Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol

Joice Cunningham; Claire M. Buckley; Patricia M. Kearney; Ciara Kelly; Leonard Browne; Emma Connolly; Rehab Elhiny; Edward W. Gregg; Jennifer Martin; Sarah M. O’ Brien; Elizabeth Oluwaseyi Bodunde; Kate N. O’ Neill; Valeria Lima Passos; Diarmuid Quinlan; Prachi Sharma; Susan M. Smith; Austin G. Stack; Emma Wallace; Linda M. O’Keeffe

doi:10.12688/hrbopenres.14205.1

Home Browse Associations between Multimorbidity Clusters and All-Cause Mortality,...

ALL Metrics

-

Views

6

Downloads

Get PDF

Get XML

Export

▬

✚

Study Protocol

Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol

[version 1; peer review: awaiting peer review]

Joice Cunningham ¹, Claire M. Buckley¹, Patricia M. Kearney¹, [...] Ciara Kelly², Leonard Browne³, Emma Connolly^4,5, Rehab Elhiny⁶, Edward W. Gregg⁷, Jennifer Martin⁸, Sarah M. O’ Brien⁹, Elizabeth Oluwaseyi Bodunde¹, Kate N. O’ Neill¹, Valeria Lima Passos¹⁰, Diarmuid Quinlan^11,12, Prachi Sharma¹, Susan M. Smith¹³, Austin G. Stack¹⁴, Emma Wallace¹², Linda M. O’Keeffe^1,15,16

Joice Cunningham ¹, Claire M. Buckley¹, [...] Patricia M. Kearney¹, Ciara Kelly², Leonard Browne³, Emma Connolly^4,5, Rehab Elhiny⁶, Edward W. Gregg⁷, Jennifer Martin⁸, Sarah M. O’ Brien⁹, Elizabeth Oluwaseyi Bodunde¹, Kate N. O’ Neill¹, Valeria Lima Passos¹⁰, Diarmuid Quinlan^11,12, Prachi Sharma¹, Susan M. Smith¹³, Austin G. Stack¹⁴, Emma Wallace¹², Linda M. O’Keeffe^1,15,16

PUBLISHED 10 Sep 2025

Author details Author details

¹ School of Public Health, University College Cork, Cork, County Cork, Ireland
² Public Health, National Health Service Improvement Team, Health Service Executive, Dublin, Ireland
³ University of Limerick School of Medicine, Limerick, County Limerick, Ireland
⁴ Trinity College Dublin, The Irish Longitudinal Study on Ageing, Dublin, Ireland
⁵ Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Leinster, Ireland
⁶ School of Pharmacy, University College Cork, Cork, County Cork, Ireland
⁷ School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Ireland
⁸ Health Service Executive, National Quality & Patient Safety Directorate, Dublin, Ireland
⁹ Health Service Executive, Office of the National Clinical Advisor and Group Lead for Chronic Disease, Clinical Design & Innovation, Office of the Chief Clinical Officer, Dublin, Ireland
¹⁰ School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland
¹¹ 15 Hogan Place, Irish College of General Practitioners, Dublin, Ireland
¹² Department of General Practice, University College Cork, Cork, Ireland
¹³ Discipline of Public Health & Primary Care, School of Medicine, Trinity College, Dublin, Trinity College, Dublin, Ireland
¹⁴ Department of Nephrology and Internal Medicine, University Hospital Limerick and University of Limerick School of Medicine, Limerick, Ireland
¹⁵ MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
¹⁶ Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Joice Cunningham
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Claire M. Buckley
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Review & Editing

Patricia M. Kearney
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Review & Editing

Ciara Kelly
Roles: Conceptualization, Methodology, Writing – Review & Editing

Leonard Browne
Roles: Conceptualization, Funding Acquisition, Writing – Review & Editing

Emma Connolly
Roles: Writing – Review & Editing

Rehab Elhiny
Roles: Investigation, Methodology, Software, Writing – Review & Editing

Edward W. Gregg
Roles: Conceptualization, Funding Acquisition, Writing – Review & Editing

Jennifer Martin
Roles: Conceptualization, Methodology, Writing – Review & Editing

Sarah M. O’ Brien
Roles: Conceptualization, Methodology, Writing – Review & Editing

Elizabeth Oluwaseyi Bodunde
Roles: Investigation, Methodology, Software, Writing – Review & Editing

Kate N. O’ Neill
Roles: Methodology, Writing – Review & Editing

Valeria Lima Passos
Roles: Writing – Review & Editing

Diarmuid Quinlan
Roles: Writing – Review & Editing

Prachi Sharma
Roles: Writing – Review & Editing

Susan M. Smith
Roles: Writing – Review & Editing

Austin G. Stack
Roles: Writing – Review & Editing

Emma Wallace
Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing

Linda M. O’Keeffe
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Visualization, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

Multimorbidity, defined as the coexistence of two or more chronic health conditions in affected individuals, is recognised as a significant global public health concern.. While previous research has identified common multimorbidity clusters, the relationships of these clusters with health outcomes is not well understood.

Aim

To systematically retrieve, synthesise, and appraise the available evidence on the association between multimorbidity clusters and mortality, quality of life (QoL), and physical function.

