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Potential use of multiple surveillance data in the forecast of hospital admissions
OBJECTIVE: This paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases. INTRODUCTION: A sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower plan...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
University of Illinois at Chicago Library
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692818/ |
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author | Lau, Eric H.Y. Ip, Dennis K.M. Cowling, Benjamin J. |
author_facet | Lau, Eric H.Y. Ip, Dennis K.M. Cowling, Benjamin J. |
author_sort | Lau, Eric H.Y. |
collection | PubMed |
description | OBJECTIVE: This paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases. INTRODUCTION: A sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower planning. In Hong Kong, the timing of influenza peak seasons are variable and early short-term indication of possible surge may facilitate preparedness which could be translated into strategies such as early discharge or reallocation of extra hospital beds. In this study we explore the potential use of multiple routinely collected syndromic data in the forecast of hospital admissions. METHODS: A multivariate dynamic linear time series model was fitted to multiple syndromic data including influenza-like illness (ILI) rates among networks of public and private general practitioners (GP), and school absenteeism rates, plus drop-in fever count data from designated flu clinics (DFC) that were created during the pandemic. The latent process derived from the model has been used as a measure of the influenza activity [1]. We compare the cross-correlations between estimated influenza level based on multiple surveillance data and GP ILI data, versus accident and emergency hospital admissions with principal diagnoses of respiratory diseases and pneumonia & influenza (P&I). RESULTS: The estimated influenza activity has higher cross-correlation with respiratory and P&I admissions (ρ=0.66 and 0.73 respectively) compared to that of GP ILI rates (Table 1). Cross correlations drop distinctly after lag 2 for both estimated influenza activity and GP ILI rates. CONCLUSIONS: The use of a multivariate method to integrate information from multiple sources of influenza surveillance data may have the potential to improve forecasting of admission surge of respiratory diseases. |
format | Online Article Text |
id | pubmed-3692818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | University of Illinois at Chicago Library |
record_format | MEDLINE/PubMed |
spelling | pubmed-36928182013-06-26 Potential use of multiple surveillance data in the forecast of hospital admissions Lau, Eric H.Y. Ip, Dennis K.M. Cowling, Benjamin J. Online J Public Health Inform ISDS 2012 Conference Abstracts OBJECTIVE: This paper describes the potential use of multiple influenza surveillance data to forecast hospital admissions for respiratory diseases. INTRODUCTION: A sudden surge in hospital admissions in public hospital during influenza peak season has been a challenge to healthcare and manpower planning. In Hong Kong, the timing of influenza peak seasons are variable and early short-term indication of possible surge may facilitate preparedness which could be translated into strategies such as early discharge or reallocation of extra hospital beds. In this study we explore the potential use of multiple routinely collected syndromic data in the forecast of hospital admissions. METHODS: A multivariate dynamic linear time series model was fitted to multiple syndromic data including influenza-like illness (ILI) rates among networks of public and private general practitioners (GP), and school absenteeism rates, plus drop-in fever count data from designated flu clinics (DFC) that were created during the pandemic. The latent process derived from the model has been used as a measure of the influenza activity [1]. We compare the cross-correlations between estimated influenza level based on multiple surveillance data and GP ILI data, versus accident and emergency hospital admissions with principal diagnoses of respiratory diseases and pneumonia & influenza (P&I). RESULTS: The estimated influenza activity has higher cross-correlation with respiratory and P&I admissions (ρ=0.66 and 0.73 respectively) compared to that of GP ILI rates (Table 1). Cross correlations drop distinctly after lag 2 for both estimated influenza activity and GP ILI rates. CONCLUSIONS: The use of a multivariate method to integrate information from multiple sources of influenza surveillance data may have the potential to improve forecasting of admission surge of respiratory diseases. University of Illinois at Chicago Library 2013-04-04 /pmc/articles/PMC3692818/ Text en ©2013 the author(s) http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/ojphi/about/submissions#copyrightNotice This is an Open Access article. Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. |
spellingShingle | ISDS 2012 Conference Abstracts Lau, Eric H.Y. Ip, Dennis K.M. Cowling, Benjamin J. Potential use of multiple surveillance data in the forecast of hospital admissions |
title | Potential use of multiple surveillance data in the forecast of hospital admissions |
title_full | Potential use of multiple surveillance data in the forecast of hospital admissions |
title_fullStr | Potential use of multiple surveillance data in the forecast of hospital admissions |
title_full_unstemmed | Potential use of multiple surveillance data in the forecast of hospital admissions |
title_short | Potential use of multiple surveillance data in the forecast of hospital admissions |
title_sort | potential use of multiple surveillance data in the forecast of hospital admissions |
topic | ISDS 2012 Conference Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3692818/ |
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