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The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000

Soyiri, Ireneous N.; Reidpath, Daniel D.

Authors

Daniel D. Reidpath



Contributors

Cécile Viboud
Editor

Abstract

Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths.Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/ temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1).The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2)This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept. © 2013 Soyiri, Reidpath.

Citation

Soyiri, I. N., & Reidpath, D. D. (2013). The Use of Quantile Regression to Forecast Higher Than Expected Respiratory Deaths in a Daily Time Series: A Study of New York City Data 1987-2000. PLoS ONE, 8(10), e78215. https://doi.org/10.1371/journal.pone.0078215

Journal Article Type Article
Acceptance Date Sep 13, 2013
Online Publication Date Oct 11, 2013
Publication Date Oct 11, 2013
Deposit Date May 15, 2019
Publicly Available Date Mar 29, 2024
Journal PLoS ONE
Print ISSN 1932-6203
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 8
Issue 10
Pages e78215
DOI https://doi.org/10.1371/journal.pone.0078215
Keywords Respiratory deaths
Public URL https://hull-repository.worktribe.com/output/1756182
Publisher URL https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0078215

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Copyright Statement
© 2013 Soyiri, Reidpath. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.





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