Our search for papers on multimorbidity that were published during the period May-August 2021 has been completed. As in previous searches, we have prepared a PDF file that can be accessed following this link.
Probably, there are some publications that were not detected by our search strategy using the terms “multimorbidity”, “multi-morbidity” and the expression “multiple chronic diseases” in PubMed (https://www.ncbi.nlm.nih.gov/pubmed), but we are sure that most publications on the subject are included in the list.
All references are also included in our library. Feel free to share with anyone interested in multimorbidity.
Multimorbidity, defined as the co-occurrence of several chronic conditions in an individual [1, 2], is increasingly common. In 2018, the Academy of Medical Sciences has declared multimorbidity a priority in global health research as it has become a norm rather than an exception for an individual to have multimorbidity [3]. Multimorbidity is a growing public health challenge as it accounts for the highest expenditure in the healthcare system [4]. In addition, multimorbidity brings about many profound implications such as decreased quality of life, functional decline, and increased healthcare utilisation among many other negative outcomes.
However, many researchers define multimorbidity differently and many different instruments were used to measure multimorbidity. For this reason, we conducted a systematic a review on the instruments used for measuring the association of the level of multimorbidity and clinically important outcomes [5]. The main objective of the systematic review was to provide a list of instruments that are suitable for use in studies aiming to measure multimorbidity in association with or for prediction of a specific outcome in community-dwelling individuals. We also provided details of the requirements, strengths and limitations of these instruments, and the chosen outcomes.
In total, we found 33 unique instruments. The most commonly used instrument was ‘Disease Count’ and it was also the only instrument that was associated with the three essential outcomes from the core outcomes set of multimorbidity research (COSmm) [6], which are quality of life, mental health and mortality. Other instruments included weighted indices and case-mix or pharmaceutical-based instruments. We hope that by describing these instruments in detail, researchers would be able to choose a suitable instrument for their research in multimorbidity.
References
Fortin M, Stewart M, Poitras ME, et al. A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med 2012;10(2):142–51. doi: https://doi.org/10.1370/afm.1337 [published Online First: 2012/03/14]
WHO. The World Health Report 2008. Primary Care – Now more than ever. 2008.
Multimorbidity: a priority for global health research The Academy of Medical Sciences 2018
Huber M, Knottnerus JA, Green L, et al. How should we define health? BMJ 2011;343:d4163. doi: 10.1136/bmj.d4163
Lee ES, Koh HL, Ho EQ, et al. Systematic review on the instruments used for measuring the association of the level of multimorbidity and clinically important outcomes. BMJ Open 2021;11(5):e041219. doi: 10.1136/bmjopen-2020-041219
Smith SM, Wallace E, Salisbury C, et al. A Core Outcome Set for Multimorbidity Research (COSmm). Ann Fam Med 2018;16(2):132-38. doi: 10.1370/afm.2178
Our search for papers on multimorbidity that were published during the period January-April 2021 has been completed. As in previous searches, we have prepared a PDF file that can be accessed following this link.
Probably, there are some publications that were not detected by our search strategy using the terms “multimorbidity”, “multi-morbidity” and the expression “multiple chronic diseases” in PubMed (https://www.ncbi.nlm.nih.gov/pubmed), but we are sure that most publications on the subject are included in the list.
All references are also included in our library. Feel free to share with anyone interested in multimorbidity.
This hybrid, one-day symposium covers three key areas in the field of multimorbidity: 1) definitions of multimorbidity, 2) Multimorbidity and clinical practice and 3) Prevention of multimorbidity, and health policy & healthcare utilization.
The event can be attended either online or physically. For now, Dutch COVID countermeasures allow 50 places available in “Het Trippenhuis”, in Amsterdam, you are most welcome to join online – that is always possible 🙂
Leading (inter)national experts will inform you about the most recent advances in these areas – accompanied by interactive panel discussions with the audience. Plenary speakers among others include prof. dr. Barbara van Munster, prof. dr. Cynthia Boyd, prof. dr. Maureen Rutten-van Mölken.
