The health needs of women prisoners: an Italian field survey

Introduction. Health care in prisons represents an important part of public health due to the interaction between prisons and society. Women prisoners have needs that distinguish them from male prisoners, however little is known about how those needs are met. The aim of the study was to gather information about the needs of women in prison and to identify which of their needs are the most or the least met. Methods. This study investigated the needs of detained women using a newly developed Questionnaire based on Gordon’s model. In this descriptive study, data were collected from a onvenient sample of women recruited from two Italian prisons. Data analysis used descriptive statistics. Results. Fifty-five women (response rate = 92%) completed the self-reported questionnaire. Our findings showed that physical needs are met worse than psychological and social needs. The majority of physical needs were related to the inability to meet food preferences and the difficulty in respecting food requirements related to disease and by religion. The women experienced a loss of privacy, and they need more time for improving the quality of their relationships. The majority of the participants (65%) declared that they suffer from psychological disorders with an alarming percentage (29%) stating that they had thoughts of self-harm. They commonly consume tobacco (87.3%), and abuse substances (20%). Discussion and conclusions. The recognition of multi-dimensional women’s needs is of primary importance to create opportunities to support incarcerated women and to build health-promoting gender-sensitive interventions.

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

Sars-Cov2 Not Detected in a Pediatric Population With Acute Respiratory Infection in Primary Care in Central and Southern Italy From November 2019 to Early March 2020

Background: In December 2019, a novel coronavirus named SARS-CoV-2 started circulating in China and this led to a major epidemic in Northern Italy between February and May 2020. Young children (aged <5 years) seem to be less affected by this coronavirus disease (COVID-19) compared to adults, although there is very little information on the circulation of this new virus among children in Italy. We retrospectively tested nasopharyngeal swabs for SARS-CoV-2 in samples collected in young children between November, 2019 and March, 2020 in the context of the RSV ComNet study. Methods: Two networks of primary care pediatricians in Lazio (Central Italy) and Puglia (Southern Italy) collected nasopharyngeal swabs from children, aged <5 years, presenting with symptoms for an acute respiratory infection (ARI). The RSV ComNet study is a multicenter study implemented to estimate the burden of RSV in young children (aged <5 years) in the community. Swabs were sent to a central reference laboratory and tested for 14 respiratory viruses through RT-PCR. All collected samples were retrospectively tested for SARS-CoV-2 using RT-PCR (Istituto Superiore di Sanità protocol). Results: A total of 293 children with ARI were identified in the two participating networks. The highest number of cases were recruited in weeks 51/2019 and 3/2020. The majority of patients (57%) came from the Lazio region. All of the 293 samples tested negative for SARS-Cov2. Rhinovirus was the most frequently detected virus (44%), followed by RSV (41%) and influenza viruses (14%). Conclusions: Our study shows that in Lazio (a region of intermediate SARS-COV-2 incidence) and Puglia (a region of low incidence), the SARS-Cov2 virus did not circulate in a sample of ARI pediatric cases consulting primary care pediatricians between November 2019 and March 2020

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

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 = 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 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.Peer reviewe

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

Limitation of life-sustaining treatment in NICU: Physicians' beliefs and attitudes in the Buenos Aires region

Objective: To explore the ethical beliefs and attitudes of Argentinean neonatologists regarding limitation of life-sustaining treatment (LST) for very sick infants. Methods: We used an anonymous questionnaire including direct questions and hypothetical clinical cases (inevitable demise and anticipated survival with severe long-term disability). Multivariable analysis was carried out to assess the relation between type of clinical case and physicians' LST attitudes. Results: Overall, 315 neonatologists in 34 units in the Buenos Aires region participated (response rate 54%). Most responders would agree with decisions to start or continue LST. In both clinical cases, continuing current treatment with no therapeutic escalation was the only form of LST limitation acceptable to a substantial proportion (about 60%) of neonatologists. Agreement with LST limitation was slightly but significantly more likely when death was inevitable. Conclusion: Argentinean neonatologists showed a conservative attitude regarding LST limitation. Patient prognosis and options of non-treatment decision significantly influenced their choices.Fil: Silberberg, Agustín. Universidad Austral. Hospital Universitario Austral; ArgentinaFil: Herich, Lena Carolin. Bambino Gesù Children's Hospital; ItaliaFil: Croci, Ileana. Bambino Gesù Children's Hospital; ItaliaFil: Cuttini, Marina. Bambino Gesù Children's Hospital; ItaliaFil: Villar, Marcelo Jose. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; ArgentinaFil: Requena Meana, Pablo. Pontificia Università della Santa Croce; Itali

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

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