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Author Archives: Jonathan Stokes

How useful are multimorbidity ‘clusters’ likely to be for important health system outcomes?

By Jonathan Stokes (left), Bruce Guthrie (center), Stewart W. Mercer (right), Nigel Rice, Matt Sutton

“The problem of multimorbidity,” both for individual patients and for health systems, has been well defined. Multimorbidity is now a well-established priority for research and medical practice [1, 2]. But, there has been little success to date in developing effective or cost-effective new models of care for these patients [3].

Multimorbidity is very different from traditional single disease intervention, however. For multimorbidity, there is huge heterogeneity with many possible combinations of conditions potentially requiring different approaches to management. For example, there are over 268 million possible unique combinations if considering 28 individual conditions. Researchers have therefore begun to search for more useful approaches to dealing with multimorbidity, beyond a count of accumulated conditions, but still simplified to a manageable handful of subgroupings, a focus on “clusters”. The hope is this approach might (i) identify target clusters for direct intervention, or, (ii) via further research on aetiological mechanisms, target clusters for preventing disease accumulation.

Two statistical approaches, (1) cluster analysis (grouping diseases), and (2) latent factor analysis (grouping patients), are commonly used to examine clusters in the general population to accomplish this stratification [4]. However, both approaches have inherent methodological and clinical/intervention complexity. To summarise, on a practical level, they risk producing results that are too abstract (e.g. unobservable latent variables) and generally over-simplifying (e.g. to a handful of combinations when there are actually a huge number that matter) compared to what can actually be observed and acted on in clinical practice: symptoms, signs, and conditions. It is also not obvious that highly prevalent clusters in the general population will be the same combinations associated with outcomes that place most pressure on the supply constraints of healthcare systems, such as costs of (potentially preventable) emergency admissions and overall costs of secondary care.

We drew instead on more simple descriptive analysis to assess all observable condition combinations and their (potentially preventable) secondary care costs [5]. We examined the distribution and top 10 unique multimorbidity combinations contributing to total secondary care costs for a cohort of patients, all (over 8 million) patients with an NHS inpatient admission in England in 2017/18. As well as contribution to total system costs, we examined the combinations with particularly high costs for individual patients. Finally, multimorbidity is dynamic and conditions can accumulate over time, so we looked at the sum of costs for all overlapping conditions in the top 10 (e.g., summing costs for all unique combinations containing, at least, diabetes + hypertension), which might offer priority targets for prevention of disease accumulation.

The main limitations were a focus on a single outcome (costs), in a single healthcare setting (secondary care), with potential under-recording of conditions. However, conditions appeared to be well-recorded and we attempted to backfill missing, healthcare costs are an extremely important outcome for policymakers, and secondary care is the highest cost healthcare setting.

Key findings/implications for policy and practice:

• There are no clear discrete disease combinations at which to target interventions, which implies a generalist/multidisciplinary team approach will remain important rather than pathways/guidelines based on a few specific disease clusters.
• Combinations containing the highest cost patients (the current focus of many interventions) were different to those accounting for the highest total costs, implying the need to develop interventions beyond only high-risk patients.
• There might be scope to use clusters to understand and develop preventative interventions, but focusing on addressing well-known disease risk factors (such as obesity, diet, exercise, and deprivation) with public health/primary care interventions might provide the most efficient route to benefit systems financially and benefit many patients with multimorbidities.

These findings also have implications for researchers/research funders, a need to re-examine how much emphasis is placed on research exploring clusters of multimorbidity, and for which specified reasons.

