COVID-19 vaccine is regarded as the most promising means of limiting the spread of or eliminating the pandemic. The success of this strategy will rely on the rate of vaccine acceptance globally. The study aims to examine the factors that influence COVID-19 vaccine acceptance, intention, and hesitancy. PubMed was searched comprehensively for articles using the keyword “COVID-19 vaccine surveys.” Of the 192 records, 22 studies were eligible for the review. Eighty-two percent of these studies were conducted among the general population. Gender, age, education, and occupation were some of the socio-demographic variables associated with vaccine acceptance. Variables such as trust in authorities, risk perception of COVID-19 infection, vaccine efficacy, current or previous influenza vaccination, and vaccine safety affected vaccine acceptance. Globally, in March 2020, the average vaccine acceptance observed was 86% which dropped to 54% in July 2020 which later increased to 72% in September 2020. Globally, the average rate of vaccine hesitancy in April 2020 was 21%, which increased to 36% in July 2020 and later declined to 16% in October 2020. Large variability in vaccine acceptance and high vaccine hesitancy can influence the efforts to eliminate the COVID-19. Addressing the barriers and facilitators of vaccines will be crucial in implementing effective and tailored interventions to attain maximum vaccine coverage.

Background: With the availability of extensive health data, artificial intelligence has an inordinate capability to expedite medical explorations and revamp healthcare.Artificial intelligence is set to reform the practice of medicine soon. Despite the mammoth advantages of artificial intelligence in the medical field, there exists inconsistency in the ethical and legal framework for the application of AI in healthcare. Although research has been conducted by various medical disciplines investigating the ethical implications of artificial intelligence in the healthcare setting, the literature lacks a holistic approach. Objective: The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework. Methodology: Electronic databases Pub Med and Google Scholar were extensively searched based on the search strategy pertaining to the purpose of this review. Further screening of the included articles was done on the grounds of the inclusion and exclusion criteria. Results: The search yielded a total of 1238 articles, out of which 16 articles were identified to be eligible for this review. The selection was strictly based on the inclusion and exclusion criteria mentioned in the manuscript. Conclusion: Artificial intelligence (AI) is an exceedingly puissant technology, with the prospect of advancing medical practice in the years to come. Nevertheless, AI brings with it a colossally abundant number of ethical and legal problems associated with its application in healthcare. There are manifold stakeholders in the legal and ethical issues revolving around AI and medicine. Thus, a multifaceted approach involving policymakers, developers, healthcare providers and patients is crucial to arrive at a feasible solution for mitigating the legal and ethical problems pertaining to AI in healthcare.

This study examined the burden of food insecurity in India’s un-notified slums, using an SDG framework to identify correlates of food insecurity. A convenience sampling approach was employed in selecting 38 slums from 675 un-notified slums across four geographic zones. Ten percent of the households in each slum site were selected from each zone, and one household member was interviewed, based on their availability and fulfilment of the eligibility criteria. Eligible individuals included those aged 18 years and above, who were resident in the selected slums and provided consent. Individuals with mental or physical challenges were excluded. A total sample of 907 study participants were included. Results showed that 43% (n = 393) of the participants were food insecure. More than half were females (73%, n = 285), who had not completed any schooling (51%, n = 202). One-third (n = 128) resided in the Northern Region of Delhi. SDG-related predictors of food insecurity included: household educational level (SDG 4 Quality education) (p = 0.03), coverage of health service needs (SDG 3 Good health and well-being) (p = 0.0002), electricity needs (SDG 7 affordable and clean energy) (p<0.0001), and employment needs (SDG 8 Decent and economic growth) (p = 0.003). Having healthcare needs that were partially or fully met was equally associated with higher food insecurity: this could be attributed to high healthcare costs and the lack of federal subsidies in un-notified slums, collectively contributing to high out-of-pocket health costs. Failure to fully meet employment needs was also significantly associated with higher food insecurity. However, met needs for electricity, finance, women’s safety and satisfactory family relationships, were associated with lower food insecurity. Household predictors of food insecurity included: number of household members, and the presence of physically disabled household members. Necessary interventions should include connecting food insecure households to existing social services such as India’s Public Distribution System, and multi-sector partnerships to address the existing challenges.

Background: An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective: In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. Methods: An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. Results: The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. Conclusions: Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers.

Schumer H, et al. Healthc Inform Res. 2018 Jan;24(1):38-45. https://doi.org/10.4258/hir.2018.24.1.38