Health Crisis Research Articles

Pharmaceutical messianism and the politics of COVID-19 in the United States

ABSTRACT Throughout the COVID-19 pandemic, public officials in the United States – from the President to governors, mayors, lawmakers, and even school district commissioners – touted unproven treatments for COVID-19 alongside, and sometimes as opposed to, mask and vaccine mandates. Utilising the framework of ‘pharmaceutical messianism’, our article focuses on three such cures – hydroxychloroquine, ivermectin, and monoclonal antibodies – to explore how pharmaceuticals were mobilised within politicised pandemic discourses. Using the states of Utah, Texas, and Florida as illustrative examples, we make the case for paying attention to pharmaceutical messianism at the subnational and local levels, which can very well determine pandemic responses and outcomes in contexts such as the US where subnational governments have wide autonomy. Moreover, we argue that aside from the affordability of the treatments being studied and the heterodox knowledge claiming their efficacy, the widespread uptake of these cures was also informed by popular medical (including immunological) knowledge, pre-existing attitudes toward ‘orthodox’ measures like vaccines and masks, and mistrust toward authorities and institutions identified with the ‘medical establishment’. Taken together, our case studies affirm the recurrent nature of pharmaceutical messianism in times of health crises – while also refining the concept and exposing its limitations.

Status of the Children's Socio-Emotional Well-being Before and During the Pandemic: A Scoping Review

Positive mental health thrives on strong social and emotional connections. Cultivating a child's ability to forge close, secure, and meaningful relationships is the cornerstone of well-being. Yet, pandemic-induced isolation has created barriers to face-to-face interaction with friends, posing a significant challenge to this critical developmental task. This scoping review delves into previous research concerning children's socio-emotional well-being before and during the pandemic. We screened studies, focusing solely on those directly addressing the core issues impacting children's emotional landscape. Our findings reveal disturbing trends: the pandemic has amplified anxieties, particularly among vulnerable children. The evidence speaks for itself – this global health crisis has inflicted a negative impact on children's socio-emotional health. In response, researchers have proposed a range of policy recommendations to equip teachers and parents with the tools to recognize and mitigate the pandemic's consequences on children. Among these, ongoing debriefing sessions for teachers can foster understanding and gradually alleviate anxieties that ripple through classrooms. Simultaneously, parents – as children's primary caregivers – must remain informed and offer unwavering support, actively monitoring and addressing anxieties within their homes.

A Comparative Study of the Tourism Carrying Capacity of the State of Baja California between 2019 and 2022

The purpose of this work is to conduct a comparative study of variations in the indicators of the tourism carrying capacity in the state of Baja California. It is crucial to consider that the state had to confront the COVID-19 pandemic, during which tourism was not deemed an essential activity. This circumstance generated numerous social, psychological, and economic effects, primarily. In this regard, the aim is to identify the consequences of organizing events that promote tourism, particularly concerning the opinions of business professionals in the region. This is a qualitative and longitudinal study; the initial phase took place in May 2019, while the second survey occurred in the summer of 2022. The statistical sample is non-probabilistic and based on convenience, comprising 320 tourism businesses. The findings indicate that the tourist destinations remained appealing, experiencing inflows just above the average and approaching their capacity limits. Significantly, there are areas for improvement in terms of their tourist load capacities across each of the dimensions studied, despite the global health crisis.

Modeling the transmission dynamics of COVID-19 with genetically resistant humans

The emergence of a coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-COV 2) has caused an unprecedented global societal and public health crisis. A mathematical model that incorporates an individual’s ability to resist COVID-19 infection is considered to study the dynamics of COVID-19. Detection and testing for COVID-19 in individuals who has ability to resist the infection, proper preventive measures, and timely medical care are essential in managing and controlling COVID-19 outbreak. These allows us to make quantifiable predictions about the effects of testing utilization on the COVID-19 prevalence. First, we analyze the disease-free state of the COVID-19 model, calculate the baseline control reproduction number, and obtain the local stability of disease-free equilibrium. The model is calibrated using data obtained from Nigeria Centre for Disease Control and key parameters of the model are estimated. Sensitivity analysis is carried out to investigate the influence of the parameters in curtailing the disease. Numerical simulations are then used to explore the behavior of the model solutions involving the novel variable (genetically resistant humans) and control measures. Our results suggest that strict intervention effort (early detection of genetically resistant individuals, testing and isolation of the infected individuals) is required for quick suppression of the disease.

