학술논문
Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups.
Document Type
Academic Journal
Author
Romero Moreno G; School of Informatics, University of Edinburgh, Edinburgh, UK. Electronic address: Guillermo.RomeroMoreno@ed.ac.uk.; Restocchi V; School of Informatics, University of Edinburgh, Edinburgh, UK.; Fleuriot JD; School of Informatics, University of Edinburgh, Edinburgh, UK.; Anand A; Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.; Mercer SW; Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.; Guthrie B; Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.
Source
Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 101647039 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2352-3964 (Electronic) Linking ISSN: 23523964 NLM ISO Abbreviation: EBioMedicine Subsets: MEDLINE
Subject
Language
English
Abstract
Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.
Methods: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.
Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.
Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.
Funding: National Institute for Health and Care Research.
Competing Interests: Declaration of interests We declare that we have no conflicts of interest.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Methods: We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.
Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.
Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.
Funding: National Institute for Health and Care Research.
Competing Interests: Declaration of interests We declare that we have no conflicts of interest.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)