Use of System Dynamics Modelling for Evidence-Based Decision Making in Public Health Practice

Author:

George Abraham1ORCID,Badrinath Padmanabhan1,Lacey Peter2,Harwood Chris2,Gray Alex2,Turner Paul2,Springer Davinia1

Affiliation:

1. Public Health Department, Kent County Council, Sessions House, County Road, Maidstone ME14 1XQ, Kent, UK

2. Whole Systems Partnership, 8 York Place, Knaresborough HG5 0AA, North Yorkshire, UK

Abstract

In public health, the routine use of linear forecasting, which restricts our ability to understand the combined effects of different interventions, demographic changes and wider health determinants, and the lack of reliable estimates for intervention impacts have limited our ability to effectively model population needs. Hence, we adopted system dynamics modelling to forecast health and care needs, assuming no change in population behaviour or determinants, then generated a “Better Health” scenario to simulate the combined impact of thirteen interventions across cohorts defined by age groups and diagnosable conditions, including “no conditions”. Risk factors for the incidence of single conditions, progression toward complex needs and levels of morbidity including frailty were used to create the dynamics of the model. Incidence, prevalence and mortality for each cohort were projected over 25 years with “do nothing” and “Better Health” scenarios. The size of the “no conditions” cohort increased, and the other cohorts decreased in size. The impact of the interventions on life expectancy at birth and healthy life expectancy is significant, adding 5.1 and 5.0 years, respectively. We demonstrate the feasibility, applicability and utility of using system dynamics modelling to develop a robust case for change to invest in prevention that is acceptable to wider partners.

Publisher

MDPI AG

Subject

Information Systems and Management,Computer Networks and Communications,Modeling and Simulation,Control and Systems Engineering,Software

Reference61 articles.

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