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Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys

Published online by Cambridge University Press:  16 June 2010

J. Alonso ,
G. Vilagut ,
S. Chatterji ,
S. Heeringa ,
M. Schoenbaum ,
T. Bedirhan Üstün ,
S. Rojas-Farreras ,
M. Angermeyer ,
E. Bromet  and
R. Bruffaerts
...Show all authors
J. Alonso
Affiliation:
Health Services Research Unit, Institut Municipal d'Investigació Mèdica (IMIM-Hospital del Mar), Barcelona, Spain CIBER en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
G. Vilagut
Affiliation:
Health Services Research Unit, Institut Municipal d'Investigació Mèdica (IMIM-Hospital del Mar), Barcelona, Spain CIBER en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
S. Chatterji
Affiliation:
World Health Organization, Geneva, Switzerland
S. Heeringa
Affiliation:
University of Michigan, Institute for Social Research, Ann Arbor, MI, USA
M. Schoenbaum
Affiliation:
National Institute of Mental Health, Bethesda, MD, USA
T. Bedirhan Üstün
Affiliation:
World Health Organization, EIP/HFS, Geneva, Switzerland
S. Rojas-Farreras
Affiliation:
Health Services Research Unit, Institut Municipal d'Investigació Mèdica (IMIM-Hospital del Mar), Barcelona, Spain
M. Angermeyer
Affiliation:
Centre for Public Mental Health, Gösing am Wagram, Austria
E. Bromet
Affiliation:
State University of New York, Stony Brook, New York, USA
R. Bruffaerts
Affiliation:
University Hospital Gasthuisberg, Leuven, Belgium
G. de Girolamo
Affiliation:
IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Brescia, Italy
O. Gureje
Affiliation:
University College Hospital, Ibadan, Nigeria
J. M. Haro
Affiliation:
Sant Joan de Déu-SSM, CIBERSAM, Barcelona, Spain
A. N. Karam
Affiliation:
Department of Psychiatry and Clinical Psychology, Saint George Hospital University Medical Center, Department of Psychiatry and Clinical Psychology, Faculty of Medicine, Balamand University Medical School, and the Institute for Development, Research, Advocacy and Applied Care (IDRAAC), Beirut, Lebanon
V. Kovess
Affiliation:
EA4069 Université Paris Descartes, Paris, France
D. Levinson
Affiliation:
Research & Planning, Mental Health Services Ministry of Health, Jerusalem, Israel
Z. Liu
Affiliation:
Institute of Mental Health, Peking University, Beijing, People's Republic of China
M. E. Medina-Mora
Affiliation:
National Institute of Psychiatry Ramon de la Fuente, Mexico City, Mexico
J. Ormel
Affiliation:
Interdisciplinary Center for Psychiatric Epidemiology, University Medical Center Groningen, The Netherlands
J. Posada-Villa
Affiliation:
Colegio Mayor de Cundinamarca University, Bogota, Colombia
H. Uda
Affiliation:
Health, Social Welfare, and Environmental Department, Osumi Regional Promotion Bureau, Kagoshima Prefecture, Japan
R. C. Kessler*
Affiliation:
Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
*
*Address for correspondence: R. C. Kessler, Ph.D., Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115, USA. (Email: [email protected])
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Abstract

Background

The methodology commonly used to estimate disease burden, featuring ratings of severity of individual conditions, has been criticized for ignoring co-morbidity. A methodology that addresses this problem is proposed and illustrated here with data from the World Health Organization World Mental Health Surveys. Although the analysis is based on self-reports about one's own conditions in a community survey, the logic applies equally well to analysis of hypothetical vignettes describing co-morbid condition profiles.

Method

Face-to-face interviews in 13 countries (six developing, nine developed; n=31 067; response rate=69.6%) assessed 10 classes of chronic physical and nine of mental conditions. A visual analog scale (VAS) was used to assess overall perceived health. Multiple regression analysis with interactions for co-morbidity was used to estimate associations of conditions with VAS. Simulation was used to estimate condition-specific effects.

Results

The best-fitting model included condition main effects and interactions of types by numbers of conditions. Neurological conditions, insomnia and major depression were rated most severe. Adjustment for co-morbidity reduced condition-specific estimates with substantial between-condition variation (0.24–0.70 ratios of condition-specific estimates with and without adjustment for co-morbidity). The societal-level burden rankings were quite different from the individual-level rankings, with the highest societal-level rankings associated with conditions having high prevalence rather than high individual-level severity.

Conclusions

Plausible estimates of disorder-specific effects on VAS can be obtained using methods that adjust for co-morbidity. These adjustments substantially influence condition-specific ratings.

Keywords

Co-morbidityepidemiologyglobal burden of diseasemental healthvisual analog scale
Type
Original Articles
Information
Psychological Medicine , Volume 41 , Issue 4 , April 2011 , pp. 873 - 886
Copyright
Copyright © Cambridge University Press 2010

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