Bacterial etiologies, antimicrobial susceptibility pattern and associated factors among patients suspected sterile body site infections at Debre Markos Comprehensive Specialized Hospital, Northwest Ethiopia.

Sterile body locations are usually associated with clinical urgency and life-threatening illnesses, and they are typically contaminated with diverse bacterial etiologies. If the bacteria acquire resistance to antimicrobial drugs, the public health crisis will only worsen. In developing countries, drug-resistant bacteria are common because of poor surveillance, diagnostic capacity, and control measures. Early diagnosis, and assessing the drug resistance and factors associated with infection are important to combat the drug resistance and treatment. This study aimed to assess the bacterial etiologies, antimicrobial susceptibility pattern, and possible associated factors among patients suspected of sterile body sites. A hospital-based cross-sectional study was conducted from June 2022 to August 2022 at Debre Markos Comprehensive Specialized Hospital in Amhara regional state, Ethiopia. One hundred seven study participants were selected using consecutive convenient sampling techniques. A structured questionnaire was used to collect socio-demographic and clinical data. Gram stain was done for a preliminary report and inoculated into blood agar, MacConkey agar, and chocolate agar and incubated aerobically and micro aerobically at 37°C for 24 h. Antimicrobial susceptibility testing was done by the modified Kirby Bauer's disk diffusion method. Data were analyzed using bivariate and multivariate logistic regression was used. A p-value less than 0.05 is considered as statistically significant. The overall magnitude of sterile body site infection among study participants was 7.5% (14/187). The majority of the isolates were Gram-negative bacteria with the predominant species Enterobacter cloacae accounting for 28.57% (4/14). Among isolates 78.57%(11/14) of them were multidrug-resistant isolates. Being inpatient, co-morbidity, and alcohol consumption were significantly associated with sterile body site infection. In our study, Gram-negative bacteria were the predominant bacteria that infects sterile body fluid. The prevalence of multi-drug resistance bacteria isolates was significantly high. Therefore, before prescribing an empirical treatment, a medical professional should identify the bacterial etiology of sterile body fluids and the susceptibility of microbes to the drug.

Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.

During the COVID-19 pandemic, Twitter (recently rebranded as "X") was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication. This study aimed to examine pediatric cancer-related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic. All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets. A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care. Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer-related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.

Economic challenges faced by migrant street vendors during a crisis: implications for social inclusion development

PurposeInformal migrant workers and street vendors have long been recognized as vulnerable groups in urban areas of Global South countries. However, limited studies exist on the economic challenges faced by migrant street vendors during crises. We aim to address this gap by shedding light on their livelihood and welfare losses during a public health crisis.Design/methodology/approachThis research uses descriptive and qualitative analyzes to triangulate the results. Data are derived from surveys and in-depth interviews with migrant street vendors in the two biggest cities in Vietnam during the COVID-19 pandemic.FindingsThe street vendors experienced significant business loss and consumption reduction during social distancing as well as encountered difficulties in recovering their businesses in the “new normal.” These adverse consequences were also found to disproportionately affect women vendors. Additionally, despite adopting various strategies and mitigation mechanisms to sustain their businesses and consumption, these efforts proved insufficient.Social implicationsThis research underscores the importance of short-term and long-term urban policies aimed at supporting and promoting the social inclusion of street vendors, particularly migrant and women vendors.Originality/valueThis research represents one of the early attempts to explore the adverse effects of a public health crisis on migrant street vendors and to examine whether the crisis disproportionately affected vendors from different genders and educational backgrounds. It also examines their business recovery in the “new normal.”

Machine learning prediction of health risk and spatial dependence of geogenic contaminated groundwater from the Hetao Basin, China

