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Category Archives: Theory, concept and measure

How can we make the measurement of multimorbidity more comparable and reproducible?


By Iris Ho and Bruce Guthrie on behalf of all authors

Measuring multimorbidity is complex. We have recently published papers from a Health Data Research UK funded study which report a systematic review of how multimorbidity is measured in the literature, and an international Delphi study seeking to identify consensus on how multimorbidity should be measured.

The systematic review examined how 566 studies defined and measured multimorbidity, finding very large variation. One in eight studies did not report which conditions were included in their multimorbidity measure. Where reported, then the number of conditions included varied from two to 285 (median 17, IQR 11-23). Most studies included at least one cardiovascular condition (98%), metabolic condition (97%), respiratory condition (93%), or musculoskeletal condition (88%). Only 78% included any mental health condition, and many other body systems were infrequently included (eg haematological conditions 24%). Only eight individual conditions (all of them physical conditions) were included by more than half of studies (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure). (Ho et al https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(21)00107-9/fulltext).

We concluded that there was an urgent need for more consistency in the field, and therefore carried out a three round Delphi consensus study with 150 professionals and 25 public panel members in round 1. Highlights of the results were that there was consensus that multimorbidity should be defined as two or more conditions. Although ‘complex multimorbidity’ (eg 3+ conditions, or 3+ conditions from 3+ body systems) was perceived as potentially useful, there was no consensus on how to define it, highlighting the need for more research into alternative definitions. Simple counts of conditions were preferred for estimating prevalence and examining clusters or trajectories, and weighted measures were preferred for risk adjustment and outcome prediction. Reflecting the variability in the systematic review, there was consensus that studies needed to be more consistent in their reporting (figure and table below are reproduced from the paper under the CC-BY licence).

Finally, there was consensus for 24 conditions that should “always” be included in multimorbidity measurement and 35 that should be “usually included (unless a good reason not to in a particular context)”. We recognise that choices should reflect context and local condition prevalence so the combined ‘always or usually include’ list can be seen as a core set of conditions with scope for local adaptation (Ho et al https://bmjmedicine.bmj.com/content/1/1/e000247).

Further research and consensus is needed to define codelists or other diagnostic criteria for the listed conditions, to examine the relative value of unweighted vs weighted measures, and to understand the additional value (if any) of different definitions of complex multimorbidity.

Like all consensus recommendations, we expect that many researchers will find at least a few items that they disagree with. BG for example finds the non-inclusion of depression in the ‘always include’ list perplexing (albeit it only just failed to reach the pre-specified 70% consensus level for inclusion, but easily reached consensus for ‘usually include’). However, unless there are pressing and explicitly justified reasons to vary from the consensus, we think it would ideal for researchers in this field to align where feasible. We hope that these studies will be interesting and useful to the research community, and welcome any feedback.

Multimorbidity as a complex systems phenomenon: a series exploring this perspective from the Journal of Evaluation in Clinical Practice

By René Melis
Multimorbidity is one of public health and healthcare’s top priorities. Yet despite this, healthcare continues to struggle to provide solutions to deal with multimorbidity in healthcare that work. It is recognized that our healthcare systems – due to their focus on the acute phase of single diseases – are not well positioned to deal with multimorbidity. Unfortunately, our dealings with changing the system do not easily translate into successes: there are so many stakeholders involved and multimorbidity is interconnected with a huge number of aspects of life and society. Where to begin? Our multimorbidity challenge has all the characteristics of a “wicked problem”: a societal problem that is so complex that is seems difficult or even impossible to solve. In the most recent Complexity Forum of the Journal of Evaluation in Clinical Practice a series of articles explore multimorbidity and how we should shape our healthcare from the perspective of complex systems thinking (http://onlinelibrary.wiley.com/doi/10.1111/jep.12723/full). Rather than a stable, albeit complicated, arrangement of individual elements with predictable results following from inputs in a linear way, a complex system is a dynamic, ecological system in which outcomes seem to emerge quite unpredictably out of the interaction of the starting conditioning. For the reasons mentioned above, this “complexity” approach might fit very well to the multimorbidity challenge.
The series starts with an introductory paper of Dr Joachim Sturmberg and colleagues. Sturmberg, who is a general practitioner as well as a longstanding expert on complexity in health (care systems), together with his colleagues explore how taking multimorbidity as a complexity phenomenon might shape integrated, personalized care differently. Following, this work is commented on by several authors from different perspectives. Being a “wicked problem” neither of these works provide a miracle potion to solve our multimorbidity issue, however, the richness of the perspectives included does shed new light. The famous Cynefin framework (https://hbr.org/2007/11/a-leaders-framework-for-decision-making) for management problems tells us that managing complex problems – and multimorbidity sure is! – has to begin with uncovering the “unknown unknowns” and we need to “probe first, then sense, and then respond”. The latter is what this series hopefully has to offer to the multimorbidity community.

