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

Peter Hanlon; Bhautesh D Jani; Barbara Nicholl; Jim Lewsey; David A McAllister; Frances S Mair

doi:10.1371/journal.pmed.1003931

. 2022 Mar 7;19(3):e1003931. doi: 10.1371/journal.pmed.1003931

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

Peter Hanlon ¹, Bhautesh D Jani ¹, Barbara Nicholl ¹, Jim Lewsey ², David A McAllister ^3,^‡, Frances S Mair ^1,^‡,^*

Editor: Sanjay Basu⁴

PMCID: PMC8901063 PMID: 35255092

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.

Introduction

Multimorbidity, the coexistence of multiple long-term conditions (LTCs) within an individual, is a global clinical and public health priority [1]. Multimorbidity is common [2], increasing in prevalence [3], and associated with adverse health outcomes [4,5]. However, important questions remain about how best to identify, quantify, prevent, and manage multimorbidity within modern healthcare. There is also growing interest in the implications of specific combinations, or clusters, of LTCs, and the identification of specific targets for intervention [6,7]. An important resource for addressing such questions are large cohort studies with linkage to routine healthcare data. One such resource is the UK Biobank.

UK Biobank is a large cohort study of approximately 500,000 people recruited from across England, Scotland, and Wales. Strengths of UK Biobank include its size, detailed baseline assessment and measurements including biological samples, follow-up assessments for certain issues, and the availability of long-term linkage to outcome data. This has allowed investigators to explore various aspects of multimorbidity including demographic patterns [4], prevalence of disease clusters [8], association with related states such as frailty or sarcopenia [9,10], the impact of lifestyle factors in the context of multimorbidity [11], and associations between multimorbidity and adverse health outcomes [4,12–15]. Many such analyses would not have been possible using routine data alone, and the sample size of UK Biobank provides considerable statistical power. However, one major limitation of UK Biobank is that its response rate to recruitment invitation, approximately 5%, is substantially lower than many cohort studies [16,17]. UK Biobank participants are recognised to be less socioeconomically deprived, have fewer lifestyle risk factors, and have a lower prevalence of LTCs than the UK population in general [18]. This presents challenges for studying multimorbidity, in particular in relation to the risk of selection bias [19,20].

It has been claimed that while UK Biobank cannot be described as representative, associations between risk factors and outcomes are nonetheless reliable [21]. Others, however, have questioned this assertion [22]. In particular, it is argued that notwithstanding the large sample size and presence of participants with multiple LTCs, selection pressures within the sample may lead to collider bias, resulting in biased associations between risk factors and outcomes. A collider is a variable that is causally influenced by 2 other variables [19]. For example, if people with multiple LTCs and people who are more likely to be hospitalised or die are less likely to be recruited into UK Biobank (i.e., a “healthy cohort” effect), this could bias the association between LTCs and those outcomes observed in UK Biobank.

A recent study compared associations between a range of risk factors and mortality in UK Biobank and in national surveys with considerably higher response rates and reported that the relative associations between risk factors and outcomes were very broadly similar between the 2 sources [21]. It is not clear, however, if the same is true of multimorbidity, as the previous study did not assess LTCs nor did it explore combinations of risk factors. Furthermore, when studying multimorbidity, absolute risks of outcomes are important as well as relative associations. This previous comparison is also limited by the fact that although the health surveys used for comparison had higher response rates than UK Biobank, they still relied on voluntary recruitment and are therefore also susceptible to selection bias. This study seeks to compare the relationship between multimorbidity and adverse health outcomes in UK Biobank with a representative, unselected population identified from routine health records. This is important as it addresses the question of whether UK Biobank analyses can produce reliable estimates of the associations between multimorbidity and adverse health outcomes. Understanding and quantifying the reliability of UK Biobank in this context is needed if the strengths mentioned above are to be harnessed to meet the rising challenge of multimorbidity.

Our study aims (i) to compare the prevalence of multimorbidity in UK Biobank with an unselected population identified from the Secure Anonymised Information Linkage (SAIL) databank; and (ii) to compare the associations between multimorbidity and all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) between the datasets, in both relative and absolute terms.

Methods

This is an observational analysis of multimorbidity in cohorts identified from UK Biobank and the SAIL databank. Unless otherwise stated in this section, all analyses were conducted according to a prespecified analysis plan (S1 Protocol). This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).

Data sources

UK Biobank

UK Biobank participants were recruited between 2006 and 2010 by postal invitation (5% response rate). Eligible participants had to be registered with a General Practitioner, aged between 40 and 70, and live within 20 miles of one of 22 assessment centres in England, Scotland, and Wales. Participants answered a touchscreen questionnaire, a nurse interview, and had physical measurements taken. Participants also consented to data linkage to healthcare records including primary care, hospital episode statistics, and mortality registers.

At present, data on linked primary care data are only available for a subset of participants. The availability of primary care data depended on the electronic medical record system used by the practice (data only currently available from certain systems) rather than any participant-level factors. This subset is representative of the UK Biobank cohort in terms of age, sex, socioeconomic status, lifestyle factors, and self-reported LTCs (S1 Table). To allow a consistent approach to identifying multimorbidity between UK Biobank and SAIL, we restricted our analysis to this subset of participants with primary care data. We further excluded participants from practices using the “Vision” primary care system, as data were not available from these practices for participants who had died since baseline assessment.

This analysis was performed as part of UK Biobank project 14151.

SAIL

Data on our unselected community comparison were taken from the SAIL databank [23]. SAIL collected linked primary care, hospital, and mortality data from participating general practices within Wales. Participating practices cover approximately 70% of the population of Wales and are representative of the wider population in terms of age, sex, and socioeconomic deprivation [24,25]. Comparison between the SAIL population and the overall population of Wales are reported elsewhere [25].

From this population, we identified all participants registered with a participating practice on 1 January 2011, and who were aged between 40 and 70 years (to mirror the age criteria of UK Biobank). This time was chosen as electronically coded data is most complete from this point and is similar to the timing of recruitment to UK Biobank.

Assessment of multimorbidity

We quantified multimorbidity using a count of 40 LTCs originally developed for a large epidemiological study in Scotland, identified using Read codes [26]. Read codes are used in primary care to record diagnoses as well as coding events such as prescriptions. The coding system is common to Wales and the rest of the UK. LTCs were identified from Read codes occurring prior to the baseline assessment, using the criteria described by Barnett and colleagues [26]. For most conditions, any diagnostic Read code occurring prior to baseline assessment was taken to indicate the presence of the LTC (as per the previous study on which the definitions were based) [26]. For some prespecified conditions, the presence of a recent diagnostic code (e.g., depression, anxiety) or of specific prescriptions dispensed in the year prior to baseline (e.g., asthma, constipation, pain, dyspepsia, migraine, epilepsy) were used to confirm if the condition were current (also according to definitions described by Barnett and colleagues) [26]. A list of all Read codes used for diagnoses and prescriptions is included in the Supporting information (S1 and S2 Files).

At the time of baseline assessment, many LTCs in the UK were managed and coded according to the Quality Outcomes Framework. These standards were applied in Wales similarly to the rest of the UK. Median time from practice registration to baseline assessment (from which diagnostic Read codes were assessed) was 14.6 years (interquartile range 7.4 to 23.3) in UK Biobank and 14.8 years (interquartile range 10.2 to 18.8) in SAIL.

For both UK Biobank and SAIL, we calculated the count of LTCs for each individual at baseline, which was treated as a numerical variable.

In order to enhance our understanding of the influence of type as well as number of LTCs, we also calculated the Cambridge multimorbidity score [5]. The Cambridge multimorbidity score has been developed based on this same list of 40 LTCs but is a weighted score used to predict mortality, hospitalisation, and primary care use. Weights are applied to each LTC and then summed to give an overall score. Separate weights are available for each outcome, as well as a generic “overall” weight. For this study, we used weights specific to each outcome where available (i.e., for mortality and hospitalisation). Using the Cambridge multimorbidity score allows us to assess the impact of type of LTC, in addition to a simple count, when looking at associations with hospitalisation and mortality using baseline data for each participant in each dataset.

We also analysed each of the 40 LTCs separately, assessing the relationship between each LTC and all-cause mortality. Finally, we identified combinations of 3 LTCs from previous studies of common co-occurring LTCs (clusters) [7,12]. We assessed the relationship between all possible combinations of the conditions within each cluster and all-cause mortality, unscheduled hospitalisation, and MACE. For example, for the cluster of coronary heart disease, diabetes, and stroke, we assessed the risk of adverse outcomes in each condition individually, in all pairwise combinations, and in people with all 3 conditions. The clusters of conditions assessed were as follows: coronary heart disease, diabetes, and stroke or transient ischaemic attack; hypertension, diabetes, and pain; pain, deafness, and irritable bowel syndrome; depression, pain, and anxiety; asthma, pain, and chronic obstructive pulmonary disease; alcohol-related disorders, illicit drug use, and pain; coronary heart disease, pain, and depression.

Covariates

Age was taken as age at baseline for both datasets (this corresponded to date of recruitment for UK Biobank, and 1 January 2011 for SAIL participants) and was treated as a numerical variable. Sex was coded as male or female and treated as categorical. Socioeconomic deprivation was assessed using Townsend scores: an area-based measure of socioeconomic deprivation [27]. Baseline UK Biobank data are linked to Townsend scores based on participant postcodes linked to previous (2001) census data. To allow comparison to a similar measure of socioeconomic deprivation in SAIL, we obtained Townsend scores based on the same census data (2001) for Local Super Output Areas in Wales, and linked these to baseline SAIL data. Townsend scores were analysed as a numerical variable.

Outcomes

Outcomes of interest were all-cause mortality, unscheduled (urgent or emergency) hospital admission, and MACEs. For both datasets, outcomes were assessed by linkage to Office for National Statistics mortality registers and to hospital inpatient data (Hospital Episode Statistics or Patient Episode Database for Wales), respectively. Unscheduled hospital admission was defined as any inpatient hospital episode coded as “urgent” or “emergency” (excluding admissions coded as “elective”). MACE was defined as hospital admission with myocardial infarction (ICD-10 code I21), stroke (I63 or I64), or cardiovascular death (ICD-10 code for underlying cause of death beginning with “I”). In SAIL, linked outcome data were available for 7.5 years follow-up (censored at 1 July 2018). Participants were censored at date of death, deregistration from a participating practice, or 7.5 years follow-up, whichever occurred first. For UK Biobank, to ensure comparable follow-up periods for outcome assessment, we censored participants similarly at death, deregistration, or 7.5 years.

Statistical analysis

SAIL data are stored and analysed within a secure repository allowing remote access. Export is limited to aggregate data. Therefore, comparison between data sources was achieved by fitting models within SAIL and then exporting model parameters from which the relationships between covariates could be summarised and presented. This process is described in detail for each comparison below. This approach was chosen because outputs from regression models are considered to be a lower risk to privacy than are frequency tables while still allowing flexibility in subsequent reporting and comparisons.

