Expansion of morbidity

The ‘expansion of morbidity’ theory was the first of the three theories to be put forward. It argues that increasing life expectancy will be accompanied by increased time spent in ill health (Scenario B in Figure 1), leading to so-called ‘failures of success’ (Gruenberg, 1977). According to this theory, medical progress will increase the survival of frail older people, such as those with dementia, so that falling mortality is associated with an increase in morbidity, leading to people living more years in poor health (Gruenberg, 1977; Kramer, 1980; Olshansky et al., 1991). This scenario would likely result in increased health care needs and expenditures.

Figure 1

Scenarios of ageing. Source: Authors’ compilation.

Another related scenario is possible, in which life expectancy increases and the onset of disease occurs at a later age, but where the proportion of years lived in ill health as a percentage of life expectancy increases (Scenario E in Figure 1). We can call this ‘relative expansion of morbidity’. In this case, years of life in good health are gained, but there are also more absolute and relative years as a share of total life expectancy spent in bad health. The implications for health and long-term care systems are unclear, but this scenario may well be associated with increased health care utilization and expenditure. Value judgements have to be made about the various implications, but this scenario may still be desirable, as it implies increased life expectancy and increased years in good health.

Compression of morbidity

The second theory, ‘compression of morbidity’, argues that increasing life expectancies can be accompanied by a later onset of disease and an overall shorter period spent in ill health. In its simplest form, it states that ‘compression of morbidity occurs if the age at first appearance of aging manifestations and chronic disease symptoms can increase more rapidly than life expectancy’ (Fries, 1983). This posits that gains in healthy life years can be greater than gains in life expectancy, reducing the absolute number of years spent in ill health. Fries further clarified that ‘[a]bsolute compression of morbidity occurs if age-specific morbidity rates decrease more rapidly than age-specific mortality rates’ (Fries, 1983). The resulting reduction in the absolute number of years spent in ill health is one possible understanding of the ‘compression of morbidity’ theory. It is illustrated in Scenario C in Figure 1 and we shall call it here ‘absolute compression of morbidity’. This scenario would likely result in reduced overall health care needs and expenditure.

Another related scenario is possible in which life expectancy increases and the onset of disease occurs at a later age (Scenario D in Figure 1). In this scenario, the absolute number of years in ill health increases slightly, but the proportion of years lived in ill health as a percentage of life expectancy decreases. We can call this ‘relative compression of morbidity’. This interpretation of the compression of morbidity scenario can also be found in the literature (GBD 2016 DALYs and Hale Collaborators 2017; Steensma, Loukine & Choi, 2017). Indeed, Fries himself noted in 1983 that ‘[r]elative compression of morbidity occurs if the amount of life after first chronic morbidity decreases as a percentage of life expectancy’ (Fries, 1983). Since the years spent in ill health only increase very slightly (by 1 year in Scenario D), compared to a major increase in years spent in good health (by 9 years in Scenario D), this scenario is also likely to be associated with a decrease in costs to the health system, presuming the years spent in good health are spent productively.

Dynamic equilibrium

The final scenario, ‘dynamic equilibrium’, points to the importance of the severity of morbidity (Scenario F in Figure 1). However, it was never formally defined and several versions have been used, all corresponding to thoughts set out by Manton (Manton, 1982; Manton, Corder & Stallard, 1997; Manton, 1998). One version of the scenario considers it to be the ‘intermediate’ between an expansion and a compression of morbidity, where mortality and morbidity decrease in proportion (Robine et al., 2020). A second version, and one which we use here for Figure 1, argues that there would be an increased prevalence of chronic diseases but that this would be counterbalanced by a decrease in the severity of these diseases. The overall prevalence of disability increases, but mainly as a consequence of increased mild or moderate disability, so the average level of disability among those affected falls (Chatterji et al., 2015). This scenario would result in decreased health care utilization and costs when compared to the expansion of morbidity scenario.

How to make use of these scenarios?

