Abstract
Multimorbidity (multiple coexisting chronic health conditions) is common and increasing worldwide, and makes care challenging for both patients and healthcare systems. To ensure care is patient-centred rather than specialty-centred, it is important to know which conditions commonly occur together and identify the corresponding patient profile. To date, no studies have described multimorbidity clusters within an unselected hospital population. Our aim was to identify and characterise multimorbidity clusters, in a large, unselected hospitalised patient population. Linked inpatient hospital episode data were used to identify adults admitted to hospital in Grampian, Scotland in 2014 who had ≥ 2 of 30 chronic conditions diagnosed in the 5 years prior. Cluster analysis (Gower distance and Partitioning around Medoids) was used to identify groups of patients with similar conditions. Clusters of conditions were defined based on clinical review and assessment of prevalence within patient groups and labelled according to the most prevalent condition. Patient profiles for each group were described by age, sex, admission type, deprivation and urban–rural area of residence. 11,389 of 41,545 hospitalised patients (27%) had ≥ 2 conditions. Ten clusters of conditions were identified: hypertension; asthma; alcohol misuse; chronic kidney disease and diabetes; chronic kidney disease; chronic pain; cancer; chronic heart failure; diabetes; hypothyroidism. Age ranged from 51 (alcohol misuse) to 79 (chronic heart failure). Women were a higher proportion in the chronic pain and hypothyroidism clusters. The proportion of patients from the most deprived quintile of the population ranged from 6% (hypertension) to 14% (alcohol misuse). Identifying clusters of conditions in hospital patients is a first step towards identifying opportunities to target patient-centred care towards people with unmet needs, leading to improved outcomes and increased efficiency. Here we have demonstrated the face validity of cluster analysis as an exploratory method for identifying clusters of conditions in hospitalised patients with multimorbidity.
| Original language | English |
|---|---|
| Article number | 5134 |
| Number of pages | 10 |
| Journal | Scientific Reports |
| Volume | 12 |
| Early online date | 24 Mar 2022 |
| DOIs | |
| Publication status | Published - 24 Mar 2022 |
Bibliographical note
FundingNHS Grampian Public Health Directorate funded the original study developing the dataset. This study was funded by NHS Grampian Endowment [Grant No. 18/10].
Acknowledgements
We thank NHS Grampian who provided data and also the Grampian Data Safe Haven, who hosted the data and provided data management support and the linkage service. This work was supported by Health Data Research UK which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust. We also acknowledge the non-financial support of our Study Steering Committee, which included clinical, epidemiological and health intelligence representation.
Keywords
- Diseases
- Health Care
- Medical research
- Risk factors
- signs and symptoms
Access to Document
- Robertson_etal_SR_Identifying_multimorbidity_cluster_VOR
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