bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decision support systems as supporters of clinical work: perspectives from physicians in Finland
Authors: Heponiemi T , Lääveri T , Kaipio J , Kainiemi E , Virtanen L , Kaihlanen A 
Published: October 2024

Abstract

Background
The digitalisation of healthcare has changed physicians’ clinical and administrative work. For example, decision support systems (DSSs) are expected to support physicians’ daily work. The present study examined physicians’ experiences of DSSs’ support for their work analysed by age, gender, employment sector and leadership position.

Methods
Altogether 4630 physicians (64% women) from Finland responded to the online survey. Logistic regression analyses were conducted to examine the associations of age, gender, employment sector, and leadership position with experiences of DSSs’ support for their work.

Results
Of the respondents, 21.3 % agreed that DSSs supported their work, whereas 43.5 % were neutral and 35.2 % disagreed with the statement. Age (Wald’s F = 15.5, p = 0.001), employment sector (Wald’s F = 84.6, p &lt; 0.001), and leadership position (Wald’s F = 16.3, p &lt; 0.001) were significantly associated with the assesment, whereas gender was not. Those aged between 45 and 54 (OR 0.65, 95% CI 0.51-0.81) and between 55 and 64 (OR 0.74, 95% CI 0.59-0.93) had lower odds for agreeing compared to those aged 35 or under. Compared to physicians in hospitals, primary care physicians had 2.18 times (95%CI=1.84-2.59) greater odds for agreeing that DSSs support their work. Those who were in leadership positions had 1.50 times (95%CI=1.23-1.82) greater odds for agreeing compared to those not in leadership positions.

Conclusions
Finnish physicians did not experience DSSs supporting their work. Especially older physicians, hospital physicians and those not in leadership positions gave negative assessments. DSSs should be better integrated to physician workflow, it is also possible that usability problems of DSSs such as alert fatigue affected user experiences. More attention should be paid to developing DSSs in a way that they support physicians work better.

Key messages
• Finnish physicians did not experience that decision support systems would actually support their work.
• Especially older physicians, hospital physicians and those not in leadership positions did not agree that decision support systems would support their work.
Machine-learning based prediction for high health care utilizers using a multi-institution registry
Authors: Tan J K , Quan L , Salim NNM , Tan J H , Goh S Y , Thumboo J , Bee Y M 
Published: October 2024

Abstract

Background
Rising healthcare costs worldwide have spurred interest in using machine learning to pinpoint major healthcare utilizers for population health efforts. Previous studies have focused on individuals who impose the highest financial burden, such as targeting high-need high-cost utilizers, a small group with limited potential for cost reduction. Therefore, we developed models to predict future healthcare utilization across various thresholds.

Methods
We leveraged data from a multi-institutional diabetes database to develop binary classification models capable of predicting healthcare utilization in the following year. The models predicted six different outcomes: patients with an annual total length of stay of ≥ 7, ≥14, and ≥30 days, and emergency department (ED) attendance of ≥ 3, ≥5, and ≥10 visits. To mitigate class imbalance, we applied random and synthetic minority oversampling techniques and compared against models trained without oversampling. Models were trained with 2019 data and tested on unseen data from 2020 and 2021.

Results
Models trained with random oversampling, including logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron, consistently demonstrated high AUC (&gt;0.80) and sensitivity (&gt;0.60) across both training-validation and test datasets. Addressing class imbalance was found to be crucial, as models without oversampling displayed satisfactory AUC (&gt;0.80) but significantly lower sensitivity (&lt;0.10). Key predictors identified included age, number of ED visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean HbA1c levels.

Conclusions
Our machine learning models successfully predict high service level utilization with strong performance, offering valuable tools for policymakers and health planners to develop targeted health initiatives based on patient utilization patterns.

Key messages
• Machine learning models can be leveraged to predict future healthcare utilization and facilitate interventional program development.
• Annual total length of stay and total emergency department visits are useful service level indicators.
Social disconnectedness and the rate of medical conditions: a Danish population-based cohort study
Authors: Laustsen L M , Lasgaard M , Momen N C , Chen D , Gradus J L , Grønkjær M S , Jensen M M , Plana-Ripoll O 
Published: October 2024

Abstract

Background
Lack of social connections has a serious impact on health, increasing the risk of medical conditions such as depression, dementia, and coronary heart disease. However, evidence is limited by a disease-specific focus. We aimed to provide a comprehensive overview of differences in incidence rates of a broad range of medical conditions according to social disconnectedness (i.e., loneliness, social isolation, and low social support).

