Survey on health literacy and related factors among firefighters of emergency management departments in Southwest China.

OBJECTIVE: Although health literacy (HL) has emerged as a critical public health concern, research on HL in emergency management departments is limited. This study aimed to investigate the awareness of HL and associated factors among firefighters of emergency management departments in southwest China to provide a basis for carrying out targeted health education. METHODS: A cross-sectional convenience sample of 1,742 firefighters from an emergency management department in southwestern China was surveyed from February to April 2023 using the Chinese Citizen's Health Literacy Questionnaire (2019 version). The chi-square test, linear trend chi-square test, Fisher's test, rank sum test, and multifactorial logistic regression model were used to identify influential factors associated with HL. RESULTS: The HL level of the 1742 respondents was 34.3%. Age, ethnicity, education level, length of service, type of job, smoking status, types of parental jobs, annual household income, time of daily internet use, etc. (P < 0.05). The results of multivariate logistic regression analysis indicate that type of job (OR = 0.648, 95%CI:0.426-0.985), length of service (OR = 0.496, 95%CI:0.251-0.981), household income (OR = 1.900, 95%CI:1.443-2.502), daily internet usage time (OR = 0.726, 95%Cl:0.588-0.896), health status (OR = 0.750, 95%Cl:0.585-0.962) and frequency of organizing HL sessions (OR = 1.603, 95%Cl:1.101-2.330) were influencing factors affecting the HL of the officers and soldiers. CONCLUSION: The health literacy level of firefighters in the Emergency Management Department in Southwest China was 34.3%. Lower levels were found in the health-related skills dimension (HRS, 30.1%) and in infectious disease control (ID, 30.7%). Health information literacy (HI, 34.3%) was lower than the national level. The type of urban and rural areas, literacy level, and household income level may be the factors affecting the level of health literacy among the respondents. Therefore, health education and promotion interventions should target high priority dimensions (HRS, HI, and ID) and should focus on strengthening health literacy levels of firefighters with rural types, low education levels, and low household income to improve their health.

A comparative national-level analysis of government food system resilience activities across four developed countries at varying stages of planning.

BACKGROUND: The COVID-19 pandemic, extreme weather events, and the Russian invasion of Ukraine have highlighted global food system vulnerabilities and a lack of preparedness and prospective planning for increasingly complex disruptions. This has spurred an interest in food system resilience. Despite the elevated interest in food system resilience, there is a lack of comparative analyses of national-level food system resilience efforts. An improved understanding of the food system resilience landscape can support and inform future policies, programs, and planning. METHODS: We conducted a cross-country comparison of national-level food system resilience activities from Australia, Aotearoa New Zealand, Sweden, and the United States. We developed upon and adapted the resilience framework proposed by Harris and Spiegel to compare actions derived from thirteen national food system resilience documents. We coded the documents based on the actions taken by the governments including: the food system resilience attributes utilized, the part of the food supply chain, the specific shocks or stressors, the implementation level, the temporal focus of action, and the expected impact on food security. We analyzed and compared countries' coded categories and subcategories, and category combinations. RESULTS: The results showed that these countries are addressing some of the same issues, are using multi-pronged policy actions to address food system resilience issues, and are focused on both retrospective reviews and prospective models of disruptive events to inform their decisions. Some work has been done towards preparing for climate change and other natural disasters, and less preparing has been done for other shocks or stressors. CONCLUSIONS: This paper develops and applies a framework rooted in literature to understand the content of national-level food system resilience documents. The analysis identified potential gaps, concentrations, and themes in national food systems resilience. The framework can be applied to augment existing policy, create new policy, as well as to supplement and complement other existing frameworks.

Formal and informal hospital emergency management practices: managing for safety and performance amid crisis.

