Pre-COVID-19-pandemic RSV epidemiology and clinical burden in pediatric primary care in Italy: a comparative analysis across two regions for the 2019/2020 season.

BACKGROUND: Respiratory syncytial virus (RSV) infection in children under 5 years have a significant clinical burden, also in primary care settings. This study investigates the epidemiology and burden of RSV in Italian children during the 2019/20 pre-pandemic winter season. METHODS: A prospective cohort study was conducted in two Italian regions. Children with Acute Respiratory Infection (ARI) visiting pediatricians were eligible. Nasopharyngeal swabs were collected and analyzed via multiplex PCR for RSV detection. A follow-up questionnaire after 14 days assessed disease burden, encompassing healthcare utilization and illness duration. Statistical analyses, including regression models, explored associations between variables such as RSV subtype and regional variations. RESULTS: Of 293 children with ARI, 41% (119) tested positive for RSV. Median illness duration for RSV-positive cases was 7 days; 6% required hospitalization (median stay: 7 days). Medication was prescribed to 95% (110/116) of RSV cases, with 31% (34/116) receiving antibiotics. RSV subtype B and regional factors predicted increased healthcare utilization. Children with shortness of breath experienced a 36% longer illness duration. CONCLUSIONS: This study highlights a significant clinical burden and healthcare utilization associated with RSV in pre-pandemic Italian primary care settings. Identified predictors, including RSV subtype and symptomatology, indicate the need for targeted interventions and resource allocation strategies. RSV epidemiology can guide public health strategies for the implementation of preventive measures.

RSV Disease Burden in Primary Care in Italy: A Multi-Region Pediatric Study, Winter Season 2022-2023.

INTRODUCTION: Human respiratory syncytial virus (RSV) is one of the most frequent causes of respiratory infections in children under 5 years of age, but its socioeconomic impact and burden in primary care settings is still little studied. METHODS: During the 2022/2023 winter season, 55 pediatricians from five Italian regions participated in our community-based study. They collected a nasal swab for RSV molecular test from 650 patients under the age of 5 with acute respiratory infections (ARIs) and performed a baseline questionnaire. The clinical and socioeconomic burden of RSV disease in primary care was evaluated by two follow-up questionnaires completed by the parents of positive children on Days 14 and 30. RESULTS: RSV laboratory-confirmed cases were 37.8% of the total recruited ARI cases, with RSV subtype B accounting for the majority (65.4%) of RSV-positive swabs. RSV-positive children were younger than RSV-negative ones (median 12.5 vs. 16.5 months). The mean duration of symptoms for all children infected by RSV was 11.47 ± 6.27 days. We did not observe substantial differences in clinical severity between the two RSV subtypes, but RSV-A positive patients required more additional pediatric examinations than RSV-B cases. The socioeconomic impact of RSV infection was considerable, causing 53% of children to be absent from school, 46% of parents to lose working days, and 25% of families to incur extra costs. CONCLUSIONS: Our findings describe a baseline of the RSV disease burden in primary care in Italy before the introduction of upcoming immunization strategies.

A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability.

Introduction: Europe works to improve cancer management through the use of artificialintelligence (AI), and there is a need to accelerate the development of AI applications for childhood cancer. However, the current strategies used for algorithm development in childhood cancer may have bias and limited generalizability. This study reviewed existing publications on AI tools for pediatric brain tumors, Europe's most common type of childhood solid tumor, to examine the data sources for developing AI tools. Methods: We performed a bibliometric analysis of the publications on AI tools for pediatric brain tumors, and we examined the type of data used, data sources, and geographic location of cohorts to evaluate the generalizability of the algorithms. Results: We screened 10503 publications, and we selected 45. A total of 34/45 publications developing AI tools focused on glial tumors, while 35/45 used MRI as a source of information to predict the classification and prognosis. The median number of patients for algorithm development was 89 for single-center studies and 120 for multicenter studies. A total of 17/45 publications used pediatric datasets from the UK. Discussion: Since the development of AI tools for pediatric brain tumors is still in its infancy, there is a need to support data exchange and collaboration between centers to increase the number of patients used for algorithm training and improve their generalizability. To this end, there is a need for increased data exchange and collaboration between centers and to explore the applicability of decentralized privacy-preserving technologies consistent with the General Data Protection Regulation (GDPR). This is particularly important in light of using the European Health Data Space and international collaborations.