Methods
We conducted a cohort study using data on loneliness, social isolation, low social support, and a composite measure from the 2013 and 2017 Danish National Health Survey. Survey data was linked with register data on 11 categories of medical conditions through 2021. Poisson regression was applied to estimate incidence rate ratios (IRRs), incidence rate differences, and explore interaction with pre-existing mental disorders.

Results
Among 162,497 survey participants, 7.6%, 3.5%, and 14.8% were identified as lonely, socially isolated, and with low social support, respectively. Lonely individuals and individuals with low social support had a higher incidence rate of all 11 categories of medical conditions (interquartile range of IRRs, loneliness 1.22-1.49 and low social support 1.10-1.14), whereas this was the case for nine categories among socially isolated individuals (interquartile range of IRRs, 1.01-1.31). Applying the composite measure, the highest IRR was 2.61 (95% CI, 2.34-2.88) for a mental disorder and the lowest 1.03 (95% CI, 0.95-1.10) for cancer. We found neither major sex differences nor substantial interaction with pre-existing mental disorders.

Conclusions
Our study confirms and extends the available evidence on social disconnectedness and the risk of medical conditions. The attained knowledge may be of significance for mapping group-specific preventative needs and guiding interventions and health practitioners aiming to reduce the disease burden among socially disconnected individuals.

Key messages
• Based on survey and register data from 162,497 individuals, our study suggests that social disconnectedness, especially loneliness, is a determinant of a broad range of medical conditions.
• The findings may be of importance for identifying and targeting the disease burden among socially disconnected individuals.
Using EEA environment and health data to communicate with the public: the European Environment and Health Atlas
Authors: Sanchez Martinez Gerardo 
Published: October 2024

Abstract
The EEA European Environment and Health Atlas presents information on how pollution and other environmental risks affect the health and well-being of Europeans. It is almost exclusively built on the datasets that the EEA collects from its network, and it summarizes only environmental information of relevance to public health, as well as health impacts from key environmental risks like air pollution, noise and industrial emissions.
6.F. Scientific session: Understanding the impact of climate change on health: the latest reports from EEA and NPHIs
Authors:   Organised by: UK Health Security Agency, RIVM (Netherlands), Robert Koch Institute (Germany), European Environment Agency,   Chair persons: Lisbeth Hall (Netherlands), Svenja Matusall (Germany)
Published: October 2024

Abstract

Climate change poses a significant threat to human life and wellbeing worldwide. Extreme weather events like heatwaves, storms or floods can have adverse impacts on health, as can many other factors influenced by climate change, such as allergens, UV radiation and vector-borne diseases. In order to support the action needed to protect and promote health and wellbeing, many National Public Health Institutes (NPHIs) aim to keep relevant stakeholders informed on the scientific evidence of the impacts of climate change on health. Reports that synthesise the evidence can help in the development of suitable and health-relevant climate change adaptation and mitigation measures. In this workshop, European Environment Agency assessments on climate and health are presented, including the European Climate Risk Assessment Report and recent outputs of the European Climate and Health Observatory. Next, three NPHIs introduce their recent national reports on the impact of climate change on health, sharing lessons learned and valuable insights into their content, process and reception, highlighting similarities and differences. The workshop will feature presentations by NPHIs from the United Kingdom (UK Health Security Agency), Germany (Robert Koch Institute), and the Netherlands (National Institute for Public Health and the Environment) as well as the European Climate and Health Observatory. The perspective of the European Climate and Health Observatory offers Europe-wide insights as a provider of tools and knowledge that support actions from NPHIs and other health actors. Our aim is to highlight the value of such reports, and inform and inspire NPHIs and other stakeholders that may be considering the publication of similar reports. The format of the workshop will be informative and interactive, with opportunity for Q&A, discussion, and engagement with the audience. By showcasing the recent reports, the workshop aims to increase awareness of climate change as a public health issue and establish NPHIs as key climate actors. Furthermore, it aims to highlight the importance of having a national, up-to-date evidence base to drive climate measures and enhance resilience.

