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Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics

Overview of attention for article published in International Journal of Health Geographics, August 2016
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  • Above-average Attention Score compared to outputs of the same age (54th percentile)

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1 Facebook page

Citations

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Title
Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics
Published in
International Journal of Health Geographics, August 2016
DOI 10.1186/s12942-016-0056-6
Pubmed ID
Authors

Junhee Han, Li Zhu, Martin Kulldorff, Scott Hostovich, David G. Stinchcomb, Zaria Tatalovich, Denise Riedel Lewis, Eric J. Feuer

Abstract

Spatial and space-time scan statistics are widely used in disease surveillance to identify geographical areas of elevated disease risk and for the early detection of disease outbreaks. With a scan statistic, a scanning window of variable location and size moves across the map to evaluate thousands of overlapping windows as potential clusters, adjusting for the multiple testing. Almost always, the method will find many very similar overlapping clusters, and it is not useful to report all of them. This paper proposes to use the Gini coefficient to help select which of the many overlapping clusters to report. The Gini coefficient provides a quick and intuitive way to evaluate the degree of the heterogeneity of the collection of clusters, which is useful to explain how well the cluster collection reveal the underlying true cluster patterns. Using simulation studies and real cancer mortality data, it is compared with the traditional approach for reporting non-overlapping clusters. The Gini coefficient can identify a more refined collection of non-overlapping clusters to report. For example, it is able to determine when it makes more sense to report a collection of smaller non-overlapping clusters versus a single large cluster containing all of them. It also fulfils a set of desirable theoretical properties, such as being invariant under a uniform multiplication of the population numbers by the same constant. The Gini coefficient can be used to determine which set of non-overlapping clusters to report. It has been implemented in the free SaTScan™ software version 9.3 ( www.satscan.org ).

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Czechia 1 1%
Unknown 85 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 16%
Researcher 12 14%
Student > Ph. D. Student 12 14%
Student > Doctoral Student 9 10%
Student > Bachelor 7 8%
Other 13 15%
Unknown 20 23%
Readers by discipline Count As %
Medicine and Dentistry 9 10%
Nursing and Health Professions 5 6%
Engineering 5 6%
Social Sciences 5 6%
Mathematics 5 6%
Other 32 37%
Unknown 26 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 July 2019.
All research outputs
#8,479,046
of 15,418,020 outputs
Outputs from International Journal of Health Geographics
#300
of 536 outputs
Outputs of similar age
#115,373
of 267,050 outputs
Outputs of similar age from International Journal of Health Geographics
#1
of 1 outputs
Altmetric has tracked 15,418,020 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 536 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one is in the 41st percentile – i.e., 41% 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 267,050 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them