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BACKGROUND: Nursing students' learning during clinical practice is largely influenced by the quality of the guidance they receive from their nurse preceptors. Students that have attended placement in nursing home settings have called for more time with nurse preceptors and an opportunity for more help from the nurses for reflection and developing critical thinking skills. To strengthen students' guidance and assessment and enhance students' learning in the practice setting, it has also been recommended to improve the collaboration between faculties and nurse preceptors. OBJECTIVE: This study explores first-year nursing students' experiences of using the Technology-Optimized Practice Process in Nursing (TOPP-N) application in 4 nursing homes in Norway. TOPP-N was developed to support guidance and assessment in clinical practice in nursing education. METHODS: Four focus groups were conducted with 19 nursing students from 2 university campuses in Norway. The data collection and directed content analysis were based on DeLone and McLean's information system success model. RESULTS: Some participants had difficulties learning to use the TOPP-N tool, particularly those who had not attended the 1-hour digital course. Furthermore, participants remarked that the content of the TOPP-N guidance module could be better adjusted to the current clinical placement, level of education, and individual achievements to be more usable. Despite this, most participants liked the TOPP-N application's concept. Using the TOPP-N mobile app for guidance and assessment was found to be very flexible. The frequency and ways of using the application varied among the participants. Most participants perceived that the use of TOPP-N facilitated awareness of learning objectives and enabled continuous reflection and feedback from nurse preceptors. However, the findings indicate that the TOPP-N application's perceived usefulness was highly dependent on the preparedness and use of the app among nurse preceptors (or absence thereof). CONCLUSIONS: This study offers information about critical success factors perceived by nursing students related to the use of the TOPP-N application. To develop similar learning management systems that are usable and efficient, developers should focus on personalizing the content, clarifying procedures for use, and enhancing the training and motivation of users, that is, students, nurse preceptors, and educators.

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In recent times, increased geogenic and human-centric activities have caused significant heavy metal(loid) (HM) contamination of soil, adversely impacting environmental, plant, and human health. Phytoremediation is an evolving, cost-effective, environment-friendly, in situ technology that employs indigenous/exotic plant species as natural purifiers to remove toxic HM(s) from deteriorated ambient soil. Interestingly, the plant's rhizomicrobiome is pivotal in promoting overall plant nutrition, health, and phytoremediation. Certain secondary metabolites produced by plant growth-promoting rhizobacteria (PGPR) directly participate in HM bioremediation through chelation/mobilization/sequestration/bioadsorption/bioaccumulation, thus altering metal(loid) bioavailability for their uptake, accumulation, and translocation by plants. Moreover, the metallotolerance of the PGPR and the host plant is another critical factor for the successful phytoremediation of metal(loid)-polluted soil. Among the phytotechniques available for HM remediation, phytoextraction/phytoaccumulation (HM mobilization, uptake, and accumulation within the different plant tissues) and phytosequestration/phytostabilization (HM immobilization within the soil) have gained momentum in recent years. Natural metal(loid)-hyperaccumulating plants have the potential to assimilate increased levels of metal(loid)s, and several such species have already been identified as potential candidates for HM phytoremediation. Furthermore, the development of transgenic rhizobacterial and/or plant strains with enhanced environmental adaptability and metal(loid) uptake ability using genetic engineering might open new avenues in PGPR-assisted phytoremediation technologies. With the use of the Geographic Information System (GIS) for identifying metal(loid)-impacted lands and an appropriate combination of normal/transgenic (hyper)accumulator plant(s) and rhizobacterial inoculant(s), it is possible to develop efficient integrated phytobial remediation strategies in boosting the clean-up process over vast regions of HM-contaminated sites and eventually restore ecosystem health.

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BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death worldwide, particularly affecting low- and middle-income countries. Food environments may be linked with the risk of CVD; however, current study findings regarding their relationship are inconsistent. A systematic review of their associations is needed to guide interventions to improve cardiovascular health. OBJECTIVE: This systematic review aimed to comprehensively assess the relationship between food environments and CVD outcomes, including incidence, hospitalization, mortality, and recurrence rates. METHOD: According to PRISMA guidelines, a systematic search was conducted until 28th March 2024, using eight databases, including PubMed, Embase, Ovid, CINAHL, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang Data. The review quality was assessed according to the Agency for Healthcare Research and Quality (AHRQ) and Newcastle-Ottawa Scale (NOS). The included studies were categorized based on their exposure factors into unhealthy, healthy, and comprehensive food environments, encompassing facilities that offer healthy and unhealthy foods. The findings were narratively synthesized according to this classification. RESULT: A total of 23 studies, encompassing 13 cross-sectional studies and 10 cohort-longitudinal studies, were included in this review. Among the 20 studies on unhealthy food environments, 13 found a positive association with CVD outcomes. Of the seven studies on healthy food environments, 3 found a negative association with CVD outcomes. Additionally, 4 out of 8 studies on comprehensive food environments found a significant but inconsistent association with CVD outcomes. CONCLUSION: This study suggested that unhealthy food environments are probably associated with CVD outcomes. At the same time, there is currently no conclusive evidence to indicate a relationship between healthy food environments or comprehensive food environments and CVD outcomes.