Methods

We will conduct a systematic search of PubMed, EMBASE, Web of Science, Ebsco APA PsyInfo and CINAHL from inception until April 2025. Eligible studies will include observational studies (e.g. cohort, cross-sectional or case control) investigating the association between multimorbidity clusters and all-cause mortality, QoL, or physical function in community- dwelling adult populations. Multimorbidity clustering will be defined as the non-random co-occurrence of chronic health conditions. Outcomes of interest include all-cause mortality, health-related quality of life (QoL) (i.e., self-perceived health status), and physical function (e.g., functional independence/physical performance)). Risk of bias will be evaluated using appropriate tools, e.g., the risk of bias in observational studies of exposures (ROBINS-E) tool. Findings will be synthesised narratively, and if feasible, a meta-analysis will be performed. Given the anticipated heterogeneity of multimorbidity clusters, a robust methodological framework, informed by existing multimorbidity literature and stakeholder engagement, will be applied to facilitate comparability across studies.

Conclusion

This review will lay out and summarise current evidence on the association between different multimorbidity clusters and key health outcomes, including all-cause mortality, QoL, and physical function. It will address methodological approaches used to investigate the multimorbidity-health outcomes associations of interest and summarise current evidence. The findings will enhance our understanding of the unique burden imposed by different clusters of chronic conditions and may inform improvements in healthcare policy, the delivery of health services and patient care.

Prospero Registration Number: CRD420251014288 (Date of Registration: 25 Jun 2025 13:08 UTC version 1.0 published)

Keywords

multimorbidity, multiple long-term conditions, clusters/patterns, all-cause mortality, quality of life, physical function

Corresponding author: Joice Cunningham

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2025 Cunningham J et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Cunningham J, M. Buckley C, M. Kearney P et al. Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol [version 1; peer review: awaiting peer review]. HRB Open Res 2025, 8:100 (https://doi.org/10.12688/hrbopenres.14205.1) First published: 10 Sep 2025, 8:100 (https://doi.org/10.12688/hrbopenres.14205.1) Latest published: 10 Sep 2025, 8:100 (https://doi.org/10.12688/hrbopenres.14205.1)

Background

Multimorbidity, or “multiple long-term conditions”, defined as the coexistence of two or more chronic conditions in the same individual, is a significant global public health challenge, placing substantial burden on individuals and on healthcare systems¹. The World Health Organization (WHO) defines chronic conditions as "health problems that require continuous management over a period of years or decades", highlighting the prolonged duration of these conditions and the necessity for ongoing care to manage symptoms, prevent complications, and enhance the quality of life (QoL) for individuals living with them². It is estimated that 37% of adults worldwide are living with multimorbidity, with over 50% of individuals aged 60 years and over affected³. The high prevalence of multimorbidity in contemporary populations reflects advances in healthcare, population ageing, and adverse trends in risk factors for chronic disease, amongst other factors⁴. Multimorbidity is associated with a range of adverse health consequences, including greater disability, poorer QoL, polypharmacy, and higher mortality⁴. Individuals living with multimorbidity have complex healthcare needs, requiring increased healthcare utilisation and expenditure, which contributes to significant strain on healthcare systems worldwide⁵. Despite this, healthcare systems, clinical practice guidelines, and healthcare provider training have traditionally been designed to manage individual chronic conditions. This single-disease paradigm fails to address the multifaceted needs of individuals with multimorbidity and has proven inadequate in delivering effective patient-centred care⁶. As a result, individuals with multimorbidity often experience fragmented, uncoordinated, and sometimes conflicting care recommendations, exacerbating their treatment burden and leading to suboptimal health outcomes⁷.

Historically, multimorbidity has been measured simply by counting the number of chronic conditions an individual has, with equal weight assigned to each condition in the “multimorbidity score”⁸. While this approach is straightforward, it fails to account for how specific combinations are associated with overall health outcomes⁹. Previous systematic reviews have shown that multimorbidity is associated with poorer outcomes, including higher mortality risk and reduced QoL^10,11. However, these studies primarily examined the association of number of conditions with outcomes, rather than exploring how specific combinations of conditions are associated with outcomes. In contrast, the concept of multimorbidity clustering focusses on the non-random co-occurrence of conditions¹². While there is variability in the multimorbidity clustering literature, some consistent clusters (e.g. cardiometabolic clusters of diabetes, hypertension, and cardiovascular diseases) have been replicated, believed to have shared underlying biological pathways and risk factors^13,14. Understanding which conditions tend to cluster together and examining the associations of these clusters with health outcomes has been identified as a key research priority¹⁵. Moreover, the associations of multimorbidity clusters on health outcomes are not uniform across all individuals. Increasingly, it is recognised that demographic factors such as age, gender, and socioeconomic status may modify the relationship between multimorbidity clusters and health outcomes¹⁶. A better understanding of the unique burden imposed by specific multimorbidity clusters on health outcomes is likely to provide new insights for developing more targeted and patient-centered healthcare delivery models that more effectively address the needs of individuals¹⁰. Furthermore, mortality and HRQoL have been identified as essential core outcomes for multimorbidity research¹⁷.

