Mortality levels are typically higher in winter, and Excess Winter Mortality (EWM) is an annual indication of this additional number of winter deaths. Obtaining an accurate measure of this construct is important in order to understand how it relates to social and environmental factors, and thus also for the purpose of designing policy and interventions to decrease preventable deaths. The current method of estimating EWM compares the number of deaths during a four-month winter period (December-March) with the average of deaths for the remaining non-winter months. Therefore, a potential limitation is that atypical mortality patterns in non-winter months can lead to over-estimation or under-estimation of EWM. In addition, the number of deaths can start to increase in October-November, leading to an unrealistically low estimation of EWM. The current project aims to explore an alternative method of estimating EWM. This method is based on seasonal adjustment for time series. Rather than using mortality data in its raw format and adopting non-winter months as a comparison baseline, the data are segregated into three components which are calculated as part of the seasonal adjustment process for time series. These components reflect a trend, seasonal variation, and a residual irregular element. The latter captures unsystematic fluctuations in the underlying pattern. As such, the irregular element can be used to derive a purer indication of EWM. This approach also has the benefit of not needing a full twelve-month span of data in order to be estimated, since this can be done on a rolling basis as more data becomes available.