Dataset Information

The societal costs and benefits of commuter bicycling: simulating the effects of specific policies using system dynamics modeling.

ABSTRACT:

Background

Shifting to active modes of transport in the trip to work can achieve substantial co-benefits for health, social equity, and climate change mitigation. Previous integrated modeling of transport scenarios has assumed active transport mode share and has been unable to incorporate acknowledged system feedbacks.

Objectives

We compared the effects of policies to increase bicycle commuting in a car-dominated city and explored the role of participatory modeling to support transport planning in the face of complexity.

Methods

We used system dynamics modeling (SDM) to compare realistic policies, incorporating feedback effects, nonlinear relationships, and time delays between variables. We developed a system dynamics model of commuter bicycling through interviews and workshops with policy, community, and academic stakeholders. We incorporated best available evidence to simulate five policy scenarios over the next 40 years in Auckland, New Zealand. Injury, physical activity, fuel costs, air pollution, and carbon emissions outcomes were simulated.

Results

Using the simulation model, we demonstrated the kinds of policies that would likely be needed to change a historical pattern of decline in cycling into a pattern of growth that would meet policy goals. Our model projections suggest that transforming urban roads over the next 40 years, using best practice physical separation on main roads and bicycle-friendly speed reduction on local streets, would yield benefits 10-25 times greater than costs.

Conclusions

To our knowledge, this is the first integrated simulation model of future specific bicycling policies. Our projections provide practical evidence that may be used by health and transport policy makers to optimize the benefits of transport bicycling while minimizing negative consequences in a cost-effective manner. The modeling process enhanced understanding by a range of stakeholders of cycling as a complex system. Participatory SDM can be a helpful method for integrating health and environmental outcomes in transport and urban planning.

SUBMITTER: Macmillan A

PROVIDER: S-EPMC3984216 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The societal costs and benefits of commuter bicycling: simulating the effects of specific policies using system dynamics modeling.

Macmillan Alexandra A Connor Jennie J Witten Karen K Kearns Robin R Rees David D Woodward Alistair A

Environmental health perspectives 20140204 4

<h4>Background</h4>Shifting to active modes of transport in the trip to work can achieve substantial co-benefits for health, social equity, and climate change mitigation. Previous integrated modeling of transport scenarios has assumed active transport mode share and has been unable to incorporate acknowledged system feedbacks.<h4>Objectives</h4>We compared the effects of policies to increase bicycle commuting in a car-dominated city and explored the role of participatory modeling to support tran ...[more]

PMID: 24496244

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Project description:BackgroundIncreasing urban bicycling has established net benefits for human and environmental health. Questions remain about which policies are needed and in what order, to achieve an increase in cycling while avoiding negative consequences. Novel ways of considering cycling policy are needed, bringing together expertise across policy, community and research to develop a shared understanding of the dynamically complex cycling system. In this paper we use a collaborative learning process to develop a dynamic causal model of urban cycling to develop consensus about the nature and order of policies needed in different cycling contexts to optimise outcomes.MethodsWe used participatory system dynamics modelling to develop causal loop diagrams (CLDs) of cycling in three contrasting contexts: Auckland, London and Nijmegen. We combined qualitative interviews and workshops to develop the CLDs. We used the three CLDs to compare and contrast influences on cycling at different points on a "cycling trajectory" and drew out policy insights.ResultsThe three CLDs consisted of feedback loops dynamically influencing cycling, with significant overlap between the three diagrams. Common reinforcing patterns emerged: growing numbers of people cycling lifts political will to improve the environment; cycling safety in numbers drives further growth; and more cycling can lead to normalisation across the population. By contrast, limits to growth varied as cycling increases. In Auckland and London, real and perceived danger was considered the main limit, with added barriers to normalisation in London. Cycling congestion and "market saturation" were important in the Netherlands.ConclusionsA generalisable, dynamic causal theory for urban cycling enables a more ordered set of policy recommendations for different cities on a cycling trajectory. Participation meant the collective knowledge of cycling stakeholders was represented and triangulated with research evidence. Extending this research to further cities, especially in low-middle income countries, would enhance generalizability of the CLDs.

Project description:BackgroundA hip fracture is a debilitating condition that consumes significant resources in the United States. Surgical treatment of hip fractures can achieve better survival and functional outcomes than nonoperative treatment, but less is known about its economic benefits.Questions/purposesWe asked: (1) Are the societal benefits of hip fracture surgery enough to offset the direct medical costs? (2) Nationally, what are the total lifetime benefits of hip fracture surgery for a cohort of patients and to whom do these benefits accrue?MethodsWe estimated the effects of surgical treatment for displaced hip fractures through a Markov cohort analysis of patients 65 years and older. Assumptions were obtained from a systematic literature review, analysis of Medicare claims data, and clinical experts. We conducted a series sensitivity analyses to assess the effect of uncertainty in model parameters on our estimates. We compared costs for medical care, home modification, and long-term nursing home use for surgical and nonoperative treatment of hip fractures to estimate total societal savings.ResultsEstimated average lifetime societal benefits per patient exceeded the direct medical costs of hip fracture surgery by USD 65,000 to USD 68,000 for displaced hip fractures. With the exception of the assumption of nursing home use, the sensitivity analyses show that surgery produces positive net societal savings with significant deviations of 50% from the base model assumptions. For an 80-year-old patient, the breakeven point for the assumption on the percent of patients with hip fractures who would require long-term nursing home use with nonoperative treatment is 37% to 39%, compared with 24% for surgical patients. Nationally, we estimate that hip fracture surgery for the cohort of patients in 2009 yields lifetime societal savings of USD 16 billion in our base model, with benefits and direct costs of USD 21 billion and USD 5 billion, respectively. For an 80-year-old, societal benefits ranged from USD 2 billion to USD 32 billion, using our range of estimates for nursing home use among nonoperatively treated patients who are immobile after the fracture.ConclusionsSurgical treatment of hip fractures produces societal savings. Although the magnitude of these savings depends on model assumptions, the finding of societal savings is robust to a range of parameter values.Level of evidenceLevel III, economic and decision analyses. See the Instructions for Authors for a complete description of levels of evidence.

Dataset Information

The societal costs and benefits of commuter bicycling: simulating the effects of specific policies using system dynamics modeling.

Background

Objectives

Methods

Results

Conclusions

Publications

The societal costs and benefits of commuter bicycling: simulating the effects of specific policies using system dynamics modeling.

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