“Multimorbidity has emerged as one of the greatest challenges facing health services, both presently and in the coming decades”. (Pearson-Stuttard et al., 2019)
Addressing the population and service challenges presented by multiple long-term conditions (multimorbidity) is a local and national priority and a major strategic priority for the NIHR. It was highlighted in the Government’s Health and Care White Paper (2021):
“In recent years, we have seen our health and care system adapt and evolve to meet the challenges facing health systems around the world. Not only is our population growing in size, people are also living longer but suffering from more long-term conditions. One in three patients admitted to hospital as an emergency has five or more health conditions, up from one in ten a decade ago.” Integration and Innovation: working together to improve health and social care for all, p5
The ambition for more integrated care, set out in the White Paper, has been central to strategies to address the challenges presented by multiple conditions. Yet as research has and is uncovering, this is far from sufficient to address the complex issues raised by the rising number of people with multiple conditions. This briefing aims to unpack the issues for those wrestling with them locally and nationally.
It explores the impact of multiple conditions both on people’s lives and on the healthcare service. It looks at the dynamic interaction between different conditions – physical and mental – and between risk factors such as people’s behaviour, and socioeconomic status. These interactions are not fully understood but they may hold the key to understanding common clusters of conditions. Clusters, in turn, offer insights into the underlying disease pathways; improved understanding will drive the development of new treatments but could also lead to new priorities in public health and healthcare services
We hope this briefing will help deepen understanding and lead to the development of more effective local strategies.
- Executive summary
- The problems and outcomes that matter most to people with multiple conditions and their carers
- Care and workforce models
- The impact on health outcomes and health and care services
- Patterns and trends in disease
- Risk Factors
- Understanding clusters
- Prevention strategies
This briefing aims to make sense of the evidence on multiple long-term conditions (multimorbidity). It builds on the Academy of Medical Science’s 2018 report “Multimorbidity: a priority for global health research”, and draws on research funded by the NIHR and others published since then. It also highlights ongoing research.
The number of people with multiple conditions is rising. More than one in four of the adult population in England lives with two or more conditions. Living with numerous and often complex health problems is becoming the norm for older people and those from disadvantaged communities. Some conditions cluster together and people can experience many different combinations of conditions. The impact of these combinations can vary.
People with multiple conditions are more likely to have poorer health, poorer quality of life and a higher risk of dying than those in the general population. Some combinations of mental and physical diseases are associated with especially poor outcomes.
Despite considerable diversity in their disease profile and circumstances, people with multiple conditions frequently share common problems. They may have reduced mobility, chronic pain, shrinking social networks, incapacity to engage with work, and lower mental wellbeing. To date, these problems have not been well-addressed by services or research. People with multiple conditions want greater service integration, more person-centred, holistic care, and better support for mental wellbeing.
The healthcare system tends to focus on individual diseases or issues. It frequently fails to respond to the needs of the whole person. This situation is reinforced by medical training models, treatment guidelines, and drug efficacy trials, all of which are typically premised on people with single conditions. The evidence on the effective management of multiple long-term conditions remains sparse. Many interventions have had difficulty demonstrating their value over regular care. This could be because interventions need to be more intense, and carried out over a more extended period, to demonstrate impact. Improving the quality of life for people with multiple conditions could also be difficult because solutions require actions beyond the remit of healthcare. There is a need for more trials of treatment and service strategies designed to manage people with common multiple condition clusters.
An area of particular importance is the interface between physical and mental health. The two are closely interconnected. Each can adversely impact the other through a number of pathways, with consequences for health and wellbeing. This duality needs to be recognised and addressed from the outset and across the whole pathway of care.
Irrespective of people’s age, multiple conditions drive increased healthcare costs and the use of hospital and primary care. One of the most common consequences of being affected by multiple health conditions is receiving numerous medications for long periods. This phenomenon, known as polypharmacy, is associated with a range of adverse health outcomes. There is a need for better evidence to identify the effects of multiple medications on those with multiple conditions.
