How to do (or not to do) using causal loop diagrams for health system research in low- and middle-income settings - step-by-step guide for designing a study

Oct 26, 2026

Causal loop diagrams (CLDs) are presented as a systems thinking method for unpacking complex health system behaviour and supporting policy and intervention learning in low- and middle-income settings. The paper explains how CLDs can be used prospectively or retrospectively to surface mechanisms and consequences, including when primary data collection is constrained by humanitarian or conflict contexts. A step-by-step study design guide addresses scope, data sources, CLD development approaches, and validation, using LMIC literature and author experience.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Abstract

Key messages

Introduction

How to do (or not to do)…using causal loop diagrams for health system research in low and middle-income settings Rachel Cassidy 1,* , Josephine Borghi 1 , Agnes Rwashana Semwanga 2 , Peter Binyaruka 3 , Neha S Singh 1 , and Karl Blanchet 4 1 Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17, Tavistock Place, London WC1H 9SH, UK 2 Information Systems Department, College of Computing and Information Sciences, Makerere University, PO Box 7062, Kampala, Uganda 3 Ifakara Health Institute, PO Box 78373, Dar Es Salaam, Tanzania 4 Geneva Centre of Humanitarian Studies, University of Geneva and the Graduate Institute, 40, Boulevard du Pont-d’Arve, 1211 Genève, Switzerland *Corresponding author. Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17, Tavistock Place, London WC1H9SH, UK. E-mail: [rachel.cassidy@lshtm.ac.uk](rachel.cassidy@lshtm.ac.uk) Accepted on 2 August 2022 Abstract Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low-and middle-income countries (LMICs) . The guidance draws on examples from the LMIC literature and authors’ own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collector source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs. Keywords: Health systems research, research methods, study design, complex systems, systems thinking, causal loop diagrams, low- and middle-income countries Key messages • Causal loop diagrams enable identification and visualization of drivers for complex system behaviour, including spill over effects or unintended consequences of policy and managerial decisions. • They can be built and validated using different sources of data, depending on resource and health system setting constraints. • It is vital that further research using a systems thinking lens be conducted in LMICs, taking into account the delivery and resource constraints experienced by facilities and actors, to further our understanding of health system functioning and optimization. Introduction Health systems are complex systems due to the large number of system elements (people, resources, processes), the varying and extensive relationships between them, and their responsiveness to their external environment (Lipsitz, 2012; Barasa et al., 2017). They produce non-linear behaviour that evolves over time (Sterman, 2000a) and in response to relationships that exist between system elements (Lipsitz, 2012) . Treating the health system as a static, linear system in evaluations results in oversight of p

FAQs

What are causal loop diagrams (CLDs) used for in health systems research?

CLDs are used to visualize and unpack complex health system behaviour. They help identify mechanisms and consequences of policies or interventions and make relationships and feedbacks more explicit.

How can researchers build CLDs when primary data collection is limited?

CLDs can be developed using secondary data, including published or grey literature, health surveys/reports, and policy documents. This approach is suitable for settings such as humanitarian or conflict contexts.

Which questions does the paper address when designing a study using CLDs?

The guidance addresses four design questions: research scope, needed data and sources, the chosen method for CLD development, and how to validate the CLD.

Oct 26, 2026

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