Browsing by Author "Rojas-Farreras, S."

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    Including information about co-morbidity in estimates of disease burden: results from the World Health Organization World Mental Health Surveys
    (CAMBRIDGE UNIV PRESS, 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA, 2011) Alonso, J.; Vilagut, G.; Chatterji, S.; Heeringa, S.; Schoenbaum, M.; Uestuen, T. Bedirhan; Rojas-Farreras, S.; Angermeyer, M.; Bromet, E.; Bruffaerts, R.; De Girolamo, G.; Gureje, O.; Haro, J.M.; Karam, A.N.; Kovess, V.; Levinson, D.; Liu, Z.; Medina-Mora, M.E.; Ormel, J.; Posada-Villa, J.; Uda, H.; Kessler, R.C.; Harvard Univ, Sch Med, Dept Hlth Care Policy, Boston, MA 02115 USA; Kessler@hcp.med.harvard.edu
    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.