Applications of system dynamics modelling to support health policy

Atkinson, Joan; Wells, Robert; Page, Andrew; Dominello, Amanda; Haines, Mary; Wilson, Andrew

doi:10.17061/phrp2531531

Cited by 97 publications

(131 citation statements)

References 22 publications

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“…7 These methods have a long history in engineering, economics, defence and ecology, and have more recently been useful in appropriately characterising the complex nature of the health sector -including evaluating operational aspects of healthcare capacity and demand, patient flows, and disease screening 7 , as well as a range of disease risk factors and outcomes. 6,9,10 An SD approach has not previously been applied to the topic of suicide; however, the complex aetiology of suicidal behaviour and the range of potentially interacting prevention strategies suggest that this approach may be useful to inform policy responses.…”

Section: A System Dynamics Modelmentioning

confidence: 99%

“…The time, scope and rigour required for conducting research to implement and evaluate combined multilevel approaches to suicide prevention using traditional epidemiological approaches often do not match with the priorities and timelines of service providers, policy makers and local communities, who usually require local or context-specific information. 6,7 Although there is some evidence for effective prevention strategies in particular contexts 8 , it is unlikely that a single intervention will have populationlevel impacts on suicide rates, given the interplay of aetiological factors associated with suicidal behaviour within a complex health and social services system. Interventions that may have promising effects on preventing suicide in one context may not be generalisable to other contexts, and their likely effects over time and in combination are unknown.…”

Section: Introductionmentioning

confidence: 99%

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A decision-support tool to inform Australian strategies for preventing suicide and suicidal behaviour

Page¹,

Atkinson²,

Heffernan³

et al. 2017

Public Health Res Pract

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Dynamic simulation modelling is increasingly being recognised as a valuable decision-support tool to help guide investments and actions to address complex public health issues such as suicide. In particular, participatory system dynamics (SD) modelling provides a useful tool for asking high-level 'what if' questions, and testing the likely impacts of different combinations of policies and interventions at an aggregate level before they are implemented in the real world.We developed an SD model for suicide prevention in Australia, and investigated the hypothesised impacts over the next 10 years (2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025) of a combination of current intervention strategies proposed for population interventions in Australia: 1) general practitioner (GP) training, 2) coordinated aftercare in those who have attempted suicide, 3) school-based mental health literacy programs, 4) brief-contact interventions in hospital settings, and 5) psychosocial treatment approaches.Findings suggest that the largest reductions in suicide were associated with GP training (6%) and coordinated aftercare approaches (4%), with total reductions of 12% for all interventions combined. This paper highlights the value of dynamic modelling methods for managing complexity and uncertainty, and demonstrates their potential use as a decision-support tool for policy makers and program planners for community suicide prevention actions.

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Section: A System Dynamics Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A decision-support tool to inform Australian strategies for preventing suicide and suicidal behaviour

Page¹,

Atkinson²,

Heffernan³

et al. 2017

Public Health Res Pract

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“…The system dynamics emphasizes understanding the structure of a system is as important as understanding each component of the system. [20][21][22][23] System dynamics model can integrate a wide range of variables that affect dengue fever and calculate the behavior of each variable in the tested model. Simulations using the model will be very useful to provide a description of the intervention that should be done based on the most appropriate place, time, and selection of method before it is applied on the field.…”

Section: -13mentioning

confidence: 99%

Ecohealth System Dynamic Model as a Planning Tool for the Reduction of Breeding Sites

