Search Results (46)

Background/Objectives: Food allergy (FA) is a growing concern in pediatric care, requiring effective avoidance strategies and timely emergency responses. The role of caregivers is central to the daily management of FA. This study aimed to assess parental knowledge, preparedness, and behaviors regarding pediatric FA management, focusing on both prevention and emergency readiness. Methods: A cross-sectional survey was conducted from December 2024 to April 2025 through the SurveyMonkey^® platform, promoted by the Italian Society of Pediatric Allergology and Immunology (SIAIP). The anonymous, structured questionnaire was distributed online and in two Italian university hospitals. A total of 129 fully completed responses from caregivers of children with FA were analyzed. The survey explored self-perceived knowledge, symptom recognition, preventive actions, emergency preparedness, and communication practices. Results: Only 9.3% of parents considered themselves “very informed,” while 54.3% reported limited or no knowledge. Just 16.0% recognized all symptoms of an allergic reaction, and only 24.0% could distinguish mild reactions from anaphylaxis. Notably, 67.4% reported not knowing how to respond to anaphylaxis, and 83.7% did not possess an epinephrine auto-injector. Preventive measures at home were inconsistently applied, and 41.1% took no precautions when eating out. Communication with external caregivers was often informal or absent. Only 33% updated physicians regularly. Conclusions: The findings reveal significant gaps in parental preparedness and highlight critical areas for educational intervention. Enhanced caregiver training, standardized communication protocols, and improved clinical follow-up are essential to strengthen pediatric FA management and safety. Full article

In the context of emergency management, individual or household decisions to engage in risk mitigation behaviors are widely recognized to be influenced by a benefit–cost perception (perceived applied value (PAV) vs. perceived economic value (PEV), respectively). To better understand how such decisions are made, we conducted a mail survey (N = 211) of households living in the Red River of the North Basin, North Dakota, in 2018. The survey is aimed at understanding the overall experience of households with flooding and their behavior toward advanced protective strategies against future floods by analyzing household PEV—their willingness to pay for the National Aeronautics and Space Administration’s (NASA) Satellite Enhanced Snowmelt Flood Prediction system. This paper presents a mediation model in which various predictors (flood risk, experience, flood knowledge, flood risk perception, flood preparedness, flood mitigation, and flood insurance) are analyzed in relation to the PAV of the new Satellite Enhanced Snowmelt Flood Predictions in the Red River of the North Basin, which, in turn, may shape the PEV of this product. We discuss the potential implications for both the emergency management research community and professionals regarding the application of advanced risk mitigation technologies to help protect and sustain communities across the country from floods and other natural disasters. This paper provides a greater understanding of the economic and social aspects of sustainability in the context of emergency management and community development. Full article

This research determines how to integrate factors related to evacuation in emergency preparedness using techniques for Multicriteria Decision-Making (MCDM). A distinctive MCDM technique that incorporates human behavior into evacuation models enhances decision-making and safety during emergencies, especially in vulnerable populations. For this purpose, a hybrid combination of MCDM methods—CRiteria Importance Through Intercriteria Correlation (CRITIC), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Weighted Aggregated Sum Product Assessment (WASPAS)—is used to rank the vulnerability of Chilean regions by considering various factors. First, the related factors are ranked by CRITIC, and the result is that the “psychosocial problem” factor has the highest priority and weight. Then, according to the hybrid methods and CRITIC, all regions of Chile are ranked first with TOPSIS, WASPAS, and a combination of them to determine which one has the highest priority. The results show that the Santiago Metropolitan Region has the highest priority for vulnerability in all three methods. Full article

Objective: To explore the possible association and trends between individual-level disaster preparedness status (for natural, technological, and human-caused hazards) and the level of pandemic preparedness during the COVID-19 pandemic among adults in the U.S. from 2021 through 2023. Methods: Multivariate logistic regression was conducted using data from the U.S. Federal Emergency Management Agency’s (FEMA) annual National Household Survey (NHS) from 2021 to 2023 to identify statistically significant variables associated with personal pandemic preparedness behavior among participants of the nationally representative survey during the COVID-19 pandemic. Results: Overall, the results showed that the proportion of respondents that were considered prepared for a pandemic peaked in 2021 (54.0%) and steadily decreased in the following years (2022: 43.3%; 2023: 41.3%) highlighting the need for pandemic preparedness resources and educational campaigns to be available prior to a pandemic occurring. The final multivariate logistic regression models featuring consistent significant covariates demonstrated a highly statistically significant relationship between individual-level disaster preparedness and pandemic preparedness across all three study years (2021: odds ratio (OR): 21.35, standard error (SE): 2.59, p < 0.001; 2022: OR: 9.26, SE: 0.87, p < 0.001; 2023: OR: 6.75, SE: 0.59, p < 0.001). Conclusions: The significant results suggest individuals who are prepared for a disaster have higher odds of being prepared for a pandemic. These findings support the continued increase in collaboration between emergency management and public health entities to jointly support the development of evidence-based resources to increase personal preparedness for both disasters and pandemics. Full article

