Age, sex, and socioeconomic differences in multimorbidity measured in four ways: UK primary care cross-sectional analysis

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Abstract

Background

Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised.

Aim

To examine variation in prevalence using different definitions of multimorbidity.

Design and setting

Cross-sectional study of 1 168 620 people in England.

Method

Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental–physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions.

Results

MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental–physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental–physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental–physical MM at 40–45 years younger, followed by MM2+ at 15–20 years younger, and MM3+ and MM3+ from 3+ at 10–15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental–physical MM.

Conclusion

Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies.

Related collections

Most cited references 25

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Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.

Karen Barnett, Stewart Mercer, Michael Norbury … (2012)

Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. In a cross-sectional study we extracted data on 40 morbidities from a database of 1,751,841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. 42·2% (95% CI 42·1-42·3) of all patients had one or more morbidities, and 23·2% (23·08-23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210,500 vs 194,996). Onset of multimorbidity occurred 10-15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9-11·2% in most deprived area vs 5·9%, 5·8%-6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59-6·90 for five or more disorders vs 1·95, 1·93-1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21-2·32 vs 1·08, 1·05-1·11). Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Scottish Government Chief Scientist Office. Copyright © 2012 Elsevier Ltd. All rights reserved.

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Defining and measuring multimorbidity: a systematic review of systematic reviews

Marjorie C Johnston, Michael A Crilly, Corri Black … (2019)

Multimorbidity, the coexistence of multiple health conditions, is a growing public health challenge. Research and intervention development are hampered by the lack of consensus regarding defining and measuring multimorbidity. The aim of this systematic review was to pool the findings of systematic reviews examining definitions and measures of multimorbidity.

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A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology.

Martin Fortin, Moira Stewart, Marie-Eve Poitras … (2012)

We sought to identify and compare studies reporting the prevalence of multimorbidity and to suggest methodologic aspects to be considered in the conduct of such studies. We searched the literature for English- and French-language articles published between 1980 and September 2010 that described the prevalence of multimorbidity in the general population, in primary care, or both. We assessed quality of included studies with a modified version of the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Results of individual prevalence studies were adjusted so that they could be compared graphically. The final sample included 21 articles: 8 described studies conducted in primary care, 12 in the general population, and 1 in both. All articles were of good quality. The largest differences in prevalence of multimorbidity were observed at age 75 in both primary care (with prevalence ranging from 3.5% to 98.5% across studies) and the general population (with prevalence ranging from 13.1% to 71.8% across studies). Apart from differences in geographic settings, we identified differences in recruitment method and sample size (primary care: 980-60,857 patients; general population: 1,099-316,928 individuals), data collection, and the operational definition of multimorbidity used, including the number of diagnoses considered (primary care: 5 to all; general population: 7 to all). This last aspect seemed to be the most important factor in estimating prevalence. Marked variation exists among studies of the prevalence of multimorbidity with respect to both methodology and findings. When undertaking such studies, investigators should carefully consider the specific diagnoses included and their number, as well as the operational definition of multimorbidity.

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Author and article information

Contributors

Clare MacRae:

ORCID: http://orcid.org/0000-0002-1007-683X

Role: Medical Research Council clinical research training fellow

Stewart W Mercer:

ORCID: http://orcid.org/0000-0002-1703-3664

Role: Professor of primary care and multimorbidity

David Henderson:

ORCID: http://orcid.org/0000-0002-4411-8532

Role: Research fellow

Megan McMinn: Role: Research fellow

Daniel R Morales:

ORCID: http://orcid.org/0000-0002-7469-0898

Role: Wellcome Trust clinical research fellow

Emily Jefferson:

ORCID: http://orcid.org/0000-0003-2992-7582

Role: Professor of health data science

Ronan A Lyons:

ORCID: http://orcid.org/0000-0001-5225-000X

Role: Professor of public Health

Jane Lyons:

ORCID: http://orcid.org/0000-0002-4407-770X

Role: Senior research officer and data scientist

Chris Dibben:

ORCID: http://orcid.org/0000-0003-1769-3774

Role: Professor of health geography

David A McAllister:

ORCID: http://orcid.org/0000-0003-3550-1764

Role: Professor and Wellcome Trust intermediate clinical fellow

Bruce Guthrie:

ORCID: http://orcid.org/0000-0003-4191-4880

Role: Professor of general practice

Journal

Journal ID (nlm-ta): Br J Gen Pract

Journal ID (iso-abbrev): Br J Gen Pract

Journal ID (hwp): bjgp

Journal ID (publisher-id): bjgp

Title: The British Journal of General Practice

Publisher: Royal College of General Practitioners

ISSN (Print): 0960-1643

ISSN (Electronic): 1478-5242

Publication date Collection: April 2023

Publication date (Electronic): 10 January 2023

Publication date PMC-release: 10 January 2023

Volume: 73

Issue: 729

Pages: e249-e256

Affiliations

Advanced Care Research Centre, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.

Centre for Population Health Sciences, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.

Division of Population Health and Genomics, University of Dundee, Dundee, UK; Department of Public Health, University of Southern Denmark, Denmark.

Health Informatics Centre, Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.

Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.

School of Geosciences, College of Science and Engineering, University of Edinburgh, Edinburgh, UK.

Public Health, School of Health and Wellbeing, University of Glasgow, Glasgow, UK.

Advanced Care Research Centre, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.

Author notes

Address for correspondence Clare MacRae, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh EH8 9AG, UK. Email: clare.macrae@ 123456ed.ac.uk