Modelling and predicting low count child asthma hospital readmissions using General Additive Models
Author(s)
Don Vicendese,
Andriy Olenko,
Shyamali Dharmage,
Mimi Tang,
Michael Abramson,
Bircan Erbas*
Affiliation(s)
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.
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.
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
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.
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.
References
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(2002) On the use of generalized additive models in time-series studies of air pollution and health. American Journal of Epidemiology, 156, 193-203. doi:10.1093/aje/kwf062
[1] Australian Centre for Asthma Monitoring. (2009) Asthma in Australian children: Findings from growing up in Australia, the longitudinal study of Australian children. AIHW, Canberra. [2] Australian Centre for Asthma Monitoring. (2008) Asthma in Australia. AIHW, Canberra. [3] Vollmer, W.M., Osborne, M.L. and Buist, A.S. (1993) Temporal trends in hospital-based episodes of asthma care in a health maintenance organization. American Review of Respiratory Disease, 147, 347-353. doi:10.1164/ajrccm/147.2.347 [4] Berry, J.G., Hall, D.E., Kuo, D.Z., Cohen, E., Agrawal, R., Feudtner, C., Hall, M., Kueser, J., Kaplan, W. and Neff, J. (2011) Hospital utilization and characteristics of patients experiencing recurrent readmissions within children’s hospitals. The Journal of the American Medical Association, 305, 682-690. doi:10.1001/jama.2011.122 [5] Watson, L., Turk, F. and Rabe, K.F. (2007) Burden of asthma in the hospital setting: An Australian analysis. International Journal of Clinical Practice, 61, 1884-1888. doi:10.1111/j.1742-1241.2007.01559.x [6] McCaul, K.A., Wakefield, M.A., Roder, D.M., Ruffin, R.E., Heard, A.R., Alpers, J.H. and Staugas, R.E. (2000) Trends in hospital readmission for asthma: Has the Australian national asthma campaign had an effect? Medical Journal of Australia, 172, 62-66. [7] Murray, C.S., Poletti, G., Kebadze, T., Morris, J., Woodcock, A., Johnston, S.L. and Custovic, A. (2006) Study of modifiable risk factors for asthma exacerbations: Virus infection and allergen exposure increase the risk of asthma hospital admissions in children. Thorax, 61, 376-382. doi:10.1136/thx.2005.042523 [8] Feudtner, C., Levin, J.E., Srivastava, R., Goodman, D.M., Slonim, A.D., Sharma, V., Shah, S.S., Pati, S., Fargason Jr., C. and Hall, M. (2009) How well can hospital readmission be predicted in a cohort of hospitalized children? A retrospective, multicenter study. Pediatrics, 123, 286-293. doi:10.1542/peds.2007-3395 [9] Bloomberg, G.R., Trinkaus, K.M., Fisher Jr., E.B., Musick, J.R. and Strunk, R.C. (2003) Hospital readmissions for childhood asthma: A 10-year metropolitan study. American Journal of Respiratory and Critical Care Medicine, 167, 1068-1076. doi:10.1164/rccm.2201015 [10] Raymond, D., Henry, R.L., Higginbotham, N. and Coory, M. (1998) Predicting readmission to hospital with asthma. Journal of Paediatrics and Child Health, 34, 534-538. doi:10.1046/j.1440-1754.1998.00297.x [11] Rasmussen, F., Taylor, D.R., Flannery, E.M., Cowan, J.O., Greene, J.M., Herbison, G.P. and Sears, M.R. (2002) Risk factors for hospital admission for asthma from childhood to young adulthood: A longitudinal population study. Journal of Allergy & Clinical Immunology, 110, 220-227. doi:10.1067/mai.2002.125295 [12] Alshehri, M., Almegamesi, T. and Alfrayh, A. (2004) Predictors of short-term readmission of asthmetic children. Journal of the Egyptian Public Health Association, 79, 165-178. [13] Reznik, M., Hailpern, S.M. and Ozuah, P.O. (2006) Predictors of early hospital readmission for asthma among inner-city children. Journal of Asthma, 43, 37-40. doi:10.1080/02770900500446997 [14] Gorelick, M., Scribano, P.V., Stevens, M.W., Schultz, T. and Shults, J. (2008) Predicting need for hospitalization in acute pediatric asthma. Pediatric Emergency Care, 24, 735-744. doi:10.1097/PEC.0b013e31818c268f [15] Blaisdell, C.J., Weiss, S.R., Kimes, D.S., Levine, E.R., Myers, M., Timmins, S. and Bollinger, M.E. (2002) Using seasonal variations in asthma hospitalizations in children to predict