Requirement Analysis of Different Variants of a Measurement and Training Station for Older Adults at Risk of Malnutrition and Reduced Mobility: Focus Group Study.

BACKGROUND: Demographic change is leading to an increasing proportion of older people in the German population and requires new approaches for prevention and rehabilitation to promote the independence and health of older people. Technical assistance systems can offer a promising solution for the early detection of nutritional and physical deficits and the initiation of appropriate interventions. Such a system should combine different components, such as devices for assessing physical and nutritional status, educational elements on these topics, and training and feedback options. The concept is that the whole system can be used independently by older adults (aged ≥70 years) for monitoring and early detection of problems in nutrition or physical function, as well as providing opportunities for intervention. OBJECTIVE: This study aims to develop technical and digital elements for a measurement and training station (MuTs) with an associated app. Through focus group discussions, target group requirements, barriers, and favorable components for such a system were identified. METHODS: Older adults (aged ≥70 years) were recruited from a community-based setting as well as from a geriatric rehabilitation center. Focus group interviews were conducted between August and November 2022. Following a semistructured interview guideline, attitudes, requirements, preferences, and barriers for the MuTs were discussed. Discussions were stimulated by videos, demonstrations of measuring devices, and participants' ratings of the content presented using rankings. After conducting 1 focus group in the rehabilitation center and 2 in the community, the interview guide was refined, making a more detailed discussion of identified elements and aspects possible. The interviews were recorded, transcribed verbatim, and analyzed using content analysis. RESULTS: A total of 21 older adults (female participants: n=11, 52%; mean age 78.5, SD 4.6 years) participated in 5 focus group discussions. There was a strong interest in the independent measurement of health parameters, such as pulse and hand grip strength, especially among people with health problems who would welcome feedback on their health development. Participants emphasized the importance of personal guidance and interaction before using the device, as well as the need for feedback mechanisms and personalized training for everyday use. Balance and coordination were mentioned as preferred training areas in a MuTs. New training options that motivate and invite people to participate could increase willingness to use the MuTs. CONCLUSIONS: The target group is generally open and interested in tracking and optimizing diet and physical activity. A general willingness to use a MuTs independently was identified, as well as a compelling need for guidance and feedback on measurement and training to be part of the station.

EuMAR stakeholder engagement: an analysis of medically assisted reproduction (MAR) data collection practices in EU countries†.

STUDY QUESTION: What are the current national medically assisted reproduction (MAR) data collection systems across EU Member States, and how can these countries contribute to a unique, cycle-by-cycle registry for the European Monitoring of Medically Assisted Reproduction (EuMAR) project? SUMMARY ANSWER: The study identified significant variation in MAR data collection practices across Member States, with differences in data types, collection methods, and reporting requirements; the EuMAR project emerges as an opportunity to enhance data standardization and improve MAR data collection in the EU. WHAT IS KNOWN ALREADY: There is a need for new approaches in MAR data collection that include long-term and cross border follow-up. The EuMAR project intends to establish a unified, cycle-by-cycle registry of data on MAR treatments in EU countries, from which accurate cumulative outcomes can be calculated. STUDY DESIGN, SIZE, DURATION: This cross-sectional study involved a survey and interviews with stakeholders from 26 EU Member States conducted in 2023 over a period of seven months. PARTICIPANTS/MATERIALS, SETTING, METHODS: Representatives from national competent authorities and professional associations involved in MAR data collection in EU countries were invited to complete the survey and interviewed to assess current data flows, information requirements, and their interest in the EuMAR project. MAIN RESULTS AND THE ROLE OF CHANCE: Half of the participating countries reported having a national MAR registry with cycle-by-cycle data (n = 13), while 31% reported having a national registry with aggregated data (n = 8) and 19% reported having no national registry (n = 5). Of the countries with a national cycle-by-cycle registry, eight countries collect identifiable data, five countries collect pseudonymized data, and one country collects fully anonymized data. Informed consent is required in 10 countries. The main advantages that participants expected from a European registry like EuMAR were the possibility of obtaining national statistics in the absence of a national registry and improving the calculation of cumulative outcomes. LIMITATIONS, REASONS FOR CAUTION: The results of the study are based on self-reported data, which may be subject to bias, however, the validity of the collected information was verified with different means, including follow-up calls for clarifications and sharing final transcript reports. The feasibility of the proposed data flow models will be tested in a pilot study. WIDER IMPLICATIONS OF THE FINDINGS: Despite the heterogeneity of data collection practices across EU countries, the results show that stakeholders have high expectations of the benefits that the EuMAR registry can bring, namely the improvement of data consistency, cross-border comparability, and cumulative live birth rates, leading to better information for patients, health care providers and policy makers. STUDY FUNDING/COMPETING INTEREST(S): The EuMAR project was co-founded by ESHRE and the European Commission (101079865-EuMAR-EU4H-2021-PJ2). No competing interests were declared. TRIAL REGISTRATION NUMBER: N/A.

