An Innovative Approach for Decision-Making on Designing Lifestyle Programs to Reduce Type 2 Diabetes on Dutch Population Level Using Dynamic Simulations

Abstract

The number of individuals suffering from type 2 diabetes is dramatically increasing worldwide, resulting in an increasing burden on society and rising healthcare costs. With increasing evidence supporting lifestyle intervention programs to reduce type 2 diabetes, and the use of scenario simulations for policy support, there is an opportunity to improve population interventions based upon cost–benefit analysis of especially complex lifestyle intervention programs through dynamic simulations. In this article, we used the System Dynamics (SD) modeling methodology aiming to develop a simulation model for policy makers and health professionals to gain a clear understanding of the patient journey of type 2 diabetes mellitus and to assess the impact of lifestyle intervention programs on total cost for society associated with prevention and lifestyle treatment of pre-diabetes and type 2 diabetes in The Netherlands. System dynamics describes underlying structure in the form of causal relationships, stocks, flows, and delays to explore behavior and simulate scenarios, in order to prescribe intervention programs. The methodology has the opportunity to estimate and simulate the consequences of unforeseen interactions in order to prescribe intervention programs based on scenarios tested through “what-if” experiments. First, the extensive knowledge of diabetes, current available data on the type 2 diabetes population, lifestyle intervention programs, and associated cost in The Netherlands were captured in one simulation model. Next, the relationships between leverage points on the growth of type 2 diabetes population were based upon available data. Subsequently, the cost and benefits of future lifestyle intervention programs on reducing diabetes were simulated, identifying the need for an integrated adaptive design of lifestyle programs while collecting the appropriate data over time. The strengths and limitations of scenario simulations of complex lifestyle intervention programs to improve the (cost)effectiveness of these programs to reduce diabetes in a more sustainable way compared to usual care are discussed.

Individual Workplace Well-Being Captured into a Literature- and Stakeholders-Based Causal Loop Diagram

“…In turn, this may provide new insights that can be used to design effective organizational workplace well-being promotion programs. In dynamic systems approaches, causal loop diagrams (CLDs) are used for mapping the multicausality of complex and dynamic issues, including bio-psychosocial issues within the health domain [ 25 , 36 , 96 , 97 ]. The aim of the current study was, therefore, to take a first step toward a systems approach by developing a conceptual causal loop diagram of individual workplace well-being.…”

“…This methodology can shed light on the consequences of unforeseen interactions based on “what-if” experiments. With respect to individual workplace well-being, such simulations can further enhance insight in the (individual) dynamics of workplace well-being and highlight the possible (or lack of) effectiveness of interventions [ 36 , 37 ]. Ultimately, these new insights could be used to determine which type of workplace well-being intervention would be most effective for whom.…”

The necessity of incorporating non-genetic risk factors into polygenic risk score models

“…Additionally, not all individuals that do have diabetes, will have received their diagnoses or have reported this to the UKB, causing noise in the data. Indeed, we do observe that the incidence of T2D is higher in Lifelines, compared to the UKB, despite the fact that the average age of Lifelines cohort is lower and that the overall diabetes prevalence in the UK (7%) 38 is similar to that in the Netherlands (6.6%) 39 . This is also despite the fact that follow-up annotation for the Lifelines cohort is shorter (approximately 8 years in Lifelines versus 12 years in the UKB).…”

Using system dynamics modelling to assess the economic efficiency of innovations in the public sector - a systematic review

“…For example, Ansah et al explored the health impact, costs, and cost savings of upstream and downstream interventions on the future number of chronic kidney disease and dialysis care patients in Singapore by 2040 [ 36 ]. Similarly, Sluijs et al developed an SD model for policy-makers to understand and assess the impact and cost-effectiveness of lifestyle intervention programs on type 2 diabetes in the Netherlands [ 37 ]. A heterogenous mix of public sector innovation types were evaluated across the studies.…”

“…DALYs consisted of Years of Life Lost (YLL) and Years of Life Lived with Disability (YLD), and the cost per averted DALY was based on a simplified calculation based on the total cost of intervention divided by the DALYs averted. Eight studies across the health, transport, water, housing, and energy sectors included cost-benefit analyses (CBA) [37,[51][52][53][54][55][56][57]. MacMillan et al, for example, compared the effects of policy innovations to increase bicycle commuting in Auckland through a participatory SD approach.…”