title: Exploratory spatial analysis of Lyme disease in Texas -what can we learn from the reported cases?
abstract: BACKGROUND
  Lyme disease (LD) is a tick-borne zoonotic illness caused by the bacterium Borrelia burgdorferi. Texas is considered a non-endemic state for LD and the spatial distribution of the state's reported LD cases is unknown.
  
  
  METHODS
  We analyzed human LD cases reported to the Texas Department of State Health Services (TX-DSHS) between 2000 and 2011 using exploratory spatial analysis with the objective to investigate the spatial patterns of LD in Texas. Case data were aggregated at the county level, and census data were used as the population at risk. Empirical Bayesian smoothing was performed to stabilize the variance. Global Moran's I was calculated to assess the presence and type of spatial autocorrelation. Local Indicator of Spatial Association (LISA) was used to determine the location of spatial clusters and outliers.
  
  
  CONCLUSIONS
  There was significant positive spatial autocorrelation of LD incidence in Texas with Moran's I of 0.41 (p = 0.001). LISA revealed significant variation in the spatial distribution of human LD in Texas. First, we identified a high-risk cluster in Central Texas, in a region that is thought to be beyond the geographical range of the main vector, Ixodes scapularis. Second, the eastern part of Texas, which is thought to provide the most suitable habitat for I. scapularis, did not appear to be a high-risk area. Third, LD cases were reported from several counties in western Texas, a region considered unsuitable for the survival of Ixodes ticks.
  
  
  CONCLUSIONS
  These results emphasize the need for follow-up investigations to determine whether the identified spatial pattern is due to: clustering of misdiagnosed cases, clustering of patients with an out-of state travel history, or presence of a clustered unknown enzootic cycle of B. burgdorferi in Texas. This would enable an improved surveillance and reporting of LD in Texas.
authors.affiliation: International Livestock Research Institute (ILRI), Box 5689, Addis Ababa, Ethiopia. b.szonyi@cgiar.org.|Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. b.szonyi@cgiar.org.
authors.forename: Barbara
authors.initials: B
authors.lastname: Szonyi
authors.affiliation: Center for Agribusiness Excellence, Tarleton State University, Stephenville, TX, USA. srinath@tarleton.edu.|Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. srinath@tarleton.edu.
authors.forename: Indumathi
authors.initials: I
authors.lastname: Srinath
authors.affiliation: Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. mesteve-gassent@cvm.tamu.edu.
authors.forename: Maria
authors.initials: M
authors.lastname: Esteve-Gassent
authors.affiliation: Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. blupiani@cvm.tamu.edu.
authors.forename: Blanca
authors.initials: B
authors.lastname: Lupiani
authors.affiliation: Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA. rivanek@cvm.tamu.edu.
authors.forename: Renata
authors.initials: R
authors.lastname: Ivanek
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D025065:Borrelia burgdorferi; D017753:Ecosystem; D006801:Humans; D015994:Incidence; D018884:Ixodes; D008193:Lyme Disease / Q000453:epidemiology*; D062206:Spatial Analysis; D013781:Texas / Q000453:epidemiology