A paper by Briggs et al. (2021) proposes one simple solution. They start by creating standard life tables. Life tables are created by estimating the probability of living one additional year for people of a given age. These can be estimated overall or as conditional probabilities by subgroup (e.g., gender, race, education, etc.).
The Briggs paper incorporates the impact of COVID-19 in three ways: on mortality, quality of life and discounting. To estimate the mortality impact, assume that q(x) is the probability of surviving from age x to age x+1. In that case, let the instantaneous death rate be: d(x) = -ln[1-q(x)]. In this case, one can estimate the impact of COVID on mortality using the standardized mortality ratio (SMR). One can estimate the number of people (out of 100,000) who would live to a given age after COVID-19 as follows:
Next one can incorporate the impact of COVID-19 on quality of life. Quality of life varies across age. Thus, the authors use an estimate of quality of life by age from Janssen and Szade (2004), which they define by the term Q(x). The impact of COVID-19–likely measured as a percentage reduction in quality of life is incorporated. One can measure quality adjusted life expectancy (QALE) for a given age (after incorporating COVID-19) as:
The authors also add in a discount factor as well, which reduces the value of quality-adjusted life year gains that are far into the future. However, one could calculate QALE with or without the discounting.
Although this is a simple and easy to use approach, this methodology assumes a standard mortality impact for all ages and an equivalent proportional reduction in quality of life. It is unclear whether those assumptions are reasonable or not. As the authors note, however, a simple life-table based analysis may be useful for diseases where the evidence evolves rapidly (such as COVID-19).
The authors also test out the use of this approach in practice. Using national statistics life tables, the authors apply this methodology to estimate the impact of COVID-19 in Canada, Israel, Norway, the UK and the US. They find that:
Contrary to some suggestions in the media, we find that even relatively elderly patients with high levels of comorbidity can still lose substantial life years and QALYs…In particular, we compare five different countries and show that differences in the average QALY losses for each COVID‐19 fatality is driven mainly by differing age distributions for those dying of the disease.
Source:
- Briggs AH, Goldstein DA, Kirwin E, Meacock R, Pandya A, Vanness DJ, Wisløff T. Estimating (quality‐adjusted) life‐year losses associated with deaths: With application to COVID‐19. Health Economics. 2020 Dec 24.