Study aim

The aim of this review is to systematically retrieve, synthesise and appraise the available evidence quantifying the association between multimorbidity clusters and all-cause mortality, QoL, and physical function.

Methods

The reporting of this systematic review protocol is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) 2015 checklist)¹⁸ can be found in the online supplementary material (see Extended data)¹⁹. Furthermore, this review is registered with the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD420251014288).

Eligibility criteria

The PECOs (Population, Exposure, Comparison or Control, Outcomes and Study Design) framework was used to structure the eligibility criteria²⁰ [Table 1].

Table 1. Inclusion and exclusion criteria based on modified PECoS.

PECoS	Inclusion Criteria	Exclusion criteria
Population	Studies that examine the general population in which groups or subgroups of participants are comprised of adults aged ≥18 years and older.	Studies including exclusively paediatric (<18 years of age) populations. Studies that include populations selected based on having a principal condition of focus (e.g., studies including only adults with diabetes). Studies that focus on nongeneral populations (e.g., individuals who are hospitalised or residing in long-term care facilities) or individuals with specific health contexts (e.g., pregnant people or homeless individuals).
Exposure	Studies that use data-driven methodology to classify conditions into multimorbidity clusters without any single condition focus.	Studies that that use non-data-driven approaches to assign conditions to clusters (e.g. studies that analyse the number of conditions without using statistical techniques to detect non-random disease associations). Studies that include fewer than ten conditions.
Comparator	Studies including single conditions or no conditions.	n/a
Outcomes	Outcomes including all-cause mortality, QoL (e.g., Short Form 36 Health Survey (SF-36), and physical function (e.g., Timed Up and Go test)	Condition-specific measures (i.e., outcomes designed for specific diagnostic groups or patient populations)
Study Type/ Design	Original quantitative research (e.g. cohort, cross-sectional or case-control study designs). No restrictions on geographical location or language.	Interventional studies, qualitative research, literature reviews or meta-analyses Non-peer reviewed articles (e.g. editorials, commentaries, case studies and opinion pieces Conference abstracts without full-text availability or study protocols

Population

We will include studies that include general population cohorts in which groups or subgroups of participants are comprised of adults aged 18 years and older. Studies must involve community-dwelling adults, defined as adults who are not living in a long-term care institution n²¹. If a relevant study is identified which includes individuals younger than 18, it will only be included if separate results are provided for those aged 18 and older. Included studies must investigate multimorbidity, defined as the co-occurrence of two or more chronic conditions within an individual, without focusing on any single index condition. We will exclude studies that focus solely on paediatric populations, individuals who are hospitalised, or those residing in long-term care institutions or in primary care settings. Studies that select participants based on a prespecified index condition (e.g., multimorbidity among individuals with diabetes) will be excluded, as these studies focus on comorbidity rather than multimorbidity. Comorbidity refers to any distinct additional health condition that exists or may occur in a patient with a specific index condition²². While individuals with comorbidity also have multimorbidity, the sample in these studies is unlikely to be representative of the broader population with multimorbidity, as the index condition and its frequently co-occurring conditions will be overrepresented²³. Additionally, studies restricted to specific health-related contexts, such as homeless individuals, veterans, or pregnant people, will also be excluded due to limited generalisability. While individuals with comorbidity also have multimorbidity, the sample in these studies is unlikely to be representative of the broader population with multimorbidity, as the index condition and its frequently co-occurring conditions will be overrepresented²³.

Exposure

We will include studies that investigate multimorbidity, defined as the co-occurrence of multiple chronic conditions in an individual, without focusing on any single condition. There is considerable variability in how multimorbidity is defined and operationalised in the literature¹⁵. Multimorbidity is typically defined as the coexistence of two or more chronic conditions²⁴. While there is a lack of a single, universally accepted definition for chronic disease²⁵, common characteristics include the non-self-limiting nature of conditions, an association with persistent and recurring health problems, and a duration, measured in months and years rather than days and weeks²⁶. Studies must employ data-driven methodology to identify and classify conditions into multimorbidity clusters or combinations. Examples of such methods include latent class analysis, hierarchical clustering, and network analysis. The statistical methods used to identify the clusters must be clearly described. Studies that apply non-data-driven approaches or analyses based solely on condition counts, without using statistical techniques to detect non-random disease associations, will be excluded. The distinction between comorbidity and multimorbidity is important, particularly concerning patient sampling²³. Studies must include a minimum of ten distinct conditions in their clustering analyses, to be included. This threshold is based on previous recommended cut-offs, where studies typically include between ten and twelve conditions to enhance the interpretability and stability of the clusters^13,27. Studies that include fewer than ten conditions in their clustering analyses will be excluded.

