Health Security最新文献

The International Health Regulations (2005) (IHR) Monitoring and Evaluation Framework is designed to assist States Parties in assessing progress toward compliance and sustainable capacities under the IHR. The States Parties Self-Assessment Annual Report (SPAR) is the only mandatory tool in the 4-component framework. The current SPAR is the third version of the tool since its inception in 2010. The revisions, while reflecting evolving requirements for health security capacity under the IHR, hinder the ability to compare capacity scores between versions and prevent analysis of historical data. In this article, we describe a methodology that aligns capacities across the 3 versions of the tool by creating umbrella terms for common themes that can be adapted or applied to any future SPAR changes, providing a sustainable framework for ongoing assessment and analysis. Our methodology enables States Parties, policymakers, and other stakeholders to view and assess country capacity across the history of self-assessment. Mapping by common themes allows for a historical understanding of national, regional, and global efforts to strengthen health security capacity.

The demands for vaccination, testing, and community outreach during the COVID-19 pandemic extended beyond public health capabilities. The role of independent pharmacies proved invaluable as they partnered with local, state, and federal public health entities to provide additional capacity. However, the complexities of federal partnering agreements and the limited awareness of independent pharmacy networks could delay engagement and support for underserved communities in future responses. Addressing this challenge requires tailored public-private partnership agreements and resilience frameworks for data sharing based on experience and exercises. This case study describes the experiences of independent pharmacies during the COVID-19 response, opportunities for sustained public health and pharmacy engagement, and recommendations for building a framework for rapid scale-up before the next public health emergency. Under the direction of the National Community Pharmacists Association Innovation Center, aggregators of independent pharmacies have collaborated to form the Independent Community Pharmacy Consortium for Federal Government Engagement. This consortium represents a majority of the 18,900 independent pharmacies in the United States and establishes a single initial point of contact for government agencies to reach independent pharmacies. The development and management of the consortium remains outside of the government, enabling continuity and flexibility. This case study offers a unique perspective of public health engagement and partnership with independent pharmacies based on documented responses, exercises, and discussions.

Cyberattacks that target critical national infrastructure, such as hospitals, pose a significant threat to the safety and wellbeing of individuals, as evidenced by incidents like the WannaCry worldwide ransomware attack. To better understand vulnerabilities within the healthcare sector and develop preventive measures, it is crucial to examine the evolving nature of cyberthreats and the types of attacks occurring. In this article, we describe a multimethod approach comprising social networks analysis, natural language processing, and machine learning, using data from GDELT (Global Database of Events, Language, and Tone), to identify the prevalence of attacks on hospitals while considering the type of attack and its date. Through this approach, meaningful patterns in the evolution of cyberattacks are revealed by analyzing the relationships between emerging cyberattacks mentioned in news reports. Findings show that the number of attacks from 2017 to 2023 increased substantially, with hospitals being more prone to critical attacks such as cyberterrorism/state actor-sponsored criminal activities, advanced persistent threats, and distributed denial of service. Mapping real-time data from diverse sources using a multimethod approach, such as the framework proposed in this article, can lead to better understanding of the threat landscape. This is a crucial step in determining necessary cyberdefenses and informing the development of policy interventions to ensure the cybersecurity of critical national infrastructure.

Natural and human-induced disasters are increasing, which affects public health and safety in many ways including disruption of healthcare. Emergency preparedness mitigates the impacts of these disasters and training improves preparedness. However, no standard for training emergency managers exists. This study aimed to explore the state of US healthcare preparedness and the impact of training on preparedness and proposes a training standard for healthcare emergency managers. Mixed methods research was conducted to understand different aspects of training, inform the design of a training standard, and explore potential barriers. The first phase included a quantitative survey with 67 participants who responded to questions about training topics, quantity, and delivery format. In the second qualitative phase, 5 focus groups with 29 participants were conducted to deepen the understanding of survey results and collect information about training topics, barriers to adopting a standard, and recommendations for overcoming barriers. Ten training topics, for a quantity of 11 hours or more per topic, were identified in the quantitative phase and reinforced in the qualitative phase. In-person training was preferred for all but 3 topics, where online synchronous delivery was preferred. Other aspects of training were further explored, and the concept of a basic versus advanced training standard emerged as a major theme. Barriers to training included financial support and time to attend training as well as gaps in leadership knowledge and support. While training is key to improved healthcare emergency preparedness, variation in training exists. This study recommends a standard in healthcare emergency manager training.

The United States' current list-based approach to biodefense is limited because it considers only known biological agents. Alternatively, developing and adopting a system based on agent-agnostic signatures would enable detection and characterization of both known and novel agents, thereby engendering greater adaptability in the face of an evolving threat landscape. Machine learning (ML) could aid in such a transition, as it can recognize and encode highly complex patterns from multiple input data modalities and has already demonstrated success in many healthcare and defense applications. Functionalizing ML for environmental biodetection requires understanding current technical capabilities. In this article, we provide a systematic review of existing ML platforms and discuss anticipated development efforts needed to achieve effective ML-enabled, agnostic biodetection.

