Entete 3

Multimorbidity in the context of Neurodegenerative Disorders

By Rafael Linden

Dear colleagues,

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.

More information:

Best regards,

Rafael Linden, M.D., Ph.D.
Associate Editor
Frontiers in Neuroscience

Professor of Neuroscience
Instituto de Biofísica Carlos Chagas Filho
Universidade Federal do Rio de Janeiro

Patient-centered innovation for multimorbidity care: the Ontario trial

By Moira Stewart

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.

  1. 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
  2. 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

Publications on multimorbidity September-December 2020

By Martin Fortin

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.

Patient-centered interdisciplinary care for multi-morbidity: A Pragmatic Mixed-Methods Randomized Controlled Trial in primary care

By Martin Fortin

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.

  1. 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: 126-134. DOI: https://doi.org/10.1370/afm.2650.

The “Journal of Comorbidity” changed to the “Journal of Multimorbidity and Comorbidity.”

By Martin Fortin

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.

  1. 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.
  2. 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.

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.

Publications on multimorbidity May-August 2020

By Martin Fortin

Our search for papers on multimorbidity that were published during the period May-August 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.

Exploring professional and lay experts’ views on the definition and measurement of multimorbidity

By Iris Ho and Bruce Guthrie
Centre for Population and Health Sciences, Usher Institute, University of Edinburgh, United Kingdom

Many people, nowadays, are living with multiple chronic conditions. However, there remains no agreed method to measure this common phenomenon.

In research, there are varying conditions included in a multimorbidity measure. The number of conditions included in a measure can range from 2 to 285. As for selection of conditions, some included disease category (e.g. gastrointestinal disease), whereas others included relatively smaller disease category (e.g. chronic liver disease) or individual conditions (e.g. hepatitis, or liver cirrhosis).

In addition, methods used to count conditions differ across studies. There are two main types of counting methods. One is using a simple count of conditions to estimate how common multimorbidity is. The other type of measures is applying weights for each chronic condition based on disease severity and its impact on an outcome, and the total weighting score is used to predict the impact of multimorbidity on the outcome, such as mortality, physical disability, hospitalisation, or quality of life. Within each type of measures, researchers have used different reference definitions and weighting schemes while counting.

Due to the inconsistent definitions of multimorbidity and measurement methods, estimates of multimorbidity prevalence and burden cannot be directly compared across studies. Therefore, we want to gather your opinions and experience relevant to multimorbidity. Your opinions can help to shape future research, clinical and policy decisions on management of multimorbidity.

We are looking for:

1) Members of the public who are interested in multiple chronic conditions or have chronic illness experiences

2) Academics, clinicians, service providers, policy makers who are interested in multimorbidity or have undertaken multimorbidity-relevant work.

This study consists of two to four rounds of survey (most likely 2-3 rounds depending on when consensus is reached). The round-one survey is currently published online and the other rounds will be distributed subsequently later.

If you are interested in the topic and want to know more about this study, please get in touch with the researcher, Iris Ho (iris.s.ho@ed.ac.uk), or the principal investigator, Bruce Guthrie (bruce.guthrie@ed.ac.uk).

Thank you!

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy: Findings from the PROPERmed individual participant data meta-analysis.

By Ana I. González (picture on the left), Andreas Meid (picture in the middle), Truc S Dinh, Jeanet W Blom, Marjan van den Akker, Petra JM Elder, Ulrich Thiem, Daniela Küllenberg de Gaudry, Kym Snell, Rafael Perera, Karin MA Swart, Henrik Rudolf, Hans-Joachim Trampish, Joerg J Meerpohl, Benno Flaig, Ghaisom Kom, Walter E Haefeli, Paul P Glasziou, Ferdinand M Gerlach and Christiane Muth (picture on the right*)

Multimorbidity and polypharmacy increase the risk for inappropriate prescriptions and underuse of appropriate medication which may lead to patient deterioration in health-related quality of life (dHRQoL) (1,2). In this heterogeneous general practice population, it would be helpful to identify those patients at high risk of dHRQoL since they may benefit most from complex interventions designed to improve their well-being (3–5).

We aimed to develop and validate a prognostic model to predict dHRQoL at six-month follow-up in older patients with at least one chronic condition and one chronic prescription in general practice (6).

We harmonized individual participant data from five cluster-randomized trials from the Netherlands and Germany. dHRQoL was defined as a decrease in the EQ-5D-3L index score of at least 5% from baseline to 6-month follow-up. Prognostic variables included baseline socio-demographics and lifestyle, morbidity, medication, functional status and well-being related variables. The model was developed using logistic regression with a stratified-intercept to account for between-study heterogeneity in baseline risk. Prognostic variables were selected in complete cases and then refitted in multiply imputed data to obtain the final model equation. Internal validation was performed using bootstrapping within studies to assess reproducibility and internal internal-external cross-validation (IECV) was used to evaluate generalisability.

The complete-case population consisted of 3,582 patients. In 1,046 (29%) patients, health-related quality of life (HRQoL) deteriorated at six-month follow-up. Selected baseline variables contributing significantly to the prediction related to single conditions (i.e. coronary heart disease), prescribed medication (i.e. drugs for acid-related disorders), inappropriate medication (i.e. systemic corticosteroids for maintenance in COPD), medication underuse (e.g. angiotensin converting enzyme inhibitors in heart failure), functional status, and well-being (i.e. HRQoL at baseline and depression), with most prognostic relevance attributable to baseline HRQoL and functional status. Bootstrap internal validation of the final model showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). With the trials as validation datasets in the IECV loop, the final model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13).

