We consider a general model for anomaly detection in a longitudinal cohort mor- tality pattern based on the joinpoint logistic regression with unknown joinpoints. We discuss backward and forward sequential procedures for selecting both the locations and the number of joinpoints. Estimation of the model parameters and the selection algorithms are illustrated with the longitudinal data on cancer mortality in a cohort of chemical workers.