DYNAMO – HIA tool Margarete Kulik, Wilma Nusselder & Hendriek Boshuizen Equity 2020, Brussels, October 17, 2012 DYNAMO-HIA tool… • …Is a ready-to-use tool to project the effects of changes in risk factor exposure due to policy measure or intervention on disease-specific and summary measures of population health • Organizes and stores necessary input data • Synthesizes according to standard causal epidemiological pathway • Projects how changes in risk factor distribution affect diseasespecific and summary measures of population health Scope of DYNAMO-HIA tool Reference scenario Description of “business as usual” situation: demographic, epidemiological and risk factor exposure Intervention scenario Changed risk factor exposure: changed prevalence and/or changed risk factor transition rates DYNAMO-HIA Estimation of change in large set of health outcomes: comparison of reference and intervention scenario DYNAMO-HIA tool: one risk factor but can be combination of risk factors Risk factor exposure: - Categories: never, current, former smokers - Continuous: mean BMI - Compound: former smokers by time since quitting Partitioning population along risk factors: - BMI*smoking - SES*smoking Large set of output measures • Future risk factor prevalence by age, sex and year • Future disease prevalence by age, sex and year • Future mortality/survival by age, sex and year • Structure of population by age, sex, diseased vs. non-diseased • Summary measures of population health - Life expectancy - Life expectancy with(out) diseases - Disability-adjusted Life expectancy cohort and population Wrapping up: DYNAMO-HIA current situation Dynamo-HIA is generic tool that: • simulates a real life population trough time (=dynamic) • models explicit risk factor states • has modest data requirements: uses population-level data • provides large set of outcome measures • is generally accessible: publicly available + no programming skills • includes database with data for large number of EU countries on: – 3 risk factors: smoking, overweight, alcohol – nine diseases: IHD, stroke, diabetes, COPD, 5 cancers – demographic situation Special needs of Equity 2020 project • Splitting up population by SES groups • Regional analyses Different options Two issues: • Data needs • Presentation of output SES groups Option 1: Each SES group as a separate population Data: all data, including mortality + IPM data of diseases by age, sex and SES ->huge challenge! Output: separate output for each SES*sex group -> user should make own figures and tables to have overview of disparities Option 2: partitioning population along combination risk factor * SES Data: Prevalence and RR by SES*risk factor (SES-low*non-smoker, etc) Output: Disease information by SES*risk factor, but not LE, mortality, etc! Regions Option 1: Each region as a separate population Data: all data, including IPM data of diseases by age, sex and region->huge challenge! Output: separate output for each region*sex group -> user should make own figures and tables to have overview of disparities Option 1b: Each region as a separate population, but use local risk factor data and national disease data Data: Demographics, prevalence RF by region, rest national Output: separate output for each region*sex -> user should make own figures and tables to have overview of disparities Wrapping up: DYNAMO-HIA Equity 2020 Data needs > available data • Some tricks are possible, but even at national level input data could not be estimated for all MS! Output: not designed for disparities (in addition to sex) • Each sub-population as population (option 1): post-processing is needed • Partitioning risk factors (option 2): important output not available by SES • This can be adapted in DYNAMO-HIA with additional funding • DYNAMO-HIA for HLY without diseases (expected mid 2013) will avoid need for IPM data in this particular case Funding • Project was funded by the Executive Agency for Health and Consumers (EAHC) • Was part of the EU Public Health Program 2003-2008 of the European Commission's Directorate General for Health and Consumer Affairs (DG SANCO) • Co-financing received from the Erasmus Medical Center Rotterdam, the Institute of Public Health and the Environment in the Netherlands, the Catalan Institute of Oncology, the International Obesity task force, the London School for Hygiene and Tropical Medicine, the Haughton Institute in Dublin, and the Instituto Tumori in Milan. THANK YOU FOR YOUR ATTENTION Example output for smoking Example output for smoking Tool can be downloaded • www.dynamo-hia.eu • Tool • User guide • Macros