Key messages
• National evidence-based reports on climate change and health are a useful tool for informing health-relevant climate change adaptation and mitigation measures.
• Through the public health functions of surveillance, monitoring, responding and evaluating, NPHIs play a crucial role in collating and assessing the evidence on the health effects of climate change.
Assessing the current health effects of climate change in the Netherlands
Authors: Hall L , Betgen C , Boekhold S 
Published: October 2024

Abstract
The National Institute for Public Health and the Environment (RIVM) has assessed the current effects of climate change on health as part of a national sector-wide process towards updating the Dutch National Adaptation Strategy. Besides health, this process includes assessing the risks of climate change for water management, agriculture, the built environment, nature and other sectors. It will provide a basis for policy, for prioritising the most urgent risks and for long-term risk monitoring. Six climate-related health topics were assessed for this update: heat, air quality, mental health, UV radiation, pollen allergies and infectious diseases. To allow comparison of the impact and likelihood of the climate-related risks across sectors, a format for factsheets on climate risks had been provided. Following this format, for each climate-related health topic we considered the climate threat, secondary effects, exposure, vulnerability, adaptive capacity, impact and cascading effects. The magnitude of the health impacts was categorised per topic as low (&lt; 10,000 people affected; 0 - 10 seriously injured/dead), medium (10,000 - 100,000 people affected; 10 - 100 seriously injured/dead) or high (&gt;100,000 people affected; &gt;100 seriously injured/dead). The likelihood of these impacts was then described as well as possible black swan events or tipping points. Finally, the policy context was outlined, including possible maladaptation, lock-ins, and equity aspects, followed by quality assurance considerations. For all topics, negative health impacts are already occurring. However, the magnitude of the impacts cannot yet be accurately quantified. In this presentation, we will discuss the results of this assessment and explore the pros and cons of being part of a sector-wide process.
Establishing disease models for the Belgian national burden of disease study: challenges and perspectives
Authors: Nayani S , Devleesschauwer B , Claerman R , De Pauw R 
Published: October 2024

Abstract
The Belgian national burden of disease study (BeBOD) was launched in 2022, and currently provides disease burden estimates for 38 key diseases. The calculation of the non-fatal component of the disease burden, the Years Lived with Disability (YLD), follows a stepwise approach that aims to establish priority outcomes; quantify prevalence “best estimates”; establish disease models; and perform expert evaluation of methods and results. The disease models visualize the relationship between the different health states associated with a given outcome. Health states include the different acute and chronic stages of the outcome (including complications), which may be stratified in different severity levels (e.g., mild, moderate, severe). Disease models used in burden of disease studies primarily aim to document the considered health states, and do not aim at a representation of the complete clinical picture of the condition. The disease models instead help in understanding how the number of cases for each health state is calculated. Models typically start with one “parent node”, which contains all cases. This parent node then gives rise to multiple “child nodes”, with the terminal child nodes representing the individual health states. In BeBOD, disease models and severity distributions are adapted from the Global Burden of Disease (GBD) study, and complemented with local data where possible. This process ensures consistency with available disability weights, while still allowing for local data to feed into the calculations. In this presentation, we will demonstrate the BeBOD process for establishing disease models based on a number of archetypical examples, which represent varying challenges linked to disease model complexity, clarity and completeness of the GBD disease model documentation, and availability of local data. Future perspectives will be discussed, including the improved leveraging of local clinical data, and the implementation of tailored active data collection.
6.I. Round table: Strengthening preparedness for public health emergencies: lessons learned and future perspectives
Authors:   Organised by: ECDC,   Chair persons: Sara Mazzilli (France), Ebba Rosendal (Portugal)
Published: October 2024

Abstract

In the face of intensifying global health threats, robust health security measures in Europe and worldwide have never been more pressing. The COVID-19 pandemic has underscored the critical need for coordinated action, adaptive strategies, and robust collaborative mechanisms to effectively respond to emerging infectious diseases and other public health emergencies. This workshop serves as a platform to examine the multifaceted landscape of health security actors, focusing on collaborative initiatives and lessons learned from recent crises. By convening experts and stakeholders from across Europe, the workshop aims to achieve several key objectives:
i) To provide an overview of collaborative initiatives established to strengthen European and international health security. By showcasing successful models of cooperation, participants will gain insights into effective strategies for fostering collaboration among diverse stakeholders.
ii) Emphasize the role of European and international institutions, such as the European Centre for Disease Prevention and Control (ECDC) and the World Health Organization (WHO), in supporting states both in coordinating regional response efforts and in developing the necessary health workforce competencies and capabilities to respond to health emergencies.
iii) Drawing on experiences from recent health emergencies, including outbreaks of high-risk pathogens, such as Ebola, the workshop will facilitate a critical examination of lessons learned. Through interactive discussions, participants will identify challenges, best practices, and opportunities for enhancing preparedness and response capacity.
iv) By interactive presentations and panel discussions, the workshop will provide a platform for knowledge exchange and networking among participants. By sharing experiences, expertise, and innovative approaches, participants will contribute to a collective understanding of the challenges and opportunities in strengthening health security.
This workshop seeks to illuminate key strategies, capacity-building efforts, and real-world lessons learnt that contribute to enhancing response capabilities to public health emergencies. Participants will gain a deeper understanding of emergency participation mechanisms, essential skills required, and available training resources to enhance preparedness.

