bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning techniques for predicting inpatient probability and LOS from EU Injury Database
Authors: Pitidis A , Fondi G , Carannante A , Giustini M 
Published: October 2024

Abstract

Background
Unlike statistical inference Machine Learning (ML) makes repeatable predictions without prior assumptions about the underlying relationships among variables. The Full Data Set (FDS) of the EU-IDB (European Injury DataBase) consent to explore risk of hospitalization due to injuries using many predictors. The Length Of Stay of patients in hospital (LOS) is a relevant proxy of complexity of treatment and resources consumption.

Methods
The IDB-FDS provides more than 3.800.000 ED records, in years 2008-19 for 19 Countries. LASSO (Least Absolute Shrinkage and Selection Operator) cross-validated linearized regression technique (linear for LOS) was used for variable selection and parameter regularization. Inpatients are those admitted or transferred to hospital. Days of hospitalization were used for LOS. A cross-validated Generalized Linear Model was performed on 5 folds randomly sampled assigning 80% of records to training and 20% to testing samples.

Results
The strongest predictors of hospital admission risk, selected by the model were in order of importance: EUROCOST-39 diagnoses categories, Age Group, Intent, Mechanism Of Injury, Activity When Injured, Transport Injury Event, Sex Of Patient, Place Of Occurrence. EUROCOST-39 represents 61,9% of explained variability and age group 19,4%. The strongest predictors of LOS were substantially the same: EUROCOST-39 86,2% of explained variability and age group 8,6%.

Conclusions
The main part of variability in the ML model is explained by diagnoses reclassified according to a disability standardization method. For instance, in the maximum training sample risk of hospitalization ranges from odd 0,76% for hand/fingers sprain up to 154,02% for brain concussion. In the median sample LOS ranges from 0,45 days for strain of hand/fingers up to 11,65 for multi-trauma. A combination of more disabling injury, older age and mechanism of injury (i.e. threat to breathing) increases enormously the risk of hospitalization and LOS.

Key messages
• Machine Learning techniques applied on EU-IDB can provide identification of relevant risk factors of hospital admission from injuries for targeting preventive measures and organizing health care.
• Artificial Intelligence consents to analyse big amount of data as dimension or analytical detail of information for identifying patterns and predictors of specific indicators for injuries or diseases.
Predictive machine learning algorithms for metabolic syndrome among Tunisian adults
Authors: Zribi M , Zaier F , Zoghlami N , Elati J , Traissac P , Aounallah-Skhiri H 
Published: October 2024

Abstract

Background
Metabolic syndrome (MS), a cluster of interconnected risk factors for cardiovascular disease and type 2 diabetes, poses a significant health burden globally. In Tunisia, amidst an ongoing epidemiological transition, the prevalence of MS presents a growing concern. Our study aimed to identify the potential risk factors of MS and propose better machine learning (ML) based models for predicting MS among Tunisian adults.

Methods
Data was sourced from the Tunisian Health Examination Survey THES-2016, encompassing individuals aged 20 years and older. MS was defined based on the criteria set by the International Diabetes Federation. Logistic regression (LR) was employed to determine risk factors for MS. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated. Five ML algorithms (Naïve Bayes, Support Vector Machine (SVM), Artificial Neural Network (ANN), AdaBoost, and Random Forest (RF)) were utilized to predict MS. Performance evaluation was conducted using accuracy, precision, recall, and area under the curve (AUC) metrics.

Results
Among the 8908 participants, the prevalence of MS was 32.8% (95% CI: 31.4% - 34.6%). LR identified age group 60-69 years (aOR: 18.5, 95% CI: 16.9-19.9, p &lt; 0.001), female sex (aOR: 1.7, 95% CI: 1.2-1.9), sedentariness (aOR: 1.6, 95% CI: 1.5-1.8) and low education level (aOR: 1.2, 95% CI: 1.1-1.4) as significant factors associated with MS. Among ML models tested, AdaBoost had the highest accuracy (89.8%), followed by SVM (89.6%). Naïve Bayes had the highest Recall (98.1%) and the most performant AUC (91.4%), while RF had the highest Precision (70.8%). The ANN had an accuracy of 89%, a precision of 70.4%, a recall of 78.7% and an AUC of 85.1%.

Conclusions
Our study highlighted the effectiveness of ML algorithms like AdaBoost and SVM in predicting MS among Tunisian adults, offering potential as early detection tools. However, further validation with larger datasets is necessary to solidify their utility in healthcare settings.

