Hanlon, P. , Jani, B. D. , Nicholl, B. , Lewsey, J. , McAllister, D. A. and Mair, F. S. (2022) Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: a comparison of longitudinal cohort studies. PLoS Medicine, 19(3), e1003931. (doi: 10.1371/journal.pmed.1003931) (PMID:35255092) (PMCID:PMC8901063)
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Abstract
Background: Cohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample. Methods and findings: These are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets. Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included. Conclusions: In this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | McAllister, Professor David and Jani, Dr Bhautesh and Hanlon, Dr Peter and Lewsey, Professor Jim and Mair, Professor Frances and Nicholl, Dr Barbara |
| Creator Roles: | Hanlon, P.Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Visualization, Writing – original draft, Writing – review and editing Jani, B. D.Conceptualization, Writing – review and editing Nicholl, B.Writing – review and editing Lewsey, J.Conceptualization, Methodology, Supervision, Writing – review and editing McAllister, D. A.Formal analysis, Methodology, Supervision, Writing – review and editing Mair, F. S.Conceptualization, Supervision, Writing – review and editing |
| Authors: | Hanlon, P., Jani, B. D., Nicholl, B., Lewsey, J., McAllister, D. A., and Mair, F. S. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > General Practice and Primary Care College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Public Health |
| Journal Name: | PLoS Medicine |
| Publisher: | Public Library of Science |
| ISSN: | 1549-1277 |
| ISSN (Online): | 1549-1676 |
| Published Online: | 07 March 2022 |
| Copyright Holders: | Copyright © 2022 Hanlon et al. |
| First Published: | First published in PLoS Medicine 19(3): e1003931 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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