Inequity in Premature Mortality: A Cohort Study of 4.3 million Adults Under 60 years With Multimorbidity

Inequity in Premature Mortality: A Cohort Study of 4.3 million Adults Under 60 years With Multimorbidity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Inequity in Premature Mortality: A Cohort Study of 4.3 million Adults Under 60 years With Multimorbidity Aman Jat, Lucy Smith, Tassella Isaac, Mehedi Hasan, Nazrul Islam, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9451391/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background : Multimorbidity is increasingly common among adults who develop multiple long-term conditions before age 60 and is linked to premature mortality. We examined whether socioeconomic disadvantage and recorded markers of structural vulnerability were associated with premature mortality in this population using the CORE20PLUS5 and PROGRESS-PLUS frameworks. Methods : We conducted a population-based cohort study using linked Clinical Practice Research Datalink (CPRD) Gold and Aurum data from England, 1987–2020. Adults aged 18–60 years with incident multimorbidity were followed until death, deregistration, study end, or age 75 years. Premature mortality was defined as all-cause death at or before age 75 years. Cox proportional hazards models estimated adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for socioeconomic, demographic, and clinical factors. Results : Among 4,303,353 adults with incident multimorbidity, 447,720 premature deaths occurred. Mortality risk was higher in the most deprived than the least deprived areas (HR 1.61, 95% CI 1.59–1.62). Recorded residential care was associated with the highest mortality risk (HR 2.92, 95% CI 2.89–2.95), followed by financial support (HR 1.44, 95% CI 1.42–1.47) and disability (HR 1.26, 95% CI 1.24–1.27). Women had a lower risk than men (HR 0.68, 95% CI 0.68–0.69). Adults aged 40–60 years at multimorbidity onset had a higher risk than those aged 18–39 years (HR 2.29, 95% CI 2.27–2.30). Compared with White individuals, Asian, Black, Mixed, and Other ethnic groups had lower adjusted risks. Mortality risk was also higher in several regions outside London. Conclusions : In adults who developed multimorbidity before age 60, premature mortality before age 75 was strongly patterned by socioeconomic disadvantage and recorded markers of structural vulnerability. These variables should be interpreted as markers of heightened vulnerability rather than direct causal determinants of mortality. Equity-oriented prevention and coordinated health and community care may help reduce avoidable inequalities. Multimorbidity Health Inequity Health Inequalities Premature Mortality Equity Framework (CORE20PLUS5 and PROGRESS-PLUS) CPRD data. Figures Figure 1 Figure 2 Figure 3 Introduction Multimorbidity – the co-occurrence of two or more chronic conditions – is a growing global and national challenge 1 – 4 . Although traditionally regarded as a condition of ageing, multimorbidity is increasingly observed among adults under 60 years, with around one in four in England living with multiple chronic conditions 5 , 6 . In this group, multimorbidity is associated with reduced quality of life, increased healthcare utilisation, and elevated risk of premature mortality 7 – 9 . Given that younger adults comprise much of the economically active population, early morbidity and mortality carry wide-ranging socioeconomic consequences 10 – 12 . Despite the scale and implications of multimorbidity in mid-life, premature mortality in this population remains underexplored. Most studies focus on older adults 13 or examine risk factors such as deprivation, disability, or clinical status in isolation, overlooking how these factors intersect to impact survival 14 – 16 . These evidence gaps point to the value of an inequity lens, recognising that structural disadvantages ultimately drive inequalities in health outcomes. Health inequity refers to unfair and avoidable differences in illness, disability, and mortality across populations, typically driven by systemic factors such as socioeconomic deprivation, geographic location, ethnicity, and other socioeconomic determinants of health 17 , 18 . For example, adults living in more deprived areas of England experience shorter lifespans and spend more years with chronic conditions such as diabetes, cardiovascular disease, or chronic pain compared with those in more affluent areas 19 . These inequities emerge from structural inequalities i.e., disparities that create barriers through systemic poverty, housing insecurity, and unequal access to healthcare, which constrain opportunities for health across the life course 20 , leading to earlier disease onset, higher prevalence of multimorbidity, and increased risk of premature death 18 . Positioning multimorbidity within this framework highlights the need to address not only clinical risk factors but also the socioeconomic and structural determinants that influence health outcomes. There is growing recognition that multimorbidity before age 60 years is strongly patterned by socioeconomic disadvantage and structural inequities, making it both a driver and a marker of health inequality across the life course 21 . However, few studies have systematically examined premature mortality in this population using equity-oriented frameworks to help quantify the impact of inequality. To address this gap, we applied two complementary inequality frameworks to capture socioeconomic and structural inequity: CORE20PLUS5, which prioritises the most deprived populations and key clinical areas of inequality 22 , and PROGRESS-PLUS, which encompasses a broader set of socioeconomic stratifies including place, race/ethnicity, occupation, sex, education, socioeconomic status, and discrimination 23 . By focusing on adults under 60 years – a consistently underrepresented group in multimorbidity research – this study aims to generate new insights using the CORE20PLUS5 and PROGRESS-PLUS inequality frameworks to examine socioeconomic disadvantage and structural inequity as determinants of premature mortality in younger adults with multimorbidity Methods Study design and data source A retrospective, population-based cohort was conducted, using routinely collected primary care data from the Clinical Practice Research Datalink (CPRD) 24 , including both the Gold 25 and Aurum 26 datasets. CPRD data include anonymised medical records from general practices across England, including clinical diagnoses, medications, referrals, and demographic details. The CPRD population is broadly representative of England in terms of age, sex, ethnicity, and socioeconomic status, as measured by the Index of Multiple Deprivation (IMD) 11 , 27 . The study period ran from 1 January 1987 to 31 December 2020. Individual records were linked to external data sources, including Hospital Episode Statistics (HES), Office for National Statistics 5 (ONS) mortality data, and NHS Digital datasets for deprivation indices and socioeconomic vulnerability indicators. Study population The CPRD database contained 7,310,752 individuals. We restricted inclusion to adults aged 18–60 years at the time of incident multimorbidity diagnosis, defined as the earliest date on which a participant was diagnosed with a second long-term condition. Multimorbidity was identified using a validated list of 59 conditions that were identifiable in CPRD using Read, SNOMED, or ICD-10 codes. 28 Participants were required to be registered with a CPRD practice for at least 12 months prior to this index date. We excluded individuals with multimorbidity onset outside the 18–60-year age range, implausible or inconsistent diagnosis dates, or incomplete demographic data. Missingness included age index (age at multimorbidity diagnosis, n = 20,042), IMD score (n = 14,596), indeterminate sex (n = 74), and a very small regional sample size from the Southeast Coast (n = 1,583). After exclusions, the final analytical cohort consisted of 4,303,353 individuals with valid data for subsequent analyses. Follow-up and outcome Participants were followed from the date of multimorbidity onset until the earliest of date of death, deregistration from the practice, the end of the study period (31 December 2020), or age cut-off at 75 years. The primary outcome was premature mortality, defined as death from any cause at age 75 years or younger. Individuals who survived beyond age 75, or died after reaching 75, were censored and classified as not having experienced premature mortality. The age threshold of 75 years was chosen in line with UK public health definitions of premature mortality, which commonly define deaths before age 75 as avoidable or early deaths and use this benchmark for national monitoring of health inequalities. Exposure variables We operationalised inequality using the CORE20PLUS5 and PROGRESS-PLUS frameworks. To address inequalities at both national and local levels, NHS England developed the CORE20PLUS5 framework to guide improvements in healthcare and promote equity. The “CORE20” refers to the most deprived 20% of the national population as defined by the Index of Multiple Deprivation (IMD). The “PLUS” highlights inclusion health groups that experience additional disadvantage, such as ethnic minority communities, adults with learning disabilities, individuals living with multimorbidity, and other socially marginalised populations. The “5” identifies five clinical priority areas such as maternity, severe mental illness (SMI), Chronic respiratory disease, early cancer diagnosis and hypertension case-finding and optimal management and lipid optimal management 29 . PROGRESS-PLUS is another widely used equity framework, originally developed by the Cochrane Equity Methods Group, to identify how health opportunities and outcomes are socioeconomically stratified 30 . The acronym stands for Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and social capital. The “PLUS” extends this framework to include additional factors such as personal characteristics associated with discrimination (e.g., age, disability), features of relationships (e.g., family structure, social support), and time-dependent factors (e.g., life-course stage, transitions such as hospital discharge or entry into long-term care) 23 . Within our dataset, we identified the following primary exposures of interest from the above frameworks: disability, financial support, residential care, and geographic region. Covariates included age, sex, IMD quintile, race/ethnicity and overall multimorbidity burden (total number of long-term conditions recorded for each individual). Disability was defined as a binary baseline indicator of any recorded disability at or before the index date, including mental health, intellectual, learning, speech, hearing, or sensory disability codes. Financial support was defined as a binary baseline indicator of any recorded receipt of financial support or financial vulnerability at or before the index date, including disability living allowance, mobility allowance, or codes indicating financial support needs or financial difficulty. Residential care was defined as a binary baseline indicator of recorded residence in a care home, nursing home, or hospice at or before the index date. Participants living in their own home, or with no recorded institutional-care code, were classified as not in residential care. Area deprivation was measured using the Index of Multiple Deprivation (IMD) quintiles, with quintile 1 representing the least deprived areas, in line with the CORE20 framework. The geographic region was derived from the CPRD practice-location variable. The source data contained 10 England region categories using ONS. One category, labelled Southeast Coast, contained a very small number of participants and was excluded a priori because sparse data would yield unstable estimates. The final analyses, therefore, included nine regional categories, with London used as the reference group in regression models. Ethnicity was categorised using the ONS five-group standard– White, Mixed, Asian, Black, Other. Age was measured as a continuous variable at multimorbidity diagnosis (index date) and categorised for subgroup analyses (18–39, 40–60, ≥ 75 years). Sex was recorded as male, female, or indeterminate in GP records. Overall multimorbidity burden was defined as the total number of long-term conditions recorded for each individual, based on a validated list of 56 conditions. Full details of all variables extracted, and their alignment with CORE20PLUS5 and PROGRESS-PLUS domains, are provided in Supplementary Table 4. Not all measures in the inequality frameworks were available in CPRD, including occupation, education, and religion. All exposure variables were defined using records available at or before the date of incident multimorbidity (index date). These variables were treated as baseline exposures and were not updated during follow-up. This approach was adopted to minimise reverse causation arising from changes in social