Entete 3

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.

A bibliometric analysis of multimorbidity from 2005 to 2019

By Mohamed Ali Ag Ahmed, José Almirall, Patrice Ngangue, Marie-Eve Poitras, Martin Fortin

A bibliometric study published by our team in 2005 demonstrated that there was a large discrepancy between the prevalence of multimorbidity in the population and the number of research studies devoted to it at that time [1]. However, the interest in the topic has increased substantially and, thanks to the contributions of many researchers, our knowledge about multimorbidity is much better today than it was only a decade ago.

This time, we conducted a bibliometric analysis of publications on multimorbidity from 2005 to 2019 aiming to identify and analyze publications on the subject, including those that most influenced this field [2]. We searched for publications containing “multimorbidity” or “multi-morbidity” using the PubMed database, and identified them with the tool iCite (https://icite.od.nih.gov/). We analyzed the number of publications, total citations, the article-level metric Relative Citation Ratio (RCR), type of study, the country of the institutional affiliation of the authors, and journals with the most cited articles.

The number of publications using “multimorbidity” has continuously increased since 2005 (2005-2009: 138; 2010-2014: 823; 2015-2019: 3068). Articles with RCR at or above the 97th percentile (RCR = 7.43) were analyzed in detail (n = 104). In 34 publications of this subgroup (33%), the word multimorbidity was used but was not the subject of study. The remaining top 70 publications included 32 observational studies, 22 reviews, five guideline statements, three analysis papers, two randomized trials, three qualitative studies, two measurement development reports, and one conceptual framework development report. The publications were produced by authors from 32 countries. They were published in 37 different journals, ranging from one to four articles in the same journal.

This study showed the important progress made in accumulating knowledge on multimorbidity, with a continuous increase that included 76% of all publications only in the last quinquennium. Nonetheless, more high impact randomized trials and qualitative studies are needed in this field of research. Our study also suggests that these numbers should be taken with caution and considered a general trend because the analysis of a subgroup of publications showed that multimorbidity was not the subject of research in one third of the publications.

The article can be accessed at the following link:

  1. Fortin M, Lapointe L, Hudon C , Vanasse A. Multimorbidity is common to family practice. Is it commonly researched? Can Fam Physician 2005;51:244-5.
  2. Ag Ahmed MA, Almirall J, Ngangue P, Poitras M-E , Fortin M. A bibliometric analysis of multimorbidity from 2005 to 2019. Journal of Comorbidity 2020;10:1-7.

Care coordination for people living with multimorbidity: An Australian perspective

By Annette Peart

Two recent papers highlight the importance of person-centred care in working with people living with multimorbidity. In Melbourne, Australia, we explored the experiences of care from the perspective of people living with multimorbidity, and the health care professionals who work with them.
Hospital Admission Risk Program (HARP) services are centred on a period of intensive care coordination, comprising comprehensive assessment and care planning, specialist medical and GP management, and a self-management approach. Care coordinators support clients to navigate the health care system and services, act as a point of contact as clients complete their care plan, link them to specialised assessment and services, and support self-management.

The first paper [1] reports on a study examining the care experience of people living with multimorbidity enrolled in the HARP. We wanted to understand their experience of planning and enacting their care, using information to make care decisions, as well as identify characteristics of care of importance to them.

The participants were HARP clients who recently received a program of care coordination. They were diverse in age, gender, country of birth, number of health conditions, and length of time on the program. Of the 23 clients we interviewed, 10 were from the heart failure stream, nine from the complex psychosocial stream, and four from the chronic respiratory condition stream.

We used phenomenology as a framework for this study. Our interpretative phenomenological analysis of the experiences of care identified three master themes. Firstly, clients perceived the benefits of expert guidance and knowledge gained from their care coordinators on their health management, including navigating the health system and changing some health behaviours. Secondly, following a period to get to know the care coordinators, who provided knowledge, helped with practical tasks, and understood the clients’ needs, a relationship of trust was formed alongside a sense of a protective circle of care. Finally, clients felt it was important for them to be treated like a person, not a patient, and described experiencing their care as personalised – tailored – to them and their needs.

The second paper [2] reports on a study exploring the experience of the HARP health care professionals providing care coordination and related services to the clients. They were employed by the health service to deliver care coordination, nursing, allied health, or medical services. We interviewed those with considerable experience in HARP.
Participants were diverse in professional backgrounds and years of experience. Of the 18 participants, 11 had qualifications in nursing, four in allied health or medicine, and three were in nonclinical leadership or management roles. Of those with a clinical background, they worked across one or a combination of the streams: chronic heart failure (six participants), complex psychosocial (four), chronic respiratory (one), and across two or three streams (four). Their experience working as a health care professional ranged from 5–35 years, and working in the HARP ranged from 6 months to 13 years.

