a1 Health Services Research Unit, Institut Municipal d'Investigació Mèdica (IMIM-Hospital del Mar), Barcelona, Spain
a2 CIBER en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
a3 World Health Organization, Geneva, Switzerland
a4 University of Michigan, Institute for Social Research, Ann Arbor, MI, USA
a5 National Institute of Mental Health, Bethesda, MD, USA
a6 World Health Organization, EIP/HFS, Geneva, Switzerland
a7 Centre for Public Mental Health, Gösing am Wagram, Austria
a8 State University of New York, Stony Brook, New York, USA
a9 University Hospital Gasthuisberg, Leuven, Belgium
a10 IRCCS Centro S. Giovanni di Dio Fatebenefratelli, Brescia, Italy
a11 University College Hospital, Ibadan, Nigeria
a12 Sant Joan de Déu-SSM, CIBERSAM, Barcelona, Spain
a13 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
a14 EA4069 Université Paris Descartes, Paris, France
a15 Research & Planning, Mental Health Services Ministry of Health, Jerusalem, Israel
a16 Institute of Mental Health, Peking University, Beijing, People's Republic of China
a17 National Institute of Psychiatry Ramon de la Fuente, Mexico City, Mexico
a18 Interdisciplinary Center for Psychiatric Epidemiology, University Medical Center Groningen, The Netherlands
a19 Colegio Mayor de Cundinamarca University, Bogota, Colombia
a20 Health, Social Welfare, and Environmental Department, Osumi Regional Promotion Bureau, Kagoshima Prefecture, Japan
a21 Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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.
(Received November 17 2009)
(Revised May 05 2010)
(Accepted May 05 2010)
(Online publication June 16 2010)