or care status occurring close to death. However, it is acknowledged that some exposures, particularly residential care and financial support, may still reflect underlying disease severity present at baseline. Statistical analysis We first carried out descriptive analyses to characterise the multimorbidity cohort. Demographic factors (age, sex, ethnicity, region, and deprivation quintile), social exposures (disability, financial support, and residential care), and clinical groups were summarised. Baseline characteristics (Table 1 ) were described overall and stratified by premature mortality status (alive/censored vs death ≤ 75 years), with distributions presented as counts and percentages. Proportional stacked bar plots (Fig. 1 ) were also used to visualise differences in mortality across age categories, sex, ethnicity, and deprivation quintiles. We used multivariable Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between socioeconomic inequality exposures and premature mortality. The adjusted model included socioeconomic inequality variables (CORE20PLUS5 and PROGRESS-PLUS), along with covariates: age at multimorbidity onset, sex, IMD quintile, ethnicity and clinical conditions. Results were visualised in a forest plot (Fig. 3 ), displaying adjusted hazard ratios with 95% confidence intervals against a reference line at HR = 1.0. Subgroup analyses and stratified Cox regression were conducted stratified by age, sex, ethnicity, and IMD quintile (2–3 vs ≥ 4). All analyses were conducted using R (4.4.3). Results Population characteristics Our final cohort comprised 4,303,353 individuals aged under 60 years with a diagnosis of at least two long-term conditions (i.e. multimorbidity). The median age at multimorbidity onset was 42 years (IQR: 33–51), 51% were female. Ethnic distribution was 82.0% White. A summary of baseline characteristics is shown in Table 1 . A large proportion of premature deaths among individuals aged 40–60 years (368,104 deaths, 82.2% of all premature deaths), compared with 79,616 deaths (17.8%) in those aged 18–39. The most common multimorbidity was cardiovascular conditions (2,251,062 individuals), followed by mental and behavioural disorders (3,243,656 individuals). Table 1 Baseline characteristics of the study population by survival status (censored alive vs. premature mortality). Variable Total Population Alive/Censored (n,%) Premature death (n,%) Age (years) 18–39 1,673,206 1,593,590 (95.2%) 79,616 (4.8%) 40–60 2,487,512 2,119,408 (85.2%) 368,104 (14.8%) Sex Male 1,893,787 1,624,685 (85.8%) 269,102 (14.2%) Female 2,409,566 2,205,458 (91.5%) 204,108 (8.5%) IMD Quintile Q1 (Least deprived) 744,642 677,319 (91.0%) 67,323 (9.0%) Q2 803,298 724,238 (90.1%) 79,060 (9.9%) Q3 824,543 738,083 (89.5%) 86,460 (10.5%) Q4 923,975 819,147 (88.7%) 104,828 (11.3%) Q5 (Most deprived) 1,006,895 871,356 (86.5%) 135,539 (13.5%) Ethnicity White 3,397,540 2,994,083 (88.1%) 403,457 (11.9%) Mixed 35,200 33,262 (94.5%) 1,938 (5.5%) Asian 204,583 189,130 (92.4%) 15,453 (7.6%) Black 131,748 120,067 (91.1%) 11,681 (8.9%) Other 71,610 66,461 (92.7%) 5,149 (7.3%) Unknown 462,672 427,140 (92.3%) 35,532 (7.7%) Region Northeast 541,037 481,807 (89.0%) 59,230 (11.0%) Northwest 167,332 146,779 (87.7%) 20,553 (12.3%) Yorkshire & Humber 897,443 788,782 (87.9%) 108,661 (12.1%) East Midlands 177,087 157,509 (89.0%) 19,578 (11.0%) West Midlands 120,363 107,652 (89.4%) 12,711 (10.6%) East of England 658,997 585,723 (88.9%) 73,274 (11.1%) Southwest 210,290 186,811 (88.9%) 23,479 (11.1%) South Central 722,500 650,467 (90.0%) 72,033 (10.0%) London 808,304 724,613 (89.6%) 83,691 (10.4%) Multimorbidity Condition Cardiovascular 2,251,062 1,912,219 (85.0%) 338,843 (15.0%) Metabolic & Endocrine 1,170,305 1,015,261 (86.8%) 155,044 (13.2%) Respiratory 1,472,064 1,291,548 (87.7%) 180,516 (12.3%) Neurological 984,182 837,500 (85.1%) 146,682 (14.9%) Cancers 777,625 540,012 (69.4%) 237,613 (30.6%) Mental & Behavioural 3,243,656 2,914,862 (89.9%) 328,794 (10.1%) Musculoskeletal 1,993,007 1,765,037 (88.6%) 227,970 (11.4%) Digestive 518,741 392,878 (75.7%) 125,863 (24.3%) Urogenital 478,644 421,381 (88.0%) 57,263 (12.0%) Haematological 34,298 29,470 (85.9%) 4,828 (14.1%) Eye 37,230 29,138 (78.3%) 8,092 (21.7%) Ear 107,334 98,850 (92.1%) 8,484 (7.9%) Infections 76,344 66,523 (87.1%) 9,821 (12.9%) Congenital 55,721 47,619 (85.4%) 8,102 (14.6%) Socioeconomic characteristics Disability (Yes) 181,369 141,247 (77.9%) 40,122 (22.1%) Financial Support (Yes) 77,558 55,583 (71.7%) 21,975 (28.3%) Residential Care (Yes) 122,130 74,356 (60.9%) 47,774 (39.1%) This figure shows the distribution of premature mortality (death before age 75 years) among 4,303,353 individuals aged 18–60 years at multimorbidity onset, identified from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases between January 1, 1987, and December 31, 2020. Mortality patterns are stratified by age group, sex, ethnicity, and Index of Multiple Deprivation (IMD) quintiles. This figure shows the selection of adults aged 18–60 years with incident multimorbidity, identified from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases between January 1, 1987, and December 31, 2020. Of 7.3 million initially identified, exclusions were applied for multimorbidity onset outside the 18–60 age range, missing data (age, sex, deprivation index), and small regional sample size. The final cohort comprised 4,303,353 adults with valid records, followed until death, deregistration, study end, or age cut off 75. Association between socioeconomic and structural inequity and premature mortality In fully adjusted Cox regression models, premature mortality was strongly associated with socioeconomic and demographic factors. Residential care carried the highest risk (HR 2.92, 95% CI: 2.89–2.95), followed by financial support (HR 1.44, 95% CI: 1.42–1.47) and disability (HR 1.26, 95% CI: 1.24–1.27). A deprivation gradient was observed, with risk increasing from quintile 2 (HR 1.11, 95% CI: 1.10–1.12) to quintile 5 (HR 1.61, 95% CI: 1.59–1.62) compared with the least deprived. Individuals aged 40–60 years had a higher risk compared with those aged 18–39 years (HR 2.29, 95% CI: 2.27–2.30). Female sex was associated with lower risk compared with male sex (HR 0.68, 95% CI: 0.68–0.69). By region, compared with London, risk was higher in the East Midlands (HR 1.12, 95% CI: 1.10–1.14), East of England (HR 1.13, 95% CI: 1.12–1.15), North West (HR 1.06, 95% CI: 1.05–1.07), Yorkshire and Humber (HR 1.05, 95% CI: 1.04–1.07), West Midlands (HR 1.05, 95% CI: 1.04–1.06), and South Central (HR 1.03, 95% CI: 1.02–1.04). Risk was lower in the Southwest (HR 0.96, 95% CI: 0.95–0.97). There was no statistically significant difference in the Northeast (HR 1.01, 95% CI: 1.00–1.03). In ethnicity, compared with White individuals, risk was lower in Mixed (HR 0.60, 95% CI: 0.57–0.63), Asian (HR 0.66, 95% CI: 0.65–0.67), Black (HR 0.71, 95% CI: 0.69–0.72), and Other (HR 0.66, 95% CI: 0.64–0.67) groups. This figure shows adjusted hazard ratios (HRs) with 95% confidence intervals from Cox proportional hazards models in a cohort of 4,303,353 Adults aged 18–60 years at multimorbidity onset, identified from CPRD Gold and Aurum databases. Premature mortality was defined as death at or before age 75 years. The vertical red dashed line at HR = 1.0 indicates the reference level. Reference categories were age 18–39 years, male sex, White ethnicity, the least deprived IMD quintile (Q1), the London region, and the absence of disability, financial support, and residential care. Factors to the right of the line indicate increased risk, and those to the left indicate reduced risk of premature mortality. Subgroup analysis and stratified Cox regression Subgroup analysis by ethnicity showed that age was associated with premature mortality. Compared with adults aged 18–39 years, those aged 40–60 years had higher risks across all ethnic groups. The hazard ratio was 3.47 for White individuals, 2.80 for Asian individuals, and 2.13 for Black individuals. These estimates reflect within-group age differences rather than direct comparisons between ethnic groups. Consistent with the main Cox model, adjusted risks were lower among ethnic minority groups than among White individuals (Asian HR 0.66, Black HR 0.71, Mixed HR 0.60, Other HR 0.66). In the main adjusted Cox model, adults in residential care had a hazard ratio of 2.92 (95% CI: 2.89–2.95) compared with those not in care. In subgroup analyses by ethnicity, deprivation, and region, hazard ratios ranged from 4.7 to 8.3 in residential care. Disability status was associated with higher risks of premature mortality, with hazard ratios ranging from 1.53 to 1.72 depending on age. Socioeconomic status, measured by the Index of Multiple Deprivation, was also associated with risk: individuals in the lowest IMD quintile had hazard ratios ranging from 1.64 to 1.78 compared with the least deprived group. Stratified Cox models confirmed independent associations between predictors and premature mortality. Residential care had the highest hazard ratio (HR 5.99, 95% CI: 5.91–6.08), followed by financial support (HR 1.76, 95% CI: 1.71–1.81) and disability (HR 1.32, 95% CI: 1.29–1.34). These associations were consistent across sex and ethnic subgroups, with no statistically significant variation in hazard ratios. Age 40–60 years remained associated with higher risk across all groups, with hazard ratios of 3.37 (95% CI: 3.32–3.41) in more deprived IMD quintiles. By ethnicity, compared with White individuals, risk was lower in those of Mixed (HR 0.42, 95% CI: 0.39–0.46), Asian (HR 0.53, 95% CI: 0.51–0.54), and Black (HR 0.61, 95% CI: 0.59–0.63) ethnicities. The higher hazard ratios observed in stratified models compared with the fully adjusted pooled model reflect differences in covariate structure and effect modification. In stratified analyses, certain variables were not simultaneously adjusted across strata, which may have resulted in larger within-group effect estimates. These findings suggest potential interaction between social vulnerability markers and demographic characteristics, rather than inconsistency in model specification. Discussion In this study, we examined premature mortality among 4.3 million individuals with multimorbidity diagnosed before age 60 years. We observed strong associations between mortality risk and a range of socioeconomic, demographic, and structural indicators. Residential care status, receipt of financial support, disability, and higher socioeconomic deprivation were all associated with increased mortality risk. Age at multimorbidity onset emerged as a major predictor, with onset between 40–60 years associated with more than double the risk of premature death compared with onset between 18–39 years. Female sex was consistently associated with lower mortality risk than male sex. Regionally, higher risks were observed outside London, particularly in the East Midlands and East of England. By ethnicity, Asian, Black, Mixed, and Other ethnic groups demonstrated lower adjusted risks of premature mortality compared with White individuals. Subgroup analyses confirmed increased risk with older age across all ethnic groups, while stratified models showed the highest mortality risk among individuals recorded as living in residential care, with additional associations observed for disability and socioeconomic disadvantage. Our findings are consistent with previous research documenting the influence of socioeconomic determinants on health outcomes in multimorbidity, although most prior studies have focused on older adults, with relatively little attention to younger populations 1 – 3 , 13 . By contrast, this study demonstrates that socioeconomic vulnerability – including residential care, financial hardship, and disability – also exert a strong effect on premature mortality at this young age group. These findings reinforce the importance of examining multimorbidity through a life-course perspective, recognising that disadvantage accumulates well before older age and may contribute to early mortality trajectories. The 2022 WHO Global Report on Health Equity similarly reported that adults living with disabilities experience earlier mortality, often driven by non-clinical barriers such as difficulties accessing healthcare, limited availability of comprehensive information, and financial or transport constraints 31 . Some exposures, such as residential care status, disability, and receipt of financial support, are likely to reflect advanced disease severity or functional decline rather than purely upstream social disadvantage. As such, the associations observed in this analysis should not be interpreted as causal pathways linking social disadvantage to mortality. Instead, these indicators highlight how social and structural vulnerability cluster around individuals with multimorbidity who experience substantially elevated mortality risk. From a health-system perspective, these markers remain highly relevant for identifying populations