bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-driven prediction of early language delay at Youth Health Care Services in the Netherlands
Authors: Putrik P , Hendriks M , van de Kraats C , Reijs R , Ravesteijn B 
Published: October 2024

Abstract

Introduction
Language development in early childhood significantly impacts future academic success, social interaction, and emotional well-being. This study aimed to develop and evaluate a prediction model using data on developmental milestones and parents’ socio-economic background known at age 2, and translate the results into an interactive digital dashboard and pilot it with Youth Health Care (YHC) professionals and parents.

Methods
A mixed-method study on retrospective cohort of children attending YHC at age 2, followed up until age 4. Data on language development, gender, parental education, and home language environment were obtained from YHC digital dossiers and linked to parental socio-economic status characteristics, and perinatal outcomes and healthcare costs using a secure remote access environment of Statistics Netherlands. Random forest and logistic regression models were computed with language development at age 4 as an outcome, initially using all available data at age 2 as predictors and then with a restricted model using variables readily available in YHC settings. Models performance was assessed using sensitivity, specificity, and AUC value. Results were visualized through an interactive dashboard and pilot-tested in simulated consultations.

Results
Among 9,148 children, 13.2% had language development delay at age 4. The full model had an AUC of 0.78, while the restricted model (gender, parental education, language environment, and language development), achieved an AUC of 0.77. YHC professionals and parents recognized the value of individualized, data-driven risk assessment for potential language delay to stimulate the discussion of preventive interventions.

Conclusions
A good quality prediction model for language development at age 4 can be derived from just a few background characteristics of the child and parents at age 2. The digital dashboard presents a practical approach of integrating prediction model results into daily YHC practice.

Key messages
• Language development at age of 4 can be predicted as early as age of 2 using just a few background characteristics of child and parents, which presents a window of opportunity for prevention.
• The digital dashboard presents a practical approach of integrating prediction model results into preventive care practice.
Reduced physical activity in youth during the COVID-19 pandemic: systematic review and meta-analysis
Authors: Ludwig-Walz H , Siemens W , Heinisch S , Dannheim I , Loss J , Bujard M 
Published: October 2024

Abstract

Background
Emerging research suggests that physical activity among children and adolescents decreased during the COVID-19 pandemic. As a summarizing analysis of changes in youth’s physical activity for Europe is lacking, we performed a systematic review and meta-analysis to close this research gap. Amongst others, we examined the relevance of restriction stringency policies and potentially vulnerable groups.

Methods
In consideration of the PRISMA statement, we searched seven databases and included studies for children and adolescents (≤19 years) of the WHO European Region that compared physical activity during the pandemic with a pre-pandemic baseline using validated measurement instruments. We used the Oxford Stringency Index as an indicator of restriction stringency. Screening for eligibility, data extraction, assessment of the study risk of bias (using ROBINS-E instrument) and certainty grading of evidence (using the GRADE approach), were all done in duplicate. Data were pooled in random effects models (Trial registration: PROSPERO: CRD42023395871).

Results
Of 14,897 non-duplicate records, 26 publications (n = 15,038 pre-pandemic, n = 13,041 during pandemic) met our inclusion criteria. Comparison before vs during the COVID-19 pandemic revealed a significant reduction in total physical activity (standardized mean difference [SMD], -0.57 [95% CI, -0.95; -0.20]) and moderate-to-vigorous physical activity (SMD, -0.43 [95% CI, -0.75; -0.10]), corresponding to a decrease of 12 min/day (a 20% reduction of the WHO recommendation). Subgroup analyses suggested that children aged 8-12 were particularly affected. The analyses outline a possible association between school closures and more reductions in physical activity.

Conclusions
A sharp decline in all forms of physical activity was recorded among European children and adolescents during the COVID-19 pandemic, particularly in middle childhood. Policy-makers, teachers and practitioners must act to reverse this decline.

Key messages
• Physical activity in European children & adolescents decreased significantly during the COVID-19 pandemic.
• Immediate action by policy-makers and practitioners is imperative to reverse this trend.
Evaluation of Previous ECG Algorithms for Identifying the Culprit Artery in Inferior STEMI
Authors: Fall S , Elhraiech A , Ghali H , Farhat S , Slim M , Ouennes S , Ben Ali I , Thabet H , Gribaa R , Neffati I 
Published: October 2024

Abstract

Background
ST elevation myocardial infarction (STEMI) is the leading cause of cardiovascular related mortality. In the context of Inferior STEMI, the culprit artery is whether the right coronary artery (RCA) or left circumflex coronary artery (LCx). An accurate prediction of culprit artery prior to primary percutaneous coronary intervention (PCI) could improve the door-to-balloon time and could prevent contrast-induced-nephropathy. However algorithms established for that purpose were not evaluated within the Tunisian population. We aimed then to assess their performance in our population

Methods
We conducted a single-center hostorical cohort study from January 2014 to December 2022 on patients admitted at Sahloul university hospital Sousse Tunisia for PCI following an inferior STEMI. We analyzed the ST segment deviations at the J-point and 80 ms after the J-point then evaluated the performance of previous algorithms.

