Loddon Mallee Integrated Cancer Service plays a key role in planning the delivery of cancer services in the Loddon Mallee Region of Victoria, Australia. Such planning relies on the accuracy of forecasting the incidence of cancer. Perhaps more importantly is the need to reflect the uncertainty of these forecasts, which is usually carried out through prediction intervals. Standard confidence levels (e.g., 90% or 95%) are typically employed when forecasting the incidence of cancer, but decision-theoretic approaches are available to help choose an optimal coverage probability by minimizing the combined risk of the interval width and noncoverage of the interval. We proceed with the decision-theoretic framework and discuss some general strategies for defining candidate loss functions for forecasting the incidence of cancer, such as the data we analyze for the Loddon Mallee Region.