A range of biological, psychological, behavioural, socioeconomic and environmental factors are associated with a higher risk of developing multiple conditions. Deprivation is particularly important, alongside obesity, poor diet, smoking, air pollution and alcohol. In addition, some medications used over time magnify the risk of acquiring another condition, and some diseases increase the risk of others. Biological mechanisms and pathways at the cellular level may trigger other conditions, even when the co-occurring conditions appear unrelated. There are also higher rates of multiple conditions in older people, women and those from some ethnic groups.
How biological, psychological, behavioural, socioeconomic and environmental risk factors interact to trigger multiple conditions is complex and still not fully understood. The evidence on possible biological mechanisms is often lacking. Better understanding is needed to improve risk stratification efforts, and drive advances in preventive strategies and more coherent treatment regimes. More research is needed to understand how risk factors interact to produce specific clusters of conditions, both in the general population and in specified population subgroups.
Understanding clusters is a potential route to improving the management of multiple conditions and setting priorities in public health and healthcare. Clusters could offer insight into underlying causal mechanisms and disease pathways; better understanding could inform the development of new drugs. However, methodological challenges remain. Different researchers have used different statistical approaches and heterogeneous lists of conditions. Results can be difficult to compare and synthesise and findings can be conflicting. The NIHR is investing in a research support facility to try to address some of the methodological issues. Many studies have found high numbers of potential cluster combinations along with a significant proportion of people in no distinct cluster category. The lack of distinct clusters means that a generalist/multidisciplinary team approach may be more important than pathways/guidelines based on specific disease clusters.
Evidence on the best approaches to prevent multiple conditions is generally lacking. The importance of deprivation and other common public health risk factors means that effective prevention is likely to require population-based strategies that tackle the environmental, social and economic determinants of health.
Addressing the rising personal and collective burden of living with multiple conditions is one of our most significant public health challenges. Research is needed in all areas. The heterogeneous nature of the underlying disease clusters and the complex interactions between different risk factors make this challenging. The substantial programme of NIHR research informed by what matters most to people with multiple conditions offers hope of improvement.
The proportion of people with multiple conditions (multimorbidity) is significant and rising. Living with numerous and often complex health problems is becoming the norm for older people and more deprived communities. In 2018, the Academy of Medical Sciences called for further research to address this challenge (see Appendix A for their research recommendations). The report summarised existing research evidence about the burden, determinants, prevention, and treatment of people with multiple conditions and set out a future research agenda.
Multiple conditions is a major strategic priority for the NIHR. The aim is to develop an evidence base which:
- Identifies the problems and outcomes that matter most to people with multiple conditions and their carers, and how they would like to see services configured to meet their needs
- Identifies and maps common clusters of disease and their trajectories among the population
- Supports design and delivery of interventions to prevent people progressing from one long-term condition to developing more..
- Delivers research that enables the health and social care system to take a patient-centred, whole person approach to the treatment and care for people with multiple conditions, including quality of life and wellbeing
This briefing builds on the Academy’s 2018 report findings, and draws on research funded by the NIHR and others, published since then. It aims to make sense of existing evidence and highlight the gaps that NIHR and others still need to fill.
Research in this area has been handicapped by the lack of a consistent definition. In this report we follow the definition set out by the Academy.
The term ‘multiple long-term conditions’ refers to the existence of two or more long-term conditions in a single individual. One of which is either:
- a physical non-communicable disease of long duration, such as cardiovascular disease or cancer
- a mental health condition of long duration, such as a mood disorder or dementia
- an infectious disease of long duration, such as HIV or hepatitis C.
Source: The Academy of Medical Sciences
NICE uses a slightly expanded version of this definition, and states that multimorbidity is the presence of two or more long-term health conditions, which can include:
- defined physical or mental health conditions, such as diabetes or schizophrenia
- ongoing conditions, such as learning disability
- symptom complexes, such as frailty or chronic pain
- sensory impairment, such as sight or hearing loss
- alcohol or substance misuse
For ease of reading, throughout this briefing we refer to ‘multiple conditions’ and ‘people with multiple conditions.’
In October 2018, The Richmond Group of Charities published the findings of an in-depth ethnographic research project describing how it feels to live with multiple conditions and the challenges people face in accessing the care and support they need. The report highlighted the almost complete absence of the perspective of lived experience in the evidence they reviewed. Despite considerable diversity in the circumstances of people with multiple conditions, there were many similar experiences. These included reduced mobility, chronic pain, shrinking social networks, losing the ability to engage with work as it is typically structured, and lower mental wellbeing.
