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

  • J. Alonso (a1) (a2), G. Vilagut (a1) (a2), S. Chatterji (a3), S. Heeringa (a4), M. Schoenbaum (a5), T. Bedirhan Üstün (a6), S. Rojas-Farreras (a1), M. Angermeyer (a7), E. Bromet (a8), R. Bruffaerts (a9), G. de Girolamo (a10), O. Gureje (a11), J. M. Haro (a12), A. N. Karam (a13), V. Kovess (a14), D. Levinson (a15), Z. Liu (a16), M. E. Medina-Mora (a17), J. Ormel (a18), J. Posada-Villa (a19), H. Uda (a20) and R. C. Kessler (a21)...

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.

Copyright

Corresponding author

*Address for correspondence: R. C. Kessler, Ph.D., Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115, USA. (Email: Kessler@hcp.med.harvard.edu)

References

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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), 127.
Baker, M, Stabile, M, Deri, C (2001). What do self-reported, objective, measures of health measure? Journal of Human Resources 39, 10671093.
Breiman, L (2001). Random forests. Machine Learning 45, 32.
Breiman, L (2009). Statistical modeling: the two cultures. Statistical Science 16, 199215.
Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ (1984). Classification and Regression Trees. Chapman & Hall: New York, NY.
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, 525542.
Center for Disease Control and Prevention (2004). Health, United States, 2004. National Center for Health Statistics: Atlanta, GA.
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, 634641.
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.
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.
Fortin, M, Soubhi, H, Hudon, C, Bayliss, EA, van den Akker, M (2007). Multimorbidity's many challenges. British Medical Journal 334, 10161017.
Friedman, JH (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics 19, 1.
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, 7685.
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, 521531.
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, 167180.
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. 1432. Cambridge University Press: New York, NY.
Hosmer, DW, Lemeshow, S (2001). Applied Logistic Regression, 2nd edn. Wiley & Sons: New York, NY.
Insinga, RP, Fryback, DG (2003). Understanding differences between self-ratings and population ratings for health in the EuroQOL. Quality of Life Research 12, 611619.
Jacoby, A, Baker, GA (2008). Quality-of-life trajectories in epilepsy: a review of the literature. Epilepsy Behavior 12, 557571.
Jasso, G (2006). Factorial survey methods for studying beliefs and judgments. Sociological Methods and Research 34, 334423.
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.
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.
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, 179186.
Krabbe, PF (2008). Thurstone scaling as a measurement method to quantify subjective health outcomes. Medical Care 46, 357365.
Krabbe, PF, Salomon, JA, Murray, CJ (2007). Quantification of health states with rank-based nonmetric multidimensional scaling. Medical Decision Making 27, 395405.
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, 16511661.
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, 337343.
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. 163175. Pro-Brook Publishing Limited: London.
Macran, S, Kind, P (2001). ‘Death’ and the valuation of health-related quality of life. Medical Care 39, 217227.
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, 13111320.
Manning, SC (1998). Configuring compliance: a professional fit. Journal of American Health Information Management Association 69, 3638.
Manning, WG, Mullahy, J (2001). Estimating log models: to transform or not to transform? Journal of Health Economics 20, 461494.
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, 289296.
McCullagh, P, Nelder, JA (1989). Generalized Linear Models, 2nd edn. Chapman & Hall: London.
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, 851858.
Mullahy, J (1998). Much ado about two: reconsidering retransformation and the two-part model in health econometrics. Journal of Health Economics 17, 247281.
Murray, CJ, Lopez, AD (1996). Evidence-based health policy – lessons from the Global Burden of Disease Study. Science 274, 740743.
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.
Ohayon, MM (2002). Epidemiology of insomnia: what we know and what we still need to learn. Sleep Medicine Review 6, 97–111.
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, 368375.
Parkin, D, Devlin, N (2006). Is there a case for using visual analogue scale valuations in cost–utility analysis? Health Economics 15, 653664.
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. 3357. Cambridge University Press: New York, NY.
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, 13641371.
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, 326332.
Salomon, JA, Murray, CJ (2004). A multi-method approach to measuring health-state valuations. Health Economics 13, 281290.
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.
Schmidt, L, Room, R (1999). Cross-cultural applicability in international classifications and research on alcohol dependence. Journal of Studies on Alcohol 60, 448462.
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, 22052214.
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, 183.
Singer, MA, Hopman, WM, MacKenzie, TA (1999). Physical functioning and mental health in patients with chronic medical conditions. Quality of Life Research 8, 687691.
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, 7687.
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.
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, 15991611.
Verbrugge, LM, Lepkowski, JM, Imanaka, Y (1989). Comorbidity and its impact on disability. Milbank Quarterly 67, 450484.
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, 468475.
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.
WHO (2004). The Global Burden of Disease: 2004 Update. World Health Organization: Geneva.
Wolter, KM (1985). Introduction to Variance Estimation. Springer-Verlag: New York, NY.
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, 7184.

Keywords

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

  • J. Alonso (a1) (a2), G. Vilagut (a1) (a2), S. Chatterji (a3), S. Heeringa (a4), M. Schoenbaum (a5), T. Bedirhan Üstün (a6), S. Rojas-Farreras (a1), M. Angermeyer (a7), E. Bromet (a8), R. Bruffaerts (a9), G. de Girolamo (a10), O. Gureje (a11), J. M. Haro (a12), A. N. Karam (a13), V. Kovess (a14), D. Levinson (a15), Z. Liu (a16), M. E. Medina-Mora (a17), J. Ormel (a18), J. Posada-Villa (a19), H. Uda (a20) and R. C. Kessler (a21)...

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