| Title |
Combinations of Chronic Conditions, Functional Limitations, and Geriatric Syndromes that Predict Health Outcomes
|
|---|---|
| Published in |
Journal of General Internal Medicine, February 2016
|
| DOI | 10.1007/s11606-016-3590-9 |
| Pubmed ID | |
| Authors |
Siran M. Koroukian, Nicholas Schiltz, David F. Warner, Jiayang Sun, Paul M. Bakaki, Kathleen A. Smyth, Kurt C. Stange, Charles W. Given |
| Abstract |
The strategic framework on multiple chronic conditions released by the US Department of Health and Human Services calls for identifying homogeneous subgroups of older adults to effectively target interventions aimed at improving their health. We aimed to identify combinations of chronic conditions, functional limitations, and geriatric syndromes that predict poor health outcomes. DESIGN, SETTING AND PARTICIPANTS Data from the 2010-2012 Health and Retirement Study provided a representative sample of U.S. adults 50 years of age or older (n = 16,640). Outcomes were: Self-reported fair/poor health, self-rated worse health at 2 years, and 2-year mortality. The main independent variables included self-reported chronic conditions, functional limitations, and geriatric syndromes. We conducted tree-based classification and regression analysis to identify the most salient combinations of variables to predict outcomes. Twenty-nine percent and 23 % of respondents reported fair/poor health and self-rated worse health at 2 years, respectively, and 5 % died in 2 years. The top combinations of conditions identified through our tree analysis for the three different outcome measures (and percent respondents with the outcome) were: a) for fair/poor health status: difficulty walking several blocks, depressive symptoms, and severe pain (> 80 %); b) for self-rated worse health at 2 years: 68.5 years of age or older, difficulty walking several blocks and being in fair/poor health (60 %); and c) for 2-year mortality: 80.5 years of age or older, and presenting with limitations in both ADLs and IADLs (> 40 %). Rather than chronic conditions, functional limitations and/or geriatric syndromes were the most prominent conditions in predicting health outcomes. These findings imply that accounting for chronic conditions alone may be less informative than also accounting for the co-occurrence of functional limitations and geriatric syndromes, as the latter conditions appear to drive health outcomes in older individuals. |
X Demographics
The data shown below were collected from the profiles of 21 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 29% |
| United States | 4 | 19% |
| Spain | 4 | 19% |
| Brazil | 1 | 5% |
| Hong Kong | 1 | 5% |
| Ireland | 1 | 5% |
| France | 1 | 5% |
| Unknown | 3 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 57% |
| Scientists | 5 | 24% |
| Practitioners (doctors, other healthcare professionals) | 3 | 14% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | <1% |
| Unknown | 124 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 17% |
| Student > Ph. D. Student | 17 | 14% |
| Student > Bachelor | 11 | 9% |
| Student > Master | 10 | 8% |
| Student > Postgraduate | 8 | 6% |
| Other | 25 | 20% |
| Unknown | 33 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 33 | 26% |
| Nursing and Health Professions | 14 | 11% |
| Social Sciences | 6 | 5% |
| Psychology | 5 | 4% |
| Computer Science | 3 | 2% |
| Other | 21 | 17% |
| Unknown | 43 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2017.
All research outputs
#3,053,331
of 25,402,889 outputs
Outputs from Journal of General Internal Medicine
#2,195
of 8,187 outputs
Outputs of similar age
#45,987
of 313,009 outputs
Outputs of similar age from Journal of General Internal Medicine
#27
of 106 outputs
Altmetric has tracked 25,402,889 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,187 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.1. This one has gotten more attention than average, scoring higher than 73% of its peers.
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