The three (and more) scenarios of ageing are useful in considering the ways in which changes in mortality and morbidity can be interlinked, and in highlighting the potential implications this has for health systems. They also illustrate in a fairly rudimentary way the difficulties in understanding the extent to which people age in good or bad health, as there can be changes in both absolute and relative terms, as well as in the severity of ill health.

However, the scenarios also come with some obvious simplifications. In Figure 1, morbidity is depicted as increasing linearly over time until death, where in reality trajectories tend to be more complicated (Fries, Bruce & Chakravarty, 2011). Furthermore, it is quite likely that a combination of scenarios occurs. The lower severity of morbidity illustrated in the ‘dynamic equilibrium’ scenario could be observed in the ‘compression of morbidity’ or the ‘relative expansion of morbidity’ scenarios. It is also important to be aware of potential differences between countries and between population groups within countries. As Fries argued, there is nothing inevitable about ‘compression of morbidity’ (Fries, Bruce & Chakravarty, 2011), as the health of older populations depends on wider societal developments, as well as health system measures to prevent, treat and manage diseases. Furthermore, even where we see an ‘expansion of morbidity’, this should not be dismissed out of hand, but may still be a worthwhile health policy objective, as it can be associated with an increase in life expectancy and the absolute number of years in good health. A final challenge with the scenarios of ageing concerns the notion of ‘morbidity’, as will be discussed in the next section.

How to measure health and disability?

Any judgement on whether people are living longer in better or worse health depends on the measure of health and disability used. While it is of interest to know about changing patterns of diagnosed disease, it is more important to understand how the health of older people affects their ability to enjoy an active and productive life. We therefore discuss the different ways in which the health status of older people can be measured.

Morbidity and the associated concept of what constitutes good or bad health are not easily defined (Fries, Bruce & Chakravarty, 2011). Morbidity in particular is an ‘imprecise term often defined in different ways’ (Fries, 2012). Changes in the ways in which diseases are measured make it difficult to assess changes over time, and the importance of disease depends to a large extent on how well symptoms can be managed and effects on capacities mitigated. Box 1 explains some of the key concepts and indicators used to measure morbidity and mortality that are discussed in greater detail throughout this brief.

Box 1

Key concepts and indicators of morbidity.

All measures of health are either subjective, objective or a combination of the two. Subjective measures of health rely on self-reporting, while objective measures are based on physical examinations and tests. Objective measures have the advantage of delivering arguably more comparable results but are more resource-intensive to collect and as a result are used less frequently. Additionally, a single assessment may not capture the effects of a condition that fluctuates over time (Stolz, Mayerl & Freidl, 2019) and may not capture the extent to which the objectively measured disease is effectively managed and effects mitigated. Self-reporting has the advantage of easy administration and of capturing how people themselves perceive their health status but can reduce comparability of data over time, and across groups or countries.

Three main strategies have been used to collect information on self-reported or ‘self-perceived’ health status or activity limitations of older people (Chatterji et al., 2015).

• Asking respondents about their overall health status using a 5-point rating scale, commonly termed self-rated (general) health or self-reported (general) health. An example is the question on self-perceived general health included in Eurostat’s measure of healthy life expectancy (see Box 1). This strategy does not capture the details of the different dimensions of health, nor any information on functional limitations.
• Asking detailed questions across several domains of health.
• Asking questions that aim to measure functional independence in ADLs and IADLs, which are then used to quantify health states and measure changes over time (Chatterji et al., 2015). An example are the self-perceived long-standing activity limitations captured for Eurostat’s measure of HLYs, but also (in a more detailed way) many surveys on ageing. Using both ADL and IADL measures (see Box 1) together can allow a meaningful measure of functional disability (Spector & Fleishman, 1998).