Prioritizing educational initiatives on emerging technologies for Italian pediatricians: bibliometric review and a survey.

BACKGROUND: Emerging technologies have demonstrated outstanding potential in improving healthcare, yet their full integration remains a challenge for all medical specialties, including pediatrics. To support the swift implementation of technologies, we identified the current trends through a bibliometric review, and we conducted a survey on Italian pediatricians to gauge educational needs and willingness to integrate technologies into clinical practice. METHODS: A working group of pediatricians representing various backgrounds designed and coordinated the study. To identify relevant topics for educational strategy development, we focused on virtual reality, telehealth, natural language processing, smartphone applications, robotics, genomics, and artificial intelligence. A bibliometric analysis limited to 2018-2023 was performed to identify trends and emerging applications within each topic. Based on the results, a questionnaire was developed and made available online to all Italian pediatricians. The results were analyzed through descriptive analysis and a multivariable logistic regression to explore associations between technology adoption and sociodemographic characteristics. RESULTS: A total of 3,253 publications were found, with Telehealth and Telemedicine having the highest number of publications and Natural Language Processing the lowest. The number of respondents to the online questionnaire was 1,540, predominantly medical doctors with over 20 years of experience working as family pediatricians. Telehealth had the highest level of knowledge (95.2%), followed by smartphone applications (89.1%) and genomics (63.2%). The greatest potential for increased use through education programs was projected for natural language processing (+ 43.1%), artificial intelligence (+ 39.6%), and virtual and mixed reality (+ 38.1%). Female respondents and older individuals were less likely to use emerging technologies. Hospital pediatricians and residents were more likely to use AI. CONCLUSIONS: We developed a replicable strategy to identify emerging themes in medical technologies relevant to pediatrics and assess the educational needs of pediatricians. A significant gap still exists between current and potential usage of emerging technologies among Italian pediatricians although they showed a positive attitude towards implementing these technologies following specific education programs. The study highlights the need for comprehensive education programs on emerging technologies in pediatrics and recommends addressing gender and age disparities in technology adoption.

Adherence to a Mediterranean Diet, Body Composition and Energy Expenditure in Outpatients Adolescents Diagnosed with Anorexia Nervosa: A Pilot Study.

Weight restoration is the primary goal of treatment for patients with Anorexia Nervosa (AN). This observational pilot study aims to describe adherence to the Mediterranean Diet (MD) and the consequent process of weight and functional recovery in outpatient adolescents diagnosed with AN. Eight patients with a median age of 15.1 (14.0-17.1) years were seen at baseline and after six months. Anthropometrics, body composition, and resting energy expenditure (REE) were assessed. The KIDMED questionnaire, the 24 h recall, and a quantitative food frequency questionnaire were used to evaluate adherence to the MD. The median KIDMED score increased from 5.5 (T0) to 10 (T1), which was not significant. Intakes of grams of carbohydrates, lipids, mono-unsaturated fatty acids, and fiber increased (p = 0.012, p = 0.036, p = 0.036, p = 0.025). Weight significantly increased (p = 0.012) as well as lean body mass (p = 0.036), with a resulting improvement of the REE (p = 0.012). No association between anthropometrics and body composition and the KIDMED score was found. The MD could represent an optimal dietary pattern for weight gain and nutritional restoration in patients with AN, and it could lead to an improvement in body composition and resting energy expenditure.

Current practices of follow-up programs for childhood cancer survivors in Italy.

