- Introduction
- Materials and Methods
- Results
- Basic description of spatial patterns for WNV disease case counts and WNV disease incidence.
- Spatial model to partition variance of WNV disease and WNND incidence at county and census tract scales.
- Hot-spot analysis for WNV disease.
- Concordance between spatial patterns based on WNV disease case count versus incidence.
- Discussion
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
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1.
Beck LR, Lobitz BM, Wood BL, 2000. Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 6: 217–227.
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2.
Kalluri S, Gilruth P, Rogers D, Szczur M, 2007. Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog 3: e116.
-
3.
Kitron U, 1998. Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35: 435–445.
-
4.
Rogers DJ, Randolph SE, 2003. Studying the global distribution of infectious diseases using GIS and RS. Nat Rev Microbiol 1: 231–237.
-
6.
Theophilides CN, Ahearn SC, Grady S, Merlino M, 2003. Identifying West Nile virus risk areas: the dynamic continuous-area space-time system. Am J Epidemiol 157: 843–854.
-
9.
Eisen L, Lozano-Fuentes S, 2009. Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Negl Trop Dis 3: e411.
-
10.
Kamadjeu R, 2009. Tracking the polio virus down the Congo River: a case study on the use of Google Earth™ in public health planning and mapping. Int J Health Geogr 8: 4.
-
11.
Lozano-Fuentes S, Elizondo-Quiroga D, Farfan-Ale JA, Loroño-Pino MA, Garcia-Rejon J, Gomez-Carro S, Lira-Zumbardo V, Najera-Vazquez R, Fernandez-Salas I, Calderon-Martinez J, Dominguez-Galera M, Mis-Avila P, Coleman M, Morris N, Moore CG, Beaty BJ, Eisen L, 2008. Use of Google Earth to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments. Bull World Health Organ 86: 718–725.
-
12.
Bell BS, Hoskins RE, Pickle LW, Wartenberg D, 2006. Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public. Int J Health Geogr 5: 49.
-
13.
Gao S, Mioc D, Yi X, Anton F, Oldfield E, Coleman D, 2009. Towards Web-based representation and processing of health information. Int J Health Geogr 8: 3.
-
14.
Eisen RJ, Eisen L, 2008. Spatial modeling of human risk of exposure to vector-borne pathogens based on epidemiological versus arthropod vector data. J Med Entomol 45: 181–192.
-
15.
Eisen RJ, Eisen L, 2007. Need for improved methods to collect and present spatial epidemiologic data for vectorborne diseases. Emerg Infect Dis 13: 1816–1820.
-
16.
Colorado Department of Public Health and Environment. Available at: http://www.cdphe.state.co.us/dc/zoonosis/wnv/index.html.
-
17.
Mostashari F, Bunning ML, Kitsutani PT, Singer DA, Nash D, Cooper MJ, Katz N, Liljebjelke KA, Biggerstaff BJ, Fine AD, Layton MC, Mullin SM, Johnson AJ, Martin DA, Hayes EB, Campbell GL, 2001. Epidemic West Nile encephalitis, New York, 1999: results of a household-based seroepidemiological survey. Lancet 358: 261–264.
-
18.
Hardin JW, Hilbe JM, 2007. Generalized Linear Models and Extensions. 2nd edition. College Station, TX: Stata Press.
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19.
Cressie N, 1993. Statistics for Spatial Data. New York: John Wiley & Sons, Inc.
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20.
Fotheringham A, Brunsdon C, Charlton M, 2002. Geographically Weighted Regression, the Analysis of Spatially Varying Relationships. West Sussex, United Kingdom: John Wiley and Sons Ltd.
-
21.
Wall M, 2004. A close look at the spatial structure implied by the CAR and SAR models. J Statist Plann Inference 121: 311–324.
-
22.
Getis A, Ord J, 1992. The analysis of spatial association by use of distance statistics. Geogr Anal 24: 189–206.
-
23.
Rytkonen MJP, 2004. Not all maps are equal: GIS and spatial analysis in epidemiology. Int J Circumpolar Health 63: 9–24.
