Multimorbidity between chronic diseases in the ageing populations

Date of Approval
Application Number
Technical Summary

This study will be a retrospective longitudinal cohort analysis. The overall aim is to investigate the occurrence and patterns of multimorbidity and explore the determinants of these clusters using routine UK primary care data. Our outcome of interest will include participants with multimorbidity which defined as coexistence of the following chronic diseases: cardiovascular diseases (CVD), cancer, arthritis, diabetes, respiratory diseases, neurological diseases and mental health conditions. 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.

Health Outcomes to be Measured

Our 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”.


Ioanna Tzoulaki - Chief Investigator - Imperial College London
Bowen Su - Corresponding Applicant - Imperial College London
Abbas Dehghan - Collaborator - Imperial College London
Azeem Majeed - 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