title: Using Nation-Wide Health Insurance Claims Data to Augment Lyme Disease Surveillance.
abstract: OBJECTIVE
  Lyme disease (LD) is the most commonly reported tick-borne illness in North America. To improve LD surveillance, we explored claims data as an adjunct data source for monitoring trends in Lyme disease incidence.
  
  
  METHODS
  We retrospectively analyzed claims from a nationwide US health insurance plan, identifying patients with newly diagnosed LD in 13 high-prevalence states over two time periods, 2004-2006 and 2010-2012.
  
  
  RESULTS
  The average LD case incidence as estimated by using claims data in 2010-2012 (75.67 per 100,000 person-years, n = 3474) was 1.50 times higher than 2004-2006 (50.25 per 100,000 person-years, n = 1965) (p < 0.001) and higher than incidence reported by the states to the Centers for Disease Control and Prevention. Among the 13 highest-prevalence states, there were 11 states with increased LD incidence over time.
  
  
  CONCLUSIONS
  Surveillance systems should explore a fusion of data sources, including payer claims that appear to be highly sensitive with limitations, with electronic laboratory data that afford high specificity, but appear to miss cases.
authors.affiliation: 1 Computational Health Informatics Program, Boston Children's Hospital , Boston, Massachusetts.
authors.forename: Yi-Ju
authors.initials: YJ
authors.lastname: Tseng
authors.affiliation: 1 Computational Health Informatics Program, Boston Children's Hospital , Boston, Massachusetts.|2 Department of Pediatrics, Harvard Medical School , Boston, Massachusetts.
authors.forename: Aurel
authors.initials: A
authors.lastname: Cami
authors.affiliation: 3 Institute for Healthcare Improvement , Cambridge, Massachusetts.|4 Division of Infectious Diseases, Boston Children's Hospital , Boston, Massachusetts.
authors.forename: Donald A
authors.initials: DA
authors.lastname: Goldmann
authors.affiliation: 5 Bureau of Infectious Disease, Massachusetts Department of Public Health, Jamaica Plain, Massachusetts.
authors.forename: Alfred
authors.initials: A
authors.lastname: DeMaria
authors.affiliation: 1 Computational Health Informatics Program, Boston Children's Hospital , Boston, Massachusetts.|2 Department of Pediatrics, Harvard Medical School , Boston, Massachusetts.|6 Department of Biomedical Informatics, Harvard Medical School , Boston, Massachusetts.
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
mesh_terms: D000293:Adolescent; D000328:Adult; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008193:Lyme Disease / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D012189:Retrospective Studies; D017282:Tick-Borne Diseases / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology; D055815:Young Adult
keywords: Claims analysis; Epidemiologic surveillance; Incidence; Lyme disease