Clustering by multiple long-term conditions and social care needs

A cross-sectional study among 10 026 older adults in England

Bibliographic Data

ID11178657
AuthorsNusrat Khan (0000-0003-3928-0022, Primary Care Research Centre, University of Southampton, Southampton, UK), Christos V Chalitsios (0000-0002-0836-9385, Primary Care Research Centre, University of Southampton, Southampton, UK), Yvonne Nartey (0000-0001-9876-679X, Primary Care Research Centre, University of Southampton, Southampton, UK), Glenn Simpson (0000-0002-1753-942X, Primary Care Research Centre, University of Southampton, Southampton, UK), Francesco Zaccardi (0000-0002-2636-6487, Leicester Real World Evidence Unit, Leicester Diabetes Centre, University of Leicester, Leicester, UK), Miriam Santer (0000-0001-7264-5260, Primary Care Research Centre, University of Southampton, Southampton, UK), Peter Roderick (0000-0001-9475-6850, NIHR Southampton Biomedical Research Centre), Paul J Roderick (Primary Care Research Centre, University of Southampton, Southampton, UK), Beth Stuart (0000-0001-5432-7437, Queen Mary University of London), Andrew J Farmer (University of Oxford), Hajira Dambha-Miller (0000-0003-0175-443X, Primary Care Research Centre, University of Southampton, Southampton, UK, corresponding author)
Year2023
Volume77
Issue12
Pages770-776
Publication date2023-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Epidemiology and Community Health (JOURNAL)
Journal identifiersISSN: 0143-005X • E-ISSN: 1470-2738
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/jech-2023-220696
PMID37620006
OpenAlexW4386120365
LanguageEN
References cited39

BACKGROUND : People with multiple long-term conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care needs (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes. METHODS : A cross-sectional study was conducted using the English Longitudinal Study of Ageing, including people with up to 10 MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, 10 measures of mobility difficulties and whether health status was limiting earning capability. Latent class analysis was performed to identify clusters. Multivariable logistic regression quantified associations between derived MLTC/SCN clusters, all-cause mortality and nursing home admission. RESULTS: Our study included 9171 people at baseline with a mean age of 66.3 years; 44.5% were men. Nearly 70.8% had two or more MLTC, the most frequent being hypertension, arthritis and cardiovascular disease. We identified five distinct clusters classified as high SCN/MLTC through to low SCN/MLTC clusters. The high SCN/MLTC included mainly women aged 70-79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR=8.71; 95% CI: 4.22 to 18). We found no association between clusters and all-cause mortality. CONCLUSIONS: We have highlighted those at risk of worse care outcomes, including nursing home admission. Distinct clusters of individuals with shared sociodemographic characteristics can help identify at-risk individuals with MLTC and SCN at primary care level

Activities of daily living · Cluster (spacecraft) · Cross-sectional study · Latent class model · Logistic regression · Physical therapy · Social support · Chronic Disease Management Strategies · Geriatric Care and Nursing Homes · Healthcare innovation and challenges · Medicine · Psychology · Gerontology

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