Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models
BMC Public Health, ISSN: 1471-2458, Vol: 15, Issue: 1, Page: 168
2015
- 7Citations
- 32Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations7
- Citation Indexes7
- CrossRef7
- Captures32
- Readers32
- 32
Article Description
Background: Emerging novel influenza outbreaks have increasingly been a threat to the public and a major concern of public health departments. Real-time data in seamless surveillance systems such as health insurance claims data for influenza-like illnesses (ILI) are ready for analysis, making it highly desirable to develop practical techniques to analyze such readymade data for outbreak detection so that the public can receive timely influenza epidemic warnings. This study proposes a simple and effective approach to analyze area-based health insurance claims data including outpatient and emergency department (ED) visits for early detection of any aberrations of ILI. Methods: The health insurance claims data during 2004-2009 from a national health insurance research database were used for developing early detection methods. The proposed approach fitted the daily new ILI visits and monitored the Pearson residuals directly for aberration detection. First, negative binomial regression was used for both outpatient and ED visits to adjust for potentially influential factors such as holidays, weekends, seasons, temporal dependence and temperature. Second, if the Pearson residuals exceeded 1.96, aberration signals were issued. The empirical validation of the model was done in 2008 and 2009. In addition, we designed a simulation study to compare the time of outbreak detection, non-detection probability and false alarm rate between the proposed method and modified CUSUM. Results: The model successfully detected the aberrations of 2009 pandemic (H1N1) influenza virus in northern, central and southern Taiwan. The proposed approach was more sensitive in identifying aberrations in ED visits than those in outpatient visits. Simulation studies demonstrated that the proposed approach could detect the aberrations earlier, and with lower non-detection probability and mean false alarm rate in detecting aberrations compared to modified CUSUM methods. Conclusions: The proposed simple approach was able to filter out temporal trends, adjust for temperature, and issue warning signals for the first wave of the influenza epidemic in a timely and accurate manner.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84928737781&origin=inward; http://dx.doi.org/10.1186/s12889-015-1500-4; http://www.ncbi.nlm.nih.gov/pubmed/25886316; https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-015-1500-4; https://dx.doi.org/10.1186/s12889-015-1500-4
Springer Science and Business Media LLC
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