Intersectional Influences on Non-Engagement Among Critical Care Survivors: A Prospective Cohort Study

Intersectional Influences on Non-Engagement Among Critical Care Survivors: A Prospective Cohort Study | 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 Intersectional Influences on Non-Engagement Among Critical Care Survivors: A Prospective Cohort Study Saira Nazeer, Alexander J Fowler, Amar Ahmad, Timothy J Stephens, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927799/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Critical care survivors face long-term health and social care needs. Barriers to engagement with essential follow-up services contribute to worse outcomes. Understanding these barriers is crucial for equitable healthcare provision and service design. Methods We conducted a prospective cohort study of adult patients admitted to a critical care unit, between April 2023 and December 2024. Eligible patients with valid contact details were sent a questionnaire electronically, designed to identify needs, 72 hours post critical care discharge. The primary outcome was service engagement, defined as completion of the electronic questionnaire. Demographic and clinical data were obtained from the local audit team. Primary exposures were index of multiple deprivation (IMD) and ethnicity. Associations were determined with multivariable logistic regression (adjusted odds ratios, aOR) and intersectional analysis. Results Of 3058 patients discharged, 2191 (72%) were eligible for analysis. The majority (1558/2191,71%) did not engage with the follow-up service. Higher non-engagement was observed in patients from more deprived areas (IMD Quintiles 1[aOR = 1.39, CI = 1.03–1.88] and 2 [aOR = 1.46, CI = 1.08–1.98]) and of Black ethnic background (aOR = 1.69, CI = 1.25–2.28). Intersectional analysis revealed further at-risk populations (915/2191, 42%), including a large group of White individuals in areas of high deprivation and a small group of Black individuals in the least deprived areas. Conclusions Intersectional analysis identified groups at-risk of non-engagement overlooked by single-factor approaches. Considering both population size and relative risk is vital for designing critical care follow-up support services to maximise impact, allocate resources efficiently, and reduce existing disparities in recovery outcomes for critical care survivors. Critical care recovery service access and engagement intersectionality Figures Figure 1 Figure 2 Figure 3 Introduction Over 200,000 people are admitted to intensive care units (ICU) in the United Kingdom annually ( 1 ). Seventy percent survive to discharge, but over half experience hospital readmission within a year and many develop new chronic conditions ( 2 – 9 ). Alongside physical recovery, psychological and cognitive challenges can occur, collectively termed Post Intensive Care Syndrome (PICS) and can lead to long-term reliance on health and social care services ( 4 , 10 – 12 ). National guidelines recommend follow-up care to address these complex needs ( 13 , 14 ). Despite this, follow-up service engagement is variable ( 15 , 16 ). Failure to address patient engagement contributes to persistent health inequalities, poorer health outcomes, and increased demand on health and social care services ( 17 ). Healthcare policies and quality improvement initiatives increasingly recognise a patient’s ability to engage as a critical factor influencing healthcare utilisation and outcomes ( 18 , 19 ). Policy makers have highlighted the importance of supporting patient engagement to improve service uptake and adherence and to reduce the impact of social determinants of health ( 18 – 21 ). Patient engagement relies on the coordinated partnerships between patients and healthcare providers, but is not shaped by a single factor ( 22 ). Rather there is a dynamic interplay of multiple social identities and structural constructs - a concept known as intersectionality ( 23 , 24 ). Ethnicity and socioeconomic status have emerged as important factors influencing patient engagement, with ethnic minority (term used for consistency with existing literature, though “global majority” is acknowledged as more accurate and inclusive) and socioeconomically deprived groups often facing greater barriers to engagement, higher admission rates and worse health outcomes ( 25 – 30 ). Conventional determinants of causal and associative factors may be too simplistic, as intersectionality is a framework determined by the overlap of identity factors (such as race, gender, socioeconomic status and disability) with systems of power (such as racism, classism and ableism), interacting to shape experiences ( 23 , 31 , 32 ). Social determinants of health and service accessibility are deeply intertwined with these intersectional identities and are key drivers of health disparities and engagement barriers ( 25 , 33 , 34 ). Critical care follow-up services design traditionally assumes a uniform capacity to engage across survivor populations. This risks systematically excluding groups whose recovery trajectories and social systems differ substantially. While some studies have examined characteristics of patients who engage with follow-up services compared with those who do not, this literature remains limited and has largely focused on clinical factors such as illness severity, length of stay, or comorbidity ( 35 , 36 ). Socio-demographic characteristics are less consistently examined, and are often treated as isolated variables rather than considered jointly. To date, no studies have explicitly explored patterns of engagement with critical care follow-up services through an intersectional lens, potentially obscuring groups at heightened risk of non-engagement. Identifying factors of non-engagement therefore requires acknowledgement of these intersecting factors and careful consideration when selecting analysis methodologies. Doing so ensures we are investigating the complex intersecting nature of non-engagement, rather than simply revealing single factor relationships. We examined our population of critical care survivors for features of non-engagement. We hypothesised that there might be a complex interaction between an individual’s socioeconomic status and ethnicity, and their ability and inclination to engage with a critical care follow up service. Accordingly we aimed to identify intersectionality in subgroups of patients who did not engage. Methods Study design We performed a prospective cohort study of adult patients admitted to the Adult Critical Care Unit at the Royal London Hospital, UK, between April 2023 and December 2024. Research ethics approval was obtained from Yorkshire and The Humber - Leeds West Research Ethics Committee (reference number 23/YH/0146, 22.08.2023). This study is reported in line with the STROBE checklist for cohort studies and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (37). Setting The Royal London Hospital is a large tertiary centre located in North-East London, which is one of the most ethnically diverse and socioeconomically deprived regions in England (38). The unit serves a mixed patient population, including trauma and neurocritical care patients. Patients can be discharged to locations across London and neighbouring regions, either being discharged directly home or via their local hospital, a rehabilitation unit, a specialist hospital or indeed discharged to supported living facilities. Data collection occurred continuously between April 2023 and December 2024. Participants - Eligibility criteria and selection Patients aged ≥16 years who survived to hospital discharge and for whom complete data sets were available were included in the analysis (see section below Excluded Datasets for more detail). Patients were sent an electronic questionnaire via text message and/or email, 72 hours post-discharge from critical care, with two reminder messages sent in the following 48 hours and a further invitation at 4 weeks post discharge from critical care. It was available in two additional languages (Polish and Bengali) selected to reflect the local population. A care navigator (a healthcare professional involved in the recovery pathway for critical care survivors) reviewed the questionnaires and referred to appropriate services. Questionnaire The electronic questionnaire consisted of four existing quality-of-life patient-reported outcome measures, chosen to reflect the breadth of symptoms experienced after critical illness: the EuroQol Five Dimensions (EQ5D), a widely used generic health-related quality of life instrument, demonstrating good validity in critically ill cohorts; the Post-ICU Presentation Screen - Community Version (PICUPS-Community), which in itself has