Key messages
• Metabolic syndrome, a complex of cardiovascular risk factors, presents a substantial global public health challenge and is particularly pronounced in Tunisia.
• Machine learning algorithms, notably AdaBoost and Support Vector Machine, demonstrate promise in forecasting and addressing metabolic syndrome.in Tunisian adults.
Pandemic-related PTSD and associated risk factors in the general adult population in the Netherlands
Authors: van Duinkerken A , Bosmans M , Baliatsas C , Dückers M 
Published: October 2024

Abstract

Introduction
The COVID-19 pandemic introduced widespread stressors and shocking events, impacting mental well-being. Understanding posttraumatic stress disorder (PTSD) prevalence during the pandemic is crucial, yet existing studies on this topic vary widely in methodological quality. This study aims to fill this gap using a large-scale national survey study to examine PTSD prevalence during the pandemic and compare pandemic-related and traditional shocking events.

Methods
This study uses data from the Dutch Public Health Monitor Adults and Elderly 2022, consisting of 365,000 Dutch adults representative for the general adult population. Pandemic-related and traditional events were assessed and anchored to a screening instrument for PTSD: the Dutch version of the PCL-5.

Results
Two-thirds of participants reported experiencing a shocking event during the pandemic. Exposure to pandemic-related events was reported more frequently than traditional events. Overall probable PTSD prevalence due to these events was 2.4%, evenly split between pandemic-related and traditional events. Among exposed individuals, probable PTSD prevalence was 3.7%. This was higher for traditional than for pandemic-related events. Risk factors for developing probable PTSD after exposure to an event during the pandemic were younger age, financial difficulties, loneliness, low resilience and experiencing multiple events.

Discussion
Exposure to shocking events during the pandemic influenced the prevalence of probable PTSD. Despite the lower relative impact of pandemic-related shocking events compared to traditional events, exposure to pandemic-related shocking events accounted for the same amount of probable PTSD cases in the Dutch general population as 5 common traditional events. This emphasizes the need to address mental health consequences of prolonged crises and the demand for healthcare, and the importance of mitigation strategies based on risk and protective factors.

Key messages
• Exposure to pandemic-related events accounted for approximately the same amount of probable PTSD cases as 5 frequently occurring traditional events.
• Despite pandemic-related events being reported more frequently, traditional events were associated with a relatively higher prevalence of PTSD.
EVFAM-BR: Sharing experiences and closure
Authors: Eshriqui I , de Almeida L Y , de Souza E L , Nascimento V E , de Mendonça JMT , Neto FTP , Rebustini F , de Medeiros E B , de Sousa AAF , Bonfim D 
Published: October 2024

Abstract
This activity will last 20 minutes. After Role Play, participants will sit in large circle and the ones that assumed the observer role will share their experiences of using EVFAM-BR. Subsequently, other participants will be invited to share their experiences and impressions about EVFAM-BR. Based on the participants’ impressions and a review of the main points covered throughout the workshop, a final discussion and closure of the workshop will be facilitated.
Health Effects of Climate Change in the UK report 2023
Authors: Duarte-Davidson R , Gillingham E L , Phalkey R , Berrang-Ford L 
Published: October 2024

Abstract
The fourth Health Effects of Climate Change in the UK report (HECC) was published by the UK Health Security Agency in 2023. The report brings together 15 independent chapters written by 90 experts from UK and international academic and research institutions and UKHSA. It provides an authoritative summary of the scientific evidence on the health effects of climate change in the UK, potential implications for public health and gaps in evidence. Chapters include a combination of new empirical analyses (using the most updated climate data - UKCP18 projections) and synthesis of new evidence. Topics covered include future UK climate, temperature effects on mortality, flooding, outdoor and indoor air quality, aeroallergens, infectious diseases, vector-borne diseases, food supply, wildfires, drought, human exposure to chemicals, solar radiation, health impacts of net zero and indicators for climate change and public health tracking.
The German Status Report on Climate Change and Health 2023
Authors: Kelleher K , Mlinaric M , Niemann H , Ziese T 
Published: October 2024

