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Published on: September 30, 2020
Multi-Morbidity in Hospitalised Older Patients: Who Are the Complex Elderly?
Milagros Ruiz1, Alex Bottle1, Susannah Long2
1Dr Foster Unit at Imperial, Dept. Primary Care and Public Health, School of Public Health, Imperial College London, Reynolds Building, St. Dunstan's Road, London, W6 6RP, United Kingdom.
Background:
No formal definition for the "complex elderly" exists; moreover, these older patients with high levels of multi-morbidity are not readily identified as such at point of hospitalisation, thus missing a valuable opportunity to manage the older patient appropriately within the hospital setting.
Objectives:
To empirically identify the complex elderly patient based on degree of multi-morbidity.
Design:
Retrospective observational study using administrative data.
Setting:
English hospitals during the financial year 2012-13.
Subjects:
All admitted patients aged 65 years and over.
Methods:
By using exploratory analysis (correspondence analysis) we identify multi-morbidity groups based on 20 target conditions whose hospital prevalence was ≥ 1%.
Results:
We examined a total of 2788900 hospital admissions. Multi-morbidity was highly prevalent, 62.8% had 2 or more of the targeted conditions while 4.7% had six or more. Multi-morbidity increased with age from 56% (65-69yr age-groups) up to 67% (80-84yr age-group). The average multi-morbidity was 3.2±1.2 (SD). Correspondence analysis revealed 3 distinct groups of older patients. Group 1 (multi-morbidity ≤2), associated with cancer and/or metastasis; Group 2 (multi-morbidity of 3, 4 or 5), associated with chronic pulmonary disease, lung disease, rheumatism and osteoporosis; finally Group 3 with the highest level of multi-morbidity (≥6) and associated with heart failure, cerebrovascular accident, diabetes, hypertension and myocardial infarction.
Conclusions:
By using widely available hospital administrative data, we propose patients in Groups 2 and 3 to be identified as the complex elderly. Identification of multi-morbidity patterns can help to predict the needs of the older patient and improve resource provision.
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