Global burden modeling refers to a set of statistical and computational methods used to estimate the burden of disease across populations, time periods, and geographic regions, particularly where data may be sparse, incomplete, or inconsistent.

To quantify and compare the health impact of diseases, injuries, and risk factors globally, enabling informed public health decision-making and resource allocation.

• Incidence and prevalence
• Mortality and cause-specific death rates
• Disability-Adjusted Life Years (DALYs)
• Years of Life Lost (YLLs)
• Years Lived with Disability (YLDs)
• Attributable burden by risk factor (e.g., PM2.5, hypertension)

• Combines data from:
  • Vital registration systems
  • Epidemiological studies
  • Health surveys
  • Hospital records
• Uses statistical modeling to:
  • Fill data gaps in countries or years lacking direct data
  • Standardize across sources
  • Produce age-, sex-, and region-specific estimates
• Common modeling techniques:
  • Bayesian meta-regression (e.g., DisMod-MR)
  • Ensemble modeling (e.g., CODEm for cause of death)
  • Covariate-driven regression (for risk factor attribution)

• Global Burden of Disease Study (GBD) by the Institute for Health Metrics and Evaluation (IHME)
• WHO Global Health Estimates

• Provides comparable, comprehensive estimates across countries and time
• Guides policy, funding, and priority-setting globally

• Dependent on model assumptions and data quality
• May obscure local variability or context-specific patterns

• Disability-Adjusted Life Year (DALY)
• Epidemiological modeling
• Ecological trend analysis