Evidence map›Paper›PMID 27488416›Full record

ArticleInternational journal of health geographics2016

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of health geographics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

74 citing papers in PubMed.

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  10. Chikungunya clusters in the state of Bahia: influence of environmental and social factors.Revista da Sociedade Brasileira de Medicina Tropical · 2025
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  13. Observational
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14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Junhee HanDivision of Biostatistics, Research Institute of Convergence for Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan, Korea.
Li ZhuSurveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA. li.zhu@nih.gov.ORCID 0000-0002-9805-6779
Martin KulldorffBrigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Scott HostovichInformation Management Services, Calverton, MD, USA.
David G StinchcombWestat, Inc, Rockville, MD, USA.
Zaria TatalovichSurveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Denise Riedel LewisSurveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Eric J FeuerSurveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSpatial 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.

methodsThe 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.

resultsThe 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.

conclusionsThe 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 ).

Indexed as

Models, StatisticalSpatial AnalysisHumansPublic Health SurveillanceResearch DesignCancer mortalityCluster detectionCluster reporting sizeDisease surveillanceGini coefficientLog likelihood ratioSaTScanScan statisticSpatial statistics

Identifiers

PMID27488416
PMCPMC4971627

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.