Enhancing causal loop diagram analysis through network measures: a comprehensive framework and an illustrative case study on maternal and child health systems in Tanzania and Zambia

Strelkovskii, N. ORCID: https://orcid.org/0000-0001-6862-1768, Rovenskaya, E. ORCID: https://orcid.org/0000-0002-2761-3443, & Borghi, J. (2024). Enhancing causal loop diagram analysis through network measures: a comprehensive framework and an illustrative case study on maternal and child health systems in Tanzania and Zambia. In: OR66 Annual Conference, 10-12 September 2024.

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Abstract

Causal loop diagrams (CLDs) offer valuable insights into complex systems by illustrating indirect relationships and feedback mechanisms. However, their complexity often poses analytical challenges. We aim to enhance CLD analysis by comprehensively integrating advanced network measures overcoming current limitations such as overlooked link polarities and unreliable component rankings. By systematically reviewing existing applications of network measures to CLD analysis, we identify best practices that address these issues, are implementable in software, accessible to non-experts, and yield actionable policy insights.
We illustrate the application of these approaches to empirical CLDs developed to explore the effects of payment for performance (P4P) schemes, involving health worker incentives, on maternal and child health systems in Tanzania and Zambia. CLDs of MCH systems in both countries were developed and validated using qualitative data from process evaluations and stakeholder dialogues. We apply network measures to the CLDs to identify key health system mechanisms affected by P4P and their impact on the key outcomes, such as the number of women and children receiving incentivized services. We also use network measures to explore variations across two maternal and child health systems in each country, and how these differences influence P4P mechanisms and outcome effects.
This study will demonstrate how network measures can assist in interpreting and comparing CLDs developed in different settings and facilitate the development of simulation models, including opportunities for P4P re-design to further strengthen health systems for better health outcomes.
This research contributes to the field by bridging the divide between CLDs and quantitative system dynamics, offering a comprehensive framework for more effective CLD analysis and use for policy. By leveraging the power of network analysis, we aim to make CLDs more accessible, insightful, and actionable for decision-makers and stakeholders in various domains, ultimately contributing to better understanding and management of complex systems.

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