兰德-技术报告（英文）-2018.6-65页-1mb

Summary of VITALITY AGE V.3 Technical Report

Core Content

Vitality Age (VA.3) is an updated health assessment tool that provides individuals with a risk-adjusted health indicator based on lifestyle choices and clinical risk factors. It estimates an individual's life expectancy by comparing their health profile to that of the average population, adjusting their chronological age accordingly. The model is designed to reflect overall health status through a single number, the Vitality Age, which indicates how many more or fewer years of life an individual is expected to have compared to the population average.

Main Viewpoints

• Conceptual Foundation: Vitality Age is a health indicator that reflects an individual's overall health status using a single number. It is calculated by adjusting actual age based on life expectancy derived from their risk factor profile.
• Formula:
  $$
  \text{Vitality Age} = \text{actual age} + \text{population life expectancy} - \text{individual life expectancy}
  $$
• Improvements Over Previous Version:
  • Incorporates age-related mortality rather than a single parameter for all ages.
  • Uses more comprehensive and consistent data sources.
  • Simulates changes in risk factor values over time rather than assuming constant values.
  • Enhances handling of missing data through predictive regression.
  • Allows for more detailed sensitivity analysis and interactive tools for users.
• Incorporation of Mental Health:
  • Mental health is introduced to the model without compromising its internal coherence.
• Data Sources:
  • Based on the Global Burden of Disease (GBD) Study, which provides data on over 300 diseases and injuries across 188 countries.
  • Uses a Bayesian meta-regression framework to combine data from multiple sources.

Key Information

• Model Purpose: To provide a snapshot of an individual's health status and guide healthier lifestyle choices.
• Stakeholder Use: Supports individual risk assessment, personalized feedback, population risk surveillance, and informs healthcare planning.
• Methodology:
  • Relative Risk Multipliers: Each risk factor is associated with a relative risk (RR) that adjusts mortality rates.
  • Counterfactuals: Compares an individual's risk profile to a "healthy" scenario to estimate the impact of risk factors.
  • Mediation Factors: Accounts for interactions between risk factors, such as the effect of obesity on glucose levels and cardiovascular disease.
• Risk Factors:
  • Includes lifestyle factors (diet, exercise, smoking, alcohol consumption).
  • Includes clinical factors (blood pressure, cholesterol levels, BMI).
  • Incorporates mental health indicators.
  • Offers more than 70 risk factors, selected based on regional importance and data availability.
• Survey Implementation:
  • Uses a short, user-friendly survey with images and categorical questions.
  • Allows integration with modern health and lifestyle trackers (e.g., Apple Watch, Fitbit).
• Geographical Adjustability:
  • Uses country-specific mortality and risk factor data from GBD.
  • Enhances applicability across different regions and populations.

Structure of the Report

• Introduction: Explains the purpose and background of the Vitality Age model.
• Review of Existing Calculators: Analyzes 15 risk calculators used in various countries, focusing on their structure, data sources, and performance.
• Methodological Framework: Describes the updated methodology for VA.3, including data selection, mortality rate calculation, and relative risk multipliers.
• Risk Factor Selection: Details the rationale, process, and specific risk factors included in the model.
• Data Collection and Processing: Explains how data is gathered and transformed for use in the model.
• Survey Questions: Describes the survey design and how it captures risk factor data.
• Conclusion: Highlights the improvements in VA.3 and its potential for future applications and enhancements.

Data Sources and Methodology

• GBD Study: Serves as the foundation for VA.3, providing consistent and comprehensive data across multiple countries and age groups.
• Risk Factor Adjustment: Uses relative risks and mediation factors to adjust mortality rates for individual risk profiles.
• Handling Missing Data: Predictive regression is used to estimate missing biometric values from other available risk factors.
• Sensitivity Analysis: VA.3 allows for detailed analysis of how changes in specific risk factors affect the final Vitality Age.

Collaborators and Funding

• Funding: The report was funded by the Discovery group of companies, part of Discovery Limited.
• Collaborators: Includes contributions from co-authors at RAND Europe and VitalityHealth UK.
• Data Quality: The GBD database offers a broad, global coverage and is updated regularly, ensuring that VA.3 remains current and accurate.

Conclusion

VA.3 represents a significant advancement in the Vitality Age model by incorporating more detailed and up-to-date data, expanding the number of risk factors, and improving the model's adaptability to different geographical and demographic contexts. It aims to support individuals in making informed health decisions and offers a more nuanced and transparent approach to health risk assessment.