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The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance
Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageou...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Swansea University
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299467/ https://www.ncbi.nlm.nih.gov/pubmed/32935015 http://dx.doi.org/10.23889/ijpds.v3i3.433 |
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author | Lix, Lisa M. Ayles, James Bartholomew, Sharon Cooke, Charmaine A. Ellison, Joellyn Emond, Valerie Hamm, Naomi C. Hannah, Heather Jean, Sonia LeBlanc, Shannon O’Donnell, Siobhan Paterson, J. Michael Pelletier, Catherine Phillips, Karen A. M. Puchtinger, Rolf Reimer, Kim Robitaille, Cynthia Smith, Mark Svenson, Lawrence W. Tu, Karen VanTil, Linda D. Waits, Sean Pelletier, Louise |
author_facet | Lix, Lisa M. Ayles, James Bartholomew, Sharon Cooke, Charmaine A. Ellison, Joellyn Emond, Valerie Hamm, Naomi C. Hannah, Heather Jean, Sonia LeBlanc, Shannon O’Donnell, Siobhan Paterson, J. Michael Pelletier, Catherine Phillips, Karen A. M. Puchtinger, Rolf Reimer, Kim Robitaille, Cynthia Smith, Mark Svenson, Lawrence W. Tu, Karen VanTil, Linda D. Waits, Sean Pelletier, Louise |
author_sort | Lix, Lisa M. |
collection | PubMed |
description | Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageous because it does not require individual-level data to be shared across jurisdictional boundaries. Our objective is to describe the process, structure, benefits, and challenges of a distributed model for chronic disease surveillance across all Canadian provinces and territories (P/Ts) using linked administrative data. The Public Health Agency of Canada (PHAC) established the Canadian Chronic Disease Surveillance System (CCDSS) in 2009 to facilitate standardized, national estimates of chronic disease prevalence, incidence, and outcomes. The CCDSS primarily relies on linked health insurance registration files, physician billing claims, and hospital discharge abstracts. Standardized case definitions and common analytic protocols are applied to the data for each P/T; aggregate data are shared with PHAC and summarized for reports and open access data initiatives. Advantages of this distributed model include: it uses the rich data resources available in all P/Ts; it supports chronic disease surveillance capacity building in all P/Ts; and changes in surveillance methodology can be easily developed by PHAC and implemented by the P/Ts. However, there are challenges: heterogeneity in administrative databases across jurisdictions and changes in data quality over time threaten the production of standardized disease estimates; a limited set of databases are common to all P/Ts, which hinders potential CCDSS expansion; and there is a need to balance comprehensive reporting with P/T disclosure requirements to protect privacy. The CCDSS distributed model for chronic disease surveillance has been successfully implemented and sustained by PHAC and its P/T partners. Many lessons have been learned about national surveillance involving jurisdictions that are heterogeneous with respect to healthcare databases, expertise and analytical capacity, population characteristics, and priorities. |
format | Online Article Text |
id | pubmed-7299467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Swansea University |
record_format | MEDLINE/PubMed |
spelling | pubmed-72994672020-09-14 The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance Lix, Lisa M. Ayles, James Bartholomew, Sharon Cooke, Charmaine A. Ellison, Joellyn Emond, Valerie Hamm, Naomi C. Hannah, Heather Jean, Sonia LeBlanc, Shannon O’Donnell, Siobhan Paterson, J. Michael Pelletier, Catherine Phillips, Karen A. M. Puchtinger, Rolf Reimer, Kim Robitaille, Cynthia Smith, Mark Svenson, Lawrence W. Tu, Karen VanTil, Linda D. Waits, Sean Pelletier, Louise Int J Popul Data Sci Population Data Science Chronic diseases have a major impact on populations and healthcare systems worldwide. Administrative health data are an ideal resource for chronic disease surveillance because they are population-based and routinely collected. For multi-jurisdictional surveillance, a distributed model is advantageous because it does not require individual-level data to be shared across jurisdictional boundaries. Our objective is to describe the process, structure, benefits, and challenges of a distributed model for chronic disease surveillance across all Canadian provinces and territories (P/Ts) using linked administrative data. The Public Health Agency of Canada (PHAC) established the Canadian Chronic Disease Surveillance System (CCDSS) in 2009 to facilitate standardized, national estimates of chronic disease prevalence, incidence, and outcomes. The CCDSS primarily relies on linked health insurance registration files, physician billing claims, and hospital discharge abstracts. Standardized case definitions and common analytic protocols are applied to the data for each P/T; aggregate data are shared with PHAC and summarized for reports and open access data initiatives. Advantages of this distributed model include: it uses the rich data resources available in all P/Ts; it supports chronic disease surveillance capacity building in all P/Ts; and changes in surveillance methodology can be easily developed by PHAC and implemented by the P/Ts. However, there are challenges: heterogeneity in administrative databases across jurisdictions and changes in data quality over time threaten the production of standardized disease estimates; a limited set of databases are common to all P/Ts, which hinders potential CCDSS expansion; and there is a need to balance comprehensive reporting with P/T disclosure requirements to protect privacy. The CCDSS distributed model for chronic disease surveillance has been successfully implemented and sustained by PHAC and its P/T partners. Many lessons have been learned about national surveillance involving jurisdictions that are heterogeneous with respect to healthcare databases, expertise and analytical capacity, population characteristics, and priorities. Swansea University 2018-10-05 /pmc/articles/PMC7299467/ /pubmed/32935015 http://dx.doi.org/10.23889/ijpds.v3i3.433 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Population Data Science Lix, Lisa M. Ayles, James Bartholomew, Sharon Cooke, Charmaine A. Ellison, Joellyn Emond, Valerie Hamm, Naomi C. Hannah, Heather Jean, Sonia LeBlanc, Shannon O’Donnell, Siobhan Paterson, J. Michael Pelletier, Catherine Phillips, Karen A. M. Puchtinger, Rolf Reimer, Kim Robitaille, Cynthia Smith, Mark Svenson, Lawrence W. Tu, Karen VanTil, Linda D. Waits, Sean Pelletier, Louise The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance |
title | The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance |
title_full | The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance |
title_fullStr | The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance |
title_full_unstemmed | The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance |
title_short | The Canadian Chronic Disease Surveillance System: A model for collaborative surveillance |
title_sort | canadian chronic disease surveillance system: a model for collaborative surveillance |
topic | Population Data Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299467/ https://www.ncbi.nlm.nih.gov/pubmed/32935015 http://dx.doi.org/10.23889/ijpds.v3i3.433 |
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