limited psychometric data but is related to the PICUPS tool and is the only tool specifically designed for ICU population; the World Health Organisation Disability Assessment Schedule 2.0 (WHODAS 2.0), a disability measure shown to be reliable and valid in the ICU population; and the 36-Item Short Form Health Survey (SF-36), which has demonstrated good reliability and validity in this population (39–42). These four existing measures were included in entirety, back to back with no adaptations made by the study team. Variables Exposures Data were collected as part of our Intensive Care National Audit and Research Centre (ICNARC) routine data collection. Demographic data included age on admission in whole years (owing to the small number of patients at either end of the age range, age was grouped into ‘≤17 years’ and ‘≥ 90 years’), sex (at birth, as recorded in medical records), and ethnicity (grouped by Office of National Statistics census categories into Black, Asian, Mixed/Other, White and Not Stated). Socioeconomic status was determined from the recorded postcode, which was then linked to Office for National Statistics lower super output area to obtain the index of multiple deprivation (IMD). IMD was grouped into quintiles based on the IMD rank. Clinical data included: level of frailty (Clinical Frailty Scale (CFS) in conjunction with clinician based assessment informed by the subjective history documented in the medical notes – owing to the small number of patients classified as having a ‘stable long term disability’, affecting their independence with activity of daily living, this category was combined with the ‘frail’ category for analytical purposes), admitting speciality (categorised into medical, surgical (this was mainly emergency as elective surgical cases are treated in a different location post-operatively), neurosurgical and trauma), critical care length of stay (LOS), maximum organ support, APACHE II and the SOFA score. The primary exposures of interest were ethnicity and IMD. Outcome measures The primary outcome was engagement. Engagement was defined as completion of part or all of the questionnaire at any time. Completion of the questionnaire represents the minimum threshold for contact with the critical care follow-up pathway and therefore serves as a pragmatic indicator of service reach and accessibility rather than patient motivation alone. For the purpose of this study, ‘non-engagement’ refers to when a patient did not complete any of the questionnaire. Data Sources and Measurement All demographic and clinical data were obtained from routine ICNARC data collection and hospital records. IMD scores were derived from patient postcode linked to the Office for National Statistics lower super output areas. Bias We attempted to minimise bias by excluding individuals with incomplete key clinical or demographic variables to reduce misclassification (see below section Excluded Datasets and figure 1). Measurement bias was minimised by using routinely collected ICNARC data, which are subject to standardised definitions and audit processes. Study Size This was a cohort defined by all eligible admissions during the study period; no formal sample size calculation was performed. Statistical analysis Descriptive statistics were used to summarise characteristics of the cohort. Associations between characteristics and engagement status were assessed using Chi-squared tests for categorical variables and Mann-Whitney-Wilcoxon tests for continuous variables. We used logistic regression for univariate analyses with engagement as the dependent variable. All variables apart from the SOFA score were included in a multivariable logistic regression. We present odds ratios with associated 95% confidence intervals. Variance inflation factors (VIFs) were calculated for all predictors in the final multivariable logistic regression model to assess multi-collinearity. We included an interaction term between ethnicity and IMD quintiles in a multivariable logistic regression model as a statistical test for intersectionality. An intersectional visualisation technique was used to explore the interaction between IMD quintiles and ethnicity. We examined patterns across these intersectional groups using a heat map, allowing visual interpretation of engagement rates and group counts. All analyses were performed using R software and associated