Outcomes

The outcomes of interest for this review include all-cause mortality, QoL (including health-related quality of life (HRQoL)), and physical function. QoL is multi-dimensional and relates to an individual’s general well-being status in relation to their value, environment, cultural and social context in which they live²⁸ While HRQoL is the self-perceived health status of individuals and includes physical, mental, emotional, and social domains²⁹. Measurement of physical functioning may range from self-report questionnaires (e.g., The Barthel Index, which measures functional disability in activities of daily living), to performance measures (e.g., The Berg Balance Scale, which assesses static balance and fall risk). Generic scales are preferred when measuring health outcomes in people with multiple conditions or comorbidities³⁰. Therefore, condition-specific measures (i.e., outcomes designed for specific diagnostic groups or patient populations) will not be eligible for inclusion. To be eligible for inclusion, studies must report outcomes specifically according to multimorbidity clusters.

Study design/type

Original quantitative research reporting multimorbidity clusters and associated outcomes of interest will be eligible for inclusion (e.g., cohort, cross-sectional, and case-control study designs). Other study designs containing quantitative data e.g. mixed method research will be considered if the quantitative results can be extracted. Interventional studies (e.g. randomized control trials), qualitative research, literature reviews or meta-analyses, non-peer reviewed articles (e.g., editorials, commentaries, case studies, and opinion pieces), and studies available only in abstract form will be excluded. In cases, where abstracts or protocols are retrieved, attempts will be made to access full texts by contacting the authors. There will be no restrictions on articles by publication date or language of publication. Where articles not in English are retrieved, translation will be done using online translation software.

Information sources

The following databases will be searched for primary research studies: Scopus (PubMed/Medline), Embase, Web of Science, Ebsco APA PsyInfo, and CINAHL. Additionally, grey literature searching will include screening the reference lists of relevant systematic reviews and/or meta-analyses previously published.

Search strategy

A comprehensive search strategy aimed at identifying relevant studies will be developed in conjunction with a medical librarian. Search terms will be based on the most relevant keywords, categories, and subheadings related to the research questions, including (1) multimorbidity, (2) clusters, (3) outcomes (all-cause mortality, QoL, HRQoL, and physical function), and (4) relevant study designs. Key words and index terms will also be identified from the title and abstract of relevant articles and will be used to inform the final search strategy. All search terms and keywords will be combined with the relevant medical subject headings (MeSH) for each of the databases using Boolean operators as appropriate. Following completion of the search, the reference lists of included articles will be used to identify additional relevant studies. An example of the search strategy for Ovid MEDLINE is shown in appendix 1.

Study selection

Following the completion of the searches, the citations of identified studies will be imported to EndNote X9, and duplicates will be removed. Microsoft Excel and PICO Portal Software for Systematic Reviews will be used to perform screening. Two independent review authors (JC and RE or EOB) will independently screen titles and abstracts for eligibility according to the eligibility criteria outlined in Table 1. Following this, potentially eligible full texts will be independently evaluated by two review authors (JC and RE or EOB). Any disagreements will be resolved through discussion, and if consensus cannot be reached, a third review author (LOK) will act as an arbitrator.

Data analysis and synthesis

Data extraction

A pre-defined data-extraction form will be used to extract data from included studies. Data extraction will be then performed by JC and reviewed by LOK. Conflicts will be resolved by consensus between two or more researchers if required. Extracted data will include study characteristics (e.g., author(s), year of publication, study design, sample size and study setting), population characteristics (e.g., age, sex, and other sociodemographic details), characteristics of multimorbidity clusters (e.g., specific conditions used to identify disease clusters, measures of disease severity, and statistical methodology used for identification of clusters), outcomes (e.g., all-cause mortality, QoL, , physical function), any relevant confounding factors considered, and risk of bias (methodological quality and risk of bias as determined by the relevant assessment tools).

Quality appraisal

Articles included in this review will be assessed for methodological quality independently by two reviewer authors (JC and LOK) using appropriate tools based on study design (e.g., risk of bias in observational studies of exposures (ROBINS-E) tool and the Joanna Briggs Institute (JBI) checklist for cross-sectional studies).

Certainty of the evidence (GRADE)

A ‘Summary of findings’ table will be used to draw conclusions about the certainty of the evidence. We will apply GRADE to assess the quality of the overall evidence for all outcomes³¹.

Synthesis

Review findings will be collated and summarised according to the review questions. The results of the literature search and study screening process will be presented in a PRISMA flow diagram, and extracted data will be presented in tabular form. Findings will initially be synthesised narratively. This will included structured summaries of study characteristics, multimorbidity cluster types, and direction/magnitude of associations with outcomes e.g. mortality. Findings will be grouped by study design (e.g., cross-sectional vs cohort), population type, and analytic method used for cluster identification. Significant heterogeneity is anticipated in terms of the multimorbidity clusters. We will consider methodological approaches used in previous reviews (e.g., grouping conditions by biological systems e.g., cardiovascular, metabolic, musculoskeletal, or using an a priori set of conditions) to ensure comparability across studies. If sufficient homogeneity exists across studies in terms of population, multimorbidity clusters, a meta-analysis will be conducted to estimate pooled effect sizes (e.g., hazard ratios, risk ratios, or odds ratios for mortality outcomes associated with multimorbidity clusters). For cohort studies, we will extract adjusted risk estimates with 95% confidence intervals. If studies are sufficiently similar in design and outcome measurement, we will use a random-effects model and assess heterogeneity using the I² statistic and Cochran’s Q test. Where appropriate, publication bias will be assessed using funnel plots and Egger’s test. Key stakeholder groups, including a patient advisory panel, will be involved to ensure the relevance of the findings.