INTRODUCTION: Quality of life in childhood cancer survivors is largely affected by survivorship care and transition from treatment to long-term follow-up (LTFU). Referring to evidence-based recommendations, we wanted to evaluate LTFU care for survivors through a survey among the Italian Association for Pediatric Hematology-Oncology (AIEOP) centers. The project aimed to evaluate the availability of services in Italy, investigate strengths and weaknesses, analyze improvements of awareness in the field, and identify the gaps that need to be addressed by different centers. METHODS: Together with the family representatives, on behalf of AIEOP's Late Effects Working Group, we developed a questionnaire on assisting childhood cancer survivors. All AIEOP centers received one questionnaire including information on local health system organizations; LTFU for childhood cancer survivors; services for adult survivors of childhood cancer; information provided to survivors/caregivers and care plan delivery. RESULTS: Forty-eight AIEOP centers were contacted and 42 replied, with a response rate of 87.5%. The majority of respondents confirmed their interest in assisting patients with a survivorship care plan (95.2%), regardless of a clinic or dedicated staff. DISCUSSION: This is the first overview of LTFU in Italy, which provides detailed results at national levels, prompting consideration of improvements in the last decade. Although there is a high level of interest in survivorship care, many centers lack resources to implement such programs. The identification of these challenges is useful for planning future strategies.

Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children.

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development. We used a LightGBM model to develop 2 sets of models for predicting pertussis and RSV infection: for each type of infection, we developed one model trained with the combination of clinical symptoms and results from routine blood test (white blood cell count, lymphocyte fraction and C-reactive protein), and one with symptoms only. All analyses were performed using Python 3.7.4 with Shapley values (Shap values) visualization package for predictor visualization. The performance of the models was assessed through confusion matrices. The models were developed on a dataset of 599 children. The recall for the pertussis model combining symptoms and routine laboratory tests was 0.72, and 0.74 with clinical symptoms only. For RSV infection, recall was 0.68 with clinical symptoms and laboratory tests and 0.71 with clinical symptoms only. The F1 score for the pertussis model was 0.72 in both models, and, for RSV infection, it was 0.69 and 0.75. ML models can support the diagnosis and surveillance of infectious diseases such as pertussis or RSV infection in children based on common symptoms and laboratory tests. ML-based clinical decision support systems may be developed in the future in large networks to create accurate tools for clinical support and public health surveillance.

Exploring the vaccine conversation on TikTok in Italy: beyond classic vaccine stances.

TikTok, a social media platform for creating and sharing short videos, has seen a surge in popularity during the COVID-19 pandemic. To analyse the Italian vaccine conversation on TikTok, we downloaded a sample of videos with a high play count (Top Videos), identified through an unofficial Application Programming Interface (consistent with TikTok's Terms of Service), and collected public videos from vaccine sceptic users through snowball sampling (Vaccine Sceptics' videos). The videos were analysed using qualitative and quantitative methods, in terms of vaccine stance, tone of voice, topic, conformity with TikTok style, and other characteristics. The final datasets consisted of 754 Top Videos (by 510 single users) plus 180 Vaccine Sceptics' videos (by 29 single users), posted between January 2020 and March 2021. In 40.5% of the Top Videos the stance was promotional, 33.9% were indefinite-ironic, 11.3% were neutral, 9.7% were discouraging, and 3.1% were ambiguous (i.e. expressing an ambivalent stance towards vaccines); 43% of promotional videos were from healthcare professionals. More than 95% of the Vaccine Sceptic videos were discouraging. Multiple correspondence analysis showed that, compared to other stances, promotional videos were more frequently created by healthcare professionals and by females, and their most frequent topic was herd immunity. Discouraging videos were associated with a polemical tone of voice and their topics were conspiracy and freedom of choice. Our analysis shows that Italian vaccine-sceptic users on TikTok are limited in number and vocality, and the large proportion of videos with an indefinite-ironic stance might imply that the incidence of affective polarisation could be lower on TikTok, compared to other social media, in the Italian context. Safety is the most frequent concern of users, and we recorded an interesting presence of healthcare professionals among the creators. TikTok should be considered as a medium for vaccine communication and for vaccine promotion campaigns.

Psychopathological Profile Associated with Food Addiction Symptoms in Adolescents with Eating Disorders.