packages ggplot2 (R software foundation, Vienna, version 4.4.3). Excluded Datasets From the total 3058 patients discharged from critical care over the data collection period, we excluded a total of 867 from the analysis either because they had no opportunity to engage with the follow-up service or because missing data could have introduced misclassification or bias (see figure 1). We excluded 604 datasets as the patients did not survive to critical care or hospital discharge and therefore would not have had the opportunity to engage with the service. We excluded 137 datasets as they did not have at least one form of valid contact detail and therefore would not have been given the opportunity to engage in the electronic format of this service. We excluded a further 126 datasets due to incomplete data to minimise the risk of misclassification, including 80 datasets missing severity of illness (APACHE II and SOFA) scores, 40 missing postcodes (precluding assignment of IMD), and 6 with missing frailty data. Results Participants From a total of 3058 critical care survivors, 2191 (72%) had complete data sets and survived to hospital discharge (Figure 1). Patient characteristics are summarised in Table 1. In brief, ICU survivors, had an average age of 53 years (range 39-67), were predominantly male (1382/2191, 63%), half were of White ethnicity (1101/2191, 50%), living in areas of high socioeconomic deprivation (1216/2191, 56% from the lowest 3 IMD deciles) and were admitted under medical (800/2191, 36%), neurosurgical (301/2191, 14%), surgical (652/2191, 30%) and trauma specialities (438/2191, 20%). We describe the characteristics and outcomes of patients with missing data (supplementary material table 1). Table 1: Patient characteristics for overall cohort (n=2191), stratified for engagement. Numbers are presented as n (% of total) unless otherwise stated. To calculate p values, Mann-Whitney U was used for continuous data and chi-squared tests for categorical data. Data regarding residence prior to admission has been omitted for data integrity concerns. IMD quintiles based on IMD rank within the cohort. Engagement Status Non-Engaged (n=1552) Engaged (n=639) p-value AGE 56 (39 to 68) 54 (39 to 66) 0.14 ETHNICITY Asian 360 (23.2%) 136 (21.3%) <0.05 Black 266 (17.1%) 74 (11.6%) <0.05 Mixed/Other 69 (4.4%) 33 (5.2%) <0.05 Not stated 103 (6.6%) 49 (7.7%) <0.05 White 754 (48.6%) 347 (54.3%) <0.05 SEX Female 565 (36.4%) 244 (38.2%) 0.46 Male 987 (63.6%) 395 (61.8%) 0.46 IMD QUINTILES 1 - most deprived 326 (21%) 112 (17.5%) <0.05 2 327 (21.1%) 111 (17.4%) <0.05 3 316 (20.4%) 122 (19.1%) <0.05 4 293 (18.9%) 145 (22.7%) <0.05 5 - least deprived 289 (18.6%) 149 (23.3%) <0.05 FRAILTY Frail 272 (17.5%) 76 (11.9%) <0.05 Not frail 1280 (82.5%) 563 (88.1%) <0.05 MAX ORGAN SUPPORT 0 29 (1.9%) 12 (1.9%) 0.47 1 816 (52.6%) 346 (54.1%) 0.47 2 489 (31.5%) 200 (31.3%) 0.47 3 201 (13%) 79 (12.4%) 0.47 4 17 (1.1%) 2 (0.3%) 0.47 ADMITTING SPECIALITY Medical 582 (37.5%) 218 (34.1%) <0.05 Neurosurgery 219 (14.1%) 82 (12.8%) <0.05 Surgical 427 (27.5%) 225 (35.2%) <0.05 Trauma 324 (20.9%) 114 (17.8%) <0.05 SOFA SCORE 4 (2 to 5) 3 (2 to 5) <0.05 APACHE II SCORE 13 (10 to 17) 12 (9 to 16) <0.05 CRITICAL CARE LOS 6 (4 to 11) 6 (4 to 10) 0.12 APACHEII Acute Physiology and Chronic Health Evaluation II, IMD Indices of Multiple Deprivation, LOS Length of Stay, SOFA Sequential Organ Failure Assessment Patient outcomes A large proportion of the patients (1558/2191, 71%) did not engage with the follow up service (Table 1). Median age and sex distribution did not differ significantly between groups, however there were significant differences in ethnicity and socioeconomic status (measured by IMD). In univariate logistic regression patients of Black ethnicity had significantly lower odds of engagement compared to those of White ethnicity (OR = 1.65, CI = 1.25-2.22). This association remained significant after adjusting for age, sex, level of frailty, admission speciality, and illness severity in the multivariable logistic regression model (aOR = 1.69, CI = 1.25-2.28). Similarly in univariate analysis, patients from more socioeconomically deprived areas had lower odds of engagement. Compared to those in the least deprived IMD quintile, individuals in more deprived quintiles had significantly lower odds of engagement, noticeably in IMD Quintile 1 (OR = 