Highly Urbanized Cities (HUCs) in the Philippines were at the forefront of public health surveillance and response during the COVID-19 pandemic. With the rapid spread of COVID-19 to Philippine cities, local government units continuously assessed, adapted, and implemented public health interventions (PHIs) and depended on available open-source government data (OSGD). This study consolidated PHIs in selected HUCs in the Philippines using high-quality OSGD to create a timeline of interventions and document good practices in local COVID-19 control. OSGD resources were collected from February 2020 to January 2023, and the data quality of OSGD was evaluated using the Journal of the American Medical Association (JAMA) benchmarks. A total of 180 metadata sources that met at least two core standards (Authorship and Currency) were included in the analysis. COVID-19 control strategies were analyzed vis-à-vis the rise of COVID-19 cases and types of PHIs, including the control of imported cases, case management, contact management, behavioral modification, and pharmaceutical intervention. Travel bans and hard lockdowns in Luzon early in the pandemic delayed the introduction of COVID-19 to other parts of the country. Good practices of LGUs for local COVID-19 control, such as quarantine passes, curfews and liquor bans, using QR-based contact tracing, massive community testing in high-risk communities, and free public swabbing centers, were implemented to slow down the local spread of COVID-19. With the evolving scenarios in city-level COVID-19 epidemics, local risk assessments based on available OSGD drove the adoption of relevant and innovative control strategies in HUCs in the Philippines. Lessons learned must be integrated into epidemic preparedness and response programs against future emerging or re-emerging infectious diseases. Full article

Background/Objectives: The coronavirus disease 2019 (COVID-19) pandemic has significantly affected global health, economies, and societies, necessitating a deeper understanding of the factors influencing its spread and severity. Methods: This study employed text network analysis to examine relationships among various risk factors associated with severe COVID-19. Analyzing a dataset of published studies from January 2020 to December 2021, this study identifies key determinants, including age, hypertension, and pre-existing health conditions, while uncovering their interconnections. Results: The analysis reveals five thematic clusters: biomedical, occupational, demographic, behavioral, and complication-related factors. Temporal trend analysis reveals distinct shifts in research focus over time. In early 2020, studies primarily addressed immediate clinical characteristics and acute complications of COVID-19. By mid-2021, research increasingly emphasized long COVID, highlighting its prolonged symptoms and impact on quality of life. Concurrently, vaccine efficacy became a dominant topic, with studies assessing protection rates against emerging viral variants, such as Alpha, Delta, and Omicron. This evolving landscape underscores the dynamic nature of COVID-19 research and the adaptation of public health strategies accordingly. Conclusions: These findings offer valuable insights for targeted public health interventions, emphasizing the need for tailored strategies to mitigate severe outcomes in high-risk groups. This study demonstrates the potential of text network analysis as a robust tool for synthesizing complex datasets and informing evidence-based decision-making in pandemic preparedness and response. Full article

