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Forecasting peak asthma admissions in London: an application of quantile regression models

Soyiri, Ireneous N.; Reidpath, Daniel D.; Sarran, Christophe


Daniel D. Reidpath

Christophe Sarran


Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs. © 2012 ISB.

Journal Article Type Article
Publication Date 2013-07
Journal International Journal of Biometeorology
Print ISSN 0020-7128
Electronic ISSN 1432-1254
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 57
Issue 4
Pages 569-578
APA6 Citation Soyiri, I. N., Reidpath, D. D., & Sarran, C. (2013). Forecasting peak asthma admissions in London: an application of quantile regression models. International Journal of Biometeorology, 57(4), 569-578.
Keywords Asthma; Emergency department; Health forecast; Hospital admission; Lag; Predictive model
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