1.5, CI = 1.12-2.01). and 2 (OR = 1.52, CI = 1.13-2.04). This relationship also remained significant in the multivariable model (IMD 1 aOR = 1.39, CI = 1.03-1.88 and IMD 2 aOR = 1.46, CI = 1.08-1.98) (Table 2, Figure 2). Scaled generalised VIFs ranged from 1.01 to 1.22, indicating no evidence of multicollinearity among the included variables. The introduction of an interaction term to the logistic model (measured with ANOVA), resulted in no significant difference. Table 2: Logistic regression analysis: Numbers are presented as OR (CI) or the p value. For categorical data, the reference category is stated in the table. UNIVARIATE ANALYSIS MULTIVARIABLE ANALYSIS VARIABLE Unadjusted OR p value Adjusted OR p value AGE 1.04 (0.99-1.09) 0.13 1.04 (0.98-1.11) 0.18 SEX Male 1.08 (0.89-1.3) 0.43 1.2 (0.99-1.47) 0.07 Female Reference category Reference category ETHNICITY Asian 1.22 (0.96-1.54) 0.1 1.24 (0.97-1.6) 0.08 Black 1.65 (1.25-2.22) <0.05 1.69 (1.25-2.28) <0.05 Mixed / Other 0.96 (0.63-1.5) 0.86 0.99 (0.64-1.55) 0.97 Not stated 0.97 (0.68-1.4) 0.86 0.98 (0.68-1.43) 0.92 White Reference category Reference category IMD QUINTILE 1 – most deprived 1.5 (1.12-2.01) <0.05 1.39 (1.03-1.88) <0.05 2 1.52 (1.13-2.04) <0.05 1.46 (1.08-1.98) <0.05 3 1.34 (1-1.78) <0.05 1.26 (0.93-1.69) 0.13 4 1.04 (0.79-1.38) 0.77 1 (0.75-1.34) 0.98 5 – least deprived Reference category Reference category MAX ORGAN SUPPORT 0 1.02 (0.53-2.11) 0.94 0.82 (0.41-1.64) 0.57 1 Reference category Reference category 2 1.04 (0.84-1.28) 0.73 1.05 (0.84-1.31) 0.69 3 1.08 (0.81-1.45) 0.61 1.13 (0.8-1.59) 0.49 4 3.6 (1.03-22.81) 0.09 0.39 (0.09-1.73) 0.21 APACHE II SCORE 1.02 (1.01-1.04) <0.05 1.02 (1-1.04) 0.11 FRAILTY Frail 1.57 (1.2-2.08) <0.05 1.5 (1.12-2.01) <0.05 Not frail Reference category Reference category CRITICAL CARE LOS (days) 1.01 (1-1.02) <0.05 1.01 (1-1.02) 0.08 ADMITTING SPECIALITY Neurosurgery 1 (0.74-1.35) 1 1.15 (0.83-1.59) 0.40 Surgical 0.71 (0.57-0.89) <0.05 0.75 (0.59-0.95) <0.05 Trauma 1.06 (0.82-1.39) 0.64 1.27 (0.96-1.69) 0.09 Medical Reference category Reference category APACHE II Acute Physiology and Chronic Health Evaluation, IMD Indices of Multiple Deprivation, LOS Length of Stay Adjusted odds ratios and 95% confidence intervals for all variables used in multivariate logistic regression analysis. Reference categories same as shown in Table 2. An odds ratio of >1 shows factors associated with non-engagement (i.e. that make you more at risk of non-engagement), whereas an odds ratio of <1 shows factors associated with engagement. Further analysis of our data was conducted to understand the intersecting impact of socioeconomic status and ethnicity on an individual’s ability to engage. A heat map (Figure 3) allowed us to distinguish patterns of non-engagement, including the groups revealed by the traditional single-factor analyses: IMD quintiles 1 and 2 and Black ethnicity groups. Obscured at-risk groups were revealed, including a large group of White individuals living in areas of high deprivation and a small group of individuals from Black ethnic backgrounds living in the least deprived areas with the highest rate of non-engagement. Although not statisticallysignificant, this analysis revealed hidden populations outside of the categories defined by the single-factor analyses; including a large group of Asian ethnicity individuals from mid to low socioeconomic deprivation (IMD quintiles 3 and 4) with poor engagement. Discussion We found distinct social and demographic patterning in non-engagement with the critical care follow-up service. Patients from more socioeconomically deprived areas and those from Black ethnic backgrounds had consistently higher odds of non-engagement, even after adjusting for confounders. Intersectional visualisation further demonstrated substantial variation in non-engagement rates across combined ethnicity and IMD strata, alongside marked differences in the size of the populations represented within each stratum (Fig. 3 ). To understand these patterns more comprehensively, we applied two analytical approaches. Using traditional single-factor logistic regression, we identified individuals from IMD quintiles 1 and 2 and those of Black ethnicity as having higher odds of non-engagement. However, when we applied an intersectional approach, visualised in Fig. 3 , we uncovered a sizeable additional group of individuals