Objectives

Traditional life tables provide valuable insights into mortality patterns but need to be adapted for quantifying the benefits of medical interventions. This article presents a methodology that modifies life tables using relative risk data to estimate incremental changes in life expectancy.

Methods

We developed a calculator that applies this approach, allowing users to input risk-factor data with and without intervention to generate adjusted life expectancy estimates. The calculations to modify life-expectancy were based on estimating ‘effective age’, a concept widely used in risk communication studies.

Results

The calculator provides a structured approach for estimating life expectancy changes that can be used for health economic evaluations. Its user-friendly interface offers policy makers and researchers a framework to explore intervention outcomes.

Conclusions

By incorporating relative risk data into life tables, this methodology supports a structured but simplified assessment of how medical interventions may influence life expectancy.

This research introduces a new method to estimate how medical interventions can change life expectancy using relative risk data. Traditional life tables are useful for understanding mortality rates but do not consider how specific health interventions might affect life expectancy. This study presents a calculator that allows users to enter risk factors with and without interventions to predict changes in life expectancy, making it easier to assess the effectiveness of medical treatments.

The tool uses the concept of "effective age," a way of expressing risk that compares an individual's health risk to that of someone older or younger. For example, a person may be 50 years old but have health risks similar to a 60-year-old. This approach provides a more personalized assessment of how interventions can impact life expectancy, which can be particularly useful for people with chronic conditions like diabetes.

The new calculator offers a straightforward method for policy makers and researchers to evaluate the long-term benefits of medical interventions. By integrating relative risk data into life tables, it provides a simplified framework, helping healthcare decision makers understand how treatments can affect patient outcomes over time. This is important for developing evidence-based health policies and improving economic evaluations in healthcare.

The tool is designed to be user-friendly and can be used on personal computers, making it accessible for local application. Its flexibility allows for personalized adjustments to life expectancy estimates, which can be especially useful when more complex computer models are unavailable.

Overall, this study fills a gap in existing tools for health intervention evaluation, offering preliminary insights into how interventions can improve life expectancy. By using this new method, healthcare providers and researchers can better understand and communicate the potential benefits of treatments, ultimately contributing to more informed healthcare decisions and strategies.

^{Note: This content was created with assistance from artificial intelligence (AI) and has been reviewed and edited by ISPOR staff. For more information or for inquiries on ISPOR’s AI policy, click here or contact us at info@ispor.org.}