Although formal preparedness for unexpected crises has long been a concern of health care policy and delivery, many hospitals struggled to manage staff and equipment shortages, precarious finances, and supply chain disruptions among other difficulties during the Coronavirus disease pandemic. Our purpose was to analyze how hospitals used formal and informal emergency management practices to maintain safe and high-quality care while responding to crisis. We conducted a qualitative study based on 26 interviews with hospital leaders and emergency managers from 12 US hospitals, purposively sampled to vary along geographic location, urban/rural delineation, size, resource availability, system membership, teaching status, and performance levels among other characteristics. In order to manage staff, space, supplies, and system- related challenges, hospitals engaged formal and informal practices around planning, teaming, and exchanging resources and information. Relying solely only on formal or informal practices proved inadequate, especially when prespecified plans, the incident command structure, and existing contracts and communication platforms failed to support resilient response. We identified emergent capabilities-imaginative planning, recombinant teaming, and transformational exchange-through which hospitals achieved harmonious interplay between the formal and informal practices of emergency management that supported safe care and resilience amid crisis. Managing emergent challenges for and amid crisis calls for health care delivery organizations to engage creative planning processes, enable motivated workers with diverse skill sets to team up, and establish rich inter- and intra-organizational partnerships that support vital exchange.

Rescue path planning for urban flood: A deep reinforcement learning-based approach.

Urban flooding is among the costliest natural disasters worldwide. Timely and effective rescue path planning is crucial for minimizing loss of life and property. However, current research on path planning often fails to adequately consider the need to assess area risk uncertainties and bypass complex obstacles in flood rescue scenarios, presenting significant challenges for developing optimal rescue paths. This study proposes a deep reinforcement learning (RL) algorithm incorporating four main mechanisms to address these issues. Dual-priority experience replays and backtrack punishment mechanisms enhance the precise estimation of area risks. Concurrently, random noisy networks and dynamic exploration techniques encourage the agent to explore unknown areas in the environment, thereby improving sampling and optimizing strategies for bypassing complex obstacles. The study constructed multiple grid simulation scenarios based on real-world rescue operations in major urban flood disasters. These scenarios included uncertain risk values for all passable areas and an increased presence of complex elements, such as narrow passages, C-shaped barriers, and jagged paths, significantly raising the challenge of path planning. The comparative analysis demonstrated that only the proposed algorithm could bypass all obstacles and plan the optimal rescue path across nine scenarios. This research advances the theoretical progress for urban flood rescue path planning by extending the scale of scenarios to unprecedented levels. It also develops RL mechanisms adaptable to various extremely complex obstacles in path planning. Additionally, it provides methodological insights into artificial intelligence to enhance real-world risk management.

Evidence of policy learning in emergency declarations as communication tools in Australia.

Emergency declarations are important legal tools for the state to protect itself and its citizens during times of crisis. Such declarations permit the exercise of extraordinary powers to address an emergency or disaster. They present an opportunity to explore policy learning in crises, through the ability to examine emergency declaration instruments and the detail of post-emergency inquiries and reviews. This paper briefly assesses Australian law that provides for emergency declarations and places it in the context of theories of policy learning and change. Two case studies reveal evidence of policy learning in emergency declaration practice in Australia. There is an emerging practice of using declarations primarily or purely as tools to communicate the seriousness of an emergency. This policy learning has occurred both within and between jurisdictions, including the federal government. This paper also probes opportunities for future research on policy learning and emergency legislation, especially in relation to the COVID-19 pandemic.

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures.

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.

Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

Next Generation Computing and Communication Hub for First Responders in Smart Cities.

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service.

Disaster literacy in disaster emergency response: a national qualitative study among nurses.