with high rates of non-engagement that was not visible through single-factor methods. Although these groups did not always exhibit the highest relative odds of non-engagement, they collectively represented a substantial proportion of the ICU survivor population. An important methodological distinction between these approaches is that odds ratios derived from logistic regression quantify relative risk but do not account for the absolute size of the populations affected. Consequently, groups with moderately elevated non-engagement rates but large population sizes may represent a substantial proportion of unmet need, despite appearing less prominent in regression-based analyses. By jointly considering non-engagement rates and population size, the intersectional approach revealed additional at-risk groups, including individuals of Black ethnicity in less deprived areas and Asian individuals from IMD quintiles 3 and 4, patterns not evident using single-factor methods alone. Taken together, these findings demonstrate that reliance on conventional regression approaches alone may obscure important population-level patterns of non-engagement, and may underestimate the true scale and distribution of unmet need, potentially overlooking groups who may benefit from targeted or tailored follow-up interventions. This may have important implications for policy development. Our results underscore the added value of intersectional approaches in uncovering both visible and obscured dimensions of inequity in access and engagement with healthcare services. Our findings largely align with existing literature, which describes lower engagement in ethnic minority groups due to barriers such as language, lower health literacy, and cultural mismatch ( 33 , 43 , 44 ). Lower engagement is associated with worse health outcomes, including higher readmission rates, increased morbidity and reduced quality of life ( 45 ). There is also a well-established association between socioeconomic disadvantage and poorer healthcare access and outcomes among critical care survivors ( 28 , 46 , 47 ). Additionally, broader healthcare utilisation disparities across ethnic groups have been documented in national English datasets ( 29 ). However, much of the evidence has examined ethnicity and socioeconomic status as separate determinants, or has focused on attendance versus non-attendence without explicitly considering how these factors interact. While some studies describe characteristics of patients who do not engage with follow-up services, few have applied quantitative approaches to identify intersecting patterns of risk across social identities ( 35 , 36 ). To that end, the additional groups identified through our intersectional analysis - specifically individuals of Black ethnicity in the least deprived areas and the large group of Asian individuals from IMD quintiles 3 and 4 - are less well described in existing literature. As these patterns do not emerge in single-factor approaches, such groups may be systematically overlooked in existing evidence, underscoring the need for further work to understand the mechanisms driving their lower engagement. Although intersectionality has been widely applied in qualitative research, it’s use in quantitative research is more recent, with growing evidence of it’s analytical value ( 48 , 49 ). However, intersectional analysis risks being applied superficially, with one systematic review highlighting that the theory is frequently misunderstood with only simplistic descriptive analysis of demographic data being conducted with no consideration of wider social context ( 50 ). Strengths and limitations A key strength of this study is the use of intersectional visualisation techniques, which allowed for a more detailed exploration of non-engagement patterns across both demographic factors and social dimensions that are often examined in isolation. This approach helped reveal both small but high risk groups and large populations with unmet need, offering novel insights to inform policy and practice. The diversity of the study population facilitated this comprehensive analysis, revealing inequities that may not have been apparent in less heterogeneous cohorts. Although those without valid contact details were excluded from data analysis, reliance on electronic communication may have disadvantaged individuals experiencing