Towards a dynamic description of major depression epidemiology

The substantial impact of major depression on population health is widely acknowledged. To date, health system responses to this condition have been largely shaped by observational findings. In the future, health policy decisions will benefit from an increasingly integrated and dynamic understanding of the epidemiology of this condition. Policy decisions can also be supported by the development of decision-support tools that can simulate the impact of alternative policy decisions on population health. Markov models are useful both in epidemiological modelling and in decision analysis.

In this project, a Markov model describing major depression epidemiology was developed. The model employed a Markov Tunnel in order to depict the dependence of recovery probabilities on episode duration. Transition probabilities, including incidence, recovery and mortality were estimated from Canadian national survey data.

Episode incidence was approximately 3% per year. Recovery rates declined exponentially over time. The model predicted point prevalence at slightly less than 1%, agreeing closely with observed prevalence data.

Epidemiological models describing the dynamic relationships between major depression incidence, prevalence, recovery and mortality can help to integrate available epidemiological data. Such models offer an attractive option for support of health policy decisions.