This study sought to assess undergraduate students’ knowledge and attitudes surrounding perceived self-efficacy and threats in various common emergencies in communities of higher education.

Self-reported perceptions of knowledge and skills, as well as attitudes and beliefs regarding education and training, obligation to respond, safety, psychological readiness, efficacy, personal preparedness, and willingness to respond were investigated through 3 representative scenarios via a web-based survey.

Among 970 respondents, approximately 60% reported their university had adequately prepared them for various emergencies while 84% reported the university should provide such training. Respondents with high self-efficacy were significantly more likely than those with low self-efficacy to be willing to respond in whatever capacity needed across all scenarios.

There is a gap between perceived student preparedness for emergencies and training received. Students with high self-efficacy were the most likely to be willing to respond, which may be useful for future training initiatives.

Disasters exacerbate inequities in health care. Health systems use the Hospital Incident Command System (HICS) to plan and coordinate their disaster response. This study examines how 2 health systems prioritized equity in implementing the Hospital Incident Command System (HICS) during the coronavirus disease 2019 (COVID-19) pandemic and identifies factors that influenced implementation.

This is a qualitative case comparison study, involving semi-structured interviews with 29 individuals from 2 US academic health systems. Strategies for promoting health equity were categorized by social determinants of health. The Consolidated Framework for Implementation Research (CFIR) guided analysis using a hybrid inductive-deductive approach.

The health systems used various strategies to incorporate health equity throughout implementation, addressing all 5 social determinants of health domains. Facilitators included HICS principles, external partnerships, community relationships, senior leadership, health equity experts and networks, champions, equity-stratified data, teaming, and a culture of health equity. Barriers encompassed clarity of the equity representative role, role ambiguity for equity representatives, tokenism, competing priorities, insufficient resource allocation, and lack of preparedness.

These findings elucidate how health systems centered equity during HICS implementation. Health systems and regulatory bodies can use these findings as a foundation to revise the HICS and move toward a more equitable disaster response.

Previous research has found that subjective cognitive decline corresponds with assessed memory impairment and could even be predictive of neurocognitive impairment. The purpose of this study was to investigate whether a single self-report item of subjective cognitive decline corresponds with the results of a performance-based measure of episodic memory.

Older adults (n = 100; age 60-90) were given the single item measure of subjective cognitive decline developed by Verfaille et al. (2018).

Those who endorsed subjective cognitive decline (n = 68) had lower scores on the CVLT-II long delay free recall than those who did not endorse such a decline (n = 32). Additionally, older adults with a neurocognitive diagnosis believed their memory was becoming worse at a higher proportion than those without.

While a single item of subjective cognitive decline should not be substituted for a comprehensive evaluation of memory, the results suggest that it may have utility as a screening item.

Through in-depth interviews, this study aimed to understand perspectives of key stakeholders regarding the decision to curtail academic operations in the setting of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak before the declaration of a pandemic on March 11, 2020, and how such processes may be optimized in the future to best protect public health and safety.

Virtual interviews with key stakeholders from 4 academic institutions were conducted from September to December 2020 using a standardized interview question template. The interviews lasted approximately 30-45 minutes and each interview was recorded with permission. The interviews were then transcribed and reviewed for qualitative analysis.

The decision to curtail academic operations involved several common themes, such as discussing how institutions would control the outbreak and the process of transitioning to virtual learning and remote work. Universities were monitoring other universities’ responses as well as evaluating the prevalence of cases nationally and globally. Risks and challenges identified included housing for international students, financial implications, and loss of academic productivity.

The decision-making process may be optimized in the future by focusing on communication within a smaller committee, prioritizing epidemiology over fiscal implications, and embracing an openness to consider new strategies. Further research regarding this topic should be pursued to best protect public health and safety.

Optimizing health care workers’ (HCWs) willingness to respond (WTR) is critical in low-and-middle-income countries (LMICs) for proper health system functioning during extreme weather events. Pakistan frequently experiences weather-related disasters, but limited evidence is available to examine HCW willingness. Our study examined the association between WTR and behavioral factors among emergency department HCWs.

A cross-sectional survey was conducted from August to September 2022 among HCWs from 2 hospitals in Karachi, Pakistan. Non-probability purposive sampling was used to recruit participants. A survey tool was informed by Witte’s Extended Parallel Process Model (EPPM). Multivariate logistic regression analyses were performed to examine the association between WTR and attitudes/beliefs as well as EPPM profiles.