BACKGROUND: As the largest group of healthcare professionals, nurses play an indispensable and crucial role in disaster response. The enhancement of nurses' disaster literacy is imperative for effective disaster emergency management. However, there is currently a lack of knowledge regarding nurses' disaster literacy. This study represents the first attempt to explore the key components and characteristics of disaster literacy among nurses. METHODS: A qualitative descriptive design was employed, and the reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (refer to File S1). The purposive sampling method was utilized. Thirty-one rescue nurses from 31 medical institutions across 25 provinces and regions in China were recruited to participate in the study. The respondents were requested to share their experiences and insights regarding disaster rescue operations. Inductive content analysis was employed for data examination. RESULTS: The results indicated that rescue nurses universally recognized that there was a pressing need to enhance the level of disaster literacy among nurses. The disaster literacy of nurses encompasses nine dimensions: physical and mental quality, disaster rescue general knowledge, professional and technical competence, professional ethics, teamwork, emotional ability, information literacy, leadership, and knowledge transformation. CONCLUSIONS: To ensure national sustainability, government departments, healthcare organizations, and hospital administrators can accurately evaluate the disaster literacy of individual clinical nurses, groups, and the workforce as a whole through nine dimensions, which also can provide evidence to support the development of precision strategies to strengthen the disaster literacy of nurses.

Terrorist attacks against emergency departments.

INTRODUCTION: Emergency departments (EDs) play an integral role in a community's response to disaster. Terrorist attacks targeting EDs have the potential to disrupt the emergency response apparatus. Understanding prior attacks against EDs can yield important lessons to mitigate the impact of future violence. METHODS: In this review, the authors used the Global Terrorism Database to conduct a search on terrorist attacks targeting EDs between 1970 and 2018. Using the search terms "doctor," "nurses," "hospital," "healthcare," "clinic," "vaccinators," and "vaccinations," a total of 2322 healthcare-specific incidents were isolated. The database was further narrowed down to terrorist attacks targeting EDs, using the search terms "emergency," "emergency department," and "emergency ward." RESULTS: A total of six attacks involving five countries were isolated. These attacks occurred between 1991 and 2016, with the majority involving the use of explosive devices, killing a total of 57 victims and leaving 26 wounded. CONCLUSION: Attacks against EDs, while rare, have the potential to lead to loss of life through both the direct attack and subsequent disruptions to healthcare.

An in-hospital stroke system to optimize emergency management of acute ischemic stroke by reducing door-to-needle time.

BACKGROUND: Door-to-needle time (DNT) is a critical consideration in emergency management of acute ischemic stroke (AIS). Deficiencies in the widely applied standard hospital workflow process, based on international guidelines, impede rapid treatment of AIS patients. We developed an in-hospital stroke system to reduce DNT and optimize hospitals' emergency procedures. OBJECTIVES: To investigate the effect of the in-hospital stroke system on the hospital workflow for AIS patients. METHODS: We performed a retrospective study on AIS patients between June 2017 and December 2021. AIS cases were assigned to a pre-intervention group (before the in-hospital stroke system was established) and a post-intervention group (after the system's establishment). We compared the two groups' demographic features, clinical characteristics, treatments and outcomes, and time metrics data. RESULTS: We analyzed 1031 cases, comprising 474 and 557 cases in the pre-intervention and post-intervention groups, respectively. Baseline data were similar for both groups. Significantly more patients in the post-intervention group (41.11%) were treated with intravenous thrombolysis (IVT) or endovascular therapy (ET) compared with those in the pre-intervention group (8.65%) (p < 0.001). DNT was markedly improved (decreasing from 118 (80.5-137) min to 26 (21-38) min among patients in the post-intervention group treated with IVT or bridging ET. Consequently, a much higher proportion of these patients (92.64%) received IVT within 60 min compared with those in the pre-intervention group (17.39%) (p < 0.001). Consequently, their hospital stays were shorter (8 [6-11] days vs. 10 [8-12] days for the pre-intervention group; p < 0.001), and they showed improved National Institutes of Health Stroke Scale (NIHSS) scores at discharge (-2 [-5-0] vs. -1 [-2-0], p < 0.001). CONCLUSION: DNT was significantly reduced following implementation of the in-hospital stroke system, which contributed to improved patient outcomes measured by the length of hospital stay and NIHSS scores.

