Using latent class analysis to inform the design of an EHR-based national chronic disease surveillance model

Details

• Alternative Title:
  Chronic Illn
• Personal Author:
  Nasuti, Laura ; Andrews, Bonnie ; Li, Wenjun ; Wiltz, Jennifer ; Hohman, Katherine H ; Patanian, Miriam
• Description:
  The Multi-state EHR-based Network for Disease Surveillance (MENDS) developed a pilot electronic health record (EHR) surveillance system capable of providing national chronic disease estimates. To strategically engage partner sites, MENDS conducted a latent class analysis (LCA) and grouped states by similarities in socioeconomics, demographics, chronic disease and behavioral risk factor prevalence, health outcomes, and health insurance coverage. Three latent classes of states were identified, which inform the recruitment of additional partner sites in conjunction with additional factors (e.g. partner site capacity and data availability, information technology infrastructure). This methodology can be used to inform other public health surveillance modernization efforts that leverage timely EHR data to address gaps, use existing technology, and advance surveillance.
• Subjects:
  Chronic Disease Chronic Disease Indicators Humans Latent Class Analysis Population Surveillance
• Keywords:
  Chronic Disease Electronic Health Record Health Information Technology Surveillance
• Source:
  Chronic Illn. 19(3):675-680
• Pubmed ID:
  35505590
• Pubmed Central ID:
  PMC10515457
• Document Type:
  Journal Article
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/ ; NU38OT000286/OT/OSTLTS CDC HHSUnited States/
• Volume:
  19
• Issue:
  3
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:23531a3b3b9b2e36b4b261ced4afb7e90d64f5b7491619e0a2ad8431a65571e8
• Download URL:
  https://stacks.cdc.gov/view/cdc/148847/cdc_148847_DS1.pdf
• File Type:
  [PDF - 249.03 KB ]