Twenty-nine percent of HCWs indicated a low WTR. HCWs using public transportation had a higher WTR. Perceived knowledge and skills, self-efficacy, and perceived impact of one’s response showed positive associations with WTR if required. Perception that one’s colleagues would report to work positively predicted WTR if asked. Consistent with the EPPM, HCWs with high efficacy and perceived threat were willing to respond to weather disasters.

Our findings highlight the need of strengthening WTR by promoting self-efficacy and enhancing accurate risk perception as a response motivator, among emergency department HCWs in Pakistan.

Emergency medical (EM) response systems require extensive coordination, particularly during mass casualty incidents (MCIs). The recognition of preparedness gaps and contextual priorities to MCI response capacity in low- and middle-income countries (LMICs) can be better understood through the components of EM reponse systems. This study aims to delineate essential components and provide a framework for effective emergency medical response to MCIs.

A scoping review was conducted using 4 databases. Title and abstract screening was followed by full-text review. Thematic analysis was conducted to identify themes pertaining to the essential components and integration of EM response systems.

Of 20,456 screened citations, 181 articles were included in the analysis. Seven major and 40 sub-themes emerged from the content analysis as the essential components and supportive elements of MCI medical response. The essential components of MCI response were integrated into a framework demonstrating interrelated connections between essential and supportive elements.

Definitions of essential components of EM response to MCIs vary considerably. Most literature pertaining to MCI response originates from high income countries with far fewer reports from LMICs. Integration of essential components is needed in different geopolitical and economic contexts to ensure an effective MCI emergency medical response.

Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day.

Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.

237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.

AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

Disasters of all varieties have been steadily increasing in frequency. Simultaneously, “big data” has seen explosive growth as a tool in business and private industries while opportunities for robust implementation in disaster management remain nascent. To more explicitly ascertain the current status of big data as applied to disaster recovery, we conducted an integrative literature review.

Eleven databases were searched using iteratively developed keywords to target big data in a disaster recovery context. All studies were dual-screened by title and abstract followed by dual full-text review to determine if they met inclusion criteria. Articles were included if they focused on big data in a disaster recovery setting and were published in the English-language peer-reviewed literature.

After removing duplicates, 25,417 articles were originally identified. Following dual title/abstract review and full-text review, 18 studies were included in the final analysis. Among those, 44% were United States-based and 39% focused on hurricane recovery. Qualitative themes emerged surrounding geographic information systems (GIS), social media, and mental health.

Big data is an evolving tool for recovery from disasters. More research, particularly in real-time applied disaster recovery settings, is needed to further expand the knowledge base for future applications.

Modern digital strategies, including Internet of Things, machine learning, and mobile applications, have revolutionized situational awareness during disaster management. Despite their importance, no review of digital strategies to support emergency food security efforts has been conducted. This scoping review fills that gap.

Keywords were defined within the concepts of food assistance, digital technology, and disasters. After the database searches, PRISMA guidelines were followed to perform a partnered, 2-round scoping literature review.

The search identified 3201 articles, and 26 articles met criteria and were included in the analysis. The data types used to describe the tools were text/opinion (42.3%), qualitative (23.1%), system architecture (19.2%), quantitative and qualitative (11.5 %), and quantitative (3.8%). The tools’ main functions were Resource Allocation (41.7%), Data Collection and Management (33%), Interagency Communications (15.4 %), Beneficiary Communications (11.5%), and Fundraising (7.7%). The platforms used to achieve these goals were Mobile Application (36%), Internet of Things (20%), Website (20%), and Mobile Survey (8%); 92% covered the disaster response phase.

Digital tools for planning, situational awareness, client choice, and recovery are needed to support emergency food assistance, but there is a lack of these tools and research on their effectiveness across all disaster phases.

The aim of this study was to test the appearance of negative dominance in coronavirus disease (COVID-19) vaccine-related information and activity online. We hypothesized that if negative dominance appeared, it would be a reflection of peaks in adverse events related to the vaccine, that negative content would attract more engagement on social media than other vaccine-related posts, and posts referencing adverse events related to COVID-19 vaccination would have a higher average toxicity score.

We collected data using Google Trends for search behavior, CrowdTangle for social media data, and Media Cloud for media stories, and compared them against the dates of key adverse events related to COVID-19. We used Communalytic to analyze the toxicity of social media posts by platform and topic.