who may benefit from earlier preventative care, enhanced monitoring, integrated social support, or timely palliative-informed interventions. While previous work has largely focused on clinical complexity, our findings reinforce the need to situate multimorbidity within its broader social context 2 , 9 , 32 . The high mortality burden associated with residential care is consistent with recent studies showing that most die within a few years of admission, with dementia accounting for a substantial share of deaths 33 . We also observed that severe mental illness was a strong predictor of premature mortality, which accords with other research in this area 34 , 35 . Although some of these patterns may reflect advanced disease, frailty, or increased vulnerability may also highlight unmet needs within care settings. In contrast to much of the existing literature 36 – 38 , we observed lower adjusted mortality risk among Asian, Black, and Mixed ethnic groups compared with White individuals. This counterintuitive pattern warrants careful interpretation and should not be understood as evidence of reduced structural disadvantage among ethnic minority populations. Several explanations may contribute, including selection effects, differential survival to multimorbidity onset, heterogeneity within broad ethnic categories, and potential under-recording or misclassification of conditions in routine data. The absence of information on country of birth, migration history, and duration of residence within CPRD limits our ability to fully explore mechanisms such as the healthy immigrant effect, whereby some migrant populations may initially exhibit better health profiles that attenuate over time 39 . Other potential explanations may include protective cultural practices, differences in healthcare utilisation, or community–level resilience factors 40 – 42 . Understanding these mechanisms is important to explain why members of the majority ethnic group do not necessarily confer a survival advantage in populations with multimorbidity. These findings have important implications for community and primary care services. Younger adults with multimorbidity who experience socioeconomic vulnerability represent a high-risk group that may benefit from proactive, coordinated care models integrating medical, social, and welfare support. Risk stratification tools within primary care could incorporate markers such as deprivation, disability, and care status to support earlier intervention. At a system level, integrated care systems and local authorities may use routinely collected data to identify geographically clustered inequities and prioritise targeted prevention and support initiatives. Strengths and Limitations A major strength of this study is its scale, comprising over 4.3 million individuals followed using linked primary care data from CPRD, providing substantial statistical power and a broadly representative picture of the English population. The use of linked datasets, including ONS mortality records, enabled robust ascertainment of premature mortality outcomes. Application of two established equity frameworks, CORE20PLUS5 and PROGRESS-PLUS, offered a structured and systematic approach to identifying inequality-relevant domains within routinely collected electronic health records. However, several limitations should be considered. The study relied on routinely collected data, which may be affected by coding variability and under-recording, particularly for socioeconomic exposures such as housing instability, informal caregiving, or social support. Some exposures, including residential care, disability, and financial support, may reflect disease severity or proximity to death, introducing potential reverse causation that cannot be fully addressed through statistical adjustment alone. Consequently, findings should be interpreted as associations rather than evidence of causal inequity pathways. The extended study period (1987–2020) spans substantial changes in healthcare delivery, social policy, diagnostic practices, and survival trends. Although period effects were not explicitly modelled, the persistence of observed socioeconomic gradients suggests enduring patterns of inequality across time. Premature mortality was defined using an age threshold of 75 years in line with UK public health standards; however, this approach results in variable follow-up duration depending on age at multimorbidity onset, which may introduce differential observation bias. Approximately 10.8% of participants had missing ethnicity data, potentially reducing precision in subgroup analyses. Additionally, CPRD lacks information on key PROGRESS-PLUS domains such as occupation, education, religion, migration history, and individual-level socioeconomic capital. Maternity was also not included, as it is not considered a long-term condition within CPRD. As such, our operationalisation of equity frameworks should be regarded as framework-informed but constrained by data availability. Finally, as this was an observational study, all associations should be interpreted as non-causal. Conclusion and Future Implications Premature mortality in adults who develop multimorbidity before the age of 60 is closely associated with socioeconomic disadvantage and markers of structural vulnerability. Residential care status, disability, financial support, socioeconomic deprivation, age at multimorbidity onset, sex, and geographic region were all associated with mortality risk. While these factors should not be interpreted as causal determinants, they identify population groups experiencing markedly elevated vulnerability within the context of multimorbidity. The application of CORE20PLUS5 and PROGRESS-PLUS frameworks supports the use of routinely collected data to inform population-level surveillance, risk stratification, and service planning for younger adults with multimorbidity. Future research should aim to disentangle modifiable social pathways from clinical severity, incorporate temporal and life-course approaches, and integrate patient-reported outcomes and lived experience to better understand mechanisms driving premature mortality. Such work is essential for developing interventions that are responsive to the complex social realities faced by younger adults living with multiple long-term conditions. Abbreviations AIM: Artificial Intelligence for Multiple Long-Term Conditions BMC: BioMed Central CI: Confidence interval CNC: Cross-NIHR Collaboration CORE20PLUS5: A National Health Service England approach to reducing healthcare inequalities, focusing on the most deprived 20% and inclusion health groups CPRD: Clinical Practice Research Datalink GP: General practitioner HES: Hospital Episode Statistics HR: Hazard ratio ICD-10: International Classification of Diseases, 10th Revision IMD: Index of Multiple Deprivation IQR: Interquartile range ISAC: Independent Scientific Advisory Committee MLTC: Multiple Long-Term Conditions NHS: National Health Service NIHR: National Institute for Health and Care Research ONS: Office for National Statistics ORCID: Open Researcher and Contributor ID PROGRESS-PLUS: Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, plus additional factors associated with disadvantage Q1–Q5: Quintiles 1 to 5 SMI: Severe mental illness SNOMED: Systematized Nomenclature of Medicine UK: United Kingdom UKHSA: UK Health Security Agency WHO: World Health Organization Declarations Funding: This report is independent research funded by the National Institute for Health Research (“NIHR202637”). HDM receives funding from Multiple Long-Term Conditions (MLTC) Cross NIHR Collaboration (CNC) (NIHR207000). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. Acknowledgements We would like to thank our patient and public contributors. Authors’ contribution HDM conceived and supervised the study and acted as the corresponding author. AJ led the study, conducted the main work, and drafted the manuscript. NI contributed to the statistical analysis. TI contributed to manuscript writing and interpretation of the findings. LS contributed to manuscript writing and critical revision of the paper. MH contributed to critical revision of the manuscript. All authors reviewed the manuscript, provided intellectual input, and approved the final version. Declaration of conflicting interests None declared. Data availability statement Data are available upon reasonable request. Requests for access to data from the study should be addressed to CPRD ( [email protected] ). All proposals requesting data access will need to specify planned uses with the approval of the study team and CPRD before the data release. Supplemental Material Supplemental material for this article is available online. Ethical approval and permission Ethical approval was granted by the University of Southampton Faculty of Medicine Research Committee (67953). The study was also approved by the Independent Scientific Advisory Group of the CPRD (ISAC protocol number 21_001667). The research was conducted in accordance with the Declaration of Helsinki and relevant governance requirements for the use of anonymised routinely collected health data. Informed consent from individual participants was not required because the study used anonymised secondary care data. Clinical trial number: not applicable. References 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. Katikireddi SV, Skivington K, Leyland AH, Hunt K, Mercer SW. The contribution of risk factors to socioeconomic inequalities in multimorbidity across the lifecourse: a longitudinal analysis of the Twenty-07 cohort. BMC Medicine. 2017;15(1):152. Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):48. Candace Imison DDoDaKMatN. Multiple long-term conditions (multimorbidity) and inequality- addressing the challenge September 2023 [Available from: https://doi.org/10.3310/nihrevidence_59977. Fortin M, Soubhi H, Hudon C, Bayliss EA, van den Akker M. Multimorbidity's many challenges. Bmj. 2007;334(7602):1016-7. UK D. One in 4 people in England living with two or more long-term conditions: The British Diabetic Association operating as Diabetes UK, a charity registered in England and Wales (no. 215199) and in Scotland (no. SC039136). ; 2021-08-17 [Available from: https://www.diabetes.org.uk/about-us/news-and-views/multiple-long-term-conditions-report. Soley-Bori M, Ashworth M, Bisquera A, Dodhia H, Lynch R, Wang Y, Fox-Rushby J. Impact of multimorbidity on healthcare costs and utilisation: a systematic review of the UK literature. British Journal of General Practice. 2021;71(702):e39-e46. Chudasama YV, Khunti K, Gillies CL, Dhalwani NN, Davies MJ, Yates T, Zaccardi F. Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: A longitudinal cohort study. PLoS Med. 2020;17(9):e1003332. Wu J, Zhang H, Shao J, Chen D, Xue E, Huang S, et al. Healthcare for Older Adults with Multimorbidity: A Scoping Review of Reviews. Clin Interv Aging. 2023;18:1723-35. Pearson-Stuttard J, Ezzati M, Gregg EW. Multimorbidity—a defining challenge for health systems. The Lancet Public Health. 2019;4(12):e599-e600. Mahadevan P, Harley M, Fordyce S, Hodgson S, Ghosh R, Myles P, et al. Completeness and representativeness of small area socioeconomic data linked with the UK Clinical Practice Research Datalink (CPRD). J Epidemiol Community Health. 2022;76(10):880-6. Singer L, Green M, Rowe F, Ben-Shlomo Y, Kulu H, Morrissey K. Trends in multimorbidity, complex multimorbidity and multiple functional limitations in the ageing population of England, 2002–2015. Journal of comorbidity. 2019;9:2235042X19872030. Salive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75-83. Nunes BP, Flores TR, Mielke GI, Thumé E, Facchini LA. Multimorbidity and mortality in older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2016;67:130-8. Jani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, Mair FS. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Medicine. 2019;17(1):74. Lewer D, Aldridge RW, Menezes D, Sawyer C, Zaninotto P, Dedicoat M, et al. Health-related quality of life and prevalence of six chronic diseases in homeless and housed people: a cross-sectional study in London and Birmingham, England. BMJ Open. 2019;9(4):e025192. Rój J. Inequity in the Access to eHealth and Its Decomposition Case of Poland. International Journal of Environmental Research and Public Health. 2022;19(4):2340. WHO. Health inequities and their causes [updated 22 February 2018. UKHSA. Health inequalities in health protection report 2025 - GOV.UK [updated 27 May 2025. Pennsylvania Uo. What Is Structural Inequality? The Center for High Impact Philanthropy [Available from: https://www.impact.upenn.edu/what-is-structural-inequality/. Oliver S, Kavanagh J, Caird J, Lorenc T, Oliver K, Harden A, et al. Health Promotion, Inequalities and Young People's health: a Systematic Review of Research. 