Electronic health records identify timely trends in childhood mental health conditions

Child and Adolescent Psychiatry and Mental Health

Research

Dimitri A. Christakis¹  Mitchell Maltenfort²  Raghuram Prasad³  Elizabeth Shenkman⁴  Suchitra Rao⁵  Levon Utidjian⁶  Christopher B. Forrest⁷  Josephine Elia⁸  Rachel Ballard⁹  Antonio Y. Hardan^1,10  Kathleen Pajer^1,11  Andres Pumariega^1,12  Peter A. Margolis^1,13  Kelly Kelleher^1,14 

[1]Center for Child Health, Behavior and Development, Department of Pediatrics, Seattle Children’s Hospital, University of Washington, Seattle, Washington, US

[2]Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, US

[3]Department of Child and Adolescent Psychiatry, Children’s Hospital of Philadelphia, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, PA, US

[4]Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, US

[5]Department of Pediatrics, Children’s Hospital of Colorado, University of Colorado, Aurora, CO, US

[6]Department of Pediatrics, Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US

[7]Department of Pediatrics, Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, US

[8]Applied Clinical Research Center, Children’s Hospital of Philadelphia, Department of Healthcare Management, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, US

[9]Department of Pediatrics, Nemours Children’s Health Delaware, Sydney Kimmel School of Medicine, Philadelphia, PA, US

[10]Department of Psychiatry and Behavioral Sciences and Pediatrics, Ann & Robert H. Lurie Children’s Hospital, Chicago, IL, US

[11]Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, US

[12]Department of Psychiatry, Faculty of Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario, Ottawa, ON, Canada

[13]Department of Psychiatry, University of Florida College of Medicine, University of Florida Health, Gainesville, FL, US

[14]James Anderson Center for Health Systems Excellence, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati, Cincinnati, OH, US

[15]The Research Institute, Nationwide Children’s Hospital, Department of Pediatrics, The Ohio State University College of Medicine, Ohio, US

关键词: Electronic Health Records;    EHR-based typology;    ICD-CM;    Pediatric Mental Health Disorders;    Demographic risks;    Covid-19;    Surveillance;    Standalone symptoms;    Adverse childhood experience;   

DOI  :  10.1186/s13034-023-00650-7

 received in 2023-03-21, accepted in 2023-08-20,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundElectronic health records (EHRs) data provide an opportunity to collect patient information rapidly, efficiently and at scale. National collaborative research networks, such as PEDSnet, aggregate EHRs data across institutions, enabling rapid identification of pediatric disease cohorts and generating new knowledge for medical conditions. To date, aggregation of EHR data has had limited applications in advancing our understanding of mental health (MH) conditions, in part due to the limited research in clinical informatics, necessary for the translation of EHR data to child mental health research.MethodsIn this cohort study, a comprehensive EHR-based typology was developed by an interdisciplinary team, with expertise in informatics and child and adolescent psychiatry, to query aggregated, standardized EHR data for the full spectrum of MH conditions (disorders/symptoms and exposure to adverse childhood experiences (ACEs), across 13 years (2010–2023), from 9 PEDSnet centers. Patients with and without MH disorders/symptoms (without ACEs), were compared by age, gender, race/ethnicity, insurance, and chronic physical conditions. Patients with ACEs alone were compared with those that also had MH disorders/symptoms. Prevalence estimates for patients with 1+ disorder/symptoms and for specific disorders/symptoms and exposure to ACEs were calculated, as well as risk for developing MH disorder/symptoms.ResultsThe EHR study data set included 7,852,081 patients < 21 years of age, of which 52.1% were male. Of this group, 1,552,726 (19.8%), without exposure to ACEs, had a lifetime MH disorders/symptoms, 56.5% being male. Annual prevalence estimates of MH disorders/symptoms (without exposure to ACEs) rose from 10.6% to 2010 to 15.1% in 2023, a 44% relative increase, peaking to 15.4% in 2019, prior to the Covid-19 pandemic. MH categories with the largest increases between 2010 and 2023 were exposure to ACEs (1.7, 95% CI 1.6–1.8), anxiety disorders (2.8, 95% CI 2.8–2.9), eating/feeding disorders (2.1, 95% CI 2.1–2.2), gender dysphoria/sexual dysfunction (43.6, 95% CI 35.8–53.0), and intentional self-harm/suicidality (3.3, 95% CI 3.2–3.5). White youths had the highest rates in most categories, except for disruptive behavior disorders, elimination disorders, psychotic disorders, and standalone symptoms which Black youths had higher rates. Median age of detection was 8.1 years (IQR 3.5–13.5) with all standalone symptoms recorded earlier than the corresponding MH disorder categories.ConclusionsThese results support EHRs’ capability in capturing the full spectrum of MH disorders/symptoms and exposure to ACEs, identifying the proportion of patients and groups at risk, and detecting trends throughout a 13-year period that included the Covid-19 pandemic. Standardized EHR data, which capture MH conditions is critical for health systems to examine past and current trends for future surveillance. Our publicly available EHR-mental health typology codes can be used in other studies to further advance research in this area.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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