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Time-series models are sequences of data points measured at successive times and spaced at uniform time intervals. They have proven value in the analysis of various forms of complex data. Dr. Vickneswary Tagore is developing and applying new time-series models to population-based administrative health databases to describe and predict the costs associated with episodes of care for chronic obstructive pulmonary disease.

Dr. Tagore’s objectives are threefold. First, she plans to use statistical derivations and computer simulations to develop a time-series model for data that have non-normal distributions and non-constant variance. Second, she intends to link to Saskatchewan’s administrative health databases for acute, emergency, primary and supportive care, where she will apply the new model to healthcare costs for chronic obstructive pulmonary disease episodes of care. And finally, the accuracy and predictive performance of conventional and new time-series models will be compared for administrative health and simulated data.

The research will produce new methodological tools for the analysis of complex time-series data and demonstrate their advantages for monitoring healthcare costs over the disease course. Dr. Tagore hopes the research will ultimately benefit provincial chronic disease management initiatives and facilitate collaborations among healthcare quality scientists, health services researchers and statisticians.

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