When patient demand exceeds hospital capacity in certain scenarios, such as natural disasters, terrorist attacks, or staffing shortages, the rapid discharge of patients identified through reverse triage methodologies can create surge capacity. The evaluation of this concept has been documented in numerous resources and studies, but current tools tend to be extensive and siloed, which may make them difficult to use during emergencies. To prepare the largest municipal healthcare system in the United States for situations requiring rapid patient discharge, NYC Health + Hospitals/Central Office Emergency Management sought to develop a short, synthesized, and user-friendly plan. After consulting experts and reviewing existing peer-reviewed articles, gray literature, and internal facility documents, the team created a 7-page rapid action checklist that synthesizes important content. The Risk-based, Abbreviated, Patient Identification Discharge (RAPID) tool was successfully used during a resident labor action in May 2023, illustrating that its utility may extend beyond the system in which it was used. Future work should be done to validate and improve upon this tool.

In this study, we identify facilitators and barriers to COVID-19 vaccination in Nairobi, Kenya, using the modified 5 Cs model for vaccine hesitancy. We conducted 33 in-person interviews in Nairobi, Kenya. Participants were recruited using convenience sampling by a member of the research team who resides in Nairobi and speaks Swahili. Interviews were audio recorded and transcripts were analyzed using thematic analysis. The modified 5 Cs model for vaccine hesitancy was applied to create a codebook prior to analysis. Participants cited misinformation, lack of trust in the science behind the vaccine, and concerns about side effects as reasons for not receiving the COVID-19 vaccine. Facilitators for choosing to receive the vaccination included concerns about the severity of COVID-19, vaccination requirements for school and employment, and communication from the government. This study is the first to organize facilitators and barriers to COVID-19 vaccine uptake in Kenya using the 5 Cs model of vaccine hesitancy. Our findings suggest that to improve vaccine uptake in Kenya, interventions should inform the public about the vaccine's safety and reduce misinformation.

The spread of bacteria that cause illness is a critical problem facing the restaurant industry worldwide. These bacteria can proliferate in various restaurants areas through airborne transmission mechanisms, increasing the risk of food contamination. In this study, our aim was to detect the presence of potential foodborne pathogenic bacteria-Escherichia coli, Staphylococcus aureus, and aerobic bacteria-in aerosols of different restaurants zones in Riyadh city in the Kingdom of Saudi Arabia. We focused on 3 important zones: preparation (Zone A), food packaging (Zone B), and handwashing (Zone C). The bacteria of interest were isolated, identified, and characterized by using selective media, biochemical, and antibiotic susceptibility tests. The results showed that all 40 of the studied restaurants were contaminated with aerobic bacteria, with a total count of 3,978 colony-forming units (CFU) in Zone C, 1,323 in Zone B, and 525 in Zone A. E coli was identified as the most prevalent illness-causing bacteria in Zone A-derived aerosols (721 CFU), while S aureus had the highest occurrence in aerosols in Zone C (528 CFU). Pertaining to the antibiotic resistance phenotype of assessed isolates, our findings revealed that Zone C-derived E coli isolates showed resistance ranging from 25% to 100% toward 8 of the 15 tested antibiotics. S aureus isolates originating from Zone B exhibited between 25% and 75% resistance to 2 antibiotics, while isolates from Zone C showed resistance ranging from 5.88% to 47.05% to 4 antibiotics. Findings from this study illustrate that restaurants' aerosols are highly contaminated with different antibiotic-resistant bacteria, which increases the risk of food poisoning and threats food security.

In Japan, the Infectious Disease Control Law designates certain institutions across the country as medical institutions for infectious diseases, with the role to respond to and prepare for epidemic or pandemic infections. Since the early stages of the COVID-19 pandemic, these designated medical institutions have provided clinical care to patients with COVID-19. While these institutions primarily handle clinical care, they are also well poised to conduct rigorous clinical research that is needed to address future health emergencies. The COVID-19 pandemic highlighted the importance of clinical research as a medical countermeasure through its role in the development of effective novel vaccines and therapeutics. Under the Japanese system, designated medical institutions that cared for patients with COVID-19 had the privilege to access the earliest cases and were uniquely positioned to contribute to scientific evidence. Based on this understanding, we conducted a nationwide survey and analyzed data from 100 designated medical institutions to better understand their experiences and involvement in clinical research during the COVID-19 pandemic and their readiness and willingness to conduct clinical research in a future health emergency. While quite a few institutions showed willingness to participate in infectious disease research in the event of a future health emergency, it was evident that many would require additional expertise and financial support to facilitate such research. Our analysis suggests that further capacity development, empowerment for clinical research, and a strong collaborative network across stakeholders are required to improve pandemic response and preparedness in Japan.