1 / 21

Characterizing Persistent Disturbing Behavior Using Longitudinal and Multivariate Techniques

This study focuses on Persistent Disturbing Behavior (PDB) and its implications within mental health care. Utilizing longitudinal and multivariate techniques, researchers at UHasselt and PZ Sancta Maria investigate the characteristics of a challenging group of patients exhibiting therapy-resistant behavior. The pilot and longitudinal studies reveal insights into patient demographics, symptom persistence over time, and the need for specialized treatment facilities. The findings underscore significant differences between PDB and non-PDB groups and suggest pathways for improved care management.

kristopher
Télécharger la présentation

Characterizing Persistent Disturbing Behavior Using Longitudinal and Multivariate Techniques

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Characterizing Persistent Disturbing Behavior Using Longitudinal and Multivariate Techniques Jan Serroyen, UHasselt Liesbeth Bruckers, UHasselt Geert Rogiers, PZ Sancta Maria Geert Molenberghs, UHasselt

  2. Outline • Persistent Disturbing Behavior (PDB) • Research questions • Pilot study • Longitudinal analysis • Cluster analysis • Concluding remarks QMSS - UHasselt

  3. Persistent Disturbing Behavior • Observation by mental health care professionals • Problematic group of patients: • Disturbing behavior • Therapy resistant • Living together is extremely difficult • Intensive supervision over 24h QMSS - UHasselt

  4. Where do they belong? • Psychiatric hospital (PH): • Definition: non-residential institution for intensive specialist care • Problem: need for a prolonged stay • Psychiatric nursing home (PNH): • Royal Decree: chronic and stabilized psychiatric conditions • Problem: instable disease status QMSS - UHasselt

  5. Research Questions • Distinguish PDB from non-PDB • Size of PDB group • Homogeneous group or subgroups QMSS - UHasselt

  6. Minimal Psychiatric Data (MPD) • Imposed by the Ministry of Public Health • Started in 1996 • Goal : • Transparency in care • Diversity of patients • Variability in care • Items • Socio demographic • Diagnostic items (DSM IV) • Psycho-social problems • Received treatment QMSS - UHasselt

  7. Pilot study • Cross-sectional study in 1998 (N = 611) • Discriminant analysis: • PDB screening by expert opinion • Discriminant function: based on MPD data • Sensitivity & Specificity: 72% - 85% • 80% correctly classified • Conclusion: PDB is a substantial group • Focus on disturbance aspect QMSS - UHasselt

  8. QMSS - UHasselt

  9. Longitudinal analysis • Aim: study persistence dimension • Discriminant analysis -> PDB-score • Calculate score at other registration occasions-> PDB-score over time QMSS - UHasselt

  10. QMSS - UHasselt

  11. QMSS - UHasselt

  12. Linear mixed-effects model QMSS - UHasselt

  13. Linear mixed-effects model • Separate models for both types of institutions • Starting model: • Mean structure: PDB group, time, time² and pairwise interactions • Variance model: • 3 group-specific random effects: intercept, time, time² • PH: group specific power-of-mean structure • PNH: group specific Gaussian serial correlation structure QMSS - UHasselt

  14. Linear mixed-effects model • Final model: • Mean structure: • Random-effects covariance matrix: QMSS - UHasselt

  15. QMSS - UHasselt

  16. QMSS - UHasselt

  17. QMSS - UHasselt

  18. QMSS - UHasselt

  19. Cluster analysis • Identify subgroups within PDB group • Gower’s distance: can handle all outcome types • Ward’s minimum variance method • Result: 2 clusters QMSS - UHasselt

  20. QMSS - UHasselt

  21. Concluding remarks • Differences PDB & non-PDB: • Mean profiles • Variance • Correlation structure • Numerous PDB patients • Need for specialized treatment facilities QMSS - UHasselt

More Related