Geogenic contaminated groundwater (GCG), characterized by elevated arsenic, fluoride, and iodine levels, present a significant challenge to public health and government management. Conventional survey-based approaches of collecting groundwater samples, conducting physicochemical tests, and performing spatial interpolation to obtain regional groundwater chemical component maps are inefficient and costly. More importantly, it does not take into account the actual hydrogeological conditions or the characteristics of pollutant transport and enrichment. To address this issue, we utilized Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to analyze the likelihood of occurrence of arsenic, fluoride, and iodine as well as their spatial distribution in shallow groundwater from the Hetao Basin. Our study incorporated 20 indicators related to meteorology, soil physicochemical properties, and groundwater conditions, along with 1505 labeled samples consisting of groundwater arsenic, fluoride, and iodine concentrations and their corresponding coordinates. Subsequently, the study automatically analyzed the meteorological, soil physicochemical properties and groundwater conditions by constructing a machine learning model using the available data. In order to optimise and select the best prediction model, this paper presents a quantitative evaluation of the prediction performance of various machine learning models. The accuracy (AC), area under curve (AUC) and mean squared error (MSE) were calculated to predict the spatial distribution of CGC. Subsequently, the optimized model for predicting the spatial distribution of GCG was selected. The results showed that the XGBoost algorithm provided optimal predictions for groundwater with arsenic concentrations above 10 μg/L and fluoride concentrations exceeding 1.5 mg/L, whereas the RF model provided the best predictions for groundwater with arsenic concentrations surpassing 50 μg/L and iodine concentrations exceeding 100 μg/L. Subsequently, groundwater health risk zones were delineated based on an optimal prediction model, and demographic analysis was conducted in both the direct and potential groundwater risk zones. Model predictions indicated that hundreds of thousands of people in the Hetao Basin were facing a public health crisis caused by high concentrations of arsenic, fluoride and iodine in groundwater. These findings underscore the significant health challenge in the study area. Considering the agricultural development and increasing groundwater use in the area, our findings can guide local governments in managing the extent of groundwater development, establishing control zones, and enhancing protection measures for populations at risk from groundwater contamination.

Financial performance of mutual and pension funds focused on the natural resources sector

The natural resource sector plays a critical role in advancing sustainable development. However, fossil fuels, precious metals, and mineral extraction require large investments, which mutual and pension funds may assume when allowing fund managers to fulfil their fiduciary duties. This study examines the financial performance of conventional, natural resource, energy, and precious metal-related mutual and pension funds, considering the effects of the COVID-19 pandemic. We adopted a five-factor model with a sample of 42 natural resource funds, 45 energy funds, 45 precious metal funds, and 2172 conventional funds from January 2016 to December 2022. The results indicate that driven by fund managers’ reverse stock-picking skills, the precious metal fund category reaches the lowest mean risk-adjusted return in relation to the energy, natural resources, and conventional fund categories. Additionally, the COVID-19 global health crisis negatively affected the mean risk-adjusted returns on the energy, precious metals, and conventional funding categories. A similar effect is observed in the natural resource pension fund category, while its matching mutual fund category achieves similar financial performance during the non-crisis and crisis periods.

The First Two Years of COVID-19 Hospitalization Characteristics and Costs: Results from the National Discharge Registry.

The COVID-19 pandemic has emerged as the primary global health challenge of the new millennium. Understanding its impact on health systems and learning from these experiences are crucial for improving system resilience against future health crises. This paper examines hospitalizations related to COVID-19 in Italy from 2020 to 2021, with a specific focus on the costs associated with these admissions. This is a retrospective, population-based study of Italian hospitalizations of patients diagnosed with COVID-19 during the 2020-2021 period, using data extracted from the National Hospital Discharge Registry. The outcome variables considered include hospital admissions, costs, and length of stay. In Italy, hospitalizations for COVID-19 totaled 357,354 in 2020 and 399,043 in 2021, with the transfer rate being three times higher than that of other patients. Hospitalizations were predominantly concentrated in the northern regions, especially during the first year. Mortality rates increased with age, while hospitalization rates peaked in the youngest and oldest age groups. The financial impact of COVID-19 hospitalizations was approximately €3.1 billion in 2020 and €3.6 billion in 2021. The cost per admission was around €8000 for standard care and €24,000 for intensive therapy in both years. Conducting a cost-benefit analysis of implementing a protective pad around the entire health system, which leverages networks of family doctors and nurses connected in real-time, could be an important step in strengthening health system resilience.

Algorithmic Doors to Community and the Trap of Visibility: TikTok for Harm Reduction Activism in the U.S. Overdose Crisis

Opioid-related overdose death continues to be a public health crisis in the United States, reaching a new peak in 2021 with more than 100,000 people dying of drug overdose; 75% of these deaths involved an opioid. Naloxone, often known by the brand name Narcan®, can be easily administered to prevent opioid overdose death. While naloxone is a central harm reduction tool, it has generated controversy as discussions of its use have been framed by longstanding stigmas associated with drug use. Critics have framed it as a “moral hazard” that encourages drug use, stigmatizing its distribution and uptake. Harm reductionists have responded by countering misinformation about drug use and harm reduction and humanizing people who use drugs. Social media platforms have become key sites for these debates. These are digital social spaces in which individuals may fuel stigma and/or build community with others by enacting identities, exchanging knowledge, and bonding over shared experiences. TikTok, a video-based platform, has become an active space for community-building around harm reduction. This article examines the experiences of people who post about naloxone on TikTok. We draw on thematic analyses of semistructured, open-ended interviews with 13 TikTok users who tagged posts with #naloxonesaveslives or #narcansaveslives hashtags from June 2020 to April 2023. Specifically, we explore how these individuals understand and leverage TikTok's association, creative, and interactive affordances as “doors” to build harm reduction community and to educate others about drug use and harm reduction. We also explore how they navigate the “traps” of visibility on TikTok that pose challenges to using the platform for harm reduction activism: stigma, burnout, mental distress, and digital silencing. Finally, we consider the implications of our findings for future research and practice related to digital harm reduction activism.