2008. A B. - Applying the CORE20PLUS5 to Address Health Inequalities for Patients Under the Rehabilitation and Recovery Service in the London Borough of Hackney. BJPsych Open. 2024;10(Suppl 1). Karran EL, Cashin AG, Barker T, Boyd MA, Chiarotto A, Dewidar O, et al. Using PROGRESS-plus to identify current approaches to the collection and reporting of equity-relevant data: a scoping review. J Clin Epidemiol. 2023;163:70-8. Herrett E, Gallagher AM, Bhaskaran K, Forbes H, Mathur R, van Staa T, Smeeth L. Data Resource Profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol. 2015;44(3):827-36. Agency MHpR. CPRD GOLD March 2025 [Available from: https://doi.org/10.48329/kfzv-jv89. Wolf A, Dedman D, Campbell J, Booth H, Lunn D, Chapman J, Myles P. Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum. Int J Epidemiol. 2019;48(6):1740-g. Agency MHpR. CPRD- Defining your study population. Dambha-Miller H, Farmer, Andrew, Nirantharakumar, K., Jackson, T., Yau, C., Walker, L., Buchan, I., Finer, S., Barnes, M.R., Reynolds, N.J., Jun, GT, Gangadharan, S, Fraser, Simon and Guthrie, Bruce Artificial intelligence for multiple long-term conditions (AIM): a consensus statement from the NIHR AIM consortia. 2023 [NIHR open research, 3 (21):[Available from: http://dx.doi.org/10.3310/nihropenres.1115210.1. NHSEngland. Core20PLUS5 (adults) – an approach to reducing healthcare inequalities [Available from: https://www.england.nhs.uk/about/equality/equality-hub/national-healthcare-inequalities-improvement-programme/core20plus5/. CochraneEquity. PROGRESS-Plus | Cochrane Equity Methods Group [Available from: https://methods.cochrane.org/equity/projects/evidence-equity/progress-plus. WHO. Xue Q, Zhang S, Yang X, Zhang Y-B, Dong Y, Li F, et al. Multimorbidity patterns and premature mortality in a prospective cohort: effect modifications by socioeconomic status and healthy lifestyles. BMC Public Health. 2025;25(1):1262. MacRae J, Ciminata G, Geue C, Lynch E, Shenkin SD, Quinn TJ, Burton JK. Mortality in long-term care residents: retrospective national cohort study. BMJ Supportive & Palliative Care. 2024:spcare-2024-005163. Andersen MP, Mills EHA, Meddis A, Sørensen KK, Butt JH, Køber L, et al. All-cause mortality among Danish nursing home residents before and during the COVID-19 pandemic: a nationwide cohort study. European Journal of Epidemiology. 2023;38(5):523-31. Morciano M, Stokes J, Kontopantelis E, Hall I, Turner AJ. Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study. BMC Medicine. 2021;19(1):71. Hayanga B, Stafford M, Bécares L. Ethnic inequalities in multiple long-term health conditions in the United Kingdom: a systematic review and narrative synthesis. BMC Public Health. 2023;23(1):178. Quiñones AR, Newsom JT, Elman MR, Markwardt S, Nagel CL, Dorr DA, et al. Racial and Ethnic Differences in Multimorbidity Changes Over Time. Med Care. 2021;59(5):402-9. Xue Q, Zhang S, Yang X, Zhang YB, Dong Y, Li F, et al. Multimorbidity patterns and premature mortality in a prospective cohort: effect modifications by socioeconomic status and healthy lifestyles. BMC Public Health. 2025;25(1):1262. Kennedy S, Kidd MP, McDonald JT, Biddle N. The Healthy Immigrant Effect: Patterns and Evidence from Four Countries. Journal of International Migration and Integration. 2015;16(2):317-32. Bécares L. Which ethnic groups have the poorest health? In: Jivraj S, Simpson L, editors. Ethnic Identity and Inequalities in Britain: The Dynamics of Diversity: Bristol University Press; 2015. p. 123-40. Nazroo JY. The structuring of ethnic inequalities in health: economic position, racial discrimination, and racism. Am J Public Health. 2003;93(2):277-84. Wallace S, Nazroo J, Bécares L. Cumulative Effect of Racial Discrimination on the Mental Health of Ethnic Minorities in the United Kingdom. American journal of public health. 2016;106:e1-e7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 05 May, 2026 Editor assigned by journal 05 May, 2026 Editor invited by journal 04 May, 2026 Submission checks completed at journal 01 May, 2026 First submitted to journal 01 May, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9451391","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634913873,"identity":"3dfc8ca6-a718-49e4-9ac1-c8b91d34dbab","order_by":0,"name":"Aman Jat","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Aman","middleName":"","lastName":"Jat","suffix":""},{"id":634913875,"identity":"ddb01693-7f44-4034-91c3-162f8dcccb74","order_by":1,"name":"Lucy Smith","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Smith","suffix":""},{"id":634913877,"identity":"baa47a01-0fff-4458-9b83-6bd1ddc72f40","order_by":2,"name":"Tassella Isaac","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Tassella","middleName":"","lastName":"Isaac","suffix":""},{"id":634913879,"identity":"a94363ed-1f1c-402e-8147-e50ac46f3cb4","order_by":3,"name":"Mehedi Hasan","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Mehedi","middleName":"","lastName":"Hasan","suffix":""},{"id":634913880,"identity":"b8aa3626-8b20-4874-9638-4aaceaee5eff","order_by":4,"name":"Nazrul Islam","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Nazrul","middleName":"","lastName":"Islam","suffix":""},{"id":634913882,"identity":"5834410c-e7d6-4e19-aecc-6d9d373dad50","order_by":5,"name":"Hajira Dambha-Miller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIie3RMUsDMRTA8VcO6hLoGoiYr3Ah4CLar/LCwd0UEFwKCk2X69i1m1+hYzcjAbvcB1Aqckehs7gKYu5uUOROOzrkv4Zf8h4BCIX+YbEdWRgYC0DVzJYAJ98Ohz2EYEuOK2MRQAJEf5K4JbyEmihzABFvT+tnDizxr0xestv54n53CRccaIpdZGyJZLrYC8OUJ8WVXhUukktIhKGp7RuM6dwN7raeqBz1iiZDRiBCoJnpIfLdk7GhNfnAjC8bMv2NnNavqJYYRHhsiPOkZzBH0jNduKQh+IDC7yIliTciJ/vO9ePNwm312p0bmlXl6w1yPp9VOzK55qOjNO4iX5/w46q+XwmFQqHQIX0CIudpWGJygm0AAAAASUVORK5CYII=","orcid":"","institution":"University of Southampton","correspondingAuthor":true,"prefix":"","firstName":"Hajira","middleName":"","lastName":"Dambha-Miller","suffix":""}],"badges":[],"createdAt":"2026-04-17 16:39:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9451391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9451391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109279157,"identity":"baaa11e6-25b5-420c-ab21-9799971686f6","added_by":"auto","created_at":"2026-05-14 16:18:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Premature Mortality by Age, Sex, Ethnicity, and Socioeconomic Deprivation (IMD Quintile) in Adults with Multimorbidity Under 60 Years in England\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis figure shows the distribution of premature mortality (death before age 75 years) among 4,303,353 individuals aged 18-60 years at multimorbidity onset, identified from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases between January 1, 1987, and December 31, 2020. Mortality patterns are stratified by age group, sex, ethnicity, and Index of Multiple Deprivation (IMD) quintiles.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9451391/v1/bb8c441e417a9da290bd51a6.png"},{"id":109297874,"identity":"a3a66297-f375-41e7-bd90-6e8c3ceac22c","added_by":"auto","created_at":"2026-05-15 09:07:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Inclusion and Exclusion Criteria for the Final Analytical Cohort of 4,303,353 adults with Multimorbidity in England \u003c/strong\u003e\u003cbr\u003e\nThis figure shows the selection of adults aged 18-60 years with incident multimorbidity, identified from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases between January 1, 1987, and December 31, 2020. Of 7.3 million initially identified, exclusions were applied for multimorbidity onset outside the 18-60 age range, missing data (age, sex, deprivation index), and small regional sample size. The final cohort comprised 4,303,353 adults with valid records, followed until death, deregistration, study end, or age cut off 75.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9451391/v1/251834ed7e78bde8f4edad70.png"},{"id":109279159,"identity":"3173ef7f-f872-4f9c-9270-2bc012adaf44","added_by":"auto","created_at":"2026-05-14 16:18:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios (HRs) for premature mortality among adults with multimorbidity in England, Clinical Practice Research Datalink (CPRD), 1987–2020.\u003c/strong\u003e\u003cbr\u003e\nThis figure shows adjusted hazard ratios (HRs) with 95% confidence intervals from Cox proportional hazards models in a cohort of 4,303,353 Adults aged 18–60 years at multimorbidity onset, identified from CPRD Gold and Aurum databases. Premature mortality was defined as death at or before age 75 years. The vertical red dashed line at HR = 1.0 indicates the reference level. Reference categories were age 18–39 years, male sex, White ethnicity, the least deprived IMD quintile (Q1), the London region, and the absence of disability, financial support, and residential care. Factors to the right of the line indicate increased risk, and those to the left indicate reduced risk of premature mortality.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9451391/v1/dc6c613f71cb747e40729be1.png"},{"id":109296445,"identity":"83a70578-3b89-446b-b4af-cfa81af3bdd7","added_by":"auto","created_at":"2026-05-15 08:47:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":514335,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9451391/v1/1650b395-07ac-488d-abf4-435a850cfda0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inequity in Premature Mortality: A Cohort Study of 4.3 million Adults Under 60 years With Multimorbidity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultimorbidity \u0026ndash; the co-occurrence of two or more chronic conditions \u0026ndash; is a growing global and national challenge\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Although traditionally regarded as a condition of ageing, multimorbidity is increasingly observed among adults under 60 years, with around one in four in England living with multiple chronic conditions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In this group, multimorbidity is associated with reduced quality of life, increased healthcare utilisation, and elevated risk of premature mortality\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Given that younger adults comprise much of the economically active population, early morbidity and mortality carry wide-ranging socioeconomic consequences\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Despite the scale and implications of multimorbidity in mid-life, premature mortality in this population remains underexplored. Most studies focus on older adults\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or examine risk factors such as deprivation, disability, or clinical status in isolation, overlooking how these factors intersect to impact survival\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. These evidence gaps point to the value of an inequity lens, recognising that structural disadvantages ultimately drive inequalities in health outcomes.\u003c/p\u003e \u003cp\u003eHealth inequity refers to unfair and avoidable differences in illness, disability, and mortality across populations, typically driven by systemic factors such as socioeconomic deprivation, geographic location, ethnicity, and other socioeconomic determinants of health\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. For example, adults living in more deprived areas of England experience shorter lifespans and spend more years with chronic conditions such as diabetes, cardiovascular disease, or chronic pain compared with those in more affluent areas\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These inequities emerge from structural inequalities i.e., disparities that create barriers through systemic poverty, housing insecurity, and unequal access to healthcare, which constrain opportunities for health across the life course\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, leading to earlier disease onset, higher prevalence of multimorbidity, and increased risk of premature death\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Positioning multimorbidity within this framework highlights the need to address not only clinical risk factors but also the socioeconomic and structural determinants that influence health outcomes.