Understanding bicycling in cities using system dynamics modelling

More People, More Active, More Often for Heart Health - Taking Action on Physical Activity

Physical inactivity is a leading contributor to increased cardiovascular morbidity and mortality. Almost 500 million new cases of preventable noncommunicable diseases (NCDs) will occur globally between 2020 and 2030 due to physical inactivity, costing just over US$300 billion, or around US$ 27 billion annually (WHO 2022). Active adults can achieve a reduction of up to 35% in risk of death from cardiovascular disease. Physical activity also helps in moderating cardiovascular disease risk factors such as high blood pressure, unhealthy weight and type 2 diabetes. For people with cardiovascular disease, hypertension, type 2 diabetes and many cancers, physical activity is an established and evidence-based part of treatment and management. For children and young people, physical activity affords important health benefits. Physical activity can also achieve important cross-sector goals. Increased walking and cycling can reduce journeys by vehicles, air pollution, and traffic congestion and contribute to increased safety and liveability in cities.

Benefits, risks, barriers, and facilitators to cycling: a narrative review

There is large potential to increase cycling participation worldwide. Participation in cycling is associated with lower risk of mortality from any cause, and incidence of cardiovascular disease and type 2 diabetes, as well as positive mental health and well-being. The largest potential for health gains likely to come from increasing participation amongst those who do not currently cycle regularly, rather than encouraging those who already cycle regularly to cycle more. Replacing car journeys with cycling can lead to reductions in air pollution emissions and lower pollutant exposure to the general population. Important gaps and uncertainties in the existing evidence base include: the extent to which the health benefits associated with cycling participation are fully causal due to the observational nature of much of the existing evidence base; the real-world economic cost-benefits of pragmatic interventions to increase cycling participation; and the most effective (combination of) approaches to increase cycling participation. To address these uncertainties, large-scale, long-term randomised controlled trials are needed to: evaluate the effectiveness, and cost-effectiveness, of (combinations of) intervention approaches to induce sustained long-term increases in cycling participation in terms of increases in numbers of people cycling regularly and number of cycling journeys undertaken, across a range of population demographic groups; establish the effects of such interventions on relevant outcomes related to health and wellbeing, economic productivity and wider societal impacts; and provide more robust quantification of potential harms of increasing cycling participation, such as collision risks.

Sabounchi N. Application of group model building in implementation research: A systematic review of the public health and healthcare literature

BACKGROUND

Group model building is a process of engaging stakeholders in a participatory modeling process to elicit their perceptions of a problem and explore concepts regarding the origin, contributing factors, and potential solutions or interventions to a complex issue. Recently, it has emerged as a novel method for tackling complex, long-standing public health issues that traditional intervention models and frameworks cannot fully address. However, the extent to which group model building has resulted in the adoption of evidence-based practices, interventions, and policies for public health remains largely unstudied. The goal of this systematic review was to examine the public health and healthcare applications of GMB in the literature and outline how it has been used to foster implementation and dissemination of evidence-based interventions.

METHODS

We searched PubMed, Web of Science, and other databases through August 2022 for studies related to public health or health care where GMB was cited as a main methodology. We did not eliminate studies based on language, location, or date of publication. Three reviewers independently extracted data on GMB session characteristics, model attributes, and dissemination formats and content.

RESULTS

Seventy-two studies were included in the final review. Majority of GMB activities were in the fields of nutrition (n = 19, 26.4%), health care administration (n = 15, 20.8%), and environmental health (n = 12, 16.7%), and were conducted in the United States (n = 29, 40.3%) and Australia (n = 7, 9.7%). Twenty-three (31.9%) studies reported that GMB influenced implementation through policy change, intervention development, and community action plans; less than a third reported dissemination of the model outside journal publication. GMB was reported to have increased insight, facilitated consensus, and fostered communication among stakeholders.

CONCLUSIONS

GMB is associated with tangible benefits to participants, including increased community engagement and development of systems solutions. Transdisciplinary stakeholder involvement and more rigorous evaluation and dissemination of GMB activities are recommended.