Chaired by prof. dr. Jako Burgers
Upcoming new research talents will pitch their research, and poster presentations will facilitate inspiring discussions during the breaks.
The Frontiers Platform has launched a Research Topic, aimed at basic research, epidemiology, clinical, neuropathological and modelling studies in the field of Multimorbidity in the context of Neurodegenerative Disorders. We encourage authors to submit either Original Articles or Reviews on this subject.
I would appreciate should IRCMo help inform scientists interested in this subject, and we encourage submission of articles related with the field.
As part of the research program entitled Patient-Centered Innovations for Persons with Multimorbidity (PACE in MM), research trials were conducted simultaneously in the Canadian provinces of Quebec and Ontario. The aim of the trials was to assess the effectiveness of a patient-centered, multi-provider intervention for patients with multimorbidity, and understand under what circumstances it worked, and did not work. The report about the Quebec trial was recently published [1], and it is our pleasure to announce that the report of the trial in Ontario is now published too [2].
Both trials used mixed-methods design with a pragmatic randomized trial and qualitative study, involving primary care sites. Outcome measures were the same: two primary outcome measures representing patient education, empowerment, and agency (the Health Education Impact Questionnaire (heiQ); and the Self-Efficacy for Managing Chronic Disease scale), and four secondary outcome measures (VR12 Health Status; EQ-5D quality of life; Kessler Psychological Distress Scale; and Health Behaviour Survey). Outcomes were assessed at baseline and at 4 months after the intervention, a period considered long enough for follow-up to the trial.
A total of 86 patients in the intervention group and 77 in the control group participated in the Ontario trial. The intervention had a neutral effect on the primary outcomes, although one subgroup (those with an income of ≥C$50 000) significantly benefitted in terms of the mental health outcome. Qualitative and fidelity findings revealed aspects of the intervention that could be improved. For example, the qualitative study found patients’ enthusiasm for a coalesced action plan, but their frustration in its absence.
As a consequence of these findings, policymakers and clinicians are encouraged to seek ways to enhance care for patients with annual incomes of <C$50 000, to optimize team composition based on an individual patient’s preferences and abilities, and to enhance and tailor follow-up care by ensuring the creation of a coherent plan with actionable steps.
Fortin M, Stewart M, Ngangue P, et al. Scaling Up Patient-Centered Interdisciplinary Care for Multimorbidity: A Pragmatic Mixed-Methods Randomized Controlled Trial. Ann Fam Med 2021;19:126-34. doi: https://doi.org/10.1370/afm.2650
Stewart M, Fortin M, Brown JB, et al. Patient-centred innovation for multimorbidity care: a mixed-methods, randomised trial and qualitative study of the patients’ experience. Br J Gen Pract 2021;71(705):e320-e30. doi: 10.3399/bjgp21X714293
Our search for papers on multimorbidity that were published during the period September-December 2020 has been completed. As in previous searches, we have prepared a PDF file that can be accessed following this link.
Probably, there are some publications that were not detected by our search strategy using the terms “multimorbidity”, “multi-morbidity” and the expression “multiple chronic diseases” in PubMed (https://www.ncbi.nlm.nih.gov/pubmed), but we are sure that most publications on the subject are included in the list.
All references are also included in our library. Feel free to share with anyone interested in multimorbidity.
The Patient-Centered Innovations for Persons with Multimorbidity research program, funded by the Canadian Institutes of Health Research, had an overall goal to build on existing structures and initiatives to evaluate patient-centered innovations relevant to multimorbidity in primary care. As part of this program, trials were conducted in 2 Canadian provinces, Quebec and Ontario. We reported the Quebec trial where the research team collaborated with a regional health care organization to implement an integrated chronic disease prevention and management program into family medicine groups (FMG), the most prevalent type of primary care practice in Quebec [1].
