title: Methodological problems and the role of statistics in cluster response studies: a framework.
abstract: More and more citizens urge public health authorities to investigate reports of disease excess in their neighbourhood. These environmental concerns are legitimate and it is part of good public health practice to respond to these complaints. However, the methodological and practical problems are severe and a lot of controversy exists about the usefulness of these investigations. To clarify the possibilities and limitations in this situation, this paper proposes a typology of cluster studies. According to this framework, cluster response is distinguished from two other types of cluster studies: Cluster monitoring. screening proactively for clusters to act as an early warning system, and cluster research, scrutinizing clustering to generate and test aetiological hypotheses. To each of these three types of cluster studies corresponds a different public health context; respectively public health action, public health surveillance and public health research. Probably, part of the controversy mentioned stems from not acknowledging sufficiently the corresponding intrinsic differences in rationality and practical constraints. Cluster response is crisis management and not scientific research. In a relatively short time, an informed decision should be taken by a multidisciplinary team of experts using readily available information and knowledge. In accordance with this point of view, cluster reports should be handled stepwise and the role of statistics is to quantify a cluster exploring different points of view as an input to the decision process.
authors.affiliation: EUROCAT, Central Registry, Brussels, Belgium.
authors.forename: P K
authors.initials: PK
authors.lastname: Quataert
authors.forename: B
authors.initials: B
authors.lastname: Armstrong
authors.forename: A
authors.initials: A
authors.lastname: Berghold
authors.forename: F
authors.initials: F
authors.lastname: Bianchi
authors.forename: A
authors.initials: A
authors.lastname: Kelly
authors.forename: M
authors.initials: M
authors.lastname: Marchi
authors.forename: M
authors.initials: M
authors.lastname: Martuzzi
authors.forename: A
authors.initials: A
authors.lastname: Rosano
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D003659:Decision Making, Organizational; D015340:Epidemiologic Research Design; D006801:Humans; D008403:Mass Screening / Q000379:methods; D015233:Models, Statistical; D010348:Patient Care Team / Q000458:organization & administration; D011159:Population Surveillance / Q000379:methods*