The cross-sectional average length of healthy life (HCAL): a measure that summarizes the history of cohort health and mortality

Abstract

Background: Healthy life years have superseded life expectancy (LE) as the most important indicator for population health. The most common approach to separate the total number of life years into those spent in good and poor health is the Sullivan method which incorporates the health dimension to the classic period life table, thus transforming the LE indicator into the health expectancy (HE) indicator. However, life years derived from a period life table and health prevalence derived from survey data are based on different conceptual frameworks. Method: We modify the Sullivan method by combining the health prevalence data with the conceptually better fitting cross-sectional average length of life (CAL). We refer to this alternative HE indicator as the "cross-sectional average length of healthy life" (HCAL). We compare results from this alternative indicator with the conventional Sullivan approach for nine European countries. The analyses are based on EU-SILC data in three empirical applications, including the absolute and relative level of healthy life years, changes between 2008 and 2014, and the extent of the gender gap. Results: HCAL and conventional HE differ in each of these empirical applications. In general, HCAL provides larger gains in healthy life years in recent years, but at the same time greater declines in the proportion of healthy life years. Regarding the gender gap, HCAL provides a more favourable picture for women compared to conventional HE. Nonetheless, the extent of these differences between the indicators is only of minor extent. Conclusions: Albeit the differences between HE and HCAL are small, we found some empirical examples in which the two indicators led to different conclusions. It is important to note, however, that the measurement of health and the data quality are much more important for the healthy life years indicator than the choice of the variant of the Sullivan method. Nonetheless, we suggest to use HCAL in addition to HE whenever possible because it widens the spectrum of empirical analyses and serves for verification of results based on the highly sensitive HE indicator.

The impact of population's educational composition on Healthy Life Years: An empirical illustration of 16 European countries

“…In terms of HLY, the period life table links together the age-specific health and mortality information from individuals with substantially different set of historical conditions and behaviors ( Guillot & Canudas-Romo, 2006 ; Vaupel et al, 1979 ; Luy et al, 2020 ). From a conceptual point of view, it is therefore more reasonable to study health and mortality from a cohort perspective ( Guillot, 2011 ; Sauerberg et al, 2020 ). This holds in particular for the case of education because educational expansion is mostly a cohort-based phenomenon and its progress differs between countries.…”

The role of education for assessing population health: An analysis of healthy life expectancy by educational attainment for 16 European countries

“…There is also a technical advantage to estimating HLE by educational attainment. HLE levels and trends have been shown to be particularly sensitive to the underlying prevalence data, as opposed to mortality information (Sauerberg, Guillot and Luy 2020). In other words, the reliability of HLE estimates depends mostly on the accuracy of prevalence data.…”

“…Thus, researchers have been working to better harmonize health data in order to measure population health across Europe more accurately (Bogaert et al 2018, Jagger andRobine 2011). Further, Luy et al (2020) show that variations in summary indicators such as LE or HLE do not necessarily reflect actual health and mortality differences over time or across countries. This is because several effects may potentially distort the comparability of HLE estimates, which particularly concerns population heterogeneity.…”

The Sensitivity of the Healthy Life Years Indicator: Approaches for Dealing with Age-Specific Prevalence Data

“…In addition, health is a stock variable sensitive to past experience (Barendregt et al 1997;Brouard/Robine 1992;Murray et al 2002) and with a complex interaction between health and mortality, making it diffi cult to accurately model age-specifi c health prevalence (Riffe et al 2016(Riffe et al , 2017. In this regard, it has been shown that some relational models provide a good fi t for modelling some types of disabilities by age (Marshall et al 2013), while alternative summary measures that incorporate the mortality history of cohorts and therefore combine health and mortality information have also been proposed (Sauerberg et al 2020). However, these methods are more important to capture the relationship between age-specifi c prevalence of disability and various disability types rather than to harmonise and extrapolate prevalence at older ages.…”