digital poverty and/or poor digital literacy. Digital poverty itself was unlikely to be a major driver of non-engagement given the small number excluded for having no telephone or email contact details (137/3058, 4% of the total population), however digital literacy remains a potentially important and underexplored barrier in this population. Comparable services within the same NHS Trust have achieved engagement rates of up to 60–80%, suggesting that digital exclusion alone does not fully explain the lower response rate observed in our population. Data integrity and inaccuracies may have introduced a source of bias and influenced the findings. Additionally, as this questionnaire was distributed via an automated service to a population which is heterogeneous in it’s recovery trajectory and timeline, it is likely that for some it did not coincide with a point at which they were clinically able to complete it or considered it relevant. To mitigate this, reminder messages were sent including a second distribution four weeks later. Finally, as this study was conducted in a single large inner-city trauma centre, the results may not be fully generalisable to other settings with different population profiles or service structures. Implications for future practice Our findings emphasise the need for tailored engagement strategies that account for intersecting identities and structural barriers, rather than focussing on single factors in isolation. Intervention developers must recognise that disadvantage rarely exists in a vacuum, but rather through the compounding intersecting effects of multiple factors. Embedding intersectionality into intervention design is therefore essential to ensure that engagement strategies meet the complex needs of critical care survivors and reduce inequities in access. Implications for future research Routinely collected hospital data fail to capture many key structural determinants of non-engagement. Evidence suggests that greater social support is associated with improved engagement ( 51 ). Components of social support such as informal care arrangements and insecure housing will affect an individual’s ability to engage, however they are poorly recorded or absent from hospital datasets. Linkage to social care records will provide some of the answers, however a comprehensive understanding of engagement in this population will require prospective and considered data collection to ensure all relevant determinants are considered. Future research will need to employ codesign methodologies to ensure relevance and responsiveness to the needs of critical care survivors and their communities. A caveat is that co-design processes often attract individuals who are already engaged, which risks overlooking the perspectives of those less likely to participate. Using pre-existing trusted community pathways and networks to reach and involve groups who might otherwise remain disengaged may mitigate this. Local faith groups, community centres, peer support networks, or voluntary organisations with established relationships can help build trust and facilitate access to underrepresented groups. It is also important to note, that studies evaluating tailored engagement interventions in other areas of healthcare rarely achieve universal engagement ( 52 – 55 ). This suggests that a more nuanced approach may be required when designing engagement strategies, one that considers multiple intersecting factors and the frequency of a variable alongside its relative odds. Conclusions The method of analysing disparities and risk factors for non-engagement can significantly affect which populations are revealed to be at risk. Failure to account for intersectionality risks designing interventions that are ineffective or inequitable, ultimately perpetuating existing disparities in recovery outcomes for critical care survivors. Incorporating intersectional approaches into the evaluation of critical care follow-up services can improve service design, resource allocation, and equity of recovery after critical illness. Declarations Ethics Approval Research ethics approval was obtained from Yorkshire and The Humber - Leeds West Research Ethics Committee (reference number 23/YH/0146, 22.08.2023). This study is reported in line with the STROBE checklist for cohort studies and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Consent