Course 10 Global Burden of Disease: Impact of Vision Loss Contribution of the GBD and dissemination of results Serge Resnikoff MD, PhD On behalf of the.

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Course 10 Global Burden of Disease: Impact of Vision Loss Contribution of the GBD and dissemination of results Serge Resnikoff MD, PhD On behalf of the GBD Vision Loss Expert Group

Why do we need data? 1. To support Advocacy “Advocacy is the process of influencing decision- makers to create change” Requires best possible information for – making effective policy decisions – mobilizing more resources

Why do we need data? 2. To support priority setting, planning, monitoring and evaluation Especially for quantifying how much needs to be prevented and treated Implies: – cause-specific data – country level data – periodically updated data – comparable data over time for trends analysis

In the Past WHO/PBD Data Bank, initiated by AD Negrel. – Two closets in a corridor – Two papers published: lists of publications

WHO/PBD Data Bank

In the Past WHO/PBD Data Bank, initiated by AD Negrel. – Two closets in a corridor – Two papers published: lists of publications – 2006: attempt of integration in the WHO InfoBase

In the Past WHO/PBD Data Bank, initiated by AD Negrel. – Two closets in a corridor – Two papers published: lists of publications – 2006: attempt of integration in the WHO InfoBase Used to generate periodic “global (and regional) estimates of Blindness and Visual impairment” – 1970, 1976, 1990 – 1996, 2002, 2004, 2008, 2010

Contribution WHO Global Estimates – initially used for the first GBD exercise – then used to feed the WHO annual statistical report (part of World Health Report) – till 2004

1996

1990 (WDR 1993) Sense Organ Cataract Glaucoma VAD Oncho Trachoma

W W WHR 2003 Glaucoma Cataract Vision loss, age related and other

Contribution WHO Global Estimates – major role in the genesis of V2020 (avoidable blindness, trends due to ageing, magnitude of URE) – Mainly used for advocacy and communication Previous GBD data (1990/96, GBD 2004) – Based on WHO/PBD estimates – high impact on Cost Effectiveness analysis (cataract, oncho, VAD…) – major role in “ranking” VI against other conditions (issue of groupings)

GBD 2001 (2006) Top 10 Causes of Years Lived with Disability Cataract Vision disorders, Age-related

Second Edition (2006) Cataract Glaucoma Trachoma Onchocerciasis Other 3.2% of total DALYs

Major issue: data are not directly comparable Resnikoff & Keys, IJO 2012

How do the findings differ?

Dissemination challenges: What is needed? country level data (prevalence and causes) for advocacy, priority setting, planning and monitoring. data easy to understand and visualize

Visualisation

Dissemination challenges: What is needed? country level data (prevalence and causes) data easy to understand and visualize data easy to access and use (web based) data regularly updated (as for mortality or demographic data) – implies specific resources and organization

Dissemination: Publication plan Published: Editorial: Global Burden of Visual Impairment and Blindness. Bourne R, Price H, Stevens G. Arch Ophthalmol. 2012;130(5): Accepted for publication : The Global Burden of Disease Project: Rationale and Methodology of the Systematic Review by the Vision Loss Group. Bourne R, Price H, Taylor H, Leasher J, Keeffe J, et al. Ophthalmic Epidemiology. Accepted 1 Sept Submitted: Global Prevalence of Vision Impairment and Blindness: Magnitude and Temporal Trends, Stevens G, White R, Flaxman S, Price H et al. PLoS Medicine

Dissemination: Publication plan Planned: – Cause specific data – at global level – Regional papers combining causes with prevalence of Vision Impairment and Blindness Also capstone papers from the GBD Core group on: – Disability Weights – DALYs.

Plan for the Future (5 years) 1.Maintain the global data base and provide periodic updates: – Update the database by annual extensions of the systematic review – Release an interim update in 2014/15. – Revisit the statistical model and provide a 5 year update in Create an internet-based portal to: – provide access to population-based prevalence data by age, by sex, by region, by country, and by cause. – model the data temporally, both retrospectively and prospectively. – also provide additional parameters such as GDP and other metrics to develop visualisations. Project is supported by BHVI