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Multi-Morbidity in Hospitalised Older Patients: Who Are the Complex Elderly?

Ruiz M, Bottle A, Long S, Aylin P - PLoS ONE (2015)

Bottom Line: 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.

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.

ABSTRACT

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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Venn diagram illustrating the overlap between the HRG definition and our proposed definition for complex elderly patients.
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pone.0145372.g003: Venn diagram illustrating the overlap between the HRG definition and our proposed definition for complex elderly patients.

Mentions: With regards to our third objective, we note that outcome indicators for the HRG complex elderly group are significantly different (p<0.0001) from those from our proposed definition (Table E in S1 File).This is not a concern. The HRG definition was based on a higher age threshold (at least 69 years.) of patients with at least 2 conditions from a specific list of clinical diagnoses and thus it focused on a smaller cohort of high-risk patients. All those patients considered complex elderly under the HRG definition made up a total of 11.6% (323 396) of the total sample. Fig 3illustrates the overlap between both definitions for the complex elderly; our definition captures 76.7% of the HRG complex elderly patients. Our complex elderly definition captures a larger elderly population, almost 3.5 times the one defined by HRG, and therefore, the previously used HRG grouping could have significantly underestimated the real cost of caring for the complex elderly within the hospital setting.


Multi-Morbidity in Hospitalised Older Patients: Who Are the Complex Elderly?

Ruiz M, Bottle A, Long S, Aylin P - PLoS ONE (2015)

Venn diagram illustrating the overlap between the HRG definition and our proposed definition for complex elderly patients.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696783&req=5

pone.0145372.g003: Venn diagram illustrating the overlap between the HRG definition and our proposed definition for complex elderly patients.
Mentions: With regards to our third objective, we note that outcome indicators for the HRG complex elderly group are significantly different (p<0.0001) from those from our proposed definition (Table E in S1 File).This is not a concern. The HRG definition was based on a higher age threshold (at least 69 years.) of patients with at least 2 conditions from a specific list of clinical diagnoses and thus it focused on a smaller cohort of high-risk patients. All those patients considered complex elderly under the HRG definition made up a total of 11.6% (323 396) of the total sample. Fig 3illustrates the overlap between both definitions for the complex elderly; our definition captures 76.7% of the HRG complex elderly patients. Our complex elderly definition captures a larger elderly population, almost 3.5 times the one defined by HRG, and therefore, the previously used HRG grouping could have significantly underestimated the real cost of caring for the complex elderly within the hospital setting.

Bottom Line: 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.

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.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus