Gestational Diabetes Prevalence Estimates from Three Data Sources, 2018

Author:

Bolduc Michele L.F.,Mercado Carla I.,Zhang Yan,Lundeen Elizabeth A.,Ford Nicole D.,Bullard Kai McKeever,Carty Denise C.

Abstract

Abstract Introduction We investigated 2018 gestational diabetes mellitus (GDM) prevalence estimates in three surveillance systems (National Vital Statistics System, State Inpatient Database, and Pregnancy Risk Assessment Monitoring Survey). Methods We calculated GDM prevalence for jurisdictions represented in each system; a subset of data was analyzed for people 18–39 years old in 22 jurisdictions present in all three systems to observe dataset-specific demographics and GDM prevalence using comparable categories. Results GDM prevalence estimates varied widely by data system and within the data subset despite comparable demographics. Discussion Understanding the differences between GDM surveillance data systems can help researchers better identify people and places at higher risk of GDM.

Publisher

Springer Science and Business Media LLC

Reference10 articles.

1. Bellamy, L., Casas, J. P., Hingorani, A. D., & Williams, D. (2009). Type 2 diabetes mellitus after gestational diabetes: A systematic review and meta-analysis. Lancet, 373(9677), 1773–1779. https://doi.org/10.1016/S0140-6736(09)60731-5.

2. Dabelea, D., Mayer-Davis, E. J., Lamichhane, A. P., D’Agostino, R. B. Jr, Liese, A. D., Vehik, K. S., Narayan, K. M., Zeitler, P., & Hamman, R. F. (2008). Association of intrauterine exposure to maternal diabetes and obesity with type 2 diabetes in youth: The SEARCH Case-Control Study. Diabetes Care, 31(7), 1422–1426. https://doi.org/10.2337/dc07-2417.

3. DeSisto, C. L., Kim, S. Y., & Sharma, A. J. (2014). Prevalence estimates of gestational diabetes mellitus in the United States, pregnancy risk Assessment Monitoring System (PRAMS), 2007–2010. Preventing Chronic Disease, 11, E104. https://doi.org/10.5888/pcd11.130415.

4. Devlin, H. M., Desai, J., & Walaszek, A. (2009). Reviewing performance of birth certificate and hospital discharge data to identify births complicated by maternal diabetes. Maternal and Child Health Journal, 13(5), 660–666. https://doi.org/10.1007/s10995-008-0390-9.

5. Dietz, P., Bombard, J., Mulready-Ward, C., Gauthier, J., Sackoff, J., Brozicevic, P., Gambatese, M., Nyland-Funke, M., England, L., Harrison, L., & Taylor, A. (2014). Validation of self-reported maternal and infant health indicators in the pregnancy risk Assessment Monitoring System. Maternal and Child Health Journal, 18, 2489–2498. https://doi.org/10.1007/s10995-014-1487-y.

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