Social vulnerability, impacts and adaptations strategies in the face of natural hazards: insight from riverine islands of Bangladesh.

BACKGROUND: Bangladesh is one of the countries at risk of natural disasters due to climate change. In particular, inhabitants of its riverine islands (char) confront ongoing climatic events that heighten their vulnerability. This study aims to assess social vulnerability, impacts, and adaptation strategies to climate change in the riverine island areas of Bangladesh. METHODS: A mixed-method approach incorporating qualitative and quantitative procedures was used on data collected from 180 households of riverine islands in Gaibandha, Bangladesh. The social vulnerability of riverine island communities was assessed based on their adaptation capacity, sensitivity, and exposure to climatic stressors. RESULTS: The findings show that char dwellers' vulnerability, impacts, and adaptation capability to climate change vary significantly depending on their proximity to the mainland. Social vulnerability factors such as geographical location, fragile and low-grade housing conditions, illiteracy and displacement, climate-sensitive occupation and low-income level, and so on caused to the in-height vulnerability level of these particular areas. This study also displays that climate change and its associated hazards cause severe life and livelihood concerns for almost all households. In this case, the riverine dwellers employed several adaptation strategies to enhance their way of life to the disaster brought on changing climate. However, low education facilities, deficiency of useful information on climate change, poor infrastructure, and shortage of money are still the supreme hindrance to the sustainability of adaptation. CONCLUSION: The findings underscore the importance of evaluating the susceptibility of local areas to climate change and emphasize the need for tailored local initiatives and policies to reduce vulnerability and enhance adaptability in communities residing in char households.

Impact of COVID-19 Disease Control Committee (CDCC) policies on prevention of the disease using Bayes network inference in west of Iran.

BACKGROUND: The start of the COVID-19 pandemic was an emergency situation that led each country to adopt specific regional strategies to control it. Given the spread of COVID-19 disease, it is crucial to evaluate which policy is more effective in reducing disease transmission. The purpose of this study was to determine the impact of policies made by COVID-19 Disease Control Committee (CDCC) to reduce the risk of the disease in Hamadan province. METHODS: In the observational study, the data were extracted from three sources in Hamadan, west of Iran; first, the session reports of CDCC; second, information on periodic evaluations conducted by the primary health care directory in Hamadan from April to August 2021 and third, expert panel opinion. Bayes network analysis was used to determine the effect of each policy on mortality rate by GeNIe software version 2.2. RESULTS: Among the policies adopted by CDCC in Hamadan, seven policies, i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene had the most impact to prevent the spread of COVID-19, respectively. In this study, the prevalence of the disease was 17.64% with the implementation of these policies. Now, if all these policies are observed 30% more, the prevalence will decrease to 14.18%. CONCLUSION: This study showed that if the seven policies (i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene) are followed simultaneously in the community, the risk of contracting the disease will be greatly reduced. Therefore, in the pandemic of infectious diseases, such policies can help prevent the spread of the disease.

A national risk analysis model (NRAM) for the assessment of COVID-19 epidemic.

COVID-19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision-making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision-making in the context of COVID-19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID-19 at the national level.

Posthurricane damage assessment using satellite imagery and geolocation features.

Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed-data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare-earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The data set curated in this work is made openly available (DOI: 10.17603/ds2-3cca-f398).

Local news sentiment towards FEMA recovery efforts after Hurricane Florence in North Carolina.

Newspaper sentiment and framing have the power to represent and inform public opinion on a variety of important issues. This study examines local news articles after Hurricane Florence struck North Carolina in the United States in September 2018 to understand the framing efforts undertaken by the outlets that produced these reports, as well as their impact on news sentiment towards the flood recovery efforts of the Federal Emergency Management Agency (FEMA). The results indicate that while most articles published in the wake of Florence have a neutral sentiment, there are a significant number of both positively and negatively coded articles that illuminate important information about how the public engaged with and comprehended the role of FEMA during recovery from the disaster, and how the media chose to cover its involvement. Such scrutiny will continue to inform how public, private, and government actors understand FEMA's role and whether it achieves its goals in the future.

