POPULATION MODELS – A SYSTEMATIC REVIEW ILLUSTRATING MODEL CHARACTERISTICS AND APPLICATIONS
Saturday, January 9, 2016: 12:00
Kai Chong Tong Auditorium, G/F (Jockey Club School of Public Health and Primary Care Building at Prince of Wales Hospital)
Beate Jahn, PhD, UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, Hall in Tyrol, Austria, Annette Conrads-Frank, PhD, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria, Gaby Sroczynski, MPH, Dr.PH, Institute of Public Health, Medical Decision Making and HTA, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T./Innsbruck, Austria, Ursula Rochau, MD, MSc, UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and HTA, Department of Public Health and HTA/ ONCOTYROL - Center for Personalized Cancer Medicine, Area 4 HTA and Bioinformatics, Hall in Tyrol/ Innsbruck, Austria, Günther Zauner, PhD, dwh GmbH, simulation services / DEXHELPP (Decision Support for Health Policy and Planning), Vienna, Austria, Michael Gyimesi, MSc, Austrian Public Health Institute, Vienna, Austria, Niki Popper, PhD, dwh Simulation Services /Technical University Vienna, Institute for Analysis and Scientific Computing / DEXHELPP (Decision Support for Health Policy and Planning), Vienna, Austria and Uwe Siebert, Prof., MD, MPH, MSc, ScD, UMIT, Dept. Public Health&HTA/ ONCOTYROL, Area 4 HTA&Bioinformatics/ Harvard T.H. Chan School Public Health, Center for Health Decision Science, Dept. Health Policy&Management/ Harvard Medical School, Institute for Technology Assessment&Dept. Radiology, Hall in Tyrol/ Innsbruck/ Boston, Austria
Purpose: Population models have become a common tool to explicitly consider population dynamics or changes when guiding decision making for health or social care policies. Applications range from prediction of burden of disease, over demand for (old age) care to economic evaluations of specific treatments or public health interventions. In our project DEXHELPP (Decision Support for Health Policy and Planning), we focus on population models, suitable modelling techniques and methodological challenges. The goal of this systematic review is to increase the insight of health policy researchers in population modelling.
Method(s): 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 structured information. The information was summarized in evidence tables and narrative comparisons. We present goals, modeling techniques, general model characteristics, model specification, model parameter estimation as well as advantages and shortcomings of chosen approaches.
Result(s): The term ‘population model’ is not used consistently. It refers to both models applied to study population dynamics and models investigating the impact of interventions on 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).
The found models are often microsimulation models. Reported challenges are: data shortage, calibration, complexity and related time and resource demands as well as difficulties understanding the outcomes. Successful microsimulation projects require continuity and resources to allow multiple applications and updates on a long perspective.
Conclusion(s):
Population models are applied to inform health policy decisions. Applications still require better data, opportunities for data linkage and long-term perspectives of funding. Research should focus on continued methodological improvement for developing and applying complex population microsimulations.
The research project DEXHELPP (Decision Support for Health Policy and Planning: Methods, Models and Technologies based on Existing Health Care Data) is in the frame of COMET-Competence Centers for Excellent Technologies. DEXHELPP is supported by BMVIT, BMWFW and the state Vienna. The COMET program is transacted by the FFG.
