to be 252, the COVID-19 group: 126, and the non-COVID-19 group: 126, and simple random sampling was used to choose the participants from the 730 total callers of the first month of telemedicine service. Their response was graded on 4 points Likert scale (1=Poor, 2=fair, 3=Good, 4=Excellent) and outcomes were analyzed by IBM SPSS (version 20.0) software. A p-value of <0.05 was considered statistically significant. OBJECTIVE: The primary objective is to estimate the level of satisfaction in both groups and compare their level of satisfaction. The secondary objective is to determine the department-specific telemedicine services requirement for people in need. RESULTS: Out of a total of 252 patients, most (54%) callers were 18-45 years old, and 44% were above 45 years old. 64% of patients were male. 90% of callers were from urban or semi-urban districts. 90% of callers had a 10th-grade or more education. 89% of patients were willing to use telemedicine services in the future. An Independent sample t-test was used to compare the means of both the groups showed a significant difference (p < 0.05) in the level of satisfaction in the COVID-19 group to the non-COVID-19 group. It showed that satisfaction in the COVID-19 group was higher than the group with non-COVID-19-related problems. CONCLUSION: COVID-19 has changed the whole spectrum of healthcare needs of the community. Our study findings showed that there is a need for separate department-wise telemedicine services to provide satisfactory service for attending to problems related to that department. For example, problems with diabetes should be attended to by an endocrinologist or an internal medicine specialist. This study finding helped us to change the policy and start department-wise telemedicine service.

Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model.

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.

An altmetric analysis of online news on India's first indigenous COVID-19 vaccine.

BACKGROUND: Covaxin is the first indigenous vaccine developed in India against COVID-19. The purpose of this study was to analyze the news stories on Covaxin published in the online media between two statements issued by Indian Council for Medical Research on 2nd and 4th July for their content, quality of information, and reporting standards. MATERIALS AND METHODS: A systematic search was performed on Google to identify the news stories related to Covaxin in the English language published between these two statements. The selected news stories were subjected to content analysis and reviewed using the screening points developed through a consultation by two independent experts using ten prevalidated criteria for health news review. The data were analyzed in MS Excel and StataMP14. RESULTS: The final analysis included 24 news stories. The mean and median score of the news stories is 10.71 and 12 (out of 20), respectively, with a score ranging from 2 to 17. The stories did not promote disease or vaccine mongering (100%), adequately mentioned the true novelty of the vaccine (95.8%), and source of the information (83.3%). However, they mostly failed to mention the information on costs, research data related to benefits, and harms and quality of the available evidence. CONCLUSION: There is a lack of reporting of detailed analysis about the methodology of development of the vaccine and limitations in its research design by health journalists. It is important to train journalists on proper reporting of health news to improve its quality in Indian media.

Expanded principles of ethics and its implementation during COVID-19 vaccine trials: A scoping evidence based research synthesis.

The COVID-19 pandemic will subside only through the emergence and distribution of an efficacious vaccine. The two main aspects that should be maintained in equilibrium: the dire necessity for speedy vaccine research and the need for safeguarding the research subjects, which is of utmost concern in research ethics. This opens up a discussion of what norms to follow during the clinical trials while developing the vaccine. As of now, various companies like Moderna, Pfizer, University of Oxford, Astra-Zeneca and so on have moved beyond the safety, efficacy and immunogenic studies. This narrative review explores and discusses the key principles of ethics: a principle of autonomy, beneficence, non-maleficence, and justice along with its ten general expanded principles. Furthermore, it delves into the different types of vaccines, their mechanisms, side effects, limitations, and advantages.

The CoVID-TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID-19.

BACKGROUND: Hospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking. OBJECTIVES: To assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19. METHODS: Among patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap. RESULTS: From March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission. CONCLUSIONS: Hospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Harnessing the potential of the primary healthcare facilities in India to respond COVID-19 pandemic: A scoping evidence-based research synthesis.

COVID-19 has resulted in an unprecedented loss of human lives and sufferings across the world. It has resulted in the collapse of public health systems and economy across the globe. As most of the national health systems lack organized surveillance infrastructure, resources, and expertise to respond to a pandemic, most of the countries failed to mount an effective response to contain the spread of this virus initially. As primary healthcare (PHC) has better access to the community, the settings where PHC services are inadequate or weak, hospitals are overwhelmed with patients, thus overburdening, and wasting meager specialist resources. PHC interventions can manage mild to moderate cases (>80% of total cases) and their contacts, along with addressing the needs of general population while only severe cases may require specialized hospital care. As PHC interventions have huge potential to tackle this pandemic, strengthening and inclusion of PHC in pandemic response could play a significant role in relieving the workload on secondary and tertiary healthcare facilities and minimizing loss of lives and its short and long term socioeconomic consequences. This article explores the scope and importance of strengthening PHC in breaking the chain of the transmission of this infectious disease, building an adequate response to minimize its disastrous consequences and prevent future emerging and reemerging disease outbreaks, if any.

COVID-19 driven changes in the air quality; a study of major cities in the Indian state of Uttar Pradesh.

Amid the COVID-19 pandemic, there has been an unprecedented cessation of outdoor anthropogenic activities leading to a significant improvement of the environment across the world. However, the positive impacts on the environment are not expected to last long as countries have started to gradually come out of lockdown and engage in aggressive measures to regain the pre-COVID-19 levels of economic activity. The present study provides for an assessment of air quality changes during the period of lockdown and unlocking across 9 major cities in the Indian state of Uttar Pradesh, including three cities (Ghaziabad, Noida, and Greater Noida) in the national capital region, which have frequently been included among the most polluted cities in the world. The pollutant load in a vertical column of air during March-July 2020 has been analyzed and compared with the corresponding period's pollution load in 2019. In addition, a detailed analysis of the ground-level changes in pollution load for Ghaziabad, Noida, and Greater Noida is also presented, along with the changes in local meteorology. A significant reduction in the total column density of NO2, CO and ground-level pollution load of PM10, PM2.5, NO2, and SO2 have been observed. In contrast, an increase in total column density of SO2 across all the cities (except Kanpur) and ground-level concentration of CO (in Noida and Greater Noida) and O3 (in Noida) was evident. The improvement in air quality (with respect to particulate matter) can primarily be attributed to the restrictions on construction and demolition activities, reduced re-suspension of roadside dust, and the restrictions on the movement of vehicles. A significant decline in the average summer temperature was recorded, and it can plausibly be attributed to lower radiative forcing due to reduced pollutant load in the atmosphere.