Results
We collected data from 156 patients, of which 130 (83.3%) hada RCA occlusion, and 26 (16.7%) a LCx occlusion. We evaluated 19 published algorithms. The criterion of zimetbaum et al (ST elevation in III&gt; II and I and/or VL &lt;-1 mm) had the highest AUC 0.77[0.66 - 0.88] YI = 52 accuracy = 0.81. The criterion of Herz et al (ST III &gt; II) had AUC ROC of 0.71 [0.58 - 0.83] YI = 0.41 accuracy = 0.78, the algorithm of Tierala et al: AUC = 0.70[0.58 - 0.83] YI = 0.40 accuracy = 0.78. The algorithm og Fiol et al: AUC = 0.79 [0.58-0.83] YI = 0.59 accuracy = 0.73.

Conclusions
The Zimetbaum criterion had the highest performance in predicting the culprit artery and Fiol’s algorithm was the most specific to the LCx occlusion in our population. Further study on the impact of these algorithms on patients outcome is necessary.

Key messages
• The prediction of the culprit artery in inferior STEMI is challenging.
• The criterion of Zimetbaum et al had a good performance for that regard.
Evaluating a Novel Hospital-Integrated Vaccination Pathway: Enhancing Coverage among Frail Patients
Authors: Di Pumpo M , Tona D M , Porcelli M , Lontano A , Regazzi L , Pascucci D , Bruno S , Cadeddu C , Laurenti P , Pastorino R 
Published: October 2024

Abstract

Background
The 2022-2025 Italian Plan for vaccine prevention (PNPV), recognizes vaccine-preventable diseases (VPDs) as significant contributors to mortality, morbidity, and healthcare expenditure. While the digitalization of the national vaccine registry is underway, initiatives aimed at enhancing digital integration between hospitals and territories are limited. There is still a gap in the development of automated systems for identifying patients who could benefit from vaccinations. The “Caring for frail patients through vaccination” (CareVax) Project aims to eliminate these gaps by providing new digital tools and integrated systems.

Methods
1500 patients will be recruited at teaching hospital “A. Gemelli” over four years. With the assistance of an automated algorithm, electronic hospital and vaccination records will be utilised to assess eligibility for vaccinations against SARS-CoV-2, H. Zoster, Influenza, S.Pneumoniae and HBV. Eligible patients will be invited to schedule a vaccination appointment and will be asked to fill a questionnaire evaluating patient-reported experience measures (PREMs). The outcomes of interest include assessing the feasibility of the pathway, measuring patients’ satisfaction and concerns, and evaluating its impact on vaccination coverage.

Results
It is expected that frail patients’ vaccine coverage will improve, being offered an expedited booking process to get the vaccines either in the ambulatories of the Local Health Authority or in Hospital. The algorithm has already been successfully validated on a sample of anonymized patients. The results of the study will provide evidence on novel, hospital-integrating, vaccination pathways.

Conclusions
The results obtained from the intervention will help reduce VDPs and facilitate identification of the frail patients, ensuring easy access to vaccination and increasing vaccination coverage.

Key messages
• Carevax will test their feasibility in an Italian teaching hospital, leveraging digital technologies to reduce the burden of VPDs novel approaches.
• Hospital-Territory integrated vaccination pathways represent a novel digital approach to reduce the burden of VPDs.
Enhancing Public Health: training an AI chatbot for complex tasks support
Authors: Aloise I , Migliara G , Franceschetti A , Pistollato A , Mancini C , Forcella C , Baccolini V , De Vito C 
Published: October 2024

Abstract

Background
The evolution of Large Language Models (LLMs) such as GPT-4, Gemini, and Copilot has transformed the field of Natural Language Processing, demonstrating human-level capabilities and potential applications in Public Health. Here, we report on the training of a chatbot model based on GPT-4 designed to assist operators with relevant and complex public health tasks.