“It's striking that people didn't speak in terms of their individual diagnoses – they instead spoke movingly about the compound impact to their lives….[the] missed opportunities to intervene; the series of losses – biomedical, relational or psychological – that add further complexity to ill health; and the accumulation of further illnesses."
Yet, the healthcare system frequently fails to respond to the needs of the whole person and instead focuses on individual diseases or issues. This can leave people with complex and uncoordinated care pathways. The NIHR has recently commissioned several pieces of work to explore what matters to people with multiple conditions and their carers (National Voices, 2019; NIHR: Policy Research Unit Older People and Frailty, 2019; Walker and Logan, 2019). Three themes emerged:
- First, the complexity and difficulty in accessing and navigating services, with a strong desire for greater service integration and coordination.
- Second, the tendency for services to focus on symptoms and conditions and fail to see the things that matter to people. There is a need for more person-centred, holistic care.
- Third, people felt that their mental health needs and emotional wellbeing were frequently ignored, which often resulted in a worsening of symptoms. Mental health services should be offered at the outset. There is a need for services to capture people’s experience of the issues that matter to them. The current NHS surveys fail to do this. Tools are available, including a PROM (patient-reported outcome measure) specifically for people with long-term conditions.
The research priorities identified by the James Lind Alliance Priority Setting Partnership for Multiple Conditions in Later Life (2018) and Safe Care for Adults with Complex Health Needs (2019) reflect many of the issues highlighted by the Richmond Group and the recent NIHR work. People with multiple conditions and their carers prioritised psychosocial issues, including the promotion of independence and wellbeing and the need for more holistic and integrated care.
As feedback from people with multiple conditions highlights, the health and care system frequently fails to respond to the needs of the whole person, and instead focuses on individual diseases or issues. This situation is reinforced by medical training, treatment guidelines, and drug efficacy trials, all of which are typically premised on people with single conditions. And, as a recent Cochrane review concluded, the evidence on effective interventions remains sparse (Smith et al., 2021).
It has been difficult to demonstrate the added value of interventions for people with multiple conditions. Interventions that have been studied include:
- integrated health and social care services, which did not improve people’s health status or experience of care (Roland et al., 2013; Stokes et al., 2020); hospital use was not reduced with extra digital support (Lugo-Palacios et al., 2019)
- health coaching, which had little impact on people’s experience and outcomes (Shah et al., 2019)
- educational and behavioural interventions to help people adhere to prescribed medicines; evidence in support is low-quality(Cross et al., 2020).
The NIHR recently funded a significant trial of perceived best practice, and more research is planned. The trial assessed a multidisciplinary patient-focused approach included a comprehensive assessment and care planning, screening for depression, and medication review. While the model of care improved people's experience, it did not improve care or service outcomes (Salisbury et al., 2018b).
Treatment guidelines and drug efficacy trials are typically based on people with single conditions. This presents a challenge to providing high-quality care for people with multiple conditions. The point is underscored by NICE in its guidance on the clinical assessment and management of multiple conditions (NICE, 2016) and by Chris Whitty in a recent BMJ article:
"Good vertical integration exists from bench to bedside for a single condition or disease, but there is little or no horizontal integration between diseases that often co-exist. This will require an intellectual shift and rethinking some elements of our research, training, and practice in virtually every discipline." (Whitty et al., 2020)
The need for new models of training, particularly the need for more generalist skills, has been highlighted by many institutions (Greenaway, 2013). Yet these are frequently lacking and many specialists feel ill-equipped to deal with the changing needs of patients (Vaughan et al., 2021).
NICE suggests that as the complexity or impact of multiple conditions increases – or the complexity of treatment or care increases – so does the need for management strategies that take specific account of multiple long-term conditions (NICE, 2016).
Whether conditions fall within the same system in the body (e.g. coronary heart disease and hypertension) will influence the complexity of care, particularly if people are under the care of different specialists (Stafford et al., 2018). Improving the quality of life for people with multiple conditions could also be difficult because solutions require actions beyond the remit of healthcare. Lastly, interventions may need to be more intense and continued over a more extended period to demonstrate impact (Salisbury et al., 2018a).