Eating disorders are considered one of the psychiatric disorders with a higher risk of death. Food addiction, related to some food addictive-like behaviours, is often in comorbidity with eating disorders and is associated with worse psychopathology. The present study aims to outline the food addiction profile, investigated using the Yale Food Addiction Scale 2.0 (YFAS 2.0), in 122 adolescents (median age: 15.6 years) suffering from eating disorders and to investigate its association with psychopathology. Patients filled out the Youth Self Report, the Multidimensional Anxiety Scale for Children 2, The Children Depression Inventory 2, and the Eating Disorder Inventory 3 (EDI-3). Pearson's chi-square test and multiple correspondence analysis were used to identify profiles. The mean symptom count was 2.8 ± 2.7. The "withdrawal" symptom was the most frequent (51%) and the most associated with clinical scores. The diagnosis of bulimia nervosa and the EDI-3 bulimia scale resulted to be the only variables to be associated with positive YFAS 2.0 symptoms. Conversely, anorexia nervosa, restrictive and atypical, was not associated with YFAS 2.0 symptoms. In conclusion, outlining the food addiction profile of eating disorders may give information about a patient's phenotype and could help to identify specific treatment models.

Public Engagement in Digital Recommendations for Promoting Healthy Parental Behaviours from Preconception through the First 1000 Days.

Web-based digital interventions may play a central role for health promoting strategies in the first "1000 days", from conception through the first 2 years of life. We developed a web platform providing evidence-based recommendations in the first 1000 days through short videos, and we studied engagement by users from preconception through parenthood in the second year of life. We described the access to videos by topic and used a multilevel model to explore the user characteristics associated with access to the video recommendations. Overall, breastfeeding, physical activity and nutrition were the most popular topics (normalized views: 89.2%, 87.2% and 86.4% respectively), while content on paternal health and smoking and alcohol was less engaging (37.3% and 42.0%). Nutrition content was the most viewed in the preconception period and during the first two trimesters of pregnancy. Nutrition and breastfeeding were also the most popular topics for users with children less than 2 years old. Higher levels of health literacy were associated only with child health content. The study findings indicate that digital strategies should be adapted according to the time period in the first 1000 days. Alternative digital promotion strategies for the less engaging topics should be considered.

Insights into the Cardiac Phenotype in 9p Deletion Syndrome: A Multicenter Italian Experience and Literature Review.

Chromosome 9p deletion syndrome is a rare autosomal dominant disorder presenting with a broad spectrum of clinical features, including congenital heart defects (CHDs). To date, studies focused on a deep characterization of cardiac phenotype and function associated with this condition are lacking. We conducted a multicentric prospective observational study on a cohort of 10 patients with a molecular diagnosis of 9p deletion syndrome, providing a complete cardiological assessment through conventional echocardiography and tissue Doppler imaging echo modality. As a result, we were able to demonstrate that patients with 9p deletion syndrome without major CHDs may display subclinical cardiac structural changes and left-ventricle systolic and diastolic dysfunction. Albeit needing validation in a larger cohort, our findings support the idea that a complete cardiac assessment should be performed in patients with 9p deletion syndrome and should be integrated in the context of a long-term follow-up.

Feasibility of a VR Intervention to Decrease Anxiety in Children with Tumors Undergoing CVC Dressing.

Virtual reality (VR) represents a promising digital intervention for managing distress and anxiety in children with tumors undergoing painful medical procedures. In an experimental cross-over study, we administered a VR intervention consisting of relaxing games during central venous catheter (CVC) dressing. The VR sessions were compared with no-VR during CVC medication. We used the distress thermometer and RCMAS-2 scale to assess distress and anxiety levels. We also explored the satisfaction level in patients and families. We enrolled 22 children. The distress levels after medication were lower in the VR group than in those without VR (VR: median 2; IQR 0-2; no-VR: median 4; IQR: 3-5). No variation in anxiety levels was detected by VR intervention. Satisfaction for using VR was very high in children and their families although a total of 12% of children were disappointed by the effect of VR. Most healthcare workers felt that VR would be useful in routine clinical practice. A VR intervention is highly acceptable, may be efficacious in decreasing distress in children with cancer undergoing painful procedures, but it is less likely that it has a measurable impact on anxiety. Evidence from larger studies is needed to assess VR translation into the clinical workflow.

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis.

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.

Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.

