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Multi-Morbidity in Hospitalised Older Patients: Who Are the Complex Elderly?
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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.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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PubMed Central - PubMed
Affiliation: Dr 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.
ABSTRACT
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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. Related in: MedlinePlus |
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pone.0145372.g001: A) Distribution of multi-morbidity in hospitalised elderly patients B) Multi-morbidity by age group C) Gender prevalence by age for patients with multi-morbidity of 2+. Mentions: Our sample consisted of a total of 2788900 hospital admissions. 52.8% were females (mean age±SD of 77.5±8.3 years) and the rest were males (mean age±SD of 76.0±7.5 years) (Table 1). 62.8% had 2 or more conditions from our list, while 4.7% had 6 or more (Table 1 and Fig 1A). The overall average morbidity (at least 1 medical condition from target list) in our sample was 2.7±1.5, and the average multi-morbidity (at least 2 coexisting medical conditions) was 3.2±1.2. |
View Article: PubMed Central - PubMed
Affiliation: Dr 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.