Comparison of multimorbidity distribution

We first plotted the distribution of LTC counts in each dataset and compared these graphically to assess the observed distribution of multimorbidity in each dataset.

We then compared the distribution of LTC count in UK Biobank with SAIL after standardising SAIL to the age/sex distribution, and then the age/sex/socioeconomic status distribution, of UK Biobank using a form of standardisation analogous to indirect standardisation. To do this, we first modelled the distribution of LTC count in SAIL on age and sex, and on age, sex, and Townsend score. We assessed Poisson and negative binomial models initially. We used up to 2 fractional polynomial terms (from the set −2, −1, −0.5, 0, 0.5, 1, 2, and 3 recommended by Royston and colleagues) to model nonlinear relationships between age and Townsend score and LTC counts [28]. The best-fitting fractional polynomials were identified using backward selection in the mfp package in R. We also explored interaction terms between each of the explanatory variables. Candidate models were assessed using the Akaike information criterion (AIC), by comparing the likelihood ratio and assessing the dispersion statistic for each model. We also compared the best-fitting models to zero-inflated models using Vuong tests. Finally, we plotted expected and observed LTC counts within strata of age, sex, and Townend score. The negative binomial model was found to fit the data well. We exported the model coefficients, variance–covariance matrix, and dispersion parameter from the secure platform, along with diagnostic plots summarising model fit (S1 Fig). This allowed us to model the expected distribution of LTC counts in SAIL conditional on age/sex and on age/sex/Townsend scores. Participants with missing data for Townsend scores were excluded from these models.

Next, we calculated the expected LTC count in UK Biobank based on the models fitted in the SAIL data. To do this, we aggregated the UK Biobank dataset by age (1-year bands), sex, and Townsend score (0.1-point intervals). We summed the total number of participants, as well as the number of participants with each LTC count, in each stratum. We used the coefficients of the negative binomial models exported from SAIL to calculate the expected mean LTC count for each stratum. Next, we used the cumulative density function for the negative binomial distribution to calculate the expected proportion of participants with each LTC count within each stratum. Finally, each expected proportion was multiplied by the number of UK Biobank participants in each stratum to obtain the expected number of participants with each LTC count. We then summed these expected values across strata to calculate the expected number of UK Biobank participants with each LTC count.

To calculate 95% confidence intervals for these estimates, we obtained 10,000 samples of the intercept, age, sex, and Townsend score coefficients by sampling from a multivariate normal distribution where the parameters were the point estimates and variance–covariance matrix for the SAIL regression models. We then repeated the above process with each of the 10,000 samples to obtain 10,000 estimates of the expected count, which we then summarised by the mean, 2.5th and 97.5th centiles to obtain 95% confidence intervals.

Modelling all-cause mortality and MACE

For all-cause mortality and MACE, we used parametric survival models to assess the relationship between LTC counts and each outcome. This allowed us to export model summary statistics from the SAIL secure platform, which could facilitate estimation of predicted event rates for any combination of covariate levels. Before fitting models, we plotted log(time) against log(−log(Kaplan–Meier estimates)) for strata of each covariate. We found that for age (5-year bands), sex, and Townsend scores (1-point intervals), lines were linear and parallel, suggesting that Weibull models were an adequate fit for the data. Up to 2 fractional polynomial terms (selected using similar methods to the previous section) and interaction terms were explored. Best-fitting models were selected by comparing AIC and likelihood ratios. We finally compared Weibull models to more flexible Generalised Gamma models. Weibull models including fractional polynomials for age and Townsend scores, as well as interaction terms between LTC count and age, and LTC count and sex, were found to fit the data best. We summarised the overall fit by plotting predicted and observed 5-year event rates (S2 and S3 Figs). Separate models were fitted for UK Biobank and SAIL. For MACE, we assessed time to first event and used cause-specific models (censoring participants dying of noncardiovascular causes and coding as event-status “0”). These models were then used to obtain predicted 5-year mortality at different values of age, sex, Townsend score, and LTC count. This allowed comparison of the absolute risk of mortality for different LTC counts between UK Biobank and SAIL. We then fit a further model treating LTC count as categorical (0, 1, 2, 3, 4, 5, and more than 5) and obtained hazard ratios to assess any difference in relative risk of mortality.

We repeated this process using the mortality weightings for the Cambridge multimorbidity score, in place of the LTC count.

Modelling unscheduled hospitalisations

For unscheduled hospitalisation, we fit count-based models using a similar strategy to that described above for modelling number of LTCs. Briefly, we compared Poisson and negative binomial models (with and without fractional polynomials and interaction terms) using likelihood ratios and dispersion statistics. We also assessed for zero inflation using Vuong tests. Overall fit of the final model was assessed by plotting observed and expected numbers of hospitalisations for each cohort as a whole, and within strata of age, sex, and socioeconomic status. Negative binomial models were found to best fit the data and included nonlinear terms for LTC count and age, as well as interactions between age and sex; age and LTC count; and sex and LTC count. Model fit is summarised in S4 and S5 Figs. Models for UK Biobank and SAIL, respectively, were used to assess hospitalisation rates per 1,000 person years at given levels of LTC count (plus age, sex, and socioeconomic status). We then repeated this process using the hospitalisation weights from the Cambridge multimorbidity score.

Modelling specific LTCs

We used Weibull models to assess the relationship between each individual LTC and mortality, adjusting for age, sex, and socioeconomic status.

Finally, we assessed the relationship between prevalent combinations of 3 LTCs and each outcome (all-cause mortality, unscheduled hospitalisation, and MACE) using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status.

Sensitivity analyses

In sensitivity analysis, we repeated analyses of the association between LTC count and mortality, hospitalisations, and MACE restricting the UK Biobank cohort to those participants who attended one of 2 assessment centres based in Wales. This was to assess if differences between UK Biobank and the community cohort were driven by differences between Wales and the rest of the UK. Separately, we also repeated the SAIL analyses restricting SAIL to participants registered in Cardiff and Swansea (the locations of the UK Biobank assessment centres in Wales). These analyses were not prespecified, being conducted following initial peer review.

Ethical approval

The UK Biobank has full ethical approval from the NHS National Research Ethics Service (16/NW/0274). All participants gave informed consent for participation in UK Biobank. Permission to access and analyse UK Biobank data was approved under UK Biobank project 14151. SAIL analyses were approved by SAIL Information Governance Review Panel (Project 0830).

Results

Primary care data were available for 211,597 UK Biobank participants and were compared to 852,349 SAIL participants. Baseline characteristics are shown in Table 1. UK Biobank had a greater proportion of female participants. UK Biobank participants also tended to be more affluent (42.0% in the most affluent quintile of the UK as a whole), whereas SAIL participants were on average less affluent than the UK population as a whole (e.g., 10.6% in the most affluent quintile). UK Biobank participants with primary care data were similar to the wider UK Biobank cohort in terms of age, sex, socioeconomic status, and self-reported LTCs (S1 Table). Median follow-up time was 7.5 years in both datasets.

Table 1. Baseline demographic characteristics.

	SAIL	UK Biobank
Total	852,349	211,597
Age
Mean (SD)	54.2 (8.6)	56.5 (8.1)
Sex
Men Women	426,372 (50.0%) 425,977 (50.0%)	96,060 (45.4%) 115,537 (54.6%)
Socioeconomic status^*
Quintile 1 (most affluent) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (most deprived) Missing	89,944 (10.6%) 96,622 (11.3%) 168,868 (19.8%) 308,080 (36.1%) 188,835 (22.2%) -	88,883 (42.0%) 29,454 (13.9%) 27,937 (13.2%) 33,324 (15.7%) 31,675 (15.0%) 324
LTC count
0 1 2 3 4 5 ≥6	281,580 (33.0%) 196,584 (23.1%) 151,043 (17.7%) 97,946 (11.5%) 58,672 (6.8%) 33,130 (3.9%) 33,394 (3.9%)	102,547 (48.5%) 51,955 (24.6%) 29,149 (13.8%) 14,924 (7.1%) 7,156 (3.4%) 3,353 (1.6%) 2,403 (1.1%)

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*Quintiles of socioeconomic status (Townsend scores) are based on quintiles for the general UK population.

LTC, long-term condition; SAIL, Secure Anonymised Information Linkage; SD, standard deviation.

Distribution of multimorbidity

The distribution of LTC counts for SAIL and UK Biobank are shown in Fig 1. Median LTC count was similar in SAIL (median 1 LTC, interquartile range 0 to 3) and UK Biobank (median 1 LTC, interquartile range 0 to 2); however, the distribution in SAIL was more skewed to the right. A far greater proportion of UK Biobank participants had no LTCs compared to SAIL. Few UK Biobank participants (2,403/211,597, 1.1%) had LTC counts greater than 5, whereas 3.8% (33,100/852,349) of SAIL participants had more than 5 LTCs.

Standardising SAIL to the age and sex distribution of UK Biobank had little impact on the difference in LTC counts (blue triangles on Fig 2)—as would be expected since the SAIL comparison was selected to have the same age range as UK Biobank. After additionally standardising by socioeconomic deprivation (red circles), however, the expected LTC counts in SAIL were closer to the observed counts in UK Biobank. These findings demonstrate that differences in socioeconomic deprivation account for a substantial proportion of the difference between UK Biobank and SAIL. Notably, however, there were still a greater proportion of UK Biobank participants with no LTCs.

Association between multimorbidity and all-cause mortality

The association between LTC count and all-cause mortality in UK Biobank and SAIL, adjusted for age, sex, and socioeconomic deprivation, is summarised in Figs 3 and 4. The predicted absolute risk is shown in Fig 3 and demonstrates that, at all levels of LTC count, the adjusted mortality rate is lower in UK Biobank than in SAIL (e.g., for a 60-year-old man with 2 LTCs, predicted 5-year mortality was 2.7% (95% CI 2.6 to 2.8) in UK Biobank and 4.6% (95% CI 4.4 to 4.8) in SAIL). This pattern is consistent across all levels of socioeconomic deprivation (S6 Fig). When using the mortality weightings from the Cambridge multimorbidity score, and thus considering the type as well as number of LTCs, the difference in absolute mortality risk between SAIL and UK Biobank persisted (e.g., for a 60-year-old man with a Cambridge mortality score of 12 [mean value in UK Biobank, approximately equivalent to a diagnosis of alcohol-related disorders: score 12.72, or the combination of diabetes, coronary heart disease, and hypertension: combined score 12.36], predicted 5-year mortality was 2.2% (95% CI 2.1 to 2.3) in UK Biobank and 4.0% (95% CI 4.0 to 4.1) in SAIL; see S7 Fig).

Fig 3 — Lines indicate the modelled 5-year mortality rate. Shaded area indicates 95% confidence interval. Predicted values are modelled at mean age for UK Biobank (56.5 years) and UK national mean Townsend score for socioeconomic status. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

Fig 4 shows adjusted hazard ratios for mortality associated with the different LTC counts for UK Biobank and SAIL. As LTC counts increase, the difference in the relative association between UK Biobank and SAIL increases, with SAIL showing higher hazard ratios at greater LTC counts.