Key messages
• Collaboration and adaptive strategies are essential for strengthening international health security in the face of emerging infectious diseases and other public health emergencies.
• Lessons learned from recent crises provide valuable insights into effective response strategies and opportunities for enhancing preparedness and response capacity.
Enhance physical, mental and academic wellbeing of pupils in Germany through digital health literacy
Authors: Renninger D , Stauch L , Fischer L , Hartmann A , Rangnow P , Dadaczynski K , Okan O 
Published: October 2024

Abstract

Background
Digital health literacy (DHL) involves the ability to find, understand, appraise, and apply health information from digital sources. It serves as the foundation for informed decision-making about one’s health and well-being using digital technology and online health information sources and services. Despite its significance, the role of DHL in relation to schoolchildren’s wellbeing remains underexplored. Therefore, this study aims to investigate how schoolchildren’s DHL influences their physical, mental, and academic well-being.

Methods
The cross-sectional survey comprised a representative sample of N = 1448 school-aged children (9-18 years) living in Germany. DHL was assessed via a translated and adapted version of the DHL instrument. The assessment of physical, mental, and academic well-being relied on the KINDL questionnaire. Linear regression analyses were conducted, with the scale score of physical, mental, and academic well-being serving as dependent variables and DHL as predictor. Additionally, potential confounding variables such as sex, age, migration background, subjective family wealth, chronic illness, and subjective health status were included in the analyses.

Results
All three regression models were significant (physical: F(9,1325) = 34.99, p &lt; .001; R2 = 0.19; mental: F(9,1320) = 27.43, p &lt; .001; R2 = 0.16; academic: F(9,1336) = 15.19, p &lt; .001; R2 = 0.09). DHL significantly predicted physical (B=.08, p=.01), mental (B=.17, p&lt;.001), and academic (B=.21, p&lt;.001) wellbeing.

Conclusions
This study provides the first evidence of the predictive power of DHL for schoolchildren’s physical, mental, and academic well-being. It emphasizes the importance of promoting DHL in schoolchildren to empower them to find, understand, appraise, and apply digital health information effectively.

Key messages
• Digital health literacy significantly predicts schoolchildren’s physical, mental, and academic well-being.
• Promoting digital health literacy can empower schoolchildren to make informed decisions about their well-being.
Limited critical and communicative health literacy is related to poorer health among older adults
Authors: Svensson P , Agerholm J , Wångdahl J 
Published: October 2024

Abstract

Background
Health literacy-related challenges are expected to increase with age due to a discrepancy between individual capacity and health care demands. Knowledge about associations between health literacy and age-related health outcomes among older adults is scarce. This is important to address and promote optimal health in older adults. The study aims to investigate the association between health literacy and health status among older adults and explore differences between the younger old and the oldest old adults.

Methods
Cross-sectional study using SWEOLD population-based data from 2022, a national representative sample of people 77+ years in Sweden. Univariate and multivariate logistic regression analysis assessed associations between communicative- and critical health literacy and health outcomes. Models were adjusted for age, sex, and socioeconomic status, and stratified by age.

Results
Significant adjusted associations were found between inadequate and problematic health literacy and less than good self-rated health (OR 2.64, 95%CI: 1.61-4.33; 2.11, 95%CI: 1.40.3.17), and requiring assistance with medicine management (OR 5.26, 95%CI: 2.67-10.38, 2.14, 95%CI 1.08-4.22). Among the younger old (77-84 years) inadequate and problematic HL was associated with having lifestyle-related conditions (OR 1.77, 95%CI: 1.07-2.93, 2.75, 95%CI: 1.33-2.71). Among the oldest old (85+), inadequate health literacy was associated with higher likelihood of poor activities of daily living (ADL) (OR 4.48, 95%CI: 1.99-10.07) and frailty (OR 3.51, 95%CI: 1.43-8.47). Level of frailty increases earlier in age for individuals with limited HL than among those with sufficient HL.

Conclusions
Limited health literacy is a prevalent issue in the older population and is associated with negative health-related outcomes. To mitigate development of poor health and frailty and to promote healthy ageing, care organisations need to meet the needs of this population in a health literacy friendly way.

Key messages
• Limited health literacy among the older population is associated with several negative health-related outcomes.
• Care organisations need to work actively to mitigate the development of poor health while promoting healthy aging among the older population in a health literacy-friendly and age sensitive manner.