Risk factors and symptoms in the definition of multimorbidity

By Tora Grauers Willadsen and Niels de Fine Olivarius
We want to share our new paper”The role of diseases, risk factors and symptoms in the definition of multimorbidity – a systematic review” (Scandinavian Journal of Primary Health Care 2016 March 8, : 1-10) here on the International Research Community on Multimorbidity’s (IRCMo) site.
Our objective was to explore how multimorbidity is defined in the scientific literature, with a focus on the role of diseases, risk factors, and symptoms in the definitions. We used systematic review as design. We searched MEDLINE (PubMed), Embase, and The Cochrane Library for relevant publications up until October 2013. One author extracted the information. Ambiguities were resolved, and consensus reached with one co-author. Our main outcome measures were: cut-off point for the number of conditions included in the definitions of multimorbidity; setting; data sources; number, kind, duration, and severity of diseases, risk factors, and symptoms.
We had the following results: In 61 (37 %) articles, out of the 163 articles we included, the cut-off point for multimorbidity was two or more conditions (diseases, risk factors, or symptoms). The most frequently used setting was the general population (68 articles, 42%), and primary care (41 articles, 25%). Sources of data were primarily self-reports (56 articles, 42%). Out of the 163 articles selected, 115 had individually constructed multimorbidity definitions, and in these articles diseases occurred in all definitions. As earlier found diabetes was the most frequent disease. Risk factors occurred in 98 (85%) and symptoms in 71 (62%) of the definitions. The severity of conditions was used in 26 (23%) of the definitions, but in different ways.
This review demonstrated, as shown previously, a heterogeneous definition of multimorbidity. Furthermore, it shows that risk factors are more often included than symptoms and that severity of conditions is seldom included in the definition. The fundamental role of risk factors in the definition of multimorbidity is one reason for the high prevalence of multimorbidity. Symptoms and severity are included less often and this contributes to making the existing definitions more usable for epidemiologists than for clinicians and patients. We believe this review adds to the discussion about more comprehensive and clinically relevant multimorbidity definitions.
To access the full manuscript, please click the following link:
At The research Unit for General Practice and Department of General Practice at The University of Copenhagen we are working on several projects about multimorbidity, both quantitative register-based studies including the whole Danish population, and qualitative studies. You are very welcome to contact us for more information. E-mail: olivarius@sund.ku.dk

Multimorbidity in two large Australian primary care practices

By Tom Brett

The Annals of Family Medicine recently published our research on multimorbidity among 7,247 patients attending two large Australian primary care practices (1). Our study set out to examine patterns and prevalence of multimorbidity and to estimate disease severity burden using the Cumulative Illness Rating Scale (CIRS).
We adhered strictly to Miller et al’s approach (2,3) in assessing number of body domains affected, the total score, the ratio of total score to number of domains (yielding a severity index), and importantly, the number of domains with maximum scores at levels 3 and 4. Highlighting the number of domains with severity scores of 3 and 4 is important as it helps guard against severity underestimation especially if there is a risk of severity index dilution with increased numbers of level 1 and 2 scores.
Our purposefully collected data, using combination of free-text electronic records, older hard copy files based on histories recorded by primary care physician, hospital discharge and outpatient letters and radiology and pathology reports, was extremely hard work and not for the fainted hearted! We feel our purposefully collected, multisource medical record data, based on 42 conditions across 14 domains and involving patients across the entire age spectrum provides further useful information for those interested in multimorbidity in primary care.
Our current research interest in the area involves patterns and prevalence of multimorbidity and disease severity burden involving disadvantaged and street-based populations.