hospitalization frequency. Journal of Asthma, 39, 567-575. doi:10.1081/JAS-120014921 [16] Moustris, K.P., Douros, K., Nastos, P.T., Larissi, I.K., Anthracopoulos, M.B., Paliatsos, A.G. and Priftis, K.N. (2011) Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece. International Journal of Environmental Health Research, 22, 93-104. doi:10.1080/09603123.2011.605876 [17] Dales, R.E., Schweitzer, I., Toogood, J.H., Drouin, M., Yang, W., Dolovich, J. and Boulet, J. (1996) Respiratory infections and the autumn increase in asthma morbidity. European Respiratory Journal, 9, 72-77. doi:10.1183/09031936.96.09010072 [18] Fleming, D.M., Cross, K.W., Sunderland, R. and Ross, A.M. (2000) Comparison of the seasonal patterns of asthma identified in general practitioner episodes, hospital admissions, and deaths. Thorax, 55, 662-665. doi:10.1136/thorax.55.8.662 [19] Wong, G.W., Ko, F.W., Lau, T.S., Li, S.T., Hui, D., Pang, S.W., Leung, R., Fok, T.F. and Lai, C.K. (2001) Temporal relationship between air pollution and hospital admissions for asthmatic children in Hong Kong. Clinical & Experimental Allergy, 31, 565-569. doi:10.1046/j.1365-2222.2001.01063.x [20] Lee, S.L., Wong, W.H. and Lau, Y.L. (2006) Association between air pollution and asthma admission among children in Hong Kong. Clinical & Experimental Allergy, 36, 1138-1146. doi:10.1111/j.1365-2222.2006.02555.x [21] Lincoln, D., Morgan, G., Sheppeard, V., Jalaludin, B., Corbett, S. and Beard, J. (2006) Childhood asthma and return to school in Sydney, Australia. Public Health, 120, 854-862. doi:10.1016/j.puhe.2006.05.015 [22] Sears, M.R. and Johnston, N.W. (2007) Understanding the September asthma epidemic. Journal of Allergy and Clinical Immunology, 120, 526-529. doi:10.1016/j.jaci.2007.05.047 [23] Jonasson, G., Lodrup Carlsen, K.C., Leegaard, J., Carlsen, K.H., Mowinckel, P. and Halvorsen, K.S. (2000) Trends in hospital admissions for childhood asthma in Oslo, Norway, 1980-95. Allergy, 55, 232-239. doi:10.1034/j.1398-9995.2000.00387.x [24] Hastie, T. and Tibshirani, R. (1986) Generalized additive models. Statistical Science, 1, 297-310. [25] Vicendese, D., Abramson, M.J., Dharmage, S.C., Tang, M.L., Allen, K.J. and Erbas, B. (2012) The influence of age, gender and seasonality on asthma readmissions among children and adolescents over time. LaTrobe University, Bundoora. [26] Chambers, M. and Clarke, A. (1990) Measuring readmission rates. British Medical Journal, 301, 1134-1136. doi:10.1136/bmj.301.6761.1134 [27] Ashton, C.M., Del Junco, D.J., Souchek, J., Wray, N.P. and Mansyur, C.L. (1997) The association between the quality of inpatient care and early readmission: A meta-analysis of the evidence. Medical Care, 35, 1044-1059. doi:10.1097/00005650-199710000-00006 [28] Davis, R.A., Dunsmuir, W.T.M. and Wang, Y. (1980) Modelling Time Series of Count Data. [29] Wood, S.N. (2006) Generalised additive models—An Introduction with R. Chapman & Hall/CRC. Taylor & Francis Group. [30] Sarkar, D. (2008) Lattice: Multivariate data visualization with R. Springer, New York. [31] Hyndman, R.J. and Athanasopoulos, G. (2012) Forecasting: Principles and practice. http://otexts.com/fpp/7/2/ [32] Schwartz, J. (1994) Non-parametric smoothing in the analysis of air pollution and respiratory illness. Canadian Journal of Statistics, 22, 471-487. doi:10.2307/3315405 [33] Katsouyanni, K., Touloumi, G., Samoli, E., Gryparis, A., Le Tertre, A., Monopolis, Y., Rossi, G., Zmirou, D., Ballester, F., Boumghar, A., Anderson, H.R., Wojtyniak, B., Paldy, A., Braunstein, R., Pekkanen, J., Schindler, C. and Schwartz, J. (2001) Confounding and effect modification in the short-term effects of ambient particles on total mortality: Results from 29 European cities within the APHEA2 project. Epidemiology, 12, 521-531. doi:10.1097/00001648-200109000-00011 [34] Jung, R.C., Kukuk, M. and Liesenfeld, R. (2006) Time series of count data: Modeling, estimation and diagnostics. Computational Statistics & Data Analysis, 51, 2350-2364. doi:10.1016/j.csda.2006.08.001 [35] Dominici, F., McDermott, A., Zeger, S.L. and Samet, J.M. (2002) On the use of generalized additive models in time-series studies of air pollution and health. American Journal of Epidemiology, 156, 193-203. doi:10.1093/aje/kwf062