\u003c/p\u003e \u003cp\u003eThere is growing recognition that multimorbidity before age 60 years is strongly patterned by socioeconomic disadvantage and structural inequities, making it both a driver and a marker of health inequality across the life course\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, few studies have systematically examined premature mortality in this population using equity-oriented frameworks to help quantify the impact of inequality. To address this gap, we applied two complementary inequality frameworks to capture socioeconomic and structural inequity: CORE20PLUS5, which prioritises the most deprived populations and key clinical areas of inequality\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and PROGRESS-PLUS, which encompasses a broader set of socioeconomic stratifies including place, race/ethnicity, occupation, sex, education, socioeconomic status, and discrimination\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. By focusing on adults under 60 years \u0026ndash; a consistently underrepresented group in multimorbidity research \u0026ndash; this study aims to generate new insights using the CORE20PLUS5 and PROGRESS-PLUS inequality frameworks to examine socioeconomic disadvantage and structural inequity as determinants of premature mortality in younger adults with multimorbidity\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eA retrospective, population-based cohort was conducted, using routinely collected primary care data from the Clinical Practice Research Datalink (CPRD)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, including both the Gold\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and Aurum\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e datasets. CPRD data include anonymised medical records from general practices across England, including clinical diagnoses, medications, referrals, and demographic details. The CPRD population is broadly representative of England in terms of age, sex, ethnicity, and socioeconomic status, as measured by the Index of Multiple Deprivation (IMD) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The study period ran from 1 January 1987 to 31 December 2020. Individual records were linked to external data sources, including Hospital Episode Statistics (HES), Office for National Statistics 5 (ONS) mortality data, and NHS Digital datasets for deprivation indices and socioeconomic vulnerability indicators.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThe CPRD database contained 7,310,752 individuals. We restricted inclusion to adults aged 18\u0026ndash;60 years at the time of incident multimorbidity diagnosis, defined as the earliest date on which a participant was diagnosed with a second long-term condition. Multimorbidity was identified using a validated list of 59 conditions that were identifiable in CPRD using Read, SNOMED, or ICD-10 codes.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Participants were required to be registered with a CPRD practice for at least 12 months prior to this index date. We excluded individuals with multimorbidity onset outside the 18\u0026ndash;60-year age range, implausible or inconsistent diagnosis dates, or incomplete demographic data. Missingness included age index (age at multimorbidity diagnosis, n\u0026thinsp;=\u0026thinsp;20,042), IMD score (n\u0026thinsp;=\u0026thinsp;14,596), indeterminate sex (n\u0026thinsp;=\u0026thinsp;74), and a very small regional sample size from the Southeast Coast (n\u0026thinsp;=\u0026thinsp;1,583). After exclusions, the final analytical cohort consisted of 4,303,353 individuals with valid data for subsequent analyses.\u003c/p\u003e\n\u003ch3\u003eFollow-up and outcome\u003c/h3\u003e\n\u003cp\u003eParticipants were followed from the date of multimorbidity onset until the earliest of date of death, deregistration from the practice, the end of the study period (31 December 2020), or age cut-off at 75 years. The primary outcome was premature mortality, defined as death from any cause at age 75 years or younger. Individuals who survived beyond age 75, or died after reaching 75, were censored and classified as not having experienced premature mortality. The age threshold of 75 years was chosen in line with UK public health definitions of premature mortality, which commonly define deaths before age 75 as avoidable or early deaths and use this benchmark for national monitoring of health inequalities.\u003c/p\u003e\n\u003ch3\u003eExposure variables\u003c/h3\u003e\n\u003cp\u003eWe operationalised inequality using the CORE20PLUS5 and PROGRESS-PLUS frameworks. To address inequalities at both national and local levels, NHS England developed the CORE20PLUS5 framework to guide improvements in healthcare and promote equity. The \u0026ldquo;CORE20\u0026rdquo; refers to the most deprived 20% of the national population as defined by the Index of Multiple Deprivation (IMD). The \u0026ldquo;PLUS\u0026rdquo; highlights inclusion health groups that experience additional disadvantage, such as ethnic minority communities, adults with learning disabilities, individuals living with multimorbidity, and other socially marginalised populations. The \u0026ldquo;5\u0026rdquo; identifies five clinical priority areas such as maternity, severe mental illness (SMI), Chronic respiratory disease, early cancer diagnosis and hypertension case-finding and optimal management and lipid optimal management\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. PROGRESS-PLUS is another widely used equity framework, originally developed by the Cochrane Equity Methods Group, to identify how health opportunities and outcomes are socioeconomically stratified\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The acronym stands for Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and social capital. The \u0026ldquo;PLUS\u0026rdquo; extends this framework to include additional factors such as personal characteristics associated with discrimination (e.g., age, disability), features of relationships (e.g., family structure, social support), and time-dependent factors (e.g., life-course stage, transitions such as hospital discharge or entry into long-term care) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin our dataset, we identified the following primary exposures of interest from the above frameworks: disability, financial support, residential care, and geographic region. Covariates included age, sex, IMD quintile, race/ethnicity and overall multimorbidity burden (total number of long-term conditions recorded for each individual). Disability was defined as a binary baseline indicator of any recorded disability at or before the index date, including mental health, intellectual, learning, speech, hearing, or sensory disability codes. Financial support was defined as a binary baseline indicator of any recorded receipt of financial support or financial vulnerability at or before the index date, including disability living allowance, mobility allowance, or codes indicating financial support needs or financial difficulty. Residential care was defined as a binary baseline indicator of recorded residence in a care home, nursing home, or hospice at or before the index date. Participants living in their own home, or with no recorded institutional-care code, were classified as not in residential care. Area deprivation was measured using the Index of Multiple Deprivation (IMD) quintiles, with quintile 1 representing the least deprived areas, in line with the CORE20 framework. The geographic region was derived from the CPRD practice-location variable. The source data contained 10 England region categories using ONS. One category, labelled Southeast Coast, contained a very small number of participants and was excluded a priori because sparse data would yield unstable estimates. The final analyses, therefore, included nine regional categories, with London used as the reference group in regression models. Ethnicity was categorised using the ONS five-group standard\u0026ndash; White, Mixed, Asian, Black, Other. Age was measured as a continuous variable at multimorbidity diagnosis (index date) and categorised for subgroup analyses (18\u0026ndash;39, 40\u0026ndash;60, \u0026ge;\u0026thinsp;75 years). Sex was recorded as male, female, or indeterminate in GP records. Overall multimorbidity burden was defined as the total number of long-term conditions recorded for each individual, based on a validated list of 56 conditions. Full details of all variables extracted, and their alignment with CORE20PLUS5 and PROGRESS-PLUS domains, are provided in Supplementary Table\u0026nbsp;4. Not all measures in the inequality frameworks were available in CPRD, including occupation, education, and religion. All exposure variables were defined using records available at or before the date of incident multimorbidity (index date). These variables were treated as baseline exposures and were not updated during follow-up. This approach was adopted to minimise reverse causation arising from changes in social or care status occurring close to death. However, it is acknowledged that some exposures, particularly residential care and financial support, may still reflect underlying disease severity present at baseline.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe first carried out descriptive analyses to characterise the multimorbidity cohort. Demographic factors (age, sex, ethnicity, region, and deprivation quintile), social exposures (disability, financial support, and residential care), and clinical groups were summarised. Baseline characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were described overall and stratified by premature mortality status (alive/censored vs death\u0026thinsp;\u0026le;\u0026thinsp;75 years), with distributions presented as counts and percentages. Proportional stacked bar plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were also used to visualise differences in mortality across age categories, sex, ethnicity, and deprivation quintiles. We used multivariable Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between socioeconomic inequality exposures and premature mortality. The adjusted model included socioeconomic inequality variables (CORE20PLUS5 and PROGRESS-PLUS), along with covariates: age at multimorbidity onset, sex, IMD quintile, ethnicity and clinical conditions. Results were visualised in a forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), displaying adjusted hazard ratios with 95% confidence intervals against a reference line at HR\u0026thinsp;=\u0026thinsp;1.0. Subgroup analyses and stratified Cox regression were conducted stratified by age, sex, ethnicity, and IMD quintile (2\u0026ndash;3 vs\u0026thinsp;\u0026ge;\u0026thinsp;4). All analyses were conducted using R (4.4.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation characteristics\u003c/h2\u003e \u003cp\u003eOur final cohort comprised 4,303,353 individuals aged under 60 years with a diagnosis of at least two long-term conditions (i.e. multimorbidity). The median age at multimorbidity onset was 42 years (IQR: 33\u0026ndash;51), 51% were female. Ethnic distribution was 82.0% White. A summary of baseline characteristics is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A large proportion of premature deaths among individuals aged 40\u0026ndash;60 years (368,104 deaths, 82.2% of all premature deaths), compared with 79,616 deaths (17.8%) in those aged 18\u0026ndash;39. The most common multimorbidity was cardiovascular conditions (2,251,062 individuals), followed by mental and behavioural disorders (3,243,656 individuals).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population by survival status (censored alive vs. premature mortality).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive/Censored (n,%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePremature death (n,%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,673,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,593,590 (95.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79,616 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,487,512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,119,408 (85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e368,104 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,893,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,624,685 (85.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e269,102 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,409,566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,205,458 (91.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204,108 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIMD Quintile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (Least deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e744,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e677,319 (91.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67,323 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e803,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e724,238 (90.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79,060 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e824,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e738,083 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86,460 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e923,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e819,147 (88.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104,828 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5 (Most deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,006,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e871,356 (86.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e135,539 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,397,540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,994,083 (88.