Related Experiment Video
Updated: Jul 2, 2026

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Explaining health care utilization for panic attacks using cusp catastrophe modeling
1Department of Family and Community Medicine, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229-3900, USA. katerndahl@uthscsa.edu
Abstract:
Despite increased health care utilization, patients with panic disorder continue to report unmet needs. The objective was to compare the fit of linear and Cusp Catastrophe Modeling in explaining changes in utilization of emergency, general and mental health settings, and self-treatments for panic symptoms. This community-based study surveyed 97 subjects with panic attacks drawn from a sample of randomly-selected adults from randomly-selected households. The stressor (splitting) variable used was Phobic Anxiety while predisposing variables included Family Health Care Utilization, Perceived Life Threat and Need For Treatment, and Treatment Experience. Outcomes consisted of the number of sites and self-treatments used for panic symptoms when first seeking care and during the 2 months prior to survey. Use of mental health sites and self-treatments demonstrated superior modeling with cusp catastrophe approaches using treatment experience as the predisposing variable, accounting for 47% and 38% of variances respectively, improving the fit by over 20% compared to the best linear models in both cases. Cusp catastrophe modeling accounted for more variance than all linear models when describing use of mental health settings and self-treatments. Cusp catastrophe may explain bimodal distributions in behavior, delays in behavior change, and sudden shifts in behavior in stressful situations.
More Related Videos
Related Concept Videos
Panic Disorder
The Availability Heuristic
Anxiety: Overview
Individuals with anxiety often experience a range of physical and emotional symptoms, including sweating, trembling, tachycardia, and disturbances in sleep patterns. These symptoms vary in intensity and frequency but are generally disruptive and distressing.
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Models of Health Promotion and Illness Prevention II
The agent-host-environment model states that disease results from...
Causality in Epidemiology

