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International Journal of Methods in Psychiatric Research logoLink to International Journal of Methods in Psychiatric Research
. 2006 Jan 6;14(1):3–13. doi: 10.1002/mpr.13

Recalibration methods to enhance information on prevalence rates from large mental health surveys

N A Taub 1, Z Morgan 1, T S Brugha 1,✉, P C Lambert 1, P E Bebbington 2, R Jenkins 3, R C Kessler 4, A M Zaslavsky 4, T Hotz 1
PMCID: PMC6878493  PMID: 16097396

Abstract

Comparisons between self‐report and clinical psychiatric measures have revealed considerable disagreement. It is unsafe to consider these measures as directly equivalent, so it would be valuable to have a reliable recalibration of one measure in terms of the other. We evaluated multiple imputation incorporating a Bayesian approach, and a fully Bayesian method, to recalibrate diagnoses from a self‐report survey interview in terms of those from a clinical interview with data from a two‐phase national household survey for a practical application, and artificial data for simulation studies.

The most important factors in obtaining a precise and accurate ‘clinical’ prevalence estimate from self‐report data were (a) good agreement between the two diagnostic measures and (b) a sufficiently large set of calibration data with diagnoses based on both kinds of interview from the same group of subjects. From the case study, calibration data on 612 subjects were sufficient to yield estimates of the total prevalence of anxiety, depression or neurosis with a precision in the region of ±2%. The limitations of the calibration method demonstrate the need to increase agreement between survey and reference measures by improving lay interviews and their diagnostic algorithms. Copyright © 2005 Whurr Publishers Ltd.

Keywords: mental disorders, diagnostic techniques and procedures, health surveys, statistical methods, anxiety, depression

