Automated Mortality Surveillance in South-Eastern Ontario for Pandemic Influenza Preparedness
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 prelimina...
Gespeichert in:
| Veröffentlicht in: | Canadian journal of public health 2010-11, Vol.101 (6), p.459-463 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 463 |
|---|---|
| container_issue | 6 |
| container_start_page | 459 |
| container_title | Canadian journal of public health |
| container_volume | 101 |
| creator | Fan, Cary van Dijk, Adam Fernando, Dillan Hall, Justin N. Wynn, Aaron Gemmill, Ian Moore, Kieran Michael |
| description | 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. Contexte : La pandémie d'influenza H1N1 (pH1N1) survenue au Canada en 2009 a montré qu'il faut accroître la surveillance au palier local et régional pour que les médecins et la santé publique puissent prendre des décisions fondées sur des données scientifiques. Nous décrivons la raison d'être, la méthode et les constatations préliminaires de la mise en œuvre d'un système automatisé de surveillance de la mortalité (SSM) dans la circonscription sanitaire de Kingston, Frontenac et Lennox et Addington (KFL&A). Méthode : Le SSM utilisait un cadre Internet automatisé avec transfert sécurisé des données. Un accord de partage des données conclu entre le médecin-hygiéniste local et la ville de Kingston a facilité l'actualisation hebdomadaire des donnée |
| doi_str_mv | 10.1007/BF03403964 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6974207</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A246535558</galeid><jstor_id>41995523</jstor_id><sourcerecordid>A246535558</sourcerecordid><originalsourceid>FETCH-LOGICAL-c659t-4b2e9dfa34a3653e47967bd1353d9485f0c3087249a0548e3073a7b57bd1cc073</originalsourceid><addsrcrecordid>eNpt0kFv0zAUB_AIgVgZXLiDok0CAcpwYjtOLkhl2qDSYJMKR2S5zkvrKrE725kYnx53GV2Dohwsx7_8Y_u9KHqZopMUIfbx8znCBOEyJ4-iSVpmKGGE5Y-jCUKoSEiW44PomXPrMMWY4afRQZZihliRTqJf086bVnio4m_GetEofxvPO3sDqmmElhArHc9N51fJmXAerI4vtRdWmbg2Nr4SuoJWyXim66YD_UfEVxY2wkKlwbnn0ZNaNA5e3I-H0c_zsx-nX5OLyy-z0-lFInNa-oQsMiirWmAicE4xEFbmbFGlmOKqJAWtkcSoYBkpBaKkAIwYFmxBt0bKMDmMPvW5m27RQiVBeysavrGqFfaWG6H4cEWrFV-aG56XjGR3AW_vA6y57sB53ionYXsFYDrHC0ozRHGaB3n0n1ybzupwOl6EIhR5jkhAxz1aiga40rUJf5XbSD7NSDgipbQIKhlRS9AQtmg01Cq8HvijES836prvo5MRFJ67Oo2mvht8EIyH334pOuf4bP59aN_s2RWIxq-caTqvjHZD-L6H0hrnLNS7WqSIb5uWPzRtwK_3q7ej_7o0gA89cGFJL8E-3Plo3Kter503dpdG0rIMVcT4L15Y-YE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>840386604</pqid></control><display><type>article</type><title>Automated Mortality Surveillance in South-Eastern Ontario for Pandemic Influenza Preparedness</title><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>JSTOR Archive Collection A-Z Listing</source><source>PubMed Central</source><source>SpringerLink Journals - AutoHoldings</source><creator>Fan, Cary ; van Dijk, Adam ; Fernando, Dillan ; Hall, Justin N. ; Wynn, Aaron ; Gemmill, Ian ; Moore, Kieran Michael</creator><creatorcontrib>Fan, Cary ; van Dijk, Adam ; Fernando, Dillan ; Hall, Justin N. ; Wynn, Aaron ; Gemmill, Ian ; Moore, Kieran Michael</creatorcontrib><description><![CDATA[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. Contexte : La pandémie d'influenza H1N1 (pH1N1) survenue au Canada en 2009 a montré qu'il faut accroître la surveillance au palier local et régional pour que les médecins et la santé publique puissent prendre des décisions fondées sur des données scientifiques. Nous décrivons la raison d'être, la méthode et les constatations préliminaires de la mise en œuvre d'un système automatisé de surveillance de la mortalité (SSM) dans la circonscription sanitaire de Kingston, Frontenac et Lennox et Addington (KFL&A). Méthode : Le SSM utilisait un cadre Internet automatisé avec transfert sécurisé des données. Un accord de partage des données conclu entre le médecin-hygiéniste local et la ville de Kingston a facilité l'actualisation hebdomadaire des données de mortalité. Les décès dus à l'influenza ont été classés selon la cause de décès (par mots clés) et par un algorithme phonétique pour saisir les orthographes alternatives. Le modèle de détection des anomalies était une version modifiée de l'algorithme de somme cumulée du système