Distinct but related concepts: impairment, disability and handicap

The concept of disability is referred to throughout this policy brief. However, it is often confused with other terms, especially impairment and handicap, each with its own specific meaning. An impairment is any loss or abnormality of psychological, physiological or anatomical structure or function. An example might be the loss of a limb. A disability is any restriction or lack (resulting from an impairment) of ability to perform an activity in the manner or within the range considered normal for a human being. A person having lost a leg, for example, might not be able to walk without support, but the use of mobility aids can considerably decrease their level of disability. A handicap is a disadvantage for a given individual that limits or prevents the fulfilment of a role that is considered normal or typical. A person having lost a leg might only be marginally handicapped. The importance of differentiating these three concepts is that an impairment need not necessarily lead to a disability, if the individual concerned can achieve the relevant tasks in other ways. Similarly, a disability need not necessarily be a handicap, but often becomes one because of a failure by society to make necessary adjustments, for example, by enabling people with disabilities access to buildings.

Presence of chronic diseases as a measure of health?

The prevalence of chronic diseases typically rises with increasing age (Barnett et al., 2012), but there are questions over whether this is a useful indicator of poor health. There are two commonly used comorbidity (multimorbidity) scoring systems which have been developed to assist in care planning (Charlson et al., 1994, Elixhauser et al., 1998). In both, people are asked whether they have been diagnosed with a chronic disease; this information is used to generate a score. This type of measure relies on self-reporting, including self-report of diagnosis made by health professionals. There are several problems with using chronic disease diagnoses to measure health status. These include:

• People in different countries or in different socioeconomic groups within the same country may have different levels of access to diagnosis and treatment, as well as different levels of health literacy, so comparisons may be difficult.
• Diagnostic thresholds change over time, so we may see more people being diagnosed, without any underlying increase in prevalence. For example, international guidelines have recently lowered the threshold for diagnosing hypertension (Narita, Hoshide & Kario, 2018) while the introduction of testing for troponin, a highly sensitive marker of myocardial damage, has increased the number of acute myocardial infarctions diagnosed (Meier et al., 2002).
• Patients may not be aware of their diagnoses or report them incorrectly.
• Diagnoses of chronic conditions are a poor measure of disability and functional limitations (Fries, Bruce & Chakravarty, 2011). An individual’s health, irrespective of having a chronic illness, is better defined by being able to execute a series of day-to-day actions and tasks (Chatterji et al., 2015). If the disease is well controlled (which it may be in some health systems but not in others), there may be no impact on other dimensions of health. If the disease is not well controlled, other health problems may follow, lowering self-assessed health and resulting in ADL limitations (Lindgren, 2016).

Finally, trends in the overall prevalence of disease can only provide limited information on whether there is a compression or expansion of disability. The reason for this is that, as populations age, more people survive into the oldest age groups. Overall prevalence of some health conditions might therefore increase, even while age-specific prevalence might be falling. In England and Wales, for example, the overall number of people with care needs was estimated to increase by 25% by 2025, but this reflected shifts in the population age structure rather than an increase in the age-standardized prevalence of poor health (Guzman-Castillo et al., 2017).

Compression of functional decline

Overall, we argue that the existence of functional limitations is a more appropriate measure of health in old age than the prevalence of chronic diseases. This is also in line with how the WHO has defined healthy ageing as ‘the process of developing and maintaining the functional ability that enables well-being in old age’ (WHO, 2015). It is therefore perhaps more appropriate to speak of ‘compression of disability’ than ‘compression of morbidity’. Even more accurately, we can speak of ‘compression of functional decline’.

While much research about ageing has used aggregate disability scores based on ADLs and IADLs, such as the Barthel scale and the Groningen Activity Restriction Scale (GARS), age-related functional decline tends to follow a hierarchy of loss of functions from those functions which are more complex (e.g. driving a car) to those that are basic for survival, such as eating (Bendayan et al., 2017). Accounting for this hierarchical order of loss could potentially help to better quantify functional decline and to inform strategies for prevention and early intervention (Gore et al., 2018).

