Healthy lifespan inequality: morbidity compression from a global perspective

Summary

Healthy lifespan inequality (HLI) indicators measure the variability in age-at-morbidity onset, so they can be used to assess the time patterns of individuals’ health deterioration.

This study documents, for the first time, the levels and trends in HLI indicators across regions and countries all over the world for a period of 30 years, from 1990 to 2019. Global HLI declined from 24.74 years (80% UI 21.59−27.74) in 1990 to 21.92 (17.79−25.80) in 2019. Generalized declines are observed across regions, except for high-income countries, where HLI has remained stagnant – and even increasing slightly since 2000. At ages above 65 years, HLI65 increased over the study period in all seven super-regions. Importantly, the values of HLI tend to be substantially larger than those of LI in all regions except for sub-Saharan Africa, and such differences increase over time. Higher values of LE tend to be associated with lower values of LI, but the relationship between HALE and HLI is considerably weaker (especially among longevity vanguard countries). In general, males tend to exhibit higher levels of LI than females, but the opposite is observed with HLI.

Interpretation

Our results suggest that, while the variability in the ages at which morbidity starts has decreased in most high-mortality countries, it has remained constant or has even increased for an ever-growing number of countries, especially among the longevity vanguards. In those countries, even if age at death is retreating and becoming increasingly concentrated at older ages [11, 15, 22,23,24], it is less clear whether morbidity onset is also being shifted towards older ages [5, 25, 26]. In general, improvements in reducing mortality rates for most causes have not been matched by similar declines in disability rates, which have either been stagnant or have increased for several causes, like diabetes or mental and substance use disorders [7]. Our findings cohere with previous studies exploring mortality and morbidity dynamics from a global perspective – while expanding and complementing those analyses in several directions.

In this paper, we find that the variability in the ages at which morbidity starts (HLI) can be much larger than the variability in the ages at death (LI), and such difference broadens over time. When this happens, the range of ages in which most individuals’ health deteriorates becomes wider than the range of ages in which most individuals die – a key finding with fundamental implications for planners aiming at improving population health and reducing health inequalities. For instance: retirement or health care policies exclusively based on (increasing) trends in life expectancy might miss the mark and have deleterious social consequences if the corresponding morbidity onset distribution widens over time. Indeed, HLI levels are estimated to be up to 36% higher than LI among women in high-income countries. The only exception to this general pattern is found in sub-Saharan Africa, where LI levels are slightly higher than those of HLI – even though they become increasingly similar over time (see Fig. 4 and SI Appendix 6). This might be attributable to the high levels of child mortality in the region [27] (which generate bimodal age-at-death distributions with high levels of lifespan inequality) together with the fact that the incidence of morbidity is relatively low at those younger ages. Further research is needed to examine what are the specific age groups and causes of death, diseases or conditions driving the trends we document.

The strong and negative association between country-specific LE and LI estimates (Fig. 5 panel A) has been widely documented [22,23,24, 28]. It suggests that the normatively desirable goals of increasing countries’ average longevity and reducing age-at-death inequality can be achieved simultaneously. However, a different picture emerges when inspecting the relationship between HALE and HLI (Fig. 5 panel B). There are many countries and regions experiencing increases in HALE concomitantly with declines in HLI – thus suggesting that a ‘compression’ [8] of morbidity is occurring in those places – (e.g., sub-Saharan Africa, south Asia, north Africa and Middle East, or other regions at the bottom of the corresponding distributions with plenty of room for improvement). At the same time, there seems to be a threshold above which further gains in HALE are not necessarily accompanied with HLI reductions. This is the case for a considerable number of countries – particularly those from central Europe, eastern Europe and central Asia and, specially, high-income countries – where it is not clear whether morbidity is ‘expanding’ [17] or ‘compressing’ [8]. Thus, while high-mortality countries have been generally successful in increasing (healthy) longevity and simultaneously reducing (healthy) lifespan inequality, low-mortality countries have made no further progress in reducing the variability in healthy lifespans. Such stagnation might be the outcome of forces pushing in opposite directions. On the one hand, improvements in living standards or the promotion of healthier lifestyles (e.g., better diets, regular exercise, avoiding alcohol consumption or smoking) [29, 30] can postpone the deterioration of individuals’ health. On the other hand, the implementation of prevention and screening programs that decrease the age at diagnosis of important diseases (e.g., cancers or mental disorders) [31, 32] as well as persistent and increasing socioeconomic inequalities [33] can contribute to widen age-at-morbidity onset distributions.

