The COVID-19 pandemic and the measures adopted are having a profound impact on a major goal of public healthcare systems: universal access to health services. The objective is to synthesize the available knowledge on access to health care for non-COVID-19 conditions and to identify knowledge gaps. A scoping review was conducted searching different databases (Medline, Google Scholar, etc.) for original articles published between December 2019 and September 2021. A total of 53 articles were selected and analyzed using the Aday and Andersen framework as a guide. Of these, 37 analyzed changes in levels of use of health services, 15 focused on the influencing factors and barriers to access, and 1 studied both aspects. Most focused on specific diseases and the early stages of the pandemic, based on a review of records. Analyses of the impact on primary care services’ use, unmet needs or inequalities in access were scarce. A generalized reduction in the use of health services was described. The most frequent access barrier described for non-COVID-19 conditions related to the services was a lack of resources, while barriers related to the population were predisposing (fear of contagion, stigma, or anticipating barriers) and enabling characteristics (worse socioeconomic status and an increase in technological barriers). In conclusion, our results show a general reduction in services’ use in the early stages of the pandemic, as well as new barriers to access and the exacerbation of existing ones. In view of these results, more studies are required on the subsequent stages of the pandemic, to shed more light on the factors that have influenced access and the pandemic’s impact on equity of access.

/pdf/changes-in-access-to-health-services-during-the-covid-19-3dgaghqt.pdf

Changes in Access to Health Services during the COVID-19 Pandemic: A Scoping Review

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbe2.297

Utility of telemedicine in sub-Saharan Africa during the COVID-19 pandemic. A rapid review

Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.

/pdf/vision-based-human-fall-detection-systems-using-deep-1cpge56y.pdf

Vision-based Human Fall Detection Systems using Deep Learning: A Review

The effectiveness of e-Health interventions on caregiver burden, depression, and quality of life in informal caregivers of patients with cancer: A systematic review and meta-analysis of randomized controlled trials.

Telehealth utilization during the Covid-19 pandemic: A systematic review.

Emotions understanding has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron virus as a case study to implement a emotions analysis framework to explore the global attitude and sentiment toward Omicron variant as an expression of Positive feeling, Neutral, and Negative feeling. Because since December 2021. Omicron variant has gained obvious attention and wide discussions on social media platforms that revealed lots of fears and anxiety feeling, due to its rapid spreading and infection ability between humans that could exceed the Delta variant infection. Therefore, this paper proposes to develop a framework utilizes techniques of natural languages processing (NLP) in deep learning methods using neural network model of Bidirectional-Long-Short-Term-Memory (Bi-LSTM) and deep neural network (DNN) to achieve accurate results. This study utilizes textual data collected and pulled from the Twitter platform (users' tweets) for the time interval from 11-Dec.-2021 to 18-Dec.-2021. Consequently, the overall achieved accuracy for the developed model is 0.946%. The produced results from carrying out the proposed framework for sentiment understanding have recorded Negative sentiment at 42.3%, Positive sentiment at 35.8%, and Neutral sentiment at 21.9% of overall extracted tweets. The acquired accuracy using data of validation for the deployed model is 0.946%. 

/pdf/omicron-virus-emotions-understanding-system-based-on-deep-1ie3o0fh.pdf

Eman Thabet Khalid

Papers

Telehealth utilization during the Covid-19 pandemic: A systematic review.

Omicron virus emotions understanding system based on deep learning architecture