Background: Globally, there is increasing research on clusters of multimorbidity, but few studies have investigated multimorbidity in urban contexts characterised by a young, multi-ethnic, deprived populations. This study identified clusters of associative multimorbidity in an urban setting.
Methods: This is a population-based retrospective cross-sectional study using electronic health records of all adults aged 18 years and over, registered between April 2005 to May 2020 in general practices in one inner London borough. Multiple correspondence analysis and cluster analysis was used to identify groups of multimorbidity from 32 long-term conditions (LTCs).
Results: The population included 41 general practices with 826,936 patients registered between 2005 and 2020, with mean age 40 (SD15·6) years. The prevalence of multimorbidity was 21% (n = 174,881), with the median number of conditions being three and increasing with age. Analysis identified five consistent LTC clusters: 1) anxiety and depression (Ratio of within- to between- sum of squares (WSS/BSS <0·01 to <0·01); 2) heart failure, atrial fibrillation, chronic kidney disease (CKD), chronic heart disease (CHD), stroke/transient ischaemic attack (TIA), peripheral arterial disease (PAD), dementia and osteoporosis (WSS/BSS 0·09 to 0·12); 3) osteoarthritis, cancer, chronic pain, hypertension and diabetes (0·05 to 0·06); 4) chronic liver disease and viral hepatitis (WSS/BSS 0·02 to 0·03); 5) substance dependency, alcohol dependency and HIV (WSS/BSS 0·37 to 0·55).
Interpretation: Mental health problems, pain, and at-risk behaviours leading to cardiovascular diseases are the important clusters identified in this young, urban population.
Funding: Impact on Urban Health, United Kingdom.
Keywords: Clustering; Correspondence analysis; LTC, long term conditions; Long term conditions; MCA, multiple correspondence analysis; Multimorbidity; QOF, quality outcomes framework; UK, United Kingdom; WSS/BSS, ratio of within- to between- sum of squares.
© 2021 The Author(s).