Prescription Trends of Psychotropics in Children and Adolescents with Autism Based on Nationwide Health Insurance Data.
Minha HongSeung Yup LeeJuhee HanJin Cheol ParkYeon Jung LeeRam HwangboHyejung ChangSeong Woo ChoSoo-Young BhangBongseog KimJun-Won HwangGeon Ho BahnPublished in: Journal of Korean medical science (2018)
Children with autism are often medicated to manage emotional and behavioral symptoms; yet, data on such pharmacotherapy is insufficient. In this study, we investigated the Korean National Health Insurance Claims Database (NHICD) information related to autism incidence and psychotropic medication use. From the 2010-2012 NHICD, we selected a total of 31,919,732 subjects under 19 years old. To examine the diagnostic incidence, we selected patients who had at least one medical claim containing an 10th revision of International Statistical Classification of Diseases and Related Health Problems (ICD-10) code for pervasive developmental disorder, F84, not diagnosed in the previous 360 days. Psychotropics were categorized into seven classes. Then, we analyzed the data to determine the mean annual diagnostic incidence and psychotropic prescription trends. Diagnostic incidence was 17,606 for the 3 years, with a mean annual incidence per 10,000 population of 5.52. Among them, 5,348 patients were prescribed psychotropics. Atypical antipsychotics were the most commonly used, followed by antidepressants. An older age, male sex, and the availability of medical aid were associated with a higher rate of prescription than observed for a younger age, female sex, and the availability of health insurance. Psychotropic drugs were used for less than one-third of patients newly diagnosed with autism, and prescription differed by sex and age. Increased diagnostic incidence is associated with an increased prescription of psychotropic drugs. Therefore, medication-related safety data and policies for psychotropic drugs in autism should be prepared.
Keyphrases
- health insurance
- newly diagnosed
- risk factors
- autism spectrum disorder
- affordable care act
- intellectual disability
- healthcare
- end stage renal disease
- electronic health record
- ejection fraction
- public health
- mental health
- big data
- chronic kidney disease
- young adults
- prognostic factors
- machine learning
- health information
- total knee arthroplasty
- peritoneal dialysis
- major depressive disorder
- physical activity
- drug induced
- smoking cessation
- deep learning
- climate change
- artificial intelligence
- single molecule