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References

  1. American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Washington DC: APA, 1994. [Google Scholar]
  2. Bebbington PE, Marsden L, Brewin CR. The need for psychiatric treatment in the general population: the Camberwell Needs for Care survey. Psychological Medicine 1997; 27: 821–34. [DOI] [PubMed] [Google Scholar]
  3. Best NG, Spiegelhalter DJ, Thomas A, Brayne C. Bayesian‐analysis of realistically complex‐models. Journal of the Royal Statistical Society Series A‐Statistics in Society 1996; 159: 323–42. [Google Scholar]
  4. Brugha TS, Bebbington P, Jenkins R, Meltzer H, Taub NA, Janas M, Vernon J. Cross validation of a household population survey diagnostic interview: a comparison of CIS‐R with SCAN ICD‐10 diagnostic categories. Psychological Medicine 1999a; 29: 1029–42. [DOI] [PubMed] [Google Scholar]
  5. Brugha TS, Bebbington PE, Jenkins R. A difference that matters: comparisons of structured and semi‐structured diagnostic interviews of adults in the general population. Psychological Medicine 1999b; 29: 1013–20. [DOI] [PubMed] [Google Scholar]
  6. Brugha TS, Jenkins R, Taub NA, Meltzer H, Bebbington P. A general population comparison of the Composite International Diagnostic Interview (CIDI) and the Schedules for Clinical Assessment in Neuropsychiatry (SCAN). Psychological Medicine 2001; 31: 1001–13. [DOI] [PubMed] [Google Scholar]
  7. Brugha TS, Nienhuis FJ, Bagchi D, Smith J, Meltzer H. The survey form of SCAN: the feasibility of using experienced lay survey interviewers to administer a semi‐structured systematic clinical assessment of psychotic and non psychotic disorders. Psychological Medicine 1999c; 29: 703–12. [DOI] [PubMed] [Google Scholar]
  8. Brugha TS, Teather D, Wills KM, Kaul A, Dignon A. Present State Examination by microcomputer: Objectives and experience of preliminary steps. International Journal of Methods in Psychiatric Research 1996; 6: 143–51. [Google Scholar]
  9. Clogg CC, Rubin DB, Shenker N, Shultz B, Weidman L. Multiple imputation of industry and occupation codes in census public‐use samples using Bayesian logistic regression. Journal of the American Statistical Association 1991; 86: 68–78. [Google Scholar]
  10. De Leeuw E. Item non‐response: prevention is better than cure. Survey Methods Newsletter 1999; 19: 40–8. [Google Scholar]
  11. Department of Health . National Service Frameworks for Mental Health. Modern standards and service models. London: Department of Health, 1999. [Google Scholar]
  12. Dunn G. Comparative calibration without a gold standard. Statistics in Medicine 1998; 17: 1294–8. [DOI] [PubMed] [Google Scholar]
  13. Foley DL, Neale MC, Kendler KS. Genetic and environmental risk factors for depression assessed by subject‐rated Symptom Check List versus Structured Clinical Interview. Psychological Medicine 2002; 31: 1413–23. [DOI] [PubMed] [Google Scholar]
  14. Frazer GE, Stram GO. Regression calibration in studies with correlated variables measured with error. American Journal of Epidemiology 2001; 154: 836–44. [DOI] [PubMed] [Google Scholar]
  15. Goodman R, Ford T, Richards H, Gatward R, Meltzer H. The Development and Well‐Being Assessment: description and initial validation of an integrated assessment of child and adolescent psychopathology. J Child Psychol Psychiatry 2000; 41: 645–55. [PubMed] [Google Scholar]
  16. Greenland S, Finkle WD. A critical look at methods for handling missing covariates in epidemiologic regression analyses. American Journal of Epidemiology 1995; 142: 1255–64. [DOI] [PubMed] [Google Scholar]
  17. Henderson S. Epidemiology of mental disorders: the current agenda. Epidemiol Rev 2000; 22: 24–8. [DOI] [PubMed] [Google Scholar]
  18. Jenkins R, Bebbington P, Brugha T, Farrell M, Gill B, Lewis G, Meltzer H, Petticrew M. The national psychiatric morbidity surveys of Great Britain – strategy and methods. Psychological Medicine 1997; 27: 765–74. [DOI] [PubMed] [Google Scholar]
  19. Kessler RC. The World Health Organization International Consortium in Psychiatric Epidemiology (ICPE): initial work and future directions – the NAPE Lecture 1998. Nordic Association for Psychiatric Epidemiology. Acta Psychiatrica Scandinavica 1999; 99: 2–9. [DOI] [PubMed] [Google Scholar]
  20. Kessler RC, McGonagle KA, Zhao S, Nelson CB, Hughes M, Eshleman S, Wittchen H‐U, Kendler KS. Lifetime and 12‐month prevalence of DSM‐III‐R psychiatric disorders in the United States. Results from the National Comorbidity Survey. Archives of General Psychiatry 1994; 51: 8–19. [DOI] [PubMed] [Google Scholar]
  21. Kessler RC, Wittchen HU, Abelson JM, McGonagle KA, Schwarz N, Kendler KS, Knauper B, Zhao S. Methodological studies of the Composite International Diagnostic Interview (CIDI) in the US National Comorbidity Survey (NCS). International Journal of Methods in Psychiatric Research 1998; 7: 33–55. [Google Scholar]
  22. Lambert PC, Abrams KR, Sanso B, Jones DR. Synthesis of Incomplete Data using Bayesian Hierarchical Models: An Illustration Based on Data Describing Ssurvival from Neuroblastoma. Leicester: University of Leicester: Epidemiology and Public Health, 1997. [Google Scholar]
  23. Lewis G, Pelosi AJ, Araya R, Dunn G. Measuring psychiatric disorder in the community: a standardized assessment for use by lay interviewers. Psychological Medicine 1992; 22: 465–86. [DOI] [PubMed] [Google Scholar]
  24. Little RJA, Rubin DB. Statistical Analysis with Missing Data. John Wiley: New York, 2002. [Google Scholar]
  25. Martin J. An overview of imputation methods and their application to survey data. Survey Methods Newsletter 1999; 19: 9–11. [Google Scholar]
  26. Meltzer H, Gill B, Petticrew M, Hinds K. OPCS Surveys of Psychiatric Morbidity in Great Britain. Report 1: The Prevalence of Psychiatric Morbidity among Adults Living in Private Households. London: HMSO, 1995. [Google Scholar]
  27. Pickles A, Dunn G, Vazquez‐Barquero JL. Screening for stratification in two‐phase (‘two‐stage’) epidemiological surveys. Statistical Methods in Medical Research 1995; 4: 73–89. [DOI] [PubMed] [Google Scholar]
  28. Regier DA, Kaelber CT, Rae DS, Farmer ME, Knauper B, Kessler RC, Norquist GS. Limitations of diagnostic criteria and assessment instruments for mental disorders. Archives of General Psychiatry 1998; 55: 109–15. [DOI] [PubMed] [Google Scholar]
  29. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: Wiley, 1987. [Google Scholar]
  30. Schafer JL. Multiple imputation: a primer. Statistical Methods in Medical Research 1999; 8: 3–15. [DOI] [PubMed] [Google Scholar]
  31. Singleton N, Bumpstead R, O'Brien M, Lee A, Meltzer H. Psychiatric Morbidity Among Adults Living in Private Households. London: The Stationary Office, 2001. [DOI] [PubMed] [Google Scholar]
  32. Spiegelhalter DJ, Thomas A, Best NG. WinBUGS User Manual Version 1.3. Cambridge: MRC Biostatistics Unit, 2000. [Google Scholar]
  33. Wing JK, Babor T, Brugha T, Burke J, Cooper JE, Giel R, Jablensky A, Regier D, Sartorius N. SCAN. Schedules for Clinical Assessment in Neuropsychiatry. Archives of General Psychiatry 1990; 47, 589–93. [DOI] [PubMed] [Google Scholar]
  34. Wittchen HU, Üstün B, Kessler RC. Diagnosing mental disorders in the community: a difference that matters? Psychological Medicine 1999; 29, 1021–7. [DOI] [PubMed] [Google Scholar]
  35. World Health Organization . The ICD‐10 Classification of Mental and Behavioural Disorders: Diagnostic Criteria for Research. Geneva: WHO, 1993. [Google Scholar]
  36. Zhou XH, Eckert GJ, Tierney WM. Multiple Imputation in Public Health Research. Statistics in Medicine 2001; 20: 1541–9. [DOI] [PubMed] [Google Scholar]

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