Entete 3

Looking for a consensus for a definition of multimorbidity: the results

We have computed the results of the survey we conducted recently on the definition of multimorbidity.

We received 55 responses from 16 countries. The distribution of respondents by country (in alphabetic order) was: Australia 4, Brazil 1, Canada 10, China 1, Egypt 1, Germany 1, India 1, Indonesia 1, Ireland 6, Netherlands 4,  South Korea 1, Spain 6, Switzerland 1, Turkey 1, United Kingdom 10 (3 of them from Scotland), United States of America 6.

Answers to the question “which definition do you think should be used for multimorbidity?” were as follows:

Comments associated with the responses to the last item are shown below as replies to this posting. We welcome more comments on this subject that can also be written as replies to the posting.

We want to thank all participants who shared their view on this subject.

The International Research Community on Multimorbidity

Rethinking health outcomes in the era of multiple concurrent chronic conditions



By Ross Upshur, Kerry Kuluski and Shawn Tracy

Outcomes are broadly considered to be the results brought about by care delivered to patients by healthcare providers. The increased focus on outcomes is understandable given the vast resources our society now devotes to health care. Outcomes that are typically measured relate to the management of specific diseases such as cancer, cardiovascular disease, and other common chronic diseases such as diabetes, osteoarthritis, and depression. On the other hand, Canadian health care systems tend to focus on outcomes that relate to the efficiency and cost effectiveness of the health care system. However, these outcomes may be missing an important phenomenon that is occurring before our eyes: multi-morbidity, or multiple concurrent chronic disease (MCCC).

This is the subject of a post authored by Ross Upshur, Kerry Kuluski and Shawn Tracy recently published in the healthydebate.ca blog. We would like to invite the readers of this blog to follow this link to read the whole text.

Improving Guidelines for Multimorbid Patients



By Cynthia M. Boyd and David M. Kent

With rare exceptions, guidelines focus on the management of a single disease, or a single disease-problem, and do not address how to optimally integrate care for individuals whose multiple problems may make guideline-recommended management of any single disease impractical, irrelevant or even harmful.  Current standards of guideline development or appraisal do not prompt guideline developers to routinely address the issue that not all patients with the same condition benefit similarly from similar therapy, nor do they provide tools for adapting the recommendations to the patient with many diseases or for prioritizing the most important recommendations within a single disease, let alone between diseases. The root of this problem, however, is not narrowly confined to guideline development and implementation. At each phase of the translational path including trial and study design and analysis, the synthesis of trial and observational study results in meta-analyses and systematic reviews, and the guideline development process, the very information necessary to support evidence-based care of the person with multimorbidity is excluded.  Needed, then, is a comprehensive approach built on a firm understanding of each of these phases of evidence generation, synthesis and integration, and guideline development. 

To address these issues, we assembled a collaborative team with complementary expertise spanning the various phases of evidence development and translation to develop a comprehensive description of the problem and provisional recommendations.  These were refined through an iterative process of feedback from researchers (from medicine, public health, biostatistics), guideline developers, and stakeholders from government, other payers and industry, which culminated at a conference on Improving Guidelines for Multimorbid Patients (Baltimore, Maryland October 2010). The results of this project are presented in 4 papers [1-4] in a symposium in the Journal of General Internal Medicine, focused on the following 3 areas: 1) evidence generation (clinical trial and observational study design and analysis), 2) evidence synthesis (systematic review, meta-analyses) and 3) guideline development.

 References

1. Boyd CM and Kent DM. Evidence-Based Medicine and the Hard Problem of Multimorbidity. JGIM 2014 Jan 18. [Epub ahead of print].
2. Weiss CO, Varadhan R, Puhan M, Vickers A, Bandeen-Roche K, Boyd CM, Kent  DM. Multimorbidity and Evidence Generation, JGIM 2014 Jan 18. [Epub ahead of print].
3. Trikalinos T, Segal J, Boyd CM. Addressing Multimorbidity in Evidence Synthesis and Integration., JGIM 2014 Jan 18. [Epub ahead of print].
4. Uhlig K, Leff B, Kent DM, Dy S, Brunnhuber K, Burgers J, Greenfield S, Guyatt G, High K, Leipzig R, Mulrow C, Schmader K, Schunemann H, Walter L, Woodcock J, and Boyd CM. A framework for crafting clinical practice guidelines that are relevant to the care and management of people with multimorbidity. JGIM 2014 Jan 18. [Epub ahead of print].

Polypharmacy patterns: unravelling systematic associations between prescribed medications



By Amaia Calderón and Alexandra Prados-Torres

We would like to share with you an article recently published in PLOS ONE [1] by our research group, EpiChron. The study demonstrates the existence of non-random associations in drug prescription, resulting in patterns of polypharmacy that exist in a significant proportion of the population. We believe that the information discovered would further the development and/or adaptation of clinical patient guidelines to patients with multimorbidity who are taking multiple drugs.

