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Electronic health records identify timely trends in childhood mental health conditions
by
Rao, Suchitra
, Maltenfort, Mitchell
, Hardan, Antonio Y.
, Utidjian, Levon
, Pajer, Kathleen
, Pumariega, Andres
, Elia, Josephine
, Kelleher, Kelly
, Prasad, Raghuram
, Christakis, Dimitri A.
, Ballard, Rachel
, Shenkman, Elizabeth
, Forrest, Christopher B.
, Margolis, Peter A.
in
Adverse childhood experiences
/ Age
/ Anxiety
/ Anxiety disorders
/ Catatonia
/ Child & adolescent mental health
/ Child & adolescent psychiatry
/ Child and Adolescent Psychiatry
/ Childhood
/ Children
/ Classification
/ Clinical Psychology
/ Codes
/ COVID-19
/ Demographic risks
/ Diseases
/ EHR-based typology
/ Electronic Health Records
/ Electronic medical records
/ Electronic records
/ Emergency medical care
/ Forensic Psychiatry
/ Gender
/ Hospitals
/ ICD-CM
/ Informatics
/ Medical records
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Medicine, Experimental
/ Mental disorders
/ Mental health
/ Mental health care
/ Pandemics
/ Patient education
/ Patients
/ Pediatric Mental Health Disorders
/ Pediatrics
/ Psychiatry
/ Psychological aspects
/ Psychosis
/ Self destructive behavior
/ Self-injury
/ Trends
/ Typology
2023
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Electronic health records identify timely trends in childhood mental health conditions
by
Rao, Suchitra
, Maltenfort, Mitchell
, Hardan, Antonio Y.
, Utidjian, Levon
, Pajer, Kathleen
, Pumariega, Andres
, Elia, Josephine
, Kelleher, Kelly
, Prasad, Raghuram
, Christakis, Dimitri A.
, Ballard, Rachel
, Shenkman, Elizabeth
, Forrest, Christopher B.
, Margolis, Peter A.
in
Adverse childhood experiences
/ Age
/ Anxiety
/ Anxiety disorders
/ Catatonia
/ Child & adolescent mental health
/ Child & adolescent psychiatry
/ Child and Adolescent Psychiatry
/ Childhood
/ Children
/ Classification
/ Clinical Psychology
/ Codes
/ COVID-19
/ Demographic risks
/ Diseases
/ EHR-based typology
/ Electronic Health Records
/ Electronic medical records
/ Electronic records
/ Emergency medical care
/ Forensic Psychiatry
/ Gender
/ Hospitals
/ ICD-CM
/ Informatics
/ Medical records
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Medicine, Experimental
/ Mental disorders
/ Mental health
/ Mental health care
/ Pandemics
/ Patient education
/ Patients
/ Pediatric Mental Health Disorders
/ Pediatrics
/ Psychiatry
/ Psychological aspects
/ Psychosis
/ Self destructive behavior
/ Self-injury
/ Trends
/ Typology
2023
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Electronic health records identify timely trends in childhood mental health conditions
by
Rao, Suchitra
, Maltenfort, Mitchell
, Hardan, Antonio Y.
, Utidjian, Levon
, Pajer, Kathleen
, Pumariega, Andres
, Elia, Josephine
, Kelleher, Kelly
, Prasad, Raghuram
, Christakis, Dimitri A.
, Ballard, Rachel
, Shenkman, Elizabeth
, Forrest, Christopher B.
, Margolis, Peter A.
in
Adverse childhood experiences
/ Age
/ Anxiety
/ Anxiety disorders
/ Catatonia
/ Child & adolescent mental health
/ Child & adolescent psychiatry
/ Child and Adolescent Psychiatry
/ Childhood
/ Children
/ Classification
/ Clinical Psychology
/ Codes
/ COVID-19
/ Demographic risks
/ Diseases
/ EHR-based typology
/ Electronic Health Records
/ Electronic medical records
/ Electronic records
/ Emergency medical care
/ Forensic Psychiatry
/ Gender
/ Hospitals
/ ICD-CM
/ Informatics
/ Medical records
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Medicine, Experimental
/ Mental disorders
/ Mental health
/ Mental health care
/ Pandemics
/ Patient education
/ Patients
/ Pediatric Mental Health Disorders
/ Pediatrics
/ Psychiatry
/ Psychological aspects
/ Psychosis
/ Self destructive behavior
/ Self-injury
/ Trends
/ Typology
2023
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Electronic health records identify timely trends in childhood mental health conditions
Journal Article
Electronic health records identify timely trends in childhood mental health conditions
2023
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Overview
Background
Electronic 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.
Methods
In 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.
Results
The 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.
Conclusions
These 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.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
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