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

Area of Science:

• Public Health
• Health Behavior Research
• Computational Modeling

Background:

• Diabetes is a growing global health concern with significant undiagnosed cases.
• Delayed diagnosis leads to severe complications and increased healthcare costs.
• Understanding health-seeking behaviors is crucial for timely diabetes detection.

Purpose of the Study:

• To develop an agent-based model integrating behavioral frameworks to predict health-seeking behaviors.
• To improve diabetes diagnosis timelines and analyze associated costs.
• To inform targeted public health interventions for diabetes management.

Main Methods:

• Developed an agent-based model incorporating the Theory of Planned Behavior (TPB), Health Belief Model (HBM), and Goal Framing Theory (GFT).
• Focused on Narromine and Gilgandra, New South Wales, Australia, analyzing diagnostic patterns, healthcare utilization, and costs.
• Conducted comparative experiments and scenario analyses to evaluate model predictive accuracy and intervention impacts.

Main Results:

• The integrated multitheory framework improved predictive accuracy by 15-30% over single-theory models.
• Spatial-temporal analysis revealed regional variations in diagnosis behaviors linked to healthcare access.
• Targeted interventions showed significant reductions in late-diagnosis rates, e.g., 15% for men in Gilgandra.

Conclusions:

• The study provides an empirically grounded, policy-oriented decision support tool for diabetes management.
• Insights gained can inform targeted interventions to improve public health outcomes in diabetes care.
• The model offers novel perspectives on health-seeking behaviors and their impact on diagnostic timelines.