Predictive Modeling of Health ConditionsIdentifying Local Hot Spots of Pediatric Chronic Diseases Using Emergency Department Surveillance

ObjectiveTo use novel geographic methods and large-scale claims data to identify the local distribution of pediatric chronic diseases in New York City.MethodsUsing a 2009 all-payer emergency claims database, we identified the proportion of unique children aged 0 to 17 with diagnosis codes for specific medical and psychiatric conditions. As a proof of concept, we compared these prevalence estimates to traditional health surveys and registry data using the most geographically granular data available. In addition, we used home addresses to map local variation in pediatric disease burden.ResultsWe identified 549,547 New York City children who visited an emergency department at least once in 2009. Though our sample included more publicly insured and uninsured children, we found moderate to strong correlations of prevalence estimates when compared to health surveys and registry data at prespecified geographic levels. Strongest correlations were found for asthma and mental health conditions by county among younger children (0.88, P = .05 and 0.99, P < .01, respectively). Moderate correlations by neighborhood were identified for obesity and cancer (0.53 and 0.54, P < .01). Among adolescents, correlations by health districts were strong for obesity (0.95, P = .05), and depression estimates had a nonsignificant, but strong negative correlation with suicide attempts (−0.88, P = .12). Using SaTScan, we also identified local hot spots of pediatric chronic disease.ConclusionsFor conditions easily identified in claims data, emergency department surveillance may help estimate pediatric chronic disease prevalence with higher geographic resolution. More studies are needed to investigate limitations of these methods and assess reliability of local disease estimates.