We used interpretative phenomenological analysis to identify four themes from descriptions of providing care, identifying and responding to a person’s needs, and the barriers and facilitators to providing person-centred care. First, participants spoke about providing care focusing on the client. This was not without its challenges, including perceptions of limitations in the care provided, especially if prioritising clients’ immediate needs and preferences. Second, they described having the time to listen and hear clients’ stories, perceived as helpful to understand client values and clients feeling they are heard. Third, participants spoke of techniques to engage clients in the service, including helping clients navigate the system. The fourth master theme involved participants describing how they viewed the client beyond the clinical features of a disease and as a whole person.

The models of care for programs such as the HARP espouse person-centred care as a key principle. However, these papers highlight the value of a new conceptualisation from the perspective of relationship-centred care. Both participant groups, clients, and health care professionals, noted the fundamental importance of developing a relationship as the basis for providing care.

Qualitative approaches to understanding the care experience are emerging, yet often provide superficial descriptions of “what” care was provided, rather than the “how”. These papers offer a rich, experiential account of care for a sample of people living with multimorbidity, and the impact of care coordination on their ability to manage their health conditions. The development of trusting relationships between clients and their health care professionals was the fundamental basis for the care experience and, for many, an improvement in their health and wellbeing.

1) Peart, A., Barton, C., Lewis, V., & Russell, G. (2020): The experience of care coordination for people living with multimorbidity at risk of hospitalisation: an interpretative phenomenological analysis, Psychology & Health, DOI: 10.1080/08870446.2020.1743293.

2) Peart, A., Lewis, V., Barton, C., & Russell, G. (2020). Healthcare professionals providing care coordination to people living with multimorbidity: An interpretative phenomenological analysis. J Clin Nurs. 00:1–12. https://doi.org/10.1111/jocn.15243

Publications on multimorbidity January-April 2020

By Martin Fortin

Our search for papers on multimorbidity that were published during the period January-April 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.

Publications on multimorbidity September-December 2019

By Martin Fortin

Our search for papers on multimorbidity that were published during the period September-December 2019 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.

OUR WELCOME POST: US Deprescribing Research Network

By Cynthia Boyd and Mike Steinman

Though medications offer the capacity to extend lives, relieve symptoms, and reduce the feared consequences of disease, they can also cause bothersome and dangerous side effects, burden older adults and their caregivers, and deplete savings. The use of multiple prescription drugs among U.S. adults age 65 and older has increased from 24% in 2000 to 39% in 2012. This significant growth is attributable to a growing older population, onset of chronic disease, and increasing availability of drugs for treatment and prevention. So, how do we handle the double-edged sword that is medication-treatment? By understanding and identifying the medications that are suitable for each patient, and deprescribing those for which these harms outweigh the benefit.

What is deprescribing? Deprescribing refers to the thoughtful and systematic process of identifying problematic medications and either reducing the dose or stopping the medication in a manner that is safe, effective, and helps people maximize their health and wellness goals.

However, this process is not easy. Little is known about how to best identify which medications are prime for deprescribing, how to safely and effectively stop them, and how to engage older adults, their loved ones, clinicians, and the health system in this process in a seamless and person-centered manner.

The National Institute on Aging recognizes the need for deprescribing medications among older adults and has awarded a five-year, $6.2 million grant to the University of California, San Francisco (UCSF) and Johns Hopkins University to establish the U.S. Deprescribing Research Network (USDeN).

Who we are and what we do – The USDeN is led by Co-Principal Investigators Michael Steinman, MD, at UCSF and the San Francisco VA Medical Center, and Cynthia Boyd, MD, MPH, at Johns Hopkins University School of Medicine. The network is comprised of a community of individuals who share the common goal of developing and disseminating high-quality evidence about deprescribing for older adults, and in doing so, helping improve medication use among older adults and the outcomes that are important to them.

The network’s key activities are designed to provide resources and create a central place for mutual learning, collaboration, building research capacity, and catalyzing work among a large and multidisciplinary group of investigators. Network activities are oriented around 4 cores and a series of working groups:

Investigator Development Core – Organizes activities to provide education and collaboration about deprescribing research, with a special focus on the needs of early-stage investigators.

Pilot and Exploratory Studies Core – Funds and supports pilot and grant planning studies related to deprescribing for older adults.

Stakeholder Engagement Core – Supports engagement of patients, caregivers, clinicians, and health system stakeholders with various activities of the network, so that the resulting research is maximally responsive to their priorities and needs.

Data and Resources Core – Provides information about prior and ongoing research on deprescribing, research resources relevant to deprescribing, and will build capacity for the use of existing electronic health record data for deprescribing research

Working Groups – Supports 4 working groups that will synthesize existing research and develop new tools for deprescribing research, including identifying high-priority targets for deprescribing, optimizing measurement tools and using electronic health data in deprescribing research, and optimizing communication around deprescribing.

We invite you to join our community of innovators by visiting us at https://deprescribingresearch.org, and following us on Twitter @DeprescribeUS. For more information contact admin@deprescribingresearch.org.

Cross-posted at https://deprescribingresearch.org/our-welcome-post-us-deprescribing-research-network/