Multimorbidity refers to the coexistence of two or more long-term illnesses in an individual, which currently affects approximately 1/3 of the general population and 2/3 of the population aged 65 years and over worldwide. Multimorbidity is predicted to increase steeply over the coming years. At the same time, health-care professionals will be faced with the challenge to treat multiple conditions in individuals because of life expectancy. This challenge is even more difficult due to the fragmented investigations of multimorbidity by health-care researchers, insufficient understanding of multimorbidity by health-care professionals and the dominance of individual disease management system in health-care settings. Failure to deliver optimised care to patients with multimorbidity leads to extra pressure on health-care resources, a decline in patients’ functional abilities and an increased risk of mortality. However, the issue of multimorbidity can be resolved if clinicians have a better understanding of the underlying pathology of the co-occurring of chronic conditions. Emerging evidence on multimorbidity suggest that common pathways may lead to different chronic diseases. Therefore, having a better understanding of multimorbidity, including its prevalence, patterns, and the causal relationships among various chronic diseases is crucial. This enhanced understanding can significantly improve patient management strategies for those afflicted with multiple chronic conditions. Furthermore, it will suggest drug repurposing opportunities for diseases that share underlying pathologies and facilitate the investigation of disease associations.
This study will be a retrospective longitudinal cohort analysis. The primary objectives are to assess the incidence and prevalence of multimorbidity, identify determinants influencing disease clusters, and evaluate drug repurposing opportunities among these disease clusters. Our outcome of interest will include participants with diagnosis of the following chronic diseases: cardiovascular diseases (CVD), cancer, arthritis, diabetes, respiratory diseases, neurological diseases and mental health conditions and multimorbidity which is defined as coexistence of the chronic diseases listed above. Participants with at least 5 years follow-up and no disease diagnosed within 12 months after their first GP practice registration were included. We will first describe multimorbidity by participants’ baseline characteristics. Continuous variables (i.e. age and years of follow-up) will be expressed as means and standard deviations (SD), and categorical variables (i.e. gender, BMI, smoking status and alcohol consumption) were expressed as number and percentage (%). Differences will be evaluated using the Kruskal-Wallis test or χ2 test as appropriate. Prevalence of multimorbidity will be inferred from cumulative incidence, with multimorbidity cases removed only at death and tabulated by different age and gender groups. Poisson regression models, with the Huber variances, will be employed to measure the progress and level of multimorbidity (defined as the number of diseases that a patient has ever had). The patterns of multimorbidity in terms of chronological order and disease patterns will be examined using the latent class models for clustering and dynamic Bayesian networks. Multistate survival model is used to investigate transactions from baseline to the index condition and from index condition to the second condition. To evaluate the effectiveness of drug repurposing, we will compare health risk hazard ratios between medication groups using Inverse Probability of Treatment Weighting (IPTW) and Cox regression, highlighting the potential of utilizing existing drugs for new therapeutic purposes in multimorbidity contexts.
The primary outcome of interest is multimorbidity. We will use the definition of multimorbidity recommended by WHO, which is the “co-existence of two or more conditions in the same individual”.
The secondary outcome is participants with diagnosis of the following chronic diseases: cardiovascular diseases (CVD), cancer, arthritis, diabetes, respiratory diseases, neurological diseases and mental health conditions.
Ioanna Tzoulaki - Chief Investigator - Imperial College London
Bowen Su - Corresponding Applicant - Imperial College London
Abbas Dehghan - Collaborator - Imperial College London
Alexander Smith - Collaborator - Imperial College London
Azeem Majeed - Collaborator - Imperial College London
Loukas Zagkos - Collaborator - Imperial College London
Marie-Joe Dib - Collaborator - Imperial College London
Mark Cunningham - Collaborator - Imperial College London
Yu Bai - Collaborator - Imperial College London
Yu Bai - Collaborator - Imperial College London
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation