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01.12.2015 | Research article | Ausgabe 1/2015 Open Access

BMC Public Health 1/2015

Exploratory spatial analysis of Lyme disease in Texas –what can we learn from the reported cases?

Zeitschrift:
BMC Public Health > Ausgabe 1/2015
Autoren:
Barbara Szonyi, Indumathi Srinath, Maria Esteve-Gassent, Blanca Lupiani, Renata Ivanek
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12889-015-2286-0) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

BS participated in the conception and design of the study, carried out data analysis and interpretation of data, and drafted the manuscript. IS was involved in data preparation, analysis, and production of maps. MEG participated in the conception and design of the study, facilitated data acquisition, and has been involved in revising the manuscript critically for intellectual content. BL participated in the conception and design of the study. RI participated in the conception and design of the study and has been involved in revising the manuscript critically for intellectual content. All authors read and approved the final manuscript.

Authors’ information

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Availability of data and materials

Not applicable.

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.

Results and Discussion

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.
Zusatzmaterial
Additional file 1: Moran’s I scatter plot of smoothed Lyme disease incidence in Texas counties, 2000–2011. The slope of the scatter plot corresponds to the value for Moran's I. The four quadrants of the scatter plot visualize the type and strength of spatial autocorrelation among neighboring counties, namely high-high, low-low (positive spatial autocorrelation) and high-low, low-high (negative spatial autocorrelation). (TIFF 409 kb)
12889_2015_2286_MOESM1_ESM.tiff
Literatur
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