[1] Calderon-Larranaga A, Gimeno-Feliu LA, Gonzalez-Rubio F, Poblador-Plou B, Lairla-San Jose M, Abad-Diez JM, Poncel-Falco A, Prados-Torres A. Polypharmacy Patterns: Unravelling Systematic Associations between Prescribed Medications. PLoS One. 2013;8(12):e84967.

Measuring Care Coordination for People with Multiple Chronic Conditions



By Eva DuGoff, Sydney Dy and Cynthia Boyd

This is the second of three posts on issues measuring quality of care in older adults with multiple chronic conditions.

Care coordination has long been considered integral to the efficient and effective delivery of health care, especially for older adults with multiple chronic health conditions. Under the Patient Protection and Affordable Care Act of 2010, Medicare has funded a number of pilot and demonstration programs testing approaches that incentivize and compensate providers for offering these services. Three of the law’s highest-profile initiatives designed to improve care coordination are Accountable Care Organizations (ACOs), the Independence at Home (IAH) demonstration program and the Community-based Care Transitions Program (CCTP).

In our article recently published in the Journal of Healthcare Quality [1], we examine how these three programs measure care coordination quality in five different domains: 1) Communication includes interpersonal communication and information transfer; 2) Continuity of care includes the capacity to monitor and respond to change, support self-management goals, and link to community resources; 3) Patient centered includes creating a proactive plan of care, assessing needs and goals, and aligning needs and resources; 4) Care transitions includes facilitation transitions as coordination needs change and facilitate transitions across settings; and, 5) Cross-Cutting assesses whether the measure applies to multiple conditions. These measurement domains are drawn from the Care Coordination Measurement Framework and NQF Multiple Chronic Condition Measurement Framework [2, 3].

The selection of the quality measures is a critical design element of these care coordination programs because it determines the empirical evidence base for assessing the success of these programs, as well as financial rewards. While quality measure selection raised a great deal of concern inside the Washington DC beltway, these issues have received little attention in the peer-reviewed literature.

In this article, we consider the ideal scenario to be as described in the following: “Ideally, quality measures in these ACA programs would reward and promote care coordination, particularly for people with MCCs (multiple chronic conditions), and have the same core measurement set to allow for comparisons between programs, and utilize measures endorsed by a national standard-setting organization, such as the National Quality Forum (NQF)” [1]. We focus on how care coordination quality is measured in three high-profile programs, ACOs, Independence at Home, and Community-based Care Transitions, and the NQF care coordination measure set. We collected all measures classified as assessing care coordination and those linked to financial incentives in the three ACA programs. Two reviewers categorized these measures independently then reconciled any differences.

What we find is far from the ideal. There is little overlap in the quality measures used to measure care coordination. While this heterogeneity may reflect the characteristics and needs of different target populations, these differences will inhibit comparison between these programs.

And, too, many aspects of care coordination are not captured by existing, selected measures. “Patient-centered care was not captured by the ACO measures, but was assessed in IAH and CCTP. None of the ACA programs measured aligning resources with patient and population needs. Care coordination activities assessing how well the health care team responds to changes in health needs, care transitions, and monitoring and follow-up were infrequently captured” [1]. In a recent article, Kathryn McDonald and colleagues come to a similar conclusion based on their analysis of care coordination quality measures for primary care practices. They find that there are no adult care coordination quality measures assessing care transitions or measuring how providers respond to changes in a patient’s health needs [4].

Further research is needed to identity meaningful care coordination quality measures that will allow policymakers to comprehensively assess these care coordination programs. There are few measures that measure care coordination in ways relevant to people with multiple chronic conditions—even those these are the people are the most in need of care coordination. In the short-term policymakers could consider aligning care coordination quality measures to the extent feasible across these three programs and future initiatives.

References

[1] DuGoff EH, Dy S, Giovannetti ER, Leff B, Boyd CM. Setting standards at the forefront of delivery system reform: aligning care coordination quality measures for multiple chronic conditions. J Healthc Qual. 2013 Sep-Oct;35(5):58-69.
[2] McDonald K, Schultz E, Albin L, Pineda N, Lonhart J, Sundaram V, Smith-Spangler C, Brustrum J, Malcolm E. Care Coordination Atlas Version 3 (Prepared by Stanford University under subcontract to Battelle on Contract No. 290-04-0020) Rockville, MD: Agency for Healthcare Research and Quality; 2010. Available at: http://www.ahrq.gov/professionals/systems/long-term-care/resources/coordination/atlas/index.html.
[3] Multiple Chronic Conditions Measurement Framework. (2012) (pp. 1-74). Washington, DC: National Quality Forum.
[4] McDonald KM, Schultz E, Pineda N, Lonhart J, Chapman T, Davies S. Care Coordination Accountability Measures for Primary Care Practice (Prepared by Stanford University under subcontract to Battelle on Contract No. 290-04-0020) Rockville, MD: Agency for Healthcare Research and Quality; 2012.