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STUDY OBJECTIVES: To use a nationally representative sample to (1) evaluate the factor structure of the PROMIS parent proxy pediatric sleep scales (Spanish translation), (2) examine the invariance of these scales across sex and across different developmental periods of childhood and adolescence, (3) confirm the information and precision of the scales using item response theory (IRT), and (4) provide age-based normative information. METHODS: Parents of a nationally representative sample of 5,525 Spanish children and adolescents ages 5-16 years (56.1% boys) completed the Spanish translation parent proxy short versions of the Sleep Disturbance and Sleep-Related Impairment scales. We conducted confirmatory factor analyses (CFA), invariance analyses, and graded response IRT analyses. RESULTS: CFAs conducted separately on males and females within three age groups (early childhood: ages 5-8 years; middle childhood: ages 9-12 years; adolescence: ages 13-16 years) indicated all items had a substantial loading with one exception (the sleep continuity item ["my child slept through the night"] had a substantially lower loading and was removed for subsequent analyses). The scores on the two scales demonstrated invariance across sex within each age group. Using IRT analyses, both scales showed a high degree of information and precision from slightly below the trait means to slightly above two standard deviations above the trait means. CONCLUSIONS: The strong psychometric properties of the short versions of the parent proxy PROMIS pediatric sleep disturbance and sleep-related impairment scales, coupled with age-based norms, suggests these scales are likely to be useful for research and clinical applications.

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In slums and urban areas with unplanned housing such as in the city of Yaounde, Cameroon, poor water quality and inadequate sanitation pose significant health risks. The absence of locally-defined sanitary boundaries, tailored to hydrogeological conditions, hinders effective zoning and land use planning, exacerbating environmental degradation and health hazards. In this study, the sanitary boundary between drinking water wells and sources of pollution in the city of Yaoundé was defined using statistical analysis techniques and Geographic Information Systems (GIS). The Groundwater Quality Index (GWQI) and certain significant parameters affecting water quality notably the transmissivity of the aquifer and well depth were used to establish the sanitary boundary equation which was then interpolated in a GIS environment to obtain the sanitary boundary map of the study area. The linear equation deduced from the significant factors was defined to map the health boundaries between wells and pollution sources for a nominal value of the GWQI (GWQI = 25). Of the 112 wells analysed, 37 % had an excellent GWQI, 16 % were good while the remaining 47 % were poor. Statistical analysis showed a strong correlation between the GWQI and significant factors of groundwater pollution, such as the distance between well and pit latrines (r = -0.753), the aquifer transmissivity of the formation (r = 0.671) and the depth of the wells (r = - 0.855) but no correlation with elevation (r = 0.017) and well age (r = 0.090). Linear regression analysis confirmed the association of the GWQI with the main factors of pollution (p ≤ 0.05). A coefficient of determination of R2 = 0.85 was obtained when validating the linear regression plot based on independent data between measured and predicted GWQI. The sanitary boundary map shows that the wells in our study area should be located between 39 m and 370 m, with an average value of 215 m. New regulations on the distance between well and pit latrines are essential to prevent groundwater pollution.

