Satscan regression: a bibliometric study based on text mining procedures

Background:Fungal metabolites known as aflatoxin M1 (AFM1) are linked to contaminated milk and milk products. Consuming food contaminated with AFM1 poses major health risks and may even be fatal.Methods:The retrieved publications were categorized in this bibliometric study using the Web of Science (WoS) database Jan 1, 1970 to Nov 30, 2022 based on a variety of factors, including the time of publication of articles, citation totals, languages, research areas, countries, affiliations, funding agencies, journals, and keywords analysis to identify any hot and developing subjects. Additionally, VOSviewer software version 1.6.18 provided the bibliometric analysis of the global collaboration network and hot research themes.Results:Overall, 679 published documents were detected. Food Control was the top-line journal in publications on AFM1 research with 540 published articles, while the USA was the best productive country in AFM1 publications as well as the major country with the maximum co-authorship collaboration. This study ensures quantitative and qualitative analyses of the top 25 journals, most cited published articles, most relevant authors and title word occurrences in published documents on AFM1 publications. Over the past two decades, there has been an enormous rise for research conducted on global AFM1.Conclusion:The assessment of the historical state and development trend in AFM1 scientific research can serve as a roadmap for future research and eventually, serve as a foundation for bettering management practices for territorial decisions, healthcare, and dairy industries.

Zero-inflated Bell scan: A more flexible spatial scan statistic

Background: Pneumoconiosis (including silicosis, coal workers’ pneumoconiosis, and asbestosis) remains a major occupational health concern, particularly in settings with sustained exposure to respirable mineral dust. Conventional epidemiological approaches often under-represent spatial heterogeneity in exposure contexts, potentially masking localized disease clusters and delaying targeted prevention. Geographic Information Systems (GIS) and spatial statistics provide a means to integrate georeferenced occupational, environmental, and health data, enabling spatially explicit surveillance and risk assessment. This review provides an overview of GIS applications in pneumoconiosis research and discusses implications for nursing practice and occupational health nursing. Methods: A structured literature search was conducted in Web of Science, Scopus, PubMed, and Google Scholar using combinations of keywords related to pneumoconiosis (e.g., “silicosis”, “coal workers’ pneumoconiosis”, “asbestosis”), GIS (e.g., “geographic information system”, “spatial analysis”), and spatial statistical methods (e.g., “Moran’s I”, “Getis-Ord Gi*”, “kernel density”, “SaTScan”, “spatial regression”, “GWR/MGWR”). Eligible publications included peer-reviewed studies applying GIS/spatial analytics to pneumoconiosis outcomes, exposure assessment, clustering/hotspot detection, or spatial risk modeling. Consistent with the template structure, findings are synthesized into three sections: (i) methods to characterize spatial patterns; (ii) hotspot/cluster analysis approaches; and (iii) spatial regression models to identify risk factors. Results: GIS-enabled mapping and spatial statistics consistently revealed non-random spatial patterns of pneumoconiosis, with clustering frequently aligned with mining, stone processing, construction corridors, and industrial zones. Hotspot methods (e.g., KDE, LISA, Getis-Ord Gi*, scan statistics) were useful in detecting localized high-risk areas for intensified screening and prevention. Spatial regression approaches (e.g., spatial lag/error models, Bayesian CAR, and GWR/MGWR) improved inference by accounting for spatial dependence and local non-stationarity, identifying associations between pneumoconiosis indicators and contextual factors such as industrial density, dust exposure proxies, sociodemographic vulnerability, and access to occupational health services. Nursing-relevant applications include improved surveillance sensitivity, geographically targeted health education, risk communication, and prioritization of screening/referral resources. Conclusions: GIS and spatial statistics strengthen pneumoconiosis research by uncovering spatial inequities in exposure and disease burden and by improving the explanatory power of risk models. For occupational health nursing, spatially informed evidence supports proactive surveillance, targeted interventions, and resource allocation. Future work should prioritize interoperable data pipelines, privacy-preserving geocoding, and capacity building to integrate GIS into routine occupational health nursing practice.

Purpose Despite the popularity of talent management (TM), very little has been published on the community of scholars that contribute to the knowledge base of the field. The purpose of this paper is to disclose the dynamics in TM research through a detailed analysis of its evolving collaboration networks (i.e. research communities) in order to identify key authors and major topics covered. Design/methodology/approach A total of 225 co-authored articles published on TM from 2001 to May 2016 were retrieved from the Web of Science and Scopus and then analyzed and mapped using social network analysis methods. Findings The authors identified two major scientific communities (one of 63 authors and the other one of 24 authors). Both communities not only have different characteristics and structure, but also focus on different topics. The authors identified key players within each community and offer a dynamic view on the main topics studied. Practical implications This paper may help practitioners and newcomers to the field to rapidly identify key players and main topics studied in the TM field. It may assist academic institutions and journal editorial teams in better assessing and identifying key scholars in this field. Originality/value This paper offers the first analysis of TM collaborative research networks.

In order to understand the global research trends and application development of artificial intelligence (AI) in information engineering from 2014 to 2024, this paper quantitatively analyzed and visualized the relevant literature in Web of Science (WoS) using VOSviewer and CiteSpace to identify the main authors, institutions and national collaboration networks of emerging research hotspots and cutting-edge topics. Through co-occurrence analysis and visual analysis, the research hotspots, evolutionary paths and development trends of artificial intelligence (AI) in information engineering applications were revealed, providing a theoretical basis for understanding the role of AI in information engineering and suggesting future research directions. The research found that : (1) Some stable collaborative networks dominated by small groups have been formed in this field; (2) A few countries have concentrated a large amount of scientific research resources in this field, and the global distribution of scientific research resources is unbalanced; (3) The depth and breadth of research in this field have expanded rapidly in the last decade, with research hotspots being rapidly updated and iterated, and technical application scenarios becoming more specific.

BackgroundThere are many applications for spatial cluster detection and more detection methods have been proposed in recent years. Most cluster detection methods are efficient in detecting circular (or circular-like) clusters, but the methods which can detect irregular-shaped clusters usually require a lot of computing time.MethodsWe propose a new spatial detection algorithm for lattice data. The proposed method can be separated into two stages: the first stage determines the significant cells with unusual occurrences (i.e., individual clustering) by applying the Choynowski’s test, and the second stage determines if there are clusters based on the information of the first stage by a binomial approximate method. We first use computer simulation to evaluate the performance of the proposed method and compare it with the scan statistics. Furthermore, we take the Taiwan Cancer data in 2000 to illustrate the detection results of the scan statistics and the proposed method.ResultsThe simulation results support using the proposed method when the population sizes are large and the study regions are irregular. However, in general, the scan statistics still have better power in detecting clusters, especially when the population sizes are not large. For the analysis of cancer data, the scan statistics tend to spot more clusters, and the clusters’ shapes are close to circular (or elliptic). On the other hand, the proposed methods only find one cluster and cannot detect small-sized clusters.ConclusionsIn brief, the proposed methods can detect both circular and non-circular clusters well when the significant cells are correctly detected by the Choynowski’s method. In addition, the binomial-based method can handle the problem of multiple testing and save the computing time. On the other hand, both the circular and elliptical scan statistics have good power in detecting clusters, but tend to detect more clusters and have lower accuracy in detecting non-circular clusters.