-
24.
Curtis A, Mills JW, Leitner M, 2006. Keeping an eye on privacy issues with geospatial data. Nature 441: 150.
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25.
Health Insurance Portability and Accountability Act of 1996. Available at: hhs.gov/HIPAAGenInfo/Downloads/HIPAALaw.pdf.
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26.
VanWey LK, Rindfuss RR, Gutmann MP, Entwisle B, Balk DL, 2005. Confidentiality and spatially explicit data: concerns and challenges. Proc Natl Acad Sci USA 102: 15337–15342.
-
27.
Openshaw S, 1984. The Modifiable Areal Unit Problem. Norwich, UK: Geo Books.
-
28.
Diuk-Wasser MA, Brown HE, Andreadis TG, Fish D, 2006. Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA. Vector Borne Zoonotic Dis 6: 283–295.
-
29.
Eisen RJ, Eisen L, Lane RS, 2006. Predicting density of Ixodes pacificus nymphs in dense woodlands in Mendocino County, California, based on geographical information systems and remote sensing versus field-derived data. Am J Trop Med Hyg 74: 632–640.
-
30.
Nielsen CF, Armijos MV, Wheeler S, Carpenter TE, Boyce WM, Kelley K, Brown D, Scott TW, Reisen WK, 2008. Risk factors associated with human infection during the 2006 West Nile virus outbreak in Davis, a residential community in northern California. Am J Trop Med Hyg 78: 53–62.
-
31.
Rodgers SE, Mather TN, 2006. Evaluating satellite sensor-derived indices for Lyme disease risk prediction. J Med Entomol 43: 337–343.
-
32.
Winters AM, Bolling BG, Beaty BJ, Blair CD, Eisen RJ, Meyer AM, Pape WJ, Moore CG, Eisen L, 2008. Combining mosquito vector and human disease data for improved assessment of spatial West Nile virus disease risk. Am J Trop Med Hyg 78: 654–665.
-
33.
Eisen RJ, Lane RS, Fritz CL, Eisen L, 2006. Spatial patterns of Lyme disease risk in California based on disease incidence data and modeling of vector-tick exposure. Am J Trop Med Hyg 75: 669–676.
-
34.
Elliot P, Wakefield J, Best N, Briggs D, 2000. Spatial Epidemiology Methods and Applications. Oxford, United Kingdom: Oxford University Press.
-
35.
Waller LA, Gotway CA, 2004. Applied Spatial Statistics for Public Health. Hoboken, NJ: John Wiley & Sons, Inc.
| Past two years | Past Year | Past 30 Days | |
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| Abstract Views | 538 | 487 | 45 |
| Full Text Views | 335 | 19 | 2 |
| PDF Downloads | 94 | 15 | 2 |
Spatial Risk Assessments Based on Vector-Borne Disease Epidemiologic Data: Importance of Scale for West Nile Virus Disease in Colorado
Anna M. Winters
Anna M. Winters
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Rebecca J. Eisen
Rebecca J. Eisen
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Mark J. Delorey
Mark J. Delorey
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Marc Fischer
Marc Fischer
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Roger S. Nasci
Roger S. Nasci
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Emily Zielinski-Gutierrez
Emily Zielinski-Gutierrez
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Chester G. Moore
Chester G. Moore
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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W. John Pape
W. John Pape
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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Lars Eisen
Lars Eisen
Division of Vector-Borne Infectious Diseases, National Center for Vector-Borne, Zoonotic and Enteric Diseases, Centers for Disease Control and Prevention, Fort Collins, Colorado; Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado; Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, Colorado
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We used epidemiologic data for human West Nile virus (WNV) disease in Colorado from 2003 and 2007 to determine 1) the degree to which estimates of vector-borne disease occurrence is influenced by spatial scale of data aggregation (county versus census tract), and 2) the extent of concordance between spatial risk patterns based on case counts versus incidence. Statistical analyses showed that county, compared with census tract, accounted for approximately 50% of the overall variance in WNV disease incidence, and approximately 33% for the subset of cases classified as West Nile neuroinvasive disease. These findings indicate that sub-county scale presentation provides valuable risk information for stakeholders. There was high concordance between spatial patterns of WNV disease incidence and case counts for census tract (83%) but not for county (50%) or zip code (31%). We discuss how these findings impact on practices to develop spatial epidemiologic data for vector-borne diseases and present data to stakeholders.
