Don Vicendese, Andriy Olenko, Shyamali Dharmage, Mimi Tang, Michael Abramson, Bircan Erbas^*

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School of Public Health, La Trobe University, Bundoora, Australia.
Department of Mathematics and Statistics, La Trobe University, Bundoora, Australia.
School of Population Health, the University of Melbourne, Australia.
Department of Allergy & Immunology, Royal Children’s Hospital, Parkville, Australia.
Department of Epidemiology & Preventive Medicine, Monash University, The Alfred Centre, Melbourne, Australia.
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Abstract: Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends. We sought to model and predict daily trends of childhood asthma readmissions over time inVictoria,Australia. Methods: We used a database of 75,000 childhood asthma admissions from the Department ofHealth,Victoria,Australiain 1997-2009. Daily admissions over time were modeled using a semi parametric Generalized Additive Model (GAM) and by sex and age group. Predictions were also estimated by using these models. Results: N = 2401 asthma readmissions within 28 days occurred during study period. Of these, n = 1358 (57%) were boys. Overall, seasonal peaks occurred in winter (30.5%) followed by autumn (28.6%) and then spring (24.6%) (p < 0.0005). Day of the week and month were significantly associated with trends in readmission. Smooth function of time was significant (p < 0.0005) and indicated declining trends in readmissions in 2001-2002 and then increasing, returning to roughly initial levels. Predictions suggested readmissions would continue to increase by 5% per year with boys in the 2 to 5 years age group experiencing the largest increase. Conclusions: GAMs are reliable methods for low count time series such as repeat admissions. Our model implied: health services may need to be revised to accommodate for seasonal peaks in readmission especially for younger age groups.

Keywords: Asthma; Readmission; Semi-Parametric Models; Seasonality; Time Trend; Low Count; Time Series

Cite this paper: Vicendese, D. , Olenko, A. , Dharmage, S. , Tang, M. , Abramson, M. and Erbas, B. (2013) Modelling and predicting low count child asthma hospital readmissions using General Additive Models. Open Journal of Epidemiology, 3, 125-134. doi: 10.4236/ojepi.2013.33019.

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