BioBricks.ai: A Versioned Data Registry for Life Sciences Data Assets.

Researchers in biomedical research, public health and the life sciences often spend weeks or months discovering, accessing, curating, and integrating data from disparate sources, significantly delaying the onset of actual analysis and innovation. Instead of countless developers creating redundant and inconsistent data pipelines, BioBricks.ai offers a centralized data repository and a suite of developer-friendly tools to simplify access to scientific data. Currently, BioBricks.ai delivers over ninety biological and chemical datasets. It provides a package manager-like system for installing and managing dependencies on data sources. Each 'brick' is a Data Version Control git repository that supports an updateable pipeline for extraction, transformation, and loading data into the BioBricks.ai backend at https://biobricks.ai. Use cases include accelerating data science workflows and facilitating the creation of novel data assets by integrating multiple datasets into unified, harmonized resources. In conclusion, BioBricks.ai offers an opportunity to accelerate access and use of public data through a single open platform.

Evaluating information management system in epidemic infectious diseases in Iran.

Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

Prioritization, resource allocation and utilization of decision support tools in animal health: Results of qualitative interviews with experts.

A follow up to an online questionnaire survey (in a kind of a sequential study design), qualitative assessment was made on the views of selected animal health experts on disease prioritization methods, resource allocation and use of decision-support tools. This was done through in-depth interviews with experts working for national or international organizations and sectors. A semi-structured question guide was formulated based on the information generated in the online questionnaire and a systematic content analysis of animal and human health manuals for disease prioritization and resource allocation. In-depth, one-on-one, online interviews on the process of disease prioritization, animal health decision-making, types of prioritization tools and aspects of improvements in the tools were conducted during March and April 2022 with 20 expert informants. Prioritization approaches reported by experts were either single criterion-based or multiple criteria-based. Experts appreciated the single-criterion-based approach (quantitative) for its objectivity in contrast to multicriteria prioritization approaches which were criticized for their subjectivity. Interviews with the experts revealed a perceived lack of quality and reliable data to inform disease prioritization, especially in smallholder livestock production systems. It was found that outputs of disease prioritization exercises do not generally directly influence resource allocation in animal health and highlighted the paucity of funding for animal health compared to other agricultural sectors. The experts considered that the available decision-support tools in animal health need improvement in terms of data visualization for interpretation, management decision making and advocacy. Further recommendations include minimizing subjective biases by increasing the availability and quality of data and improving the translation of disease prioritization outputs into actions and the resources to deliver those actions. DATA AVAILABILITY STATEMENT: The data can be obtained from the corresponding author upon request.

Untapping the Prognostic Value of Patient-Generated Health Data in Locally Advanced Non-small Cell Lung Cancer.