Author Notes
Financial support: The study was funded, in part, by a grant from the Centers for Disease Control and Prevention (T01/CCT822307) and a contract from the National Institutes of Allergy and Infectious Diseases (N01-AI-25489).
Authors' addresses: Anna M. Winters, Rebecca J. Eisen, Mark J. Delorey, Marc Fischer, Roger S. Nasci, and Emily Zielinski-Gutierrez, Division of Vector-Borne Infectious Diseases, Centers for Disease Control and Prevention, Fort Collins, CO. Chester G. Moore and Lars Eisen, Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, CO. W. John Pape, Communicable Disease Program, Colorado Department of Public Health and Environment, Denver, CO.
- Introduction
- Materials and Methods
- Results
- Basic description of spatial patterns for WNV disease case counts and WNV disease incidence.
- Spatial model to partition variance of WNV disease and WNND incidence at county and census tract scales.
- Hot-spot analysis for WNV disease.
- Concordance between spatial patterns based on WNV disease case count versus incidence.
- Discussion
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Beck LR, Lobitz BM, Wood BL, 2000. Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 6: 217–227.
-
2.
Kalluri S, Gilruth P, Rogers D, Szczur M, 2007. Surveillance of arthropod vector-borne infectious diseases using remote sensing techniques: a review. PLoS Pathog 3: e116.
-
3.
Kitron U, 1998. Landscape ecology and epidemiology of vector-borne diseases: tools for spatial analysis. J Med Entomol 35: 435–445.
-
4.
Rogers DJ, Randolph SE, 2003. Studying the global distribution of infectious diseases using GIS and RS. Nat Rev Microbiol 1: 231–237.
-
6.
Theophilides CN, Ahearn SC, Grady S, Merlino M, 2003. Identifying West Nile virus risk areas: the dynamic continuous-area space-time system. Am J Epidemiol 157: 843–854.
-
9.
Eisen L, Lozano-Fuentes S, 2009. Use of mapping and spatial and space-time modeling approaches in operational control of Aedes aegypti and dengue. PLoS Negl Trop Dis 3: e411.
-
10.
Kamadjeu R, 2009. Tracking the polio virus down the Congo River: a case study on the use of Google Earth™ in public health planning and mapping. Int J Health Geogr 8: 4.
-
11.
Lozano-Fuentes S, Elizondo-Quiroga D, Farfan-Ale JA, Loroño-Pino MA, Garcia-Rejon J, Gomez-Carro S, Lira-Zumbardo V, Najera-Vazquez R, Fernandez-Salas I, Calderon-Martinez J, Dominguez-Galera M, Mis-Avila P, Coleman M, Morris N, Moore CG, Beaty BJ, Eisen L, 2008. Use of Google Earth to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments. Bull World Health Organ 86: 718–725.
-
12.
Bell BS, Hoskins RE, Pickle LW, Wartenberg D, 2006. Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public. Int J Health Geogr 5: 49.
-
13.
Gao S, Mioc D, Yi X, Anton F, Oldfield E, Coleman D, 2009. Towards Web-based representation and processing of health information. Int J Health Geogr 8: 3.
-
14.
Eisen RJ, Eisen L, 2008. Spatial modeling of human risk of exposure to vector-borne pathogens based on epidemiological versus arthropod vector data. J Med Entomol 45: 181–192.
-
15.
Eisen RJ, Eisen L, 2007. Need for improved methods to collect and present spatial epidemiologic data for vectorborne diseases. Emerg Infect Dis 13: 1816–1820.
-
16.
Colorado Department of Public Health and Environment. Available at: http://www.cdphe.state.co.us/dc/zoonosis/wnv/index.html.
-
17.