The Academy suggested that there are opportunities to learn from clinical areas where it is the norm for people to have multiple conditions or multiple body systems affected. Examples include the care of older people, children with complex needs, end of life care and some rare diseases. Common multiple condition clusters may also help. A recent Spanish study undertook risk stratification based on cluster analysis and risk assessment of 1-year mortality. Using this approach, they identified three distinct groups of people with multiple conditions (Bretos-Azcona et al., 2020). They had heterogeneous needs but traditional models would have treated them similarly.
An area of particular importance is the interface between physical and mental health. As highlighted above, physical and mental health are closely interconnected. One can adversely impact the other through a number of pathways, with significant consequences for health and wellbeing. For people with severe mental illness, addressing the underlying risk factors for physical health problems will be critical to good outcomes (Firth et al., 2019).
People with multiple conditions have poorer quality of life (Williams and Egede, 2016) and a higher mortality risk (Nunes et al., 2016) than the general population. Some combinations of diseases are associated with especially poor outcomes. People with severe mental illness can expect to live 10-20 years less than the general population, primarily due to poor physical health (World Health Organization, 2018).
"The high rate of physical comorbidity, which often has poor clinical management, drastically reduces life expectancy for people with mental illness, and also increases the personal, social, and economic burden of mental illness across the lifespan." (Firth et al., 2019)
Conditions that affect the heart, lung, and renal systems, are associated with higher mortality (Gijsen et al., 2001).
One of the most common consequences of having multiple conditions is receiving multiple medications for long periods. This phenomenon, known as polypharmacy, is associated with a range of negative health outcomes, including drug-related problems, adverse drug events, physical and cognitive function, hospitalisation, and mortality. However, it is inherently difficult to establish a causal relationship as health status is a confounding factor (Chen et al., 2020).
There is growing evidence that having multiple conditions is a more important driver of costs in the health and social care system than other factors such as age (Kasteridis et al., 2015). Depression was a particularly significant driver of cost and utilisation of care (Soley-Bori et al., 2021).
Around one in four adults in England, that is, more than 14 million people, have two or more health conditions (Stafford et al., 2018). The age at which people are acquiring multiple conditions is falling. People living in the most disadvantaged communities can expect to have two or more conditions 10 years earlier than those in the least deprived (Stafford et al., 2018).
The numbers of people with multiple conditions are high and growing in older adults (Singer et al., 2019a). One study projects a rise in the prevalence of two or more conditions in people aged over 65, from 54% (2015) to 68% (2035). This is alongside the number of people with four or more conditions doubling by 2035 to nearly 2.5 million (17%) (Kingston et al., 2018).
Some conditions are much more prevalent than others in those with multiple conditions. Analysis of GP data in England suggested hypertension (18.2%), depression/anxiety (10.3%) and chronic pain (10.1%) were the three most common (Cassell et al., 2018).
Some cluster together more than others, but people with multiple conditions can experience a wide array of different combinations (Busija et al., 2019). In some cases, co-existing conditions are similar in their origin and treatment requirements, which is referred to as concordant multimorbidity. For example, coronary heart disease and various conditions that affect the brain's blood vessels share a common cause and frequently co-exist. In other cases – termed discordant multimorbidity – the co-existing conditions appear to be unrelated to each other or require different treatment. An example would be chronic physical and mental health conditions. Patterns of clustering differ according to demography and geography. For example, more deprived groups are more likely to have clusters that include mental health conditions (Singer et al., 2019b). The recent research exploring clusters is described further below.
A range of biological, psychological, behavioural, socioeconomic and environmental factors affect the likelihood of having multiple long-term conditions.
The Academy Report summarised evidence about lifestyle factors that either increase or reduce the risk of having multiple conditions (see Table 1). It stressed that much of this evidence does not come from longitudinal studies, so the paths of causation and interdependency are not clear. Many of the risk factors are linked to single conditions. It may be this association that is observed. More research is needed to determine which factors are most strongly, and most directly, associated with multimorbidity.
