Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-09T14:12:08.027Z Has data issue: false hasContentIssue false

Polytomous logistic regression analysis of the General Health Questionnaire and the Present State Examination

Published online by Cambridge University Press:  09 July 2009

F. W. Wilmink*
Affiliation:
Departments of Social Psychiatry and Statistics and Measurement Theory, University of Groningen, The Netherlands
T. A. B. Snijders
Affiliation:
Departments of Social Psychiatry and Statistics and Measurement Theory, University of Groningen, The Netherlands
*
1Address for correspondence: Dr F. W Wilmink, Psychiatric University Clinic, Oostersingel 59, 9713 EZ Groningen, The Netherlands.

Synopsis

First, two examples of dichotomous logistic regression analysis are presented. The probability of being a psychiatric case according to the Present State Examination is predicted from the total score on the General Health Questionnaire and from the general practitioner's judgement on the presence of a mental health problem. Subjects were 292 primary care attenders. Results are compared with those from prior studies.

Next, the extension to the polytomous case is demonstrated. The probability of being at any given level of the Index of Definition (computed from PSE data) is estimated from the General Health Questionnaire total score by an ordered polytomous logistic regression model. Several applications of the polytomous logistic regression model are discussed. These range from estimating the proportion of psychiatric cases among individuals who refuse to be interviewed to the formulation of sampling schemes which can be expected to reduce costs while at the same time yielding optimal information for testing specific hypotheses.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 1989

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, J. A. & Philips, P. R. (1981). Regression, discrimination and measurement models for ordered categorical variables. Applied Statistics 30, 2231.Google Scholar
Bishop, Y. M. M., Fienberg, S E. & Holland, P. W. (1975). Discrete Multivariate Analysis. MIT Press: Cambridge, Mass. and London, England.Google Scholar
Chan, D. W. & Chan, T. S. C. (1983). Reliability, validity and the structure of the General Health Questionnaire in a Chinese context. Psychological Medicine 13, 363371.CrossRefGoogle Scholar
Cox, D. R. (1970). The Analysis of Binary Data. Methuen: London.Google Scholar
Engel, J. (1988). Polytomous logistic regression. Statistica Neerlandica 42, 233252.Google Scholar
Fienberg, S. E. (1977). The Analysis of Cross-Classified Categorical Data. MIT Press: Cambridge, Mass, and London, England.Google Scholar
Finlay-Jones, R. A. & Murphy, E. (1979). Severity of psychiatric disorder and the 30-item General Health Questionnaire. British Journal of Psychiatry 134, 609616.CrossRefGoogle ScholarPubMed
Fleiss, J. L., Williams, J. B. W. & Dubro, A. F. (1986). The logistic regression analysis of psychiatric data. Journal of Psychiatric Research 20, 195209.CrossRefGoogle ScholarPubMed
Giel, R., Ormel, J., Frankenberg, W. & Van de Willige, G. (1989). Assessment and management of mental health problems in a Dutch family practice. (In preparation.)Google Scholar
Goldberg, D. (1972). The Detection of Psychiatric Illness by Questionnaire, Maudsley Monograph No. 21. Oxford University Press: London.Google Scholar
Goldberg, D., Kay, C. & Thompson, L. (1976). Psychiatric morbidity in general practice and the community. Psychological Medicine 6, 565569.Google Scholar
Goldberg, D. P.Bridges, K., Duncan-Jones, P. & Grayson, D. (1987). Dimensions of neuroses seen in primary-care settings. Psychological Medicine 17, 461470.CrossRefGoogle ScholarPubMed
Goodchild, M. E. & Duncan-Jones, P. (1985). Chronicity and the General Health Questionnaire. British Journal of Psychiatry 146, 5561.CrossRefGoogle ScholarPubMed
Henderson, S., Duncan-Jones, P., Byrne, D. G., Scott, R. & Adcock, S. (1979). Psychiatric disorder in Canberra. A standardized study of prevalence. Acta Psychiatrica Scandinavica 60, 375391.CrossRefGoogle ScholarPubMed
Hodiamont, P. P. G. & Veling, S. H. J. (1984). Een model voor het bepalen van psychiatrische prevalentie: de relatie GHQ-PSE. Tijdschrift voor Psychiatrie 26, 592607Google Scholar
Hodiamont, P. P. G., Peer, N. & Syben, N. (1987). Epidemiological aspects of psychiatric disorder in a Dutch health area. Psychological Medicine 17, 485505.Google Scholar
Hutchison, D. (1984). Ordinal variable regression using the McCullagh (proportional odds) model. GLIM newsletter, no. 9, pp. 917.Google Scholar
McCullagh, P., (1980). Regression models for ordinal data. Journal of the Royal Statistical Society, Ser. B 42, 109142.Google Scholar
Wijesinha, A., Begg, C. B., Funkenstein, H. H. & McNeil, B. J. (1983). Methodology for the differential diagnosis of a complex data set. Medical Decision Making 3, 133154.CrossRefGoogle ScholarPubMed
Williams, P., Tarnopolsky, A., Hand, D. & Shepherd, M. (1986). Minor psychiatric morbidity and general practice consultations: the West London Survey. Psychological Medicine monograph supplement 9.Google Scholar
Wilmink, F. W., Ormel, J., Giel, R., Krol, B., Lindeboom, E. G., van der Meer, K. & Soeteman, J. H. (1989). General practitioners' characteristics and the assessment of psychiatric illness. Journal of Psychiatric Research (in the press).Google Scholar
Wing, J. K. & Sturt, E. (1978). The PSE-ID-CATEGO System: A Supplementary Manual. MRC Social Psychiatry Unit: London.Google Scholar
Wing, J. K., Mann, S. A., Leff, J. P. & Nixon, J. M. (1978). The concept of a ‘case’ in psychiatric population surveys. Psychological Medicine, 8, 103117.Google Scholar