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Distribution of symptom dimensions across Kraepelinian divisions

Published online by Cambridge University Press:  02 January 2018

Dimitris G. Dikeos*
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK and Department of Psychiatry, University of Athens, Greece
Harvey Wickham
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Colm McDonald
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK and Department of Psychiatry, National University of Ireland, Galway, Ireland
Muriel Walshe
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Thordur Sigmundsson
Affiliation:
Department of Psychiatry, Landspitalinn, The University Hospital, Reykjavik, Iceland
Elvira Bramon
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Anton Grech
Affiliation:
Mount Carmel Hospital, Attard, Malta
Timothea Toulopoulou
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Robin Murray
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK
Pak C. Sham
Affiliation:
Division of Psychological Medicine, Institute of Psychiatry, King's College London, UK and Department of Psychiatry, University of Hong Kong
*
Dr Dimitris G. Dikeos, Institute of Psychiatry, SGDP Building, De Crespigny Park, Denmark Hill, London SE5 8AF, UK. Tel: +44(0)20 7848 0854; email d.dikeos@iop.kcl.ac.uk
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Abstract

Background

Dimensional structures are established for many psychiatric diagnoses, but dimensions have not been compared between diagnostic groups.

Aims

To examine the structure of dimensions in psychosis, to analyse their correlations with disease characteristics and to assess the relative contribution of dimensions v. diagnosis in explaining these characteristics.

Method

Factor analysis of the OPCRIT items of 191 Maudsley Family Study patients with schizophrenia, mood disorders with psychosis, schizoaffective disorder, and other psychotic illnesses, followed by regression of disease characteristics from factor scores and diagnosis.

Results

Five factors were identified (mania, reality distortion, depression, disorganisation, negative); all were more variable in schizophrenia than in affective psychosis. Mania was the best discriminator between schizophrenia and affective psychosis; the negative factor was strongly correlated with poor premorbid functioning, insidious onset and worse course. Dimensions explained more of the disease characteristics than did diagnosis, but the explanatory power of the latter was also high.

Conclusions

Kraepelinian diagnostic categories suffice for understanding illness characteristics, but the use of dimensions adds substantial information.

Information

Type
Papers
Copyright
Copyright © Royal College of Psychiatrists, 2006 
Figure 0

Table 1 Diagnosis and demographic characteristics of the sample

Figure 1

Table 2 Five-factor solution. Item loadings after varimax rotation. Bold type indicates the item loadings that contribute to each factor.

Figure 2

Fig. 1 Distribution of the factor scores in patients with schizophrenia (filled bars) and mood disorders (shaded bars). The y-axis represents percentage of cases within each diagnostic category.

Figure 3

Table 3 Regressions of clinical characteristics relating to premorbid features, onset and course on the five-factor scores. Values represent beta (standard error in parentheses) for logistic, linear or ordinal regression as appropriate. Within each cell, values of the top line are controlled for gender and age, and values of the bottom line are controlled for gender, age and diagnosis

Figure 4

Fig. 2 Negative symptoms factor score of patients with schizophrenia separated into groups according to the course of their illness. 2: multiple episodes with good recovery between; 3: multiple episodes with partial recovery between; 4: continuous chronic illness; 5: continuous chronic illness with deterioration. Dots show means for each group and bars represent 95% confidence intervals.

Figure 5

Table 4 Regressions of clinical characteristics on gender, age, diagnosis and factor scores.1 Values in the first column are R-squares (based on the Cox & Snell calculation); values in the last four columns are R-square differences between regression models

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