Clustering by Multiple Long-Term Conditions and Social Care Needs: A cohort study amongst 10,025 older adults in England

Abstract

AbstractBackgroundPeople with Multiple Long-Term Conditions (MLTC) face health and social care challenges. This study aimed to classify people by MLTC and social care need (SCN) into distinct clusters and quantify the association between derived clusters and care outcomes.MethodsA cohort study was conducted using the English Longitudinal Study of Ageing (ELSA), including people with up to ten MLTC. Self-reported SCN was assessed through 13 measures of difficulty with activities of daily living, ten 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 SCN/MLTC clusters, all-cause mortality, and nursing home admission.ResultsThe cohort included 9171 people at baseline with a mean age of 66·3 years; 44·5% were males. 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 to 79 years who were white and educated to the upper secondary level. This cluster was significantly associated with higher nursing home admission (OR = 8·97; 95% CI: 4·36 to 18·45). We found no association between clusters and all-cause mortality.ConclusionsThis results in five clusters with distinct characteristics that permit the identification of high-risk groups who are more likely to have worse care outcomes, including nursing home admission. This can inform targeted preventive action to where it is most needed amongst those with MLTC.What is already known on this topicWhile it is established that multiple long-term conditions are linked to an increased risk of hospitalisation, nursing home admission and mortality, no previous research has examined this risk in relation to clusters of MLTC and social care needs in England.What this study addsUsing latent class analysis, this study identified five clusters by multiple long-term conditions and social care needs with distinct characteristics and quantified their relationship with nursing home admission and mortality.How this study might affect research, practice or policyThe findings permit the identification of high-risk groups who are more likely to have worse care outcomes, including nursing home admission in the future. This can inform targeted preventive action to where it is most needed amongst those with MLTC. Recognition of MLTC and SCN clusters may also aid clinicians in moving away from a single disease management approach in older adults.