Monitoring Public Perception of Health Risks in Brazil and Italy: Cross-Cultural Research on the Risk Perception of Choking in Children

Hochdorn, Alexander; Oliveira, A.E.; Lorenzoni, Giulia; Francavilla, Andrea; Baldas, Solidea; Berchialla, Paola; Oliveira, Alessandra Ribeiro Ventura; Alves, Vicente Paulo; Gregori, Darío; Azzolina, Danila

doi:10.3390/children8070541

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…Excessive activity while eating, such as laughing, walking, and crying when handling toys or other small materials, increase choking risk in children, but many parents are less awere about it [29]. Choking cases occur due to the lack of knowledge of the children's parents and caregivers, so it is realized that the community's knowledge regarding this matter must be improved [30], [31]. Parents and caregivers need to be knowledgeable in order to recognize food and non-food objects that can cause choking in children [32].…”

Section: Resultsmentioning

confidence: 99%

Education for parents regarding choking prevention and handling on children: a scoping review

Pondete

Barlıanto

Suryanto

2022

IJPHS

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Choking is a pediatric emergency that threatens life. Choking occurs in 80% of under three-year-old children worldwide. Many children do not receive help since the aid provider fears causing harm due to the absence of choking case knowledge. Improving parent’s knowledge can decrease choking risk in children that threatens life. This scoping review aimed to identify education provision regarding foreign body aspiration on children. This study was a scoping review with choking handling articles to identify education provisions regarding choking handling on children. Article search was carried out using internet databases, i.e., PubMed, Proquest, and Sciencedirect. The selection was performed using a PRISMA flow diagram. The search results obtained 1,081 literatures, title-screening excluded 948 articles and obtaining 98 articles to be tested for eligibility. The 91 articles with unrelated objectives were excluded. Seven remaining articles following the inclusion criteria were identified. Providing education for parents about choking prevention and handling on children is an essential component of the public health approach as video-based e-learning program, direct learning, installation of hazard warning of choking. Educational programs will be effective when utilizing various information sources and assigning additional healthcare providers tasks to educate parents and the community.

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Section: Resultsmentioning

confidence: 99%

Education for parents regarding choking prevention and handling on children: a scoping review

Pondete

Barlıanto

Suryanto

2022

IJPHS

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“…Injury surveillance is made difficult by a series of logistic and structural challenges, the most important of which is the accurate coding of injury mechanisms, products involved, types of injury, and body parts involved, given that emergency department (ED) admission and discharge records are largely based on narrative free-text notes [ 7 ]. Injury surveillance integrated with timely data dissemination is crucial for planning and evaluating prevention policies [ 8 ] and quantifying injury burden and related risk factors [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach

Azzolina,

Bressan,

Lorenzoni

et al. 2023

JMIR Public Health Surveill

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Background Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. Objective This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. Methods The study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. Results The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. Conclusions This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals’ efforts in manually classifying diagnoses for research purposes.

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Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach (Preprint)

Azzolina,

Bressan,

Lorenzoni

et al. 2022

Preprint

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BACKGROUND Unintentional injury is the leading cause of death in young children. Emergency department (ED) diagnoses are a useful source of information for injury epidemiological surveillance purposes. However, ED data collection systems often use free-text fields to report patient diagnoses. Machine learning techniques (MLTs) are powerful tools for automatic text classification. The MLT system is useful to improve injury surveillance by speeding up the manual free-text coding tasks of ED diagnoses. OBJECTIVE This research aims to develop a tool for automatic free-text classification of ED diagnoses to automatically identify injury cases. The automatic classification system also serves for epidemiological purposes to identify the burden of pediatric injuries in Padua, a large province in the Veneto region in the Northeast Italy. METHODS The study includes 283,468 pediatric admissions between 2007 and 2018 to the Padova University Hospital ED, a large referral center in Northern Italy. Each record reports a diagnosis by free text. The records are standard tools for reporting patient diagnoses. An expert pediatrician manually classified a randomly extracted sample of approximately 40,000 diagnoses. This study sample served as the gold standard to train an MLT classifier. After preprocessing, a document-term matrix was created. The machine learning classifiers, including decision tree, random forest, gradient boosting method (GBM), and support vector machine (SVM), were tuned by 4-fold cross-validation. The injury diagnoses were classified into 3 hierarchical classification tasks, as follows: injury versus noninjury (task A), intentional versus unintentional injury (task B), and type of unintentional injury (task C), according to the World Health Organization classification of injuries. RESULTS The SVM classifier achieved the highest performance accuracy (94.14%) in classifying injury versus noninjury cases (task A). The GBM method produced the best results (92% accuracy) for the unintentional and intentional injury classification task (task B). The highest accuracy for the unintentional injury subclassification (task C) was achieved by the SVM classifier. The SVM, random forest, and GBM algorithms performed similarly against the gold standard across different tasks. CONCLUSIONS This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals’ efforts in manually classifying diagnoses for research purposes.

show abstract

Monitoring Public Perception of Health Risks in Brazil and Italy: Cross-Cultural Research on the Risk Perception of Choking in Children

Cited by 3 publications

References 54 publications

Education for parents regarding choking prevention and handling on children: a scoping review

Education for parents regarding choking prevention and handling on children: a scoping review

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach (Preprint)

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