Abstract
The German Status Report on Climate Change and Health was coordinated by the Robert Koch Institute, Germany’s public health institute, and published in 2023 through a collaboration of approximately 100 authors from 30 German institutions. It is an update of a similar report from 2010 and includes review articles and syntheses on the effects of climate change on human health through various influences such as heat, extreme weather, UV radiation, air pollutants and allergens, as well as the effect of climate change on infectious diseases through vector-borne, water-borne or food-borne pathogens. Literature reviews on the potential impact on antimicrobial resistance and mental health explore less well researched effects of climate change on health. The report discusses climate change communication and climate justice as cross-cutting issues and closes with an analysis of the options for action recommended throughout the preceding chapters. While the coordination of this report was funded by the German Federal Ministry of Health, the content was compiled by an advisory board of scientists and the authors recruited by them. The status report is published as 14 individual scientific papers in three issues of the peer-reviewed Journal of Health Monitoring and is available open access in English and in German in order to reach a large scientific audience. Besides the scientific audience, another target group are local authorities contributing towards adaptation and mitigation measures. Insights from the report are being communicated by diverse channels specifically to this latter group and are taken into consideration in the process of shaping the German Strategy for Adaptation to Climate Change.
Methodological insights into severity distribution of non-fatal cancer burden in Belgium using a microsimulation model (2004-2019)
Authors: De Pauw R , Gorasso V , Devleesschauwer B 
Published: October 2024

Abstract
This study leverages a registry-based microsimulation model to analyze the non-fatal burden of cancer in Belgium from 2004 to 2019, emphasizing the methodology for severity distribution. Utilizing incidence- and prevalence-based approaches, we estimated Years Lived with Disability (YLD) through detailed national cancer registry data coupled with tailored disease models. The microsimulation model employed enabled nuanced translation of cancer incidence into prevalence estimates, incorporating the longitudinal impacts of disease severity influenced by treatment. Methodologically, the model integrated expert elicitation to adjust for treatment-related disabilities, such as surgical aftereffects, enhancing the accuracy of disability weight assignment across different cancer stages. This approach allowed for a dynamic assessment of disease burden, reflecting real-world complexities of cancer prognosis and treatment pathways. Significant findings include a substantial increase in the age-standardized non-fatal burden for non-melanoma skin cancer, highlighting the model’s capability to detect shifts in disease impact over time. By focusing on the microsimulation’s capacity to delineate severity distributions within the cancer burden landscape, this analysis underscores the model’s utility in refining health policy decisions and resource allocation. Ultimately, this study presents a robust framework for national burden of disease studies, facilitating detailed evaluations of health interventions and their longitudinal effectiveness.
6.H. Round table: Harnessing Patient-Reported Data for Health Innovation: Insights from the OECD PaRIS Initiative
Authors:   Organised by: PaRIS Consortium, EUPHA-HSR,   Chair persons: Johan Hansen (EUPHA-HSR), Mieke Rijken (Netherlands)
Published: October 2024

Abstract

Summary
This round table will disseminate knowledge and foster discussions on data reported by patients with a chronic disease in primary care settings in 20 countries for enhancing health system responsiveness and effectiveness. By delving into the OECD’s Patient-Reported Indicator Surveys (PaRIS) survey’s development, adaptation, and analytical strategies, participants will leave with a richer understanding of how patient-reported data can inform and transform health policy and practice in Europe and beyond.

Rationale
The round table aligns with the Conference theme by focusing on the transformative potential of patient-reported data in health systems. OECD PaRIS provide a unique lens through which to examine patient-centered care, showcasing innovative methods to capture and utilize data across diverse health system contexts. This round table will explore the development, adaptation, and analysis of this flagship survey, revealing its critical role in shaping health policies and practices that are attuned to patient needs and experiences.

Objectives
1. Understanding Development and Testing Processes: To gain insights into the systematic approaches used in developing patient-reported instruments that are both robust and sensitive across multiple healthcare settings; 2. Exploring Co-development and Adaptation: To discuss the challenges and strategies involved in adapting health surveys to various national contexts while ensuring stakeholder engagement and data relevancy; 3. Analyzing Data for Policy Making: To comprehend the analytical frameworks used in processing patient-reported data for crafting effective health policies and identifying factors that influence patient outcomes.

Key messages
• Patient reported data on experiences and outcomes provide essential information for health systems performance.
• Unique insights into the systematic collection of such information can be gathered through the proposed discussion on the experience of designing and implementing the PaRIS Survey.
Patient-Reported Indicator Surveys (PaRIS): making the case for co-development and adaptation to national contexts
Authors: Kendir Candan 
Published: October 2024

