Online journal of public health informatics最新文献

Background: Disaster medicine education increasingly emphasizes situational awareness and a proactive disaster mindset as crucial competencies for effective response. Situational awareness involves comprehending the disaster environment to make informed decisions under pressure, while a disaster mindset encompasses psychological resilience and effective functioning amid chaos. Integrating technologies into simulation training allows experiential learning that bridges these theoretical concepts with practical application.

Objective: This study aims to investigate the current status of teaching these concepts and the use of technology in fostering situational awareness and a disaster mindset within disaster medicine education by reviewing the existing literature.

Methods: This study used a scoping review of scientific studies (2005-2025), obtained from PubMed, Scopus, and Web of Science databases, complemented by a Google Scholar search. From December 1, 2024, to the end of January 2025, 3 reviewers searched, compiled, reviewed, and selected eligible studies in English, discussing the use of technology in fostering situational awareness and disaster mindset.

Results: Out of 217 initially identified records, 49 studies met the inclusion criteria after a 2-stage screening and full-text review process. Of these, 42 were peer-reviewed scientific articles and 7 were official documents. Approximately 86% (42/49) of the studies addressed situational awareness, while only 2% (1/49) explicitly focused on the concept of disaster mindset. Most of the included studies highlighted the use of immersive technologies such as virtual and augmented reality, geographic information systems, and artificial intelligence-driven tools to enhance real-time information processing and decision-making in disaster education contexts. By strategically incorporating these advanced tools into educational frameworks, the divide between theoretical knowledge and practical application can effectively be bridged, fostering essential experiential learning and developing robust psychological readiness for future challenges.

Conclusions: Simulation training enhances situational awareness and disaster mindset, bridging the gap between theory and practice through experiential learning. The findings from this review highlight current pedagogical approaches and technological applications, identifying gaps and future directions for enhancing disaster medicine education.

Background: With the increasing use of social media, platforms like Twitter (rebranded as X in 2023) have become popular channels for disseminating health information. In Saudi Arabia, Twitter is widely used, making it an effective tool for health awareness. However, the accuracy of nutrition-related content on social media is often questioned.

Objective: The study aims to evaluate the accuracy and evidence-based quality of nutrition-related tweets posted by reputable Saudi health and nutrition awareness providers.

Methods: A mixed methods content analysis was conducted on tweets published by 7 Saudi health organizations, examining content in Arabic and English over 12 months. Nutrition-related tweets were analyzed for accuracy, popularity, and evidence inclusion by a panel of experts in clinical nutrition, food science, and technology.

Results: A total of 531 nutrition-related tweets were included in the study. Findings indicate that 445 (84%) of the tweets were accurate, of which only 17 (4%) included cited evidence. Yet, only 13 (2%) were inaccurate. The highest number of tweets are from Saudi Food and Drug Administration (SFDA) 96 (18%), Gulf Health Council (GHC) 91 (17%), Saudi Society for Clinical Nutrition (SSCN) 89 (16%), Kayl Association for Combating Obesity 83 (16%) and National Nutrition Committee (NNC) 80 (15%) and the lowest is Ministry of Health (MOH) 31 (5%). Significant relationships were observed between tweet accuracy and the source organization (P=.009, 95% CI 0.008-0.01), content type (P=.03, 95% CI 0.03-0.03), and tweet timing (P=.04, 95% CI 0.04-0.04). Governmental sources had higher popularity and were more frequently accurate compared to nongovernmental sources.

Conclusions: Reputable Twitter accounts in Saudi Arabia generally provide accurate nutrition-related content, though evidence citation is minimal. Users are encouraged to rely on reputable accounts for health information, and further research is suggested to explore the quality of evidence in such posts.

Unlabelled: This viewpoint highlights the critical need for proactive and strategic integration of digital health tools into heat-health action plans (HHAPs) across Europe. Drawing insights from the digital health surge during the COVID-19 pandemic and recent heat-related health impacts, we identify response gaps and suggest specific strategies to strengthen current plans. Key recommendations include leveraging mobile health communication, expanding telemedicine usage, adopting wearable health monitoring devices, and using advanced data analytics to improve responsiveness and equity. This perspective aims to guide policymakers, health authorities, and health care providers in systematically enhancing heat-health preparedness through digital health innovation.

Background: Threats to data integrity have always existed in online human subjects research, but it appears these threats have become more common and more advanced in recent years. Researchers have proposed various techniques to address satisficers, repeat participants, bots, and fraudulent participants; yet, no synthesis of this literature has been conducted.

Objective: This study undertakes a scoping review of recent methods and ethical considerations for addressing threats to data integrity in online research.

Methods: A PubMed search was used to identify 90 articles published from 2020 to 2024 that were written in English, that discussed online human subjects research, and that had at least one paragraph dedicated to discussing threats to online data integrity.

Results: We cataloged 16 types of techniques for addressing threats to online data integrity. Techniques to authenticate personal information (eg, videoconferencing and mailing incentives to a physical address) appear to be very effective at deterring or identifying fraudulent participants. Yet such techniques also come with ethical considerations, including participant burden and increased threats to privacy. Other techniques, such as Completely Automated Public Turing test to tell Computers and Humans Apart (reCAPTCHA; Google LLC), scores, and checking IP addresses, although very common, were also deemed by several researchers as no longer sufficient protections against advanced threats to data integrity.

Conclusions: Overall, this review demonstrates the importance of shifting online research protocols as bots and fraudulent participants become more sophisticated.