Background and Objectives: The COVID-19 pandemic has posed unprecedented challenges to healthcare workers, leading to significant psychological distress, altered health-related behaviors, and reliance on various coping mechanisms. Understanding these impacts is critical for developing targeted interventions to support healthcare professionals. This study aimed to evaluate the psychological stressors, emotional responses, changes in healthy behaviors, and coping mechanisms employed by healthcare workers during the COVID-19 pandemic. The study further examined differences across demographic and professional groups and explored correlations between stressors, coping strategies, and emotional outcomes. Materials and Methods: A cross-sectional survey was conducted among 338 healthcare workers, including physicians and nurses, in urban and rural healthcare settings during the pandemic. Data were collected using validated instruments to measure emotional responses (anxiety and anger), lifestyle behaviors (dietary habits, sleep patterns, physical activity, and smoking), and coping strategies. Statistical analyses included descriptive, inferential, and correlation techniques to assess relationships between variables. Results: Fear of infecting family members (M = 3.36, SD = 0.86) and concerns about inadequate protective equipment (M = 2.80, SD = 0.95) were the most significant stressors, strongly associated with heightened anxiety and anger. Changes in healthy behaviors were observed: 69.2% maintained a healthy meal schedule, 56.5% reported disrupted sleep patterns, and only 39.6% engaged in regular physical activity. Among smokers (27.5%), 31.1% increased smoking as a maladaptive coping strategy, while 21.1% reduced smoking. Nurses predominantly relied on emotion-focused strategies, such as religious coping and venting, whereas physicians favored problem-focused strategies like planning and active coping. Social support emerged as a protective factor, mitigating stress and facilitating adaptive coping. Conclusions: The study revealed significant psychological and behavioral impacts on healthcare workers during the COVID-19 pandemic. Key stressors included the fear of infecting family members, concerns about inadequate protective measures, and the prolonged uncertainty of the pandemic, which contributed to heightened levels of anxiety and anger. Changes in healthy behaviors, such as disrupted sleep patterns, decreased physical activity, and increased reliance on maladaptive coping mechanisms, further underscored the multifaceted challenges faced by healthcare professionals. Although the acute phase of the pandemic has passed, the long-term consequences on the mental health and well-being of healthcare workers remain critical concerns. Further research is essential to develop effective strategies for monitoring, preventing, and addressing psychological distress among healthcare professionals, ensuring their preparedness for future public health crises. Full article

This study investigates the relationship between SARS-CoV-2 RT–PCR cycle threshold (Ct) values and key COVID-19 transmission and outcome metrics across five years of the pandemic in Jalisco, Mexico. Utilizing a comprehensive time-series analysis, we evaluated weekly median Ct values as proxies for viral load and their temporal associations with positivity rates, reproduction numbers (Rt), hospitalizations, and mortality. Cross-correlation and lagged regression analyses revealed significant lead–lag relationships, with declining Ct values consistently preceding surges in positivity rates and hospitalizations, particularly during the early phases of the pandemic. Granger causality tests and vector autoregressive modeling confirmed the predictive utility of Ct values, highlighting their potential as early warning indicators. The study further observed a weakening association in later pandemic stages, likely influenced by the emergence of new variants, hybrid immunity, changes in human behavior, and diagnostic shifts. These findings underscore the value of Ct values as scalable tools for public health surveillance and highlight the importance of contextualizing their analysis within specific epidemiological and temporal frameworks. Integrating Ct monitoring into surveillance systems could enhance pandemic preparedness, improve outbreak forecasting, and strengthen epidemiological modeling. Full article

An in-depth understanding of gas (oxygen and methane) seepage characteristics in coal mine goafs is essential for the safe production of mines and for advancing sustainable development practices within the mining industry. However, the gas distribution and its flow processes still remain ambiguous. In this article, we developed a three-dimensional porous media mining goaf mathematical model (considering the heterogeneity) to analyze the methane and oxygen flow features. Firstly, based on the variation laws of the “three zones”—the free caving zone, fracture zone, and subsidence zone—porosity changes in the vertical direction were set. A three-dimensional physical model of a fully mechanized caving mining area with a “U”-shaped ventilation system was established as the basis, and a COMSOL Multiphysics multi-field coupled model was built. Secondly, based on the established model, the characteristics of porosity distribution, mixed gas pressure changes, and the volume fraction of oxygen in the goaf were analyzed. The results show that as the distance from the working face increases, the compaction intensity in the mined-out area gradually rises, resulting in a decreasing porosity trend. The porosity distribution characteristics significantly impact the mechanical behavior and gas flow. The gas pressure inside the mined-out area is much higher than the surroundings, decreasing with depth. The upper and middle parts have the highest-pressure concentrations, requiring focused assessment and targeted monitoring measures based on the pressure characteristics of different regions. The oxygen concentration gradually decreases with depth due to poor ventilation, leading to potential explosive gas mixtures, necessitating ventilation system optimization, enhanced monitoring, and emergency preparedness. The gas exhibits vertical stratification, with higher concentrations in the upper and deep regions. Targeted drainage and ventilation methods can effectively control the gas concentration and ensure production safety. Full article

With the acceleration of urbanization, the increasing frequency of building fires has caused a large number of deaths and economic losses. In order to delve into the evacuation route selection behaviors seen in library fires and analyze the impacts of individual evacuation psychologies on route decisions during escaping, based on practical survey data from the library on the Boda campus of Xinjiang University, this study built a mixed Logit (ML) model irrespective of latent psychological variables and a hybrid choice model (HCM) considering the latent variables of adaptive evacuation psychologies to investigate the internal formation mechanism of evacuees’ route decisions. The results indicate that evacuees’ non-adaptive conformity psychology, adaptive altruism psychology, and environmental familiarity have significant impacts on their route decisions. The stronger the evacuees’ non-adaptive inertia psychology, the more they lean towards the shortest route. Meanwhile, altruistic adaptive evacuation psychology has a significant negative impact on the probability of choosing the longest route. The stronger the evacuees’ environmental familiarity, the more they tend to choose the evacuation route with good emergency lighting. Personal socio-economic attributes have varying impacts on peoples’ evacuation route decisions. The findings of our study provide theoretical support for sustainable planning, preparedness, and the design of fire evacuations. This contribution aids in advancing sustainable practices for emergency responses. Full article

