Understanding Delayed Diabetes Diagnosis: An Agent-Based Model of Health-Seeking Behavior

Firouzeh Rosa Taghikhah, Araz Jabbari, Kevin C. Desouza, Arunima Malik and Hadi A. Khorshidi
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Firouzeh Rosa Taghikhah: Business School, University of Sydney, NSW, Australia
Araz Jabbari: Faculty of Business Administration, Université Laval, QC, Canada
Kevin C. Desouza: Faculty of Business and Law, Queensland University of Technology, QLD, Australia
Arunima Malik: School of Physics, University of Sydney, Camperdown, NSW, Australia
Hadi A. Khorshidi: Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia

Medical Decision Making, 2025, vol. 45, issue 4, 399-425

Abstract: Background Diabetes is a rapidly growing global health issue, with the hidden burden of undiagnosed cases leading to severe complications and escalating health care costs. Methods This study investigated the potential of integrated behavioral frameworks to predict health-seeking behaviors and improve diabetes diagnosis timelines through the development of an agent-based model. Focusing on Narromine and Gilgandra in New South Wales, Australia, the model captured the integrative influence of 3 social theories—theory of planned behavior (TPB), health belief model (HBM), and goal framing theory (GFT)—on health care decisions across behavioral and nonbehavioral variables, providing a robust analysis of temporal diagnostic patterns, health care utilization, and costs. Results Our comparative experiments indicated that this multitheory framework improved predictive accuracy by 15% to 30% compared with single-theory models, effectively capturing the interplay of planned, belief-driven, and context-based health behaviors. Spatial-temporal analysis highlighted key regional and demographic variations in diagnosis behaviors. While early, planned medical visits were prevalent in regions with better access (Gilgandra), areas with limited infrastructure saw a reliance on hospital-based diagnoses (Narromine). Health care cost analysis demonstrated a nonlinear expenditure pattern, suggesting that these theories defy conventional linear cost trends. Scenario analysis demonstrated the impact of targeted interventions. Gender-specific awareness initiatives in Gilgandra reduced late-diagnosis rates among men by approximately 15%, while enhanced access to care in Narromine decreased hospital-based late diagnoses from a baseline of 80% to around 60%. Conclusions This study contributes an empirically grounded, policy-oriented decision support tool to inform targeted interventions, offering novel insights to improve diabetes management. Highlights We explored the delay in diabetes diagnosis, particularly within remote Australian communities, through looking into the health care–seeking behavior of individuals displaying diabetes symptoms. We developed an innovative agent-based model to craft a dynamic decision support tool for policy makers by providing unique insights into the health behaviors of diabetes patients. Our study contributes significantly to the understanding of public health management with particular concerns around diabetes, as well as equips the New South Wales Ministry of Health with impactful insights into the consequences of their decisions.

Keywords: decision analytics; decision support systems; health behavior; noncommunicable disease; complex systems simulation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:4:p:399-425

DOI: 10.1177/0272989X251326908

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