BACKGROUND: Patient-generated health data (PGHD), which includes patient-reported outcomes (PROs) and wearable device data, may have prognostic value for cancer patients. We tested that hypothesis using data from several prospective trials where patients with locally advanced non-small cell lung cancer (LA-NSCLC) were treated with definitive chemoradiotherapy. METHODS: Cox proportional hazards models were utilized to identify the baseline patient-reported symptom that best predicted progression-free survival (PFS) duration in a trial that involved PRO-CTCAE collection (Cohort 1). Using data from trials that included EORTC QLQ-C30 questionnaires and wearable devices (Cohort 2), the same symptom was tested as a predictor of PFS. Baseline physical inactivity was also tested as a predictor of PFS. A simple risk stratification tool utilizing PROs and physical activity was proposed. RESULTS: In Cohort 1 (n = 50), anorexia was the only pretreatment PRO that was significantly associated with PFS after Bonferroni correction (HR = 3.94, P = .002). In Cohort 2 (n = 58), baseline anorexia was also significantly associated with PFS (HR = 2.48, P = .018), as was physical inactivity (HR = 3.11, P < .001). Median PFS duration for patients in Cohort 2 with anorexia or physical inactivity was 6 months, compared to 18 months for other patients (HR = 3.08, P < .001). Median overall survival duration for patients with anorexia or physical inactivity was 19 months, compared to 65 months for other patients (HR = 2.44, P = .021). CONCLUSION: PGHD, including PROs and wearable device data, can provide valuable prognostic information for LA-NSCLC patients treated with definitive chemoradiotherapy. These findings should be validated using larger datasets.

Adoption of the OMOP CDM for Cancer Research using Real-world Data: Current Status and Opportunities.

Background: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem. Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research. Materials and Methods: Published literature databases were searched to retrieve OMOP CDM and cancer-related English language articles published between January 2010 and December 2023. A charting form was developed for two main themes, i.e., clinically focused data analysis studies and infrastructure development studies in the cancer domain. Results: In total, 50 unique articles were included, with 30 for the data analysis theme and 23 for the infrastructure theme, with 3 articles belonging to both themes. The topics covered by the existing body of research was depicted. Conclusion: Through depicting the status quo of research efforts to improve or leverage the potential of the OMOP CDM ecosystem for advancing cancer research, we identify challenges and opportunities surrounding data analysis and infrastructure including data quality, advanced analytics methodology adoption, in-depth phenotypic data inclusion through NLP, and multisite evaluation.

Prevalence and risks of intravenous chemotherapy-induced severe neutropenia in solid cancers: a multicenter retrospective cohort study on real-life data.

PURPOSE: We aimed at identifying prevalence, clinical outcomes and prognostic factors in cancer patients with intravenous chemotherapy-induced severe neutropenia (ICISN). METHODS: In this multicenter retrospective cohort study on the clinical data warehouse of Greater Paris University Hospitals (AP-HP), we included all adult patients with solid cancer hospitalized between 2016 and 2021 with intravenous chemotherapy within 30 days prior to severe neutropenia (D70 or D611 ICD-10 codes AND a neutrophil count < 500/mm3). The primary endpoint was referral to intensive care unit (ICU) or death within 30 days. We collected cancer, patient, and treatment characteristics. RESULTS: Among 141,586 cancer inpatients, 40,660 received chemotherapy among whom 661 (1.6%) had ICISN. Median age was 63 years (interquartile range (IQR), 54-70) and 330 patients (49%) were female. The median Charlson score was 10 (IQR, 8-11). Main primary cancers were lung (n = 204, 31%) and breast (n = 87, 13%). Advanced cancers were found in 551 patients (83%), 331 (50%) were in 1st line of chemotherapy, 284 (42%) in the 1st cycle of the current line and 149 (22%) had primary G-CSF. Documented bacterial (mostly gram-negative bacilli) and fungal infections were observed in 113 (17%) and 19 (3%) patients; 58 (9%) were transferred to ICU and 82 (12%) died within 30 days, 372 (56%) patients received subsequent chemotherapy. Independent prognostic factors were the level of monocyte, lymphocyte counts or albuminemia and a documented bacterial infection, while Charlson index and primary prophylactic G-CSF were not associated with patient clinical outcomes. CONCLUSION: Despite the use of primary G-CSF, ICISN remains a frequent event, which leads to ICU death in one on five cases Some prognostic factors of severity have been highlighted and could help clinicians to prevent severe complications.

The impact of limited access to digital health records on doctors and their willingness to adopt electronic health record systems.

Objective: Research over the past decade has extensively covered the benefits of electronic health records in developing countries. Yet, the specific impact of their limited access on doctors' workload and clinical decision-making, particularly in Bangladesh, remains underexplored. This study investigates current patients' medical history storage mechanisms and associated challenges. It explores how doctors in Bangladesh obtain and review patients' past medical histories, identifying the challenges they face. Additionally, it examines whether limited access to digital health records is an obstacle in clinical decision-making and explores factors influencing doctors' willingness to adopt electronic health record systems in such contexts. Method: An online cross-sectional survey of 105 doctors with Bachelor of Medicine, Bachelor of Surgery/Bachelor of Dental Surgery (MBBS/BDS) degrees and at least 2 years of experience was conducted, covering (a) personal information, (b) workload, (c) patient history challenges, and (d) decision-making. Results: Out of 105 participants, 51.4% of them use paper-based methods with 56% facing challenges, versus 20% using digital methods. Most (94.3%) interview patients directly, and 80.9% are interested in a web-based, comprehensive medical history system. An ordinal regression model identified that the physicians' disciplines, workload, and efficiency level of the current workplace in facilitating patient history-taking variables significantly affected willingness to adopt the described electronic health record in the survey. Conclusion: Doctors in Bangladesh encounter significant challenges related to workload and clinical decision-making, largely attributed to restricted access to patients' past medical histories. Despite the prevalent use of paper-based records, there is a notable willingness among these medical professionals to embrace electronic health record systems, indicating a potential shift towards more efficient healthcare practices in the region.

A longitudinal investigation of parenting, depression, and counseling use among Asian American adolescents.

INTRODUCTION: Asian American adolescents are equally or more likely to experience depression but less likely to seek treatment for depression than adolescents from other racial and ethnic groups in the US. The current study examined the long-term effects of parental care, parental control, and parental closeness on depression and counseling use among Asian American adolescents. METHODS: Using data from the National Longitudinal Study of Adolescent Health (Add Health), we conducted a cross-lagged path analysis with 270 Asian American adolescents (48.1% female; 51.9% male). The study used data from Waves I, II, and III (1994-2002) with participants' mean ages ranging from 14 to 23. RESULTS: Findings indicated that the cross-sectional relationships between parenting characteristics and depression were stronger than the longitudinal relationships suggesting that parenting practices may be a stronger proximal, rather than distal, predictor of depression. Specifically, parental closeness was associated with less depression in early and mid-adolescence (age 12-18), but the relationship changed direction in young adulthood (age 18-26). Additionally, a significant interaction suggested that parental care was related to fewer depressive symptoms for those who reported high, compared to low, parental control in mid-adolescence (age 14-18). Furthermore, high parental care was associated with more counseling use at high levels of control. However, high parental care was associated with less counseling use at low levels of parental control in early adolescence. CONCLUSION: These results highlight the importance of understanding parenting characteristics using longitudinal designs when examining the development of depression and help-seeking behaviors among Asian American adolescents.

Barriers encountered with clinical data warehouses: Recommendations from a focus group.

BACKGROUND AND OBJECTIVE: The increasing implementation and use of electronic health records over the last few decades has made a significant volume of clinical data being available. Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. However, the implementation of clinical data warehouses encounters a set of barriers: ethical, legislative, technical, human and organizational. This paper proposes an overview of difficulties and barriers encountered during a clinical data warehouse (CDW) development and implementation project. METHODS: We conducted a focus group at the 2023 Medical Informatics Europe Conference and invited professionals involved in the implementation of CDW. These experts described their CDW and the difficulties and barriers they encountered at each phase: (i) launching of the data warehouse project, (ii) implementing the data warehouse and (iii) using a data warehouse in routine operations. They were also asked to propose solutions they were able to implement to address the barriers previously reported. RESULTS: After synthesis and consensus, a total of 26 barriers were identified, 10 pertained to tasks, 5 to tools and technologies, 4 to persons, 4 to organization, and 3 to the external environment. To address these challenges, a set of 15 practical recommendations was offered, covering essential aspects such as governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization. CONCLUSIONS: These recommendations serve as a valuable resource for healthcare institutions seeking to establish and optimize CDWs, offering a roadmap for leveraging clinical data for research, quality enhancement, and improved patient care.

