Quantitative Subgroup: Administrative Health Data Analysis

Specific Research Objectives:

  1. To conduct inter- and intra-provincial comparisons of cancer diagnostic, treatment and survivorship phases of breast cancer care with a focus on aspects of that care which might be influenced by primary care.
  2. To identify subgroups of patients at risk of sub-optimal access and outcomes, with a special focus on specific vulnerable populations.

The Data:

The study cohorts consist of all breast cancer patients in each of five Canadian provinces (British Columbia, Alberta, Manitoba, Ontario and Nova Scotia) who were diagnosed from 2007-2011. The research infrastructure will contain standardized variables derived from linked databases. The databases include cancer registries, census data, physician claims, ambulatory care and inpatient hospital data. Other databases such as laboratory, pharmacy, emergency services, homecare services, and Citizenship and Immigration Canada data are also being used by some provinces based on data availability. We are studying diagnosis, treatment, and survivorship concurrently. Analyses will be conducted separately at designated research centres in each province using similar strategies. Methods developed in one province or one phase will be transferred to others to allow comparisons across provinces and phases

Increasing Research Capacity: We will increase Canada’s cancer health services research capacity in primary care by building on extant work of team members. We will:

  1. standardize methods for identifying study populations
  2. choose and define risk and outcome variables
  3. develop and apply standardized algorithms
  4. standardize documentation for the analytic methods, algorithms, programs, and data dictionaries.

This approach will develop critical capacity, minimize methodological heterogeneity in inter-provincial comparisons, and provide the infrastructure for ongoing quality surveillance.

The Administrative Health Data subgroup presented a webinar on October 17, 2019 as part of the Population Data Science Webinar series. Click here to view the webinar recording and learn about key findings from participating provinces, challenges and lessons learned.