Journal
PLOS ONE
Volume 10, Issue 12, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0145372
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Funding
- NIHR Biomedical Research Centre
- Dr Foster Intelligence (an independent health service research organisation)
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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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