Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

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. Peter Hanlon and colleagues compare the associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank. Author summary Why was this study done? Multimorbidity, the presence of multiple long-term conditions (LTCs), is associated with a range of adverse health outcomes. The UK Biobank cohort study has gathered and linked genetic, physical, and clinical information on a population scale providing unique opportunities to study the impact of multimorbidity. However, participants in UK Biobank appear on average to be healthier than the general population (“healthy volunteer bias”) and it is not clear if this selection bias affects estimates of the impact of multimorbidity using UK Biobank. What did the researchers do and find? We compared the prevalence of multimorbidity, and the impact of multimorbidity on adverse health outcomes, in UK Biobank and in a representative sample of people from Wales, UK (SAIL databank). While multimorbidity was less common in UK Biobank, the relationship between number of LTCs and mortality, hospital admissions, and major adverse cardiovascular events (MACEs) was similar between UK Biobank and SAIL at lower levels of multimorbidity (e.g., 2 or 3 LTCs) and for many common LTCs (e.g., hypertension, coronary artery disease, and chronic obstructive pulmonary disease). However, for people with higher LTC counts (e.g., 4 or more), or with specific LTCs such as mental health conditions, UK Biobank underestimates the risk of mortality, hospitalisation, and MACEs. What do these findings mean? The wide range of measures gathered by UK Biobank make it a valuable resource for studying multimorbidity, and our study suggests that analyses of modest levels of multimorbidity (such as people with 2 or 3 LTCs) are likely to yield reliable estimates. However, for people with a higher number of LTCs or with LTCs such as mental health conditions, alcohol-related disorders, or addiction, estimates based on UK Biobank data are likely to be conservative compared to a representative sample. Ideally, future LTC and multimorbidity research should combine insights from both representative routine data and information rich research cohorts such as UK Biobank. These analyses are limited by a lack of data on the severity of LTCs.