Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-24T06:45:53.010Z Has data issue: false hasContentIssue false
  • English
  • Français

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])
Get access Rights & Permissions [Opens in a new window]

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

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

Andlin-Sobocki, P, Jonsson, B, Wittchen, HU, Olesen, J (2005). Cost of disorders of the brain in Europe. European Journal of Neurology 12 (Suppl. 1), 1–27.Google Scholar
Baker, M, Stabile, M, Deri, C (2001). What do self-reported, objective, measures of health measure? Journal of Human Resources 39, 1067–1093.CrossRefGoogle Scholar
Breiman, L (2001). Random forests. Machine Learning 45, 32.Google Scholar
Breiman, L (2009). Statistical modeling: the two cultures. Statistical Science 16, 199–215.Google Scholar
Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ (1984). Classification and Regression Trees. Chapman & Hall: New York, NY.Google Scholar
Buntin, MB, Zaslavsky, AM (2004). Too much ado about two-part models and transformation? Comparing methods of modeling Medicare expenditures. Journal of Health Economics 23, 525–542.CrossRefGoogle ScholarPubMed
Center for Disease Control and Prevention (2004). Health, United States, 2004. National Center for Health Statistics: Atlanta, GA.Google Scholar
Craig, BM, Busschbach, JJ, Salomon, JA (2009). Modeling ranking, time trade-off, and visual analog scale values for EQ-5D health states: a review and comparison of methods. Medical Care 47, 634–641.Google Scholar
Donohue, JM, Pincus, HA (2007). Reducing the societal burden of depression: a review of economic costs, quality of care and effects of treatment. Pharmacoeconomics 25, 7–24.Google Scholar
Duan, N, Manning, WG, Morris, CN, Newhouse, JP (1984). Choosing between the sample-selection model and the multi-part model. Journal of Business and Economic Statistics 2, 289.Google Scholar
Fortin, M, Soubhi, H, Hudon, C, Bayliss, EA, van den Akker, M (2007). Multimorbidity's many challenges. British Medical Journal 334, 1016–1017.CrossRefGoogle ScholarPubMed
Friedman, JH (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics 19, 1.Google Scholar
Gabilondo, A, Rojas-Farreras, S, Vilagut, G, Haro, JM, Fernandez, A, Pinto-Meza, A, Alonso, J (2010). Epidemiology of major depressive episode in a southern European country: results from the ESEMeD-Spain project. Journal of Affective Disorders 120, 76–85.CrossRefGoogle Scholar
Gudex, C, Dolan, P, Kind, P, Williams, A (1996). Health state valuations from the general public using the visual analogue scale. Quality of Life Research 5, 521–531.Google Scholar
Haro, JM, Arbabzadeh-Bouchez, S, Brugha, TS, de Girolamo, G, Guyer, ME, Jin, R, Lepine, JP, Mazzi, F, Reneses, B, Vilagut, G, Sampson, NA, Kessler, RC (2006). Concordance of the Composite International Diagnostic Interview version 3.0 (CIDI 3.0) with standardized clinical assessments in the WHO World Mental Health surveys. International Journal of Methods in Psychiatric Research 15, 167–180.Google Scholar
Heeringa, SG, Wells, JE, Hubbard, F, Mneimneh, Z, Chiu, WT, Sampson, N, Berglund, PA (2008). Sample designs and sampling procedures. In The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders (ed. Kessler, R. C. and Üstün, T. B.), pp. 14–32. Cambridge University Press: New York, NY.Google Scholar
Hosmer, DW, Lemeshow, S (2001). Applied Logistic Regression, 2nd edn. Wiley & Sons: New York, NY.Google Scholar
Insinga, RP, Fryback, DG (2003). Understanding differences between self-ratings and population ratings for health in the EuroQOL. Quality of Life Research 12, 611–619.Google Scholar
Jacoby, A, Baker, GA (2008). Quality-of-life trajectories in epilepsy: a review of the literature. Epilepsy Behavior 12, 557–571.Google Scholar