Figure 2 shows a hypothetical model of the compression of functional decline, where the goal is to shift Trajectory 1 towards the rectangular ideal, resulting in Trajectory 2. In the new trajectory, higher levels of capability are maintained for longer, although life expectancy only increases slightly. If this aim could be achieved on a population level, a compression of functional decline would occur (Gore et al., 2018).

Figure 2

Compression of functional decline (CFD). Source: Gore et al., 2018.

Summary measures of population health

Several aggregated population level indicators combine information on mortality and non-fatal health outcomes in a single number. These are commonly called summary measures of population health (SMPH) or health expectancies. They combine information on life expectancy and prevalence of good health, and thus provide hints on whether the period of disability at the end of life is increasing or shortening (Christensen et al., 2009).

Two of the most widely used SMPHs are HALE and its converse DALYs, both developed by the WHO (see Box 1). Both measures are used in the Global Burden of Disease (GBD) study, led by the Institute of Health Metrics at the University of Washington, which provides annually updated estimates of the worldwide burden of disease from major disease and injuries (GBD 2017 DALYs and HALE Collaborators, 2018).

HALE estimates the expected number of ‘healthy’ years of life in a given population, if current disability and mortality patterns in that population continue. It is obtained by subtracting from life expectancy the number of years lived with a disability multiplied by a weighting that represents the severity of each condition. DALYs express the years of healthy life lost by being in a state of poor health or disability as well as those lost due to premature death. The sum of DALYs across a population represents the burden of disease. It measures the gap between the current health status and an ideal health status where the entire population lives to an advanced age, free of disease and disability. The morbidity components of HALE and DALYS, as calculated in the GBD study, are based on disability weights estimated for the sequelae of, in the most recent version, 354 diseases and injuries (GBD 2017 Disease and Injury Incidence and Prevalence Collaborators, 2018).

A comparison of gains in life expectancy and in HALE has been used to make statements about compression or expansion of morbidity, such as in Canada (Steensma, Loukine & Choi, 2017) or globally (GBD 2016 DALYs and HALE Collaborators, 2017), but HALE, although using health-related quality of life measures to determine disability weights, captures the extent of the burden of disease and disability generally and not the extent of functional limitations as such, which, as discussed above, are a more relevant measure of healthy ageing.

Eurostat publishes data on HLY, based on answers to one question on self-perceived activity limitations (Bogaert et al., 2018), and on healthy life expectancy, based on one question on self-perceived general health. Similar to the concepts of HALE and DALY, both comprise a combination of mortality and self-reported health or disability. HLY is the number of years that a person is expected to continue to live free of self-perceived long-standing activity limitations as captured by data collected for the EU Statistics on Income and Living Conditions (EU-SILC) (Eurostat, 2018b). The relevant EU-SILC question concerning the long-standing activity limitation is: ‘For at least the past six months, to what extent have you been limited because of a health problem in activities people usually do? Would you say you have been: severely limited?, limited but not severely?, not limited at all?’ (Eurostat, 2018b). Since the underlying measure of health used to calculate HLY is activity limitation, it is a disability-free life expectancy (DFLE), similar to HALE, although without considering the presence of disease as such. As argued above, the measure of activity limitations is most appropriate for capturing how well people age, and HLYs, although they are based on a global activity limitation measure, can give an approximate indication of compression or expansion of morbidity. They have been used in projections of EU targets taking into account the different ageing scenarios (Lagiewka, 2012).

Eurostat’s indicator on healthy life expectancy is based on self-perceived general health and combines data on mortality and self-perceived general health. The latter is based on a 5-point scale (How is your health in general? Is it … [very good] [good] [fair] [bad] [very bad]). Self-rated general health has been used in studies that assess the extent of healthy ageing, such as in Germany (Sperlich, Tetzlaff & Geyer, 2019) or Switzerland (Remund et al., 2019). However, it is a rather broad measure and does not allow for distinguishing between people’s perceptions of their somatic and mental health (Remund et al., 2019), nor does it capture the extent of functional limitations.