Developing and Implementing a Patient-Centered Opioid Prescribing Algorithm among Gynecological Oncology Patients.

Background: The opioid epidemic is a public health crisis. However, opioid prescription recommendations have not been established in gynecological oncology, and guidelines that incorporate patient-reported pain are lacking. Objectives: The article aims to evaluate prescribing patterns, utilization, and patient-reported pain control in gynecological oncology patients at a large tertiary academic center. Methods: This was a two-phase, prospective cohort study. For Phase 1, patients undergoing hysterectomy through the gynecological oncology division at the University of New Mexico were enrolled. Postoperative opioid use was collected and standardized to oral morphine milligram equivalents (MMEs). The factors associated with outpatient opioid use were used to develop an opioid prescription algorithm. In Phase 2, we evaluated the implementation of the prescription algorithm. For both phases, patients completed a demographic survey, satisfaction survey, and validated pain questionnaires. Results: In Phase 1, the amount of opioids used was significantly lower than the amount of opioids prescribed. Factors that correlated with postoperative opioid use included surgical procedures and last 24-hour inpatient MME use. A standardized opioid prescription algorithm was developed by incorporating these factors. In Phase 2, the opioid prescribing algorithm there was no significant difference in pain scores between the two phases. Conclusions: Opioids were substantially overprescribed in gynecological oncology patients undergoing hysterectomy. Our study found that the surgical route and last 24-hour MME inpatient usage were reliable predictors of outpatient opioid use. We developed and implemented a standardized opioid prescription algorithm that was validated by comparing the pain control measures in the two phases.

Bridging the gap or widening the divide? Exploring the dual role of social capital in healthcare financing in rural Uttar Pradesh, India

Social capital, defined as the nature of the social relationship and the resources embedded within the social network of an individual or community, influences how individuals within a group interact and collaborate within their communities or organizations. While it is acknowledged that social capital can be drawn from as a coping strategy to mitigate financial stress, there is a notable absence of the lived experience in the literature on how social capital influences households to tap resources from their social network. We have investigated the role of social capital in healthcare financing in rural Uttar Pradesh, India, highlighting the challenges faced by households in managing healthcare expenses. We took a qualitative research approach, conducting in-depth interviews with 24 households in the Hardoi District of Uttar Pradesh in August 2017 to explore participants’ lived experience of accruing support from their community during their healthcare crisis. Data analysis followed a thematic content analysis approach. The study finds that households leverage social capital for both financial and non-financial support during health crises. Social networks, trust, and community cohesion play critical roles in resource acquisition. However, overreliance on social capital can be coercive, leading to inequity, privacy invasion, and dependency. Though social capital serves as a crucial resource of support in healthcare emergencies, its unequal distribution and potential for misuse highlight the need for more structured health financing policies in India. The findings underscore the importance of integrating community-driven resources into broader health financing strategies, considering local social structures and community dynamics.

Engaging communities as partners in health crisis response: a realist-informed scoping review for research and policy

BackgroundHealth is increasingly affected by multiple types of crises. Community engagement is recognised as being a critical element in successful crisis response, and a number of conceptual frameworks and global guideline documents have been produced. However, little is known about the usefulness of such documents and whether they contain sufficient information to guide effective community engagement in crisis response. We undertake a scoping review to examine the usefulness of conceptual literature and official guidelines on community engagement in crisis response using a realist-informed analysis [exploring contexts, mechanisms, and outcomes(CMOs)]. Specifically, we assess the extent to which sufficient detail is provided on specific health crisis contexts, the range of mechanisms (actions) that are developed and employed to engage communities in crisis response and the outcomes achieved. We also consider the extent of analysis of interactions between the mechanisms and contexts which can explain whether successful outcomes are achieved or not.Scope and findingsWe retained 30 documents from a total of 10,780 initially identified. Our analysis found that available evidence on context, mechanism and outcomes on community engagement in crisis response, or some of their elements, was promising, but few documents provided details on all three and even fewer were able to show evidence of the interactions between these categories, thus leaving gaps in understanding how to successfully engage communities in crisis response to secure impactful outcomes. There is evidence that involving community members in all the steps of response increases community resilience and helps to build trust. Consistent communication with the communities in time of crisis is the key for effective responses and helps to improve health indicators by avoiding preventable deaths.ConclusionsOur analysis confirms the complexity of successful community engagement and the need for strategies that help to deal with this complexity to achieve good health outcomes. Further primary research is needed to answer questions of how and why specific mechanisms, in particular contexts, can lead to positive outcomes, including what works and what does not work and how to measure these processes.

