Identifying Children With Medical Complexity From the National Survey of Children’s Health Combined 2016–17 Data Set

Abstract

OBJECTIVES: To develop a method of identifying children with medical complexity (CMC) from the National Survey of Children’s Health (NSCH) 2016–2017 combined data set, to compare this approach to existing CMC identification strategies, and to describe sociodemographic characteristics of our CMC sample. METHODS: Using survey items pertinent to the medical complexity domains in the style by Cohen et al (chronic health conditions, health service needs, health care use, and functional limitations), we created a schema to categorize children as CMC by applying a 95th percentile cutoff for survey item positivity. We applied existing CMC identification techniques to the NSCH. We used 2-proportion z tests to compare the classification output of our CMC identification method to those of existing approaches. We used χ2 analyses to examine relationships between child and family characteristics, comparing CMC with children with special health care needs (CSHCN) and children with no special health care needs. RESULTS: Among the 71 811 children in the sample, 1.5% were classified as CMC by our method, representing almost 1.2 million children (weighted) in the United States in 2016–2017. CSHCN and children with no special health care needs represented 17.2% (weighted n = 12.6 million) and 81.2% (weighted n = 59.6 million) of the sample, respectively. Our approach classified a significantly smaller number of CSHCN as CMC than existing CMC identification methods, which classified 3.9% to 13.2% of the 2016–2017 NSCH sample as more complex (P < .001). CMC status was significantly associated with male sex, minority race or ethnicity, and experiencing socioeconomic adversity (all P < .001). CONCLUSIONS: This method enables standardized identification of CMC from NSCH data sets, thus allowing for an examination of CMC health outcomes, pertinent to pediatric hospitalist medicine, contained in the survey.