These troubling trade-offs between health equality and efficiency become even more pronounced when inspecting trends at ages above 65 years. Our findings indicate that countries’ overall success in increasing LE65 and HALE65 inevitably comes at a cost: a simultaneous increase in both LI65 and HLI65, indicating greater heterogeneity among the elder population. These results cohere with recent studies reporting increasing trends in lifespan inequality at age 75 and a positive relationship between LE65 and LI65 [23], and suggest that the variability in morbidity onset at older ages (HLI65) is going in the same direction. Indeed, Seaman et al. also find that the variability in the age at morbidity onset in Denmark (as measured by the age at first hospital admission among adults aged 60 and above) also tends to increase from the 1990s onwards [9]. Unfortunately, the approach followed in that study cannot be replicated at a global scale because of its reliance on hospital admission data – which is difficult to access and is not easily comparable across countries. Likewise, our findings on increasing health variability among the elder are in line with recent studies documenting an increase in the diversification of the causes from which individuals die in low-mortality countries – especially among individuals dying at ages above 50 [34]. Put together, these studies posit that, as mortality is pushed towards increasingly higher ages, the health profiles of the survivors are increasingly diverse (i.e., with an increasingly heterogeneous mix of robust and frail individuals).

The findings presented here consistently point towards a compositional shift in health inequalities within countries: as longevity increases, morbidity-related inequalities are gaining prominence with respect to mortality-related ones. That is, generalized improvements in living standards, medical innovations, the spread of technological breakthroughs and public health policies might have contributed to reduce mortality-related inequalities across world countries. This is reflected in the decreasing inequalities in basic survival indicators like life expectancy at birth, mostly driven by the reduction of infant and child mortality [5, 27]. However, these improvements have in turn contributed to the emergence of new layers of morbidity-related inequalities among adults at older ages, often to the advantage of privileged countries or socio-economic groups [10, 35]. Stated otherwise: the same structural improvements that have contributed to increase the survival chances of the worse-off have in turn delayed the emergence of health-related inequalities to older ages. This is yet another instance of the successive waves of convergence-divergence cycles in health stipulated by the health transition theory suggested by Frenk et al. [36] and later adopted by Vallin and Meslé [37].

Furthermore, our results shed new light on the male-female health and mortality differences. Women tend to live longer, and the length of their lives are less unequally distributed than men (Fig. 4) [6]. However, the female advantage is less obvious, or even disappears, when looking at the length of healthy life. It is well known that the sex gaps in HALE are considerably smaller than those in LE [5, 6]. As regards healthy lifespan inequality, we find that in approximately three quarters of our country-year observations female HLI levels are higher than those of males. These findings suggest that, not only women spend longer fractions of their lives in less-than-good health as postulated in the ‘health-survival paradox’ [6, 38], but that they tend to face greater uncertainty than men regarding the ages at morbidity onset. Further research on the determinants of the increasing sex-differences in HLI is also needed.

Limitations

Our paper has several limitations. First, our estimates are exclusively based on period life tables. Unfortunately, longitudinal methods can only be applied in a highly reduced number of data-rich countries. Hence, we rely upon the synthetic cohort approach in which individuals are subject to period-specific mortality and morbidity conditions along their lifetimes, something that is customarily used in the estimation of LE, LI and HALE indicators. Second, the methods used to generate age-at-morbidity onset distributions implicitly rely on the assumption that individuals cannot recover from their ‘less-then-healthy’ status. While somewhat unrealistic, this is the simplifying assumption underlying the Sullivan method [39], which under mild regularity conditions is generally acceptable for monitoring long-term trends in HALE [40], and has been widely used for estimating HALE indicators [6], also by GBD [5]. In addition, the definition of HALE is exclusively based on the prevalence and severity of diseases and health conditions, but fails to take into consideration other, more holistic, dimensions of health (like self-reported health, or pain and discomfort levels) typically included in other approaches, like the EQ-5D measure of the EuroQol Group [41]. Third, the quality of the mortality and, specially, morbidity data varies considerably across countries (an issue that is partially attributable to the different sampling strategies followed to obtain health information), which is reflected in the uncertainty intervals of our HLI estimates. All our analyses are based on levels and trends of the point estimates, but conclusions should be formulated with caution given their uncertainty [19]. Lastly, the outbreak of Covid-19 pandemic has not been included in our analyses. A comprehensive study on the potential impact that the pandemic might have on the population health indicators investigated here is extremely important, but should probably await better data.

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