Beyond Obesity and Overweight: The Clinical Assessment and Treatment of Excess Body Fat in Children : Part 1 - Insulin Resistance as the Root Cause of Pediatric Obesity.

Pediatric obesity and comorbidities related to insulin resistance continue to be a growing public health crisis. If lifestyle measures are unsuccessful, pharmacological and surgical interventions are offered. In this paper, we describe the driving force of the obesity crisis: hyperinsulinemia and the development of insulin resistance. We give historical background of key policy issues which have contributed to this pandemic as well as the physiologic mechanisms of insulin resistance. The prevalence of obesity will continue to rise unless the root cause of hyperinsulinemia is addressed. Current research on insulin resistance demonstrates that a decreased consumption of carbohydrates is an effective first-line dietary intervention for the treatment of obesity and related metabolic diseases. Evidence shows it is safe and beneficial. A low-carbohydrate eating pattern can be helpful to address pediatric obesity. However, there must be policy guardrails in place to ensure that this is a sustainable and viable option for children and their families. There must be a change in the nutritional environment to help individuals battle the chronic disease of obesity.

Beyond Obesity and Overweight: the Clinical Assessment and Treatment of Excess Body Fat In Children : Part 2 - the Prescription of Low-Carbohydrate Eating as the First Approach.

Pediatric obesity and comorbidities related to insulin resistance continue to be a growing public health crisis. If lifestyle measures are unsuccessful, pharmacological and surgical interventions are offered. In this paper, we describe the driving force of the obesity crisis: hyperinsulinemia and the development of insulin resistance. We give historical background of key policy issues which have contributed to this pandemic as well as the physiologic mechanisms of insulin resistance. The prevalence of obesity will continue to rise unless the root cause of hyperinsulinemia is addressed. Current research on insulin resistance demonstrates that a decreased consumption of carbohydrates is an effective first-line dietary intervention for the treatment of obesity and related metabolic diseases. Evidence shows it is safe and beneficial. A low-carbohydrate eating pattern can be helpful to address pediatric obesity. However, there must be policy guardrails in place to ensure that this is a sustainable and viable option for children and their families. There must be a change in the nutritional environment to help individuals battle the chronic disease of obesity.

Food Sales Activities Associated to Family Survival Strategies During Covid-19 Lockdown in Brazzaville, Congo

The COVID-19 pandemic has a negative impact on people's living conditions, particularly in sub-Saharan Africa in vulnerable and low-income families. The aim of this work is to analyze the socio-demographic profile of people who engaged in food sales activities during the COVID-19 lockdown in Brazzaville, as well as their coping and survival strategies. The study, which used a mixed (qualitative and quantitative) approach, included 716 people (241 men and 475 women) aged 18 to 72 years residing in four communes in the southern zone of Brazzaville, Republic of Congo. The main study inclusion criterion was the practice of selling food during COVID-19 lockdown. However, we did not include people whose profession had been selling these products prior to the health crisis. The qualitative data collected during the interviews concerned information on vendors, sales locations, coping strategies used to find the products sold, and sales prices. As for the qualitative data assessed using a food questionnaire, they were summarized in terms of the quality and quantity of food consumed, which made it possible to assess daily energy intake and therefore the level of food security. The results showed that the sellers, with an average age of 42.7, had a relatively low level of education and lived in a medium-sized family (8). Of these, 21.8% were food insecure. Foodstuffs sold were dominated by miscellaneous products (48.2%), street foods (26.7%), ready-made meals (22.3%), cassava flour (17.4%) and fruit and vegetables (7.5%). Salted fish, smoked fish and frozen (imported) meat products were also sold. As far as prepared dishes were concerned, grilled meats of various kinds were the most popular. Sales outlets included state markets, neighborhood markets, storefronts and street markets for auctions. The use of rickshaws and wheelbarrows by a few money-seeking youngsters enabled the products purchased to be transported from the places of purchase to the various markets or sales sites. In short, given the absence of an effective distribution and marketing policy in the food system in times of crisis in the Congo, our results call into question the resilience of vulnerable populations in the face of the country's sociosanitary crises.

