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Category Archives: Multimorbidity methods

The measurement of multimorbidity

By Kathryn Nicholson
Answering an invitation to contribute with an article to a special issue of the journal Health Psychology, we wrote the article entitled “The measurement of multimorbidity” that was recently published [1]. The article was written with the purpose of providing a review of the literature published between 1974 and 2018 that have utilized measures for multimorbidity and to provide guidance on measures to consider when conducting a research study on multimorbidity. The article introduces the reader to the two main groups of measures of multimorbidity that can be distinguished. The first group of measures is constituted by a simple count from various lists of chronic conditions. The second group of measures introduces weighting for included chronic conditions thus creating a “weighted index” of multimorbidity. These two main groups are not mutually exclusive as the list of medical conditions in some weighted indexes can be used as a list of conditions without weighting. This classification does not include measures of multimorbidity which are not based on lists of medical conditions, such as the Cumulative Illness Rating Scale, which includes areas or domains that are grouped under body systems instead of medical conditions. The article shows the variety of existing measurements, highlighting their differences, to provide an overview of the possibilities that are available to a researcher intending to measure multimorbidity. Finally, the article outlines some guidelines for the choice of a measurement of multimorbidity for research studies. We hope that this review of the existing literature will help inform the careful use of these tools by researchers moving forward. In addition to this review, it is advised that readers attempt to keep updated on the ever-increasing multimorbidity literature.
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1. Nicholson K, Almirall J, Fortin M: The measurement of multimorbidity. Health Psychol 2019. Apr 25. doi: 10.1037/hea0000739. [Epub ahead of print]

‘Multimorbidity Treatment Burden Questionnaire’ (MTBQ) – a new measure of treatment burden

By Polly Duncan and Chris Salisbury
A group of researchers from the University of Bristol, UK, have developed a new simply worded, concise questionnaire, named the ‘Multimorbidity Treatment Burden Questionnaire’ (MTBQ) to measure treatment burden (the perceived effort of looking after one’s health and the impact that this has on day to day life) in patients with multimorbidity (multiple long-term conditions).  The study has been published in the BMJ Open [1].
Treatment burden includes everything that the patient has to do to look after their health – from ordering, collecting and taking medicines; to co-ordinating, arranging transport for and attending health appointments with multiple different health professionals; to monitoring blood sugar or blood pressure levels; to learning about your health conditions; and taking on lifestyle advice.
To understand how new health care interventions impact on treatment burden, we need to be able to measure it, and a recent study published in the Annals of Family Medicine highlighted treatment burden as one of the core outcome measures for research studies involving patients with multimorbidity [2].
The MTBQ was developed as part of a large research study called the 3D Study [3].  The research team identified and reviewed three existing measures of treatment burden that were not specific to a medical condition.  A further measure has since been published.  We found that the existing measures had limitations (e.g. they did not cover all of the areas of treatment burden or they required good literacy levels and so were not suitable for our study population of mainly older people) and so we decided to develop and validate a new measure.
We discussed the concept of treatment burden and an existing framework of treatment burden that had been developed in the United States with members of a patient and public involvement group.  Using this framework as a guide, we developed a questionnaire to include all the important areas of treatment burden.  We interviewed patients with multimorbidity and asked them to comment on the layout and wording of questions, how easy the questions were to understand and to ‘think aloud’ as they answered the questionnaire – what did the questions mean to them and what answer would they give if they were completing the questionnaire?
The MTBQ was then completed by over 1500 mostly elderly patients (average age 71 years) with three or more long-term conditions who took part in the 3D Study.  The research team assessed the questionnaire against the ISOQOL international standards for developing and validating questionnaires and found that it performed well, demonstrating good face validity (e.g. it measured what it set out to measure), construct validity (e.g. patients with high disease burden and poor quality of life reported higher treatment burden), reliability and responsiveness to change  (e.g. as expected, patients who reported reduce quality of life over time also reported higher treatment burden over time) [1].
Strengths of the MTBQ include:
- simple wording
- a concise measure with ten questions
- all the important aspects of treatment burden are included
- it was tested in patients for whom it was intended – elderly patients (means age 71 years) with three or more long-term conditions
Further information about the MTBQ can be found here:
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References:
1. Duncan P, Murphy M, Man MS, et al. Development and validation of the Multimorbidity Treatment Burden Questionnaire (MTBQ). BMJ Open 2018;8(4):e019413. doi: 10.1136/bmjopen-2017-019413 [published Online First: 2018/04/12]
2. Smith SM, Wallace E, Salisbury C, et al. A Core Outcome Set for Multimorbidity Research (COSmm). Ann Fam Med 2018;16(2):132-38.
3. Man MS, Chaplin K, Mann C, et al. Improving the management of multimorbidity in general practice: protocol of a cluster randomised controlled trial (The 3D Study). BMJ Open 2016;6(4):e011261. doi: 10.1136/bmjopen-2016-011261

Assessing and measuring chronic multimorbidity in the older population

By Amaia Calderón-Larrañaga and Davide L Vetrano
Multimorbidity is one of the main challenges facing health systems worldwide. While its definition as “the simultaneous presence of two or more chronic diseases” is well established, its operationalization is not yet agreed. This study aimed to provide a clinically-driven comprehensive list of chronic conditions to be included when measuring multimorbidity.
Based on a consensus definition of chronic disease, all codes from the International Classification of Diseases 10th revision (ICD-10) were classified as chronic or not by an international team of physicians and epidemiologists specialized in geriatrics and family medicine, and were subsequently grouped into broader categories. Last, we showed proof of concept by applying the classification to older adults from the Swedish National study of Aging and Care in Kungsholmen (SNAC-K).
An initial number of 918 chronic ICD-10 codes were identified and grouped into 60 chronic disease categories. In SNAC-K, 88.6% had ≥2 of these 60 disease categories, 73.2% had ≥3, and 55.8% had ≥4. Once validated, this operational measure of multimorbidity may enable the advancement and evolution of conceptual and theoretical aspects of multimorbidity that will eventually lead to better care.
The publication can be found in the following link:

Methods for identifying 30 chronic conditions: application to administrative data



By Marcello Tonelli

From a list of 40 common chronic conditions, we identified validated algorithms that use ICD-9 CM/ICD-10 data for 30 of these [1]. Algorithms with both positive predictive value and sensitivity ≥70% were graded as “high validity”; those with positive predictive value ≥70% and sensitivity <70% were graded as “moderate validity”. Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. We then applied the algorithms to a large cohort of Alberta residents to show proof of concept. In our opinion, using a standard set of algorithms could facilitate the study and surveillance of multimorbidity across jurisdictions. We encourage other groups to consider using this scheme in their studies.

1. Marcello Tonelli et al. Methods for identifying 30 chronic conditions: application to administrative data. BMC Medical Informatics and Decision Making. 2015;15:31.