Methods
A scoping review of existing studies on the application of LLMs in Public Health settings was conducted to understand the landscape and identify gaps in their current implementations. We then proceeded to analyse the necessary skills and competencies required for public health professionals to adequately instruct the model on required tasks. Finally, we selected the Italian regulations and guidelines with the greatest relevance in the public health field to provide the knowledge base to the model.

Results
The review revealed no relevant documents on the use of LLMs in areas related to Public Health. Skills and competencies were retrieved from the Guidelines of the Italian Society of Hygiene for Public Health operators and used to instruct the model on how to address public health tasks. As for the knowledge base, we retrieved 25 documents regulating specific areas of the public health field, such as vaccinations, hygiene and safety at work, infection prevention, and school hygiene. Such documents were used to train the model, after formatting them appropriately.

Conclusions
Despite challenges with non-standardized and technically complex documentation, preliminary testing of our model has shown its versatility and utility in supporting daily public health operations. A validation phase is under definition and will involve a DELPHI process, in which products of the custom GPT model will be evaluated by a panel of public health operators to evaluate its performance in real-world settings, ensuring compliance with national health regulations.

Key messages
• Training a GPT-4-based chatbot to support complex tasks in Public Health, tailored by relevant national guidelines.
• Our custom model, informed by public health guidelines, showed potential in preliminary tests.
Application of artificial intelligence to study the causality in public health: a systematic review
Authors: La Mastra C , Barchitta M , Maugeri A , Agodi A 
Published: October 2024

Abstract

Introduction
In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

Methods
A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health.

Results
From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment.

Discussion
While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings.

Key messages
• In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions.
• This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.
Investigating the impact of the WHO Hive platform for health emergencies preparedness and response
Authors: Farhat M , Latinovic L , von Harbou K , Emiroglu N , Hang F , Yau B , Kovačev N 
Published: October 2024

Abstract

Background
Recent global health emergencies, such as the COVID-19 pandemic, showed there is a growing need for enhanced collaboration and knowledge sharing across global health communities. The WHO Hive platform provides a centralized hub for public health experts to connect, share and access valuable resources. This study aims to analyze the impact of the Hive following its launch of 4 tools within the Hive to enhance engagement and collaboration: Community space, Chat and discussion forum, Video conferencing, Hive drive document repository. The study will explore further technological and structural needs required to increase its usage in health emergency preparedness and response.

Methods
This study employs a structured survey for users of the Hive that will be anonymized and stratified by non-identifying data such as professional roles and geographic locations to highlight trends and patterns within specific subgroups. This approach ensures that the analysis is representative of the diverse user base on the Hive platform, with currently over 700 public health experts from more than 200 organizations across 105 countries.

Expected Outcomes
The study is expected to provide a comprehensive evaluation of Hive’s operational effectiveness in enhancing community collaboration, engagement, and knowledge-sharing, and highlighting its strengths and areas for improvement. The results will help inform future development to enhance collaboration and engagement.

Conclusions
The WHO Hive offers a unique value proposition as a platform developed by a trusted global health authority, enhancing credibility and fostering greater participation between health professionals. Feedback from the users is crucial to optimize future development to sustain it as a resource for health emergencies preparedness, readiness and response.

Key messages
• Digital tools for stronger health emergency preparedness and response to enhance collaboration and engagement.
• Highlighting key areas for digital integration to strengthen health emergency preparedness and response.
Clustering Portuguese municipalities based on population health indicators
Authors: Magalhães C M , Cruz M , Matos C 
Published: October 2024

Abstract

Background
The large number of publicly available health-related indicators poses challenges in health planning to policymakers. Identifying patterns among municipalities may assist these processes, by grouping similar contexts and pointing out relevant differences among neighbouring locations. The aim of this work was to define clusters of municipalities based on health-related indicators.

Methods
All health-related indicators available at municipality level for the year 2021 were obtained from the Portuguese National Statistics Office website. Missing values were imputed with the closest available year or excluded. Indicators were standardized and K-means clustering was applied. Elbow criterion was used to select the optimal number of clusters. Sensitivity analysis was performed with a priori exclusion of indicators less directly associated with health outcomes.

Results
The clustering analysis revealed five distinct clusters of municipalities. Clusters were associated with specific socio-demographic patterns, providing insights into common health challenges and strengths within each group. Interestingly, geographic profiles emerged (e.g. coastal vs inland), despite no geographic information being specifically provided. One outlier municipality was identified (Lisbon), highlighting a need for special consideration.

