html5-img
1 / 19

Global Burden of Disease 2010 : Estimating burden of disease attributable to mental disorders

Global Burden of Disease 2010 : Estimating burden of disease attributable to mental disorders. Amanda Baxter Queensland Centre for Mental Health Research The University of Queensland Brisbane, AUSTRALIA Australian Society for Psychiatric Research Dunedin, 2011. 1.

maya
Download Presentation

Global Burden of Disease 2010 : Estimating burden of disease attributable to mental disorders

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Global Burden of Disease 2010 : Estimating burden of disease attributable to mental disorders Amanda Baxter Queensland Centre for Mental Health Research The University of Queensland Brisbane, AUSTRALIA Australian Society for Psychiatric Research Dunedin, 2011 1

  2. The Global Burden of Disease Study, 1990 • A major finding of the study was the magnitude of burden associated with chronic disease, particularly mental disorders. • 15-44 yr age group: 5 of 10 leading causes of burden in the world were mental disorders • Depression was the leading cause of disease burden • Ref: Murray & Lopez, 1996

  3. Changes what is being done differently in GBD2005 • More disorders • Emphasis on empirical evidence • New disease modeling tool (Dismod3) • - Derive missing data, use of co-variates • Disability weights • Discounting • Age weighting

  4. Mental disorders – GBD1990 and 2010

  5. Illicit drug use disorders – GBD1990 and 2010

  6. Results of Systematic review

  7. Changes what is being done differently in GBD2005 More disorders Emphasis on empirical evidence New disease modeling tool (Dismod3) - derive missing data, use of co-variates 4. Disability weights 5. Discounting 6. Age weighting

  8. 3. Disease modeling • Disease models derived using a new software application Dismod3 developed at the IHME, University of Washington. • Will derive estimates for countries where no/little data available • Can apply an adjustment factor to estimates based study characteristics • eg. Autism - Adjust estimates from studies where case finding is passive (Case Registries) to approximate estimates from studies where case finding is active (Birth cohorts) • Derive missing data taking into account country/population characteristics • eg. Anxiety disorders and major depression in conflict and post-conflict countries.

  9. Changes what is being done differently in GBD2005 • More disorders • Emphasis on empirical evidence • New disease modeling tool (Dismod3) • - Derive missing data, use of co-variates • Disability weights • Discounting • Age weighting

  10. essential optional DALYs: Social values 1. How to compare years lost due to death with years lived in poor health?  DW values between 0 and 1 2. Value of health year of life equal at all ages?  age weights 3. Value of future years of life?  discounting http://www.who.int/healthinfo/global_burden_disease/daly_disability_weight/en/index.html

  11. 4. Disability Weights • In GBD, non-fatal consequences of diseases and injuries understood as transitions through different ‘health states’ • YLD calculation requires aggregate assessments of the overall decrements in health associated with particular health states  disability weights • DWs are measures of overall levels of health rather than contribution of health to overall welfare • GBD1990 : • DW elicited from panel of health professionals following explicit protocol evaluating 22 indicator conditions in an intensive group exercise with ‘deliberative phase’ using person trade-off (PTO) method. Responses averaged across participants

  12. New Disability Weight Project • The new DW will have a greater emphasis on paired comparisons, anchored by time trade-off methods. • It also aims to engage members of the general community (including those in developing countries) to a greater degree. • The DW project is being carried out in two stages: • a community household survey in selected regions, and • an online open-access survey.

  13. 5. Discounting • Discounting common practice in economic analyses. • Assumes that individuals value their health more now than at some point in the future. So the further in the future health loss occurs the more they are discounted. • GBD1990 used 3% discounting

  14. Why discount? Consistency with cost-effectiveness analyses Prevent giving ‘excessive’ weight to deaths at younger ages

  15. 6. Age weighting • Used to reflect a social preference that values a year lived by young adult more highly than that of young children or the elderly. • Eg. An Australian survey found that respondents considered saving four 20-year olds as important as saving ten 60-year olds (Nord et al, 1996 and 1998) • Not related to productivity but ‘social’ role in caring for the young and old

  16. Age weighting Arguments against: • Unacceptable on equity grounds • Does not reflect actual social values But: everyone potentially lives through every age  not inequitable 1.6 1.2 relative value 0.8 0.4 0.0 0 20 40 60 80 100 age

  17. Source: Murray and Lopez, 2006. Chapter 5

  18. Conclusions and consequences • Ranking of mental disorders and illicit drug use disorders? • More disorders • MD considered risk for other health outcomes • ...but if no discounting & no age weighting .... • Vastly expanded evidence base • Disability weights ?

  19. Acknowledgements Co-Chairs - Mental Disorders and Illicit Drug Use Disorders Expert Group Prof Harvey Whiteford Prof Louisa Degenhardt GBD Technical Assistance Prof Theo Vos Rosana Norman Research Team Alize Ferrari Fiona Charlson Adele Somerville Roman Schuerer Holly Erskine

More Related