ABSTRACTBackground and objectiveThis study provides a comprehensive bibliometric and scientometric analysis of research trends in schizophrenia genetics from 2020 to 2025. Driven by rapid technological advances such as genome‐wide association studies (GWAS) and next‐generation sequencing (NGS), the field has experienced significant growth. The objective was to map the thematic evolution, key contributors, and collaboration networks shaping psychiatric genomics research.MethodsAn integrated bibliometric and scientometric approach was employed. A dataset of 5001 English‐language publications authored by 27,692 researchers was retrieved from PubMed, Scopus, and web of science (Wos) databases. Following deduplication and eligibility screening, VOSviewer (version 1.6.20) was used to generate network visualizations analyzing co‐authorship, keyword co‐occurrence, citation, and co‐citation patterns.ResultsThe analysis revealed a notable surge in publications after 2022, increasing from 431 articles in 2020 to 1,645 articles in 2022, before stabilizing above 900 publications annually in 2023 and 2024. Six major thematic clusters were identified: (1) genetic and cellular mechanisms; (2) neurochemical and behavioral studies; (3) neuroimaging; (4) clinical and epidemiological features including polygenic risk scoring; (5) methodological approaches; and (6) comorbidities with pharmacogenetic implications. Prominent contributors included Ole A. Andreassen and Vince D. Calhoun, while the United States, China, and the United Kingdom emerged as central hubs of international collaboration.ConclusionThis study elucidates the leading researchers, prevailing themes, and collaborative networks driving schizophrenia genetics research from 2020 to 2025. The findings demonstrate the efficacy of bibliometric and scientometric methods, particularly VOSviewer, in revealing the dynamic landscape of psychiatric genomics and guiding future multidisciplinary research efforts.This graphical abstract illustrates the integrated workflow of the study “mapping the scientific landscape of schizophrenia genetics (2020–2025)”. The process combines bibliometric and scientometric analyses of 5,001 publications using VOSviewer to identify thematic clusters, key contributors, and international collaboration networks that define global research trends in schizophrenia genetics.

The potential existence of spatial clusters in childhood cancer incidence is a debated topic. Identification of rare disease clusters in general may help to better understand disease etiology and develop preventive strategies against such entities.The incidence of newly diagnosed childhood malignancies under 15 years of age is 140/1,000,000. In this context, the subgroup of nephroblastoma represents an extremely rare entity with an annual incidence of 7/1,000,000. We evaluated widely used statistical approaches for spatial cluster detection in childhood cancer (Ref. [22] Schündeln et al., 2021, Cancer Epidemiology). For the simulation study, random high risk clusters of 1 to 50 adjacent districts (NUTS-level 3, nomenclature des unités territoriales statistiques) were generated on the basis of the 402 German administrative districts. Each cluster was simulated with different relative risk levels (1 to 100). For each combination of cluster size and risk level 2000 iterations were performed. Simulated data was then analyzed by three local clustering tests: Besag-Newell method, spatial scan statistic and the Bayesian Besag-York-Mollié approach (fit by Integrated Nested Laplace Approximation). The performance characteristics of all three methods were systematically documented (sensitivity, specificity, positive/negative predictive values, exact- and minimum power, correct classification, positive/negative diagnostic likelihood and false positive/negative rate).This data article links to a Mendeley online repository which includes the raw data of simulated high-risk clusters and simulated cases on the district level for an all-childhood-malignancy scenario as well as for cases of nephroblastoma. These data was used for the evaluation of the three cluster detection methods. The R code for simulation and analysis are available from GitHub.The article also includes analyzed data summarizing the performance of the cluster detection tests in very rare disease entities, using the example of simulated nephroblastoma cases.The raw data from the study can be used for benchmarking analyses applying different spatial statistical methods systematically and evaluating their performance characteristics comparatively. The analyzed data from the nephroblastoma example can be useful to interpret the performance of the three applied local cluster detection tests in the setting of extremely rare disease entities. As a practical application, data and R code can be used for performance analyses when planning to establish surveillance systems for rare disease entities.

ObjectiveTo conduct a comprehensive bibliometric analysis of the application of artificial intelligence (AI) in Rare diseases (RDs), with a focus on analyzing publication output, identifying leading contributors by country, assessing the extent of international collaboration, tracking the emergence of research hotspots, and detecting trends through keyword bursts.MethodsIn this bibliometric study, we identified and retrieved publications on AI applications in RDs spanning 2003 to 2023 from the Web of Science (WoS). We conducted a global research landscape analysis and utilized CiteSpace to perform keyword clustering and burst detection in this field.ResultsA total of 1501 publications were included in this study. The evolution of AI applications in RDs progressed through three stages: the start-up period (2003–2010), the steady development period (2011–2018), and the accelerated growth period (2019–2023), reflecting this field’s increasing importance and impact at the time of the study. These studies originated from 85 countries, with the United States as the leading contributor. “Mutation”, “Diagnosis”, and “Management” were the top three keywords with high frequency. Keyword clustering analysis identified gene identification, effective management, and personalized treatment as three primary research areas of AI applications in RDs. Furthermore, the keyword burst detection indicated a growing interest in the areas of “biomarker”, “predictive model”, and “data mining”, highlighting their potential to shape future research directions.ConclusionsOver two decades, research on the AI applications in RDs has made remarkable progress and shown promising results in the development. Advancing international transboundary cooperation is essential moving forward. Utilizing AI will play a more crucial role across the spectrum of RDs management, encompassing rapid diagnosis, personalized treatment, drug development, data integration and sharing, and continuous monitoring and care.

