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Environmental risk factors for Lyme disease identified with geographic information systems

Author

Listed:
  • Glass, G.E.
  • Schwartz, B.S.
  • Morgan III, J.M.
  • Johnson, D.T.
  • Noy, P.M.
  • Israel, E.

Abstract

Objectives. A geographic information system was used to identify and locate residential environmental risk factors for Lyme disease. Methods. Data were obtained for 53 environmental variables at the residences of Lyme disease case patients in Baltimore County from 1989 through 1990 and compared with data for randomly selected addresses. A risk model was generated combining the geographic information system with logistic regression analysis. The model was validated by comparing the distribution of cases in 1991 with another group of randomly selected addresses. Results. In crude analyses, 11 environmental variables were associated with Lyme disease. In adjusted analyses, residence in forested areas (odds ratio [OR] = 3.7, 95% confidence interval [CI] = 1.2, 11.8), on specific soils (OR = 2.1, 95% CI = 1.0, 4.4), and in two regions of the county (OR = 3.5, 95% CI = 1.6, 7.4) (OR = 2.8, 95% CI = 1.0, 7.7) was associated with elevated risk of getting Lyme disease. Residence in highly developed regions was protective (OR = 0.3, 95% CI = 0.1, 1.0). The risk of Lyme disease in 1991 increased with risk categories defined from the 1989 through 1990 data. Conclusions. Combining a geographic information system with epidemiologic methods can be used to rapidly identify risk factors of zoonotic disease over large areas.

Suggested Citation

  • Glass, G.E. & Schwartz, B.S. & Morgan III, J.M. & Johnson, D.T. & Noy, P.M. & Israel, E., 1995. "Environmental risk factors for Lyme disease identified with geographic information systems," American Journal of Public Health, American Public Health Association, vol. 85(7), pages 944-948.
    • Handle: RePEc:aph:ajpbhl:1995:85:7:944-948_6
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    Cited by:

    1. Reaser, Jamie & Tabor, Gary M. & Becker, Daniel & Muruthi, Philip & Witt, Arne & Woodley, Stephen J. & Ruiz-Aravena, Manuel & Patz, Jonathan Alan MD, MPH & Hickey, Valerie & Hudson, Peter, 2020. "Land use-induced spillover: priority actions for protected and conserved area managers," EcoEvoRxiv bmfhw, Center for Open Science.
    2. Yifan Li & Juanle Wang & Mengxu Gao & Liqun Fang & Changhua Liu & Xin Lyu & Yongqing Bai & Qiang Zhao & Hairong Li & Hongjie Yu & Wuchun Cao & Liqiang Feng & Yanjun Wang & Bin Zhang, 2017. "Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China," IJERPH, MDPI, vol. 14(6), pages 1-18, May.
    3. Donohoe, Holly & Pennington-Gray, Lori & Omodior, Oghenekaro, 2015. "Lyme disease: Current issues, implications, and recommendations for tourism management," Tourism Management, Elsevier, vol. 46(C), pages 408-418.
    4. José Miguel Barrios & Willem W. Verstraeten & Piet Maes & Jean-Marie Aerts & Jamshid Farifteh & Pol Coppin, 2012. "Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases," IJERPH, MDPI, vol. 9(12), pages 1-19, November.
    5. Maliyoni, Milliward & Chirove, Faraimunashe & Gaff, Holly D. & Govinder, Keshlan S., 2019. "A stochastic epidemic model for the dynamics of two pathogens in a single tick population," Theoretical Population Biology, Elsevier, vol. 127(C), pages 75-90.
    6. Haozhe Zhang & Jinyi Li, 2024. "Mapping the urban and rural planning response paths to pandemics of infectious diseases," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

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