Participatory Causal Loop Mapping of the Adoption of Organic Farming in Nigeria

Contrary to the expectations of promoters of organic agriculture, the adoption of the technology by smallholder farmers in Africa has been low and slow, for reasons not well understood. Existing studies on the topic mostly estimated the effect of some variables on the adoption of the technology. But adoption is characterized by complex and dynamic interactions of many interconnected factors, which existing studies overlooked. The underlying causal structures and feedback mechanisms that dynamically interact to affect the adoption of organic farming in urban and rural Africa are also not well known. To bridge these gaps, we used a system dynamics tool called participatory causal loop diagraming to map the underlying causal factors and feedback mechanisms driving the adoption of organic farming in rural and urban Nigeria. We conducted loop and network analyses of the group causal loop diagrams, which were created during the participatory system dynamics modeling workshops with the organic farmers in our study areas. Our findings underscore the importance of the knowledge of organic farming, demand- and supply-side-oriented awareness creation, and the economic viability of organic farming for widespread adoption of the technology. We suggested the potential leverages around which interventions can be built to boost the adoption rates of the technology.

Systems science for developing policy to improve physical activity, the Caribbean

The World Health Organization (WHO) Global Action Plan on Physical Activity recommends adopting a systems approach to implementing and tailoring actions according to local contexts. We held group model-building workshops with key stakeholders in the Caribbean region to develop a causal loop diagram to describe the system driving the increasing physical inactivity in the region and envision the most effective ways of intervening in that system to encourage and promote physical activity. We used the causal loop diagram to inform how the WHO Global Action Plan on Physical Activity might be adapted to a local context. Although the WHO recommendations aligned well with our causal loop diagram, the diagram also illustrates the importance of local context in determining how interventions should be coordinated and implemented. Some interventions included creating safe physical activity spaces for both sexes, tackling negative attitudes to physical activity in certain contexts, including in schools and workplaces, and improving infrastructure for active transport. The causal loop diagram may also help understand how policies may be undermined or supported by key actors or where policies should be coordinated. We demonstrate how, in a region with a high level of physical inactivity and low resources, applying systems thinking with relevant stakeholders can help the targeted adaptation of global recommendations to local contexts.

Suburb-level changes for active transport to meet the SDGs: Causal theory and a New Zealand case study

The 2030 Agenda for Sustainable Development and its Sustainable Development Goals (SDGs) represent a historic global linking of health, equity and environmental sustainability. Accumulated evidence suggests that improving urban neighbourhoods to make them safer and more attractive for walking and cycling can accelerate progress towards the SDGs. The pathways to change are complex, non-linear and involve multiple pathways and multiple SDG outcomes, yet the SDG goals are often considered in isolation. Further, there have been few studies of environmental interventions for healthier transport that foreground equity. The aim of this paper is to describe and demonstrate practically how integrated interventions for placemaking and active transport can contribute to a wide range of SDG targets. First, we take an evidence-based approach to describing how such interventions are connected to targets within the SDGs. Second, we propose a complex causal theory of the pathways to change and the inter-relationships between SDGs. Third, we show, with concrete examples, how a case study project in Auckland, New Zealand illustrates these pathways, contributing to achieving the SDG targets, including barriers and challenges. We find that by addressing Goal 11 in particular ways that focus on equity (Goal 10), eight of the other goals can also be advanced. Our causal theory describes one balancing and 12 reinforcing patterns of behaviour that link interventions improvements to neighbourhoods with ten of the SDGs in a complex system. Our case study demonstrates that it is possible to successfully put this causal theory into practice through interventions, but these require strong partnerships between researchers, public health practitioners, policy-makers and communities, long-term evaluation and addressing both physical and social environments.

Integrating complex systems science into road safety research and practice, part 1: review of formative concepts

Many of our most persistent public health problems are complex problems. They arise from a web of factors that interact and change over time and may exhibit resistance to intervention efforts. The domain of systems science provides several tools to help injury prevention researchers and practitioners examine deep, complex and persistent problems and identify opportunities to intervene. Using the increase in pedestrian death rates as an example, we provide (1) an accessible overview of how complex systems science approaches can augment established injury prevention frameworks and (2) a straightforward example of how specific systems science tools can deepen understanding, with a goal of ultimately informing action.

Systems thinking in the context of road safety: Can systems tools help us realize a true "Safe Systems" approach?

Purpose of review

Road traffic injuries are one of the leading causes of death in the U.S. and globally. We introduce the Safe Systems approach as a promising paradigm for road safety practice and describe how systems thinking tools can help bridge the gap between the current status quo and a Safe Systems approach.

