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Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects

Author

Listed:
  • Xun Shi

    (Department of Geography, Dartmouth College, 6017 Fairchild, Hanover, NH 03755, USA)

  • Stephanie Miller

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

  • Kevin Mwenda

    (Department of Geography, University of California at Santa Barbara, Santa Barbara, CA 93106, USA)

  • Akikazu Onda

    (Department of Geography, Dartmouth College, 6017 Fairchild, Hanover, NH 03755, USA)

  • Judy Rees

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

  • Tracy Onega

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

  • Jiang Gui

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

  • Margaret Karagas

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

  • Eugene Demidenko

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

  • John Moeschler

    (The Children's Environmental Health and Disease Prevention Center, The Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA)

Abstract

Background: Limited by data availability, most disease maps in the literature are for relatively large and subjectively-defined areal units, which are subject to problems associated with polygon maps. High resolution maps based on objective spatial units are needed to more precisely detect associations between disease and environmental factors. Method: We propose to use a Restricted and Controlled Monte Carlo (RCMC) process to disaggregate polygon-level location data to achieve mapping aggregate data at an approximated individual level. RCMC assigns a random point location to a polygon-level location, in which the randomization is restricted by the polygon and controlled by the background (e.g., population at risk). RCMC allows analytical processes designed for individual data to be applied, and generates high-resolution raster maps. Results: We applied RCMC to the town-level birth defect data for New Hampshire and generated raster maps at the resolution of 100 m. Besides the map of significance of birth defect risk represented by p- value, the output also includes a map of spatial uncertainty and a map of hot spots. Conclusions: RCMC is an effective method to disaggregate aggregate data. An RCMC-based disease mapping maximizes the use of available spatial information, and explicitly estimates the spatial uncertainty resulting from aggregation.

Suggested Citation

  • Xun Shi & Stephanie Miller & Kevin Mwenda & Akikazu Onda & Judy Rees & Tracy Onega & Jiang Gui & Margaret Karagas & Eugene Demidenko & John Moeschler, 2013. "Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects," IJERPH, MDPI, vol. 10(9), pages 1-14, September.
    • Handle: RePEc:gam:jijerp:v:10:y:2013:i:9:p:4161-4174:d:28599
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    References listed on IDEAS

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    1. Qiang Cai & Gerard Rushton & Budhendra Bhaduri, 2012. "Validation tests of an improved kernel density estimation method for identifying disease clusters," Journal of Geographical Systems, Springer, vol. 14(3), pages 243-264, July.
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    Cited by:

    1. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.

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