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Characterizing Persistent Disturbing Behavior Using Longitudinal and Multivariate Techniques

Characterizing Persistent Disturbing Behavior Using Longitudinal and Multivariate Techniques. Jan Serroyen, UHasselt Liesbeth Bruckers , UHasselt Geert Rogiers, PZ Sancta Maria Geert Molenberghs, UHasselt. Outline. Persistent Disturbing Behavior (PDB) Research questions Pilot study

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Characterizing Persistent Disturbing Behavior Using Longitudinal and Multivariate Techniques

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  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

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