Estimating life expectancy and years of life lost in epidemiological studies: a review of methods using an example from multimorbidity

Abstract

Background and objective: There has been an increasing interest in using life expectancy metrics, such as years of life lost (YLL), to explore epidemiological associations. YLL is easier to understand for both healthcare professionals and the lay people and has become a common measure for evaluating public health priorities. As the literature presents a range of approaches to estimate it, this review aims to: (1) summarise the key methods; (2) show how to implement them using current software; (3) apply them in a real-world example. Methods: We investigated simpler nonparametric as well as parametric, model-based methods to estimate of YLL, including: (1) Years of potential life lost (YPLL); (2) Global Burden of Disease (GBD) approach; (3) Chiangs life tables; (4) Epi-demographic approach; and (5) Flexible Royston-Parmar parametric survival model. We used data from the UK Biobank with baseline measures collected in 2006-2010 and linkage to mortality records. We selected 36 chronic conditions: participants with two or more conditions were categorised as having multimorbidity. Results: For the YPLL and GBD method, the analytical procedures allow only to quantify the average YLL within each group (with and without multimorbidity) and, from them, their difference. Conversely, for the Chiangs life tables, the epi-demographic approach, and the Royston-Parmar survival model, both the remaining life expectancy within each group and the YLL could be estimated. In 499,992 UK Biobank participants (white ethnicity, 94%; women, 55%) with a median (IQR) age of 58 (50-63) years, 98,605 (20%) had multimorbidity and 11,871 deaths occurred during the follow-up. The YLLs comparing subjects with vs without multimorbidity varied significantly according to the technique and the modelling approach used: from a longer life expectancy in subjects with multimorbidity using the YPLL and the GBD method to a shorter one using the other three methods (i.e., at 65 years, the YLL were 1.8, 1.3, and 4.6 years using Chiangs, epi-demographic, and Royston-Parmar approach, respectively). Conclusions: When comparing the burden of a disease on life expectancy across studies caution is needed as methods may estimate different quantities. While deciding among different methods to estimate YLL, researchers should consider such differences in relation to the purpose of the research and the type of available data.

Flexible parametric methods for calculating life expectancy in small populations

“…For example, the default knot placement for exible parametric methods in the statistical package Stata ('stpm2') [10,11] for four degrees of freedom is at the 0 th , 25 th , 50 th , 75 th and 100 th percentile of the event distribution for a speci c population. In this study, we used age as the timescale of interest as this is a natural choice for life expectancy calculations and aligns with previous research in this area [12][13][14][15]. The knots were, therefore, placed according the distributions of age at death in the study population.…”