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Introduction This study examines the geographic distribution and temporal trends of Zika virus (ZIKV) outbreaks in India from 2016 to 2023 using data from the Integrated Disease Surveillance Programme (IDSP). The burden of ZIKV in India has risen due to its rapid spread and significant health impacts. Existing literature highlights seasonal and geographic patterns but lacks a comprehensive, long-term analysis specific to India. This study addresses this gap by analyzing trends over seven years to inform better public health responses. Methods A secondary data analysis was conducted using publicly available data from the IDSP on reported Zika cases from January 2016 to December 2023. Descriptive statistical methods and geographic information system (GIS) mapping techniques were employed to analyze the geographic distribution and temporal trends of ZIKV outbreaks in India. The data were analyzed and visualized using R software version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria), with heat maps and choropleth maps to identify hotspots, and line diagrams to identify temporal trends. Results Zika outbreaks predominantly occurred during the post-monsoon season, accounting for 47.62% (n = 10) of the total 21 outbreaks, followed by the monsoon season with 33.33% (n = 7), and summer with 19.05% (n = 4). Two deaths were reported during a significant outbreak in Madhya Pradesh in 2018. Temporal trends indicated notable spikes in cases in 2018 (131 cases) and 2021 (234 cases), with no cases reported in 2019 and 2020. The geographic distribution maps highlighted significant concentrations of ZIKV outbreaks in specific districts within Uttar Pradesh, Madhya Pradesh, and Kerala. Discussion The study identified seasonal patterns, with most cases occurring in the post-monsoon season. The geographic spread of the ZIKV was observed in eight states from 2016 to 2023. GIS identified three hotspots in Uttar Pradesh, Madhya Pradesh, and Kerala. Conclusion The study highlights the need for heightened surveillance and targeted intervention preparedness during high-risk seasons. Enhancing testing facilities and data reporting systems could improve outbreak identification, management, and response.

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Background: The management of extensive longitudinal data in cohort studies presents significant challenges, particularly in middle-income countries like Malaysia where technological resources may be limited. These challenges include ensuring data integrity, security, and scalability of storage solutions over extended periods. Objective: This article outlines innovative methods developed and implemented by The Malaysian Cohort project to effectively manage and maintain large-scale databases from project inception through the follow-up phase, ensuring robust data privacy and security. Methods: We describe the comprehensive strategies employed to develop and sustain the database infrastructure necessary for handling large volumes of data collected during the study. This includes the integration of advanced information management systems and adherence to stringent data security protocols. Outcomes: Key achievements include the establishment of a scalable database architecture and an effective data privacy framework that together support the dynamic requirements of longitudinal healthcare research. The solutions implemented serve as a model for similar cohort studies in resource-limited settings. The article also explores the broader implications of these methodologies for public health and personalized medicine, addressing both the challenges posed by big data in healthcare and the opportunities it offers for enhancing disease prevention and management strategies. Conclusion: By sharing these insights, we aim to contribute to the global discourse on improving data management practices in cohort studies and to assist other researchers in overcoming the complexities associated with longitudinal health data.

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Purpose: Assess the effectiveness of standardizing multidisciplinary case conferences (MDCs). Methods: Anonymous electronic surveys gauged opinions of abdominal radiologists engaged in recurring MDCs. A standardized Excel template, following Cancer Care Ontario guidelines and relevant literature, was distributed to MDC managers. Physicians were instructed to send cases 36 hours prior to MDC. Template adherence was assessed at 1.5 and 8 months. A follow-up survey at 4 months evaluated the intervention's effectiveness. Results: 27/34 abdominal radiologists provided 47 baseline responses, and 12 delegated radiologists provided 23 follow-up responses. "Often/always" being provided the image's location increased from 36% (17/47) at baseline to 70% (16/23) at follow-up. Non-adherence to the 36-hour cut-off decreased from 36% (16/45) to 17% (4/23). 72% disagreed that uploading remote imaging to hospital servers is easy (33/46), similar to follow-up (18/23, 78%). In assessing the intervention, 41% noted improved standardization (9/22), another 41% considered MDCs already standardized (9/22), and 18% reported no change (4/22). Those reporting no change experienced a higher frequency of non-adherence to the 36-hour cut-off (3/4, 75%) than others (1/18, 6%), and less frequent "often/always" ratings for image location being provided (3/4, 75%) than others (2/18, 11%). 89% (25/28) of MDCs adhered to the template. Issues regarding last-minute add-on cases may be mitigated through EPIC force functions. Artificial intelligence advancements may assist in retrieving external images and patient information. Conclusion: Adherence to MDC standardization was high, allowing for more efficient preparation, potentially reducing radiologist administrative burdens. Future force functions and artificial intelligence integration into electronic patient records may further augment this.