1.    Brett T, Arnold-Reed DE, Popescu A, et al. Multimorbidity in patients attending 2 Australian primary care practices. Ann Fam Med 2013; 11(6): 535-542.
2.    Miller MD, Towers A. A manual of guidelines for scoring the Cumulative Illness Rating Scale for geriatrics (CIRS-G). Pittsburg, PA: University of Pittsburgh, 1991.
3.    Miller MD, Paradis CF, Houch PR, et al. Rating chronic illness burden in geropsychiatric practice and research: application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992; 41 (3): 237-248.

The definition of multimorbidity: looking for a consensus

By Martin Fortin

We published recently a bibliometric study of English language publications indexed from 1970 to 2012 that showed a diversity of terms used to describe the presence of multiple concurrent diseases: comorbidity, multimorbidity, polymorbidity, polypathology, pluripathology, multipathology, multicondition [1]. Comorbidity was overwhelmingly used when one disease/condition was designated as index, as described by Feinstein [2]. Multimorbidity was the term most often used when no disease/condition was designated as index, but several different definitions exist.
We would like to invite you to have a look at the results of the bibliometric study and then to complete a two-question survey that should take you approximately one minute to complete. The questions are:
1-Which definition do you think should be used for multimorbidity?
a) Multiple co-occurring chronic or long-term diseases or conditions, none considered as index disease.
b) Multiple co-occurring diseases or conditions, none considered as index disease.
c) Any of the above definitions.
d) Another definition (please, provide a definition or a reference):


2-What is your country of origin? ____________________________

The deadline to participate in the survey is January 31, 2014. We would like to receive input from as many people as possible. Please consider completing the survey as your input is very important. Many thanks for your help.
Results of the survey will be posted in February 2014.

Click here to complete the survey

[1] Almirall J, Fortin M. The coexistence of terms to describe the presence of multiple concurrent diseases. Journal of Comorbidity. 2013;3(1):4-9.
[2] Feinstein AR. The pre-therapeutic classification of co-morbidity in chronic diseases. J Chronic Diseases. 1970;23:455-469.

Comparisons of multi-morbidity in family practice – issues and biases

By Moira Stewart, Martin Fortin, Helena Britt, Christopher Harrison, and Heather Maddocks

A recent study published in Family Practice “Comparisons of multi-morbidity in family practice – issues and biases” [1] compared the methods and results of three separate prevalence studies of multi-morbidity from; i) the Saguenay region of Quebec [2]; ii) a sub-study of the Bettering Evaluation and Care of Health (BEACH) program in Australia [3,4]; and iii) the Deliver Primary Health Care Information (DELPHI) project in South-western Ontario [5,6].

A re-estimate of the prevalence rates using identical age-sex groups found multi-morbidity prevalence to vary by as much as 61%, where reported prevalence was 95% among females aged 45–64 in the Saguenay study, 46% in the BEACH sub-study and 34% in the DELPHI study.

Several aspects of the methods and study designs were identified as differing among the studies, including the sampling of frequent attenders, sampling period, source of data, and both the definition and count of chronic conditions.

The paper offers a guide for authors reporting the methods used in multi-morbidity prevalence research, recommending detailed descriptions of the type of sampling, completeness and accuracy of the source of data, and the definition of chronic conditions.

Further comparisons among multi-morbidity data using agreed upon standards for the definition of chronic conditions and the way to count multi-morbidity are recommended to assess the impact of these methodological variations.


1 Stewart M, Fortin M, Britt H, Harrison C, Maddocks H.  Comparisons of multi-morbidity in family practice – issues and biases.  Family Practice. May 2013.  doi: 10.1093/fampra/cmt012.

