title: Answering cluster investigation requests: the value of simple simulations and statistical tools.
abstract: 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.
authors.affiliation: French Institute of Health and Medical Research, INSERM, U170, IFR69, Université Paris XI, 16, av. Paul Vaillant-Couturier, F-94807 cedex, Villejuif, France. bellec@vjf.inserm.fr
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Bellec
authors.forename: Denis
authors.initials: D
authors.lastname: Hémon
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Clavel
mesh_terms: D000293:Adolescent; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004812:Epidemiologic Methods; D005602:France / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology*; D011634:Public Health; D012042:Registries