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INTRODUCTION: Kazakhstan has made significant strides in digital health implementation since 2013, transitioning from paper-based systems to wide implementation of digital solutions. However, challenges persist, notably the misalignment between existing data collection requirements and ongoing digital transformations. To address this obstacle, the development of a new digital-friendly requirements for health data collection at provider levels was initiated. MATERIAL AND METHODS: A comparative analysis of all existing requirements on health and care data collection set a foundation for further actions. A dedicated technical working group, established by the Ministry of Healthcare, oversaw this process, while several specialized expert groups participated in detailed discussions to ensure alignment with healthcare needs. We took as a basis the legislative act enforcing use of data forms inherited from pre-digital era. Data elements from most used forms were cataloged, checked for consistency, and standardized. ISO 13940 standard guided transformation of rigid paper-based forms into flexible digital-friendly data sets. RESULTS: Analysis of the healthcare legislation reveals significant optimization opportunities. We categorized 157 forms into four groups by their content and role and focusing on clinical and administrative records for transformation. Data elements' analysis led to developing 209 standardized classifications with data values. 83 data sets, encompassing clinical and administrative domains, were developed to substitute 63 (out of 157) forms. Therefore, the foundation for better data and structure interoperability was set. DISCUSSION: The top-down approach to digital health standardization, combined with open market policies, results in inconsistent data collection practices. Developed data sets align with the goal of developing the national Electronic Health Records system as enabler for interoperability and adds a bottom-up perspective to digital health standardization. This effort addresses the lack of guidance for digital data model development, however additional efforts required to change the current practice and finalize the national legislation transformation into digital-friendly mode.

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BACKGROUND: The global community has set an ambitious goal to end HIV/AIDS as a public health threat by 2030. Significant progress has been achieved in pursuing these objectives; however, concerns remain regarding the lack of disaggregated routine data for key populations (KPs) for a targeted HIV response. KPs include female sex workers, transgender populations, gay men and other men who have sex with men, people who are incarcerated, and people who use drugs. From an epidemiological perspective, KPs play a fundamental role in shaping the dynamics of HIV transmission due to specific behaviors. In South Africa, routine health information management systems (RHIMS) do not include a unique identifier code (UIC) for KPs. The purpose of this protocol is to develop the framework for improved HIV monitoring and programming through piloting the inclusion of KPs UIC in the South African RHIMS. OBJECTIVE: This paper aims to describe the protocol for a multiphased study to pilot the inclusion of KPs UIC in RHIMS. METHODS: We will conduct a multiphased study to pilot the framework for the inclusion of KPs UIC in the RHIMS. The study has attained the University of Johannesburg Research Ethics Committee approval (REC-2518-2023). This study has four objectives, including a systematic review, according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (objective 1). Second, policy document review and in-depth stakeholder interviews using semistructured questionnaires (objective 2). Third, exploratory data analysis of deidentified HIV data sets (objective 3), and finally, piloting the framework to assess the feasibility of incorporating KPs UIC in RHIMS using findings from objectives 1, 2, and 3 (objective 4). Qualitative and quantitative data will be analyzed using ATLAS.ti (version 6; ATLAS.ti Scientific Software Development GmbH) and Python (version 3.8; Python Software Foundation) programming language, respectively. RESULTS: The results will encompass a systematic review of literature, qualitative interviews, and document reviews, along with exploratory analysis of deidentified routine program data and findings from the pilot study. The systematic review has been registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42023440656). Data collection is planned to commence in September 2024 and expected results for all objectives will be published by December 2025. CONCLUSIONS: The study will produce a framework to be recommended for the inclusion of the KP UIC national rollout. The study results will contribute to the knowledge base around the inclusion of KPs UIC in RHIMS data. TRIAL REGISTRATION: PROSPERO CRD42023440656; https://tinyurl.com/msnppany. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55092.

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The Northern Areas of Pakistan encompass the Hindukush, Karakoram, and Himalayan mountain ranges witnessing glacier surging, exacerbated by climate warming. As glaciers rapidly melt, ravines experience heightened blockage and migration, obstructing stream discharges and forming expansive ice-dammed lakes. The rupture of these natural dams triggers Glacial Lake Outburst Floods downstream in the primary glacier's ravine. The catastrophic Glacial Lake Outburst Floods in 2022 across the Karakoram ranges in Northern Pakistan prompted this study. It focuses on Shishper Glacier Lake. The aim is to provide complete flood observations and their devastating effects on downstream communities. Analysis of Landsat 08 Imagery reveals the evolution of Shishper Glacier Lake from its initiation in November 2018 to the catastrophic GLOF in May 2022. The lake reached a maximum area of 0.32 km2 in 2019 and its successive breaches on June 22, 2019, and May 29, 2020, reduced it to 0.018 km2. Draining continued until July 2021, shrinking the lake area to 0.009 km2. A noteworthy 2.73 °C temperature increase in 2022 correlated with an expansion of the lake area to 0.33 km2, culminating in the GLOF on May 7th, 2022. The study emphasizes the critical need for mapping, assessing, and monitoring surging glaciers and glacier-formed lakes in the Karakoram ranges to safeguard downstream communities from potential hazards.