Veterinary Services'use of the Global Burden of Animal Diseases to prioritise interventions, monitor impact and develop critical competencies.

Attracting and sustaining investment in Veterinary Services and animal health programmes from national government budgets, development aid and grants, and philanthropic donors requires economic rationale using relevant, reliable and validated analytical approaches. The complex interwoven relationships between animal health, livestock husbandry systems, national food security, global health security and environmental sustainability emphasise the importance of improving data governance and stewardship and applying economic analysis to understand animal disease burdens. These efforts should enable prioritised investment of limited resources and effective monitoring of the impact of programmes over time. Data governance and stewardship capacities are fundamental to development, implementation and performance monitoring of evidence-based policies in animal health. There are challenges in data availability for national and subnational livestock populations in different sectors, for disease incidence and prevalence, and for animal health expenditure in support of optimised allocation of scarce resources, be they finance, land, labour, or management attention and policy focus. Animal health data systems governance and stewardship and economic analysis are core skills for Veterinary Services in developing and applying evidence-based policy, but capability probably varies among World Organisation for Animal Health (WOAH) Members. The WOAH Performance of Veterinary Services programme has several critical competencies that are relevant to economics of animal health and to data governance and stewardship, but these have not yet been targeted for coordinated capacity development. Implementation of publicâ€"private partnership approaches for animal health programmes creates increasing expectations of robust data and methods for prioritisation, options analysis, and assessing impacts and costs. Experience and examples from national systems in New Zealand, Australia, Ethiopia and Indonesia illustrate current challenges associated with prioritisation of animal health programmes using economic analysis. The Global Burden of Animal Diseases programme intends to support WOAH Members and partners to develop capacities for and standardise approaches to economic analysis and prioritisation in animal health programmes.

Les investissements dans les Services vétérinaires et dans les programmes de santé animale à partir des budgets publics nationaux, des aides et subventions au développement et des fonds alloués par des donateurs philanthropiques peuvent être encouragés et pérennisés au moyen d'une argumentation économique solide fondée sur des méthodes analytiques pertinentes, fiables et validées. La complexité et l'imbrication des relations entre la santé animale, les systèmes d'élevage, la sécurité de l'approvisionnement alimentaire à l'échelle nationale, la sécurité sanitaire mondiale et la durabilité environnementale imposent d'améliorer la gouvernance et la gestion des données et de recourir à des analyses économiques pour mieux comprendre l'impact des maladies animales. Ces efforts devraient permettre de définir les investissements prioritaires dans un contexte de ressources limitées et d'assurer un suivi efficace de l'impact des programmes dans le temps. L'existence de capacités de gouvernance et de gestion des données est donc une condition essentielle pour concevoir et mettre en oeuvre des politiques de santé animale fondées sur des données factuelles et pour suivre leurs performances. Les données disponibles sur les populations d'animaux d'élevage des différentes filières aux niveaux national ou infranational, sur l'incidence et la prévalence des maladies ou sur les dépenses de santé animale sont parfois insuffisantes pour étayer une utilisation optimale de ressources limitées, qu'il s'agisse de moyens financiers, des terres, de la main-d'oeuvre, voire des efforts de gestion ou de la volonté politique. La gouvernance et la gestion des systèmes de données de santé animale et la conduite d'analyses économiques sont des compétences cruciales des Services vétérinaires, que ceux-ci mobilisent pour concevoir et mettre en oeuvre des politiques fondées sur des données factuelles ; il est néanmoins peu probable que ces capacités soient d'un niveau homogène parmi tous les Membres de l'Organisation mondiale de la santé animale (OMSA). Le Processus d'évaluation de la Performance des Services vétérinaires mis en place par l'OMSA définit un certain nombre de compétences critiques dans le domaine de l'économie de la santé animale et de la gouvernance et gestion des données, mais ces compétences n'ont pas encore été intégrées dans un effort coordonné de renforcement des capacités. Les stratégies consistant à confier la mise en oeuvre de programmes de santé animale à des partenariats public-privé suscitent des besoins accrus en données et en méthodes robustes pour l'établissement des priorités, l'analyse des options et l'évaluation des impacts et des coûts. Les auteurs mentionnent les expériences et exemples de systèmes nationaux en Nouvelle-Zélande, en Australie, en Ethiopie et en Indonésie pour illustrer les enjeux actuels liés à l'utilisation des analyses économiques pour définir les priorités des programmes de santé animale. Le programme " Impact mondial des maladies animales " vise à aider les Membres et les partenaires de l'OMSA à renforcer leurs capacités dans le domaine de l'analyse économique et de la définition des priorités des programmes de santé animale et à normaliser leurs approches en la matière.