Against a backdrop of globalization, dynamic shifts in consumer demand, and climate change impact, the intricacies of agri-food supply chains have become increasingly convoluted, necessitating innovative measures to guarantee agri-food security and authenticity. Blockchain technology emerges as a promising solution, offering transparency, immutability, traceability, and efficiency in the overall supply chain. This study aims to investigate determinants impacting both the intention to use and the actual usage of blockchain-driven agri-food supply chain platforms. To achieve this, an expanded and adapted conceptual model rooted in the Unified Theory of Acceptance and Use of Technology (UTAUT) was formulated and empirically examined through Partial Least Squares Structural Equation Modeling using data from 175 respondents from agri-food companies across eight European countries. Agri-Food Supply Chain Partner Preparedness (FSCPP) emerged as the pivotal factor with the highest degree of influence on the intention to use blockchain-driven supply chain platforms. Additionally, the results from this study offer support for the significant influence of Performance Expectancy (PE), Effort Expectancy (EE), and Perceived Trust (PT) on usage intention, while also revealing the positive impact of Organizational Blockchain Readiness (OBR) on expected Usage Behavior (UB). This study provides significant insights into blockchain adoption within agri-food supply chains, contributing to the existing literature through an extended UTAUT framework. Full article

Tsunamis are a substantial natural threat in Thailand, as evidenced by the 2004 Indian Ocean tsunami. Effective evacuation is vital to reduce casualties and property damage. However, despite improved warning systems, high death tolls still occur, indicating complex evacuation behavior influenced by various factors. This study examines these factors among diverse groups in Phuket and Phang Nga, Thailand. A survey of 1000 locals, non-local workers, and travelers assesses threat and coping appraisals, past tsunami experiences, gender, age, and tsunami evacuation intention and action selection. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze the data based on the hypotheses related to the Protection Motivation Theory (PMT). The results of the analyses show that threat and coping appraisals significantly predict tsunami evacuation intention, and gender influences threat perception related to evacuation. Variations among respondent types emphasize the need for tailored disaster preparedness and response strategies. This study offers crucial insights for policymakers, emergency responders, and disaster management stakeholders, underlining the significance of further research into the intricate interplay of individual and contextual factors shaping tsunami evacuation behavior. Full article

Background: This study examined where women sought healthcare during the COVID-19 pandemic and their reasons for doing so. We aim to understand further how women accessed care during the COVID-19 pandemic to inform future preparedness and response efforts. This knowledge gained from this study can inform strategies to address existing gaps in access and ensure that women’s health needs are adequately considered during emergencies. Methods: This study used an interpretive phenomenological-analysis approach to analyze data on women’s experiences with healthcare in Nigeria as the COVID-19 pandemic progressed. Semi-structured interviews were conducted with 24 women aged 15 to 49 between August and November 2022 and were supplemented with three focus-group discussions. Results: Following our analysis, three superordinate themes emerged: (i) barriers to seeking timely and appropriate healthcare care, (ii) the influence of diverse health practices and beliefs on health-seeking behavior, and (iii) gendered notions of responsibility and of coping with financial challenges. Conclusions: This paper examined women’s decision to seek or not seek care, the type of care they received, and where they went for care. Women felt that the COVID-19 pandemic affected their decision to seek or not seek care. Full article

This paper describes the results from a study designed to illustrate the use of machine learning analytical techniques from a household consumer perspective. The outcome of interest in this study is a household’s degree of financial preparedness as indicated by the presence of an emergency fund. In this study, six machine learning algorithms were evaluated and then compared to predictions made using a conventional regression technique. The selected ML algorithms showed better prediction performance. Among the six ML algorithms, Gradient Boosting, kNN, and SVM were found to provide the most robust degree of prediction and classification. This paper contributes to the methodological literature in consumer studies as it relates to household financial behavior by showing that when prediction is the main purpose of a study, machine learning techniques provide detailed yet nuanced insights into behavior beyond traditional analytic methods. Full article

Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018–2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research. Full article