Limits...
Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models.

Chan TC, Teng YC, Hwang JS - BMC Public Health (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, 115 Nankang, Taipei, Taiwan. dachianpig@gmail.com.

ABSTRACT

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.

Show MeSH

Related in: MedlinePlus

Daily observed influenza-like illness emergency department visits in southern Taiwan in 2009 (A), and results of aberration detection by proposed method (B), by modified CUSUM applied to the Pearson residuals (C). *Note: Detected aberration signals are marked with a red x at the top. The time period between the two dashed lines was August 2009.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4352259&req=5

Fig7: Daily observed influenza-like illness emergency department visits in southern Taiwan in 2009 (A), and results of aberration detection by proposed method (B), by modified CUSUM applied to the Pearson residuals (C). *Note: Detected aberration signals are marked with a red x at the top. The time period between the two dashed lines was August 2009.

Mentions: In Figure 4, Figure 5, Figure 6, Figure 7, the seasonal influenza epidemics and the pandemic outbreaks were all detected in central and southern Taiwan with our approach. The intensities of the aberrations were high in ED visits, and earlier aberrations were also found in ED visits. However, the modified CUSUM method applied to Pearson residuals still caused many false alarms (Figure 5 and Figure 7).Figure 4


Detection of influenza-like illness aberrations by directly monitoring Pearson residuals of fitted negative binomial regression models.

Chan TC, Teng YC, Hwang JS - BMC Public Health (2015)

Daily observed influenza-like illness emergency department visits in southern Taiwan in 2009 (A), and results of aberration detection by proposed method (B), by modified CUSUM applied to the Pearson residuals (C). *Note: Detected aberration signals are marked with a red x at the top. The time period between the two dashed lines was August 2009.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4352259&req=5

Fig7: Daily observed influenza-like illness emergency department visits in southern Taiwan in 2009 (A), and results of aberration detection by proposed method (B), by modified CUSUM applied to the Pearson residuals (C). *Note: Detected aberration signals are marked with a red x at the top. The time period between the two dashed lines was August 2009.
Mentions: In Figure 4, Figure 5, Figure 6, Figure 7, the seasonal influenza epidemics and the pandemic outbreaks were all detected in central and southern Taiwan with our approach. The intensities of the aberrations were high in ED visits, and earlier aberrations were also found in ED visits. However, the modified CUSUM method applied to Pearson residuals still caused many false alarms (Figure 5 and Figure 7).Figure 4

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, 115 Nankang, Taipei, Taiwan. dachianpig@gmail.com.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus