Using a conceptual framework that recognizes the multiple levels of influence on children’s health outcomes, our team will develop an evidence based analytic approach to examine the extent of the geographic variation in children’s mental health and well-being and its multilevel determinants using existing, linked individual- and area-level databases. Our primary goal is to identify disparities in children’s mental health and well-being across U.S. counties and Census Divisions, and related modifiable factors at the individual- and area-level. This will be achieved by: 1) using multilevel analysis and post-stratification to generate small area estimates for selected children’s health indicators, and 2) exploring the feasibility of comparative and/or integrative data analysis strategies that synergize population-based health surveys to overcome estimation and modelling difficulties associated with small samples and heterogeneity across surveys. To achieve these goals, our project will develop and implement statistical methodologies that compare and/or integrate data from the National Survey of Children’s Health with other surveys with child health questionnaires such as the National Health Interview Survey-Child, Behavioral Risk Factor Surveillance System (e.g., Children’s Health Assessment Survey), and/or the California Health Interview Survey-Child/Adolescent Modules. Our detailed aims include:
Aim 1a: Explanatory Modeling – Identify the multilevel (individual, county, and state) determinants of children’s health and well-being outcomes including but not limited to 1) Attention-Deficit/Hyperactivity Disorder, 2) Tourette’s Syndrome, and 3) Autism and Autism Spectrum Disorders
Aim 1b: Predictive Modeling – Create and map county- and state-level estimates of selected indicators of children’s mental health and well-being
Aim 2: Comparative/Integrative Data Analysis – Determine the feasibility of comparing small area estimates across multiple population-based surveys and conducting integrative data analysis to overcome estimation and modelling difficulties associated with small samples and survey design heterogeneity
The significance of this project is two-fold. First, this research will demonstrate the utilize of the NSCH data, alone and compared/integrated with other population-based surveys, for producing estimates at granular levels of geography not amenable to direct estimation. Secondly, the research is valuable for rural communities, who have notable child health disparities when aggregated, but very little local information by which to base resource allocation and intervention decisions.