Jasso, G (2006). Factorial survey methods for studying beliefs and judgments. Sociological Methods and Research 34, 334–423.Google Scholar
Kessler, RC, Üstün, TB (2004). The World Mental Health (WMH) Survey Initiative Version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research 13, 93–121.Google Scholar
Kessler, RC, Üstün, TB (eds) (2008). The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders. Cambridge University Press: New York, NY.Google Scholar
Knight, M, Stewart-Brown, S, Fletcher, L (2001). Estimating health needs: the impact of a checklist of conditions and quality of life measurement on health information derived from community surveys. Journal of Public Health in Medicine 23, 179–186.CrossRefGoogle ScholarPubMed
Krabbe, PF (2008). Thurstone scaling as a measurement method to quantify subjective health outcomes. Medical Care 46, 357–365.Google Scholar
Krabbe, PF, Salomon, JA, Murray, CJ (2007). Quantification of health states with rank-based nonmetric multidimensional scaling. Medical Decision Making 27, 395–405.Google Scholar
Krabbe, PF, Stalmeier, PF, Lamers, LM, Busschbach, JJ (2006). Testing the interval-level measurement property of multi-item visual analogue scales. Quality of Life Research 15, 1651–1661.CrossRefGoogle ScholarPubMed
Kraemer, HC, Kazdin, AE, Offord, DR, Kessler, RC, Jensen, PS, Kupfer, DJ (1997). Coming to terms with the terms of risk. Archives of General Psychiatry 54, 337–343.Google Scholar
Lopez, AD, Mathers, CD (2007). Inequalities in health status: findings from the 2001 Global Burden of Disease study. In The Global Forum Update on Research for Health, vol. 4 (ed. Matlin, S.), pp. 163–175. Pro-Brook Publishing Limited: London.Google Scholar
Macran, S, Kind, P (2001). ‘Death’ and the valuation of health-related quality of life. Medical Care 39, 217–227.Google Scholar
Maddigan, SL, Feeny, DH, Johnson, JA (2005). Health-related quality of life deficits associated with diabetes and comorbidities in a Canadian National Population Health Survey. Quality of Life Research 14, 1311–1320.Google Scholar
Manning, SC (1998). Configuring compliance: a professional fit. Journal of American Health Information Management Association 69, 36–38.Google Scholar
Manning, WG, Mullahy, J (2001). Estimating log models: to transform or not to transform? Journal of Health Economics 20, 461–494.Google Scholar
Marquie, L, Raufaste, E, Lauque, D, Marine, C, Ecoiffier, M, Sorum, P (2003). Pain rating by patients and physicians: evidence of systematic pain miscalibration. Pain 102, 289–296.CrossRefGoogle ScholarPubMed
McCullagh, P, Nelder, JA (1989). Generalized Linear Models, 2nd edn. Chapman & Hall: London.CrossRefGoogle Scholar
Moussavi, S, Chatterji, S, Verdes, E, Tandon, A, Patel, V, Ustun, B (2007). Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 370, 851–858.CrossRefGoogle ScholarPubMed
Mullahy, J (1998). Much ado about two: reconsidering retransformation and the two-part model in health econometrics. Journal of Health Economics 17, 247–281.CrossRefGoogle ScholarPubMed
Murray, CJ, Lopez, AD (1996). Evidence-based health policy – lessons from the Global Burden of Disease Study. Science 274, 740–743.Google Scholar
Murray, CJL, Lopez, AD, Mathers, CD, Stein, C (2001). The Global Burden of Disease 2000 Project: Aims, Methods and Data Sources. World Health Organization: Geneva.Google Scholar
Ohayon, MM (2002). Epidemiology of insomnia: what we know and what we still need to learn. Sleep Medicine Review 6, 97–111.Google Scholar
Ormel, J, Petukhova, M, Chatterji, S, Aguilar-Gaxiola, S, Alonso, J, Angermeyer, MC, Bromet, EJ, Burger, H, Demyttenaere, K, de Girolamo, G, Haro, JM, Hwang, I, Karam, E, Kawakami, N, Lepine, JP, Medina-Mora, ME, Posada-Villa, J, Sampson, N, Scott, K, Ustun, TB, Von Korff, M, Williams, DR, Zhang, M, Kessler, RC (2008). Disability and treatment of specific mental and physical disorders across the world. British Journal of Psychiatry 192, 368–375.Google Scholar
