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
University of Manitoba, Winnipeg, MB CANADA.
New Brunswick Department of Health, Fredericton, NB CANADA.
Int J Popul Data Sci. 2018 Oct 5;3(3):433. doi: 10.23889/ijpds.v3i3.433.
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.
慢性病对全球人口和医疗保健系统有着重大影响。行政卫生数据是慢性病监测的理想资源，因为它们以人群为基础且是常规收集的。对于多辖区监测而言，分布式模型具有优势，因为它不需要跨辖区边界共享个人层面的数据。我们的目标是描述使用关联行政数据在加拿大所有省和地区（P/T）开展慢性病监测的分布式模型的流程、结构、益处和挑战。加拿大公共卫生局（PHAC）于2009年建立了加拿大慢性病监测系统（CCDSS），以促进对慢性病患病率、发病率和结局进行标准化的全国性估计。CCDSS主要依赖于关联的健康保险登记文件、医生计费索赔和医院出院摘要。针对每个P/T的数据应用标准化的病例定义和通用分析方案；汇总数据与PHAC共享，并汇总用于报告和开放获取数据计划。这种分布式模型的优点包括：它利用了所有P/T中可用的丰富数据资源；它支持所有P/T中的慢性病监测能力建设；PHAC可以轻松开发监测方法的变更并由P/T实施。然而，也存在挑战：各辖区行政数据库的异质性以及数据质量随时间的变化威胁到标准化疾病估计数的生成；所有P/T共有的数据库有限，这阻碍了CCDSS的潜在扩展；需要在全面报告与P/T披露要求之间取得平衡以保护隐私。PHAC及其P/T合作伙伴已成功实施并维持了CCDSS慢性病监测分布式模型。在涉及医疗保健数据库、专业知识和分析能力、人口特征及优先事项存在异质性的辖区进行国家监测方面，我们吸取了许多经验教训。