Para atraer y mantener las inversiones en los Servicios Veterinarios y los programas de sanidad animal procedentes de los presupuestos de los gobiernos nacionales, la ayuda para el desarrollo y las subvenciones, así como de donantes filántropos, se requiere un razonamiento económico en el que se utilicen enfoques analíticos pertinentes, fiables y validados. Las complejas relaciones entre la sanidad animal, los sistemas de ganadería, la seguridad alimentaria nacional, la seguridad sanitaria mundial y la sostenibilidad ambiental ponen de relieve la importancia de mejorar la gobernanza y la gestión de datos y de aplicar el análisis económico para comprender el impacto de las enfermedades animales. Estos esfuerzos deberían permitir establecer prioridades para la inversión de los limitados recursos y realizar un seguimiento eficaz de las repercusiones de los programas a lo largo del tiempo. Las capacidades de gobernanza y gestión de datos son fundamentales para el desarrollo y la implementación de políticas de sanidad animal con una base empírica y para el seguimiento de sus resultados. Existen dificultades en cuanto a la disponibilidad de datos sobre las cabañas ganaderas nacionales y subnacionales de los distintos sectores, la incidencia y prevalencia de las enfermedades y el gasto en sanidad animal que plantean problemas a la hora de optimizar la asignación de unos recursos que son escasos, ya sean los recursos financieros, las tierras, la mano de obra o la atención a la gestión y la orientación de las políticas. La gobernanza y la gestión de los sistemas de datos zoosanitarios y el análisis económico son competencias esenciales para que los Servicios Veterinarios elaboren y apliquen políticas con base empírica, pero es probable que la capacidad varíe entre los Miembros de la Organización Mundial de Sanidad Animal (OMSA). El Proceso de Prestaciones de los Servicios Veterinarios de la OMSA abarca varias competencias esenciales que son relevantes para la economía de la sanidad animal y para la gobernanza y la gestión de datos, pero que aún no han sido objeto de actividades coordinadas de desarrollo de capacidades. La aplicación de enfoques de asociación público-privada para los programas de sanidad animal aumenta aún más las expectativas de datos y métodos sólidos para el establecimiento de prioridades, el análisis de opciones y la evaluación de las repercusiones y los costos. La experiencia y los ejemplos de los sistemas nacionales de Nueva Zelanda, Australia, Etiopía e Indonesia ilustran los retos actuales asociados al establecimiento de prioridades en los programas de sanidad animal mediante el análisis económico. El programa sobre el impacto global de las enfermedades animales pretende ayudar a los Miembros y socios de la OMSA a desarrollar capacidades y armonizar enfoques para el análisis económico y el establecimiento de prioridades en los programas de sanidad animal.

Health Data Sciences and Cardiovascular Disease in Africa: Needs and the Way Forward.