Recent findings

Systems thinking tools can help us align with a Safe Systems approach by identifying latent risks in the transportation system, examining factors that coalesce to produce high travel speeds and kinetic energy transfer, and supporting safety prioritization through goal alignment.

Summary

The Safe Systems approach represents a significant change in the way we have historically designed transportation systems; it puts safety at the forefront and calls for designing a system that accounts for human fallibility. Operationalizing holistic Safe Systems concepts may be difficult, but systems thinking tools can help. Systems thinking tools provide a common language for individuals from diverse disciplines and sectors to express their unique understanding of the interconnected factors shaping road safety problems and support discussions about potential solutions that align with a Safe Systems approach.

System dynamics applications to injury and violence prevention: a systematic review

Purpose of review

System dynamics (SD) is an approach to solving problems in the context of dynamic complexity. The purpose of this review was to summarize SD applications in injury prevention and highlight opportunities for SD to contribute to injury prevention research and practice.

Recent findings

While SD has been increasingly used to study public health problems over the last few decades, uptake in the injury field has been slow. We identified 18 studies, mostly conducted in the last 10 years. Applications covered a range of topics (e.g., road traffic injury; overdose; violence), employed different types of SD tools (i.e., qualitative and quantitative), and served a variety of research and practice purposes (e.g., deepen understanding of a problem, policy analysis).

Summary

Given the many ways that SD can add value and complement traditional research and practice approaches (e.g., through novel stakeholder engagement and policy analysis tools), increased investment in SD-related capacity building and opportunities that support SD use are warranted.

Development of the Impacts of Cycling Tool (ICT): A modelling study and web tool for evaluating health and environmental impacts of cycling uptake

BACKGROUND

A modal shift to cycling has the potential to reduce greenhouse gas emissions and provide health co-benefits. Methods, models, and tools are needed to estimate the potential for cycling uptake and communicate to policy makers the range of impacts this would have.

METHODS AND FINDINGS

The Impacts of Cycling Tool (ICT) is an open source model with a web interface for visualising travel patterns and comparing the impacts of different scenarios of cycling uptake. It is currently applied to England. The ICT allows users to visualise individual and trip-level data from the English National Travel Survey (NTS), 2004-2014 sample, 132,000 adults. It models scenarios in which there is an increase in the proportion of the population who cycle regularly, using a distance-based propensity approach to model which trips would be cycled. From this, the model estimates likely impact on travel patterns, health, and greenhouse gas emissions. Estimates of nonoccupational physical activity are generated by fusing the NTS with the English Active People Survey (APS, 2013-2014, 559,515 adults) to create a synthetic population. Under 'equity' scenarios, we investigate what would happen if cycling levels increased equally among all age and gender categories, as opposed to in proportion to the profile of current cyclists. Under electric assist bike (pedelecs or 'e-bike') scenarios, the probability of cycling longer trips increases, based on the e-bike data from the Netherlands, 2013-2014 Dutch Travel Survey (50,868 adults).Outcomes are presented across domains including transport (trip duration and trips by mode), health (physical activity levels, years of life lost), and car transport-related CO2 emissions. Results can be visualised for the whole population and various subpopulations (region, age, gender, and ethnicity). The tool is available at www.pct.bike/ict. If the proportion of the English population who cycle regularly increased from 4.8% to 25%, then there would be notable reductions in car miles and passenger related CO2 emissions (2.2%) and health benefits (2.1% reduction in years of life lost due to premature mortality). If the new cyclists had access to e-bikes, then mortality reductions would be similar, while the reduction in car miles and CO2 emissions would be larger (2.7%). If take-up of cycling occurred equally by gender and age (under 80 years), then health benefits would be marginally greater (2.2%) but reduction in CO2 slightly smaller (1.8%). The study is limited by the quality and comparability of the input data (including reliance on self-report behaviours). As with all modelling studies, many assumptions are required and potentially important pathways excluded (e.g. injury, air pollution, and noise pollution).

CONCLUSION

This study demonstrates a generalisable approach for using travel survey data to model scenarios of cycling uptake that can be applied to a wide range of settings. The use of individual-level data allows investigation of a wide range of outcomes, and variation across subgroups. Future work should investigate the sensitivity of results to assumptions and omissions, and if this varies across setting.