Table 1: Multiple Conditions: Lifestyle risk factors
|Increase risk||Reduces risk|
|Poor diet||Healthy diet|
|Smoking||Strong social networks|
Analysis based on the Academy Report (The Academy of Medical Sciences, 2018)
The prevalence of multiple conditions is higher, and the age of onset is younger in those living in more deprived areas (Barnett et al., 2012). One study found almost a 50 per cent increased odds of multimorbidity in those with the least wealth compared to those with the greatest (Singer et al., 2019b). There are even greater inequalities among those with three or more chronic conditions in three or more body systems (Singer et al., 2019a).
More deprived populations have higher rates of many lifestyle risk factors. Even so, one study showed that the top five risk factors explained less than half the socioeconomic differences in the prevalence of multiple long-term conditions (Katikireddi et al., 2017).
There is evidence that as well as influencing the risk of multiple conditions, factors such as education will influence its impact. Chen et al found that the impact of multiple conditions on health and costs was greater in those with less education (Chen et al., 2020).
The Academy highlighted higher rates of multiple long-term conditions in older people, women and people from some ethnic groups. It is not clear whether this is due to non-modifiable biological factors or other modifiable factors. For example, the association between the prevalence of multiple conditions and age could be due to the opportunity to accumulate chronic conditions over time.
Some diseases magnify the risk of others. Most notably, physical and mental health are closely interconnected and affect each other through a number of pathways (Prince et al., 2007). For example, people with mental illnesses have a 1.4 to 2 fold increased risk of obesity, diabetes and cardiovascular diseases compared with the general population (Firth et al., 2019). The Academy highlighted a range of evidence that type 2 diabetes and depression each magnify the risk of the other. Depression can worsen diabetes outcomes, in part through people not taking their treatment as prescribed. But there may potentially be a direct effect of antidepressants on blood glucose levels. Depression also increases the risk of morbidity and mortality in populations with type 2 diabetes. In turn, poor diabetes control intensifies the symptoms of depression, leading to a cyclical relationship between the two conditions (The Academy of Medical Sciences, 2018).
While the above example demonstrates the interdependence between diseases, one study has suggested that underlying risk factors are the primary drivers of the development of multiple conditions over time (Singer et al., 2019b).
Some drugs used over a long time, increase the risk of other conditions. For example, antiretroviral therapies (ART) for the treatment of HIV are associated with insulin resistance, elevated blood lipids, and central fat accumulation, each of which can ultimately contribute to the development of type 2 diabetes and cardiovascular diseases (Thienemann et al., 2013). Some psychotropic drugs are associated with an increased risk of several physical conditions, including obesity, diabetes, and cardiovascular diseases (Correll et al., 2015).
There is a need for better evidence to identify the effects of multiple medications on those with multiple conditions. There is also a need for more clinical decision support, given the evidence that the benefits and harms associated with medications may not always be understood by GPs (Treadwell et al., 2020). The unintended adverse effects of medications exacerbate the complexity of managing people with multiple conditions.
The Academy cited evidence that biological mechanisms and pathways at the cellular level may trigger other long-term conditions, even when the co-occurring conditions appear unrelated. In general, they found that evidence on the possible biological mechanisms is lacking. Better understanding in this area is needed to improve risk stratification efforts and drive advances in preventive strategies. Identifying common biological mechanisms for several conditions could permit the development of new approaches to treatment which targets common pathways. Such treatments could have benefits for multiple conditions simultaneously.
How biological, psychological, behavioural, socioeconomic and environmental risk factors interact to trigger multiple long-term conditions is complex and still not fully understood. Some have argued for combined socio-economic and clinical interventions, the so-called 'syndemic' approach. It would address macro-level social factors which promote disease clustering at the population level at the same time as treating disease at the individual level (Hart and Horton, 2017).
"By addressing both the roots of sickness (inequality) and the treatment of symptoms (clinical care), syndemic intervention can strengthen strategies of prevention and care by considering the full scope of syndemic vulnerabilities, rather than treating disorders individually and ignoring the complex contexts in which they occur." (Mendenhall, 2017) p890
Understanding clusters is a potential route to improving the management of multiple conditions and setting priorities for public health and healthcare.
