SUPPORTING POLICY DECISIONS UNDER UNCERTAINTY USING POPULATION MODELS – A SYSTEMATIC REVIEW

Purpose: To guide decision making for health or social care policies, population models have become a common tool that explicitly considers population dynamics or changes. Applications range from prediction of burden of disease, over demand for care to economic evaluations of specific treatments or public health interventions. In our project DEXHELPP (Decision Support for Health Policy and Planning), we reconsider definitions of population models and focus on suitable modeling techniques and methodological challenges. The goal of this systematic review is to increase the insight of health policy researchers in population modeling.

Method: We performed a systematic review on population models, focusing on the development and application for health policy questions. We identified existing models and systematically extracted information. The information was summarized in evidence tables and standardized narrative comparisons. We present goals, modeling techniques, general model characteristics, model specification, validation, calibration as well as advantages and shortcomings of chosen approaches.

Result: The term ‘population model’ is not used consistently. It refers to both models applied to study the dynamics of a population and models investigating the impact of interventions on the level of entire populations. Population models consider open (dynamic) rather than closed cohorts. In general, populations can be projected into the future using micro- or macro simulations, time can be continuous or discrete, and a modular structure can allow studying several diseases and applications. Comprehensive population models that have been applied for several research questions exist, for example, in Canada (POHEM), Sweden (SESIM), Australia (APPSIM) or Austria (GEPOC) or by OECD/WHO (CDP). The identified models are often microsimulation models. Reported challenges are: data shortage, calibration, complexity and related time and resource demands as well as quantifying uncertainty around model projections.

Conclusion: We identified several complex models with high quality, used for multiple research questions. The application of population models still requires better data, opportunities for data linkage and consistent reporting standards. Research should focus on continued methodological improvement for developing and applying complex population microsimulations.