From isolation to revival: trade recovery amid global health crises

BackgroundThe COVID-19 pandemic has highlighted the importance of designing effective trade recovery measures in response to global health events (GHEs). This study combines international trade risk management theory and multi-case comparative analysis of past GHEs to present a theoretical framework for designing national trade recovery measures for future events.ResultsThe research finds that during GHEs, trade risks shift to fundamental uncertainty, requiring spatial–temporal-subject dimension recovery measures. The study suggests changing the focus of trade recovery policy design from emergency-oriented and single-dimension measures to reserve-oriented and enduring-effect measures of comprehensive dimensions at micro- and macroeconomic levels.ConclusionThe study contributes to the debate on managing trade risks in times of crisis, where there is a need to develop effective trade recovery measures that account for the complexities of global trade and the unique challenges of GHEs. The findings provide practical guidance for trade officials and policymakers to design measures in response to GHEs to improve a country’s overall trade recovery.

A psychometric assessment of a novel scale for evaluating vaccination attitudes amidst a major public health crisis

Despite abundant scientific evidence supporting immunization benefits, vaccine hesitancy remains a significant global health concern, particularly during public health crises. Exploring public attitudes towards vaccination is crucial. This study aimed to develop and validate a tailored Public Vaccination Attitudes Scale specifically under the unique circumstances of a public health crisis. A psychometric evaluation was conducted using a cross-sectional study during the peak of a major public health crisis. The scale was developed and its psychometric properties validated using three approaches: (1) generating the item pool through literature research and focus group discussions; (2) assessing the items through expert consultation; and (3) evaluating construct validity, content validity, and internal consistency reliability through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Data from a total of 3921 respondents were randomly divided into two subsets, one for EFA (n = 1935) and the other for CFA (n = 1986). A 22-item draft scale with five factors was created after literature research and focus group discussion. The content validity of this scale ranged between 0.88 and 1.00. EFA showed a 17-item scale with four factors (Cronbach’s α > 0.7) accounting for 68.044% of the total variance. CFA showed that the values of the fit indices, including convergent validity and discriminant validity, were excellent or acceptable. The overall Cronbach’s α was 0.874, and each factor ranged from 0.726 to 0.885. This study introduces a valuable tool for assessing vaccination attitudes during public health crises, aiding researchers, policymakers, and nurses in combating vaccine hesitancy. Emphasizing the importance of fostering vaccine acceptance, it enhances disease control during emergencies, contributing to the knowledge needed for more effective public health strategies and crisis responses

Spatiotemporal Variations of COVID-19 Variants in 100 Countries: Exploring Spatial Patterns and Time-Lag Effects

Learning from the COVID-19 pandemic is vital for future global health crises. Forecasting pandemic spread across countries in the presence of viral mutations, such as SARS-CoV-2 variant strains, remains a challenge. Previous studies indicate spatial and temporal regularities in SARS-CoV-2 variant distribution, yet limitations persist: (1) static geographical patterns overlook dynamic changes and their causes, and (2) the association between temporal variations in SARS-CoV-2 lineages and epidemic outbreaks is underexplored. To address these gaps, we propose an analysis framework using more than 10 million SARS-CoV-2 genome sequence metadata from 100 countries. Our framework identifies spatial heterogeneity patterns in the relative frequency of SARS-CoV-2 variants through clustering, examines factors influencing spatial heterogeneity using explainable machine learning, and analyzes the time lag effect of temporal variation on COVID-19 infection waves based on spatial patterns. Our findings demonstrate the following: (1) The distribution of SARS-CoV-2 variant strains among 100 countries exhibits a spatial pattern characterized by geographic proximity, with observed dynamic changes. (2) Bilateral distance consistently dominates, and factors related to globalization and response policy contribute to nongeographic proximity and temporal variation of the spatial pattern. (3) Leveraging this spatial pattern allows for the forecast of time lags (mainly ranging from twenty to thirty-eight days) between the emergence of new strains and subsequent infection waves. Our study clarifies the spatial pattern and time lag effect of the dynamic distribution of SARS-CoV-2 variant strains. It offers a spatial basis and temporal reference for early warning in future global public health crises from a geographical perspective.