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.

Neighborhood Sociodemographic Predictors of Serious Emotional Disturbance (SED) in Schools: Demonstrating a Small Area Estimation Method in the National Comorbidity Survey (NCS-A) Adolescent Supplement

“…Although the K6 had only fair concordance with SED at the individual level, K6 scores could be used to generate highly accurate estimates of school-level SED prevalence (Li and Zaslavsky 2010). This result suggests that it may be productive to continue research to refine very short measures, like the K6, to allow school-level SED to be estimated from information about the distribution of responses to very short screening scales (Green et al 2010b).…”

“…Further, a number of neighborhood characteristics have been found consistently to predict resident mental health in community epidemiological studies of child-adolescent mental disorders (Dupéré et al 2009; Mair et al 2008; Pickett and Pearl 2001; Xue et al 2005). However, the accuracy of small-area estimates of mental illness based on small-area Census data has been questioned, as the strength of associations between aggregate BG variables and the mental illness of residents is often modest, resulting in imprecise small-area estimates of prevalence (Hudson 2009; Kessler et al 1999; Li and Zaslavsky 2010). Furthermore, synthetic estimation relies on the assumption that the prevalence of disorders is constant for demographic strata, so area prevalence depends only on demographic composition; for example, low-income adolescent boys might be assumed to have the same rates of SED in low- and high-poverty areas.…”

Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model

“…We directly define the association between the binary ( Y ) and Gaussian ( Z ) variables through two conditionally specified regression models. The crux of the difference between our approach and that of Li and Zaslavsky (2010) is that we do not require latent variables. Avoiding latent variables simplifies interpretation and better enables us to capture strong dependence.…”

“…To our knowledge, the work that is most closely related to ours considers bivariate small‐area estimation for binary and Gaussian responses in the context of estimating mental illness prevalence in schools (Li and Zaslavsky, 2010). To model the association between the binary and continuous responses conditional on random effects for small areas, they introduce an additional latent variable with a normal distribution, essentially enforcing a probit link function for the binary response variable.…”

Modeling a bivariate residential‐workplace neighborhood effect when estimating the effect of proximity to fast‐food establishments on body mass index

“…Although there already exist models with bivariate (or more generally multivariate) random effects, these differ from the situation considered here as they account for bivariate outcomes, multiple random effects (eg, random‐intercepts, random‐slopes) for the same clustering variable, or stratified random effects such as allowing each neighborhood to have different effects at every observation time . To our knowledge, the case of a bivariate random effect for a clustering variable that classifies a single observation multiple ways (eg, by residential and employment neighborhood) has not been considered previously.…”