Association between multimorbidity and unscheduled hospitalisation

The absolute and relative associations between LTC counts and unscheduled hospital admission are shown in Figs 5 and 6, respectively. Up to an LTC count of 3, the LTC count-specific rate of hospital admissions was similar between UK Biobank and SAIL. Above this level, the rate was higher in SAIL than in UK Biobank. At all levels of LTC count, the incident rate ratio for SAIL was higher than for UK Biobank. Also, as LTC count increases, the difference in relative association between UKB and SAIL increases. As with mortality, the pattern was similar when using the weighted Cambridge multimorbidity score, with similar hospitalisation rates at low levels but higher rates in SAIL at higher levels.

Fig 5 — Lines indicate the modelled rate of unscheduled hospitalisations per 1,000 person years observation. Shaded area indicates 95% confidence interval. Predicted values are modelled at mean age for UK Biobank (56.5 years) and UK national mean Townsend score for socioeconomic status. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

Fig 6 — Points indicate IRRs adjusted for age, sex, and socioeconomic status. Whiskers indicate 95% CIs. *N =* total number of participants per LTC count, n events = number of events. CI, confidence interval; IRR, incidence rate ratio; LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

Association between multimorbidity and MACE

Findings for MACE are shown in Fig 7 (for absolute risk) and Fig 8 (for relative associations). As for mortality and hospitalisation, absolute risk was lower in UK Biobank; however, the relative association was similar at lower LTC counts (below 5).

Relationship between individual LTCs and all-cause mortality

The prevalence and hazard ratios for all-cause mortality for each individual LTC are shown in Fig 9. Results are adjusted for age, sex, and socioeconomic deprivation. Most LTCs were more prevalent in SAIL than in UK Biobank; however, the magnitude of difference was highly variable. For example, chronic kidney disease was more than 6 times more prevalent in SAIL than in UK Biobank, and dementia and illicit drug use were more than 5 times more prevalent. Several common conditions, such as coronary heart disease, hypertension, chronic obstructive pulmonary disease, and asthma, were all more than twice as prevalent in SAIL than in UK Biobank. However, a few conditions (e.g., multiple sclerosis, anorexia or bulimia, and Parkinson’s disease) were slightly more common in UK Biobank than in SAIL.

For many common conditions, the hazard ratios for all-cause mortality were similar between UK Biobank and SAIL (e.g., chronic kidney disease, coronary heart disease). Other conditions had notably higher hazard ratios in SAIL than in UK Biobank (e.g., alcohol-related disorders, illicit drug use, and chronic liver disease).

Association between combinations of LTCs and clinical outcomes

The relationship between specific combinations of LTCs and each outcome (mortality, hospitalisation, and MACE) are shown in the Supporting information (S11–S34 Figs). Associations between LTC combinations and outcomes were similar between the 2 datasets for some LTC types (most notably multiple cardiovascular conditions, or cardiovascular and respiratory conditions). Risk of outcomes was underestimated in UK Biobank for other combinations, particularly those including depression, anxiety, or pain (S11–S34 Figs).

Sensitivity analyses

In a sensitivity analysis of the relationship between LTC count and each outcome (all-cause mortality, hospitalisations, and MACE), we restricted UK Biobank participants to those attending assessment centres in Wales. The adjusted event rates were slightly lower in the Welsh UK Biobank subset than in the whole UK Biobank cohort (S35–S37 Figs). Separately, we restricted the SAIL analyses to Cardiff and Swansea (the location of UK Biobank assessment centres), finding similar rates to the main analysis for mortality and MACE. For hospitalisation, the difference between SAIL participants from Cardiff and Swansea and UK Biobank participants from Wales was less than the difference between each of the full cohorts (S36 Fig), although rates in SAIL remained higher than UK Biobank. This suggests that differences between SAIL and UK Biobank are unlikely to be driven by differences between Wales and the rest of the UK.

Discussion

Multimorbidity in UK Biobank, as identified through linked primary care Read code data, is both less common and associated with a lower absolute risk of all-cause mortality than in a similarly aged representative sample from Wales (SAIL databank), whether one assesses multimorbidity via a simple count or uses a weighted scale that takes type of LTC into account. Differences in socioeconomic deprivation account for some, but not all, of the observed difference in the distribution of multimorbidity. The difference in absolute mortality risk persisted after adjustment for socioeconomic deprivation. Despite this, the relative association between multimorbidity and mortality and hospitalisation seen in UK Biobank were comparable to SAIL at lower LTC counts (below 4). Moreover, although generally less prevalent in UK Biobank, many common LTCs such as hypertension, chronic kidney disease, chronic obstructive pulmonary disease, and coronary heart disease had a similar relative risk of mortality between the 2 datasets. These findings suggest that, given its large sample size and extensive baseline assessment, UK Biobank is a valuable resource for exploring risks associated with chronic illness or multimorbidity with lower LTC counts. However, UK Biobank is likely to underestimate the impact of multimorbidity in people with greater numbers of LTCs or with specific LTCs such as mental health conditions. This difference was most obvious among participants with LTC counts or 4 or more.

There are a number of possible explanations for the lower risk of mortality and hospitalisation associated with a given LTC count in UK Biobank. First, the specific LTCs present in UK Biobank participants with multimorbidity are likely to differ, particularly as we found that the differences in prevalence are not evenly distributed across all LTCs (e.g., dementia, schizophrenia, and illicit drug use are particularly underrepresented). This is unsurprising as many of these conditions are likely to impact the likelihood of volunteering for recruitment to this type of study, and show strong relationships with mortality. However, the difference in mortality and hospitalisation was similar when using the Cambridge multimorbidity score, which weights each LTC by its respective risk of each outcome, suggesting that type of LTC alone cannot explain the observed difference.

Second, LTCs in UK Biobank may be less severe or advanced than in the population in general. This could occur if severe disease resulted in increased functional limitation which in turn reduced the likelihood people would volunteer to attend for UK Biobank assessment. This hypothesis is supported by our findings for conditions such as chronic liver disease, in which the prevalence was similar in UK Biobank and SAIL, but the hazard ratio for mortality was 2-fold higher in SAIL compared to UK Biobank. Unfortunately, it is usually not possible to estimate severity of LTCs using routine data alone.

Finally, additional factors are likely to influence the risk of adverse outcomes such as behavioural and environmental risk factors. Also, area-based measures of socioeconomic deprivation may not pick up all relevant elements. Within a given area (within which individuals will be assigned the same value for socioeconomic status), there is likely to be variation in factors such as income, education, disability, and behaviours such as smoking and alcohol consumption. These factors may influence the likelihood of volunteering for studies such as UK Biobank, but may not be adequately reflected in an area-based measure of socioeconomic status. UK Biobank participants are recognised to have lower levels of various lifestyle risk factors, such as smoking, than the UK population, and this is likely to mitigate the impact of multimorbidity [18]. Indeed, previous UK Biobank analyses have demonstrated that lifestyle factors influence life expectancy in people with multimorbidity [11]. Also, unhealthy lifestyle factors disproportionately impact mortality risk in the context of higher socioeconomic deprivation [29]. Selection bias of UK Biobank on these features is therefore likely to influence the relationship between multimorbidity and adverse health outcomes.

Compared to individuals without LTCs, the association between LTC count and outcome was more similar across SAIL and UK Biobank at lower counts (e.g., 1, 2, 3 conditions) for both death and hospitalisation. One possible explanation for this is differential selection pressures. As the number of LTCs increase, individuals able to undergo the UK Biobank visits and procedures are likely to be increasingly atypical of individuals in the community with that number of conditions (e.g., they may have less severe disease or less functional impairment). Similarly, for certain conditions (such as mental health conditions or addiction, where the difference in prevalence is large), people volunteering for UK Biobank may be less typical of people with these conditions in the community. It is also possible that, for some people, living with a condition may be a motivation for participation in UK Biobank. Furthermore, some LTCs have been noted to be more common in more affluent populations [30]. We speculate that this may be one possible explanation for the higher prevalence of some conditions (e.g., Parkinson’s disease) in UK Biobank compared to SAIL.

Previous studies have compared associations in UK Biobank to national surveys with higher response rates (e.g., Health Survey for England and Health Survey for Scotland) [18,21]. To our knowledge, this is the first study to directly compare estimates from UK Biobank with those from routinely collected data from a representative sample, and also the first to do so for multimorbidity. Batty and colleagues reported that associations between a wide range of established risk factors and mortality were similar between UK Biobank and more representative surveys, suggesting that associations were largely generalizable and reassured that UK Biobank was a useful resource for studying aetiological mechanisms [21]. However, like UK Biobank, surveys may involve visits and procedures, and so biases based on health and functional status are likely to operate in both settings. Furthermore, for some risk factors, differences were observed between estimates from UK Biobank and more representative surveys.

While some of our findings, such as the relationship between specific LTCs and mortality, support this previous study [21], other findings highlight important caveats relating to multimorbidity research. First, some underrepresented conditions may not give reliable estimates. Second, at higher LTC counts (where UK Biobank data is sparce), estimates become less reliable. Finally, assessing absolute event rates is particularly likely to yield underestimates.

A previous study assessing the impact of cardiometabolic multimorbidity analysed UK Biobank alongside pooled data from 91 cohorts in the Emerging Risk Factors collaboration [12]. This study found that diabetes, stroke, and coronary heart disease (alone or in combination) were associated with similar risks across these datasets. We found similar results for these common conditions, both alone and in combination. However, we found greater differences with other conditions, which are underrepresented, such as mental health conditions, addiction, and pain.

Previous studies of multimorbidity using UK Biobank have acknowledged the lack of representativeness and cautioned that estimates of association are likely to be conservative [4,9]. Our findings support this hypothesis, suggesting that both relative and absolute risks of multimorbidity estimated using UK Biobank are likely to be lower than in the population in general. Moreover, where multimorbidity counts are included as a potential confounder, rather than as the exposure of interest, the direction of effect may be less predictable.

Observational studies are crucial to advancing our understanding of multimorbidity, particularly as people with multimorbidity are often excluded from clinical trials [25,31]. Furthermore, UK Biobank contains a wealth of phenotypic and lifestyle data that is not discoverable from routine data alone. In addition, follow-up questionnaires facilitate analysis of the relationships between multimorbidity and diverse factors not recorded in routine data [32]. Therefore, studies such as UK Biobank offer valuable opportunities to understand the causes, phenotypic manifestation, and wide-ranging implications of multimorbidity. However, our findings reinforce the need for caution in interpreting findings, particularly as some estimates are likely to be conservative. Furthermore, people with multimorbidity with high LTC counts are underrepresented in UK Biobank: 1.1% of UK Biobank participants had LTC counts greater than 5, compared to 3.8% of people in SAIL. While these people are a small proportion of the overall population, many clinical and public health challenges are concentrated and intensified in such individuals.

