Spatiotemporal Trends of Birth Defects in North Carolina, 2003-2015.

Details

• Alternative Title:
  Annals of Epidemiology, 2026, v. 115: Spatiotemporal Trends of Birth Defects in North Carolina, 2003-2015.
• Journal Article:
  Annals of Epidemiology
• Personal Author:
  Lu, Haidong ; Olshan, Andrew F. ; Serre, Marc L. ; Anthony, Kurtis M. ; Fry, Rebecca C. ; Forestieri, Nina E. ; Keil, Alexander P.
• Description:
  Birth defects are a leading cause of infant mortality in the United States, but little is known about causes of many types of birth defects. Spatiotemporal disease mapping to identify high-prevalence areas is a potential strategy to narrow the search for potential environmental and other causes that aggregate over space and time. We described the spatial and temporal trends of the prevalence of birth defects in North Carolina during 2003-2015, using data on live births obtained from the North Carolina Birth Defects Monitoring Program. By employing a Bayesian space-time Poisson model, we estimated spatial and temporal trends of non-chromosomal and chromosomal birth defects. During 2003-2015, 52,524 (3.3 %) of 1598,807 live births had at least one recorded birth defect. The prevalence of non-chromosomal birth defects decreased from 3.8 % in 2003-2.9 % in 2015. Spatial modeling suggested a large geographic variation in non-chromosomal birth defects at census-tract level, with the highest prevalence in southeastern North Carolina. The strong spatial heterogeneity revealed in this work allowed us to identify geographic areas with higher prevalence of non-chromosomal birth defects in North Carolina. This variation will help inform future research focused on epidemiologic studies of birth defects to identify etiologic factors.
• Content Notes:
  Author manuscript
• Subjects:
  • Humans
  • Male
  • Female
  • Infant, Newborn
  • Spatio-Temporal Analysis
  • Geographic Mapping
  • Prevalence
  • Congenital Abnormalities
  • Epidemiological Monitoring
  • Bayes Theorem
  More +
• Keywords:
  • birth defects
  • Bayesian disease mapping
  • spatiotemporal analysis
• Source:
  Annals of Epidemiology, 2026, v. 115
• DOI:
  https://doi.org/10.1016/j.annepidem.2026.01.012
• ISSN:
  1047-2797 ; 1873-2585
• Pubmed ID:
  41698779
• Pubmed Central ID:
  PMC12912798
• Document Type:
  Text
• Funding:
  R01 ES029531/ES/NIEHS NIH HHSUnited States/ ; U50 DD422096/DD/NCBDD CDC HHSUnited States/
• Genre:
  Journal Article
• Place as Subject:
  North Carolina
• Pages in Document:
  85-90 (13 pdf pages)
• Volume:
  115
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:29b3de83ea471662d0d04ca1ef8e205ff0cdf3be3dde84d03be53749ca47f037bf6d4bb2e123c8d1b21e44ed21d10c48ddbc4812da7bb1c17a41ddf8d82cb47b