2 Fortin M, Bravo G, Hudon C, Vanasse A, Lapointe L. Prevalence of multimorbidity among adults seen in family practice. Ann Fam Med 2005; 3: 223–8.

3 Britt HC, Harrison CM, Miller GC, Knox SA. Prevalence and patterns of multimorbidity in Australia. Med J Aust 2008; 189: 72–7.

4 Knox SA, Harrison CM, Britt HC, Henderson JV. Estimating prevalence of common chronic morbidities in Australia. Med J Aust 2008; 189: 66–70.

5 Stewart M, Thind A, Terry AL, et al. Multimorbidity in primary care: a study using electronic medical record (EMR) data. In: Thirty-Seventh Annual Meeting of North American Primary Care Research Group, Quebec, Canada, 14–18 November, 2009.

6 Stewart M, Thind A, Terry A, Chevendra V, Marshall JN. Implementing and maintaining a researchable database from electronic medical records—a perspective from an academic family medicine department. Healthc Policy 2009; 5: 26–39.

Multimorbidity measures

By Alyson Huntley

In the Academic Unit of Primary Health Care at the University of Bristol, one of our key research themes is organisation and delivery of care led by Professor Chris Salisbury.  This theme relates to providing evidence about the impact of changes in how primary health care is organised and delivered. Our research often combines quantitative, qualitative and economic methods. We have conducted a number of large scale multi-centre evaluations of important initiatives. We are particularly interested in the impact of these new models of care on core values of primary care such as access to care, generalism, co-ordination and continuity of care.

An important part of this research is the study of multimorbidity. We have several projects running in this area at the moment including:

  • The impact of multimorbidity on the use of resources in primary care
  • Complex consultations. The impact of multimorbidity on consultations.
  • A systematic review of measures of multimorbidity.

We have recently published the systematic review on multimorbidity measures in the Annals of Family Medicine (Huntley AL, Johnson R, Purdy S, Valderas JM, Salisbury C. Measures of multimorbidity and morbidity burden for use in primary care and community settings: a systematic review and guide. Ann Fam Med. 2012;10(2):134-41).

The aims of this review were to identify measures of multimorbidity and morbidity burden suitable for use in research in primary care and community populations, and to investigate their validity in relation to anticipated associations with patient characteristics, process measures, and health outcomes.

We found that the measures most commonly used in primary care and community settings are disease counts, Charlson index, ACG System, CIRS, CDS, and DUSOI. Different measures are most appropriate according to the outcome of interest. Choice of measure will also depend on the type of data available. More research is needed to directly compare performance of different measures.

The Disease Burden Morbidity Assessment (DBMA) by self-report

By Marie-Eve Poitras, RN. M.Sc.

Studies on multimorbidity should rely on valid and robust measurement to assess the disease burden experienced by patients with chronic diseases. There are many instruments designed to measure multimorbidity, however, most of them have to be administered by professionals because of the medical background required to complete them. This is a limitation to using these instruments in large samples of patients either in primary care settings or the general population.

Previous studies have shown that a measure that includes a weighting for severity is a better predictor of patient-related outcomes than a measure based on a simple disease count [1-2]. Severity can be judged on purely clinical grounds by health professionals or on the basis of the illness experienced by patients themselves. However, impact on daily living seems to be best evaluated by the patient because self-reported disease burden correlates with quality of life outcomes more strongly than measures of comorbidity based on other methods of data collection [3].

 The Disease Burden Morbidity Assessment (DBMA) is a self-report questionnaire that seems promising because: 1) it can be administered to large samples of patients and 2) it judges severity on the basis of the illness experienced by patients themselves [3].

 We conducted a study to test and to measure the properties of the French translation of the DBMA (DBMA-Fv). The DBMA-Fv’s properties were similar to its English counterpart as to its median sensitivity and specificity compared to chart reviews. It correlated moderately with an established index of multimorbidity, the Cumulative Illness Rating Scale (CIRS). A high percentage of patients were able to complete the test correctly as a mail questionnaire and it showed high test-retest reliability.

 The article describing the study can be accessed freely on line [4], where the readers can also find both the English and French versions of the DBMA questionnaire as appendices to the paper.

