All Title Author
Keywords Abstract

PLOS ONE  2014 

Bayesian Spatio-Temporal Analysis and Geospatial Risk Factors of Human Monocytic Ehrlichiosis

DOI: 10.1371/journal.pone.0100850

Full-Text   Cite this paper   Add to My Lib


Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005–2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms. The best fitting model revealed that spatio-temporal autocorrelation in Kansas increased steadily from 2005–2012, and identified poverty status, relative humidity, and an interactive factor, ‘diurnal temperature range x mixed forest area’ as significant county-level risk factors for HME. The identification of significant spatio-temporal pattern and new risk factors are important in the context of HME prevention, for future research in the areas of ecology and evolution of HME, and as well as climate change impacts on tick-borne diseases.


[1]  Pavelites JJ, Prahlow JA (2011) Fatal human monocytic ehrlichiosis: a case study. Forensic Science Medicine and Pathology 7: 287–293. doi: 10.1007/s12024-010-9219-0
[2]  CDC Ehrlichiosis Website. Available: Accessed 2014 Jun 12.
[3]  Yabsley MJ, Wimberly MC, Stallknecht DE, Little SE, Davidson WR (2005) Spatial analysis of the distribution of Ehrlichia chaffeensis, causative agent of human monocytotropic ehrlichiosis, across a multi-state region. American Journal of Tropical Medicine and Hygiene 72: 840–850.
[4]  Wimberly MC, Baer AD, Yabsley MJ (2008) Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens. International Journal of Health Geographics 7: 15 doi:10.1186/1476-072X-7-15.
[5]  Manangan JS, Schweitzer SH, Nibbelink N, Yabsley MJ, Gibbs SEJ, et al. (2007) Habitat factors influencing distributions of Anaplasma phagocytophilum and Ehrlichia chaffeensis in the Mississippi alluvial valley. Vector-Borne and Zoonotic Diseases 7: 563–573. doi: 10.1089/vbz.2007.0116
[6]  Fotheringham AS, Wong DWS (1991) The modifiable areal unit problem in multivariate statistical-analysis. Environment and Planning A 23: 1025–1044. doi: 10.1068/a231025
[7]  Raghavan RK, Brenner KM, Harrington JA Jr, Higgins JJ, Harkin KR (2013) Spatial scale effects in environmental risk-factor modelling for diseases. Geospatial health 7: 169–182.
[8]  Patz JA, Graczyk TK, Geller N, Vittor AY (2000) Effects of environmental change on emerging parasitic diseases. International Journal for Parasitology 30: 1395–1405. doi: 10.1016/s0020-7519(00)00141-7
[9]  Raghavan RK, Almes K, Goodin DG, Harrington JA, Stackhouse PW, (In press) Spatially heterogeneous land-cover/land-use and climatic risk factors of tick-borne feline cytauxzoonosis. Vector-Borne and Zoonotic Diseases.
[10]  Dorny P, Phiri IK, Vercruysse J, Gabriel S, Willingham AL, et al. (2004) A Bayesian approach for estimating values for prevalence and diagnostic test characteristics of porcine cysticercosis. International Journal for Parasitology 34: 569–576. doi: 10.1016/j.ijpara.2003.11.014
[11]  Lawson AB (2013) Bayesian disease mapping: hierarchical modeling in spatial epidemiology. Vol. 32 . CRC Press.
[12]  CDC National Notifiable Diseases Surveillance System (NNDSS) Website. Available: Accessed 2014 Jun 12.
[13]  Multi-Resolution Land Characteristics Consortium (MRLC) Website. Available: Accessed 2014 Jun 12.
[14]  USGS Land Processes Distributed Active Archive Center (LP DACC) Website. Available: Accessed 2014 Jun 12.
[15]  Eckman RS, Stackhouse PW Jr (2012) CEOS contributions to informing energy management and policy decision making using space-based Earth observations. Applied Energy 90: 206–210. doi: 10.1016/j.apenergy.2011.03.001
[16]  National Historical Geographic Information System (NHGIS) Website. Available: Accessed 2014 Jun 12.
[17]  Demma LJ, Holman RC, McQuiston JH, Krebs JW, Swerdlow DL (2005) Epidemiology of human ehrlichiosis and anaplasmosis in the United States, 2001–2002. American Journal of Tropical Medicine and Hygiene 73: 400–409. doi: 10.1196/annals.1374.017
[18]  CDC Ehrlichiosis Website. Available: Accessed 2014 Jun 2.
[19]  R-INLA Random Walk Model of Order 1 Website. Available: Accessed 2014 Jun 12.
[20]  Lawson AB (2013) Bayesian disease mapping. Hierarchical modeling in spatial epidemiology. Second Edition. CRC Press New York.
[21]  Hosmer DW, Lemeshow S (1990) Model-Building Strategies and Methods for Logistic Regression. Applied Logistic Regression. Second Edition ed. pp. 91–142.
[22]  R-INLA Project Website. Available: Accessed 2014 Jun 12.
[23]  Beocat Documentation Website. Available: Accessed 2014 Jun 2.
