» Articles » PMID: 27654897

Simulation Modelling As a Tool for Knowledge Mobilisation in Health Policy Settings: a Case Study Protocol

Overview
Publisher Biomed Central
Date 2016 Sep 23
PMID 27654897
Citations 20
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Evidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making.

Methods/design: The study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants' engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change.

Discussion: The study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings.

Citing Articles

Stochastic simulation modeling of the economics of providing additional living space for housed dairy cows.

Thompson J, Hudson C, Huxley J, Kaler J, Green M Front Vet Sci. 2024; 11:1473696.

PMID: 39703408 PMC: 11656588. DOI: 10.3389/fvets.2024.1473696.


Guiding prevention initiatives by applying network analysis to systems maps of adverse childhood experiences and adolescent suicide.

Maldonado B, Schuerkamp R, Martin C, Rice K, Nataraj N, Brown M Netw Sci (Camb Univ Press). 2024; 12(3):234-260.

PMID: 39664320 PMC: 11633372. DOI: 10.1017/nws.2024.8.


The effectiveness of secondary-school based interventions on the future physical activity of adolescents in Aotearoa New Zealand: a modelling study.

Bergen T, Richards J, Kira G, Kim A, Signal L, Mizdrak A Int J Behav Nutr Phys Act. 2024; 21(1):114.

PMID: 39375727 PMC: 11460133. DOI: 10.1186/s12966-024-01653-z.


Impact of an innovative bundled payment to TB health care providers in China: an economic simulation analysis.

Xu P, Ying Y, Xu D, Huan S, Zhao L, Wang H BMC Health Serv Res. 2024; 24(1):577.

PMID: 38702650 PMC: 11069261. DOI: 10.1186/s12913-024-11034-8.


A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates.

Hrzic R, Cade M, Wong B, McCreesh N, Simon J, Czabanowska K J Public Health (Oxf). 2023; 46(1):127-135.

PMID: 38061776 PMC: 10901273. DOI: 10.1093/pubmed/fdad248.


References
1.
Brownson R, Royer C, Ewing R, McBride T . Researchers and policymakers: travelers in parallel universes. Am J Prev Med. 2006; 30(2):164-72. DOI: 10.1016/j.amepre.2005.10.004. View

2.
Rizzo T, Metzger B, Dooley S, Cho N . Early malnutrition and child neurobehavioral development: insights from the study of children of diabetic mothers. Child Dev. 1997; 68(1):26-38. View

3.
Atkinson J, Wells R, Page A, Dominello A, Haines M, Wilson A . Applications of system dynamics modelling to support health policy. Public Health Res Pract. 2015; 25(3):e2531531. DOI: 10.17061/phrp2531531. View

4.
Morisset A, St-Yves A, Veillette J, Weisnagel S, Tchernof A, Robitaille J . Prevention of gestational diabetes mellitus: a review of studies on weight management. Diabetes Metab Res Rev. 2009; 26(1):17-25. DOI: 10.1002/dmrr.1053. View

5.
Bellamy L, Casas J, Hingorani A, Williams D . Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet. 2009; 373(9677):1773-9. DOI: 10.1016/S0140-6736(09)60731-5. View