RESEARCH POSTER PRESENTATION DESIGN © 2015 www.PosterPresentations.com Electronic Health Records (EHR) provide a rich source of population data that can be used to systematically understand multimorbidity. Robust measures of association are hypothesis-generating if their co-occurrence is higher than expected. However, prevalent methodological framework are unstable when evidence is limited. Associations between conditions canbe assembled into a network of multimorbidity, where network analysis can be used to understand the role of diseases, ﬁnd clusters, or study disease progression. The effects of unreliable association measures get aggregated and intensified when performing network analysis. These problems affect particularly to minorities (small but important subgroups), for which there is less data available. We have developed a Bayesian inference framework that is robust to sparse data in the analysis of multimorbidity patterns. The framework also has the following benefits: - Explicit estimation of uncertainty - Robust to decisions in the methodology - Robust to noise in rare conditions Background and motivation Methods 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. Results 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. 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. Multimorbidity analysis with low condition counts:​ A robust Bayesian approach for small but important subgroups​ Guillermo Romero Moreno, Valerio Restocchi, Jacques Fleuriot, Atul Anand, Steward Mercer, Bruce Guthrie Conclusion Reference Romero Moreno G., Restocchi V., Fleuriot J., Anand A., Mercer S., Guthrie B., “Multimorbidity analysis with low condition counts:​ A robust Bayesian approach for small but important subgroups”​, eBioMedicine, Volume 102, 2024, 105081, https://www.sciencedirect.com/science/article/pii/S2352396424001166​ This study/project is funded by the NIHR under the AI for Multiple Long-Term Conditions (AIM) programme, NIHR202639. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. Model This model of multimorbidity generation assumes: i) hypothetical risk factors affecting the appearance of each LTC independent of the others, ii) hypothetical common mechanisms that simultaneously affect a pair of LTCs and lead to their co-appearance, e.g. common risk factors or one LTC increasing the risk of acquiring the other. ABC reflects the ratio of generation by common mechanisms as compared to independent factors: ABC = 1 + fij / fi fj These are then embedded in a fully Bayesian framework: Priors Code The code for implementing our framework is publicly available at https://github.com/Juillermo/ABC. It has also be published as an easily installable python package, via pip install abc-network ( https://pypi.org/pro