Discussion

Multimorbidity represents a global health challenge. Existing literature has established that the interplay between chronic conditions is not merely additive but often synergistic and may be associated with poorer health outcomes³². While previous studies have shown that multimorbidity is linked to negative health consequences, such as increased disability, diminished HRQoL, and higher mortality risk, the interaction effect between different co-occurring conditions remain underexplored. Moreover, a condition count fails to explore whether specific combinations of conditions are responsible for driving poorer health outcomes. This systematic review aims to provide a more nuanced understanding of how specific multimorbidity clusters are associated with patient outcomes. By delineating which clusters pose the greatest threats to health, this review may inform the development of integrated care pathways that address the specific needs of patients with complex health profiles.

Ethics and consent

As the study protocol is for a systematic review of existing literature, ethical approval is not required.

Ethics and dissemination

Upon completion of the review, the findings will be disseminated through multiple channels to ensure broad reach and impact:

Academic dissemination: The research findings will be presented at international scientific conferences to engage the academic community. Additionally, the results will be submitted for publication in a high-impact peer-reviewed journal relevant to the field, ensuring that the findings contribute to advancing knowledge in multimorbidity research.

Patient, Public, and Policy-oriented dissemination: To enhance the relevance and accessibility of the findings for patient, public, and policymaker audiences, a summary of key results will be developed in clear, non-technical language and disseminated to key stakeholder groups.

Data availability

Underlying data/extended data

Open Science Framework:

This project contains the following extended data: Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol: https://doi.org/10.17605/OSF.IO/6UESJ¹⁹

