Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes
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 cer...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
|
| Series: | Children |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9067/11/10/1271 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205641959800832 |
|---|---|
| author | Lin Ammar Kristin Bird Hui Nian Angela Maxwell-Horn Rees Lee Tan Ding Corinne Riddell Tebeb Gebretsadik Brittney Snyder Tina Hartert Pingsheng Wu |
| author_facet | Lin Ammar Kristin Bird Hui Nian Angela Maxwell-Horn Rees Lee Tan Ding Corinne Riddell Tebeb Gebretsadik Brittney Snyder Tina Hartert Pingsheng Wu |
| author_sort | Lin Ammar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8fe8ab867b5d430eab07b73f01d80d4c |
| institution | OA Journals |
| issn | 2227-9067 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj-art-8fe8ab867b5d430eab07b73f01d80d4c2025-08-20T02:11:03ZengMDPI AGChildren2227-90672024-10-011110127110.3390/children11101271Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases CodesLin Ammar0Kristin Bird1Hui Nian2Angela Maxwell-Horn3Rees Lee4Tan Ding5Corinne Riddell6Tebeb Gebretsadik7Brittney Snyder8Tina Hartert9Pingsheng Wu10Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN 37232, USADepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Pediatrics, College of Medicine, University of Arizona, Tucson, AZ 85721, USADepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USADivision of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, CA 94720, USADepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USADepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USAObjective: 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.https://www.mdpi.com/2227-9067/11/10/1271Down syndromeadministrative databasesInternational Classification of Diseasesbirth certificate |
| spellingShingle | Lin Ammar Kristin Bird Hui Nian Angela Maxwell-Horn Rees Lee Tan Ding Corinne Riddell Tebeb Gebretsadik Brittney Snyder Tina Hartert Pingsheng Wu Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes Children Down syndrome administrative databases International Classification of Diseases birth certificate |
| title | Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes |
| title_full | Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes |
| title_fullStr | Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes |
| title_full_unstemmed | Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes |
| title_short | Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes |
| title_sort | development and validation of a diagnostic algorithm for down syndrome using birth certificate and international classification of diseases codes |
| topic | Down syndrome administrative databases International Classification of Diseases birth certificate |
| url | https://www.mdpi.com/2227-9067/11/10/1271 |
| work_keys_str_mv | AT linammar developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT kristinbird developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT huinian developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT angelamaxwellhorn developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT reeslee developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT tanding developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT corinneriddell developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT tebebgebretsadik developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT brittneysnyder developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT tinahartert developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes AT pingshengwu developmentandvalidationofadiagnosticalgorithmfordownsyndromeusingbirthcertificateandinternationalclassificationofdiseasescodes |