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INTRODUCTION: Conducting clinical studies is an integral part of the clinical research repertoire of university hospitals. A wealth of organizational competences must always be available in a central location and in an up-to-date form for appropriate administration. Information such as the number of ongoing studies, and the number of enrolled participants is required for tasks related to e.g. sponsor quality management and KPIs. A registry for clinical trials can answer these questions and enhance the exchange of information. METHODS: Requirements for an in-house registry for clinical trials were defined in a multidisciplinary task force. The requirements included interfaces and key abilities to create customized reports to fulfill the obligation to provide evidence. RESULTS: The study registry is productive since May 2020 and internal interfaces have been implemented to ensure consistency between systems and the documented studies. Manually recorded data is enhanced by interfaces to primary registers. The comprehensive data set in the study register enables the creation of individual queries at any time for a variety of questions. DISCUSSION: The UKSH study register has already demonstrated its usefulness in various applications and several projects. The extensive data set and the modular realization allows many current and future requirements to be met.

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BACKGROUND: Data quality in health information systems has a complex structure and consists of several dimensions. This research conducted for identify Common data quality elements for health information systems. METHODS: A literature review was conducted and search strategies run in Web of Knowledge, Science Direct, Emerald, PubMed, Scopus and Google Scholar search engine as an additional source for tracing references. We found 760 papers, excluded 314 duplicates, 339 on abstract review and 167 on full-text review; leaving 58 papers for critical appraisal. RESULTS: Current review shown that 14 criteria are categorized as the main dimensions for data quality for health information system include: Accuracy, Consistency, Security, Timeliness, Completeness, Reliability, Accessibility, Objectivity, Relevancy, Understandability, Navigation, Reputation, Efficiency and Value- added. Accuracy, Completeness, and Timeliness, were the three most-used dimensions in literature. CONCLUSIONS: At present, there is a lack of uniformity and potential applicability in the dimensions employed to evaluate the data quality of health information system. Typically, different approaches (qualitative, quantitative and mixed methods) were utilized to evaluate data quality for health information system in the publications that were reviewed. Consequently, due to the inconsistency in defining dimensions and assessing methods, it became imperative to categorize the dimensions of data quality into a limited set of primary dimensions.

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Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

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The role of immunization in public health is crucial, offering widespread protection against infectious diseases and underpinning societal well-being. However, achieving optimal vaccination coverage is impeded by vaccine hesitancy, a significant challenge that necessitates comprehensive strategies to understand and mitigate its effects. We propose the integration of Population Health Management principles with Immunization Information Systems (IISs) to address vaccine hesitancy more effectively. Our approach leverages systematic health determinants analysis to identify at-risk populations and tailor interventions, thereby promoting vaccination coverage and public health responses. We call for the development of an enhanced version of the Italian National Vaccination Registry, which aims to facilitate real-time tracking of individuals' vaccination status while improving data accuracy and interoperability among healthcare systems. This registry is designed to overcome current barriers by ensuring robust data protection, addressing cultural and organizational challenges, and integrating behavioral insights to foster informed public health campaigns. Our proposal aligns with the Italian National Vaccination Prevention Plan 2023-2025 and emphasizes proactive, evidence-based strategies to increase vaccination uptake and contrast the spread of vaccine-preventable diseases. The ultimate goal is to establish a data-driven, ethically sound framework that enhances public health outcomes and addresses the complexities of vaccine hesitancy within the Italian context and beyond.

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BACKGROUND: District-based public private mix (DPPM) tuberculosis in Purwakarta district was strengthened by the MitraTB application. This research is aimed to explore perception of user about MitraTB application and measure their perception of this application in dimensions; design, usefulness, ease of use, and acceptance. METHODS: This study was exploratory sequential mixed methods research. A qualitative study was first conducted in order to gain an in-depth understanding about user's perception of MitraTB application through in-depth interviews. Data were analyzed through coding and categorizing. Based on qualitative finding, a questionnaire was developed and used in the following quantitative study. A cross sectional study was then conducted in quantitative phase. Data were analyzed using Rasch modeling. RESULT: The design of the MitraTB application looks simple and attractive to users. This application is useful to make it easier for private practitioners to report TB cases and it is easy to use. Respondents can accept the MitraTB application well. Most respondents have good perception about MitraTB application in dimensions; design (56.25%), usefulness (69.79%), ease of use (55.20%), and acceptance (73.96%). CONCLUSIONS: MitraTB application has a good design feature, useful, easy to use, and acceptable. This application facilitates the private sector to be involved in the TB program by reporting TB cases. Follow-up and local regulations are required for the continued use of this application.

