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Sociodemographic characteristics and longitudinal progression of multimorbidity: A multistate modelling analysis of a large primary care records dataset in England.
- Source :
-
PLoS medicine [PLoS Med] 2023 Nov 03; Vol. 20 (11), pp. e1004310. Date of Electronic Publication: 2023 Nov 03 (Print Publication: 2023). - Publication Year :
- 2023
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Abstract
- Background: Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.<br />Methods and Findings: We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 ([1.74, 1.78]; p < 0.001) for those with no preexisting conditions and decreases to 0.95 ([0.75, 1.21]; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 ([1.95, 2.12], p < 0.001), compared to transitions from CVD 1.50 ([1.43, 1.58], p < 0.001), CKD 1.37 ([1.30, 1.44], p < 0.001), and HF 1.55 ([1.34, 1.79], p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results.<br />Conclusions: Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.<br />Competing Interests: JB has previously received funding for unrelated work from F. Hoffmann-La Roche Ltd. KN reports grants from NIHR, MRC, Diabetes UK, Vifor, and AstraZeneca, and personal fees from Merck Sharp & Dohme, Sanofi, and Boehringer Ingelheim, outside of and unrelated to the submitted work. TM has previously received funding for unrelated work: from Cancer Research UK and from the Health Research Board (Ireland) for advisory board participation and travel. SJG received payment from Novo Nordisk, Napp and Astra Zeneca for lectures at 6 educational events over the last 3 years. Napp supported SJG’s attendance at EASD 2018 and he received payment from Eli Lilly associated with membership of an independent data monitoring committee for a randomised trial of a medication to lower glucose. PK, SR and KN are co-investigators on the HDRUK grant titled Measuring & Understanding Multimorbidity using Routine Data in the UK (MUrMuR-UK). CY has received remuneration for unrelated consultancy services to F. Hoffmann-La Roche and Singula Bio. The other authors have declared that no competing interests exist.<br /> (Copyright: © 2023 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Subjects :
- Humans
Multimorbidity
Cross-Sectional Studies
England epidemiology
Chronic Disease
Primary Health Care
Diabetes Mellitus, Type 2 diagnosis
Diabetes Mellitus, Type 2 epidemiology
Cardiovascular Diseases
Heart Failure diagnosis
Heart Failure epidemiology
Renal Insufficiency, Chronic diagnosis
Renal Insufficiency, Chronic epidemiology
Subjects
Details
- Language :
- English
- ISSN :
- 1549-1676
- Volume :
- 20
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- PLoS medicine
- Publication Type :
- Academic Journal
- Accession number :
- 37922316
- Full Text :
- https://doi.org/10.1371/journal.pmed.1004310