CLUSTER-ANALYSIS AND DISEASE MAPPING - WHY, WHEN, AND HOW - A STEP-BY-STEP GUIDE

Growing public awareness of environmental hazards has led to an increa sed demand for public health authorities to investigate geographical c lustering of diseases. Although such cluster analysis is nearly always ineffective in identifying causes of disease, it often has to be used to address public concern about environmental hazards. Interpreting t he resulting data is not straightforward, however, and this paper pres ents a guide for the non-specialist. The pitfalls include the fact tha t cluster analyses are usually done post hoc, and not as a result of a prior hypothesis. This is particularly true for investigations prompt ed by reported clusters, which have the inherent danger of overestimat ing the disease rate through ''boundary shrinkage'' of the population from which the cases are assumed to have arisen. In disease surveillan ce the problem of making multiple comparisons can be overcome by testi ng for clustering and autocorrelation. When rates of disease are illus trated in disease maps undue focus on areas where random fluctuation i s greatest can be minimised by smoothing techniques. Despite the fact that cluster analyses rarely prove fruitful in identifying causation, they may-like single case reports-have the potential to generate new k nowledge.