Katharine B. Parodi , Emily D. Barnes , Jennifer Greif Green, Melissa K. Holt, Amie E. Grills
{"title":"A review of US nationally representative data sources of child and adolescent anxiety","authors":"Katharine B. Parodi , Emily D. Barnes , Jennifer Greif Green, Melissa K. Holt, Amie E. Grills","doi":"10.1016/j.xjmad.2024.100047","DOIUrl":null,"url":null,"abstract":"<div><p>Anxiety is one of the most prevalent mental health conditions among youth; however, scholars have commented on the limited data collection of anxiety measures in United States (US) nationally representative surveys. Inadequate data has hampered researchers’ ability to estimate recent prevalence and track changes over time. Monitoring anxiety, particularly among children and adolescents, including subpopulations facing marked mental health disparities (e.g., females, sexual and gender diverse youth), is crucial for informing policies which direct healthcare provision and tailoring prevention and intervention. This study describes a comprehensive review of US nationally representative datasets that could be used to generate prevalence estimates of anxiety symptoms and/or disorders among children and adolescents from 2011 to present. Results of this five-step search process identified 11 data series meeting inclusion criteria. Of these, seven data series included a valid measure of mental, emotional, or behavioral health, which included at least one anxiety-related question in the scale. This overview of US population-based data on child and adolescent anxiety symptoms and disorders contributes to the field by identifying data sources that could be used to estimate recent prevalence and highlighting gaps in the availability of empirically validated instruments of anxiety in US nationally representative data sets.</p></div>","PeriodicalId":73841,"journal":{"name":"Journal of mood and anxiety disorders","volume":"5 ","pages":"Article 100047"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950004424000014/pdfft?md5=40513584aa34614f062b175c06e2d0d8&pid=1-s2.0-S2950004424000014-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of mood and anxiety disorders","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950004424000014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Anxiety is one of the most prevalent mental health conditions among youth; however, scholars have commented on the limited data collection of anxiety measures in United States (US) nationally representative surveys. Inadequate data has hampered researchers’ ability to estimate recent prevalence and track changes over time. Monitoring anxiety, particularly among children and adolescents, including subpopulations facing marked mental health disparities (e.g., females, sexual and gender diverse youth), is crucial for informing policies which direct healthcare provision and tailoring prevention and intervention. This study describes a comprehensive review of US nationally representative datasets that could be used to generate prevalence estimates of anxiety symptoms and/or disorders among children and adolescents from 2011 to present. Results of this five-step search process identified 11 data series meeting inclusion criteria. Of these, seven data series included a valid measure of mental, emotional, or behavioral health, which included at least one anxiety-related question in the scale. This overview of US population-based data on child and adolescent anxiety symptoms and disorders contributes to the field by identifying data sources that could be used to estimate recent prevalence and highlighting gaps in the availability of empirically validated instruments of anxiety in US nationally representative data sets.