Mostashari F, Bunning ML, Kitsutani PT, Singer DA, Nash D, Cooper MJ, Katz N, Liljebjelke KA, Biggerstaff BJ, Fine AD, Layton MC, Mullin SM, Johnson AJ, Martin DA, Hayes EB, Campbell GL, 2001. Epidemic West Nile encephalitis, New York, 1999: results of a household-based seroepidemiological survey. Lancet 358: 261–264.
-
18.
Hardin JW, Hilbe JM, 2007. Generalized Linear Models and Extensions. 2nd edition. College Station, TX: Stata Press.
-
19.
Cressie N, 1993. Statistics for Spatial Data. New York: John Wiley & Sons, Inc.
-
20.
Fotheringham A, Brunsdon C, Charlton M, 2002. Geographically Weighted Regression, the Analysis of Spatially Varying Relationships. West Sussex, United Kingdom: John Wiley and Sons Ltd.
-
21.
Wall M, 2004. A close look at the spatial structure implied by the CAR and SAR models. J Statist Plann Inference 121: 311–324.
-
22.
Getis A, Ord J, 1992. The analysis of spatial association by use of distance statistics. Geogr Anal 24: 189–206.
-
23.
Rytkonen MJP, 2004. Not all maps are equal: GIS and spatial analysis in epidemiology. Int J Circumpolar Health 63: 9–24.
-
24.
Curtis A, Mills JW, Leitner M, 2006. Keeping an eye on privacy issues with geospatial data. Nature 441: 150.
-
25.
Health Insurance Portability and Accountability Act of 1996. Available at: hhs.gov/HIPAAGenInfo/Downloads/HIPAALaw.pdf.
-
26.
VanWey LK, Rindfuss RR, Gutmann MP, Entwisle B, Balk DL, 2005. Confidentiality and spatially explicit data: concerns and challenges. Proc Natl Acad Sci USA 102: 15337–15342.
-
27.
Openshaw S, 1984. The Modifiable Areal Unit Problem. Norwich, UK: Geo Books.
-
28.
Diuk-Wasser MA, Brown HE, Andreadis TG, Fish D, 2006. Modeling the spatial distribution of mosquito vectors for West Nile virus in Connecticut, USA. Vector Borne Zoonotic Dis 6: 283–295.
-
29.
Eisen RJ, Eisen L, Lane RS, 2006. Predicting density of Ixodes pacificus nymphs in dense woodlands in Mendocino County, California, based on geographical information systems and remote sensing versus field-derived data. Am J Trop Med Hyg 74: 632–640.
-
30.
Nielsen CF, Armijos MV, Wheeler S, Carpenter TE, Boyce WM, Kelley K, Brown D, Scott TW, Reisen WK, 2008. Risk factors associated with human infection during the 2006 West Nile virus outbreak in Davis, a residential community in northern California. Am J Trop Med Hyg 78: 53–62.
-
31.
Rodgers SE, Mather TN, 2006. Evaluating satellite sensor-derived indices for Lyme disease risk prediction. J Med Entomol 43: 337–343.
-
32.
Winters AM, Bolling BG, Beaty BJ, Blair CD, Eisen RJ, Meyer AM, Pape WJ, Moore CG, Eisen L, 2008. Combining mosquito vector and human disease data for improved assessment of spatial West Nile virus disease risk. Am J Trop Med Hyg 78: 654–665.
-
33.
Eisen RJ, Lane RS, Fritz CL, Eisen L, 2006. Spatial patterns of Lyme disease risk in California based on disease incidence data and modeling of vector-tick exposure. Am J Trop Med Hyg 75: 669–676.
-
34.
Elliot P, Wakefield J, Best N, Briggs D, 2000. Spatial Epidemiology Methods and Applications. Oxford, United Kingdom: Oxford University Press.
-
35.
Waller LA, Gotway CA, 2004. Applied Spatial Statistics for Public Health. Hoboken, NJ: John Wiley & Sons, Inc.
| Past two years | Past Year | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 538 | 487 | 45 |
| Full Text Views | 335 | 19 | 2 |
| PDF Downloads | 94 | 15 | 2 |