Abstract / Description of output

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 (3,039 men, 8,970 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.
Original languageEnglish
Article number105081
Pages (from-to)1-12
Number of pages12
Issue numberApril 2024
Publication statusPublished - 21 Mar 2024

Keywords / Materials (for Non-textual outputs)

  • multimorbidity
  • association measures
  • network analysis
  • relative risk
  • Bayesian inference
  • low counts


Dive into the research topics of 'Multimorbidity analysis with low condition counts: A robust Bayesian approach for small but important subgroups'. Together they form a unique fingerprint.

Cite this