Understanding the impact of relapsed lymphoid cancers

Project number: 
Approval date: 
Thursday, July 4, 2019
Principal Investigator: 
BC Cancer
Funding Agency: 
Not Available
Datasets requested: 
Deaths (BC Vital Statistics Agency)
Hospital Separations (BC Ministry of Health)
Consolidation file (BC Ministry of Health)
bc cancer-external
Medical Services Plan (BC Ministry of Health)
Consolidation - demographic (Ministry of Health)
consolidation - census geocodes
Research objective: 

The Research Questions specific to this DAR are as follows:

What is the pattern of resource utilization experienced by relapse lymphoma patients in current practice in British Columbia's health care system?
What is the cost impact and what are the potential cost savings from implementing cross-provincial personalized lymphoid cancer treatment - both at first diagnosis and at relapse?
What are some of the key drivers of cost and savings?
What impact would implementation of new bio marker assays at the point of relapse have on the health care budget?


Lymphoid cancer management guided by genomic profiling - by identifying subpopulations of patients that will not respond to certain types of drugs or other medical interventions - will lead to a reduction in health care expenditure on treatments from which patients do not derive benefit, avoiding unnecessary and expensive treatment.
Similarly, by identifying populations with particularly favorable responses to standard treatment, it may be possible to reduce treatment and treatment-associated costs without forfeiting treatment benefit.
the use of genomic markers may result in quality of life and survival benefits for patients, either by determining eligibility for novel medical interventions (i.e., drugs that target a specific gene expression pathway) or by identifying those who are in need of additional adjuvant care (e.g., intensified chemotherapy regimens).
Resource utilization of lymphoid cancers (specifically the four most common lymphoid cancers - diffuse large B cell lymphoma, follicular lymphoma, Hodgkin lymphoma, and chronic lymphocytic leukemia) will depend on factors such as age, sex, clinical stage, and other standard prognostic factors. For the purposes of this research study, we require an accurate description of costs experienced by comparable control groups for these four types of lymphoma cancer patients who have already undergone treatment and experienced relapse to establish a sound baseline of resource utilization patterns prior to implementation of these new genomic analytic tools. Economic modeling and statistical techniques will be applied during the model building and analysis stages to ensure we account for any uncertainties related to treatment and/ or costs of these new analytic techniques to current practice.

Page last revised: September 2, 2019