Parkin, D, Devlin, N (2006). Is there a case for using visual analogue scale valuations in cost–utility analysis? Health Economics 15, 653–664.Google Scholar
Pennell, B-E, Mneimneh, Z, Bowers, A, Chardoul, S, Wells, JE, Viana, MC, Dinkelmann, K, Gebler, N, Florescu, S, He, Y, Huang, Y, Tomov, T, Vilagut, G (2008). Implementation of the World Mental Health Surveys. In The WHO World Mental Health Surveys: Global Perspectives on the Epidemiology of Mental Disorders (ed. Kessler, R. C. and Üstün, T. B.), pp. 33–57. Cambridge University Press: New York, NY.Google Scholar
Roth, T, Jaeger, S, Jin, R, Kalsekar, A, Stang, PE, Kessler, RC (2006). Sleep problems, comorbid mental disorders, and role functioning in the National Comorbidity Survey Replication. Biological Psychiatry 60, 1364–1371.CrossRefGoogle ScholarPubMed
Saarni, SI, Suvisaari, J, Sintonen, H, Pirkola, S, Koskinen, S, Aromaa, A, Lonnqvist, J (2007). Impact of psychiatric disorders on health-related quality of life: general population survey. British Journal of Psychiatry 190, 326–332.Google Scholar
Salomon, JA, Murray, CJ (2004). A multi-method approach to measuring health-state valuations. Health Economics 13, 281–290.Google Scholar
Salomon, JA, Tandon, A, Murray, CJ (2004). Comparability of self rated health: cross sectional multi-country survey using anchoring vignettes. British Medical Journal 328, 258.Google Scholar
Schmidt, L, Room, R (1999). Cross-cultural applicability in international classifications and research on alcohol dependence. Journal of Studies on Alcohol 60, 448–462.Google Scholar
Schnadig, ID, Fromme, EK, Loprinzi, CL, Sloan, JA, Mori, M, Li, H, Beer, TM (2008). Patient–physician disagreement regarding performance status is associated with worse survivorship in patients with advanced cancer. Cancer 113, 2205–2214.CrossRefGoogle ScholarPubMed
Schoenborn, CA, Adams, PF, Schiller, JS (2003). Summary health statistics for the U.S. population: National Health Interview Survey, 2000. Vital Health and Statistics 10, 1–83.Google Scholar
Singer, MA, Hopman, WM, MacKenzie, TA (1999). Physical functioning and mental health in patients with chronic medical conditions. Quality of Life Research 8, 687–691.Google Scholar
Stiggelbout, AM, de Vogel-Voogt, E (2008). Health state utilities: a framework for studying the gap between the imagined and the real. Value Health 11, 76–87.Google Scholar
Tandon, A, Murray, CJL, Salomon, JA, King, G (2002). Statistical Models for Enhancing Cross-Population Comparability. Global Programme on Evidence for Health Policy Discussion Paper no. 42. World Health Organization: Geneva.Google Scholar
Taubman, SL, Robins, JM, Mittleman, MA, Hernan, MA (2009). Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. International Journal of Epidemiology 38, 1599–1611.Google Scholar
Verbrugge, LM, Lepkowski, JM, Imanaka, Y (1989). Comorbidity and its impact on disability. Milbank Quarterly 67, 450–484.Google Scholar
Wang, PS, Simon, GE, Kessler, RC (2008). Making the business case for enhanced depression care: the National Institute of Mental Health-Harvard Work Outcomes Research and Cost-effectiveness Study. Journal of Occupational and Environmental Medicine 50, 468–475.Google Scholar
Whiteford, H (2000). Unmet need: a challenge for governments. In Unmet Need in Psychiatry: Problems, Resources, Responses (ed. Andrews, G. and Henderson, S.), pp. 8–10. Cambridge University Press: Cambridge, UK.Google Scholar
WHO (2004). The Global Burden of Disease: 2004 Update. World Health Organization: Geneva.Google Scholar
Wolter, KM (1985). Introduction to Variance Estimation. Springer-Verlag: New York, NY.Google Scholar
Young, JG, Hernan, MA, Picciotto, S, Robins, JM (2010). Relation between three classes of structural models for the effect of a time-varying exposure on survival. Lifetime Data Analysis 16, 71–84.Google Scholar