- PubMed Search Strategy
- PRISMA-P-checklist

Faculty Opinions recommended

References

1. Pearson-Stuttard J, Ezzati M, Gregg EW: Multimorbidity—a defining challenge for health systems. Lancet Public Health. 2019; 4(12): e599–e600. PubMed Abstract | Publisher Full Text
2. Temkin SM, Barr E, Moore H, et al.: Chronic conditions in women: the development of a National Institutes of Health framework. BMC Womens Health. 2023; 23(1): 162. PubMed Abstract | Publisher Full Text | Free Full Text
3. Chowdhury SR, Chandra Das D, Sunna TC, et al.: Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine. 2023; 57: 101860. PubMed Abstract | Publisher Full Text | Free Full Text
4. Skou ST, Mair FS, Fortin M, et al.: Multimorbidity. Nat Rev Dis Primers. 2022; 8(1): 48. PubMed Abstract | Publisher Full Text | Free Full Text
5. Tran PB, Kazibwe J, Nikolaidis GF, et al.: Costs of multimorbidity: a systematic review and meta-analyses. BMC Med. 2022; 20(1): 234. PubMed Abstract | Publisher Full Text | Free Full Text
6. Salisbury C: Multimorbidity: redesigning health care for people who use it. Lancet. 2012; 380(9836): 7–9. PubMed Abstract | Publisher Full Text
7. Prior A, Vestergaard CH, Vedsted P, et al.: Healthcare fragmentation, multimorbidity, potentially inappropriate medication, and mortality: a Danish nationwide cohort study. BMC Med. 2023; 21(1): 305. PubMed Abstract | Publisher Full Text | Free Full Text
8. Ho ISS, Azcoaga-Lorenzo A, Akbari A, et al.: Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies. Lancet Public Health. 2021; 6(8): e587–e597. PubMed Abstract | Publisher Full Text
9. Huntley AL, Johnson R, Purdy S, et al.: 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. PubMed Abstract | Publisher Full Text | Free Full Text
10. Makovski TT, Schmitz S, Zeegers MP, et al.: Multimorbidity and Quality of Life: systematic literature review and meta-analysis. Ageing Res Rev. 2019; 53: 100903. PubMed Abstract | Publisher Full Text
11. Nunes BP, Flores TR, Mielke GI, et al.: Multimorbidity and mortality in older adults: a systematic review and meta-analysis. Arch Gerontol Geriatr. 2016; 67: 130–8. PubMed Abstract | Publisher Full Text
12. Vetrano DL, Roso-Llorach A, Fernández S, et al.: Twelve-year clinical trajectories of multimorbidity in a population of older adults. Nat Commun. 2020; 11(1): 3223. PubMed Abstract | Publisher Full Text | Free Full Text
13. Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, et al.: Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014; 67(3): 254–66. PubMed Abstract | Publisher Full Text
14. Busija L, Lim K, Szoeke C, et al.: Do replicable profiles of multimorbidity exist? Systematic review and synthesis. Eur J Epidemiol. 2019; 34(11): 1025–1053. PubMed Abstract | Publisher Full Text
15. Ferris J, Fiedeldey LK, Kim B, et al.: Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol. BMJ Open. 2023; 13(12): e076496. PubMed Abstract | Publisher Full Text | Free Full Text
16. Schäfer I, Hansen H, Schön G, et al.: The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC Health Serv Res. 2012; 12(1): 89. PubMed Abstract | Publisher Full Text | Free Full Text
17. Smith SM, Wallace E, Salisbury C, et al.: A Core Outcome Set for multimorbidity research (COSmm). Ann Fam Med. 2018; 16(2): 132–138. PubMed Abstract | Publisher Full Text | Free Full Text
18. Moher D, Shamseer L, Clarke M, et al.: Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) 2015 statement. Syst Rev. 2015; 4(1): 1. PubMed Abstract | Publisher Full Text | Free Full Text
19. Cunningham J, O' Keeffe L: Associations between multimorbidity clusters and all-cause mortality, Quality of Life and physical function: a systematic review protocol. Open Science Framework (OSF). 2025. http://www.doi.org/10.17605/OSF.IO/6UESJ
20. Morgan RL, Whaley P, Thayer KA, et al.: Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018; 121(Pt 1): 1027–1031. PubMed Abstract | Publisher Full Text | Free Full Text
21. Peek STM, Wouters EJM, van Hoof J, et al.: Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform. 2014; 83(4): 235–48. PubMed Abstract | Publisher Full Text
22. Feinstein AR: The pre-therapeutic classification of co-morbidity in chronic disease. J Chronic Dis. 1970; 23(7): 455–68. PubMed Abstract | Publisher Full Text
23. Harrison C, Fortin M, van den Akker M, et al.: Comorbidity versus multimorbidity: why it matters. J Multimorb Comorb. 2021; 11: 2633556521993993. PubMed Abstract | Publisher Full Text | Free Full Text
24. Johnston MC, Crilly M, Black C, et al.: Defining and measuring multimorbidity: a systematic review of systematic reviews. Eur J Public Health. 2019; 29(1): 182–189. PubMed Abstract | Publisher Full Text
25. Goodman RA, Posner SF, Huang ES, et al.: Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Prev Chronic Dis. 2013; 10: 120239. PubMed Abstract | Publisher Full Text | Free Full Text
26. Thrall JH: Prevalence and costs of chronic disease in a health care system structured for treatment of acute illness. Radiology. 2005; 235(1): 9–12. PubMed Abstract | Publisher Full Text
27. Fortin M, Stewart M, Poitras ME, et al.: A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med. 2012; 10(2): 142–51. PubMed Abstract | Publisher Full Text | Free Full Text
28. Phyo AZZ, Freak-Poli R, Craig H, et al.: Quality of Life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health. 2020; 20(1): 1596. PubMed Abstract | Publisher Full Text | Free Full Text
29. Karimi M, Brazier J: Health, Health-Related Quality of Life, and Quality of Life: what is the difference? Pharmacoeconomics. 2016; 34(7): 645–9. PubMed Abstract | Publisher Full Text
30. Salaffi F, Carotti M, Grassi W: Health-Related Quality of Life in patients with hip or knee osteoarthritis: comparison of generic and disease-specific instruments. Clin Rheumatol. 2005; 24(1): 29–37. PubMed Abstract | Publisher Full Text
31. Atkins D, Best D, Briss PA: Grading quality of evidence and strength of recommendations. BMJ. 2004; 328(7454): 1490. PubMed Abstract | Publisher Full Text | Free Full Text
32. Marventano S, Ayala A, Gonzalez N, et al.: Multimorbidity and functional status in community-dwelling older adults. Eur J Intern Med. 2014; 25(7): 610–6. PubMed Abstract | Publisher Full Text

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 10 Sep 2025

Author details Author details

¹ School of Public Health, University College Cork, Cork, County Cork, Ireland
² Public Health, National Health Service Improvement Team, Health Service Executive, Dublin, Ireland
³ University of Limerick School of Medicine, Limerick, County Limerick, Ireland
⁴ Trinity College Dublin, The Irish Longitudinal Study on Ageing, Dublin, Ireland
⁵ Discipline of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Leinster, Ireland
⁶ School of Pharmacy, University College Cork, Cork, County Cork, Ireland
⁷ School of Population Health, RCSI University of Medicine and Health Sciences, Dublin, Ireland
⁸ Health Service Executive, National Quality & Patient Safety Directorate, Dublin, Ireland
⁹ Health Service Executive, Office of the National Clinical Advisor and Group Lead for Chronic Disease, Clinical Design & Innovation, Office of the Chief Clinical Officer, Dublin, Ireland
¹⁰ School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland
¹¹ 15 Hogan Place, Irish College of General Practitioners, Dublin, Ireland
¹² Department of General Practice, University College Cork, Cork, Ireland
¹³ Discipline of Public Health & Primary Care, School of Medicine, Trinity College, Dublin, Trinity College, Dublin, Ireland
¹⁴ Department of Nephrology and Internal Medicine, University Hospital Limerick and University of Limerick School of Medicine, Limerick, Ireland
¹⁵ MRC Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK
¹⁶ Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Joice Cunningham
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Claire M. Buckley
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Review & Editing

Patricia M. Kearney
Roles: Conceptualization, Funding Acquisition, Investigation, Methodology, Writing – Review & Editing

Ciara Kelly
Roles: Conceptualization, Methodology, Writing – Review & Editing

Leonard Browne
Roles: Conceptualization, Funding Acquisition, Writing – Review & Editing