This first IPD-based prognostic model for dHRQoL in older patients with multiple chronic conditions and medication in general practice performed well in discrimination, calibration, and generalisability and might thus help clinicians identify older patients at high risk of dHRQoL.

This work was supported by the German Innovation Funds according to § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGB V – Fünftes Buch Sozialgesetzbuch), grant number: 01VSF16018.The funder had no role in developing the protocol for this review.

The abstract of the article can be accessed at the following link: https://pubmed.ncbi.nlm.nih.gov/33065164/

*Photo: Universität Bielefeld/S. Jonek


  1. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: A cross-sectional study. Lancet. 2012;380(9836):37–43.
  2. Saqlain M, Ali H, Kamran S, Munir MU, Jahan S, Mazhar F. Potentially inappropriate medications use and its association with health-related quality of life among elderly cardiac patients. Qual Life Res. 2020 May 20;
  3. Romskaug R, Skovlund E, Straand J, Molden E, Kersten H, Pitkala KH, et al. Effect of Clinical Geriatric Assessments and Collaborative Medication Reviews by Geriatrician and Family Physician for Improving Health-Related Quality of Life in Home-Dwelling Older Patients Receiving Polypharmacy. JAMA Intern Med. 2020 Feb 1;180(2):181.
  4. Rankin A, Cadogan CA, Patterson SM, Kerse N, Cardwell CR, Bradley MC, et al. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database Syst Rev. 2018 Sep 3;
  5. Smith SM, Wallace E, O’Dowd T, Fortin M. Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev [Internet]. 2016 Mar 15; Available from: http://doi.wiley.com/10.1002/14651858.CD006560.pub3
  6. González-González AI, Meid AD, Dinh TS, Blom JW, van den Akker M, Elders PJM, et al. A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy. J Clin Epidemiol. 2021;130:1–12.

Role of clinical, functional and social factors in the association between multimorbidity and quality of life: Findings from the Survey of Health, Ageing and Retirement in Europe (SHARE)

By Tatjana T. Makovski (picture on the left), Gwenaëlle Le Coroller, Polina Putrik, Yun Hee Choi, Maurice P. Zeegers, Saverio Stranges, Maria Ruiz Castell, Laetitia Huiart, Marjan van den Akker (picture on the right)

Quality of life (QoL) is often mentioned among the main consequences of multimorbidity and it has been set as one of the core outcomes in multimorbidity research [1].

In exploring the association between multiple conditions and quality of life, researchers most often account for socio-economic factors [2], although it has been recognised a while ago that other aspects such as perceived social support [3] and limitation with activities of daily living [4] play a significant role in this relationship.

With the current study, we intended to underline the relevance of some of these factors, as well as to test other elements for significance. Factors of interest were: symptoms; indicators of treatment burden such as polypharmacy, unmet care needs and frequency of utilisation of care; size of social network, participation in social activities, personal and financial help, loneliness as a proxy for perceived social support [5]; and activities of daily living (ADL) with instrumental activities (IADL).

The study included individuals aged 50+ (n = 67 179) in 18 countries who participated in wave 6 of the population-based Survey of Health, Ageing and Retirement in Europe (SHARE). Wave 6 measured presence of 17+ conditions and applied the Control, Autonomy, Self-Realization and Pleasure (CASP-12v1) QoL questionnaire. We used 3-level random slope linear regression model to test for the association between number of diseases and QoL. The base model adjusted for socio-economic factors only, while factors of interest were subsequently added in the base model, one at the time. A change of ≥15% in the β-coefficient of the number of conditions compared to the β-coefficient in the base model indicated a relevant effect on the association.

Symptoms, loneliness, ADL/IADL and polypharmacy instigated a set change of the coefficient in the models individually. Adding all relevant factors together in the final model attenuated the strength of the association between number of diseases and QoL, as demonstrated with QoL slope of -2.44 [95% CI: -2.72; -2.16] in the base model and much lesser but still statistically significant decline of -0.76 [95%CI: -0.97; -0.55] in the final model.

The study confirmed that other factors beyond socio-economic circumstances explain the relationship between multimorbidity and QoL, and should be considered when further exploring this question. Maybe one of the most interesting contributions of the paper is including elements of the treatment burden as an adjustment factor in the analyses. Treatment burden is gaining increasingly on research interest. It is abundant in patients with multimorbidity and is likely to take its share in the association between multiple conditions and QoL.

The findings may be useful in supporting a comprehensive assessment of patient’s health status and needs during a personalised care planning.

SHARE countries also displayed interesting differences in the findings.

The article can be assessed at the following link:

  1. Smith SM, Wallace E, Salisbury C, Sasseville M, Bayliss E, Fortin M. A Core Outcome Set for Multimorbidity Research (COSmm). Ann Fam Med. 2018;16(2):132-138.
  2. Makovski TT, Schmitz S, Zeegers MP, Stranges S, van den Akker M. Multimorbidity and quality of life: Systematic literature review and meta-analysis. Ageing Res Rev. 2019;53:100903.
  3. Fortin M, Bravo G, Hudon C, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83-91.
  4. Barile JP, Thompson WW, Zack MM, Krahn GL, Horner-Johnson W, Haffer SC. Activities of daily living, chronic medical conditions, and health-related quality of life in older adults. J Ambul Care Manage. 2012;35*(4):292-303.
  5. Bernardon S, Babb KA, Hakim-Larson J, Gragg M. Loneliness, attachment, and the perception and use of social support in university students. Can J Behav Sci. 2011;43(1):40-51.