Potential for selection bias is a problem for cohort studies in general, not just UK Biobank. This study emphasises the importance of assessing population demographics compared to the general population and being mindful of the potential for estimates to be biased by a lack of representativeness. Our findings suggest that UK Biobank may be biased (and likely conservative) in making inferences for the population of people with multimorbidity involving high LTC counts, many of whom live in areas of high socioeconomic deprivation. However, the impact of selection bias is unpredictable, and this pattern may not necessarily be the same in other cohorts susceptible to selection bias. In the absence of a direct comparison with a representative sample, investigators should be cautious in their interpretation of findings derived from highly selected cohorts. This suggests a need for future research cohorts to intensify efforts to target recruitment to ensure people with multimorbidity or living in areas of high socioeconomic deprivation are included. It will also be worth considering how routine healthcare data can be linked with other information resources or enhanced by collection of additional data to enable more mechanistic research for those with complex multimorbidity and those living in areas of high socioeconomic deprivation [33].

Strengths of this analysis include the use of Read coded primary care data to identify multimorbidity in both data sources. This allowed identical definitions to be applied to both UK Biobank and SAIL. The use of linked hospitalisation and mortality data allowed reliable estimation of outcomes. Therefore, our analyses are likely to give an accurate representation of the differences in multimorbidity and association with outcomes between UK Biobank and an unselected community population from within the UK. However, some conditions, such as chronic pain, may be underidentified by using Read coded data compared with self-report. Also, SAIL includes data from Wales only, whereas UK Biobank comprises England, Scotland, and Wales, although this is unlikely to have a substantial impact on our findings. There may also be some overlap between UK Biobank participants in Wales and people included in SAIL databank; however, we were unable to identify UK Biobank participants in SAIL. As discussed above, we lacked data on the severity of the included LTCs (in both datasets), as well as data on ethnicity and broader lifestyle factors from SAIL. It is therefore not possible to disentangle the relative impact of LTC type, severity, and wider factors on the differences in mortality and hospitalisation risk. It is likely that the differences in association seen are a combination of these factors. Finally, we were only able to analyse a subset of the UK Biobank cohort as, to date, primary care data linkage is only available for this subset. However, these participants are similar to the wider cohort in terms of age, sex, socioeconomic status, and prevalence of multimorbidity (S1 Table).

In conclusion, UK Biobank accurately represents the increased relative risks associated with multimorbidity involving LTC counts of 3 or less and therefore can be a useful resource for multimorbidity research. However, its lack of representativeness limits inferences about the minority of people with higher levels of multimorbidity, most noticeably those with LTC counts of 4 or more. The rich lifestyle, environmental, phenotypic, and genetic data available in UK Biobank offers unique opportunities to understand factors associated with development of multimorbidity as well as relationships between multimorbidity and various associated phenomena. Nonetheless, researchers should understand that where LTCs are the exposure, UK Biobank is likely to provide results that are more conservative than one would find in a general population cohort. Ideally, future research should combine insights from both representative routine data and information rich research cohorts such as UK Biobank.

Declarations

Ethics committee approval

This study had ethical approval as part of UK Biobank project 14151 (NHS National Research Ethics Service 16/NW/0274). SAIL analyses were approved by SAIL Information Governance Review Panel (Project 0830).

Dissemination

Published findings will be returned to UK Biobank, from whom findings are made available to UK Biobank participants via the UK Biobank website.

Supporting information

S1 Protocol. Analysis plan.

(DOCX)

Click here for additional data file.^{(117.4KB, docx)}

S1 Checklist. STROBE reporting checklist.

(DOCX)

Click here for additional data file.^{(33.4KB, docx)}

S1 File. Read codes for included LTCs.

LTC, long-term condition.

(CSV)

Click here for additional data file.^{(327.6KB, csv)}

S2 File. Read codes for drugs used in LTC definitions.

LTC, long-term condition.

(CSV)

Click here for additional data file.^{(240.6KB, csv)}

S1 Table. Comparison of UK Biobank participants with linked GP data versus those without GP data available.

GP, General Practice.

(DOCX)

Click here for additional data file.^{(14.2KB, docx)}

S2 Table. Observed and expected LTC counts in UK Biobank.

Includes confidence intervals pertaining to Fig 2. LTC, long-term condition.

(DOCX)

Click here for additional data file.^{(13.7KB, docx)}

S3 Table. Observed event rates in UK Biobank and SAIL.

Observed rates of all-cause mortality, unscheduled hospitalisation, and MACE in UK Biobank and SAIL, by number of baseline LTCs. LTC, long-term condition; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage.

(DOCX)

Click here for additional data file.^{(20.9KB, docx)}

S4 Table. Unadjusted hazard ratios.

Includes unadjusted hazard ratios for those presented in the main analysis.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S5 Table. Cambridge multimorbidity score: Comparison of UK Biobank and SAIL.

Descriptive statistics of Cambridge multimorbidity score in UK Biobank and SAIL. SAIL, Secure Anonymised Information Linkage.

(DOCX)

Click here for additional data file.^{(13.2KB, docx)}

S1 Fig. Model diagnostic for SAIL LTC model.

Observed and expected LTC counts in SAIL, stratified by age, sex, and socioeconomic status. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(78.9KB, pdf)}

S2 Fig. Comparison of model fit: All-cause mortality in UK Biobank and SAIL.

Line indicates the modelled values for each cohort (stratified by age), shaded area indicates 95% CIs, points indicate the observed proportion of deaths within each stratum of age and LTC count. Size of the point indicates the number of events per strata. CI, confidence interval; LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(19.8KB, pdf)}

S3 Fig. Comparison of model fit: MACE in UK Biobank and SAIL.

(PDF)

Click here for additional data file.^{(19.8KB, pdf)}

S4 Fig. Model fit, negative binomial model for unscheduled hospitalisations.

Observed and expected number of unscheduled hospitalisations. SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(45.3KB, pdf)}

S5 Fig. Model fit, negative binomial model for unscheduled hospitalisations, stratified by age, sex, and socioeconomic status.

Observed and expected number of unscheduled hospitalisations. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(72.3KB, pdf)}

S6 Fig. Association of multimorbidty and all-cause mortality by age, sex, and socioeconomic status.

Line indicates the modelled values for each cohort; shaded area indicates 95% CIs. Plots are stratified by age, sex, and socioeconomic status quintiles. CI, confidence interval; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(112.2KB, pdf)}

S7 Fig. Cambridge score and all-cause mortality.

Line indicates the modelled values for each cohort; shaded area indicates 95% CIs. CI, confidence interval; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(68.8KB, pdf)}

S8 Fig. Association of multimorbidty and unscheduled hospitalisation by age, sex, and socioeconomic status.

(PDF)

Click here for additional data file.^{(119.6KB, pdf)}

S9 Fig. Cambridge score and unscheduled hospitalisation.

Line indicates the modelled values for each cohort; shaded area indicates 95% CIs. CI, confidence interval; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(69.9KB, pdf)}

S10 Fig. Association of multimorbidty and MACE by age, sex, and socioeconomic status.

(PDF)

Click here for additional data file.^{(110.7KB, pdf)}

S11 Fig. HRs for all-cause mortality: Coronary heart disease, diabetes, and stroke.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; TIA, transient ischaemic attack; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.3KB, png)}

S12 Fig. HRs for all-cause mortality: Hypertension, diabetes, and pain.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.4KB, png)}

S13 Fig. HRs for all-cause mortality: Irritable bowel syndrome, deafness, and pain.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.7KB, png)}

S14 Fig. HRs for all-cause mortality: Depression, pain, and anxiety.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.6KB, png)}

S15 Fig. HRs for all-cause mortality: Asthma, pain, and COPD.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.1KB, png)}

S16 Fig. HRs for all-cause mortality: Alcohol-related disorders, illicit drug use, and pain.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.4KB, png)}

S17 Fig. HRs for all-cause mortality: Coronary heart disease, diabetes, and atrial fibrillation.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.2KB, png)}

S18 Fig. HRs for all-cause mortality: Pain, coronary heart disease, and depression.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10KB, png)}

S19 Fig. IRRs for unscheduled hospitalisation: Coronary heart disease, diabetes, and stroke.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; TIA, transient ischaemic attack; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.8KB, png)}

S20 Fig. IRRs for unscheduled hospitalisation: Hypertension, diabetes, and pain.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.5KB, png)}

S21 Fig. IRRs for unscheduled hospitalisation: Irritable bowel syndrome, deafness, and pain.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.9KB, png)}

S22 Fig. IRRs for unscheduled hospitalisation: Depression, pain, and anxiety.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.7KB, png)}

S23 Fig. IRRs for unscheduled hospitalisation: Asthma, pain, and COPD.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.4KB, png)}

S24 Fig. IRRs for unscheduled hospitalisation: Alcohol-related disorders, illicit drug use, and pain.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.4KB, png)}

S25 Fig. IRRs for unscheduled hospitalisation: Coronary heart disease, diabetes, and atrial fibrillation.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.4KB, png)}

S26 Fig. IRRs for unscheduled hospitalisation: Pain, coronary heart disease, and depression.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.3KB, png)}

S27 Fig. HRs for MACE: Coronary heart disease, diabetes, and stroke.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; TIA, transient ischaemic attack; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.5KB, png)}

S28 Fig. HRs for MACE: Hypertension, diabetes, and pain.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.5KB, png)}

S29 Fig. HRs for MACE: Irritable bowel syndrome, deafness, and pain.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.6KB, png)}

S30 Fig. HRs for MACE: Depression, pain, and anxiety.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.5KB, png)}

S31 Fig. HRs for MACE: Asthma, pain, and COPD.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(8.9KB, png)}

S32 Fig. HRs for MACE: Alcohol-related disorders, illicit drug use, and pain.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.5KB, png)}

S33 Fig. HRs for MACE: Coronary heart disease, diabetes, and atrial fibrillation.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.2KB, png)}

S34 Fig. HRs for MACE: Pain, coronary heart disease, and depression.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.1KB, png)}

S35 Fig. Sensitivity analysis: All-cause mortality.

Comparison of full cohort and area-specific estimates. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(36KB, pdf)}

S36 Fig. Sensitivity analysis: Unscheduled hospitalisation.

Comparison of full cohort and area-specific estimates. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(53.9KB, pdf)}

S37 Fig. Sensitivity analysis: MACE.

Comparison of full cohort and area-specific estimates. LTC, long-term condition; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(36KB, pdf)}

Abbreviations

AIC: Akaike information criterion
LTC: long-term condition
MACE: major adverse cardiovascular event
SAIL: Secure Anonymised Information Linkage

Data Availability

UK Biobank data can be obtained from UK Biobank project site, subject to successful registration and application process. Further details can be found at https://www.ukbiobank.ac.uk/. SAIL data are available upon application to the SAIL Information Governance Review Panel. Further details can be found at https://saildatabank.com/application-process/. All syntax underlying the analysis presented will be returned to UK Biobank for record, along with all model outputs from SAIL, as per the UK Biobank material transfer agreement. This syntax will be available to third party researchers from UK Biobank.