EARS (Early Aberration Reporting System). Résultats : Nous avons établi les taux de mortalité de référence dans la région par une analyse rétrospective des données municipales de mortalité sur une période de 10 ans. Les données du SSM surveillées durant la saison d'influenza pH1N1 n'ont montré aucun impact significatif sur le fardeau de mortalité ni sur le moment des décès dans la circonscription sanitaire de KFL&A. Conclusion : Les données municipales ont permis de surveiller la mortalité dans la région de KFL&A et d'actualiser les résultats chaque semaine. D'autres municipalités pourraient participer à ce projet de surveillance en utilisant le modèle de Kingston sans avoir à y consacrer des sommes importantes sur une base permanente. On poursuit les efforts pour améliorer la qualité des données fournies par les médecins et leur transcription. L'intégration des données de mortalité et d'autres flux de données en temps réel dans un tableau de bord électronique de la santé publique pourrait permettre aux décideurs d'obtenir de l'information en temps utile durant les urgences sanitaires.]]></description><identifier>ISSN: 0008-4263</identifier><identifier>EISSN: 1920-7476</identifier><identifier>DOI: 10.1007/BF03403964</identifier><identifier>PMID: 21370781</identifier><identifier>CODEN: CJPEA4</identifier><language>eng</language><publisher>Cham: Canadian Public Health Association</publisher><subject>Causes of death ; Data collection ; Data processing ; Datasets ; Death ; Decision making ; Disease models ; Disease Outbreaks - prevention & control ; Epidemiology ; Health aspects ; Health surveys ; Humans ; Influenza A Virus, H1N1 Subtype - isolation & purification ; Influenza, Human - mortality ; Influenza, Human - prevention & control ; Information management ; Medical statistics ; Medicine ; Medicine & Public Health ; Methods ; Mortality ; Ontario ; Ontario - epidemiology ; Pandemics ; Personal health ; Population Surveillance - methods ; Public Health ; Public Health Administration - methods ; Public Health Informatics - methods ; QUANTITATIVE RESEARCH ; Reporting requirements ; Statistics ; Surveillance ; Technology application ; Vital statistics</subject><ispartof>Canadian journal of public health, 2010-11, Vol.101 (6), p.459-463</ispartof><rights>Canadian Public Health Association, 2010 © Association canadienne de santé publique, 2010</rights><rights>The Canadian Public Health Association 2010</rights><rights>COPYRIGHT 2010 Springer</rights><rights>Copyright Canadian Public Health Association Nov/Dec 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c659t-4b2e9dfa34a3653e47967bd1353d9485f0c3087249a0548e3073a7b57bd1cc073</citedby><cites>FETCH-LOGICAL-c659t-4b2e9dfa34a3653e47967bd1353d9485f0c3087249a0548e3073a7b57bd1cc073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/41995523$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/41995523$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,727,780,784,803,885,27923,27924,41487,42556,51318,53790,53792,58016,58249</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21370781$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fan, Cary</creatorcontrib><creatorcontrib>van Dijk, Adam</creatorcontrib><creatorcontrib>Fernando, Dillan</creatorcontrib><creatorcontrib>Hall, Justin N.</creatorcontrib><creatorcontrib>Wynn, Aaron</creatorcontrib><creatorcontrib>Gemmill, Ian</creatorcontrib><creatorcontrib>Moore, Kieran Michael</creatorcontrib><title>Automated Mortality Surveillance in South-Eastern Ontario for Pandemic Influenza Preparedness</title><title>Canadian journal of public health</title><addtitle>Can J Public Health</addtitle><addtitle>Can J Public Health</addtitle><description><![CDATA[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. Contexte : La pandémie d'influenza H1N1 (pH1N1) survenue au Canada en 2009 a montré qu'il faut accroître la surveillance au palier local et régional pour que les médecins et la santé publique puissent prendre des décisions fondées sur des données scientifiques. Nous décrivons la raison d'être, la méthode et les constatations préliminaires de la mise en œuvre d'un système automatisé de surveillance de la mortalité (SSM) dans la circonscription