Title |
Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping
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Published in |
International Journal of Health Geographics, October 2011
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DOI | 10.1186/1476-072x-10-54 |
Pubmed ID | |
Authors |
Kristen H Hampton, Marc L Serre, Dionne C Gesink, Christopher D Pilcher, William C Miller |
Abstract |
Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. |
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Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 4 | 6% |
Portugal | 1 | 1% |
Ghana | 1 | 1% |
Switzerland | 1 | 1% |
Unknown | 62 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 15 | 22% |
Researcher | 12 | 17% |
Student > Doctoral Student | 9 | 13% |
Student > Master | 8 | 12% |
Professor > Associate Professor | 5 | 7% |
Other | 11 | 16% |
Unknown | 9 | 13% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 14 | 20% |
Environmental Science | 8 | 12% |
Earth and Planetary Sciences | 6 | 9% |
Agricultural and Biological Sciences | 6 | 9% |
Computer Science | 5 | 7% |
Other | 19 | 28% |
Unknown | 11 | 16% |