Funding Statement

PH is funded through a Clinical Research Training Fellowship from the Medical Research Council (Grant reference: MR/S021949/1). DM is funded via an Intermediate Clinical Fellowship and Beit Fellowship from the Wellcome Trust - 201492/Z/16/Z. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Whitty CJ, MacEwen C, Goddard A, Alderson D, Marshall M, Calderwood C, Atherton F, McBride M, Atherton J, Stokes-Lampard H, Reid W. Rising to the challenge of multimorbidity. BMJ. 2020. Jan 6;368. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina AM. Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies. J Comorb. 2019;9:2235042X19870934. doi: 10.1177/2235042X19870934 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.van Oostrom SH, Gijsen R, Stirbu I, Korevaar JC, Schellevis FG, Picavet HSJ, et al. Time trends in prevalence of chronic diseases and multimorbidity not only due to aging: data from general practices and health surveys. PLoS ONE. 2016;11(8):e0160264. doi: 10.1371/journal.pone.0160264 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Jani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, et al. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Med. 2019;17(1):74. doi: 10.1186/s12916-019-1305-x [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Payne RA, Mendonca SC, Elliott MN, Saunders CL, Edwards DA, Marshall M, et al. Development and validation of the Cambridge Multimorbidity Score. CMAJ. 2020;192(5):E107–E14. doi: 10.1503/cmaj.190757 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Vetrano DL, Roso-Llorach A, Fernández S, Guisado-Clavero M, Violán C, Onder G, et al. Twelve-year clinical trajectories of multimorbidity in a population of older adults. Nat Commun. 2020;11(1):1–9. doi: 10.1038/s41467-019-13993-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Zhu Y, Edwards D, Mant J, Payne RA, Kiddle S. Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study. BMC Med. 2020;18:1–11. doi: 10.1186/s12916-019-1443-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Zemedikun DT, Gray LJ, Khunti K, Davies MJ, Dhalwani NN. editors. Patterns of multimorbidity in middle-aged and older adults: an analysis of the UK Biobank data. Mayo Clin Proc. 2018;93(7):857–66. doi: 10.1016/j.mayocp.2018.02.012 [DOI] [PubMed] [Google Scholar]
9.Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FS. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health. 2018;3(7):e323–e32. 10.1016/S2468-2667(18)30091-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Dodds RM, Granic A, Robinson SM, Sayer AA. Sarcopenia, long-term conditions, and multimorbidity: findings from UK Biobank participants. J Cachexia Sarcopenia Muscle. 2020;11(1):62–8. doi: 10.1002/jcsm.12503 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Chudasama YV, Khunti KK, Zaccardi F, Rowlands AV, Yates T, Gillies CL, et al. Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study. BMC Med. 2019;17(1):108. doi: 10.1186/s12916-019-1339-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, et al. Association of cardiometabolic multimorbidity with mortality. JAMA. 2015;314(1):52–60. doi: 10.1001/jama.2015.7008 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Chiang JI, Hanlon P, Li T-C, Jani BD, Manski-Nankervis J-A, Furler J, et al. Multimorbidity, mortality, and HbA1c in type 2 diabetes: A cohort study with UK and Taiwanese cohorts. PLoS Med. 2020;17(5):e1003094. doi: 10.1371/journal.pmed.1003094 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Hanlon P, Jani BD, Butterly E, Nicholl B, Lewsey J, McAllister DA, et al. An analysis of frailty and multimorbidity in 20,566 UK Biobank participants with type 2 diabetes. Commun Med (London). 2021;1(1):1–9. 10.1038/s43856-021-00029-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Sullivan MK, Jani BD, McConnachie A, Hanlon P, McLoone P, Nicholl BI, et al. Hospitalisation events in people with chronic kidney disease as a component of multimorbidity: parallel cohort studies in research and routine care settings. BMC Med. 2021;19(1):278. doi: 10.1186/s12916-021-02147-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: Current status and what it means for epidemiology. Health Policy Technol. 2012;1(3):123–6. 10.1016/j.hlpt.2012.07.003. [DOI] [Google Scholar]
17.Manolio TA, Collins R. Enhancing the Feasibility of Large Cohort Studies. JAMA. 2010;304(20):2290–1. doi: 10.1001/jama.2010.1686 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–34. doi: 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun. 2020;11(1):5749. doi: 10.1038/s41467-020-19478-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Infante-Rivard C, Cusson A. Reflection on modern methods: selection bias—a review of recent developments. Int J Epidemiol. 2018;47(5):1714–22. doi: 10.1093/ije/dyy138 [DOI] [PubMed] [Google Scholar]
21.Batty GD, Gale CR, Kivimäki M, Deary IJ, Bell S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ. 2020;368. doi: 10.1136/bmj.m131 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Keyes KM, Westreich D. UK Biobank, big data, and the consequences of non-representativeness. Lancet. 2019;393(10178):1297. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Jones KH, Ford DV, Thompson S, Lyons R. A profile of the Sail Databank on the UK secure research platform. Int J Popul Data Sci. 2019;4(2). doi: 10.23889/ijpds.v4i2.1134 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Schnier C, Wilkinson T, Akbari A, Orton C, Sleegers K, Gallacher J, et al. The secure Anonymised information linkage databank dementia e-cohort (SAIL-DeC). Int J Popul Data Sci. 2020;5(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Hanlon P, Hannigan L, Rodriguez-Perez J, Fischbacher C, Welton NJ, Dias S, et al. Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies: an individual-level participant data analysis. BMC Med. 2019;17(1):201. doi: 10.1186/s12916-019-1427-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2 [DOI] [PubMed] [Google Scholar]
27.Townsend P, Phillimore P, Beattie A. Health and deprivation: inequality and the North: Routledge; 1988. [Google Scholar]
28.Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28(5):964–74. doi: 10.1093/ije/28.5.964 [DOI] [PubMed] [Google Scholar]
29.Foster HM, Celis-Morales CA, Nicholl BI, Petermann-Rocha F, Pell JP, Gill JM, et al. The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort. Lancet Public Health. 2018;3(12):e576–e85. doi: 10.1016/S2468-2667(18)30200-7 [DOI] [PubMed] [Google Scholar]
30.Yang F, Johansson AL, Pedersen NL, Fang F, Gatz M, Wirdefeldt K. Socioeconomic status in relation to Parkinson’s disease risk and mortality: A population-based prospective study. Medicine. 2016;95(30). doi: 10.1097/MD.0000000000004337 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Hanlon P, Butterly E, Lewsey J, Siebert S, Mair FS, McAllister DA. Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions. BMC Med. 2020;18 (1):1–12. doi: 10.1186/s12916-019-1443-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Hanlon P, McCallum M, Jani BD, McQueenie R, Lee D, Mair FS. Association between childhood maltreatment and the prevalence and complexity of multimorbidity: A cross-sectional analysis of 157,357 UK Biobank participants. J Comorb. 2020;10:2235042X10944344. doi: 10.1177/2235042X10944344 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Lawlor DA, Tilling K, Davey SG. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86. doi: 10.1093/ije/dyw314 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS Med. doi: 10.1371/journal.pmed.1003931.r001

Decision Letter 0

Louise Gaynor-Brook

13 Sep 2021

Dear Dr. Hanlon,

Thank you very much for submitting your manuscript "Associations between multimorbidity and adverse health outcomes: comparison of cohorts from UK Biobank and a representative community sample" (PMEDICINE-D-21-02504) for consideration at PLOS Medicine.

Your paper was evaluated by three independent reviewers, including a statistical reviewer, and was discussed among all the editors here and with an academic editor with relevant expertise. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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We look forward to receiving your revised manuscript.

Sincerely,

Louise Gaynor-Brook, MBBS PhD

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

General comments:

Throughout the paper, please adapt reference call-outs to the following style: "... adverse health outcomes [4,5]." (noting the absence of spaces within the square brackets).

Title: Please revise your title according to PLOS Medicine's style, placing the study design in the subtitle (ie, after a colon). We suggest “Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies” or similar

Abstract:

Abstract Methods and Findings:

Please include the study design and years during which data was collected for the UKBB / SAIL cohorts

Please provide brief demographic details of the study population (e.g. sex, age, ethnicity, etc) in each of the databanks studied.

Please quantify the results presented in your abstract (with 95% CIs), ensuring that all numbers presented in the abstract are identical to numbers presented in the main manuscript text.

In the last sentence of the Abstract Methods and Findings section, please describe 2-3 of the main limitations of the study's methodology.

Abstract Conclusions:

Please begin your Abstract Conclusions with "In this study, we observed ..." or similar, to summarize the main findings from your study, without overstating your conclusions. Please emphasize what is new and address the implications of your study, being careful to avoid assertions of primacy. Please be more specific as to which outcomes you are referring to.

Author Summary:

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

In the final bullet point of ‘What Do These Findings Mean?’, please describe the main limitations of the study in non-technical language.

Introduction:

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Methods:

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section. If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript. If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and if/when reported analyses differed from those that were planned. Changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale. If a reported analysis was performed based on an interesting but unanticipated pattern in the data, please be clear that the analysis was data-driven.

Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)." The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/ When completing the checklist, please use section and paragraph numbers, rather than page numbers which will likely no longer correspond to the appropriate sections after copy-editing.

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Results:

Please provide a table showing the baseline characteristics of the study population (Table 1).

Please note that figures and tables in the Supplementary Information cannot be grouped together in one document (https://journals.plos.org/plosmedicine/s/supporting-information). Please refer to a specific figure or table e.g. Figure S1, Table S1, etc. throughout the main text.

Please report percentages to at least one decimal place.

Please present numerators and denominators for percentages, at least in the Tables [not necessarily each time they're mentioned].

Line 224 - please clarify what is represented by the numbers in brackets

Where adjusted analyses are presented, please also provide the unadjusted analyses (this may be in a Table) and please indicate which factors are adjusted for.

Where possible, please provide the actual numbers of events for the outcomes (this may be in a Table), not just HRs.

Discussion:

Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

Please remove all subheadings within your Discussion e.g. Strengths and Limitations, Conclusion

Figures:

Please provide titles and legends for all figures (including those in Supporting Information files).

Please define all abbreviations used in the respective figure legend.

Fig 3a, 4a - please indicate what is represented by the coloured areas and central lines in your graphs; 95% CI?

Fig 3b - please clarify whether adjusted HRs are shown. Please indicate in the figure caption the meaning of the boxes and whiskers shown for UKBB ; why are these not provided for SAIL data?

References:

Please ensure that journal name abbreviations match those found in the National Center for Biotechnology Information (NCBI) databases, and are appropriately formatted and capitalised.

Please also see https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references for further details on reference formatting.

Supplementary files:

Please provide titles and legends for each individual table and figure in the Supporting Information.

Please note that figures and tables in the Supporting Information cannot be grouped together in one document. Please see https://journals.plos.org/plosmedicine/s/supporting-information for our supporting information guidelines.