[24]  Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Posterior simulation: In: Bayesian Data Analysis. Boca Raton, FL: Chapmann and Hall/CRC. pp 283–310.
[25]  CDC Approximate Distribution of the Lone Star Tick Website. Available: Accessed 2014 Jun 12.
[26]  Bishopp FC, Trembley HL (1945) Distribution and hosts of certain North American ticks. Journal of Parasitology 31: 1–54. doi: 10.2307/3273061
[27]  Hair JA, Sauer JR, Durham KA (1975) Water-balance and humidity preference in 3 species of ticks. Journal of Medical Entomology 12: 37–47.
[28]  Rodgers SE, Zolnik CP, Mather TN (2007) Duration of exposure to suboptimal atmospheric moisture affects nymphal blacklegged tick survival. Journal of Medical Entomology 44: 372–375. doi: 10.1603/0022-2585(2007)44[372:doetsa];2
[29]  Yoder JA, Hedges BZ, Benoit JB (2012) Water balance of the American dog tick, Dermacentor variabilis, throughout its development with comparative observations between field-collected and laboratory-reared ticks. International Journal of Acarology 38: 334–343. doi: 10.1080/01647954.2011.647073
[30]  Wimberly MC, Yabsley MJ, Baer AD, Dugan VG, Davidson WR (2008) Spatial heterogeneity of climate and land-cover constraints on distributions of tick-borne pathogens. Global Ecology and Biogeography 17: 189–202. doi: 10.1111/j.1466-8238.2007.00353.x
[31]  Goodin DG, Mitchell JE, Knapp MC, Bivens RE (2004) Climate and weather atlas of Kansas. An Introduction. Accessed: 2014 Jun 12.
[32]  Suess J, Gerstengarbe F-W (2008) What makes ticks tick? Climate change, ticks and tick-borne diseases. Parasitology Research 103: S157–S158. doi: 10.1111/j.1708-8305.2007.00176.x
[33]  Berger KA, Wang Y, Mather TN (2013) MODIS-derived land surface moisture conditions for monitoring blacklegged tick habitat in southern New England. International Journal of Remote Sensing 34: 73–85. doi: 10.1080/01431161.2012.705447
[34]  Randolph SE, Storey K (1999) Impact of microclimate on immature tick-rodent host interactions (Acari: Ixodidae): Implications for parasite transmission. Journal of Medical Entomology 36: 741–748.
[35]  Ochanda H (2006) Comparison of the survival of Theileria parva-infected adult Rhipicephalus appendiculatus (Acari: Ixodidae) and their infection under simulated climate conditions in the laboratory and in the field. International Journal of Tropical Insect Science 26: 101–107. doi: 10.1079/ijt2006107
[36]  Schwartz MD (1995) Detecting structural climate-change - an air mass-based approach in the north central united-states, 1958-1992. Annals of the Association of American Geographers 85: 553–568. doi: 10.1111/j.1467-8306.1995.tb01812.x
[37]  Gray JS, Dautel H, Estrada-Pena A, Kahl O, Lindgren E (2009) Effects of climate change on ticks and tick-borne diseases in Europe. Interdisciplinary perspectives on infectious diseases 2009: 593232–593232. doi: 10.1155/2009/593232
[38]  Gilbert L (2010) Altitudinal patterns of tick and host abundance: a potential role for climate change in regulating tick-borne diseases? Oecologia 162: 217–225. doi: 10.1007/s00442-009-1430-x
[39]  Randolph SE (2010) To what extent has climate change contributed to the recent epidemiology of tick-borne diseases? Veterinary Parasitology 167: 92–94. doi: 10.1016/j.vetpar.2009.09.011
[40]  Karl TR, Kukla G, Razuvayev VN, Changery MJ, Quayle RG, et al. (1991) Global warming - evidence for asymmetric diurnal temperature-change. Geophysical Research Letters 18: 2253–2256. doi: 10.1029/91gl02900
[41]  Braganza K, Karoly DJ, Arblaster JM (2004) Diurnal temperature range as an index of global climate change during the twentieth century. Geophysical Research Letters 31..
[42]  Karl TR, Jones PD, Knight RW, Kukla G, Plummer N, et al. (1993) A new perspective on recent global warming - asymmetric trends of daily maximum and minimum temperature. Bulletin of the American Meteorological Society 74: 1007–1023. doi: 10.1175/1520-0477(1993)074<1007:anporg>;2
[43]  Sumilo D, Bormane A, Asokliene L, Vasilenko V, Golovljova I, et al. (2008) Socio-economic factors in the differential upsurge of tick-borne encephalitis in Central and Eastern Europe. Reviews in Medical Virology 18: 81–95. doi: 10.1002/rmv.566
[44]  Stefanoff P, Rosinska M, Samuels S, White DJ, Morse DL, et al.. (2012) A National Case-Control Study Identifies Human Socio-Economic Status and Activities as Risk Factors for Tick-Borne Encephalitis in Poland. Plos One 7..
[45]  Godfrey ER, Randolph SE (2011) Economic downturn results in tick-borne disease upsurge. Parasites & Vectors 4.
[46]  County Health Ranking Website. Available: Accessed 2014 Jun 12.


comments powered by Disqus