Abstract
In this presentation we discuss the early implementation of the OECD’s Patient-Reported Indicator Surveys (PaRIS) in primary care, highlighting the importance of co-development and national adaptation. The PaRIS initiative focuses on collecting patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) from individuals with chronic conditions and their primary care providers. This data collection is critical as populations age and the prevalence of chronic conditions increases, necessitating health systems that prioritize patient-centered care. The co-development process of the PaRIS survey involved extensive international collaboration, incorporating feedback from policymakers, healthcare providers, patients, and academics, thereby enhancing the relevance and ownership of the project. An example of successful stakeholder engagement is the creation of the PaRIS Patient Advisory Panel, which included representatives from various patient organizations who participated in all stages of the survey’s development. Challenges encountered during pilot implementations varied by country, influenced by factors like existing data infrastructures and regulatory environments. For instance, countries lacking robust data collection systems faced more significant hurdles in adapting the survey to their contexts. The article emphasizes that while rigorous standardization is essential for making international comparisons, adaptation to local contexts is crucial for addressing specific implementation challenges. The joint efforts of all stakeholders are vital for the success of international initiatives like the PaRIS survey. Such collaborative efforts not only facilitate the collection of meaningful data but also contribute to the enhancement of primary care quality through informed policymaking and improved patient care practices.
Health literacy in pregnant women facing prenatal screening: a short report from “SaperePer” project
Authors: Bellisario V , Squillacioti G , El Sherbiny S , Ghelli F , Mazzoli M , Sciarrone A , Carletto S , Marozio L , Bono R 
Published: October 2024

Abstract

Advancements in health promotion and prenatal medicine have made noninvasive prenatal testing (NIPT) a common practice during pregnancy, but understanding prenatal health information becomes crucial not only for the well-being of the mother but also for that of the unborn child. Analyze health-related information during pregnancy is fundamental, and this work fits perfectly with this topic, reporting preliminary results from the pilot study “SaperePer”. A health-related questionnaire was administered to 358 women in the first trimester of pregnancy, aiming to study different outcomes: 1.level of knowledge of NIPT; 2.number of screening tests undergone; 3.willingness to undergo a diagnostic test with a high outcome risk of screening test; 4.willingness to end the pregnancy following a diagnosis of pathology. The questionnaire was divided into 3 sections: personal characteristics and lifestyle habits, prenatal health behavior scale (PHBS), and knowledge of NIPT. Preliminary results showed that the level of knowledge of NIPT was higher in older, atheist, working, wealthy highly educated, and preliminary informed women, as well as the higher number of screening tests undergone and the propensity to undergo an invasive diagnostic test after a high-risk screening result (KS and MW tests p &lt; 0.05). On the contrary, women with lower education, faithful and, even more, not preliminary and appropriately informed during prenatal counseling showed lower knowledge of NIPT, lower number of tests undergone, and meager chances to undergo an invasive diagnostic test or to end the pregnancy after a positive diagnosis of pathology (KS and MW tests p &lt; 0.05). In conclusion, this study evaluated health-related information during the first but crucial stages of pregnancy, opening a new understanding of how health literacy and correct prenatal counseling could increase awareness and health promotion in pregnant women, particularly in those women with lower cultural tools.

Key messages
• Health literacy and correct prenatal counseling could increase awareness and health promotion in pregnant women.
• Understanding prenatal health information becomes crucial both for maternal and prenatal well-being.
Using PPI and UDL principles to design a gastrointestinal endoscopy patient information video series
Authors: Fennessy S , Mulcahy L , O’Donnell B , Mulcahy H , Spooner M , McDermott E 
Published: October 2024

Abstract

Introduction
Patients undergoing GI endoscopy often experience anxiety and due to limited health literacy. Traditional paper-based information leaflets are often not sufficient to accommodate for the diverse way in which people consume information. Public and patient involvement (PPI) and universal design for learning are two well-described pedagogical principles. We wanted to use these principles to design more effective patient education materials.

Objectives
Given the heterogeneity of resources available to patients, the authors wanted to develop a high-quality and trustable video resources for patients that would be available on our hospital website. These videos would be co-designed by patients and ancillary staff, through the formation of a key stakeholder panel.

Results
Using PPI principles, we were able to improve the relevance and quality of our content, through the use of storyboards and the expertise of the stakeholder panel. Through the three key UDL principles of multiple means of representation, engagement and action and expression, we were able to ensure that our videos were accessible and appropriate for all of our patients. Initial feedback from stakeholders and patients provided valuable insights, prompting necessary adjustments for accuracy and patient understanding. This comprehensive approach underscores the commitment to enhancing healthcare delivery. Our project is the first guide in combining both pedagogical principles in the development of patient information and education materials.

Conclusions
The meticulous steps taken, from stakeholder engagement to video production, reflect a commitment to inclusivity and patient-centred care. Ongoing evaluation and feedback mechanisms ensure continuous improvement, contributing to the overall success and sustainability of the digital service.

Key messages
• This is the first research/patient education piece designed using both PPI and UDL principles.
• The importance of patient-centred educational design is key to improving health literacy and education in our patients.