Comments from the reviewers:

Reviewer #1: "Associations between multimorbidity and adverse health outcomes: comparison of cohorts from UK Biobank and a representative community sample" compares two datasets: a UK Biobank cohort (n=211,597), and a Wales-based SAIL databank (n=852,055) covering some 70% of the Welsh population, for multimorbidity on n=40 long-term conditions (LTC). Various adjusted associations were examined with both unweighted and weighted (Cambridge) scores, and the datasets were found to be similar for associations between multimorbidity and various outcomes when LTC<=3, i.e. UK Biobank appears broadly representative for such cases. However, for LTC>3, UK Biobank estimates were found to likely be conservative. Some plausible explanations for the divergence in observed associations for LTC>3 were discussed.

The analysis appears generally well-executed, and should help to justify the validity of UK Biobank cohort data for research involving populations with relatively low multimorbidities. A number of comments follow:

1. It might be briefly commented as to whether the LTC definitions, and LTC classification by medical personnel, is likely to be applied very similarly for both UK Biobank and SAIL, i.e. are there any significant variances in medical practice between the component countries of the U.K.?

2. It might also be commented as to whether there exists possible overlaps between UK Biobank and SAIL data, for Welsh participants, i.e. might some Welsh participants be considered as part of both datasets?

3. Related to the above, it might be interesting to consider country-level sensitivity analyses if convenient/possible for UK Biobank - in particular UK Biobank (Wales) - vs. SAIL.

4. In Line 147, it is stated that "Age was taken as age at recruitment for UK Biobank, and calculated for 1st January 2011 for SAIL participants". It might be clarified as to whether the data involved must correspond to age at recruitment (for UK Biobank participants) or to 2011 (for SAIL participants); in particular, is it possible that relatively out-of-date data is involved in the analysis (e.g. SAIL participant had last check-up in say 2008, but those results are reported with his age in 2011)?

5. In Line 172, it is stated that "We used fractional polynomials to model non-linear relationships between age and Townsend score and LTC counts". The specific methodology might be described in supplementary material and/or cited as appropriate.

6. In Line 184, it is stated that "Weibull models including fractional polynomials for age and Townsend scores, as well as interaction terms between LTC count and age, and LTC count and sex, were found to fit the data best". It might be clarified as to what this "best" is compared against.

7. In general, for the Statistical Analysis section, it might be considered to state the (Supplementary) Figure/Table corresponding to each analysis, and label the Supplementary Figures/Tables accordingly.

8. For Figure 2, it might be illustrative to also display raw unadjusted counts for SAIL, if possible.

Reviewer #2:

The study covers an important topic in multimorbidity research, with most likely implications for other cohort studies (also beyond the UK). The methods and results are well presented with systematic comparison with various methods and outcomes. I enjoyed reading the paper.

Major comments:

1) how did the authors handle differences in follow-up time, or completeness of follow-up across datasets? I can image that differences in attrition or (predicted) time-horizons may contribute to differences in absolute risks of adverse health outcomes over time.

2) The authors restricted their analyses to a sample of 40 to 70 years old adults. Why was this done? (40-70 was baseline recruiting age for UK Biobank, but I suppose accrued follow-up now also allows for broader analyses on an age-scale)

3) I commend the authors with their great description of their sophisticated statistical analyses, that is provided in great detail.

4) It may perhaps be useful to readers to know why a cut-off of 3 for LTCs was chosen to compare findings across studies?. Especially since absolute risks for mortality and hospitalization between the two datasets are systematically higher in SAIL compared to UK biobank, regardless of multimorbidity count. Addition of an analysis plan that describes these analyses would benefit the paper, as well as adhering to STROBE guidelines (including for example the checklist)

5) what were reasons to predict the risk of MACE instead of the actual observation risk? Using the cumulative incidence function, it is perhaps more useful to simply show the observed (absolute) rates. Introducing the prediction term, introduces potential confounding of model misspecification or lack of generalizaiblity of the underlying predictive model.

6) in the methods section, SAIL is introduced as a representative sample of Wales. 70% of the population is covered. Can the authors provide some information on the missing 30% of people? Are they by any chance expected to be any different from the ones that are included?

1) When presenting the prevalence of individual LTCs in UK Biobank and SAIL, the authors note that most LTCs were more prevalent in SAIL, but some LTCs (eg. Parkinsons' disease) were more common in UK Biobank. I'd be helpful to discuss possible explanations for this.

7) The authors hypothesise that LTCs in UK Biobank may be less severe or advanced which would explain the difference in risks of mortality / hospitalisation, but did not offer any explanation as to why LTCs in UK Biobank might be less severe. Since UK Biobank offers information about participants beyond the routine data, it might be possible to explore this further and try to support this hypothesis by assessing the severity of LTCs using those extra information.

8) In the supplementary results, the authors elegantly stratified for depreviation scores. It however seems that regardless of these scores, risks for adverse health outcomes are systematically higher for SAIL compared to UK Biobank participants. Could the authors speculate what drove these higher risks, apparently something beyond social-economic status, age or sex.

Minor comments:

1) In the introduction section, the authors introduce the term collider bias. Not all readers may be familiar with this term, could the authors briefly explain this in the introduction section.

2) Figure 1 depicts the distribution of LTCs. The authors subsequently report that the mean is higher in SAIL than in UK-biobank. However, this distribution is highly skewed, and I would either suggest to transform it to a normal distribution or report median or range instead of mean.

3) Figure 2. The triangles and circles are relatively small compared to the bars, perhaps enlarge to increase readability.

4) can the authors clarify to readers what this score exactly means? "Cambridge mortality score of 12 [mean value in UK Biobank], predicted 5-year mortality was 2.2% (2.1 to 2.3) in UK Biobank and 4.0% (4.0-4.1) in SAIL, see supplementary appendix)." For example, what kind of combinations does one then have?

5) It would be very helpful to discuss the generalizability of the findings to other cohort studies with risk of selection bias, and its implications for multimorbidity studies in general.

6) The authors might consider adding confidence intervals to the modelled counts in Figure 2.

7) the results section contains very valuable information, but also covers a lot of different aspects of the paper. Perhaps subheadings may help guide the reader through these different parts.

Thank you, Silvan Licher

Reviewer #3: Review

This is an interesting paper, but I feel the analysis could do with expanding (in terms of description, at least). I am also concerned by the fact that UKBB includes people from England, Wales and Scotland (but only those near the assessment centres), whereas SAIL includes participants from across Wales.

Main comments

1. The comparison is between UK Biobank and a Welsh representative sample - so it is possible that differences between the rest of the UK and Wales could account for some/all of the differences observed here.

2. There were only two UKBB assessment centres in Wales, in Swansea and Cardiff. It would be useful to consider restricting the SAIL analysis to participants registered in Swansea or Cardiff, to ensure comparability.

3. "LTCs were identified from Read codes occurring prior to the baseline assessment, using the criteria described by Barnett et al." It would be useful to have more detail here, e.g. how long before the baseline assessment could the Read code occur? (there is presumably a cut-off?)? Was the % of participants with GP records going back 1,2, 5 however-many years the same across UKBB and SAIL? Any differences in READ recording between UK and UKBB? Could the list of READ codes be provided in supplementary material?

4. Similarly, "Finally, we identified combinations of three LTCs from previous studies of multimorbidity clusters,7,12 and assessed all possible combinations of the conditions within each cluster." - more information both in main text and as supplementary would be useful - I could not understand from this description what the outcomes were.

5. For outcomes, what was the cut-off date in each dataset (i.e. censoring date)? Is length of follow-up comparable?

6. I found the description of the standardisation a little confusing. I am used to standardisation being either direct or indirectly standardised rates, i.e. here would have direct standardisation within strata of age/sex (with age grouped) and then within strata of age/sex/SES, comparing the number of cases (multimorbidity rates) in UKBB with the number expected from SAIL rates. I think something similar is done here, except that a parametric model is used rather than strata and direct comparison of numbers of rates. More detail on how this model was derived would be helpful - i.e. exactly how were fractional polynomials used, what degree did you go up to, how did you assess model fit, etc. Did the negative binomial models fit well? Were any other models considered?

The fit of the models in SAIL is crucial to the interpretation of their fit to the UKBB data.

How did you estimate the confidence intervals for the UKBB estimates?

7. There is a similar lack of detail for the survival models. Weibull were found to fit best, but what other options were considered? What criteria were used to arrive at the best-fitting model? This information is unclear: "censoring participants dying of non-cardiovascular causes and coding as event status '0'". Was there an overall censoring date, and did it differ between participants (and was length of follow-up similar for SAIL and UKBB)?

8. Why was Poisson used for unscheduled hospitalisation? What (exactly) was the outcome? Why was time to first unscheduled hospitalisation not used? Were there many people with multiple unscheduled hospitalisations and how were they dealt with? Was the assumption made that 1 person with 5 unscheduled hospitalisations over 5 years contributes the same information as 5 people each with 1 unscheduled hospitalisation over 1 year? I would think it was possible that there might be over-dispersion, or that some people may be prone to several unscheduled hospitalisations?

9. Results - figure 1 -I would actually like to see a table for both figure 1 and 2 with observed and expected numbers in it, rates and their CIs. It is hard to compare rates on the figure when the prevalences are quite low.

10. Results - "After additionally standardizing by socioeconomic deprivation (red circles), however, the expected LTC counts in SAIL". Are these the expected LTC counts in SAIL, or the expected counts in UKBB given the rates in SAIL? I had assumed the latter, given the description of the methods. Thus on Figure 2, I would expect to see observed UKBB counts, and UKBB expected given SAIL age/sex model and UKBB age/sex distribution, and UKBB expected given SAIL age/sex/SES model and UKBB age/sex/SES distribution. To me "SAIL age/sex standardised to UKBB" implies predicted SAIL counts given UKBB age/sex model and SAIL age/sex distribution.

11. I would like to see confidence intervals on all figures. Would be useful also to see obs/expected (as in the more usual standardisation), especially for the higher counts of multimorbidity as the prevalences are so low it is hard to compare them on the figure.

12. What was the age/sex/SES distribution of UKBB and SAIL? I would expect to see this in a table in the main paper.

13. In figure 3a, is the absolute mortality risk shown for a particular age/SES group, or averaged over the population as a whole, or something else? I can't relate the text about a specific age group to the figure?

14. For figure 3b, would be better to have points not blocks and then show the confidence intervals for both SAIL and UKBB. The same for figure 4b and 5b. And figure 6.

15. This sentence "Importantly, the rank-order of LTCs in terms of their mortality risk was broadly similar between the two datasets." - what does this mean? How do you judge "broadly similar"? If this is important, I'd expect to see a table/figure showing the rank-order of LTCs in terms of mortality in the two datasets and some sort of comparison. If this is about the HRs shown in Figure 6, then having figure 6 ordered either by SAIL or UKBB HR would help (am not sure what it is ordered by, is roughly mortality risk but doesn't seem to be exactly HR in either dataset?).

