Common data model for sickle cell disease surveillance: considerations and implications
Abstract
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. Sickle cell disease (SCD) is a complex health disorder that requires frequent scheduled and unscheduled healthcare visits. In the United States, we do not have comprehensive data on the numbers of persons with SCD and where they receive healthcare. In order to understand this better, The Centers for Disease Control and Prevention (CDC) is supporting SCD surveillance through state-level Sickle Cell Data Collection Programs. Because the data are collected within each state, they are not standardized across states. In order to standardize the datasets across states, we have constructed a common data model. This will allow participating states to standardize their data so that it can be presented together for multi-state projects. In our model, each state keeps its own data, but can work with other states to combine information. We hope this common data model will be useful to other groups working with SCD data or data on other rare diseases.
Publication Title
JAMIA Open
Recommended Citation
Smeltzer, M., Reeves, S., Cooper, W., Attell, B., Strouse, J., Takemoto, C., Kanter, J., Latta, K., Plaxco, A., Davis, R., Hatch, D., Reyes, C., Dombkowski, K., Snyder, A., Paulukonis, S., Singh, A., & Kayle, M. (2023). Common data model for sickle cell disease surveillance: considerations and implications. JAMIA Open, 6 (2) https://doi.org/10.1093/jamiaopen/ooad036
