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. 2024 Jun 28:74:102703.
doi: 10.1016/j.eclinm.2024.102703. eCollection 2024 Aug.

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

Stefanie J Krauth  1   2 , Lewis Steell  1   3   4 , Sayem Ahmed  5 , Emma McIntosh  5 , Grace O Dibben  6 , Peter Hanlon  1 , Jim Lewsey  5 , Barbara I Nicholl  1 , David A McAllister  5 , Susan M Smith  7 , Rachael Evans  8 , Zahira Ahmed  8 , Sarah Dean  9 , Colin Greaves  10 , Shaun Barber  9   11 , Patrick Doherty  12 , Nikki Gardiner  13 , Tracy Ibbotson  1 , Kate Jolly  14 , Paula Ormandy  15 , Sharon A Simpson  6 , Rod S Taylor  6   16 , Sally J Singh  8 , Frances S Mair  1 , Bhautesh Dinesh Jani  1 ; PERFORM research team
Affiliations

Association of latent class analysis-derived multimorbidity clusters with adverse health outcomes in patients with multiple long-term conditions: comparative results across three UK cohorts

Stefanie J Krauth et al. EClinicalMedicine. .

Abstract

Background: It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use.

Methods: Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n = 1,825,289), UK Biobank (n = 502,363), and the UK Household Longitudinal Study (UKHLS) (n = 49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL).

Findings: Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR = 4.47, hospitalisation IRR = 1.84; GP use IRR = 2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR = 4.52, hospitalisation IRR = 1.53, GP use IRR = 2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR = 2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years.

Interpretation: Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs.

Funding: This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).

Keywords: Clustering; Hospitalisation; Mortality; Multimorbidity; Primary health care; Service use.

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Conflict of interest statement

SJS is Clinical Lead for National Respiratory Audit Programme–Pulmonary Rehabilitation and also a National Institute for Health Research (NIHR) Senior Investigator. This work was supported by the NIHR Leicester Biomedical Research Centre (BRC). KJ declares funding from NIHR Applied Research Collaboration West Midlands and Sub-committee chair for NIHR Programme Grants for Applied Health Research (payment to institution). BIN declares receiving funding from NIHR, vs. Arthritis, NIHR-EPSRC, Glasgow Knowledge Exchange Fund and for external PhD examination. RAE declares receipt of speaker fees (Boeringher June 2021; Moderna April 2023) and ERS Group 01.02 Pulmonary Rehabilitation and Chronic Care Secretary (unpaid), and ATS Pulmonary Rehabilitation Assembly Chair (unpaid). SD declares NIHR Applied Research Collaboration: South West Peninsula (PenARC; payment to institution), receipt of the following NIHR grants (payment to institution): NIHR151938; NIHR204099; RP-PG-0514-20,002; NIHR201038; NIHR201070; NIHR200428, receipt of grants (payment to institution): Gillings Family foundation (ID 943008); The Stroke Association (ID 901902); NIHR School for Primary Care Research—Exeter internal fund (ID 856766); Academic Health Science Network South West (ID 1355693), receipt of textbook royalties (John Wiley & Sons), support for meeting attendance from NIHR (p-PG-0514- 20,002) and Health Research Council New Zealand (21/826; 18/254), and membership of NIHR Programme Grant for Applied Research funding panel committee and The Stroke Association research funding panel. SJK declares receipt of conference funding from School of Health and Wellbeing, University of Glasgow. SAS declares presidency of the UK Society of Behavioural Medicine, membership of HTA Clinical Evaluations and Trials Committee (2016–2020), membership of Commissioning Panel for the National Institute of Health Research (NIHR) Policy Research Programme (2019–2022), and membership of Chief Scientist Office HIPS committee (2018–2023). FSM declares grants from NIHR during the conduct of the study; grants from Wellcome, grants from Innovate UK, grants from Innovative Medicines Initiative (IMI2), grants from UKRI, grants from EPSRC, grants from MRC, grants from Chief Scientist office Scotland, outside the submitted work; and MRC CARP Panel membership. Dr Greaves reports grants from National Institute for Health Research (NIHR) during the conduct of the study.

Figures

Fig. 1
Fig. 1
Predicted mortality rates by MLTC clusters over a 10-year follow up in SAIL. All other covariates in the model were set to their mean value in the respective age group. Error bars represent 95% confidence intervals.
Fig. 2
Fig. 2
Predicted number of hospitalisations by MLTC clusters over a 10-year follow up in SAIL. All other covariates in the model were set to their mean value in the respective age group. Error bars represent 95% confidence intervals.
Fig. 3
Fig. 3
Predicted number of GP use by MLTC clusters over a 10-year follow up in SAIL. All other covariates in the model were set to their mean value in the respective age group. Error bars represent 95% confidence intervals.
Fig. 4
Fig. 4
SAIL and UK Biobank LTC clusters ranked by Years of Life Lost.
See this image and copyright information in PMC

References

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