Precision of incidence predictions based on poisson distributed observations

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Abstract
Disease incidence predictions are useful for a number of administrative and scientific purposes. The simplest ones are made using trend extrapolation, on either an arithmetic or a logarithmic scale. This paper shows how approximate confidence prediction intervals can be calculated for such predictions, both for the total number of cases and for the age‐adjusted incidence rates, by assuming Poisson distribution of the age and period specific numbers of incident cases. Generalizations for prediction models, for example, using power families and extra‐Poisson variation, are also presented. Cancer incidence predictions for the Stockholm‐Gotland Oncological Region in Sweden are used as an example.

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