We conducted a concurrent triangulation mixed methods study, with convergent quantitative and qualitative components. The first component was a pragmatic randomized controlled trial with a delayed intervention in the control group to evaluate the effect of the intervention on patient’s self-management and self-efficacy for managing chronic diseases. The second concurrent component used a descriptive qualitative approach.
Primary outcomes were the Health Education Impact Questionnaire (heiQ) and Self-Efficacy for Managing Chronic Diseases. Secondary outcomes included health status measured by the Veterans RAND 12 Item Health Survey (VR-12), quality of life measured with the EuroQol 5-dimensions questionnaire, psychological distress, measured with the Kessler 6-item Psychological Distress Scale Questionnaire (K6), and health behaviors (tobacco smoking, physical activity, healthy eating, and high risk alcohol consumption) assessed with specific questions from the Enquête de santé du Saguenay–Lac-Saint Jean 2007 and the Behavioral Risk Factor Surveillance System.
The trial randomized 284 patients (144 in intervention group, 140 in control group). After 4 months, the intervention showed a neutral effect for the primary outcomes, but there was significant improvement in 2 health behaviors (healthy eating, and physical activity).
The descriptive qualitative evaluation revealed that the patients reinforced their self-efficacy and improved their self-management which was divergent from the quantitative results. Qualitatively, the intervention was evaluated as positive.
The combination of qualitative and quantitative designs proved to be a good design for evaluating this complex intervention.
Formerly, we could find in the description of the Journal of Comorbidity that it published “original clinical and experimental research articles on the pathophysiology, diagnosis, prevention and management of patients with comorbidity/multimorbidity.” Now, in the description of the Journal of Multimorbidity and Comorbidity, one reads that it publishes the same type of articles on “comorbidity and multimorbidity.”
The change in the name of the journal and the change in the description from “comorbidity/multimorbidity” to “comorbidity and multimorbidity” may seem natural for those working on multimorbidity or those who are familiar with its meaning. However, for many who still consider both words as interchangeable, writing “comorbidity/multimorbidity” could have been seen as normal and the separation in “comorbidity and multimorbidity” could be seen as redundant.
In 1996, van den Akker and colleagues [1] pointed out the prevailing ambiguity around the use of both terms at that time, and suggested distinct definitions for them. Since then, there has been an increasing awareness about the difference between both terms and the importance of using them correctly. A benefit in using both terms adequately is that publications are then correctly classified, leading to an improvement in the quality of search queries and ultimately to better research.
However, although the first alert on the ambiguity in the use of the terms was published 25 years ago, it has taken a long time for the recognition of the difference between both terms and its effect in slowing down the advance of our knowledge on the subject. For example, in the National Library of Medicine of the National Institutes of Health (NIH), the term “multimorbidity” was a subheading under the Medical Subject Heading (MeSH) “comorbidity” until 2017. It was only in 2018 that the term “multimorbidity” appeared with the hierarchy of a MeSH.
In the editorial of the Journal of Multimorbidity and Comorbidity explaining the change in the name of the journal [2], it is well explained that multimorbidity and comorbidity are distinct concepts in research design, intervention development and healthcare delivery. However, there is not a universal recognition of this distinction yet.
We welcome the change in the name of the journal as another step in clarifying the use of the terms, hoping that it will contribute to our main goal which is to improve the health outcomes of our patients.
van den Akker M, Buntinx F and Knottnerus JA. Comorbidity or multimorbidity: what’s in a name? A review of literature. Eur J Gen Pract 1996; 2: 65-70.
Harrison C, Fortin M, van den Akker M, et al. Comorbidity versus multimorbidity: Why it matters. Journal of Multimorbidity and Comorbidity 2021; 11. Article first published online: March 2, 2021. DOI: https://doi.org/10.1177/2633556521993993.
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.
Academy of Medical Sciences. Multimorbidity: a priority for global health research. 2018.
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.
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.
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.
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.
