The Current Employment Statistics (CES) State and Area program publishes seasonally adjusted data for over 2000 series each month, covering over 400 subnational geographic areas. The seasonal factors used to make these adjustments have traditionally been developed using historical employment data forecasted for one year. A particular challenge in concurrent adjustment of these series is the outsized impact of various events on localized areas. These events range from natural disasters and strikes to unusual weather, and are often difficult to properly identify and model at the end of a time series. We describe the monitoring, screening, and review process used to model these events during concurrent adjustment. The empirical research tests how a stricter critical value policy would affect simulated time series data after an exogenous event is added to the concurrent adjustment process. This exercise provides insight into how the procedure would fare under realistic circumstances and how to handle potential outliers in the data. The results provide evidence that a stricter critical value policy may be beneficial.