From causal loop diagrams to future scenarios: Using the cross-impact balance method to augment understanding of urban health in Latin America

Abstract

Urban health is shaped by a system of factors spanning multiple levels and scales, and through a complex set of interactions. Building on causal loop diagrams developed via several group model building workshops, we apply the cross-impact balance (CIB) method to understand the strength and nature of the relationships between factors in the food and transportation system, and to identify possible future urban health scenarios (i.e., permutations of factor states that impact health in cities). We recruited 16 food and transportation system experts spanning private, academic, non-government, and policy sectors from six Latin American countries to complete an interviewer-assisted questionnaire. The questionnaire, which was pilot tested on six researchers, used a combination of questions and visual prompts to elicit participants’ perceptions about the bivariate relationships between 11 factors in the food and transportation system. Each participant answered questions related to a unique set of relationships within their domain of expertise. Using CIB analysis, we identified 21 plausible future scenarios for the system. In the baseline model, ‘healthy’ scenarios (with low chronic disease, high physical activity, and low consumption of highly processed foods) were characterized by high public transportation subsidies, low car use, high street safety, and high free time, illustrating the links between transportation, free time and dietary behaviors. In analyses of interventions, low car use, high public transport subsidies and high free time were associated with the highest proportion of factors in a healthful state and with high proportions of ‘healthy’ scenarios. High political will for social change also emerged as critically important in promoting healthy systems and urban health outcomes. The CIB method can play a novel role in augmenting understandings of complex urban systems by enabling insights into future scenarios that can be used alongside other approaches to guide urban health policy planning and action.

Drivers, uncertainties, and future scenarios of the Iranian health system

“…Despite the complexity of the relations in the HS, a sectoral look at the system with paying too much attention to details lead to ambiguities and challenges in designing and implementing policies and achieving the HS’s final goal. While many similar studies in the HS have evaluated and measured the factors affecting each dimension of the HS [ 12 – 14 ], the current study has tried to present tangible images and different and believable stories of probable and compatible scenarios of the Iran HS’s future, while considering all dimensions and functions, such as financing, resources provision, providing services and stewardship, as well as considering the necessary features to adopt a future-oriented approach, including the complexity, ambiguity, uncertainty and the need for holism.…”

“…Many studies have examined the interactions among critical variables of the health sector. For example, identifying the interactions and communication networks among critical factors affecting direct out of pocket payments [ 13 ], Stankov et al’s study in 2019 aimed at identifying the factors affecting urban health through a systemic approach and using the cross-impact analysis matrix to understand the power and nature of the component’s relationships and to identify possible future urban health scenarios in Latin America [ 14 ], using the cross-impact analysis matrix to develop possible scenarios for hospital supply chain resilience [ 15 ], and as well as the use of interaction matrices to examine risk factors for diseases such as cardiovascular disease [ 16 ]. However, in the present study, the interaction of the components in all dimensions of the HS has been investigated.…”

Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis

“…This paper focuses on a novel, visually oriented approach for eliciting the expert knowledge about relationships between key factors in a system that must be collected as part of any CIB analysis. This approach is described by using a case study which applied CIB analysis to examine how the interrelated influence of food and transportation system factors impact future urban health scenarios in Latin American cities [ 2 ]. The approach outlined in this paper adds to the CIB methodological toolbox and could be used as a more visual, and simpler, alternative to the standard approach to eliciting judgements from experts.…”

Qualitative systems mapping for complex public health problems: A practical guide

“…The authors therefore advise to be cautious when using such metrics as a supportive tool during analysis and always ground findings in qualitative evidence. Other methods to quantify causal loop diagrams and to select desirable future scenarios have been described in the literature [11,32].…”