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visualizing disease and injury rates

visualizing disease and injury rates. Ian Bolliger Institute for Health Metrics and Evaluation. Background. DisMod 3 model produces country-age-sex-year-specific prevalence, incidence, excess mortality Over 20,000 estimates per disease cause Models can be very sensitive to parameter choice

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visualizing disease and injury rates

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  1. visualizing disease and injury rates Ian Bolliger Institute for Health Metrics and Evaluation

  2. Background • DisMod 3 model produces country-age-sex-year-specific prevalence, incidence, excess mortality • Over 20,000 estimates per disease cause • Models can be very sensitive to parameter choice • Need a way to evaluate plausibility of each disease model along several criteria • Solution: Visualizations

  3. Challenges • Model results stored in several formats (i.e. CSV, JSON) • Model is still evolving • Result formats will continue to change • Need to adapt to researchers desiring new visualizations

  4. Implementation • Two types of summary figures deemed most valuable • Age pattern of disease by region • Scatterplot of age-standardized rate in 1990 and 2010 by country • Class developed to standardize format of model results at country and region level • MeanEstimates • Methods within this class written to produce plots

  5. Results

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