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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2018
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.
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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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