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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Gestational Diabetes Prevalence Estimates from Three Data Sources, 2018.

Michele L F Bolduc1, Carla I Mercado2, Yan Zhang3

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Maternal and Child Health Journal
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PubMed
Summary

Gestational diabetes mellitus (GDM) prevalence estimates varied across three major US surveillance systems in 2018. Understanding these differences is key for identifying populations and locations with higher GDM risk.

Keywords:
Birth certificatesDiabetesEpidemiologyGestationalHealth surveysHospital recordsMaternal healthPregnancy

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Surveillance

Background:

  • Investigated 2018 gestational diabetes mellitus (GDM) prevalence estimates using three distinct surveillance systems: National Vital Statistics System, State Inpatient Database, and Pregnancy Risk Assessment Monitoring Survey.
  • Examined variations in GDM prevalence reporting across these systems.

Purpose of the Study:

  • To compare GDM prevalence estimates derived from different data sources.
  • To identify potential biases or discrepancies in GDM surveillance data.
  • To inform researchers on selecting appropriate data systems for GDM risk assessment.

Main Methods:

  • Calculated GDM prevalence for all represented jurisdictions within each system.
  • Analyzed a subset of data for individuals aged 18-39 in 22 consistent jurisdictions.
  • Compared dataset-specific demographics and GDM prevalence using standardized categories.

Main Results:

  • Significant variations in GDM prevalence estimates were observed across the three data systems.
  • Discrepancies in prevalence persisted even when analyzing comparable demographic groups within the data subset.
  • Dataset-specific characteristics influenced GDM prevalence reporting.

Conclusions:

  • Differences between GDM surveillance data systems impact prevalence estimates.
  • Awareness of these system-specific variations is crucial for accurate GDM risk identification.
  • Researchers can better target high-risk populations and geographic areas by understanding data system nuances.