The application of artificial intelligence (AI) systems is emerging in many fields in recent years, due to the increased computing power available at lower cost. Although its applications in various branches of medicine, such as pediatric oncology, are many and promising, its use is still in an embryonic stage. The aim of this paper is to provide an overview of the state of the art regarding the AI application in pediatric oncology, through a systematic review of systematic reviews, and to analyze current trends in Europe, through a bibliometric analysis of publications written by European authors. Among 330 records found, 25 were included in the systematic review. All papers have been published since 2017, demonstrating only recent attention to this field. The total number of studies included in the selected reviews was 674, with a third including an author with a European affiliation. In bibliometric analysis, 304 out of the 978 records found were included. Similarly, the number of publications began to dramatically increase from 2017. Most explored AI applications regard the use of diagnostic images, particularly radiomics, as well as the group of neoplasms most involved are the central nervous system tumors. No evidence was found regarding the use of AI for process mining, clinical pathway modeling, or computer interpreted guidelines to improve the healthcare process. No robust evidence is yet available in any of the domains investigated by systematic reviews. However, the scientific production in Europe is significant and consistent with the topics covered in systematic reviews at the global level. The use of AI in pediatric oncology is developing rapidly with promising results, but numerous gaps and challenges persist to validate its utilization in clinical practice. An important limitation is the need for large datasets for training algorithms, calling for international collaborative studies.

Early factors associated with risk of developmental coordination disorder in very preterm children: A prospective area-based cohort study in Italy.

BACKGROUND: Developmental coordination disorder (DCD) is a motor disorder of unknown aetiology that may have long-term consequences on daily activities, and psychological and physical health. Studies investigating risk factors for DCD have so far provided inconsistent results. OBJECTIVES: To assess, using a parent-report screening tool, risk of DCD in school-age very preterm children born in Italy, and investigate the associated early biomedical and sociodemographic factors. METHODS: A prospective area-based cohort (804 children, response rate 73.4%) was assessed at 8-11 years of age in three Italian regions. Perinatal data were abstracted from medical records. DCD risk was measured using the Italian-validated version of the Developmental Coordination Disorder Questionnaire (DCDQ-IT). For this study, children with cognitive deficit (i.e. intelligence quotient <70), cerebral palsy, severe vision and hearing disabilities, and other impairments affecting movement were excluded. A total of 629 children were analysed. We used inverse probability weighting to account for loss to follow-up, and multilevel, multivariable modified Poisson models to obtain adjusted risk ratio (aRR) and 95% confidence interval (CI). Missing values in the covariates were imputed. RESULTS: 195 children (weighted proportion 31.8%, 95% CI 28.2, 35.6) scored positive on the DCDQ-IT, corresponding to the 15th centile of the reference Movement-ABC test. Factors associated with overall DCD risk were male sex (aRR 1.35, 95% CI 1.05, 1.73), intrauterine growth restriction (aRR 1.45, 95% CI 1.14, 1.85), retinopathy of prematurity (aRR 1.62, 95% CI 1.07, 2.45), and older maternal age at delivery (aRR 1.39, 95% CI 1.09, 1.77). Complete maternal milk feeding at discharge from the neonatal unit and higher parental socio-economic status were associated with decreased risk. CONCLUSIONS: Both biomedical and sociodemographic factors increase DCD risk. These findings can contribute to elucidating the origins of this disorder, and assist in the identification of children at risk for early referral and intervention.

Predictors of survival in paediatric mitral valve surgery: the impact of age at operation at late follow-up.

