Version 1
: Received: 2 September 2024 / Approved: 3 September 2024 / Online: 3 September 2024 (14:21:46 CEST)
How to cite:
Ammar, L.; Bird, K.; Nian, H.; Maxwell-Horn, A.; Lee, R.; Ding, T.; Riddell, C.; Gebretsadik, T.; Snyder, B.; Hartert, T.; Wu, P. Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes. Preprints2024, 2024090231. https://doi.org/10.20944/preprints202409.0231.v1
Ammar, L.; Bird, K.; Nian, H.; Maxwell-Horn, A.; Lee, R.; Ding, T.; Riddell, C.; Gebretsadik, T.; Snyder, B.; Hartert, T.; Wu, P. Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes. Preprints 2024, 2024090231. https://doi.org/10.20944/preprints202409.0231.v1
Ammar, L.; Bird, K.; Nian, H.; Maxwell-Horn, A.; Lee, R.; Ding, T.; Riddell, C.; Gebretsadik, T.; Snyder, B.; Hartert, T.; Wu, P. Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes. Preprints2024, 2024090231. https://doi.org/10.20944/preprints202409.0231.v1
APA Style
Ammar, L., Bird, K., Nian, H., Maxwell-Horn, A., Lee, R., Ding, T., Riddell, C., Gebretsadik, T., Snyder, B., Hartert, T., & Wu, P. (2024). Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes. Preprints. https://doi.org/10.20944/preprints202409.0231.v1
Chicago/Turabian Style
Ammar, L., Tina Hartert and Pingsheng Wu. 2024 "Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes" Preprints. https://doi.org/10.20944/preprints202409.0231.v1
Abstract
Objective: To develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data. Methods: We identified a cohort of children born 2000-2017, enrolled in a state Medicaid program, who either had DS coded on the birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at 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. 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 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.
Keywords
Down syndrome; administrative databases; International Classification of Diseases; birth certificate
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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