Int. J. Environ. Res. Public Health 2013, 10(9), 4161-4174; doi:10.3390/ijerph10094161
Article

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

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Received: 10 July 2013; in revised form: 23 August 2013 / Accepted: 27 August 2013 / Published: 6 September 2013
(This article belongs to the Special Issue Spatial Epidemiology)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: birth defects; aggregate data; disaggregation; Monte Carlo; disease mapping; New Hampshire
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MDPI and ACS Style

Shi, X.; Miller, S.; Mwenda, K.; Onda, A.; Rees, J.; Onega, T.; Gui, J.; Karagas, M.; Demidenko, E.; Moeschler, J. Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects. Int. J. Environ. Res. Public Health 2013, 10, 4161-4174.

AMA Style

Shi X, Miller S, Mwenda K, Onda A, Rees J, Onega T, Gui J, Karagas M, Demidenko E, Moeschler J. Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects. International Journal of Environmental Research and Public Health. 2013; 10(9):4161-4174.

Chicago/Turabian Style

Shi, Xun; Miller, Stephanie; Mwenda, Kevin; Onda, Akikazu; Rees, Judy; Onega, Tracy; Gui, Jiang; Karagas, Margaret; Demidenko, Eugene; Moeschler, John. 2013. "Mapping Disease at an Approximated Individual Level Using Aggregate Data: A Case Study of Mapping New Hampshire Birth Defects." Int. J. Environ. Res. Public Health 10, no. 9: 4161-4174.

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