Publications by authors named "Dan Metes"
Signs and symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) are present at preschool ages and often not identified for early intervention. We aimed to use machine learning to detect ADHD early among kindergarten-aged children using population-level administrative health data and a childhood developmental vulnerability surveillance tool: Early Development Instrument (EDI). The study cohort consists of 23,494 children born in Alberta, Canada, who attended kindergarten in 2016 without a diagnosis of ADHD.
View Article and Find Full Text PDFIntroduction: In the context of the COVID-19 pandemic, it becomes important to comprehend service utilization patterns and evaluate disparities in mental health-related service access among children.
Objective: This study uses administrative health records to investigate the association between early developmental vulnerability and healthcare utilization among children in Alberta, Canada from 2016 to 2022.
Methods: Children who participated in the 2016 Early Development Instrument (EDI) assessment and were covered by public Alberta health insurance were included (N = 23 494).
Article Synopsis
- - This study investigates factors contributing to developmental vulnerability in kindergarten children, aiming to connect social and health data from a large population of 23,494 kids in Alberta, Canada.
- - Key findings show that major risk factors include mental health issues, being biologically male, and having a low socioeconomic status, which significantly increase developmental vulnerability.
- - The research supports the idea that both social and health influences are crucial in understanding developmental challenges, suggesting a need for comprehensive intervention strategies at various community levels.
Objective: Opioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases.
View Article and Find Full Text PDFBackground: Croup is a common pediatric respiratory illness. While croup is generally a mild disease, exacerbations may be seen in the Emergency Department (ED) setting. The objective of this study was to describe the epidemiology of croup presentations to EDs made by infants (