Emma Connolly
Roles: Writing – Review & Editing

Rehab Elhiny
Roles: Investigation, Methodology, Software, Writing – Review & Editing

Edward W. Gregg
Roles: Conceptualization, Funding Acquisition, Writing – Review & Editing

Jennifer Martin
Roles: Conceptualization, Methodology, Writing – Review & Editing

Sarah M. O’ Brien
Roles: Conceptualization, Methodology, Writing – Review & Editing

Elizabeth Oluwaseyi Bodunde
Roles: Investigation, Methodology, Software, Writing – Review & Editing

Kate N. O’ Neill
Roles: Methodology, Writing – Review & Editing

Valeria Lima Passos
Roles: Writing – Review & Editing

Diarmuid Quinlan
Roles: Writing – Review & Editing

Prachi Sharma
Roles: Writing – Review & Editing

Susan M. Smith
Roles: Writing – Review & Editing

Austin G. Stack
Roles: Writing – Review & Editing

Emma Wallace
Roles: Conceptualization, Funding Acquisition, Methodology, Writing – Review & Editing

Linda M. O’Keeffe
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 10 Sep 2025, 8:100

https://doi.org/10.12688/hrbopenres.14205.1

Copyright

© 2025 Cunningham J et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download

Export To

metrics

VIEWS

49

downloads

6

Citations

0

SEE MORE DETAILS

CITE

how to cite this article

Cunningham J, M. Buckley C, M. Kearney P et al. Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol [version 1; peer review: awaiting peer review]. HRB Open Res 2025, 8:100 (https://doi.org/10.12688/hrbopenres.14205.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Open Peer Review

Comments on this article Comments (0)

Version 1

VERSION 1 PUBLISHED 10 Sep 2025

Open Peer Review

Reviewer Status

AWAITING PEER REVIEW

Comments on this article

All Comments(0)

Add a comment

Sign up for content alerts

[1] 1. Pearson-Stuttard J, Ezzati M, Gregg EW: Multimorbidity—a defining challenge for health systems. Lancet Public Health. 2019; 4(12): e599–e600. PubMed Abstract | Publisher Full Text

[2] 2. Temkin SM, Barr E, Moore H, et al.: Chronic conditions in women: the development of a National Institutes of Health framework. BMC Womens Health. 2023; 23(1): 162. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Chowdhury SR, Chandra Das D, Sunna TC, et al.: Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. EClinicalMedicine. 2023; 57: 101860. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Skou ST, Mair FS, Fortin M, et al.: Multimorbidity. Nat Rev Dis Primers. 2022; 8(1): 48. PubMed Abstract | Publisher Full Text | Free Full Text

[5] 5. Tran PB, Kazibwe J, Nikolaidis GF, et al.: Costs of multimorbidity: a systematic review and meta-analyses. BMC Med. 2022; 20(1): 234. PubMed Abstract | Publisher Full Text | Free Full Text

[6] 6. Salisbury C: Multimorbidity: redesigning health care for people who use it. Lancet. 2012; 380(9836): 7–9. PubMed Abstract | Publisher Full Text

[7] 7. Prior A, Vestergaard CH, Vedsted P, et al.: Healthcare fragmentation, multimorbidity, potentially inappropriate medication, and mortality: a Danish nationwide cohort study. BMC Med. 2023; 21(1): 305. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Ho ISS, Azcoaga-Lorenzo A, Akbari A, et al.: Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies. Lancet Public Health. 2021; 6(8): e587–e597. PubMed Abstract | Publisher Full Text

[9] 9. Huntley AL, Johnson R, Purdy S, et al.: 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. PubMed Abstract | Publisher Full Text | Free Full Text

[10] 10. Makovski TT, Schmitz S, Zeegers MP, et al.: Multimorbidity and Quality of Life: systematic literature review and meta-analysis. Ageing Res Rev. 2019; 53: 100903. PubMed Abstract | Publisher Full Text

[11] 11. Nunes BP, Flores TR, Mielke GI, et al.: Multimorbidity and mortality in older adults: a systematic review and meta-analysis. Arch Gerontol Geriatr. 2016; 67: 130–8. PubMed Abstract | Publisher Full Text

[12] 12. Vetrano DL, Roso-Llorach A, Fernández S, et al.: Twelve-year clinical trajectories of multimorbidity in a population of older adults. Nat Commun. 2020; 11(1): 3223. PubMed Abstract | Publisher Full Text | Free Full Text

[13] 13. Prados-Torres A, Calderón-Larrañaga A, Hancco-Saavedra J, et al.: Multimorbidity patterns: a systematic review. J Clin Epidemiol. 2014; 67(3): 254–66. PubMed Abstract | Publisher Full Text

[14] 14. Busija L, Lim K, Szoeke C, et al.: Do replicable profiles of multimorbidity exist? Systematic review and synthesis. Eur J Epidemiol. 2019; 34(11): 1025–1053. PubMed Abstract | Publisher Full Text