PURPOSE OF REVIEW: The rising burden of cardiovascular disease (CVD) in Africa is of great concern. Health data sciences is a rapidly developing field which has the potential to improve health outcomes, especially in low-middle income countries with burdened healthcare systems. We aim to explore the current CVD landscape in Africa, highlighting the importance of health data sciences in the region and identifying potential opportunities for application and growth by leveraging health data sciences to improve CVD outcomes. RECENT FINDINGS: While there have been a number of initiatives aimed at developing health data sciences in Africa over the recent decades, the progress and growth are still in their early stages. Its maximum potential can be leveraged through adequate funding, advanced training programs, focused resource allocation, encouraging bidirectional international partnerships, instituting best ethical practices, and prioritizing data science health research in the region. The findings of this review explore the current landscape of CVD and highlight the potential benefits and utility of health data sciences to address CVD challenges in Africa. By understanding and overcoming the barriers associated with health data sciences training, research, and application in the region, focused initiatives can be developed to promote research and development. These efforts will allow policymakers to form informed, evidence-based frameworks for the prevention and management of CVDs, and ultimately result in improved CVD outcomes in the region.

Health Data Sciences and Cardiovascular Diseases in South Asia: Innovations and Challenges in Digital Health.

PURPOSE OF REVIEW: Health data sciences can help mitigate high burden of cardiovascular disease (CVD) management in South Asia by increasing availability and affordability of healthcare services. This review explores the current landscape, challenges, and strategies for leveraging digital health technologies to improve CVD outcomes in the region. RECENT FINDINGS: Several South Asian countries are implementing national digital health strategies that aim to provide unique health account numbers for patients, creating longitudinal digital health records while others aim to digitize healthcare services and improve health outcomes. Significant challenges impede progress, including lack of interoperability, inadequate training of healthcare workers, cultural barriers, and data privacy concerns. Leveraging digital health for CVD management involves using big data for early detection, employing artificial intelligence for diagnostics, and integrating multiomics data for health insights. Addressing these challenges through policy frameworks, capacity building, and international cooperation is crucial for improving CVD outcomes in region.

NFDI4Health Workflow and Service for Synthetic Data Generation, Assessment and Risk Management.

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI); however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany's National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch Institute (RKI).

Demonstrating the data integrity of routinely collected healthcare systems data for clinical trials (DEDICaTe): A proof-of-concept study.

Introduction/aims: Healthcare systems data (also known as real-world or routinely collected health data) could transform the conduct of clinical trials. Demonstrating integrity and provenance of these data is critical for clinical trials, to enable their use where appropriate and avoid duplication using scarce trial resources. Building on previous work, this proof-of-concept study used a data intelligence tool, the "Central Metastore," to provide metadata and lineage information of nationally held data. Methods: The feasibility of NHS England's Central Metastore to capture detailed records of the origins, processes, and methods that produce four datasets was assessed. These were England's Hospital Episode Statistics (Admitted Patient Care, Outpatients, Critical Care) and the Civil Registration of Deaths (England and Wales). The process comprised: information gathering; information ingestion using the tool; and auto-generation of lineage diagrams/content to show data integrity. A guidance document to standardise this process was developed. Results/Discussion: The tool can ingest, store and display data provenance in sufficient detail to support trust and transparency in using these datasets for trials. The slowest step was information gathering from multiple sources, so consistency in record-keeping is essential.

Machine learning methods to discover hidden patterns in well-being and resilience for healthy aging.

