Open AccessEstimating prevalence of serious emotional disturbance in schools using a brief screening scale
Author(s) -
Li Fan,
Green Jennifer Greif,
Kessler Ronald C.,
Zaslavsky Alan M.
Publication year - 2010
Publication title -
international journal of methods in psychiatric research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.275
H-Index - 73
eISSN - 1557-0657
pISSN - 1049-8931
DOI - 10.1002/mpr.315
Subject(s) - bivariate analysis , scale (ratio) , mental health , small area estimation , national comorbidity survey , psychology , population , sample (material) , multilevel model , demography , clinical psychology , medicine , statistics , gerontology , psychiatry , environmental health , geography , mathematics , cartography , sociology , chemistry , chromatography , estimator
Information about the prevalence of serious mental illness (SMI) among adults or serious emotional disturbance (SED) among youth in small domains such as counties, states, or schools is valuable for mental health policy planning purposes, but prohibitively expensive to collect with semi‐structured surveys. Commonly used synthetic estimation methods yield imprecise estimates. An improved method is described here that combines information about socio‐demographic covariates with screening scale scores obtained from a sample of individuals, using a prediction equation derived from a Bayesian multilevel regression model with bivariate outcomes fitted to a larger population survey. This method is illustrated using K6 screening scale scores to predict school‐level prevalence of SED in the sample of 282 schools that participated in the National Comorbidity Survey Replication Adolescent Supplement. Respondents completed a diagnostic interview that was used to define DSM‐IV SED. SED prevalence varied significantly across schools and was strongly correlated with aggregate K6 scores (ρ = 0.70). Calculations suggest that near‐maximum precision of school‐level SED prevalence estimates could be attained with K6 samples of 200 students per school. This modeling approach holds great promise for generating accurate estimates of SMI/SED in small‐area planning units based on K6 scores collected in ongoing health tracking surveys. Copyright © 2010 John Wiley & Sons, Ltd.