A Commentary on the U.S. HHS Initiative, Multiple Chronic Conditions: A Strategic Initiative. A Special Issue of the Journal of Comorbidity



By William A. Satariano and Cynthia M. Boyd

“Multiple Chronic Conditions:  A Strategic Framework” is a seminal report and the heart of a US strategic initiative, released by the U.S. Department of Health and Human Services (HHS) in December 2010.  The purpose of the special initiative is to focus the attention and resources of the US government on the research, practice, and policy implications of multiple chronic conditions (MCCs) [1].   The Journal of Comorbidity (JoC) recently published a special issue to consider the MCC report from a broader international perspective, to our knowledge, the first scientific journal to do so [2].
As co-editors of the special issue, we, together with Sandra Cox, the JoC Senior Editor, invited a team of international scholars to respond to the MCC report, as summarized for the special issue by Anand K. Parekh and Richard A. Goodman from the US HHS.  Both Parekh and Goodman were instrumental in the development of the MCC report; and, we thought, uniquely qualified to summarize, for our purposes, the key research objectives of the special initiative.
Each scholar was asked to address one of the key research objectives of the MCC report and, whenever possible, to highlight some of the current research, practice, and policy from their home countries.  The scholars and their topics are Martin Fortin and Susan M. Smith (Canada and Ireland), Improving the external validity of clinical trials:  the case of multiple chronic conditions; Francois G. Schellevis (the Netherlands), Epidemiology of multiple chronic conditions:  an international perspective; Jose M. Valderas (United Kingdom), Increasing clinical,, community, and patient-centered health research for preventing and managing multimorbidity; and Efrat Shadmi (Israel), Disparities in multiple chronic conditions within populations.
We believe that the JoC special issue underscores the importance of international collaboration for a better understanding of both the common and unique themes associated with the global impact of MCCs.  We trust that this special issue will help to simulate further research, discussion, and guidance to that end.

[1] U.S. Department of Health & Human Services.  Multiple chronic conditions:  A strategic framework.  Optimum health and quality of life for individuals with multiple chronic conditions.  Washington, DC:  U.S. Department of Health & Human Services; 2010. Available from:  http:www.hhs.gov/ash/initiatives/mcc/mcc_framework.pdf

[2] Special Issue:  A commentary on the U.S. HHS Initiative, Multiple Chronic Conditions:  A strategic initiative.  Journal of Comorbidity2013;3(2):18-50.  Available from:  http:www.jcomorbidity.com/index.php/test/issue/current/showToc

HAPPY HOLIDAYS 2014

Quality indicators for functional status and quality of life in people with multimorbidity: Are we ready?



Sydney Dy, Elizabeth Pfoh, and Cynthia Boyd

Patients with multimorbidity suffer from multiple concurrent diseases that may affect their functional status and quality of life. Quality measurement that incorporates functional status and quality of life domains could be a valuable approach to complement the many disease-centered quality measures, and might help guide improvement in care. However, since multiple factors can influence functional status and quality of life, incorporating these outcomes into measures of the quality of health care may be challenging, especially in the population with multimorbidity.  To address this concern, we published a review in the Journal of the American Geriatrics Society [1] that aimed to inform initiatives that are developing quality indicators addressing functional status and health-related quality of life for patients with multimorbidity.  

We reviewed key sources of indicators (such as the National Quality Forum and Agency for Healthcare Research and Quality) to find quality indicators for quality of life and functional status relevant to this population in the outpatient arena. We also interviewed key informants who are using these outcomes in quality indicator projects that include or focus on the population with multimorbidity.

We found few relevant quality indicators for people with multimorbidity; existing indicators are used only for specific populations or settings, are challenging to implement, and have issues with validity. Key informants discussed concerns about the validity of existing indicators for differentiating quality of care between systems, and concerns about their use in a population where physical function for many people is naturally declining over time. Additionally, they raised concerns about consistent documentation for these quality indicators across providers.  Another challenge is defining the appropriate sampling population for the indicators.  

Across countries, quality measurement is used for varied purposes, ranging from financial incentives to accountability and quality improvement.  Avoiding unintended consequences through the use of such quality indicators is essential, as it may be most challenging to improve these outcomes in the most vulnerable populations.  Fundamental to the consideration of using quality indicators that focus on functional status and quality of life is improving the evidence base for how to improve these outcomes for people with multimorbidity – and this is still a limited, albeit growing, evidence base.

A potential first step to better integrate these outcomes into quality initiatives might be to implement standards for infrastructure to routinely collect this data from patients in clinical care.

1. Dy SM, Pfoh ER, Salive ME, Boyd CM. Health-Related Quality of Life and Functional Status Quality Indicators for Older Persons with Multiple Chronic Conditions. Epub, J Am Geriatr Soc. 2013 Dec 9.

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.