Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-27T20:32:46.528Z Has data issue: false hasContentIssue false
  • English
  • Français

Patterns of psychiatric diagnosis in general practice: the Second National Morbidity Survey

Published online by Cambridge University Press:  09 July 2009

Graham Dunn
Graham Dunn*
Affiliation:
Biometrics Unit, Institute of Psychiatry, London
*
1Address for correspondence: Dr Graham Dunn, Biometrics Unit, Institute of Psychiatry, De Crespigny Park, Denmark Hill, London SE5 8AF.
Get access Rights & Permissions [Opens in a new window]

Synopsis

Multidimensional scaling, in the form of principal coordinates analysis and two-way correspondence analysis, is used to illustrate inter-practice variation in patterns of psychiatric diagnoses provided by data from the longitudinal file of the Second National Morbidity Survey. The results strongly support the view that general practitioners' diagnostic habits should be validated before their records are used to provide data on ‘official’ estimates of psychiatric morbidity. It is recommended that, whatever the quality of the data, large tables of official socio-economic or medical statistics should be supplemented by graphical summaries, as they quite often are in France.

Type
Research Article
Information
Psychological Medicine , Volume 16 , Issue 3 , August 1986 , pp. 573 - 581
Copyright
Copyright © Cambridge University Press 1986

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

Alvey, N., Galwey, N. & Lane, P. (1982). An Introduction to GENSTAT. Academic Press: London.Google Scholar
Ashford, J. R. (1972). Patient contacts in general practice in the National Health Service. The Statistician 21, 265–289.CrossRefGoogle Scholar
Ashford, J. R. & Hunt, R. G. (1974). The distribution of doctor–patient contacts in the National Health Service. Journal of the Royal Statistical Society, Series A 137, 347–383.CrossRefGoogle Scholar
Ashford, J. R. & Pearson, N. G. (1970). Who uses the health services and why? Journal of the Royal Statistical Society, Series A 133, 295–357.CrossRefGoogle Scholar
College of General Practitioners Research Committee of Council (1963). A classification of disease. Amended version. Journal of the College of General Practitioners 6, 207–216.Google Scholar
Deville, J-C. & Malinvaud, E. (1983). Data analysis in official socio-economic statistics. Journal of the Royal Statistical Society, Series A 146, 335–361.CrossRefGoogle Scholar
Duncan-Jones, P. (1981). The natural history of neurosis: probability models. In What is a Case? Problems of Definition in Psychiatric Community Surveys (ed. Wing, J. K., Bebbington, P. E. and Robins, L.), pp. 161–180. Grant-McIntyre: London.Google Scholar
Dunn, G. (1983). Longitudinal records of anxiety and depression in general practice: the Second National Morbidity Survey. Psychological Medicine 13, 897–906.CrossRefGoogle ScholarPubMed
Dunn, G. (1985). Records of psychiatric morbidity in general practice: the National Morbidity Surveys. Psychological Medicine 15, 223–226.CrossRefGoogle ScholarPubMed
Dunn, G. & Everitt, B. S. (1983). An Introduction to Mathematical Taxonomy. Cambridge University Press: Cambridge.Google Scholar
Dunn, G. & Skuse, D. (1981). The natural history of depression in general practice: stochastic models. Psychological Medicine 11, 755–764.CrossRefGoogle ScholarPubMed
Eaton, W. W. (1974). Mental hospitalization as a reinforcement process. American Sociological Review 39, 252–260.CrossRefGoogle ScholarPubMed
Froggatt, P., Dudgeon, M. Y. & Merrett, J. D. (1969). Consultations in general practice. Analysis of individual frequencies. British Journal of Preventive and Social Medicine 23, 1–11.Google ScholarPubMed
Gower, J. C. (1966). Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325–338.CrossRefGoogle Scholar
Greenacre, M. J. (1984). Theory and Application of Correspondence Analysis. Academic Press: London.Google Scholar
Kilpatrick, S. J. (1977). The empirical study of the distribution of episodes of illness recorded in the 1970–71 National Morbidity Survey. Applied Statistics 26, 26–33.CrossRefGoogle Scholar
Lebart, L., Morineau, A. & Warwick, K. M. (1984). Multivariate Descriptive Statistical Analysis, Correspondence Analysis and Related Techniques for Large Matrices. John Wiley and Sons: New York.Google Scholar
Marks, J. N., Goldberg, D. P. & Hillier, V. F. (1979). Determinants of the ability of general practitioners to detect psychiatric illness. Psychological Medicine 9, 337–353.CrossRefGoogle ScholarPubMed
Marshall, A. W. & Goldhamer, H. (1955). An application of Markov processes to study the epidemiology of mental disease. Journal of the American Statistical Association 50, 99–129.CrossRefGoogle Scholar
Prim, R. C. (1957). Shortest connection matrix network and some generalizations. Bell System Technical Journal 36, 1389–1401.CrossRefGoogle Scholar
Royal College of General Practitioners (1980). Second National Morbidity Survey. Journal of the Royal College of General Practitioners 30, 547–550.Google Scholar
Shepherd, M., Cooper, B., Brown, A. C. & Kalton, G. W. (1966). Psychiatric Illness in General Practice. Oxford University Press: London.Google Scholar
Smith, C. A. B. (1977). A note on genetic distance. Annals of Human Genetics 40, 463–479.CrossRefGoogle ScholarPubMed
Young, F. W. & Lewyckyj, R. (1980). ALSCAL User's Guide. Institute for Research in the Social Sciences: Chapel Hill, N.C.Google Scholar