"Better understanding of needs and outcomes of individuals with different profiles of multimorbidity will enable the transition from disease-centred to person-centred health care for individuals with multiple chronic conditions." (Busija et al., 2019) p1050
This approach could also improve understanding of the underlying causal mechanisms and pathways that increase the likelihood of conditions co-occurring. It could help understand drug interactions, and lead to the development of new drugs that target these underlying pathways.
Since 2018, the NIHR and others have put significant funding into research into disease clusters. Projects include:
- A joint MRC-NIHR call in 2018 for research into disease clusters, their trajectories and risk factors. Five studies were funded, ranging from methodological research to studies that focus on specific disease clusters.
- “Tackling multimorbidity at scale: understand disease clusters, determinants, & biological pathways”.(2019)
- Artificial Intelligence for Multiple Long-Term Conditions (Multimorbidity) (AIM) (2020). A call for research into the use of AI and data science to address the challenges of multiple conditions, including developing the understanding of disease clusters.
There are growing numbers of published studies identifying disease clusters. For example, an analysis of UK Biobank data by Zemedikun et al. (Zemedikun et al., 2018) used a two-stage statistical analysis to map clusters of conditions in people aged 40 to 69 years. They identified common clusters and associations of conditions within the clusters.
The study identified three core clusters:
- The first cluster contained only myocardial infarction and angina with a strong association. The co-occurrence of these two conditions was 13 times higher than in isolation.
- The second cluster had diabetes at its core, with strong associations with heart failure, chronic kidney disease, liver failure, and stroke.
- In the third cluster, cluster, hypertension, asthma, depression, and cancer were at the centre.
The study suggested that better management and prevention of conditions such as diabetes and hypertension – which are at the centre of disease clusters and potentially part of several other chronic conditions' trajectories – would improve outcomes.
Several studies have focused on clusters with the worst outcomes or that make the most significant service demands. One (Zhu et al., 2020) analysed general practice data on 113 thousand adults aged 18 and over, who had multiple conditions. The study identified 20 patient clusters across different age groups (see Table X). All unique combinations of conditions had a prevalence of less than 1% in the total population. Pain featured in 13 clusters. Some clusters had significantly higher mortality and service use than others. The study suggested that these groups of people with multiple conditions should have tailored approaches to their care.
Table 2: Disease clusters with highest mortality and service use by age band
|Age Range||Highest Mortality||Highest Service Use|
|18-64||Psychoactive substance and alcohol misuse.
Mortality - 18 times higher than those without multiple conditions.
|Depression, anxiety and pain|
|65-84||Coronary heart disease, depression and pain||Coronary heart disease, depression and pain|
|85+||Coronary heart disease, heart failure and atrial fibrillation||Coronary heart disease, heart failure and atrial fibrillation|
A prospective cohort study over five years included people over the age of 65 with multiple conditions treated in primary care in Spain. The study found that a large proportion of people fell into one of six clusters (Guisado-Clavero et al., 2018). Five affected a specific system in the body, for example, cardiovascular. One, the largest cluster, had a nonspecific pattern. The paper noted that multimorbidity patterns were constant over time. By contrast, a similar cohort study looking at people aged over 60 in Sweden (Vetrano et al., 2020) noted the highly dynamic evolution of clusters at both 6 and 12 years, with participants moving from one cluster to another. However, Vetrano et al. identified some commonality amongst the cluster trajectories that suggests the potential to better tailor care in the future. They called for more longitudinal research to explore these trajectories.
Stokes et al. (Stokes et al., 2021) analysed data from all hospital admissions from 2017/2018 (more than 8 million people with multiple conditions). They identified over 60,000 unique disease combinations. No combination accounted for more than 3.2% of the total costs for people with multiple conditions. They concluded that the lack of distinct clusters means that a generalist/multidisciplinary team approach was needed rather than pathways/guidelines based on a few specific disease clusters. They also found that disease clusters with the highest average cost per patient were not the same as those with the highest total cost.
Several papers have flagged the methodological challenges associated with identifying disease clusters. One systematic review of 51 studies (Busija et al., 2019) found 407 different profiles of multiple conditions based on different statistical approaches and heterogeneous lists of conditions. They recommended more rigour and consistency in the analytic approaches used. The NIHR AIM research call (described above) includes funding for a Research Support Facility to help address these issues.
