Clustering populations by health and social care with multiple long-term conditions: a cohort study - the English Longitudinal Study of Ageing (ELSA)

Dambha-Miller, Hajira, Nartey, Yvonne, Khan, Nusrat, Simpson, Glenn, Lin, Sharon, Akyea, Ralph and Farmer, Andrew (2023) Clustering populations by health and social care with multiple long-term conditions: a cohort study - the English Longitudinal Study of Ageing (ELSA). The British journal of general practice : the journal of the Royal College of General Practitioners, 73 (Suppl. 1). (doi:10.3399/bjgp23X734337).

Abstract

Background: The integration of health and social care services is a potential solution for improving care, despite monetary constraints and increasing demand. How two or more multiple long-term conditions (MLTC) cluster, interact and associate with socioeconomic factors, and affect access to unscheduled primary healthcare services is understudied.
Aim: To cluster an MLTC population by health and social care, examine clusters, and quantify associations with health outcomes.
Method: A retrospective cohort study was conducted using the ELSA database (2002 to 2019) on 19802 participants aged ≥50 years. Ten major health conditions, and social care need, including difficulty in activities of daily living (ADL) and mobility, for example, were used to cluster MLTC by latent class modelling. Multivariate logistic regression models were used to establish further association.
Results: The mean age of the participants at baseline (wave 2) was about 66 years and 55% of participants were female, with more than 60% developing MLTC in their lifetime (waves 2 to 9). Of the five distinct latent clusters, cluster 5 was the most significant cluster composed of lung diseases, stroke, dementia, and high ADL and mobility difficulty scores. The majority of the participants were aged 70-79 years, female, and married. The odds of having a longer nursing home stay were 8.97 (95% confidence interval = 4.36 to 18.45), and death was 10% higher in this cluster compared to the highest probability cluster 4 in the maximally adjusted regression model.
Conclusion: This study identified MLTC clusters by social care need with the highest primary care demand. Targeting clinical practice to prevent MLTC progression for these groups may lessen future pressures on primary care demand.

This record has no associated files available for download.

Contributors

Author: Hajira Dambha-Miller

Author: Glenn Simpson

Download statistics