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Common data model for sickle cell disease surveillance: considerations and implications
OBJECTIVE: Population-level data on sickle cell disease (SCD) are sparse in the United States. The Centers for Disease Control and Prevention (CDC) is addressing the need for SCD surveillance through state-level Sickle Cell Data Collection Programs (SCDC). The SCDC developed a pilot common informati...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224800/ https://www.ncbi.nlm.nih.gov/pubmed/37252051 http://dx.doi.org/10.1093/jamiaopen/ooad036 |
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author | Smeltzer, Matthew P Reeves, Sarah L Cooper, William O Attell, Brandon K Strouse, John J Takemoto, Clifford M Kanter, Julie Latta, Krista Plaxco, Allison P Davis, Robert L Hatch, Daniel Reyes, Camila Dombkowski, Kevin Snyder, Angela Paulukonis, Susan Singh, Ashima Kayle, Mariam |
author_facet | Smeltzer, Matthew P Reeves, Sarah L Cooper, William O Attell, Brandon K Strouse, John J Takemoto, Clifford M Kanter, Julie Latta, Krista Plaxco, Allison P Davis, Robert L Hatch, Daniel Reyes, Camila Dombkowski, Kevin Snyder, Angela Paulukonis, Susan Singh, Ashima Kayle, Mariam |
author_sort | Smeltzer, Matthew P |
collection | PubMed |
description | OBJECTIVE: Population-level data on sickle cell disease (SCD) are sparse in the United States. The Centers for Disease Control and Prevention (CDC) is addressing the need for SCD surveillance through state-level Sickle Cell Data Collection Programs (SCDC). The SCDC developed a pilot common informatics infrastructure to standardize processes across states. MATERIALS AND METHODS: We describe the process for establishing and maintaining the proposed common informatics infrastructure for a rare disease, starting with a common data model and identify key data elements for public health SCD reporting. RESULTS: The proposed model is constructed to allow pooling of table shells across states for comparison. Core Surveillance Data reports are compiled based on aggregate data provided by states to CDC annually. DISCUSSION AND CONCLUSION: We successfully implemented a pilot SCDC common informatics infrastructure to strengthen our distributed data network and provide a blueprint for similar initiatives in other rare diseases. |
format | Online Article Text |
id | pubmed-10224800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102248002023-05-29 Common data model for sickle cell disease surveillance: considerations and implications Smeltzer, Matthew P Reeves, Sarah L Cooper, William O Attell, Brandon K Strouse, John J Takemoto, Clifford M Kanter, Julie Latta, Krista Plaxco, Allison P Davis, Robert L Hatch, Daniel Reyes, Camila Dombkowski, Kevin Snyder, Angela Paulukonis, Susan Singh, Ashima Kayle, Mariam JAMIA Open Brief Communications OBJECTIVE: Population-level data on sickle cell disease (SCD) are sparse in the United States. The Centers for Disease Control and Prevention (CDC) is addressing the need for SCD surveillance through state-level Sickle Cell Data Collection Programs (SCDC). The SCDC developed a pilot common informatics infrastructure to standardize processes across states. MATERIALS AND METHODS: We describe the process for establishing and maintaining the proposed common informatics infrastructure for a rare disease, starting with a common data model and identify key data elements for public health SCD reporting. RESULTS: The proposed model is constructed to allow pooling of table shells across states for comparison. Core Surveillance Data reports are compiled based on aggregate data provided by states to CDC annually. DISCUSSION AND CONCLUSION: We successfully implemented a pilot SCDC common informatics infrastructure to strengthen our distributed data network and provide a blueprint for similar initiatives in other rare diseases. Oxford University Press 2023-05-27 /pmc/articles/PMC10224800/ /pubmed/37252051 http://dx.doi.org/10.1093/jamiaopen/ooad036 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Brief Communications Smeltzer, Matthew P Reeves, Sarah L Cooper, William O Attell, Brandon K Strouse, John J Takemoto, Clifford M Kanter, Julie Latta, Krista Plaxco, Allison P Davis, Robert L Hatch, Daniel Reyes, Camila Dombkowski, Kevin Snyder, Angela Paulukonis, Susan Singh, Ashima Kayle, Mariam Common data model for sickle cell disease surveillance: considerations and implications |
title | Common data model for sickle cell disease surveillance: considerations and implications |
title_full | Common data model for sickle cell disease surveillance: considerations and implications |
title_fullStr | Common data model for sickle cell disease surveillance: considerations and implications |
title_full_unstemmed | Common data model for sickle cell disease surveillance: considerations and implications |
title_short | Common data model for sickle cell disease surveillance: considerations and implications |
title_sort | common data model for sickle cell disease surveillance: considerations and implications |
topic | Brief Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224800/ https://www.ncbi.nlm.nih.gov/pubmed/37252051 http://dx.doi.org/10.1093/jamiaopen/ooad036 |
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