There are well-evidenced prevention strategies for many of the component diseases of multiple condition clusters (Head et al., 2021).
- tobacco cessation to prevent cardiovascular, respiratory and several neoplastic diseases
- a reduction in blood pressure to prevent coronary disease, ischaemic stroke, cerebral haemorrhage, congestive heart failure and chronic kidney disease, and
- LDL-cholesterol lowering to prevent coronary heart disease and ischaemic stroke
The Academy report suggested that the benefits are magnified when strategies are delivered in parallel. For example, the combination of smoking cessation, a 15mmHg reduction in systolic blood pressure and a 1mmol/L reduction in LDL-cholesterol will reduce the incidence of cardiovascular diseases, chronic obstructive pulmonary disease (COPD) and smoking-related cancers by more than half.
As described above, the complex interaction of biological, psychological, behavioural, socioeconomic and environmental factors makes developing effective prevention strategies difficult. Relatively little is known about the independent risk factors or the accumulation trajectories for multiple conditions (Head et al., 2021). The clinical courses of older adults with multiple conditions are dynamic and complex. But some studies have demonstrated common risk factors and development pathways that suggest the potential for more tailored interventions (Vetrano et al., 2020). An increased understanding of which conditions most commonly cluster, along with their underlying risk factors, would help prioritise strategies for early diagnosis, screening and prevention.
A 2017 Cochrane review found limited evidence on effective prevention of multiple conditions. It found some evidence of benefit where interventions targeted risk factors such as depression or specific functional difficulties such as walking (Smith et al., 2017). A recent large study of biobank data found that healthy lifestyles improved health outcomes, irrespective of whether someone had multiple conditions (Chudasama et al., 2020). Analysis of data from the Whitehall II cohort study found that socioeconomic status has an impact on the risk of multiple conditions but not on mortality after the onset of these conditions. Thus primary prevention is key to reducing social inequalities in the risk of dying (Dugravot et al., 2020).
However, interventions that rely on individual behaviour change can make inequalities worse (Head et al., 2021). For example, a recent study of an exercise referral scheme found that people from the most deprived groups were least likely to be referred and least likely to take part (Morgan et al., 2020).
Effective prevention of multiple conditions is likely to require population-based strategies that tackle the environmental, social and economic determinants of health (Head et al., 2021; Katikireddi et al., 2017).
"We need to challenge the common narrative that multimorbidity is inevitable in a modern ageing society. To do this, the focus on multimorbidity must shift from solely management of high-risk older individuals to include integrated population-level prevention strategies throughout the life-course to address the drivers of multimorbidity." (Head et al., 2021) p243
Addressing the rising personal and collective burden of living with multiple conditions is one of our most significant public health challenges.
People with multiple conditions and their carers have called for research on psychosocial issues, including the promotion of independence and wellbeing, as well as on the delivery of more holistic and integrated care. The evidence for effective interventions remains sparse. There are potential opportunities to learn from clinical areas where it is the norm for people to have multiple conditions or multiple body systems affected, for example, the care of children with complex needs, older people or some rare diseases.
How biological, psychological, behavioural, socioeconomic and environmental risk factors interact to trigger multiple conditions is complex and still not fully understood. The evidence on the possible biological mechanisms is lacking. Better understanding in this area is needed to improve risk stratification efforts and drive advances in preventive strategies. More research is needed to understand how risk factors interact to produce specific clusters of conditions, both in the general population and in specified population subgroups. An increased understanding of which conditions most commonly cluster together, and their underlying risk factors, could also help prioritise strategies for early diagnosis, screening and prevention. There is also a need for better evidence to identify the effects of multiple medications on those with multiple conditions.
Addressing the challenge of multiple conditions is a strategic priority for the NIHR and other research funders, including MRC and UKRI. All invest in this area and coordinate their efforts through a cross-funder multimorbidity research framework (2020). There is a collective ambition to address many of the questions highlighted in this review.
How to cite this Collection
NIHR Collection: Multiple long-term conditions (multimorbidity): making sense of the evidence; March 2021; doi:10.3310/collection_45881
The author of this collection was Candace Imison, Associate Director of Evidence and Dissemination at the NIHR Centre for Engagement and Dissemination
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