Objective: We investigated the incidence and seasonal patterns of child maltreatment hospitalisations in Hong Kong.
Design: A retrospective study of subjects aged under 19 years with a primary diagnosis of child maltreatment admitted to hospitals in Hong Kong from 2001 to 2010. Data were retrieved from the centralised database of all 42 public hospitals in the Hospital Authority.
Main Outcome Measures: Child maltreatment incidence rate.
Results: A consistent seasonal pattern was found for non-sexual maltreatment in children aged 6-18 years (p<0.001). Hospitalisations peaked in May and October but dipped in August and January. No significant seasonal patterns were found for sexual maltreatment or among children under 6 years. The seasonal pattern of child maltreatment coincided with the two school examination periods. The annual child maltreatment hospitalisation rate in Hong Kong in 2010 was 73.4 per 100 000 children under 19 years, more than double that in 2001.
Conclusions: A peculiar seasonal pattern and an alarming increasing trend in child maltreatment hospitalisation were observed in Hong Kong, which we speculated to be related to school examination stress and increasing socioeconomic disparity. Our findings highlighted differences in the trends of child maltreatment between Hong Kong and the West. Professionals and policymakers should be made aware of these trends and develop effective strategies to tackle child maltreatment.
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http://dx.doi.org/10.1136/archdischild-2015-310151 | DOI Listing |
Prehosp Emerg Care
January 2025
Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO.
Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.
View Article and Find Full Text PDFTurk Arch Pediatr
January 2025
Department of Forensic Medicine, İstanbul Health and Technology University Faculty of Medicine, İstanbul, Türkiye.
This review synthesizes current research on domestic violence and sexual assault, focusing on their short-term and long-term effects on family dynamics, particularly on the development and well-being of children and adolescents. The article employs a curated body of literature, including surveys, reviews, program evaluations, and international health reports, to elucidate the direct and collateral damage caused by such trauma within families. The review critically examines the intersecting consequences of abuse, including immediate psychological distress and long-term socio-economic and educational disruptions for affected youths.
View Article and Find Full Text PDFHeliyon
January 2025
Departments of Health Informatics, College of Health Sciences, Mattu University, Mattu, Ethiopia.
Background: Child sexual abuse is a grave issue with significant consequences for the well-being and development of children worldwide. Understanding the scope of this problem is essential, particularly in Ethiopia, where protecting the nation's youth is crucial. Although child sexual abuse is a critical issue, there is a lack of comprehensive assessment of its prevalence and associated factors in Ethiopia.
View Article and Find Full Text PDFContemp Clin Trials
January 2025
Department of Statistical Science, University College London, Room 120, 1-19 Torrington Pl, London WC1E 7HB, UK. Electronic address:
Background: Sexual exploitation of children and adolescents (SECA) is a mostly invisible phenomenon, having negative impacts on adolescents' health and well-being.. There is increasing awarenessof preventative strategies to reduce sexual exploitation of children and adolescents, but limited evidence on their effectiveness and mechanisms.
View Article and Find Full Text PDFChild Abuse Negl
January 2025
Johns Hopkins School of Medicine, United States of America. Electronic address:
Background: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.
Methods: We analyzed data from the Maryland Health Services Cost Review Commission (2015-2020) for patients aged 0-19 years.
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