Characterizing Persistent Disturbing Behavior Using Longitudinal and

Multivariate Techniques Jan Serroyen, UHasselt

Liesbeth Bruckers, UHasselt

Geert Rogiers, PZ Sancta Maria

Geert Molenberghs, UHasselt

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Outline

Persistent Disturbing Behavior (PDB)

Research questions

Pilot study

Longitudinal analysis

Cluster analysis

Concluding remarks

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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

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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

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Research Questions

Distinguish PDB from non-PDB

Size of PDB group

Homogeneous group or subgroups

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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

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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

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Longitudinal analysis

Aim: study persistence dimension

Discriminant analysis -> PDB-score

Calculate score at other registration occasions

-> PDB-score over time

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Linear mixed-effects model

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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

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Linear mixed-effects model

Final model:Mean structure:

Random-effects covariance matrix:

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Cluster analysis

Identify subgroups within PDB group

Gower’s distance:

can handle all outcome types

Ward’s minimum variance method

Result: 2 clusters

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Concluding remarks

Differences PDB & non-PDB:Mean profilesVarianceCorrelation structure

Numerous PDB patients

Need for specialized treatment facilities