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Panic Disorder01:27

Panic Disorder

Panic disorder is an anxiety disorder characterized by recurrent and sudden minutes-long episodes of intense fear, known as panic attacks. These attacks may feel like heart attacks and often happen without warning or a specific cause. They can include symptoms such as rapid heart rate, shortness of breath, chest pain, trembling, sweating, dizziness, and a sense of helplessness. During a panic attack, individuals may feel as though they are experiencing a heart attack or are in a...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Anxiety: Overview01:18

Anxiety: Overview

Anxiety is a common mental disorder featuring excessive worry, fear, and apprehension, significantly affecting daily life. People with anxiety disorders experience persistent and intense anxiety, interrupting their everyday functioning.
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 Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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 II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...

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Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
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Explaining health care utilization for panic attacks using cusp catastrophe modeling.

David Katerndahl1

  • 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

Nonlinear Dynamics, Psychology, and Life Sciences
|September 4, 2008
PubMed
Summary
This summary is machine-generated.

Cusp catastrophe modeling better explains how panic disorder patients use mental health services and self-treatments. This approach reveals sudden behavioral shifts and delays in care, improving understanding of patient needs.

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Area of Science:

  • Psychology
  • Health Services Research
  • Behavioral Science

Background:

  • Patients with panic disorder frequently experience unmet healthcare needs despite increased service utilization.
  • Understanding the dynamics of healthcare seeking behavior in panic disorder is crucial for improving patient outcomes.

Purpose of the Study:

  • To compare the efficacy of linear modeling versus Cusp Catastrophe Modeling in explaining healthcare utilization patterns for panic symptoms.
  • To identify the best model for understanding changes in the use of emergency, general, and mental health services, as well as self-treatments.

Main Methods:

  • A community-based survey of 97 adults experiencing panic attacks was conducted.
  • Phobic Anxiety was used as the stressor variable, with Family Health Care Utilization, Perceived Life Threat, Need For Treatment, and Treatment Experience as predisposing variables.
  • Outcomes measured included the number of sites and self-treatments used for panic symptoms at initial care-seeking and in the two months prior to the survey.

Main Results:

  • Cusp catastrophe modeling significantly outperformed linear models in explaining the use of mental health sites (47% variance) and self-treatments (38% variance).
  • The 'Treatment Experience' predisposing variable was particularly effective within the cusp catastrophe framework for both outcomes.
  • This modeling approach improved the fit by over 20% compared to the best linear models for mental health site usage and self-treatment.

Conclusions:

  • Cusp catastrophe modeling offers a superior framework for understanding complex healthcare-seeking behaviors in panic disorder, particularly concerning mental health service utilization and self-treatment.
  • This approach may elucidate bimodal distributions in behavior, delayed behavioral changes, and abrupt shifts in response to stressful situations.
  • Findings suggest that catastrophe modeling can provide deeper insights into patient needs and treatment-seeking dynamics in panic disorder.