Health Security最新文献

During the COVID-19 pandemic, vaccination rates among pregnant women were notably lower due to concerns about vaccine safety and effectiveness. We investigated the impact of COVID-19 vaccination on hospitalization and illness severity due to COVID-19 among pregnant women. Data were obtained from the Korea Disease Control and Prevention Agency's COVID-19 National Health Insurance Service cohort, including 2,235 pregnant women and 6,733 nonpregnant women (1:3 matched) infected with COVID-19 between October 2020 and December 2021. COVID-19 vaccination status was categorized as unvaccinated, first dose only, and fully vaccinated (2 or more doses). Logistic regression was performed to identify factors associated with hospitalization and illness severity. Among pregnant women infected with COVID-19, 88.4% were unvaccinated, 4.6% received 1 dose, and 7.1% were fully vaccinated, compared to 61.3%, 8.1%, and 30.6%, respectively, for nonpregnant women. The odds ratio (OR) for hospitalization was significantly higher for pregnant women compared to nonpregnant women (OR=1.78). Within the pregnant women cohort, the OR for hospitalization was 0.92 for those who received the first vaccine dose and 0.37 for those who were fully vaccinated, demonstrating a significantly lower hospitalization rate only in the fully vaccinated group. A similar pattern was observed for illness severity, with ORs of 0.74 for the first dose and 0.33 for full vaccination, indicating a significantly lower rate of severe illness only in the fully vaccinated group. Additionally, high-risk pregnant women exhibited significantly higher odds of both hospitalization and severe illness. These findings demonstrate that full COVID-19 vaccination coverage (2 or more doses) is strongly associated with decreased hospitalization and severe illness among pregnant women. Addressing vaccine hesitancy through prenatal care discussions and improving vaccine accessibility is essential to enhance maternal health outcomes.

New York City has been the epicenter of multiple recent infectious disease outbreaks, including COVID-19 and mpox, due to its position as one of the largest international travel hubs in the United States. In response to the imperative need to transport patients to specialized biocontainment units during high-consequence infectious disease outbreaks, the Health and Human Services Region 2 Regional Emerging Special Pathogen Treatment Center at New York City Health + Hospitals/Bellevue and the NYC Department of Health and Mental Hygiene spearheaded a comprehensive patient transport system. Informed by real-world experiences, quarterly drills, and regional partner engagement, the updated Region 2 patient transport concept of operations (CONOPS) ensures safe and seamless patient transfers. This article elucidates key components of the patient transport CONOPS, the multifaceted partner engagement approach used to develop it, and the collaborative workshop that fine-tuned the plan. Organizational skills, partner engagement, and adaptability were all necessary for refining and operationalizing a robust patient transport CONOPS. The finalization of this plan speaks to the collaborative spirit and commitment of regional leaders to ensure the effective management of high-consequence infectious disease outbreaks and the safeguarding of public health within Region 2 and beyond.

US Supreme Court opinions from the inception of the COVID-19 pandemic to date are reshaping national and subnational abilities to respond to public health emergencies. Substantial impacts in emergency legal preparedness and response arise in multiple legal areas including: (1) federal executive authorities; (2) access to health services and emergency care; (3) public health emergency interventions and mandates; (4) race-based limitations in resource allocations; (5) misinformation; and (6) scope of liability during and after emergencies. Against this backdrop an array of legal options and critical takeaways may help mitigate the impacts of US Supreme Court opinions and advance effective emergency responses ahead.

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.