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Simulation as a Strategy for Linking Epidemiologic Data to Health Policy Decisions

Background:
Epidemiologic estimates for mood and anxiety disorders have been available for several decades, but using this information to improve population mental health is a difficult challenge. In distinction to traditional epidemiologic methods (e.g. estimation of population parameters), simulation involves developing a representation of the epidemiology, allowing exploration of “what if” scenarios reflecting alternative policy options. A simulation model representing the epidemiology of mood and anxiety disorders in Canada has recently been developed and played a role in shaping Canada’s recently published national mental health strategy.

Methods:
The modeling was carried out by RiskAnalytica using their “Life at Risk” platform. Specification and calibration of the model occurred in consultation with national and international experts. Data sources included demographic data, literature reviews, published estimates, supplementary analysis of survey data and meta-analysis.

Results:
Reconciliation of incidence and prevalence data required representation of recall bias in the model. This suggests that population surveys may underestimate lifetime prevalence and that cross-sectional data may provide misleading impressions about secular trends in prevalence. Increases in the absolute number, but not percentage, of Canadians with mood and anxiety disorders are projected in upcoming decades as a result of population growth. These increases will be most pronounced in elderly age groups.

Conclusions:
This “base” model provides an overview of mood and anxiety disorder epidemiology. Simulation models can act as a platform for generating economic analyses and epidemiologic projections. These can support the rapid exploration of “what if” scenarios and policy options, thereby informing policy decisions. "