tetano
Editor, Senior Moderator
Can J Public Health. 2010 Nov-Dec;101(6):459-63.
Automated mortality surveillance in south-eastern Ontario for pandemic influenza preparedness.
Fan C, van Dijk A, Fernando D, Hall JN, Wynn A, Gemmill I, Moore KM.
Queen's University, Kingston, ON. cary.fan@queensu.ca
Abstract
BACKGROUND: The recent Canadian experience with pandemic H1N1 (pH1N1) influenza in 2009 highlighted the need for enhanced surveillance at local and regional levels to support evidence-based decision making by physicians and public health. We describe the rationale, methodology, and provide preliminary findings from the implementation of an automated Mortality Surveillance System (MSS) in the Kingston, Frontenac and Lennox & Addington (KFL&A) health unit.
METHODS: The MSS utilized an automated web-based framework with secure data transfer. A data sharing agreement between the local Medical Officer of Health and the City of Kingston facilitated weekly updates of mortality data. Deaths due to influenza were classified using keywords in the cause of death and a phonetic algorithm to capture alternate spellings. Anomaly detection was modeled on the modified cumulative sum algorithm implemented in the Early Aberration Reporting System.
RESULTS: Retrospective analysis of municipal mortality data over a 10-year period established baseline mortality rates in the region. MSS data monitored during the pH1N1 influenza season showed no significant impact on the burden or timing of mortality in the KFL&A health unit.
CONCLUSION: Municipal data enabled surveillance of mortality in the KFL&A region with weekly updates. Other municipalities may participate in this surveillance project using the Kingston model without significant ongoing investment. Efforts to improve data quality at the physician and transcription level are ongoing. Integration of mortality data and other real-time data streams into an integrated electronic public health dashboard could provide decision-makers with timely information during public health emergencies.
PMID: 21370781 [PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/21370781
Automated mortality surveillance in south-eastern Ontario for pandemic influenza preparedness.
Fan C, van Dijk A, Fernando D, Hall JN, Wynn A, Gemmill I, Moore KM.
Queen's University, Kingston, ON. cary.fan@queensu.ca
Abstract
BACKGROUND: The recent Canadian experience with pandemic H1N1 (pH1N1) influenza in 2009 highlighted the need for enhanced surveillance at local and regional levels to support evidence-based decision making by physicians and public health. We describe the rationale, methodology, and provide preliminary findings from the implementation of an automated Mortality Surveillance System (MSS) in the Kingston, Frontenac and Lennox & Addington (KFL&A) health unit.
METHODS: The MSS utilized an automated web-based framework with secure data transfer. A data sharing agreement between the local Medical Officer of Health and the City of Kingston facilitated weekly updates of mortality data. Deaths due to influenza were classified using keywords in the cause of death and a phonetic algorithm to capture alternate spellings. Anomaly detection was modeled on the modified cumulative sum algorithm implemented in the Early Aberration Reporting System.
RESULTS: Retrospective analysis of municipal mortality data over a 10-year period established baseline mortality rates in the region. MSS data monitored during the pH1N1 influenza season showed no significant impact on the burden or timing of mortality in the KFL&A health unit.
CONCLUSION: Municipal data enabled surveillance of mortality in the KFL&A region with weekly updates. Other municipalities may participate in this surveillance project using the Kingston model without significant ongoing investment. Efforts to improve data quality at the physician and transcription level are ongoing. Integration of mortality data and other real-time data streams into an integrated electronic public health dashboard could provide decision-makers with timely information during public health emergencies.
PMID: 21370781 [PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/21370781