国家健康：健康强则经济强

Stronger health, February 2026 Table of contents Estimate the health improvements from scalingselected health interventions3 Estimate the incremental cost and economic return This appendix outlinesthe methodology and key assumptions underlying the Prioritizing Health model,which assesses the global disease burden and models the potential impact of scaling proven cost-effective This analysis represents an “art of the possible” approach, aiming to estimate the potential benefitsof expanding access to proven health interventions on a global scale. While it provides a high-levelperspective on the opportunities and impact, it is important to acknowledge that there are inherent The model comprises three main analytical steps: —Estimate the health improvements from scaling selected health interventions. —Quantify the economic benefit of improved health. —Estimate the incremental cost and economic return. Estimate the health improvements from scaling selected health interventions The model adopted a bottom-up approach, assessing individual health conditions in detail and estimatingthe potential reduction in disease burden achievable through the scale-up of specific interventions. Itencompassed approximately 90 conditions, representing about 85 percent of the total global diseaseburden, and incorporated risk-attributable burden from around 27 risk factors, which together accounted For the remaining conditions—constituting the residual 15 percent of the global disease burden—an averageimpact proportion was applied, derived from comparable conditions within the same disease group or 1. Select conditions for in-depth analysis The model used the Institute for Health Metrics and Evaluation (IHME) Global Burden of Disease (GBD)2021 data suite as the analytical baseline. Projections of future disease burden were drawn from IHME’sforecast estimates, which are based on a comprehensive and systematically curated global health database.Nevertheless, forecasts of disease burden are inherently uncertain, and unanticipated health shocks, policy Within the GBD data set, disease burden is quantified using disability-adjusted life years (DALYs) andcauses are organized in a hierarchical structure: Level 0 aggregates all causes; Level 1 distinguishesbroad cause groupings of communicable, noncommunicable, and injury causes; Level 2 disaggregatesdisease burden into 22 major disease groups, including cancers, cardiovascular disorders, and mentaldisorders; and Levels 3 and 4 comprise progressively more detailed, condition-specific classifications regions, income archetypes,2and age groups. Additional conditions were included where needed to ensurerepresentativeness. This process resulted in a preliminary list of 46 conditions, presented in Exhibit 2. Web Exhibit <2> of <5> severity reduction, or both. For certain broad disease categories, more detailed analysis at a lowerclassification level was deemed necessary to improve accuracy. In such cases, Level 3 categories werereplaced with their corresponding Level 4 disease classifications. This refinement was applied to conditionssuch as diabetes mellitus (assessed separately as type 1 and type 2), stroke (ischemic stroke, intracerebralhemorrhage, and subarachnoid hemorrhage), headache disorders (migraine and tension-type), blindness and Following these adjustments, the final deep-dive analysis covered 89 diseases, with all disease groups wellrepresented, covering about 85 percent of the total global disease burden. 2. Estimate risk-attributable disease burden For conditions in which disease burden is attributable to specific risk factors (for example, type 2 diabeteslinked to high BMI or ischemic heart disease associated with high blood pressure), more-granular risk-attributable disease burden estimates were incorporated into the model. The analysis included 27 riskfactors spanning metabolic, environmental or occupational, and behavioral categories, aligned with the According to the IHME risk burden data set, most conditions are associated with multiple risk factors thatcontribute jointly to the overall disease burden. These risk factors are often interdependent—for instance,metabolic risks such as high fasting plasma glucose and high BMI may overlap in their contribution todisease outcomes. To prevent double counting when estimating the potential impact of interventions 3. Identify and categorize interventions A review of clinical literature was conducted to identify cost-effective and scalable interventions withthe greatest potential for health impact, including both preventive measures aimed at reducing diseaseincidence and therapeutic interventions addressing established disease. The objective of this process wasto determine high-impact interventions that could substantially reduce disease burden if implemented moreeffectively and if access gaps were minimized. Sources used for the intervention review included clinicalg