Conclusions
Clustering techniques provide a data-driven approach to inform public health planning. Our findings show previously unrecognized patterns and relationships between municipalities. Using these clusters, policymakers may implement interventions based on what has been proven to work in similar contexts, enhance collaboration opportunities and promote a tailored policy development, contributing to an overall improvement of public health outcomes.

Key messages
• Clustering machine learning techniques provide a data-driven approach to inform public health planning.
• Identifying comparable municipalities based on health-related indicators allows targeted public health interventions.
Beneficial effect of honey from algae extract-fed bees on quality of life
Authors: Hajzer Z , Oláh C , Oláh Z , Szekér Z , Oláh B , Shaikh A , Daróczi L , Prokisch J 
Published: October 2024

Abstract

Background
Our team has been researching and developing special honeys from bees fed with syrups containing natural plant extracts for decades. Several pre-clinical studies have demonstrated that honey from bees fed with Chlorella algae extract contains a number of algal active ingredients with beneficial effects on the human body.

Methods
We conducted a prospective double-blind functional food study with 30 healthy volunteers with ethical approval. Based on strict inclusion and exclusion criteria, 30 gramms of honey has been eaten twice a day for 3 weeks under 150 ml of yoghurt. 15 people were fed honey from bees with special algae extract, while 15 concerned - as a control group - consumed acacia honey. Before and after the study, sleep quality was assessed by the SQS questionnaire, and quality of life by the SF-36, EORTC Q-C30 and EQ-5D tests.

Results
The specific algae extract contained honey improved the people’s sleep quality, their EQRTC parameters, their appetite, their digestion, and it also significantly reduced pain (p = 0.01), boosted the patients cognitive function, and reduced both constipation and diarrhoea. Significant differences in SF-36 parameters: pain decreased (12.9±1.6vs 6.6±1.1; p = 0.01), physical function (21.2±6.3vs 27.1±3.6; p = 0.01) and energy (11.2±2.7vs 13.9±1.4; p = 0.01) improved. No such changes occurred in control group.

Conclusions
Honey from bees fed with algae extract has a number of beneficial physiological effects on the human body, improving many parameters of quality of life.

Key messages
• The consumption of honey from bees fed with algae extract improves sleep, reduces pain, and has a positive effect on digestive and gastrointestinal symptoms.
• Within public health, quality of life is an increasingly important issue and improving it with functional foods is an important area of research.
Blood pressure and its control in adults: the Italian Health Examination Survey 2023-CUORE Project
Authors: Marcozzi B , Lo Noce C , Di Lonardo A , Vannucchi S , Damiano C , Galeone D , Bellisario P , Palmieri L , Donfrancesco C 
Published: October 2024

Abstract

Background
The WHO recommends a 25% relative reduction in the prevalence of raised blood pressure (RBP) by 2025, considering 2010 as baseline. To monitor the achievement of this target, prevalence of RBP and mean blood pressure were measured in the Italian general adult population within periodic health examination surveys (HESs) conducted within the CUORE Project.

Methods
In 2023 a new HES started including, up to now, data from random samples of residents aged 35-74 years (706 men, 727 women) in 7 Regions (of 20 regions) distributed in North, Centre and South of Italy. Blood pressure was measured at the right arm, in sitting position, by automated oscillometric device using standardized procedures and methods; the average of two measurements performed with 1 minute interval was used for the analysis. RBP is defined as systolic and/or diastolic blood pressure equal or greater than 140/90 mmHg. The survey is funded by the Italian Ministry of Health-CCM.

Results
Preliminary analysis shows that prevalence of RBP is 37% (95% Confidence Interval-CI: 30-46%) in men and 23% (17-29%) in women. Mean value of systolic blood pressure is 134 mmHg (133-136 mmHg) in men and 126 mmHg (125-127 mmHg) in women. Mean value of diastolic blood pressure is 80 mmHg (78-81 mmHg) in men and 75 mmHg (74-76 mmHg) in women. Among those with RBP and/or in treatment (49% of men and 39% of women), 35% of men and 19% women are unaware they could have BP control problems, 8% and 9% are aware but not in treatment, 24% and 40% are under treatment and at target, 33% and 22% are under treatment but not at target, respectively.

Conclusions
These preliminary data underline that RBP is still high in the Italian adult population. Significant reductions in RBP prevalence occurred in comparison to previous HES data, 20% from 25 years ago, about 10% from 15 year ago, while a stable trend was found in comparison to 5 years ago. Improvements on RBP awareness and control are still necessary.

Key messages
• Raised blood pressure is still high in the Italian general adult population.
• Implementing periodic HES is fundamental to measure blood pressure and estimate awareness and control of hypertension in the general population.