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e403,457 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33,262 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,938 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204,583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189,130 (92.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,453 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131,748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120,067 (91.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,681 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71,610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66,461 (92.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,149 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e462,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e427,140 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35,532 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNortheast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e541,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e481,807 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59,230 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167,332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146,779 (87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20,553 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYorkshire \u0026amp; Humber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e897,443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e788,782 (87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108,661 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157,509 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,578 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Midlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120,363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,652 (89.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,711 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast of England\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e658,997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e585,723 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73,274 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186,811 (88.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23,479 (11.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth Central\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e722,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e650,467 (90.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72,033 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLondon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e808,304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e724,613 (89.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83,691 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultimorbidity Condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,251,062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,912,219 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e338,843 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic \u0026amp; Endocrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,170,305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,015,261 (86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155,044 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,472,064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,291,548 (87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180,516 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e984,182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e837,500 (85.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146,682 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e777,625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e540,012 (69.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237,613 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental \u0026amp; Behavioural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,243,656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,914,862 (89.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e328,794 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,993,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,765,037 (88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227,970 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518,741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e392,878 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125,863 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrogenital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e478,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421,381 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57,263 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaematological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29,470 (85.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,828 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEye\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29,138 (78.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,092 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98,850 (92.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,484 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76,344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66,523 (87.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9,821 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongenital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55,721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,619 (85.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,102 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocioeconomic characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181,369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141,247 (77.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40,122 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial Support (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77,558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55,583 (71.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21,975 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential Care (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74,356 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47,774 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThis figure shows the distribution of premature mortality (death before age 75 years) among 4,303,353 individuals aged 18\u0026ndash;60 years at multimorbidity onset, identified from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases between January 1, 1987, and December 31, 2020. Mortality patterns are stratified by age group, sex, ethnicity, and Index of Multiple Deprivation (IMD) quintiles.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThis figure shows the selection of adults aged 18\u0026ndash;60 years with incident multimorbidity, identified from the Clinical Practice Research Datalink (CPRD) Gold and Aurum databases between January 1, 1987, and December 31, 2020. Of 7.3\u0026nbsp;million initially identified, exclusions were applied for multimorbidity onset outside the 18\u0026ndash;60 age range, missing data (age, sex, deprivation index), and small regional sample size. The final cohort comprised 4,303,353 adults with valid records, followed until death, deregistration, study end, or age cut off 75.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between socioeconomic and structural inequity and premature mortality\u003c/h3\u003e\n\u003cp\u003eIn fully adjusted Cox regression models, premature mortality was strongly associated with socioeconomic and demographic factors. Residential care carried the highest risk (HR 2.92, 95% CI: 2.89\u0026ndash;2.95), followed by financial support (HR 1.44, 95% CI: 1.42\u0026ndash;1.47) and disability (HR 1.26, 95% CI: 1.24\u0026ndash;1.27). A deprivation gradient was observed, with risk increasing from quintile 2 (HR 1.11, 95% CI: 1.10\u0026ndash;1.12) to quintile 5 (HR 1.61, 95% CI: 1.59\u0026ndash;1.62) compared with the least deprived. Individuals aged 40\u0026ndash;60 years had a higher risk compared with those aged 18\u0026ndash;39 years (HR 2.29, 95% CI: 2.27\u0026ndash;2.30). Female sex was associated with lower risk compared with male sex (HR 0.68, 95% CI: 0.68\u0026ndash;0.69).\u003c/p\u003e \u003cp\u003eBy region, compared with London, risk was higher in the East Midlands (HR 1.12, 95% CI: 1.10\u0026ndash;1.14), East of England (HR 1.13, 95% CI: 1.12\u0026ndash;1.15), North West (HR 1.06, 95% CI: 1.05\u0026ndash;1.07), Yorkshire and Humber (HR 1.05, 95% CI: 1.04\u0026ndash;1.07), West Midlands (HR 1.05, 95% CI: 1.04\u0026ndash;1.06), and South Central (HR 1.03, 95% CI: 1.02\u0026ndash;1.04). Risk was lower in the Southwest (HR 0.96, 95% CI: 0.95\u0026ndash;0.97). There was no statistically significant difference in the Northeast (HR 1.01, 95% CI: 1.00\u0026ndash;1.03).\u003c/p\u003e \u003cp\u003eIn ethnicity, compared with White individuals, risk was lower in Mixed (HR 0.60, 95% CI: 0.57\u0026ndash;0.63), Asian (HR 0.66, 95% CI: 0.65\u0026ndash;0.67), Black (HR 0.71, 95% CI: 0.69\u0026ndash;0.72), and Other (HR 0.66, 95% CI: 0.64\u0026ndash;0.67) groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis figure shows adjusted hazard ratios (HRs) with 95% confidence intervals from Cox proportional hazards models in a cohort of 4,303,353 \u003cem\u003eAdults\u003c/em\u003e aged 18\u0026ndash;60 years at multimorbidity onset, identified from CPRD Gold and Aurum databases. Premature mortality was defined as death at or before age 75 years. The vertical red dashed line at HR\u0026thinsp;=\u0026thinsp;1.0 indicates the reference level. Reference categories \u003cem\u003ewere\u003c/em\u003e age 18\u0026ndash;39 years, male sex, White ethnicity, the least deprived IMD quintile (Q1), the London region, and the absence of disability, financial support, and residential care. Factors to the right of the line indicate increased risk, and those to the left indicate reduced risk of premature mortality.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis and stratified Cox regression\u003c/h2\u003e \u003cp\u003eSubgroup analysis by ethnicity showed that age was associated with premature mortality. Compared with adults aged 18\u0026ndash;39 years, those aged 40\u0026ndash;60 years had higher risks across all ethnic groups. The hazard ratio was 3.47 for White individuals, 2.80 for Asian individuals, and 2.13 for Black individuals. These estimates reflect within-group age differences rather than direct comparisons between ethnic groups. Consistent with the main Cox model, adjusted risks were lower among ethnic minority groups than among White individuals (Asian HR 0.66, Black HR 0.71, Mixed HR 0.60, Other HR 0.66). In the main adjusted Cox model, adults in residential care had a hazard ratio of 2.92 (95% CI: 2.89\u0026ndash;2.95) compared with those not in care. In subgroup analyses by ethnicity, deprivation, and region, hazard ratios ranged from 4.7 to 8.3 in residential care. Disability status was associated with higher risks of premature mortality, with hazard ratios ranging from 1.53 to 1.72 depending on age. Socioeconomic status, measured by the Index of Multiple Deprivation, was also associated with risk: individuals in the lowest IMD quintile had hazard ratios ranging from 1.64 to 1.78 compared with the least deprived group.\u003c/p\u003e \u003cp\u003eStratified Cox models confirmed independent associations between predictors and premature mortality. Residential care had the highest hazard ratio (HR 5.99, 95% CI: 5.91\u0026ndash;6.08), followed by financial support (HR 1.76, 95% CI: 1.71\u0026ndash;1.81) and disability (HR 1.32, 95% CI: 1.29\u0026ndash;1.34). These associations were consistent across sex and ethnic subgroups, with no statistically significant variation in hazard ratios. Age 40\u0026ndash;60 years remained associated with higher risk across all groups, with hazard ratios of 3.37 (95% CI: 3.32\u0026ndash;3.41) in more deprived IMD quintiles. By ethnicity, compared with White individuals, risk was lower in those of Mixed (HR 0.42, 95% CI: 0.39\u0026ndash;0.46), Asian (HR 0.53, 95% CI: 0.51\u0026ndash;0.54), and Black (HR 0.61, 95% CI: 0.59\u0026ndash;0.63) ethnicities. The higher hazard ratios observed in stratified models compared with the fully adjusted pooled model reflect differences in covariate structure and effect modification. In stratified analyses, certain variables were not simultaneously adjusted across strata, which may have resulted in larger within-group effect estimates. These findings suggest potential interaction between social vulnerability markers and demographic characteristics, rather than inconsistency in model specification.