8237928 Creating directed acyclic graphs (DAGs) with domain experts for healthy employment among aging workers

<h3>Background</h3> Directed Acyclic Graphs (DAGs) are visual tools used in epidemiology to represent and clarify assumptions about causal relationships between variables. While they are valuable tools, the quality of DAGs depends on the incorporation of domain expertise. However, structured approaches to meaningfully involve domain experts in the creation of DAGs—especially in complex occupational health contexts—remain underdeveloped. <h3>Objective</h3> This study aims to evaluate a structured, participatory approach to DAG development in collaboration with domain experts. The focus is on understanding how individual vulnerabilities and organizational factors (e.g. sectoral policies) interact to influence healthy and sustained employment among ageing workers. <h3>Methods</h3> The study involves workshops in which domain experts from occupational health, human resources, labour unions, and other areas, collaboratively construct DAGs based on a defined causal question. The process, informed by existing guidance (e.g. Rodrigues et al., 2022), includes brainstorming, refinement, and validation phases. Participants identify key variables, mechanisms, and sources of bias, while building consensus around causal assumptions. <h3>Preliminary Insights</h3> Preliminary insights suggest that the structured, collaborative approach helps uncover both shared assumptions and divergent perspectives across professional backgrounds. Participants found it made implicit causal reasoning more explicit and encouraged reflection on context-specific mechanisms. The process also fostered interdisciplinary dialogue and critical discussions about confounding and data limitations. However, balancing depth and feasibility proved challenging: conversations risked becoming too broad and not manageable. Final DAGs are still in development, but early findings indicate that this method enhances both the relevance of DAGs for applied research and mutual learning among experts. <h3>Conclusion</h3> Although still ongoing, the study demonstrates the potential of expert-guided DAG development to enrich causal modelling in occupational health research. The approach may help bridge the gap between theory, data, and policy by integrating diverse forms of expertise into the analytical design phase.