Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes

• Main Authors: Lin Ammar (Author), Kristin Bird (Author), Hui Nian (Author), Angela Maxwell-Horn (Author), Rees Lee (Author), Tan Ding (Author), Corinne Riddell (Author), Tebeb Gebretsadik (Author), Brittney Snyder (Author), Tina Hartert (Author), Pingsheng Wu (Author)
• Format: Book
• Published: MDPI AG, 2024-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.3390/children11101271; 2227-9067
• Keywords: Down syndrome; administrative databases; International Classification of Diseases; birth certificate; Pediatrics; RJ1-570
• Source: Children, Vol 11, Iss 10, p 1271 (2024)

Objective: We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. Methods: We identified a cohort of children born between 2000 and 2017, enrolled in the Tennessee Medicaid Program (TennCare), who either had DS coded on their birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at Vanderbilt University Medical Center, a comprehensive academic medical center, in the United States. Children with suspected DS were defined as having DS if they had (a) karyotype-confirmed DS indicated on their birth certificate; (b) karyotype-pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or (c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life. The positive predictive value (PPV) of the algorithm and 95% confidence interval (CI) were reported. Results: Of the 411 children with suspected DS, 354 (86.1%) were defined as having DS by the algorithm. According to medical chart review, the algorithm correctly identified 347 children with DS (PPV = 98%, 95%CI: 96.0-99.0%). Of the 57 children the algorithm defined as not having DS, 50 (97.7%, 95%CI: 76.8-93.9%) were confirmed as not having DS by medical chart review. Conclusions: An algorithm that accurately identifies individuals with DS using birth certificate data and/or ICD codes provides a valuable tool to study DS using administrative data.