Background: Nigeria has made significant investments in client-level electronic health systems, including the Nigeria Medical Record System (NMRS) and the National Data Repository (NDR), with funding from the US President's Emergency Plan for AIDS Relief through the US Centers for Disease Control and Prevention (US CDC). A biometric system was used across the US CDC-supported program in Nigeria to consistently track and monitor service uptake by people living with HIV during this period. The system was used to conduct deduplication analysis with the goal of preventing double counting and improving data integrity across all the US CDC-supported treatment sites (health facilities and community sites).

Objective: We describe the fingerprint biometric system in Nigeria and the process used for deduplicating health records of people living with HIV, including preliminary results.

Methods: The fingerprint biometric system leveraged the availability of the electronic NMRS at health facilities and the NDR. The integration of the fingerprint biometric module into the NMRS enabled fingerprints capture using SecuGen devices. Stakeholder engagement and capacity building were conducted with people living with HIV and health facility staff for fingerprint capture, storage, and transmission of the fingerprint templates to the NDR. Deduplication of the fingerprint templates was conducted in the automated biometric information system that is integrated with the NDR.

Results: We implemented fingerprint capture for 1,538,971 people living with HIV to deduplicate records from 1,141 treatment sites to improve the reliability and uniqueness of the system of records. Preliminary data showed that of the 1,538,971 records assessed by 30th June 2024, 1,520,187 of the active records (98.78%) had valid fingerprints, and 1,264,299 (83.17%) of the records with valid fingerprints were unique.

Conclusions: The implementation of a biometric system using fingerprint data allowed the identification of potentially duplicate records for resolution, thereby improving the quality of HIV treatment data for HIV program planning.

Background: The COVID-19 pandemic continues to offer valuable insights into crisis management and risk communication, particularly through retrospective analyses that allow a more comprehensive understanding. Emotional responses played a crucial role in shaping how individuals processed information and built trust in different objects in the early stages of the COVID-19 pandemic.

Objective: This study aimed to investigate how negative emotions influence online information engagement and trust in 4 distinct entities: government, scientists, health care providers, and other people (relatives, friends, family, and strangers).

Methods: A nationwide survey was conducted in China from January 31 to February 9, 2020, involving 1568 adult participants. The data collection was particularly valuable due to the limited access to national samples in China during the early stages of the public health crisis. Participants were asked questions related to negative emotions, engagement with online information, and their trust in 4 different entities (government, scientists, other people, and health care providers) during the pandemic. Mediation analyses were performed to test the associations between the examined variables. A 95% bootstrap CI approach was used to estimate the mediation effects.

Results: This study reveals that negative emotions not only had a direct effect on trust but also indirectly fostered trust in the government and scientists through increased information engagement. There was a positive association (B=0.219, SE 0.023; P<.001) between negative emotions and information engagement. In addition, individuals experiencing more negative emotions tended to trust more in the government (B=0.191, SE 0.022; P<.001) and scientists (B=0.184, SE 0.017; P<.001). However, this effect did not extend to trust in health care providers or interpersonal trust.

Conclusions: The research findings reveal that while negative emotions directly and indirectly enhanced trust in the government and scientists through increased information engagement, they did not significantly impact trust in health care providers or interpersonal relationships in the Chinese context. These findings highlight the different pathways through which emotions and information behaviors affect trust during public health crises, offering critical lessons for future public health emergencies and risk communication.

Background: Neonatal disease and its outcomes are important indicators for a responsive health care system and encompass the effects of socioeconomic and environmental factors on new-borns and mothers. Ethiopia is working to achieve the Sustainable Development Goal target for the reduction of 12 or less per 1000 birth by 2030 and 21 per 1000 livebirths by 2025 as part of the second Ethiopian Health Sector Transformation Plan.

Objective: This study aimed to compare the performance of classical time-series models with that of deep learning models and to forecast the neonatal mortality rate in Ethiopia to verify whether Ethiopia will achieve national and international targets.

Methods: Data were extracted from the official World Bank database. Classical time-series models, such as autoregressive integrated moving average (ARIMA) and double exponential smoothing, and neural network-based models, such as multilayer perceptron, convolutional neural network, and long short-term memory, have been applied to forecast neonatal mortality rates from 2021 to 2030 in Ethiopia. During model building, the first 21 years of data (from 1990 to 2010) were used for training, and the remaining 10 years of data were used to test model performance. Model performance was evaluated using R², mean absolute percentage error (MAPE), and root mean squared error (RMSE). Finally, the best model was used to forecast the neonatal mortality rate over the next 10 years from 2021 to 2030, with a 95% prediction interval (PI).

Results: The results showed that the double exponential smoothing model was the best, with a maximum R2 of 99.94% and minimum MAPE and RMSE of 0.002 and 0.0748, respectively. The worst performing among the 5 models was the CNN, with an R2 of 93.71% and a maximum RMSE of 0.79. Neonatal mortality in Ethiopia is forecasted to be 23.20 (PI 22.20-24.40) per 1000 live births in 2025 and 19.80 (PI 17.10-22.80) per 1000 live births in 2030.

Conclusions: This study revealed that national and international targets for neonatal mortality cannot be realized if the current trend continues. This highlights the need for urgent interventions to strengthen the health system to fasten the decline rate of neonatal mortality and collaborative effort with concerned stakeholders for improved and responsive neonatal and child health services in order to achieve these targets.

Unlabelled: The COVID-19 pandemic accelerated the adoption of remote consultations across health care, requiring rapid adjustments in service delivery. Consequently, there is an urgent need to understand the impact of remote consultations on health pathways. This viewpoint paper explores key challenges in data sources in England that hinder research on the impact of remote consultations on health outcomes. Based on our experience conducting research on this topic, we present variations in observational study findings and their validity, considering differences in population characteristics and data sources. We provide recommendations to enhance data quality for future research, including improvements in data recording platforms and strengthened structures for linking primary and secondary care electronic health records.