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Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

Overview of attention for article published in International Journal of Health Geographics, October 2011
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Title
Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping
Published in
International Journal of Health Geographics, October 2011
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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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
Portugal 1 1%
Ghana 1 1%
Switzerland 1 1%
Unknown 61 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 22%
Researcher 11 16%
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 %
Medicine and Dentistry 14 21%
Environmental Science 7 10%
Earth and Planetary Sciences 6 9%
Agricultural and Biological Sciences 6 9%
Computer Science 5 7%
Other 19 28%
Unknown 11 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2012.
All research outputs
#18,305,773
of 22,664,644 outputs
Outputs from International Journal of Health Geographics
#511
of 627 outputs
Outputs of similar age
#111,289
of 133,865 outputs
Outputs of similar age from International Journal of Health Geographics
#2
of 2 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 627 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 133,865 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.