While our first hypothesis was partially supported, with peaks in search behavior for image and YouTube videos driven by adverse events, we did not find negative dominance in other types of searches or patterns of attention by news media or on social media.

We did not find evidence in our data to prove the negative dominance of adverse events related to COVID-19 vaccination on social media. Future studies should corroborate these findings and, if consistent, focus on explaining why this may be the case.

Influenza vaccination remains the most effective primary prevention strategy for seasonal influenza. This research explores the percentage of emergency medical services (EMS) clinicians who received the seasonal flu vaccine in a given year, along with their reasons for vaccine acceptance and potential barriers.

A survey was distributed to all EMS clinicians in Virginia during the 2018-2019 influenza season. The primary outcome was vaccination status. Secondary outcomes were attitudes and perceptions toward influenza vaccination, along with patient care behaviors when treating an influenza patient.

Ultimately, 2796 EMS clinicians throughout Virginia completed the survey sufficiently for analysis. Participants were mean 43.5 y old, 60.7% male, and included the full range of certifications. Overall, 79.4% of surveyed EMS clinicians received a seasonal flu vaccine, 74% had previously had the flu, and 18% subjectively reported previous side effects from the flu vaccine. Overall, 54% of respondents believed their agency has influenza or respiratory specific plans or procedures.

In a large, state-wide survey of EMS clinicians, overall influenza vaccination coverage was 79.4%. Understanding the underlying beliefs of EMS clinicians remains a critical priority for protecting these frontline clinicians. Agencies should consider practical policies, such as on-duty vaccination, to increase uptake.

The purpose of this study was to demonstrate the use of social network analysis to understand public discourse on Twitter around the novel coronavirus disease 2019 (COVID-19) pandemic. We examined different network properties that might affect the successful dissemination by and adoption of public health messages from public health officials and health agencies.

We focused on conversations on Twitter during 3 key communication events from late January to early June of 2020. We used Netlytic, a Web-based software that collects publicly available data from social media sites such as Twitter.

We found that the network of conversations around COVID-19 is highly decentralized, fragmented, and loosely connected; these characteristics can hinder the successful dissemination of public health messages in a network. Competing conversations and misinformation can hamper risk communication efforts in a way that imperil public health.

Looking at basic metrics might create a misleading picture of the effectiveness of risk communication efforts on social media if not analyzed within the context of the larger network. Social network analysis of conversations on social media should be an integral part of how public health officials and agencies plan, monitor, and evaluate risk communication efforts.

The lack of radiation knowledge among the general public continues to be a challenge for building communities prepared for radiological emergencies. This study applied a multi-criteria decision analysis (MCDA) to the results of an expert survey to identify priority risk reduction messages and challenges to increasing community radiological emergency preparedness.

Professionals with expertise in radiological emergency preparedness, state/local health and emergency management officials, and journalists/journalism academics were surveyed following a purposive sampling methodology. An MCDA was used to weight criteria of importance in a radiological emergency, and the weighted criteria were applied to topics such as sheltering-in-place, decontamination, and use of potassium iodide. Results were reviewed by respondent group and in aggregate.

Sheltering-in-place and evacuation plans were identified as the most important risk reduction measures to communicate to the public. Possible communication challenges during a radiological emergency included access to accurate information; low levels of public trust; public knowledge about radiation; and communications infrastructure failures.

Future assessments for community readiness for a radiological emergency should include questions about sheltering-in-place and evacuation plans to inform risk communication.

Obesity is a major risk factor for osteoarthritis (OA) whilst there is some evidence that diabetes also increases risk. Metformin is a common oral treatment for those with diabetes.

The aim is to investigate whether metformin reduces the risk of OA.

This was a cohort study set within the Consultations in Primary Care Archive, with 3217 patients with type 2 diabetes. Patients at 13 general practices with recorded type 2 diabetes in the baseline period (2002–2003) and no prior record of OA were identified. Exposure was a prescription for metformin. Outcome was an OA record during follow up. Cox proportional hazard models with Gamma frailty term were fitted: adjusted for age, gender, deprivation, and comorbidity.

There was no association between prescribed metformin treatment at baseline and OA (adjusted HR: 1.02, 95% CI: 0.91, 1.15). A similar non- significant association was found when allowing exposure status of prescription of metformin to vary over time.