 1. Fortin, M., et al., Comparative assessment of three different indices of multimorbidity for studies on health-related quality of life. Health Qual Life Outcomes, 2005. 3:74.
2.Fortin, M., et al., Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res, 2006. 15:83-91.
3. Bayliss, E.A., J.L. Ellis, and J.F. Steiner, Subjective assessments of comorbidity correlate with quality of life health outcomes: Initial validation of a comorbidity assessment instrument. Health and Quality of life Outcomes, 2005. 3:51.
4. Poitras, M.-E., et al., Validation of the disease burden morbidity assessment by self-report in a French-speaking population. BMC Health Service Research, 2012. 12:35.

Can we exclude exclusion criteria?

By Graham Watt
I keep a cartoon in which a patient tells a flummoxed psychiatrist,

” I have neither illusions nor delusions, Doc. My problem is that I exist day after day in grim reality”.

In seeking to pigeon-hole the patient as a case, the doctor ignores her reality.
Does the same thing happen in multiple morbidity?
Shakespeare first noted, “When troubles come, they come not single spies but in batallions”

That seems true of multiple morbidity in socio-economically deprived areas, defined as the “number, severity and complexity of health and social problems within families”.

Operational definitions of multiple morbidity in research studies, based on counts of conditions, get nowhere near this, largely missing out on social and family aspects.

The problem is heterogenity, something that research tries to eliminate.
How can multiple morbidity research put heterogenity centre stage, so that results inform the majority of patients’ circumstances and not only those meeting case definitions?

Of course, asking the question is the easy part.

Measuring Patients’ Perceptions of Patient-Centered Care

By Catherine Hudon

As many people affected by multimorbidity frequently interact with a family physician, [1-2] this professional is in a privileged position to play a significant role in their health. In patient-physician interactions, patient-centered care is widely acknowledged as a core value in family medicine [3-5] and has been associated with short term positive outcomes. [6-8] We decided to conduct a systematic review to identify and compare instruments, subscales or items assessing patient perception of patient-centered care in family medicine. We identified two instruments dedicated to measuring patient-centered care and eleven instruments that address some dimensions of this concept. The two instruments dedicated to patient-centered care measure key dimensions of this concept but are visit-based, limiting their applicability for long-term care processes such as chronic illness management. Relevant items from the eleven other instruments provide partial coverage of the concept but these instruments were not designed to provide a specific assessment of patient-centered care.
This article is published in the Mar/Apr 2011 issue of Annals of Family Medicine. To have free access to this article, click on this link:

1. Starfield B, Lemke K, Bernbardt T, Foldes S, Forrest C, Weiner J: Comorbidity: implications for the importance of primary care in case management. Annals of Family Medicine 2003, 1:8-14.
2. Broemeling A, Watson D, Prebtani F: Population patterns of chronic health conditions, co-morbidity and healthcare use in Canada: implication for policy and practices. Healthcare Quaterly 2008, 11:70-76.
3. World Health Organization. Former les personnels de santé du XXe siècle: le défi des maladies chroniques [http://www.who.int/chp/knowledge/publications/workforce_report_fre.pdf]
4. World Health Organization. The Innovative Care for Chronic Condition (ICCC). [http://www.who.int/diabetesactiononline/about/ICCC/en/index.html]
5. Wagner EH, Austin BT, Von Korff M: Organizing care for patients with chronic illness. Milbank Quarterly 1996, 74:511-544.
6. Stewart M, Brown JB, Donner A, McWhinney IR, Oates J, Jordan J: The impact of patient-centered care on outcomes. Journal of Family Practice 2000, 49:796-804.
7. Stewart M, Brown JB, Weston WW, Freeman TR: Patient-centred medicine: transforming the clinical method. 2nd edn. United Kingdom: Radcliffe Medical Press; 2003.
8. Little P, Everitt H, Williamson I, Warner G, Moore M, Gould C, Ferrier K, Payne S: Observational study of effect of patient centredness and positive approach on outcomes of general practice consultations. BMJ 2001, 323:908-911.