OBJECTIVES: The aim of this study was to elucidate predictors of death and reintervention after mitral valve (MV) surgery in children. METHODS: A single-centre retrospective study was performed enrolling 142 patients younger than 18 years who underwent primary index surgical mitral repair or replacement at Bambino Gesù Children's Hospital in Rome from July 1982 to April 2020. Patients with complete, transitional or partial atrioventricular septal defect and patients with single ventricle physiology were excluded. Patients were stratified according to the age group: group 1 (<1 year old), group 2 (1-5 years old) and group 3 (>5 years old). The composite primary outcome was freedom from death or transplant. The secondary outcome was freedom from redo MV surgery. RESULTS: Transplant-free survival was 89% at 5 years and 88% at 10 years. Stratified by age, group 1 had poorer outcome in comparison with other groups (log-rank test P = 0.105). Both univariate and multivariate analyses showed that age <1 year was a significant risk factor for death or transplant (P = 0.044). Age <1 year was associated with increased risk of reoperation (aHR = 3.38, P = 0.009), while the presence of genetic syndrome (aHR = 0.22) and preoperative EF% (aHR = 0.97) were protective factors for reoperation. CONCLUSIONS: The overall survival and freedom from reoperation in children undergoing MV surgery still need improvements. Younger age was a significant risk factor for death and reintervention both after repair and replacement of the MV. In particular, infants and neonates have a three-fold risk for death compared to children.

Maternal education and cognitive development in 15 European very-preterm birth cohorts from the RECAP Preterm platform.

BACKGROUND: Studies are sparse and inconclusive about the association between maternal education and cognitive development among children born very preterm (VPT). Although this association is well established in the general population, questions remain about its magnitude among children born VPT whose risks of medical and developmental complications are high. We investigated the association of maternal education with cognitive outcomes in European VPT birth cohorts. METHODS: We used harmonized aggregated data from 15 population-based cohorts of children born at <32 weeks of gestational age (GA) or <1500 g from 1985 to 2013 in 13 countries with information on maternal education and assessments of general development at 2-3 years and/or intelligence quotients between 4 and 15 years. Term-born controls (≥37 weeks of GA) were available in eight cohorts. Maternal education was classified as: low (primary/lower secondary); medium (upper secondary/short tertiary); high (bachelor's/higher). Pooled standardized mean differences (SMDs) in cognitive scores were estimated (reference: high educational level) for children assessed at ages 2-3, 4-7 and 8-15 years. RESULTS: The study included 10 145 VPT children from 12 cohorts at 2-3 years, 8829 from 12 cohorts at 4-7 years and 1865 children from 6 cohorts at 8-15 years. Children whose mothers had low, compared with high, educational attainment scored lower on cognitive measures [pooled unadjusted SMDs: 2-3 years = -0.32 (95% confidence intervals: -0.43 to -0.21); 4-7 years = -0.57 (-0.67; -0.47); 8-15 years = -0.54 (-0.72; -0.37)]. Analyses by GA subgroups (<27 vs ≥27 weeks) in children without severe neonatal morbidity and term controls yielded similar results. CONCLUSIONS: Across diverse settings and regardless of the degree of prematurity, low maternal education was associated with lower cognition.

Intention of Parents to Immunize Children against SARS-CoV-2 in Italy.

Several countries have targeted adolescents for immunization against SARS-CoV-2 to mitigate COVID-19 spread. In Italy, immunization for children ≥ 12 years has been available starting from June 2021. We conducted a cross-sectional study to investigate the knowledge, attitude and intention to vaccinate children < 18 years in Italian families. We used a multinomial logistic regression model to investigate factors associated with intention to vaccinate. We collected a total of 1696 responses. Among the 491 families of children ≥ 12 years, 41.2% would not vaccinate their children and 21.2% were uncertain, while among the 1205 families of children < 12 years, 36.1% would not vaccinate and 33.8% were uncertain. Determinants of intention to vaccinate both age groups were perceived safety and efficacy of vaccines and perceived risk of transmitting infection to adults. For children < 12 years, additional determinants were perceived risk of being infected and being hospitalized because of COVID-19. In view of the expanding strategy to vaccinate adolescents and the availability of immunization for children < 12 years, our results call for a communication strategy targeted at families of children focused on the safety and efficacy of COVID-19 vaccine in children and on the dynamics of infection spread across different age groups. As perceptions in families are volatile and may change rapidly over time, repeated surveys for measuring attitudes to vaccinate would be advisable.

Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study.

BACKGROUND: Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. OBJECTIVE: This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. METHODS: We compared the number of sessions by users with a COVID-19-positive contact and users with COVID-19-compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. RESULTS: We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. CONCLUSIONS: Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.