[15] 15. Ferris J, Fiedeldey LK, Kim B, et al.: Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol. BMJ Open. 2023; 13(12): e076496. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Schäfer I, Hansen H, Schön G, et al.: The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. First results from the multicare cohort study. BMC Health Serv Res. 2012; 12(1): 89. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 17. Smith SM, Wallace E, Salisbury C, et al.: A Core Outcome Set for multimorbidity research (COSmm). Ann Fam Med. 2018; 16(2): 132–138. PubMed Abstract | Publisher Full Text | Free Full Text

[18] 18. Moher D, Shamseer L, Clarke M, et al.: Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) 2015 statement. Syst Rev. 2015; 4(1): 1. PubMed Abstract | Publisher Full Text | Free Full Text

[19] 19. Cunningham J, O' Keeffe L: Associations between multimorbidity clusters and all-cause mortality, Quality of Life and physical function: a systematic review protocol. Open Science Framework (OSF). 2025. http://www.doi.org/10.17605/OSF.IO/6UESJ

[20] 20. Morgan RL, Whaley P, Thayer KA, et al.: Identifying the PECO: a framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018; 121(Pt 1): 1027–1031. PubMed Abstract | Publisher Full Text | Free Full Text

[21] 21. Peek STM, Wouters EJM, van Hoof J, et al.: Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform. 2014; 83(4): 235–48. PubMed Abstract | Publisher Full Text

[22] 22. Feinstein AR: The pre-therapeutic classification of co-morbidity in chronic disease. J Chronic Dis. 1970; 23(7): 455–68. PubMed Abstract | Publisher Full Text

[23] 23. Harrison C, Fortin M, van den Akker M, et al.: Comorbidity versus multimorbidity: why it matters. J Multimorb Comorb. 2021; 11: 2633556521993993. PubMed Abstract | Publisher Full Text | Free Full Text

[24] 24. Johnston MC, Crilly M, Black C, et al.: Defining and measuring multimorbidity: a systematic review of systematic reviews. Eur J Public Health. 2019; 29(1): 182–189. PubMed Abstract | Publisher Full Text

[25] 25. Goodman RA, Posner SF, Huang ES, et al.: Defining and measuring chronic conditions: imperatives for research, policy, program, and practice. Prev Chronic Dis. 2013; 10: 120239. PubMed Abstract | Publisher Full Text | Free Full Text

[26] 26. Thrall JH: Prevalence and costs of chronic disease in a health care system structured for treatment of acute illness. Radiology. 2005; 235(1): 9–12. PubMed Abstract | Publisher Full Text

[27] 27. Fortin M, Stewart M, Poitras ME, et al.: A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med. 2012; 10(2): 142–51. PubMed Abstract | Publisher Full Text | Free Full Text

[28] 28. Phyo AZZ, Freak-Poli R, Craig H, et al.: Quality of Life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health. 2020; 20(1): 1596. PubMed Abstract | Publisher Full Text | Free Full Text

[29] 29. Karimi M, Brazier J: Health, Health-Related Quality of Life, and Quality of Life: what is the difference? Pharmacoeconomics. 2016; 34(7): 645–9. PubMed Abstract | Publisher Full Text

[30] 30. Salaffi F, Carotti M, Grassi W: Health-Related Quality of Life in patients with hip or knee osteoarthritis: comparison of generic and disease-specific instruments. Clin Rheumatol. 2005; 24(1): 29–37. PubMed Abstract | Publisher Full Text

[31] 31. Atkins D, Best D, Briss PA: Grading quality of evidence and strength of recommendations. BMJ. 2004; 328(7454): 1490. PubMed Abstract | Publisher Full Text | Free Full Text

[32] 32. Marventano S, Ayala A, Gonzalez N, et al.: Multimorbidity and functional status in community-dwelling older adults. Eur J Intern Med. 2014; 25(7): 610–6. PubMed Abstract | Publisher Full Text

Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol

Abstract

Background

Aim

Methods

Conclusion

Keywords

Background

Study aim

Methods

Eligibility criteria

Table 1. Inclusion and exclusion criteria based on modified PECoS.

Population

Exposure

Outcomes

Study design/type

Information sources

Search strategy

Study selection

Data analysis and synthesis

Data extraction

Quality appraisal

Certainty of the evidence (GRADE)

Synthesis

Discussion

Ethics and consent

Ethics and dissemination

Data availability

Underlying data/extended data

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Comments on this article

Are you a HRB-funded researcher?

Thank you!

Associations between Multimorbidity Clusters and All-Cause Mortality, Quality of Life and Physical Function: A Systematic Review Protocol

Abstract

Background

Aim

Methods

Conclusion

Keywords

Background

Study aim

Methods

Eligibility criteria

Table 1. Inclusion and exclusion criteria based on modified PECoS.

Population

Exposure

Outcomes

Study design/type

Information sources

Search strategy

Study selection

Data analysis and synthesis

Data extraction

Quality appraisal

Certainty of the evidence (GRADE)

Synthesis

Discussion

Ethics and consent

Ethics and dissemination

Data availability

Underlying data/extended data

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Comments on this article

Competing Interests Policy

Stay Updated

Are you a HRB-funded researcher?

Thank you!