Editorial: Disease Clusters and Ecological Studies

Wakefield, Jon; Quinn, Mike; Raab, Gillian M.

doi:10.1111/1467-985x.00179

Cited by 8 publications

(6 citation statements)

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“…This term refers to the fact that group‐level data can hide and misrepresent individual behaviour. These disciplines recognise that inference about a relationship or process has to derive from information at the scale where it operates or risk aggregation problems (Wakefield & Salway 2001). Typically, individual organisms are not of interest, but that is the scale where competition and important responses to weather occur.…”

Section: Introductionmentioning

confidence: 99%

Individual-scale variation, species-scale differences: inference needed to understand diversity

Clark¹,

Bell²,

Hersh³

et al. 2011

Ecology Letters

142

172

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As ecological data are usually analysed at a scale different from the one at which the process of interest operates, interpretations can be confusing and controversial. For example, hypothesised differences between species do not operate at the species level, but concern individuals responding to environmental variation, including competition with neighbours. Aggregated data from many individuals subject to spatio-temporal variation are used to produce species-level averages, which marginalise away the relevant (process-level) scale. Paradoxically, the higher the dimensionality, the more ways there are to differ, yet the more species appear the same. The aggregate becomes increasingly irrelevant and misleading. Standard analyses can make species look the same, reverse species rankings along niche axes, make the surprising prediction that a species decreases in abundance when a competitor is removed from a model, or simply preclude parameter estimation. Aggregation explains why niche differences hidden at the species level become apparent upon disaggregation to the individual level, why models suggest that individual-level variation has a minor impact on diversity when disaggregation shows it to be important, and why literature-based synthesis can be unfruitful. We show how to identify when aggregation is the problem, where it has caused controversy, and propose three ways to address it.

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Section: Introductionmentioning

confidence: 99%

Individual-scale variation, species-scale differences: inference needed to understand diversity

Clark¹,

Bell²,

Hersh³

et al. 2011

Ecology Letters

142

172

View full text Add to dashboard Cite

show abstract

“…All these difficulties may partly explain why casecontrol studies of clusters have often failed to identify new risk factors [2,[4][5][6][7]17]. This remark does not apply to investigations of clusters where a marked excess and a particular factor exist in a small area.…”

Section: Discussionmentioning

confidence: 99%

“…Given the possibility of false alarms and the underlying financial and human costs, many authors have wondered whether it is worth undertaking indepth investigation, especially when an excess of cases is reported by the general public or media [2,[4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Answering Cluster Investigation Requests: The Value of Simple Simulations and Statistical Tools

2005

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Cluster investigations remain an important public health issue as the number of reported clusters and public concern increase. This study shows how statistical considerations and a simulation tool may be helpful in providing communities with proper answers to the questions usually raised in such situations: How surprising is an observed childhood cancer excess? What could be learned from a statistical test? What could be learned from a case-control study? Using real demographic and incidence-rate data together with simulations based on the hypothesis that incidence rates are homogeneous, the probabilities of observing given situations were estimated. A number of real situations have been used as examples. The results of the simulation study showed, in detail, that no reliable information on the reality of an observed excess could be obtained a posteriori from a statistical test. A cluster of the same size may or may not be surprising, depending on the spatial area and time window to which the cases are related (i.e., the expected number of cases), and depending on the size of the referential territory to which this area is associated. The lack of power of a case-control study if no particular unusual exposure is present is also addressed. The approach described in this paper can easily be reproduced and adapted to many situations. It may be of assistance to health departments conducting cluster investigations and communicating with the public.

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“…The topic has been the subject of lively discussion in the literature (Cuzick and Edwards 1990; Jacquez et al 1996b; Waller 2000; Wartenberg 1995; Wartenberg and Greenberg 1993). Several national and international workshops have also addressed this issue: the 1992 Workshop on “Statistics and Computing in Disease Clustering” in Port Jefferson, New York; the 1994 Conference on “Statistics and Computing in Disease Clustering” sponsored by the NCI in Vancouver, British Columbia, Canada; and the 1997 World Health Organization “Disease Mapping and Risk Assessment for Public Health” in Rome, Italy (Jacquez et al 1993, 1996a; Lawson et al 2000; Wakefield et al 2001). As a result of the increased intensity in the field of cluster statistics, more than 100 analytic methods are currently available (NAACCR 2002).…”

Section: Recent Environmental Health Initiatives That Impact Cluster mentioning

confidence: 99%

An Update on Cancer Cluster Activities at the Centers for Disease Control and Prevention

Kingsley

Schmeichel

Rubin

2007

Environ Health Perspect

116

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The Centers for Disease Control and Prevention (CDC) continues to be aware of the need for response to public concern as well as to state and local agency concern about cancer clusters. In 1990 the CDC published the “Guidelines for Investigating Clusters of Health Events,” in which a four-stage process was presented. This document has provided a framework that most state health departments have adopted, with modifications pertaining to their specific situations, available resources, and philosophy concerning disease clusters. The purpose of this present article is not to revise the CDC guidelines; they retain their original usefulness and validity. However, in the past 15 years, multiple cluster studies as well as scientific and technologic developments have affected cluster science and response (improvements in cancer registries, a federal initiative in environmental public health tracking, refinement of biomarker technology, cluster identification using geographic information systems software, and the emergence of the Internet). Thus, we offer an addendum for use with the original document. Currently, to address both the needs of state health departments as well as public concern, the CDC now a) provides a centralized, coordinated response system for cancer cluster inquiries, b) supports an electronic cancer cluster listserver, c) maintains an informative web page, and d) provides support to states, ranging from laboratory analysis to epidemiologic assistance and expertise. Response to cancer clusters is appropriate public health action, and the CDC will continue to provide assistance, facilitate communication among states, and foster the development of new approaches in cluster science.

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Editorial: Disease Clusters and Ecological Studies

Cited by 8 publications

References 0 publications

Individual-scale variation, species-scale differences: inference needed to understand diversity

Individual-scale variation, species-scale differences: inference needed to understand diversity

Answering Cluster Investigation Requests: The Value of Simple Simulations and Statistical Tools

An Update on Cancer Cluster Activities at the Centers for Disease Control and Prevention

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