sanitaire de Kingston, Frontenac et Lennox et Addington (KFL&A). Méthode : Le SSM utilisait un cadre Internet automatisé avec transfert sécurisé des données. Un accord de partage des données conclu entre le médecin-hygiéniste local et la ville de Kingston a facilité l'actualisation hebdomadaire des données de mortalité. Les décès dus à l'influenza ont été classés selon la cause de décès (par mots clés) et par un algorithme phonétique pour saisir les orthographes alternatives. Le modèle de détection des anomalies était une version modifiée de l'algorithme de somme cumulée du système EARS (Early Aberration Reporting System). Résultats : Nous avons établi les taux de mortalité de référence dans la région par une analyse rétrospective des données municipales de mortalité sur une période de 10 ans. Les données du SSM surveillées durant la saison d'influenza pH1N1 n'ont montré aucun impact significatif sur le fardeau de mortalité ni sur le moment des décès dans la circonscription sanitaire de KFL&A. Conclusion : Les données municipales ont permis de surveiller la mortalité dans la région de KFL&A et d'actualiser les résultats chaque semaine. D'autres municipalités pourraient participer à ce projet de surveillance en utilisant le modèle de Kingston sans avoir à y consacrer des sommes importantes sur une base permanente. On poursuit les efforts pour améliorer la qualité des données fournies par les médecins et leur transcription. L'intégration des données de mortalité et d'autres flux de données en temps réel dans un tableau de bord électronique de la santé publique pourrait permettre aux décideurs d'obtenir de l'information en temps utile durant les urgences sanitaires.]]></description><subject>Causes of death</subject><subject>Data collection</subject><subject>Data processing</subject><subject>Datasets</subject><subject>Death</subject><subject>Decision making</subject><subject>Disease models</subject><subject>Disease Outbreaks - prevention & control</subject><subject>Epidemiology</subject><subject>Health aspects</subject><subject>Health surveys</subject><subject>Humans</subject><subject>Influenza A Virus, H1N1 Subtype - isolation & purification</subject><subject>Influenza, Human - mortality</subject><subject>Influenza, Human - prevention & control</subject><subject>Information management</subject><subject>Medical statistics</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Methods</subject><subject>Mortality</subject><subject>Ontario</subject><subject>Ontario - epidemiology</subject><subject>Pandemics</subject><subject>Personal health</subject><subject>Population Surveillance - methods</subject><subject>Public Health</subject><subject>Public Health Administration - methods</subject><subject>Public Health Informatics - methods</subject><subject>QUANTITATIVE RESEARCH</subject><subject>Reporting requirements</subject><subject>Statistics</subject><subject>Surveillance</subject><subject>Technology application</subject><subject>Vital statistics</subject><issn>0008-4263</issn><issn>1920-7476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpt0kFv0zAUB_AIgVgZXLiDok0CAcpwYjtOLkhl2qDSYJMKR2S5zkvrKrE725kYnx53GV2Dohwsx7_8Y_u9KHqZopMUIfbx8znCBOEyJ4-iSVpmKGGE5Y-jCUKoSEiW44PomXPrMMWY4afRQZZihliRTqJf086bVnio4m_GetEofxvPO3sDqmmElhArHc9N51fJmXAerI4vtRdWmbg2Nr4SuoJWyXim66YD_UfEVxY2wkKlwbnn0ZNaNA5e3I-H0c_zsx-nX5OLyy-z0-lFInNa-oQsMiirWmAicE4xEFbmbFGlmOKqJAWtkcSoYBkpBaKkAIwYFmxBt0bKMDmMPvW5m27RQiVBeysavrGqFfaWG6H4cEWrFV-aG56XjGR3AW_vA6y57sB53ionYXsFYDrHC0ozRHGaB3n0n1ybzupwOl6EIhR5jkhAxz1aiga40rUJf5XbSD7NSDgipbQIKhlRS9AQtmg01Cq8HvijES836prvo5MRFJ67Oo2mvht8EIyH334pOuf4bP59aN_s2RWIxq-caTqvjHZD-L6H0hrnLNS7WqSIb5uWPzRtwK_3q7ej_7o0gA89cGFJL8E-3Plo3Kter503dpdG0rIMVcT4L15Y-YE</recordid><startdate>20101101</startdate><enddate>20101101</enddate><creator>Fan, Cary</creator><creator>van Dijk, Adam</creator><creator>Fernando, Dillan</creator><creator>Hall, Justin N.</creator><creator>Wynn, Aaron</creator><creator>Gemmill, Ian</creator><creator>Moore, Kieran Michael</creator><general>Canadian Public Health Association</general><general>Springer International Publishing</general><general>Springer</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>0-V</scope><scope>3V.