Canadian Partnership Against Cancer - Who We are

Transcription

1 Canadian Partnership Against Cancer - Who We are The Canadian Partnership Against Cancer is an independent organization funded by the federal government to accelerate action on cancer control for all Canadians. The Partnership s work spans the cancer control continuum, from prevention and screening to research and supportive care. Our collaborative approach is mindful of the patient voice, and strives to be culturally responsive to the needs of First Nations, Inuit and Métis communities.

2 Partnership goals 2

3 Measuring Long Term Impact Business case: act today, to reduce the risk cancer poses to Canadians in the future Both from a disease burden and economic perspective Need a method to demonstrate the impact of coordinated action on long term goals

4 Cancer Risk Management Model - Objectives Provide a shared platform that is a comprehensive, webbased, decision-support modeling platform that projects population-based health and economic impacts of a coordinated cancer control strategy to Canadians. Help inform policy decisions and reduce the burden of costs and disease posed by cancer on the population. Leverage the investment use it for the pan-canadian strategy but develop in a way to be useful for provinces

5 Cancer Risk Management Model Overview The Cancer Risk Management Model uses microsimulation to project population-based impacts of cancer control interventions in Canada. The objective of this modeling initiative is to develop an evaluative tool that would provide a shared platform to evaluate population health benefits and economic effects of cancer control interventions. Available online via a secure log-in, the model provides national and provincial cancer projections based on user s unique cancer control questions and user-defined variables. The model can be used by cancer control community to inform program planning that addresses current policy questions. 5

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7 CRMM: Microsimulation overview Various statistical methods and data used to estimate equations and synthesize individual processes into one large system Data sources Output of simulation Data analysis (SAS, Stata, etc) Model assessment compare to data sources used to build model (internal) and to other data sources (external); Learn: identify data inconsistencies and data gaps Simulation process Dynamic models and Parameters Hazards (birth, death, immigration) Incidence rates Risk equations (regression equations) Survival distributions (Weibull, Exponential) Transition probabilities (Markov chains, joint dist.) Distributions (education, income ) Input to simulation Aggregate results for population (by sex, age, region, year ) Track individual trajectories Functional form of dynamic models programmed into Microsimulation Aging of person, subject to dynamic models Aggregate data Individual-level data 7

8 How does microsimulation work?s microsimulation work? Large, representative sample population, aged from birth to death Age 0 Age 1 Age 15 Age 56 Distant recurrence Terminal Care Male Starts smoking Cumulative cigarette smoking Increased risk of lung cancer Treatment Remission Trt On-going care Lung cancer diagnosed at age 56.8 Stage II Method: (1) Calculate Probability : Agesex specific incidence rate multiplied by relative risk associated with smoking and Radon (2) Draw random number (3) If Random number less than Probability, cancer is diagnosed Case- Fatality 8

9 How does microsimulation work? microsimulation work? Large, representative sample population, aged from birth to death Age 0 Age 1 Age x Age y Male Female Add up individual lives to report population level results Model calibrated to observed Canadian incidence, prevalence and mortality data Death Weighted to full Canadian population 9