Background: Physiotherapy research supports the advancement of evidence-based practice and the development of a highly skilled workforce. This study aims to visualize the highly cited scientific output of Indian physiotherapists from 1999 to 2018. Methods: A descriptive study design was adopted to visualize the highly cited scientific output of Indian physiotherapists using the Web of Science (WoS) database from 1999 to 2018. A search was carried out using the following keywords "((TS=(physiotherapy) OR TS=("physical rehabilitation") OR TS=("physical therapy")) AND AD=(India))Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI, CCR-EXPANDED, IC Timespan=1999-2018". Data collected were analyzed using Incites from WoS and VOSviewer software. Results: A total of 488 articles were published between 1999 and 2018, with a peak of 103 in 2016 with 2419 citations. A decline in publication count was observed after 2016. The journal International Journal of Physiotherapy published the highest number of articles (n=35). Manipal University (n=36) was found to be the most active institution for physiotherapy research in India, as determined by publishing the most articles. Indian physiotherapists published the highest number of research articles in collaboration with US authors (n=24). Conclusion: There is an increasing trend in the scientific output of Indian physiotherapists over the past two decades; however, a decline is observed after 2016. It is recommended that research collaborations across the globe are increased and scientific output should be improved, leading to a higher number of citations. Future research should explore factors influencing the scientific production of Indian physiotherapists and devise appropriate strategies to attain further improvement.

The paper explores teachers and students’ training culture in clinical psychology in an Italian university in order to understand whether the educational context support the development of useful professional skills support the access of the newly graduated psychologists into the job market. To this aim, students and teachers’ interview transcriptions (n = 47) were collected into two corpora. Both corpora underwent Emotional Text Mining (ETM), which is a text mining procedure allowing for the identification of the social mental functioning (culture) setting people’s behaviors, sensations, expectation, attitudes, and communication. The results show 4 clusters and 3 factors for each corpus highlighting similarity in the representation of the training culture between student and teachers. The education and the professional training are perceived as two different processes with different goals. The text mining procedure turned out to be an enlightening tool to explore the university training culture: The research results were presented to the Master Coordinator who decided to plan a group activity for the students aiming to improve the educational process.

This bibliometric study examines the integration of gamification elements within educational contexts, highlighting trends, influential research, and collaborative networks in the field. Gamification, increasingly recognized for its potential to enhance student engagement and motivation, has become a focal point in educational research. However, there remains a need for a comprehensive analysis to map its development, identify key contributors, and evaluate its impact. This study addresses these gaps by analyzing 1,637 documents from the Scopus database, focusing on publications related to gamification in education. Methodologically, Scopus Analyzer was used to identify publication trends, while VOSviewer software facilitated keyword mapping and co-authorship network analysis. The results reveal a steady increase in research activity, particularly since 2020, reflecting a growing acceptance of gamification as a viable educational tool. Key findings reveal that "gamification," "engagement," and "motivation" are frequently occurring keywords, indicating core themes within the field. The United States, Spain, and the United Kingdom emerged as the leading contributors in terms of publications and collaborations, highlighting significant international interest. Additionally, prominent authors have contributed substantially to advancing theoretical and practical knowledge on gamification's application in diverse educational settings. Despite this progress, gaps remain in standardized frameworks and longitudinal studies assessing gamification's long-term impact. In conclusion, the study underscores the extensive research interest in gamification within education, its established benefits, and the collaborative efforts across countries. Thus, future research should focus on addressing identified gaps by developing structured frameworks and conducting studies that evaluate the sustained effectiveness of gamified interventions across various educational contexts. This bibliometric analysis provides a foundation for educators, researchers, and policymakers to understand the evolving landscape of gamification in education.

