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

20181 7 108363 2 328185 document 108363 OR66_NA_CLD.pdf application/pdf 7ae8fc466a87c4a2fff6f22dd7e9577c MD5 2887956 2024-12-12 16:01:53 https://pure.iiasa.ac.at/id/eprint/20181/1/OR66_NA_CLD.pdf 20181 1 1 application/pdf text en public cc_by_nc_4

OR66_NA_CLD.pdf

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/108363 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/108363 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/108363 archive 353 disk0/00/02/01/81 2024-12-12 16:05:42 2025-03-12 05:01:03 2024-12-12 16:05:42 conference_item show 1 Strelkovskii N. 8719 0000-0001-6862-1768 Rovenskaya E. 8206 0000-0002-2761-3443 Borghi J. 9448 0000-0002-0482-5451 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 pub ASA CAT EM SYRR 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. 2024-09-09 published 298 260 3380 public paper OR66 Annual Conference 10-12 September 2024 conference FALSE info:eu-repo/semantics/conferenceObject