16. Discussion - "than in a similarly aged representative UK sample from SAIL databank," - I thought the SAIL sample was only Welsh?

17. "Moreover, although generally less prevalent in UK Biobank, many LTCs had a similar relative risk of mortality between the two datasets.". This is a bit vague. How many? How do you judge "similar"?

18. "However, UK Biobank is likely to underestimate the impact of multimorbidity in people with greater numbers of LTCs (e.g. 4 or more) ". I think care is needed here - in all cases the comparison is to having no LTCs - whereas the sentence implies that the comparison is limited to those having 4 or more LTCs. The issue is that the difference between predicted impact of LTCs in SAIl and UKBB increases as the number of LTCs increases.

19. Was ethnicity examined at all here? Was ethnicity similarly distributed across SAIL and UKBB?

20. "SAIL includes data from Wales only, whereas UK Biobank comprises England, Scotland and Wales, although this is unlikely to have a substantial impact on our findings". This feels like quite a big claim to make with no evidence - you could compare the SAIL results to UKBB both only including people from Cardiff and Swansea - although numbers will be smaller, it would help to see how much the differences are due to Wales vs England vs Scotland?

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2022 Mar 7;19(3):e1003931. doi: 10.1371/journal.pmed.1003931.r002

Author response to Decision Letter 0

3 Oct 2021

Attachment

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PLoS Med. doi: 10.1371/journal.pmed.1003931.r003

Decision Letter 1

Louise Gaynor-Brook

16 Nov 2021

Dear Dr. Hanlon,

Thank you very much for re-submitting your manuscript "Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies" (PMEDICINE-D-21-02504R1) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

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------------------------------------------------------------

Requests from Editors:

Comments from Reviewers:

Reviewer #1: We thank the authors for addressing our previous comments, and have no further major issues to report. It may however be considered to further account for the significant difference in prior socioeconomic status distribution between SAIL and UK Biobank from Table 1 (which moreover might be expected to be 20% for each quintile, in a truly representative sample), if possible.

Reviewer #3:

1. I think the abstract should mention that UKBB is UK-wide (2 centres in Wales) and that the SAIL is Wales.

2. Also can you clarify in the abstract why UKBB has half the expected number of participants? (I think because of linkage, but it would be useful to have this info in the abstract).

3. Abstract - I don't think UKBB accurately estimates risks (because you say absolute risks are underestimated), but relative risk/risk ratio/hazard ratio?

4. Figure 2 could have confidence intervals on the expected number? I would expect CIs to be in the vertical plane not the horizontal. Also it would be useful if the legend for "expected" indicated that this was expected given the model in SAIL.

5. Supplementary Table 2 indicates that once SES is taken into account, there seem to be more people in UKBB with the high number of morbidities, compared to what would be expected from SAIL. The confidence intervals also look very narrow - though I appreciate this could be due to rounding to the nearest whole number?

6. "We then compared the distribution of LTC count in UK Biobank with SAIL after standardizing SAIL to the age/sex distribution, and then the age/sex/socioeconomic status distribution, of UK Biobank.". I found this description a bit confusing. My reading of the rest of the methods is that you fitted a model in SAIL, then used it to predict outcomes in UK Biobank. I don't see how this means that SAIL was standardized to the age/sex/SES distribution of UK biobank? I would have described it as the expected number in each LTC category if the rates in SAIL applied in UKBB? But this may just be semantics and the description of the methods is clear.

7. Thanks for the sensitivity analyses restricting to the populations of Cardiff and Swansea. For mortality I agree that the main results are unaffected - but for unscheduled hospitalisations, the results appear much closer in the sensitivity analysis,

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2022 Mar 7;19(3):e1003931. doi: 10.1371/journal.pmed.1003931.r004

Author response to Decision Letter 1

23 Nov 2021

Attachment

Submitted filename: PLOS medicine reviewer response round 2.docx

Click here for additional data file.^{(17.1KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003931.r005

Decision Letter 2

Louise Gaynor-Brook

26 Jan 2022

Dear Dr Hanlon,

On behalf of my colleagues and the Academic Editor, Dr. Sanjay Basu, I am pleased to inform you that we have agreed to publish your manuscript "Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies" (PMEDICINE-D-21-02504R2) in PLOS Medicine.

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Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Louise Gaynor-Brook, MBBS PhD

Associate Editor

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Protocol. Analysis plan.

(DOCX)

Click here for additional data file.^{(117.4KB, docx)}

S1 Checklist. STROBE reporting checklist.

(DOCX)

Click here for additional data file.^{(33.4KB, docx)}

S1 File. Read codes for included LTCs.

LTC, long-term condition.

(CSV)

Click here for additional data file.^{(327.6KB, csv)}

S2 File. Read codes for drugs used in LTC definitions.

LTC, long-term condition.

(CSV)

Click here for additional data file.^{(240.6KB, csv)}

S1 Table. Comparison of UK Biobank participants with linked GP data versus those without GP data available.

GP, General Practice.

(DOCX)

Click here for additional data file.^{(14.2KB, docx)}

S2 Table. Observed and expected LTC counts in UK Biobank.

Includes confidence intervals pertaining to Fig 2. LTC, long-term condition.

(DOCX)

Click here for additional data file.^{(13.7KB, docx)}

S3 Table. Observed event rates in UK Biobank and SAIL.

(DOCX)

Click here for additional data file.^{(20.9KB, docx)}

S4 Table. Unadjusted hazard ratios.

Includes unadjusted hazard ratios for those presented in the main analysis.

(DOCX)

Click here for additional data file.^{(17KB, docx)}

S5 Table. Cambridge multimorbidity score: Comparison of UK Biobank and SAIL.

Descriptive statistics of Cambridge multimorbidity score in UK Biobank and SAIL. SAIL, Secure Anonymised Information Linkage.

(DOCX)

Click here for additional data file.^{(13.2KB, docx)}

S1 Fig. Model diagnostic for SAIL LTC model.

Observed and expected LTC counts in SAIL, stratified by age, sex, and socioeconomic status. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(78.9KB, pdf)}

S2 Fig. Comparison of model fit: All-cause mortality in UK Biobank and SAIL.

(PDF)

Click here for additional data file.^{(19.8KB, pdf)}

S3 Fig. Comparison of model fit: MACE in UK Biobank and SAIL.

(PDF)

Click here for additional data file.^{(19.8KB, pdf)}

S4 Fig. Model fit, negative binomial model for unscheduled hospitalisations.

Observed and expected number of unscheduled hospitalisations. SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(45.3KB, pdf)}

S5 Fig. Model fit, negative binomial model for unscheduled hospitalisations, stratified by age, sex, and socioeconomic status.

Observed and expected number of unscheduled hospitalisations. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(72.3KB, pdf)}

S6 Fig. Association of multimorbidty and all-cause mortality by age, sex, and socioeconomic status.

(PDF)

Click here for additional data file.^{(112.2KB, pdf)}

S7 Fig. Cambridge score and all-cause mortality.

Line indicates the modelled values for each cohort; shaded area indicates 95% CIs. CI, confidence interval; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(68.8KB, pdf)}

S8 Fig. Association of multimorbidty and unscheduled hospitalisation by age, sex, and socioeconomic status.

(PDF)

Click here for additional data file.^{(119.6KB, pdf)}

S9 Fig. Cambridge score and unscheduled hospitalisation.

Line indicates the modelled values for each cohort; shaded area indicates 95% CIs. CI, confidence interval; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(69.9KB, pdf)}

S10 Fig. Association of multimorbidty and MACE by age, sex, and socioeconomic status.

(PDF)

Click here for additional data file.^{(110.7KB, pdf)}

S11 Fig. HRs for all-cause mortality: Coronary heart disease, diabetes, and stroke.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; TIA, transient ischaemic attack; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.3KB, png)}

S12 Fig. HRs for all-cause mortality: Hypertension, diabetes, and pain.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.4KB, png)}

S13 Fig. HRs for all-cause mortality: Irritable bowel syndrome, deafness, and pain.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.7KB, png)}

S14 Fig. HRs for all-cause mortality: Depression, pain, and anxiety.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.6KB, png)}

S15 Fig. HRs for all-cause mortality: Asthma, pain, and COPD.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.1KB, png)}

S16 Fig. HRs for all-cause mortality: Alcohol-related disorders, illicit drug use, and pain.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.4KB, png)}

S17 Fig. HRs for all-cause mortality: Coronary heart disease, diabetes, and atrial fibrillation.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.2KB, png)}

S18 Fig. HRs for all-cause mortality: Pain, coronary heart disease, and depression.

CI, confidence interval; HR, hazard ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10KB, png)}

S19 Fig. IRRs for unscheduled hospitalisation: Coronary heart disease, diabetes, and stroke.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; TIA, transient ischaemic attack; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.8KB, png)}

S20 Fig. IRRs for unscheduled hospitalisation: Hypertension, diabetes, and pain.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.5KB, png)}

S21 Fig. IRRs for unscheduled hospitalisation: Irritable bowel syndrome, deafness, and pain.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.9KB, png)}

S22 Fig. IRRs for unscheduled hospitalisation: Depression, pain, and anxiety.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.7KB, png)}

S23 Fig. IRRs for unscheduled hospitalisation: Asthma, pain, and COPD.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.4KB, png)}

S24 Fig. IRRs for unscheduled hospitalisation: Alcohol-related disorders, illicit drug use, and pain.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.4KB, png)}

S25 Fig. IRRs for unscheduled hospitalisation: Coronary heart disease, diabetes, and atrial fibrillation.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.4KB, png)}

S26 Fig. IRRs for unscheduled hospitalisation: Pain, coronary heart disease, and depression.

CI, confidence interval; IRR, incidence rate ratio; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.3KB, png)}

S27 Fig. HRs for MACE: Coronary heart disease, diabetes, and stroke.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; TIA, transient ischaemic attack; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.5KB, png)}

S28 Fig. HRs for MACE: Hypertension, diabetes, and pain.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.5KB, png)}

S29 Fig. HRs for MACE: Irritable bowel syndrome, deafness, and pain.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.6KB, png)}

S30 Fig. HRs for MACE: Depression, pain, and anxiety.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(9.5KB, png)}

S31 Fig. HRs for MACE: Asthma, pain, and COPD.

CI, confidence interval; COPD, chronic obstructive pulmonary disease; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(8.9KB, png)}

S32 Fig. HRs for MACE: Alcohol-related disorders, illicit drug use, and pain.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.5KB, png)}

S33 Fig. HRs for MACE: Coronary heart disease, diabetes, and atrial fibrillation.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.2KB, png)}

S34 Fig. HRs for MACE: Pain, coronary heart disease, and depression.

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage; UKB, UK Biobank.

(PNG)

Click here for additional data file.^{(10.1KB, png)}

S35 Fig. Sensitivity analysis: All-cause mortality.

Comparison of full cohort and area-specific estimates. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(36KB, pdf)}

S36 Fig. Sensitivity analysis: Unscheduled hospitalisation.