</scope><scope>4S-</scope><scope>4U-</scope><scope>7QP</scope><scope>7QR</scope><scope>7RV</scope><scope>7T2</scope><scope>7TK</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>88J</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FQ</scope><scope>8FV</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>AN0</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DPSOV</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>KC-</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M2L</scope><scope>M2O</scope><scope>M2R</scope><scope>M3G</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0X</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20101101</creationdate><title>Automated Mortality Surveillance in South-Eastern Ontario for Pandemic Influenza Preparedness</title><author>Fan, Cary ; van Dijk, Adam ; Fernando, Dillan ; Hall, Justin N. ; Wynn, Aaron ; Gemmill, Ian ; Moore, Kieran Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c659t-4b2e9dfa34a3653e47967bd1353d9485f0c3087249a0548e3073a7b57bd1cc073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Causes of death</topic><topic>Data collection</topic><topic>Data processing</topic><topic>Datasets</topic><topic>Death</topic><topic>Decision making</topic><topic>Disease models</topic><topic>Disease Outbreaks - prevention & control</topic><topic>Epidemiology</topic><topic>Health aspects</topic><topic>Health surveys</topic><topic>Humans</topic><topic>Influenza A Virus, H1N1 Subtype - isolation & purification</topic><topic>Influenza, Human - mortality</topic><topic>Influenza, Human - prevention & control</topic><topic>Information management</topic><topic>Medical statistics</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Methods</topic><topic>Mortality</topic><topic>Ontario</topic><topic>Ontario - epidemiology</topic><topic>Pandemics</topic><topic>Personal health</topic><topic>Population Surveillance - methods</topic><topic>Public Health</topic><topic>Public Health Administration - methods</topic><topic>Public Health Informatics - methods</topic><topic>QUANTITATIVE RESEARCH</topic><topic>Reporting requirements</topic><topic>Statistics</topic><topic>Surveillance</topic><topic>Technology application</topic><topic>Vital statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fan, Cary</creatorcontrib><creatorcontrib>van Dijk, Adam</creatorcontrib><creatorcontrib>Fernando, Dillan</creatorcontrib><creatorcontrib>Hall, Justin N.</creatorcontrib><creatorcontrib>Wynn, Aaron</creatorcontrib><creatorcontrib>Gemmill, Ian</creatorcontrib><creatorcontrib>Moore, Kieran Michael</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>BPIR.com Limited</collection><collection>University Readers</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Social Science Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Canadian Business & Current Affairs Database</collection><collection>Canadian Business & Current Affairs Database (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Social Science Premium Collection</collection><collection>British Nursing Database</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>eLibrary</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Politics Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Politics Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Political Science Database</collection><collection>Research Library</collection><collection>Social Science Database</collection><collection>CBCA Reference & Current Events</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Canadian journal of public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fan, Cary</au><au>van Dijk, Adam</au><au>Fernando, Dillan</au><au>Hall, Justin N.