- Microsimulation in health
- Population health model
- The Cancer Risk Management Model
- STAR team project
- Physical activity models
- Neurological disease models
The Health Analysis Division of Statistics Canada is a pioneer of policy-relevant health-related computer simulation models. These models are tools to evaluate the impact of health interventions and policies at the population level. No single data source can ever be expected to provide enough information about treatment options, health outcomes, equity and cost-effectiveness when choices have to be made between and among different policy and program interventions.
Drawing from the rich banks of data within Statistics Canada, these microsimulation models realistically represent the Canadian population with attributes such as risk factor exposures, health histories and demographic characteristics typical of Canadians. The models simulate histories for individual persons in continuous time and add the individuals up to create aggregate results for the total population. The models generate realistic future projections of status quo trends and provide users with the ability to test “what if scenarios” related to potential policy and program interventions.
The Population Health Model (POHEM) is a microsimulation model of diseases and risk factors in which the basic unit of analysis is the individual person. The simulation creates and ages a large sample population representative of Canada, one individual at a time, until death. The life trajectory of each simulated person unfolds by exposure to different life-like events, such as smoking initiation and cessation, changes in weight, and incidence and progression of diseases such as osteoarthritis, cancer, diabetes and heart disease.
POHEM combines data from a wide range of sources, including nationally representative cross-sectional and longitudinal surveys, cancer registries, hospitalization databases, vital statistics, Census, treatment cost data as well as parameters in the published literature.
The model inputs may also be altered at the user's request to investigate ‘what if‘ scenarios. These scenarios can be very useful for policy makers, by providing information beyond what is available from retrospective population studies.
Earlier versions of POHEM were used to estimate lifetime costs of breast and colorectal cancer, as well as assessments of health technology on cancer control, such as chemotherapy options for advanced stage lung cancer, use of preventive Tamoxifen on Canadian women, and impact of population-based colorectal cancer screening.
More recent generations of POHEM models have been developed for other common diseases such as osteoarthritis, acute myocardial infarction and diabetes, as well as for disease risk factors such as obesity and physical inactivity. The risk factor modules enable users to simulate the impacts of the changes of obesity or physical activities on key health outcomes.
Statistics Canada was selected by the Canadian Partnership Against Cancer to develop the Cancer Risk Management Platform. The platform is a sophisticated, state-of-the-art, web-enabled microsimulation model designed for direct and easy use by cancer control and other health policy decision makers. The model can evaluate cancer control strategies such as prevention, screening and treatment for two leading cancers, lung and colorectal. The model can compare 20-year projections of incidence, mortality, resource needs, direct health care costs and broader economic impacts, such as lost wages. The platform will be further developed in 2010-11 and 2011-12 to include models of cervical and breast cancer.
The STAR team is a Canadian network of health modellers and decision makers. The objective is to apply simulation technology to advance applied health services research and improve policy planning and decision-making in Canada. Key elements are the development of a set of validated simulation models of obesity-sensitive chronic diseases (cancer, heart disease, diabetes, and osteoarthritis), to use the models to project future health outcomes under different policy scenarios, and to lay the foundation for the first multi-disease multi-risk factor simulation model. Our research team is based on a partnership of leading investigators from three major academic centres (University of British Columbia, McGill University and University of Ottawa) and experts from Statistics Canada. The team is working with an advisory team of senior decision makers from federal and provincial health ministries, health regions and health organisations to establish key policy questions and to conduct demonstration projects.
Developed with the Public Health Agency of Canada, Statistics Canada has developed evidence-based simulation tools to study the impact of physical activity on population health outcomes, with the ability to test different scenarios.
The Physical Activity Simulation Static Model (PASSM) explores the physical activity associated with activities of daily life, with scope for increasing physical activity (given 24 hours per day) and observing resulting changes in mortality and life expectancy.
A dynamic model explores patterns of physical activity over the life course and their impacts on health. This model enables users to view the future impact on population health of different physical activity scenarios.
New models are under development with the Public Health Agency of Canada to project the future health and economic burden of key neurological diseases.
The first phase will be a dynamic model of Canadians with neurological diseases that will project the incidence and prevalence of key neurological conditions, the impact on mortality, life expectancy, disability-adjusted life years and health-adjusted life expectancy (HALE) and the direct costs of treatment and indirect costs (lost wages and tax revenue).
The second phase will extend the dynamic model to include impact on families and aregivers. It will examine the quality of life of family members and caregivers and the indirect economic costs associated with caregiving (lost wages, lost tax revenue).
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