Comparison of full cohort and area-specific estimates. LTC, long-term condition; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(53.9KB, pdf)}

S37 Fig. Sensitivity analysis: MACE.

Comparison of full cohort and area-specific estimates. LTC, long-term condition; MACE, major adverse cardiovascular event; SAIL, Secure Anonymised Information Linkage.

(PDF)

Click here for additional data file.^{(36KB, pdf)}

Attachment

Submitted filename: PLOS Medicine response to reviewers submitted.docx

Click here for additional data file.^{(58.9KB, docx)}

Attachment

Submitted filename: PLOS medicine reviewer response round 2.docx

Click here for additional data file.^{(17.1KB, docx)}

Data Availability Statement

[pmed.1003931.ref001] 1.Whitty CJ, MacEwen C, Goddard A, Alderson D, Marshall M, Calderwood C, Atherton F, McBride M, Atherton J, Stokes-Lampard H, Reid W. Rising to the challenge of multimorbidity. BMJ. 2020. Jan 6;368. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref002] 2.Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina AM. Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies. J Comorb. 2019;9:2235042X19870934. doi: 10.1177/2235042X19870934 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref003] 3.van Oostrom SH, Gijsen R, Stirbu I, Korevaar JC, Schellevis FG, Picavet HSJ, et al. Time trends in prevalence of chronic diseases and multimorbidity not only due to aging: data from general practices and health surveys. PLoS ONE. 2016;11(8):e0160264. doi: 10.1371/journal.pone.0160264 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref004] 4.Jani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, et al. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Med. 2019;17(1):74. doi: 10.1186/s12916-019-1305-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref005] 5.Payne RA, Mendonca SC, Elliott MN, Saunders CL, Edwards DA, Marshall M, et al. Development and validation of the Cambridge Multimorbidity Score. CMAJ. 2020;192(5):E107–E14. doi: 10.1503/cmaj.190757 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref006] 6.Vetrano DL, Roso-Llorach A, Fernández S, Guisado-Clavero M, Violán C, Onder G, et al. Twelve-year clinical trajectories of multimorbidity in a population of older adults. Nat Commun. 2020;11(1):1–9. doi: 10.1038/s41467-019-13993-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref007] 7.Zhu Y, Edwards D, Mant J, Payne RA, Kiddle S. Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study. BMC Med. 2020;18:1–11. doi: 10.1186/s12916-019-1443-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref008] 8.Zemedikun DT, Gray LJ, Khunti K, Davies MJ, Dhalwani NN. editors. Patterns of multimorbidity in middle-aged and older adults: an analysis of the UK Biobank data. Mayo Clin Proc. 2018;93(7):857–66. doi: 10.1016/j.mayocp.2018.02.012 [DOI] [PubMed] [Google Scholar]

[pmed.1003931.ref009] 9.Hanlon P, Nicholl BI, Jani BD, Lee D, McQueenie R, Mair FS. Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493 737 UK Biobank participants. Lancet Public Health. 2018;3(7):e323–e32. 10.1016/S2468-2667(18)30091-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref010] 10.Dodds RM, Granic A, Robinson SM, Sayer AA. Sarcopenia, long-term conditions, and multimorbidity: findings from UK Biobank participants. J Cachexia Sarcopenia Muscle. 2020;11(1):62–8. doi: 10.1002/jcsm.12503 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref011] 11.Chudasama YV, Khunti KK, Zaccardi F, Rowlands AV, Yates T, Gillies CL, et al. Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study. BMC Med. 2019;17(1):108. doi: 10.1186/s12916-019-1339-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref012] 12.Di Angelantonio E, Kaptoge S, Wormser D, Willeit P, Butterworth AS, Bansal N, et al. Association of cardiometabolic multimorbidity with mortality. JAMA. 2015;314(1):52–60. doi: 10.1001/jama.2015.7008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref013] 13.Chiang JI, Hanlon P, Li T-C, Jani BD, Manski-Nankervis J-A, Furler J, et al. Multimorbidity, mortality, and HbA1c in type 2 diabetes: A cohort study with UK and Taiwanese cohorts. PLoS Med. 2020;17(5):e1003094. doi: 10.1371/journal.pmed.1003094 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref014] 14.Hanlon P, Jani BD, Butterly E, Nicholl B, Lewsey J, McAllister DA, et al. An analysis of frailty and multimorbidity in 20,566 UK Biobank participants with type 2 diabetes. Commun Med (London). 2021;1(1):1–9. 10.1038/s43856-021-00029-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref015] 15.Sullivan MK, Jani BD, McConnachie A, Hanlon P, McLoone P, Nicholl BI, et al. Hospitalisation events in people with chronic kidney disease as a component of multimorbidity: parallel cohort studies in research and routine care settings. BMC Med. 2021;19(1):278. doi: 10.1186/s12916-021-02147-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref016] 16.Allen N, Sudlow C, Downey P, Peakman T, Danesh J, Elliott P, et al. UK Biobank: Current status and what it means for epidemiology. Health Policy Technol. 2012;1(3):123–6. 10.1016/j.hlpt.2012.07.003. [DOI] [Google Scholar]

[pmed.1003931.ref017] 17.Manolio TA, Collins R. Enhancing the Feasibility of Large Cohort Studies. JAMA. 2010;304(20):2290–1. doi: 10.1001/jama.2010.1686 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref018] 18.Fry A, Littlejohns TJ, Sudlow C, Doherty N, Adamska L, Sprosen T, et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am J Epidemiol. 2017;186(9):1026–34. doi: 10.1093/aje/kwx246 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref019] 19.Griffith GJ, Morris TT, Tudball MJ, Herbert A, Mancano G, Pike L, et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun. 2020;11(1):5749. doi: 10.1038/s41467-020-19478-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref020] 20.Infante-Rivard C, Cusson A. Reflection on modern methods: selection bias—a review of recent developments. Int J Epidemiol. 2018;47(5):1714–22. doi: 10.1093/ije/dyy138 [DOI] [PubMed] [Google Scholar]

[pmed.1003931.ref021] 21.Batty GD, Gale CR, Kivimäki M, Deary IJ, Bell S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ. 2020;368. doi: 10.1136/bmj.m131 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref022] 22.Keyes KM, Westreich D. UK Biobank, big data, and the consequences of non-representativeness. Lancet. 2019;393(10178):1297. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref023] 23.Jones KH, Ford DV, Thompson S, Lyons R. A profile of the Sail Databank on the UK secure research platform. Int J Popul Data Sci. 2019;4(2). doi: 10.23889/ijpds.v4i2.1134 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref024] 24.Schnier C, Wilkinson T, Akbari A, Orton C, Sleegers K, Gallacher J, et al. The secure Anonymised information linkage databank dementia e-cohort (SAIL-DeC). Int J Popul Data Sci. 2020;5(1). [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref025] 25.Hanlon P, Hannigan L, Rodriguez-Perez J, Fischbacher C, Welton NJ, Dias S, et al. Representation of people with comorbidity and multimorbidity in clinical trials of novel drug therapies: an individual-level participant data analysis. BMC Med. 2019;17(1):201. doi: 10.1186/s12916-019-1427-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref026] 26.Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43. doi: 10.1016/S0140-6736(12)60240-2 [DOI] [PubMed] [Google Scholar]

[pmed.1003931.ref027] 27.Townsend P, Phillimore P, Beattie A. Health and deprivation: inequality and the North: Routledge; 1988. [Google Scholar]

[pmed.1003931.ref028] 28.Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol. 1999;28(5):964–74. doi: 10.1093/ije/28.5.964 [DOI] [PubMed] [Google Scholar]

[pmed.1003931.ref029] 29.Foster HM, Celis-Morales CA, Nicholl BI, Petermann-Rocha F, Pell JP, Gill JM, et al. The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort. Lancet Public Health. 2018;3(12):e576–e85. doi: 10.1016/S2468-2667(18)30200-7 [DOI] [PubMed] [Google Scholar]

[pmed.1003931.ref030] 30.Yang F, Johansson AL, Pedersen NL, Fang F, Gatz M, Wirdefeldt K. Socioeconomic status in relation to Parkinson’s disease risk and mortality: A population-based prospective study. Medicine. 2016;95(30). doi: 10.1097/MD.0000000000004337 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref031] 31.Hanlon P, Butterly E, Lewsey J, Siebert S, Mair FS, McAllister DA. Identifying frailty in trials: an analysis of individual participant data from trials of novel pharmacological interventions. BMC Med. 2020;18 (1):1–12. doi: 10.1186/s12916-019-1443-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref032] 32.Hanlon P, McCallum M, Jani BD, McQueenie R, Lee D, Mair FS. Association between childhood maltreatment and the prevalence and complexity of multimorbidity: A cross-sectional analysis of 157,357 UK Biobank participants. J Comorb. 2020;10:2235042X10944344. doi: 10.1177/2235042X10944344 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1003931.ref033] 33.Lawlor DA, Tilling K, Davey SG. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86. doi: 10.1093/ije/dyw314 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

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

Peter Hanlon

Bhautesh D Jani

Barbara Nicholl

Jim Lewsey

David A McAllister

Frances S Mair

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Data sources

UK Biobank

SAIL

Assessment of multimorbidity

Covariates

Outcomes

Statistical analysis

Comparison of multimorbidity distribution

Modelling all-cause mortality and MACE

Modelling unscheduled hospitalisations

Modelling specific LTCs

Sensitivity analyses

Ethical approval

Results

Table 1. Baseline demographic characteristics.

Distribution of multimorbidity

Fig 1. Distribution of counts in UK Biobank and SAIL.

Fig 2. Observed and expected LTC counts in UK Biobank.

Association between multimorbidity and all-cause mortality

Fig 3. Relationship between LTC count and absolute mortality risk for UK Biobank and SAIL.

Fig 4. Relationship between LTC count and hazard ratio for mortality in UK Biobank and SAIL.

Association between multimorbidity and unscheduled hospitalisation

Fig 5. Relationship between LTC count and absolute risk of unscheduled hospitalisation for UK Biobank and SAIL.

Fig 6. Relationship between LTC count and IRR for unscheduled hospitalisation in UK Biobank and SAIL.

Association between multimorbidity and MACE

Fig 7. Relationship between LTC count and absolute risk of MACE for UK Biobank and SAIL.

Fig 8. Relationship between LTC count and incident rate ratio for unscheduled hospitalisation in UK Biobank and SAIL.

Relationship between individual LTCs and all-cause mortality

Fig 9. Relationship between each LTC and all-cause mortality in UK Biobank and SAIL.

Association between combinations of LTCs and clinical outcomes

Sensitivity analyses

Discussion

Declarations

Ethics committee approval

Dissemination

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Louise Gaynor-Brook

Roles

Author response to Decision Letter 0

Decision Letter 1

Louise Gaynor-Brook

Roles

Author response to Decision Letter 1

Decision Letter 2

Louise Gaynor-Brook

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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