</au><au>Wynn, Aaron</au><au>Gemmill, Ian</au><au>Moore, Kieran Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated Mortality Surveillance in South-Eastern Ontario for Pandemic Influenza Preparedness</atitle><jtitle>Canadian journal of public health</jtitle><stitle>Can J Public Health</stitle><addtitle>Can J Public Health</addtitle><date>2010-11-01</date><risdate>2010</risdate><volume>101</volume><issue>6</issue><spage>459</spage><epage>463</epage><pages>459-463</pages><issn>0008-4263</issn><eissn>1920-7476</eissn><coden>CJPEA4</coden><abstract><![CDATA[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. Contexte : La pandémie d'influenza H1N1 (pH1N1) survenue au Canada en 2009 a montré qu'il faut accroître la surveillance au palier local et régional pour que les médecins et la santé publique puissent prendre des décisions fondées sur des données scientifiques. Nous décrivons la raison d'être, la méthode et les constatations préliminaires de la mise en œuvre d'un système automatisé de surveillance de la mortalité (SSM) dans la circonscription sanitaire de Kingston, Frontenac et Lennox et Addington (KFL&A). Méthode : Le SSM utilisait un cadre Internet automatisé avec transfert sécurisé des données. Un accord de partage des données conclu entre le médecin-hygiéniste local et la ville de Kingston a facilité l'actualisation hebdomadaire des données de mortalité. Les décès dus à l'influenza ont été classés selon la cause de décès (par mots clés) et par un algorithme phonétique pour saisir les orthographes alternatives. Le modèle de détection des anomalies était une version modifiée de l'algorithme de somme cumulée du système EARS (Early Aberration Reporting System). Résultats : Nous avons établi les taux de mortalité de référence dans la région par une analyse rétrospective des données municipales de mortalité sur une période de 10 ans. Les données du SSM surveillées durant la saison d'influenza pH1N1 n'ont montré aucun impact significatif sur le fardeau de mortalité ni sur le moment des décès dans la circonscription sanitaire de KFL&A. Conclusion : Les données municipales ont permis de surveiller la mortalité dans la région de KFL&A et d'actualiser les résultats chaque semaine. D'autres municipalités pourraient participer à ce projet de surveillance en utilisant le modèle de Kingston sans avoir à y consacrer des sommes importantes sur une base permanente. On poursuit les efforts pour améliorer la qualité des données fournies par les médecins et leur transcription. L'intégration des données de mortalité et d'autres flux de données en temps réel dans un tableau de bord électronique de la santé publique pourrait permettre aux décideurs d'obtenir de l'information en temps utile durant les urgences sanitaires.]]></abstract><cop>Cham</cop><pub>Canadian Public Health Association</pub><pmid>21370781</pmid><doi>10.1007/BF03403964</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0008-4263 |
| ispartof | Canadian journal of public health, 2010-11, Vol.101 (6), p.459-463 |
| issn | 0008-4263 1920-7476 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6974207 |
| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; JSTOR Archive Collection A-Z Listing; PubMed Central; SpringerLink Journals - AutoHoldings |
| subjects | Causes of death Data collection Data processing Datasets Death Decision making Disease models Disease Outbreaks - prevention & control Epidemiology Health aspects Health surveys Humans Influenza A Virus, H1N1 Subtype - isolation & purification Influenza, Human - mortality Influenza, Human - prevention & control Information management Medical statistics Medicine Medicine & Public Health Methods Mortality Ontario Ontario - epidemiology Pandemics Personal health Population Surveillance - methods Public Health Public Health Administration - methods Public Health Informatics - methods QUANTITATIVE RESEARCH Reporting requirements Statistics Surveillance Technology application Vital statistics |
| title | Automated Mortality Surveillance in South-Eastern Ontario for Pandemic Influenza Preparedness |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T06%3A50%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Automated%20Mortality%20Surveillance%20in%20South-Eastern%20Ontario%20for%20Pandemic%20Influenza%20Preparedness&rft.jtitle=Canadian%20journal%20of%20public%20health&rft.au=Fan,%20Cary&rft.date=2010-11-01&rft.volume=101&rft.issue=6&rft.spage=459&rft.epage=463&rft.pages=459-463&rft.issn=0008-4263&rft.eissn=1920-7476&rft.coden=CJPEA4&rft_id=info:doi/10.1007/BF03403964&rft_dat=%3Cgale_pubme%3EA246535558%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=840386604&rft_id=info:pmid/21370781&rft_galeid=A246535558&rft_jstor_id=41995523&rfr_iscdi=true |