\u003c/p\u003e \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we examined premature mortality among 4.3\u0026nbsp;million individuals with multimorbidity diagnosed before age 60 years. We observed strong associations between mortality risk and a range of socioeconomic, demographic, and structural indicators. Residential care status, receipt of financial support, disability, and higher socioeconomic deprivation were all associated with increased mortality risk. Age at multimorbidity onset emerged as a major predictor, with onset between 40\u0026ndash;60 years associated with more than double the risk of premature death compared with onset between 18\u0026ndash;39 years. Female sex was consistently associated with lower mortality risk than male sex. Regionally, higher risks were observed outside London, particularly in the East Midlands and East of England. By ethnicity, Asian, Black, Mixed, and Other ethnic groups demonstrated lower adjusted risks of premature mortality compared with White individuals. Subgroup analyses confirmed increased risk with older age across all ethnic groups, while stratified models showed the highest mortality risk among individuals recorded as living in residential care, with additional associations observed for disability and socioeconomic disadvantage.\u003c/p\u003e \u003cp\u003eOur findings are consistent with previous research documenting the influence of socioeconomic determinants on health outcomes in multimorbidity, although most prior studies have focused on older adults, with relatively little attention to younger populations\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. By contrast, this study demonstrates that socioeconomic vulnerability \u0026ndash; including residential care, financial hardship, and disability \u0026ndash; also exert a strong effect on premature mortality at this young age group. These findings reinforce the importance of examining multimorbidity through a life-course perspective, recognising that disadvantage accumulates well before older age and may contribute to early mortality trajectories. The 2022 WHO Global Report on Health Equity similarly reported that adults living with disabilities experience earlier mortality, often driven by non-clinical barriers such as difficulties accessing healthcare, limited availability of comprehensive information, and financial or transport constraints\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSome exposures, such as residential care status, disability, and receipt of financial support, are likely to reflect advanced disease severity or functional decline rather than purely upstream social disadvantage. As such, the associations observed in this analysis should not be interpreted as causal pathways linking social disadvantage to mortality. Instead, these indicators highlight how social and structural vulnerability cluster around individuals with multimorbidity who experience substantially elevated mortality risk. From a health-system perspective, these markers remain highly relevant for identifying populations who may benefit from earlier preventative care, enhanced monitoring, integrated social support, or timely palliative-informed interventions.\u003c/p\u003e \u003cp\u003eWhile previous work has largely focused on clinical complexity, our findings reinforce the need to situate multimorbidity within its broader social context\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The high mortality burden associated with residential care is consistent with recent studies showing that most die within a few years of admission, with dementia accounting for a substantial share of deaths\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. We also observed that severe mental illness was a strong predictor of premature mortality, which accords with other research in this area\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Although some of these patterns may reflect advanced disease, frailty, or increased vulnerability may also highlight unmet needs within care settings.\u003c/p\u003e \u003cp\u003eIn contrast to much of the existing literature\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, we observed lower adjusted mortality risk among Asian, Black, and Mixed ethnic groups compared with White individuals. This counterintuitive pattern warrants careful interpretation and should not be understood as evidence of reduced structural disadvantage among ethnic minority populations. Several explanations may contribute, including selection effects, differential survival to multimorbidity onset, heterogeneity within broad ethnic categories, and potential under-recording or misclassification of conditions in routine data. The absence of information on country of birth, migration history, and duration of residence within CPRD limits our ability to fully explore mechanisms such as the healthy immigrant effect, whereby some migrant populations may initially exhibit better health profiles that attenuate over time\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Other potential explanations may include protective cultural practices, differences in healthcare utilisation, or community\u0026ndash;level resilience factors\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Understanding these mechanisms is important to explain why members of the majority ethnic group do not necessarily confer a survival advantage in populations with multimorbidity.\u003c/p\u003e \u003cp\u003eThese findings have important implications for community and primary care services. Younger adults with multimorbidity who experience socioeconomic vulnerability represent a high-risk group that may benefit from proactive, coordinated care models integrating medical, social, and welfare support. Risk stratification tools within primary care could incorporate markers such as deprivation, disability, and care status to support earlier intervention. At a system level, integrated care systems and local authorities may use routinely collected data to identify geographically clustered inequities and prioritise targeted prevention and support initiatives.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eA major strength of this study is its scale, comprising over 4.3\u0026nbsp;million individuals followed using linked primary care data from CPRD, providing substantial statistical power and a broadly representative picture of the English population. The use of linked datasets, including ONS mortality records, enabled robust ascertainment of premature mortality outcomes. Application of two established equity frameworks, CORE20PLUS5 and PROGRESS-PLUS, offered a structured and systematic approach to identifying inequality-relevant domains within routinely collected electronic health records.\u003c/p\u003e \u003cp\u003eHowever, several limitations should be considered. The study relied on routinely collected data, which may be affected by coding variability and under-recording, particularly for socioeconomic exposures such as housing instability, informal caregiving, or social support. Some exposures, including residential care, disability, and financial support, may reflect disease severity or proximity to death, introducing potential reverse causation that cannot be fully addressed through statistical adjustment alone. Consequently, findings should be interpreted as associations rather than evidence of causal inequity pathways.\u003c/p\u003e \u003cp\u003eThe extended study period (1987\u0026ndash;2020) spans substantial changes in healthcare delivery, social policy, diagnostic practices, and survival trends. Although period effects were not explicitly modelled, the persistence of observed socioeconomic gradients suggests enduring patterns of inequality across time. Premature mortality was defined using an age threshold of 75 years in line with UK public health standards; however, this approach results in variable follow-up duration depending on age at multimorbidity onset, which may introduce differential observation bias.\u003c/p\u003e \u003cp\u003eApproximately 10.8% of participants had missing ethnicity data, potentially reducing precision in subgroup analyses. Additionally, CPRD lacks information on key PROGRESS-PLUS domains such as occupation, education, religion, migration history, and individual-level socioeconomic capital. Maternity was also not included, as it is not considered a long-term condition within CPRD. As such, our operationalisation of equity frameworks should be regarded as framework-informed but constrained by data availability. Finally, as this was an observational study, all associations should be interpreted as non-causal.\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusion and Future Implications","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003cp\u003ePremature mortality in adults who develop multimorbidity before the age of 60 is closely associated with socioeconomic disadvantage and markers of structural vulnerability. Residential care status, disability, financial support, socioeconomic deprivation, age at multimorbidity onset, sex, and geographic region were all associated with mortality risk. While these factors should not be interpreted as causal determinants, they identify population groups experiencing markedly elevated vulnerability within the context of multimorbidity.\u003c/p\u003e \u003cp\u003eThe application of CORE20PLUS5 and PROGRESS-PLUS frameworks supports the use of routinely collected data to inform population-level surveillance, risk stratification, and service planning for younger adults with multimorbidity. Future research should aim to disentangle modifiable social pathways from clinical severity, incorporate temporal and life-course approaches, and integrate patient-reported outcomes and lived experience to better understand mechanisms driving premature mortality. Such work is essential for developing interventions that are responsive to the complex social realities faced by younger adults living with multiple long-term conditions.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAIM: Artificial Intelligence for Multiple Long-Term Conditions\u003c/p\u003e\n\u003cp\u003eBMC: BioMed Central\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003eCNC: Cross-NIHR Collaboration\u003c/p\u003e\n\u003cp\u003eCORE20PLUS5: A National Health Service England approach to reducing healthcare inequalities, focusing on the most deprived 20% and inclusion health groups\u003c/p\u003e\n\u003cp\u003eCPRD: Clinical Practice Research Datalink\u003c/p\u003e\n\u003cp\u003eGP: General practitioner\u003c/p\u003e\n\u003cp\u003eHES: Hospital Episode Statistics\u003c/p\u003e\n\u003cp\u003eHR: Hazard ratio\u003c/p\u003e\n\u003cp\u003eICD-10: International Classification of Diseases, 10th Revision\u003c/p\u003e\n\u003cp\u003eIMD: Index of Multiple Deprivation\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile range\u003c/p\u003e\n\u003cp\u003eISAC: Independent Scientific Advisory Committee\u003c/p\u003e\n\u003cp\u003eMLTC: Multiple Long-Term Conditions\u003c/p\u003e\n\u003cp\u003eNHS: National Health Service\u003c/p\u003e\n\u003cp\u003eNIHR: National Institute for Health and Care Research\u003c/p\u003e\n\u003cp\u003eONS: Office for National Statistics\u003c/p\u003e\n\u003cp\u003eORCID: Open Researcher and Contributor ID\u003c/p\u003e\n\u003cp\u003ePROGRESS-PLUS: Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, plus additional factors associated with disadvantage\u003c/p\u003e\n\u003cp\u003eQ1\u0026ndash;Q5: Quintiles 1 to 5\u003c/p\u003e\n\u003cp\u003eSMI: Severe mental illness\u003c/p\u003e\n\u003cp\u003eSNOMED: Systematized Nomenclature of Medicine\u003c/p\u003e\n\u003cp\u003eUK: United Kingdom\u003c/p\u003e\n\u003cp\u003eUKHSA: UK Health Security Agency\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis report is independent research funded by the National Institute for Health Research (\u0026ldquo;NIHR202637\u0026rdquo;). HDM receives funding from Multiple Long-Term Conditions (MLTC) Cross NIHR Collaboration (CNC) (NIHR207000). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank our patient and public contributors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHDM conceived and supervised the study and acted as the corresponding author. AJ led the study, conducted the main work, and drafted the manuscript. NI contributed to the statistical analysis. TI contributed to manuscript writing and interpretation of the findings. LS contributed to manuscript writing and critical revision of the paper. MH contributed to critical revision of the manuscript. All authors reviewed the manuscript, provided intellectual input, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request. Requests for access to data from the study should be addressed to CPRD ([email protected]). All proposals requesting data access will need to specify planned uses with the approval of the study team and CPRD before the data release.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplemental material for this article is available online.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and permission\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the University of Southampton Faculty of Medicine Research Committee (67953). The study was also approved by the Independent Scientific Advisory Group of the CPRD (ISAC protocol number 21_001667). The research was conducted in accordance with the \u003cstrong\u003eDeclaration of Helsinki\u003c/strong\u003e and relevant governance requirements for the use of anonymised routinely collected health data. Informed consent from individual participants was not required because the study used anonymised secondary care data. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarnett 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.\u003c/li\u003e\n\u003cli\u003eKatikireddi SV, Skivington K, Leyland AH, Hunt K, Mercer SW. The contribution of risk factors to socioeconomic inequalities in multimorbidity across the lifecourse: a longitudinal analysis of the Twenty-07 cohort. BMC Medicine. 2017;15(1):152.\u003c/li\u003e\n\u003cli\u003eSkou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity. Nat Rev Dis Primers. 2022;8(1):48.\u003c/li\u003e\n\u003cli\u003eCandace Imison DDoDaKMatN. Multiple long-term conditions (multimorbidity) and inequality- addressing the challenge September 2023 [Available from: https://doi.org/10.3310/nihrevidence_59977.\u003c/li\u003e\n\u003cli\u003eFortin M, Soubhi H, Hudon C, Bayliss EA, van den Akker M. Multimorbidity\u0026apos;s many challenges. Bmj. 2007;334(7602):1016-7.\u003c/li\u003e\n\u003cli\u003eUK D. One in 4 people in England living with two or more long-term conditions: The British Diabetic Association operating as Diabetes UK, a charity registered in England and Wales (no. 215199) and in Scotland (no. SC039136). ; 2021-08-17 [Available from: https://www.diabetes.org.uk/about-us/news-and-views/multiple-long-term-conditions-report.\u003c/li\u003e\n\u003cli\u003eSoley-Bori M, Ashworth M, Bisquera A, Dodhia H, Lynch R, Wang Y, Fox-Rushby J. Impact of multimorbidity on healthcare costs and utilisation: a systematic review of the UK literature. British Journal of General Practice. 2021;71(702):e39-e46.\u003c/li\u003e\n\u003cli\u003eChudasama YV, Khunti K, Gillies CL, Dhalwani NN, Davies MJ, Yates T, Zaccardi F. Healthy lifestyle and life expectancy in people with multimorbidity in the UK Biobank: A longitudinal cohort study. PLoS Med. 2020;17(9):e1003332.\u003c/li\u003e\n\u003cli\u003eWu J, Zhang H, Shao J, Chen D, Xue E, Huang S, et al. Healthcare for Older Adults with Multimorbidity: A Scoping Review of Reviews. Clin Interv Aging. 2023;18:1723-35.\u003c/li\u003e\n\u003cli\u003ePearson-Stuttard J, Ezzati M, Gregg EW. Multimorbidity\u0026mdash;a defining challenge for health systems. The Lancet Public Health. 2019;4(12):e599-e600.\u003c/li\u003e\n\u003cli\u003eMahadevan P, Harley M, Fordyce S, Hodgson S, Ghosh R, Myles P, et al. Completeness and representativeness of small area socioeconomic data linked with the UK Clinical Practice Research Datalink (CPRD). J Epidemiol Community Health. 2022;76(10):880-6.\u003c/li\u003e\n\u003cli\u003eSinger L, Green M, Rowe F, Ben-Shlomo Y, Kulu H, Morrissey K. Trends in multimorbidity, complex multimorbidity and multiple functional limitations in the ageing population of England, 2002\u0026ndash;2015. Journal of comorbidity. 2019;9:2235042X19872030.\u003c/li\u003e\n\u003cli\u003eSalive ME. Multimorbidity in older adults. Epidemiol Rev. 2013;35:75-83.\u003c/li\u003e\n\u003cli\u003eNunes BP, Flores TR, Mielke GI, Thum\u0026eacute; E, Facchini LA. Multimorbidity and mortality in older adults: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2016;67:130-8.\u003c/li\u003e\n\u003cli\u003eJani BD, Hanlon P, Nicholl BI, McQueenie R, Gallacher KI, Lee D, Mair FS. Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank cohort. BMC Medicine. 2019;17(1):74.\u003c/li\u003e\n\u003cli\u003eLewer D, Aldridge RW, Menezes D, Sawyer C, Zaninotto P, Dedicoat M, et al. Health-related quality of life and prevalence of six chronic diseases in homeless and housed people: a cross-sectional study in London and Birmingham, England. BMJ Open. 2019;9(4):e025192.\u003c/li\u003e\n\u003cli\u003eR\u0026oacute;j J. Inequity in the Access to eHealth and Its Decomposition Case of Poland. International Journal of Environmental Research and Public Health. 2022;19(4):2340.\u003c/li\u003e\n\u003cli\u003eWHO. Health inequities and their causes [updated 22 February 2018. \u003c/li\u003e\n\u003cli\u003eUKHSA. Health inequalities in health protection report 2025 - GOV.UK [updated 27 May 2025. \u003c/li\u003e\n\u003cli\u003ePennsylvania Uo. What Is Structural Inequality? The Center for High Impact Philanthropy [Available from: https://www.impact.upenn.edu/what-is-structural-inequality/.\u003c/li\u003e\n\u003cli\u003eOliver S, Kavanagh J, Caird J, Lorenc T, Oliver K, Harden A, et al. Health Promotion, Inequalities and Young People\u0026apos;s health: a Systematic Review of Research. 2008.\u003c/li\u003e\n\u003cli\u003eA B. - Applying the CORE20PLUS5 to Address Health Inequalities for Patients Under the Rehabilitation and Recovery Service in the London Borough of Hackney. BJPsych Open. 2024;10(Suppl 1).\u003c/li\u003e\n\u003cli\u003eKarran EL, Cashin AG, Barker T, Boyd MA, Chiarotto A, Dewidar O, et al. Using PROGRESS-plus to identify current approaches to the collection and reporting of equity-relevant data: a scoping review. J Clin Epidemiol. 2023;163:70-8.\u003c/li\u003e\n\u003cli\u003eHerrett E, Gallagher AM, Bhaskaran K, Forbes H, Mathur R, van Staa T, Smeeth L. Data Resource Profile: Clinical Practice Research Datalink (CPRD). Int J Epidemiol. 2015;44(3):827-36.\u003c/li\u003e\n\u003cli\u003eAgency MHpR. CPRD GOLD March 2025 [Available from: https://doi.org/10.48329/kfzv-jv89.\u003c/li\u003e\n\u003cli\u003eWolf A, Dedman D, Campbell J, Booth H, Lunn D, Chapman J, Myles P. Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum. Int J Epidemiol. 2019;48(6):1740-g.\u003c/li\u003e\n\u003cli\u003eAgency MHpR. CPRD- Defining your study population.\u003c/li\u003e\n\u003cli\u003eDambha-Miller H, Farmer, Andrew, Nirantharakumar, K., Jackson, T., Yau, C., Walker, L., Buchan, I., Finer, S., Barnes, M.R., Reynolds, N.J., Jun, GT, Gangadharan, S, Fraser, Simon and Guthrie, Bruce Artificial intelligence for multiple long-term conditions (AIM): a consensus statement from the NIHR AIM consortia. 2023 [NIHR open research, 3 (21):[Available from: http://dx.doi.org/10.3310/nihropenres.1115210.1.\u003c/li\u003e\n\u003cli\u003eNHSEngland. Core20PLUS5 (adults) \u0026ndash; an approach to reducing healthcare inequalities [Available from: https://www.england.nhs.uk/about/equality/equality-hub/national-healthcare-inequalities-improvement-programme/core20plus5/.\u003c/li\u003e\n\u003cli\u003eCochraneEquity. PROGRESS-Plus | Cochrane Equity Methods Group [Available from: https://methods.cochrane.org/equity/projects/evidence-equity/progress-plus.\u003c/li\u003e\n\u003cli\u003eWHO. \u003c/li\u003e\n\u003cli\u003eXue Q, Zhang S, Yang X, Zhang Y-B, Dong Y, Li F, et al. Multimorbidity patterns and premature mortality in a prospective cohort: effect modifications by socioeconomic status and healthy lifestyles. BMC Public Health. 2025;25(1):1262.\u003c/li\u003e\n\u003cli\u003eMacRae J, Ciminata G, Geue C, Lynch E, Shenkin SD, Quinn TJ, Burton JK. Mortality in long-term care residents: retrospective national cohort study. BMJ Supportive \u0026amp;amp; Palliative Care. 2024:spcare-2024-005163.\u003c/li\u003e\n\u003cli\u003eAndersen MP, Mills EHA, Meddis A, S\u0026oslash;rensen KK, Butt JH, K\u0026oslash;ber L, et al. All-cause mortality among Danish nursing home residents before and during the COVID-19 pandemic: a nationwide cohort study. European Journal of Epidemiology. 2023;38(5):523-31.\u003c/li\u003e\n\u003cli\u003eMorciano M, Stokes J, Kontopantelis E, Hall I, Turner AJ. Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study. BMC Medicine. 2021;19(1):71.\u003c/li\u003e\n\u003cli\u003eHayanga B, Stafford M, B\u0026eacute;cares L. Ethnic inequalities in multiple long-term health conditions in the United Kingdom: a systematic review and narrative synthesis. BMC Public Health. 2023;23(1):178.\u003c/li\u003e\n\u003cli\u003eQui\u0026ntilde;ones AR, Newsom JT, Elman MR, Markwardt S, Nagel CL, Dorr DA, et al. Racial and Ethnic Differences in Multimorbidity Changes Over Time. Med Care. 2021;59(5):402-9.\u003c/li\u003e\n\u003cli\u003eXue Q, Zhang S, Yang X, Zhang YB, Dong Y, Li F, et al. Multimorbidity patterns and premature mortality in a prospective cohort: effect modifications by socioeconomic status and healthy lifestyles. BMC Public Health. 2025;25(1):1262.\u003c/li\u003e\n\u003cli\u003eKennedy S, Kidd MP, McDonald JT, Biddle N. The Healthy Immigrant Effect: Patterns and Evidence from Four Countries. Journal of International Migration and Integration. 2015;16(2):317-32.\u003c/li\u003e\n\u003cli\u003eB\u0026eacute;cares L. Which ethnic groups have the poorest health? In: Jivraj S, Simpson L, editors. Ethnic Identity and Inequalities in Britain: The Dynamics of Diversity: Bristol University Press; 2015. p. 123-40.\u003c/li\u003e\n\u003cli\u003eNazroo JY. The structuring of ethnic inequalities in health: economic position, racial discrimination, and racism. Am J Public Health. 2003;93(2):277-84.\u003c/li\u003e\n\u003cli\u003eWallace S, Nazroo J, B\u0026eacute;cares L. Cumulative Effect of Racial Discrimination on the Mental Health of Ethnic Minorities in the United Kingdom. American journal of public health. 2016;106:e1-e7. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, Health Inequity, Health Inequalities, Premature Mortality, Equity Framework (CORE20PLUS5 and PROGRESS-PLUS), CPRD data.","lastPublishedDoi":"10.21203/rs.3.rs-9451391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9451391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Multimorbidity is increasingly common among adults who develop multiple long-term conditions before age 60 and is linked to premature mortality. We examined whether socioeconomic disadvantage and recorded markers of structural vulnerability were associated with premature mortality in this population using the CORE20PLUS5 and PROGRESS-PLUS frameworks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a population-based cohort study using linked Clinical Practice Research Datalink (CPRD) Gold and Aurum data from England, 1987–2020. Adults aged 18–60 years with incident multimorbidity were followed until death, deregistration, study end, or age 75 years. Premature mortality was defined as all-cause death at or before age 75 years. Cox proportional hazards models estimated adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for socioeconomic, demographic, and clinical factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among 4,303,353 adults with incident multimorbidity, 447,720 premature deaths occurred. Mortality risk was higher in the most deprived than the least deprived areas (HR 1.61, 95% CI 1.59–1.62). Recorded residential care was associated with the highest mortality risk (HR 2.92, 95% CI 2.89–2.95), followed by financial support (HR 1.44, 95% CI 1.42–1.47) and disability (HR 1.26, 95% CI 1.24–1.27). Women had a lower risk than men (HR 0.68, 95% CI 0.68–0.69). Adults aged 40–60 years at multimorbidity onset had a higher risk than those aged 18–39 years (HR 2.29, 95% CI 2.27–2.30). Compared with White individuals, Asian, Black, Mixed, and Other ethnic groups had lower adjusted risks. Mortality risk was also higher in several regions outside London.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: In adults who developed multimorbidity before age 60, premature mortality before age 75 was strongly patterned by socioeconomic disadvantage and recorded markers of structural vulnerability. These variables should be interpreted as markers of heightened vulnerability rather than direct causal determinants of mortality. Equity-oriented prevention and coordinated health and community care may help reduce avoidable inequalities.\u003c/p\u003e","manuscriptTitle":"Inequity in Premature Mortality: A Cohort Study of 4.3 million Adults Under 60 years With Multimorbidity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 16:18:12","doi":"10.21203/rs.3.rs-9451391/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-05T15:03:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T07:37:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T12:01:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T09:55:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-05-01T09:50:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a034745c-e03e-4439-a447-db70d5fe1dca","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"2","date":"2026-05-05T15:03:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T07:37:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T12:01:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T09:55:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-05-01T09:50:07+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T16:18:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 16:18:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9451391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9451391","identity":"rs-9451391","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}