The article can be assessed at the following link: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003514


  1. Academy of Medical Sciences. Multimorbidity: a priority for global health research. 2018.
  2. Whitty CJM, MacEwen C, Goddard A, Alderson D, Marshall M, Calderwood C, et al. Rising to the challenge of multimorbidity. BMJ. 2020;368:l6964. doi: 10.1136/bmj.l6964.
  3. Smith SM, Soubhi H, Fortin M, Hudon C, O’Dowd T. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev. 2016;4.
  4. Ng SK, Tawiah R, Sawyer M, Scuffham P. Patterns of multimorbid health conditions: a systematic review of analytical methods and comparison analysis. Int J Epidemiol. 2018;47(5):1687-704. Epub 2018/07/18. doi: 10.1093/ije/dyy134. PubMed PMID: 30016472.
  5. Stokes J, Guthrie B, Mercer SW, Rice N, Sutton M. Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study. PLOS Medicine. 2021;18(1):e1003514. doi: 10.1371/journal.pmed.1003514.

The Foundations Framework for Developing and Reporting New Models of Care for Multimorbidity

By Jonathan Stokes

There is a lack of evidence on effective intervention for managing patients with multimorbidity. A major barrier to progress in this area is the lack of consensus over how best to describe models of care for multimorbidity. If evidence is to drive clinical innovation, we need to build the evidence base through ongoing evaluation and review. That process is hampered, however, by incomplete descriptions of models in publications. Without accurate descriptions, researchers cannot replicate studies or identify ‘active ingredients’. There have been many examples in recent years of reporting frameworks that have improved the utility of health services research (http://www.equator-network.org/). In this paper [1], we describe a framework as a starting point for addressing this need in the high-profile area of multimorbidity.

Our framework describes each model in terms of the foundations:
• its theoretical basis (i.e. the clear and explicit aims, values and assumptions of what it is trying to achieve)
• the target population (‘multimorbidity’ is a somewhat vague term, so there is the need to define the group carefully, e.g. a patient with diabetes and hypertension might have very different care needs than a patient with dementia and depression)
• the elements of care implemented to deliver the model

We categorised elements of care into 3 types: (1) clinical focus (e.g. a focus on mental health), (2) organisation of care (e.g. offering extended appointment times for those who have multimorbidity), (3) support for model delivery (e.g. changing the IT system to better share electronic records between primary and secondary care).

Using the framework to look at current approaches to care for multimorbid patients, we found:

• The theoretical basis of most current models is the Chronic Care Model (CCM). This was initially designed for single disease-management programmes, and arguably not sensitive to the needs of multimorbidity.
• That current models mostly focus on a select group, usually elderly or high risk/cost. It is important to remember that in absolute terms, there are more people with multimorbidity aged under 65 years. Similarly, high risk patients are an obvious target, but there may be too few of them to make a significant impact on overall system costs. It is important that models incorporate the needs of younger and (currently) lower risk/cost patients (potentially with most scope for effects of preventing future deterioration).
• There is a need for increased attention for low-income populations (where multimorbidity is known to be more common), and for a focus on mental health (multimorbid patients with a mental health issue are at increased risk for detrimental outcomes).
• The literature suggests that the large emphasis in current models of care on self-management may not always be appropriate for multimorbid patients who frequently have barriers to self-managing their diseases. The emphasis on case management (intensive individual management of high-risk patients) should take into account the evidence that while patient satisfaction can be improved, cost and self-assessed health are not significantly affected.

We also looked at how approaches have changed over time, comparing newer to older models. Newer models tend to favour expansion of primary care services in a single location (e.g. increased co-location of social care services and extended chronic disease appointments), rather than coordination across multiple providers or at home (e.g. decreased care planning and integration with other social and community care services, decreased home care).

Health systems have only recently begun to implement new models of care for multimorbidity, with
limited evidence of success. Careful design, implementation, and reporting can assist in the development of the evidence base in this important area. We hope our framework can encourage more standardised reporting and research on the theoretical basis and target population for interventions, as well as the contribution of different elements (including interactions between them) needed to provide cost-effective care and support redesign of health systems for those who use them most.

This free to read article can be found at the following link:

[1] Stokes J, Man M-S, Guthrie B, Mercer SW, Salisbury C, Bower P. The Foundations Framework for Developing and Reporting New Models of Care for Multimorbidity. The Annals of Family Medicine. 2017;15(6):570-7.