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Underlying mechanisms of complex interventions addressing the care of older adults with multimorbidity: a realist review
  1. Monika Kastner1,2,3,
  2. Leigh Hayden1,
  3. Geoff Wong4,
  4. Yonda Lai3,
  5. Julie Makarski1,
  6. Victoria Treister3,
  7. Joyce Chan1,
  8. Julianne H Lee3,
  9. Noah M Ivers5,6,
  10. Jayna Holroyd-Leduc7,
  11. Sharon E Straus3,8
  1. 1Knowledge Translation and Implementation, Research and Innovation, North York General Hospital, Toronto, Ontario, Canada
  2. 2Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  3. 3Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Ontario, Canada
  4. 4Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
  5. 5Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
  6. 6Family Medicine, Women’s College Hospital, Toronto, Ontario, Canada
  7. 7Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
  8. 8Medicine, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr Monika Kastner; monika.kastner{at}utoronto.ca

Abstract

Objectives To understand how and why effective multi-chronic disease management interventions influence health outcomes in older adults 65 years of age or older.

Design A realist review.

Data sources Electronic databases including Medline and Embase (inception to December 2017); and the grey literature.

Eligibility criteria for selecting studies We considered any studies (ie, experimental quasi-experimental, observational, qualitative and mixed-methods studies) as long as they provided data to explain our programme theories and effectiveness review (published elsewhere) findings. The population of interest was older adults (age ≥65 years) with two or more chronic conditions.

Analysis We used the Realist And MEta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and publication criteria for our synthesis aimed at refining our programme theories such that they contained multiple context-mechanism-outcome configurations describing the ways different mechanisms fire to generate outcomes. We created a 3-step synthesis process grounded in meta-ethnography to separate units of data from articles, and to derive explanatory statements across them.

Results 106 articles contributed to the analysis. We refined our programme theories to explain multimorbidity management in older adults: (1) care coordination interventions with the best potential for impact are team-based strategies, disease management programmes and case management; (2) optimised disease prioritisation involves ensuring that clinician work with patients to identify what symptoms are problematic and why, and to explore options that are acceptable to both clinicians and patients and (3) optimised patient self-management is dependent on patients’ capacity for selfcare and to what extent, and establishing what patients need to enable selfcare.

Conclusions To optimise care, both clinical management and patient self-management need to be considered from multiple perspectives (patient, provider and system). To mitigate the complexities of multimorbidity management, patients focus on reducing symptoms and preserving quality of life while providers focus on the condition that most threaten morbidity and mortality.

PROSPERO registration number CRD42014014489.

  • multimorbidity
  • older adults
  • complex interventions
  • realist review
  • chronic disease management

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Contributors MK: Manuscript development and final approval, methods design, data acquisition, data extraction, data analysis, research question development. LH: Manuscript development and final approval, data extraction, data analysis. GW: Manuscript development and final approval, methods design and data interpretation. YL: Manuscript development and final approval, data extraction, data analysis, methods. JM: Manuscript development and final approval, data extraction, data analysis, methods. VT: Manuscript development and final approval, data extraction, data analysis, methods design. JC: Manuscript development and final approval, data extraction, data analysis. JL: Manuscript development and final review, data extraction, data analysis. NMI: Manuscript development and final approval, methods design, data acquisition. JH-L: Manuscript development and final approval, methods design, data acquisition. SES: Manuscript development and final approval, methods design, data acquisition.

  • Funding This research was supported by an Ontario, Canada Ministry of Health and Long-term Care (MOHLTC) Health Systems Research Fund (HSRF) Capacity Award. The funder was not involved in conducting the realist review. Monika Kastner is funded by a Canadian Institutes of Health Research (CIHR) New Investigator Award. Geoff Wong is partly funded by The Evidence Synthesis Working Group of the United Kingdom’s National Institute for Health Research School for Primary Care Research (NIHR SPCR) [Project Number 390]. Noah Ivers is funded by a CIHR New Investigator Award and a Clinician Scientist Award from the Department of Family and Community Medicine, University of Toronto. Jayna Holroyd-Leduc is funded by a University of Calgary BSF Chair in Geriatric Medicine. Sharon Straus is funded by a Tier 1 Canada Research Chair in Knowledge Translation.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement We included most of the data generated or analysed for this study in this published article and associated appendices. Any additional datasets are available from the corresponding author upon request.

  • Patient consent for publication Not required.