title: Using a short screening scale for small-area estimation of mental illness prevalence for schools.
abstract: We use data collected in the National Comorbidity Survey - Adolescent (NCS-A) to develop a methodology to estimate the small-area prevalence of serious emotional distress (SED) in schools in the United States, exploiting the clustering of the main NCS-A sample by school. The NCS-A instrument includes both a short screening scale, the K6, and extensive diagnostic assessments of the individual disorders and associated impairment that determine the diagnosis of SED. We fitted a Bayesian bivariate multilevel regression model with correlated effects for the probability of SED and a modified K6 score at the individual and school levels. Our results provide evidence for the existence of variation in the prevalence of SED across schools and geographical regions. Although the concordance between the modified K6 scale and SED is only modest for individuals, the school-level random effects for the two measures are strongly correlated. Under this model we obtain a prediction equation for the rate of SED based on the mean K6 score and covariates. This finding supports the feasibility of using short screening scales like the K6 as an alternative to more comprehensive lay assessments in estimating school-level rates of SED. These methods may be applicable to other studies aiming at small-area estimation for geographical units.
authors.affiliation: Department of Statistical Science, Duke University.
authors.forename: Fan
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Health Care Policy, Harvard Medical School.
authors.forename: Alan M
authors.initials: AM
authors.lastname: Zaslavsky
keywords: Bayesian; National Comorbidity Survey; bivariate; hierarchical models; prediction; serious emotional distress; small-area estimation; survey