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BACKGROUND: With the increasing number of protracted refugee crises globally, it is essential to ensure strong national health information systems (HIS) in displacement settings that include refugee-sensitive data and disaggregation by refugee status. This multi-country study aims to assess the degree of integration of refugee health data into national HIS in Jordan, Lebanon, and Uganda and identify the strengths and weaknesses of their national HIS in terms of collecting and reporting on refugee-related health indicators. METHODS: The study employs a comparative country analysis approach using a three-phase framework. The first phase involved reviewing 4120 indicators compiled from global health organizations, followed by a multi-stage refinement process, resulting in 45 indicators distributed across five themes. The second phase consisted of selecting relevant criteria from the literature, including data sources, annual reporting, disaggregation by refugee status, refugee population adjustments, accuracy, and consistency. The third phase involved assessing data availability and quality of the selected indicators against these criteria. RESULTS: Our analysis uncovered significant challenges in assessing the health status of refugees in Jordan, Lebanon, and Uganda, primarily stemming from limitations in the available health data and indicators. Specifically, we identified significant issues including incomplete local data collection with reliance on international data sources, fragmented data collection from various entities leading to discrepancies, and a lack of distinction between refugees and host populations in most indicators. These limitations hinder accurate comparisons and analyses. In light of these findings, a set of actionable recommendations was proposed to guide policymakers in the three countries to improve the integration of refugee health data into their national HIS ultimately enhancing refugees' well-being and access to healthcare services. CONCLUSION: The current status of refugee-related health data in Jordan, Lebanon, and Uganda indicates the need for improved data collection and reporting practices, disaggregation by refugee status and better integration of refugee health data into national HIS to capture the health status and needs of refugees in host countries. Key improvement strategies include establishing a centralized authority for consistent and efficient data management, fostering transparent and inclusive data governance, and strengthening workforce capacity to manage refugee health data effectively.

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Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

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Data quality in health information systems (HIS) is essential for informed decision-making in the health sector, particularly in sub-Saharan Africa (SSA) where these systems face many challenges like resource limitations and weak infrastructure. This systematic review assessed the quality of HIS data in the region, focusing on the dimensions, and factors influencing this quality. It highlights the importance of systematic evaluation, ongoing training for data collectors in the analysis and use of data for decision-making, and the adoption of information and communication technologies in the healthcare system to improve data quality. These findings point the way to better use of health data and the need for a more integrated approach to digital health in SSA.

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Three-dimensional (3D) simulations and precise landscape visualizations are crucial for various applications, like landscape management and planning, computer and connection of the landscape, evaluation, and tracking of land use. The consequences of several plans and a large scene cannot be communicated using older methods of comprehensive environmental planning and development in a timely, rational, and coordinated manner. Architects have trouble incorporating ideas into other comprehensive planning implementation processes. Architects did not thoroughly investigate the neighbourhood's demographics and matching behavioural needs and lacked critical thinking. The 3D dynamic landscape simulation is a detailed computerized three-dimensional simulation of the environment that can be dynamically presented. With the aid of Artificial Intelligence (AI) technology, the system possesses a strong sense of reality, a user-friendly interface, and interactive features that can be tailored to the requirements of the contemporary urban environmental landscape. Regarding exterior publicity, domestic assistance, environmental land use planning, and information systems. The novelty of the proposed Interactive Design System based on AI (IDS-AI) is to create a 3D dynamic landscape model based on a real-life environmental scene, utilizing a Geographic Information System (GIS) to optimize landscape vision. Secondly, 3D environmental landscape design simulation was implemented using GIS spatial analysis in conjunction with the Fuzzy Analytical Hierarchical Process (FAHP) to reduce the data overlap rate and help make an accurate decision. Finally, the design incorporates the development of the interactive interface system application of landscape design and environmental resources for viewing the landscape, the factors that affect them, and the area coverage ratio of various land cover types. The experimental outcomes show that the suggested IDS model increases the gradient sensitivity level of 98.3 % and area coverage ratio of 93.4 % compared to other existing models.