Environmental factors are considered to be one of the elements responsible for the development of certain diseases. Examples of these environmental factors include deficiency of some elements (e.g., certain vitamins) that are necessary for maintaining a person's health or environmental contamination of an area by hazardous chemicals. For people living in a contaminated area, they are prone to get sick. If residential places are fixed for all people, then we can easily identify contaminated areas by locating clusters of patients with similar diseases. Unfortunately, this is not the case especially in modern society since people change their residential places frequently. In this paper we use patients' residential history (also called geospatial lifeline) to locate contaminated areas. In various domains, such as epidemiology and public health research, detection of space-time clusters is an important task. Current cluster analysis methods can only identify general hot spot in a given time period but cannot pinpoint the exact area where environmental factors may be responsible for the development of a disease. We propose a novel method to identify possible relationships between a disease and the locations where environmental factors might be responsible for the development of a disease. This method differs from previous methods in two ways. Firstly, we adopt the concept of geospatial lifeline which is actually a piecewise linear trajectory in three dimensional space (x, y dimensions plus time dimension). Secondly, based on disease principles, we divide a patient's geospatial lifeline into four periods: normal period, the period of being exposed to a contaminated area (exposure period), latent period, and sick period. Therefore, a geospatial lifeline is not only a spatial-temporal trajectory but also has useful semantic information in different parts of the trajectory. Based on patients' geospatial lifelines, this new method helps unearth unknown contaminated areas responsible for the development of a given disease and disclose other useful disease related information.

Introduction: The identification of climate temperature-sensitive pathogens and infectious diseases is essential in addressing health risks resulting from global warming. Such research is especially crucial in regions where climate change may have a more significant impact like Russia. Recent studies have reasoned that the abundance of non-toxigenic V. cholerae is environmentally driven and can be part of early global warming signals for Russian territory. The aim of the study is to investigate the spatial-temporal trends and thermo-climatic sensitivity of non-toxigenic V. cholerae abundance in Russia.
 Methods: This study employed Kulldorff’s space-time statistics to identify persistent clusters of the V. cholerae ctx- isolation and areas for exploring temperature-depended patterns of the vibrio distribution. Correlation analysis was used to identify regions with temperature-driven Vibrio abundance in water samples.
 Results: The spatial analysis detected 16 persistent (7-8 year) clusters of V. cholerae ctx- across the study period 2005-2021. The number of clusters with RR >1 abandoning from the south to the north and the total number of persistent clusters (9) is greater in the period of 2014(5)-2021 compared with the period 2005-2013 (7). A distinct significant thermo-climatic effect on the abundance of V. cholerae ctx- in water basins was found in three Russian regions with temperate marine (the Kaliningrad region) and sharp continental climatic conditions (the Irkutsk region and the Republic of Sakha). The temperature and Vibrio prevalence trend curves are peaky (the Kaliningrad region and the Republic of Sakha) or bell-shaped (the Irkutsk region) changed and closely followed together.
 Conclusion: The persistent clusters should become targeted areas to improve sanitation conditions. The study offers valuable outcomes to support simplified empirical evaluations of the potential hazards of vibrio abundance that might be useful locally for public health authorities and globally as a part of Russia's warning system of climate change effects.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in socialization. The amount of scientific research results on motherhood related to ASD has grown exponentially; however, there are no bibliometric studies in this field. Objective: This article aimed to analyze scientific research on motherhood related to the autism spectrum published in WoS. Articles on motherhood related to ASD were retrieved from the Web of Science (WoS). The advanced search interface used was “Title of the article, , Keywords”. The analysis and visualization of the selected documents and their data were performed using a wide range of tools and software such as MS Excel (v16.0), VOS viewer (version 1.6.15), and R packages (Biblioshiny, version 2.0). A total of 1660 articles were included in this study. Most of the publications were original articles. The United States published the most significant number of articles among the countries identified. P.R. Hastings, M.M. Seltzer, and J. Van de Water were the main authors. The Journal of Autism and Developmental Disorders was the most productive and impactful journal. The main research topics were related to mental health and social support in the role of motherhood. This desk study provides researchers with a comprehensive understanding of ASD-related maternity research trends by evaluating relevant publications in recent decades. The results of this bibliometric analysis can serve as a basis and orientation for new studies.