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How medical advances and health interventions will shape future longevity

  • L. A. Gitsels, E. Kulinskaya and N. R. Wright

Abstract

Medicine-related research includes numerous studies on the hazards of mortality and what risk factors are associated with these hazards, such as diseases and treatments. These hazards are estimated in a sample of people and summarised over the observed period. From these observations, inferences can be made about the underlying population and consequently inform medical guidelines for intervention. New health interventions are usually based on these estimated hazards obtained from clinical trials. A lengthy lead time would be needed to observe their effect on population longevity. This paper shows how estimated mortality hazards can be translated to hypothetical changes in life expectancies at the individual and population levels. For an individual, the relative hazards are translated into the number of years gained or lost in “effective age”, which is the average chronological age with the same risk profile. This translation from hazard ratio to effective age could be used to explain to individuals the consequences of various diseases and lifestyle choices and as a result persuade clients in life and health insurance to pursue a healthier lifestyle. At the population level, a period life expectancy is a weighted average of component life expectancies associated with the particular risk profiles, with the weights defined by the prevalences of the risk factor of interest and the uptake of the relevant intervention. Splitting the overall life expectancy into these components allows us to estimate hypothetical changes in life expectancy at the population level at different morbidity and uptake scenarios. These calculations are illustrated by two examples of medical interventions and their impact on life expectancy, which are beta blockers in heart attack survivors and blood pressure treatment in hypertensive patients. The second example also illustrates the dangers of applying the results from clinical trials to much wider populations.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

*Correspondence to: Elena Kulinskaya, School of Computing Sciences, Sciences 2.03, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK. Tel: +44 (0)1603 59 2692. E-mail: E.Kulinskaya@uea.ac.uk

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