Respati

Raksanagara

Djuhaeni

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract.Dengue is still one of major health problem in Indonesia. Dengue transmission is influenced by dengue prevention and eradication program, community participation, housing environment and climate. The complexity of the disease coupled with limited resources necessitates different approach for prevention methods that include factors contribute to the transmission. One way to prevent the dengue transmission is by reducing the mosquito's breeding sites. Four factors suspected to influence breeding sites are dengue prevention and eradication program, community participation, housing environment, and weather condition. In order to have an effective program in reducing the breeding site it is needed to have a model which can predict existence of the breeding sites while the four factors under study are controlled. The objective of this study is to develop an Ecohealth model using system dynamic as a planning tool for the reduction of breeding sites to prevent dengue transmission with regard to dengue prevention and eradication program, community participation, housing environment, and weather condition. The methodology is a mixed method study using sequential exploratory design. The study comprised of 3 stages: first a qualitative study to 14 respondents using in-depth interview and 6 respondents for focus group discussion. The results from the first stage was used to develop entomology and household survey questionnaires for second stage conducted in 2036 households across 12 sub districts in Bandung City. Ecohealth system dynamic model was developed using data from first and second stages. Analyses used are thematic analysis for qualitative data; spatial, generalized estimating equation (GEE) and structural equation modeling for quantitative data; also average mean error (AME) and average variance error (AVE) for dynamic system model validation. System dynamic model showed that the most effective approach to eliminate breeding places was by ensuring the availability of basic sanitation for all houses. Weather factors such as precipitation can be compensated with the eradication of breeding sites activities which is conducted as scheduled and at the same time for the whole areas. Conclusion of this study is that dengue prevention and eradication program, community participation, and housing environment contributed to breeding places elimination influenced the existence of the breeding sites. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd basic sanitation and breeding places eradication program done timely and collectively are the most effective approach to eradicate breeding sites. Ecohealth dynamic system model can be used as a tool for the planning of breeding sites eradication program to prevent disease transmissions at city level.

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“…Participatory model development also serves to engage stakeholders and potential end‐users – improving communication and garnering broader support for collaborative action based on model outputs 21, 22, 23, 24, 25. While models of alcohol consumption behaviour have been developed 26, 27, 28, 29, a participatory approach to the application of agent‐based simulation modelling to estimate the impact over time of different trading hour policies has not been previously reported.…”

Section: Introductionmentioning

confidence: 99%

Impacts of licensed premises trading hour policies on alcohol‐related harms

et al. 2018

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Background and aimEvaluations of alcohol policy changes demonstrate that restriction of trading hours of both ‘on’‐ and ‘off’‐licence venues can be an effective means of reducing rates of alcohol‐related harm. Despite this, the effects of different trading hour policy options over time, accounting for different contexts and demographic characteristics, and the common co‐occurrence of other harm reduction strategies in trading hour policy initiatives, are difficult to estimate. The aim of this study was to use dynamic simulation modelling to compare estimated impacts over time of a range of trading hour policy options on various indicators of acute alcohol‐related harm.MethodsAn agent‐based model of alcohol consumption in New South Wales, Australia was developed using existing research evidence, analysis of available data and a structured approach to incorporating expert opinion. Five policy scenarios were simulated, including restrictions to trading hours of on‐licence venues and extensions to trading hours of bottle shops. The impact of the scenarios on four measures of alcohol‐related harm were considered: total acute harms, alcohol‐related violence, emergency department (ED) presentations and hospitalizations.ResultsSimulation of a 3 a.m. (rather than 5 a.m.) closing time resulted in an estimated 12.3 ± 2.4% reduction in total acute alcohol‐related harms, a 7.9 ± 0.8% reduction in violence, an 11.9 ± 2.1% reduction in ED presentations and a 9.5 ± 1.8% reduction in hospitalizations. Further reductions were achieved simulating a 1 a.m. closing time, including a 17.5 ± 1.1% reduction in alcohol‐related violence. Simulated extensions to bottle shop trading hours resulted in increases in rates of all four measures of harm, although most of the effects came from increasing operating hours from 10 p.m. to 11 p.m.ConclusionsAn agent‐based simulation model suggests that restricting trading hours of licensed venues reduces rates of alcohol‐related harm and extending trading hours of bottle shops increases rates of alcohol‐related harm. The model can estimate the effects of a range of policy options.

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Applications of system dynamics modelling to support health policy

Cited by 97 publications

References 22 publications

A decision-support tool to inform Australian strategies for preventing suicide and suicidal behaviour

A decision-support tool to inform Australian strategies for preventing suicide and suicidal behaviour

Ecohealth System Dynamic Model as a Planning Tool for the Reduction of Breeding Sites

Impacts of licensed premises trading hour policies on alcohol‐related harms

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