Local emergency management's use of social media during disasters: a case study of Hurricane Irma.

Governments' use of social media during all phases of emergency management, especially during disasters, has increased dramatically in the past 20 years. Yet, implementation at the local government level in the United States remains haphazard. As technology and the role of social media evolve, there persists a need to understand the socio-technical aspects of social media's employment in times of disaster. This study contributes to the growing social media literature by asking the following questions: what challenges remain and what lessons learned are being institutionalised at the local level of government? A qualitative analysis of 26 after action reports on Hurricane Irma (September 2017) by county, state, and federal governments and a four-hour focus-group session revealed dominant and subdominant themes, including: push/pull information; capacity and technical issues; inconsistent messaging; one-way versus two-way communication; timing of messages; and data collection. The paper concludes by discussing lessons learned, remaining challenges, evidence of organisational learning, and recommendations for future research.

Dynamics of interorganisational emergency communication on Twitter: the case of Hurricane Irma.

This study examines how county-level emergency management offices (EMOs) used Twitter to communicate with other public agencies and non-profit and for-profit organisations before, during, and after Hurricane Irma in 2017. It assesses the strategies that EMOs and other stakeholders employed to communicate risks on Twitter, concluding that its potential has not been fully exploited. EMOs only frequently interacted with a few non-profit and for-profit organisations, despite their involvement in emergency communication. While EMOs and other public agencies emphasised information dissemination and called on citizens to act and be prepared for the hurricane, non-profits tended to stress service and resource-related information, encouraged others to assist with disaster response, and provided emotional support. For-profits, meanwhile, actively addressed customers' concerns through direct two-way communication. Our findings indicate that EMOs should integrate non-profit and for-profit organisations' communication efforts, engaging them in important conversations on Twitter and advocating the use of highly relevant hashtags at different disaster management stages.

International humanitarian narratives of disasters, crises, and Indigeneity.

Narratives are a means of making sense of disasters and crises. The humanitarian sector communicates stories widely, encompassing representations of peoples and events. Such communications have been critiqued for misrepresenting and/or silencing the root causes of disasters and crises, depoliticising them. What has not been researched is how such communications represent disasters and crises in Indigenous settings. This is important because processes such as colonisation are often at the origin but are typically masked in communications. A narrative analysis of humanitarian communications is employed here to identify and characterise narratives in humanitarian communications involving Indigenous Peoples. Narratives differ based upon how the humanitarians who produce them think that disasters and crises should be governed. The paper concludes that humanitarian communications reflect more about the relationship between the international humanitarian community and its audience than reality, and underlines that narratives mask global processes that link audiences of humanitarian communications with Indigenous Peoples.

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues.

With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big "sensed" data leads to an effective and efficient management of disaster situations so as to prevent human and economic losses. The advancement of built-in sensing technologies in smart mobile devices enables crowdsourcing of sensed data, which is known as mobile crowdsensing (MCS). This systematic literature review investigates the use of mobile crowdsensing in disaster management on the basis of the built-in sensor types in smart mobile devices, disaster management categories, and the disaster management cycle phases (i.e., mitigation, preparedness, response, and recovery activities). Additionally, this work seeks to unveil the frameworks or models that can potentially guide disaster management authorities towards integrating crowd-sensed data with their existing decision-support systems. The vast majority of the existing studies are conceptual as they highlight a challenge in experimental testing of the disaster management solutions in real-life settings, and there is little emphasis on the use cases of crowdsensing through smartphone sensors in disaster incidents. In light of a thorough review, we provide and discuss future directions and open issues for mobile crowdsensing-aided disaster management.