BACKGROUND: A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ability to collect data between clinical care visits. Machine learning/artificial intelligence methods have the potential to uncover insights into healthy aging. Nurses and nurse informaticians have a unique lens to shape the future use of this technology. METHODS: The purpose of this research was to apply machine learning methods to MyStrengths+MyHealth de-identified data (N = 988) for adults 45 years of age and older. An exploratory data analysis process guided this work. RESULTS: Overall (n = 988), the average Strength was 66.1% (SD = 5.1), average Challenges 66.5% (SD = 7.5), and average Needs 60.06% (SD = 3.1). There was a significant difference between Strengths and Needs (p < 0.001), between Challenges and Needs (p < 0.001), and no significant differences between average Strengths and Challenges. Four concept groups were identified from the data (Thinking, Moving, Emotions, and Sleeping). The Thinking group had the most statistically significant challenges (11) associated with having at least one Thinking Challenge and the highest average Strengths (66.5%) and Needs (83.6%) compared to the other groups. CONCLUSION: This retrospective analysis applied machine learning methods to de-identified whole person health resilience data from the MSMH application. Adults 45 and older had many Strengths despite numerous Challenges and Needs. The Thinking group had the highest Strengths, Challenges, and Needs, which aligns with the literature and highlights the co-occurring health challenges experienced by this group. Machine learning methods applied to consumer health data identify unique insights applicable to specific conditions (e.g., cognitive) and healthy aging. The next steps involve testing personalized interventions with nurses leading artificial intelligence integration into clinical care.

Integrating Health and Disability Data Into Academic Information Systems: Workflow Optimization Study.

Background: Integrating health information into university information systems holds significant potential for enhancing student support and well-being. Despite the growing body of research highlighting issues faced by university students, including stress, depression, and disability, little has been done in the informatics field to incorporate health technologies at the institutional level. Objective: This study aims to investigate the current state of health information integration within university systems and provide design recommendations to address existing gaps and opportunities. Methods: We used a user-centered approach to conduct interviews and focus group sessions with stakeholders to gather comprehensive insights and requirements for the system. The methodology involved data collection, analysis, and the development of a suggested workflow. Results: The findings of this study revealed the shortcomings in the current process of handling health and disability data within university information systems. In our results, we discuss some requirements identified for integrating health-related information into student information systems, such as privacy and confidentiality, timely communication, task automation, and disability resources. We propose a workflow that separates the process into 2 distinct components: a health and disability system and measures of quality of life and wellness. The proposed workflow highlights the vital role of academic advisors in facilitating support and enhancing coordination among stakeholders. Conclusions: To streamline the workflow, it is vital to have effective coordination among stakeholders and redesign the university information system. However, implementing the new system will require significant capital and resources. We strongly emphasize the importance of increased standardization and regulation to support the information system requirements for health and disability. Through the adoption of standardized practices and regulations, we can ensure the smooth and effective implementation of the required support system.

Potentially avoidable hospitalizations and associated factors among older people in French Guiana using the French National Health Data System.

Knowing the prevalence of potentially avoidable hospitalizations (PAHs) and the factors associated with them is essential if preventive action is to be taken. Studies on PAHs mainly concern adults, and very few have been carried out in South America. To the best of our knowledge, there has been no study on PAHs in French Guiana, particularly among older adults. This case-control study aimed to estimate the prevalence of PAHs in the Guianese population aged over 65 and to analyze their associated factors. We used the 2017-2019 data from the French National Health Service database (Système National des Données de Santé). The patients were age- and sex-matched 1 : 3 with controls without any PAH in 2019. Factors associated with PAHs were investigated through two conditional logistic regression models [one including the Charlson comorbidity index (CCI) and one including each comorbidity of the CCI], with calculation of the adjusted odds ratio (aOR) and 95% confidence interval (CI). The PAH incidence was 17.4 per 1000 inhabitants. PAHs represented 6.6% of all hospitalizations (45.6% related to congestive heart failure or hypertension). A higher CCI was associated with PAHs [aOR 2.2 (95% CI: 1.6, 3.0) and aOR 4.8 (95% CI: 2.4, 9.9) for 1-2 and ≥3 comorbidities, respectively, versus 0], as was immigrant health insurance status [aOR 2.3 (95% CI: 1.3, 4.2)]. Connective tissue disease, chronic pulmonary disease, congestive heart failure, diabetes, and peripheral vascular disease were comorbidities associated with an increased risk of PAHs. While the prevention of PAHs among immigrants is probably beyond the reach of the Guianese authorities, primary care and a public health policy geared toward prevention should be put in place for the French Guianese population suffering from cardiovascular disease in order to reduce PAHs.