to participate Informed consent was obtained by continuation to complete questionnaire. Consent to publish Not applicable as using data in an anonymised manner in publication. Availability of data and materials Source data from this study is not publicly available as GDPR compliant de-identification within a geographically limited dataset addressing protected characteristics and sensitive healthcare data cannot be ensured. The lead author is happy to consider reasonable requests for aggregated results for additional analyses. Competing Interests ZP has received honoraria for consultancy from GlaxoSmithKline, Lyric Pharmaceuticals, Faraday Pharmaceuticals and Fresenius-Kabi, educational support from Baxter and Nestle Health Science and speaker fees from Orion, Baxter, Sedana, Fresenius-Kabi and Nestle. RP undertakes paid consultancy work for Mode Sensors. SN, AJF, AA, TS, JS, JP and YW have no competing interests to declare. Funding This study was supported by Barts Charity (IRAS number 318066). They had no role in the design, collection, analysis or interpretation of the data, nor in writing or decision to submit the manuscript. Authors’ contributions SN, TS, JS and ZP conceived and designed the study. SN, AA and AJF performed the analyses with input from JP, YW and RP. SN, AJF and ZP drafted the initial manuscript. All authors approved the final version of the manuscript. Acknowledgments The authors thank the critical care audit team at The Royal London Hospital and all patients whose data contributed to this study. References ICNARC [Internet]. [cited 2025 Feb 20]. Case Mix Programme Public Report 2022-23. Available from: https://www.icnarc.org/reports/case-mix-programme-public-report-2022-23/ Prescott HC, Harrison DA, Rowan KM, Shankar-Hari M, Wunsch H. 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Ethnic disparities in hospitalisation and hospital-outcomes during the second wave of COVID-19 infection in east London. Sci Rep. 2022 Mar 8;12:3721. Apea VJ, Wan YI, Dhairyawan R, Puthucheary ZA, Pearse RM, Orkin CM, et al. Ethnicity and outcomes in patients hospitalised with COVID-19 infection in East London: an observational cohort study. BMJ Open. 2021 Jan 17;11(1):e042140. Jain S, Murphy TE, Falvey JR, Leo-Summers L, Zang E, Gill TM, et al. Associations between Social Determinants of Health and Post-Hospitalization Rehabilitation among Critically Ill Older Adults. Annals of the American Thoracic Society [Internet]. 2025 Sep 30 [cited 2025 Oct 9]; Available from: https://www.atsjournals.org/doi/epdf/10.1513/AnnalsATS.202504-387OC?role=tab Toal CM, Fowler AJ, Pearse RM, Puthucheary Z, Prowle JR, Wan YI. Health Resource Utilisation and Disparities: an Ecological Study of Admission Patterns Across Ethnicity in England Between 2017 and 2020. 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Chrispin PS, Scotton H, Rogers J, Lloyd D, Ridley SA. Short Form 36 in the intensive care unit: assessment of acceptability, reliability and validity of the questionnaire. Anaesthesia. 1997;52(1):15–23. Frederic. Effective methods of engaging Black and minority ethnic communities within health care settings [Internet]. Race Equality Foundation. 2010 [cited 2025 May 23]. Available from: https://raceequalityfoundation.org.uk/health-and-care/effective-methods-of-engaging-black-and-minority-ethnic-communities-within-health-care-settings/ The King’s Fund [Internet]. [cited 2025 May 23]. The Health Of People From Ethnic Minority Groups In England. Available from: https://www.kingsfund.org.uk/insight-and-analysis/long-reads/health-people-ethnic-minority-groups-england Hibbard JH, Greene J. What The Evidence Shows About Patient Activation: Better Health Outcomes And Care Experiences; Fewer Data On Costs. Health Affairs. 2013 Feb;32(2):207–14. McHenry RD, Moultrie CEJ, Quasim T, Mackay DF, Pell JP. Association Between Socioeconomic Status and Outcomes in Critical Care: A Systematic Review and Meta-Analysis. Critical Care Medicine. 2023 Mar;51(3):347. McHenry RD, Moultrie CE, Corfield AR, Lone NI, Mackay DF, Pell JP. The association between socioeconomic status and outcomes in critical illness: A national cohort study of emergency admissions to critical care units in Scotland 2010–2021. Journal of the Intensive Care Society. 2025 May 14;17511437251338608. Roman M, Cheng A, Lai FY, Aujla H, Sanders J, Dearling J, et al. Intersectionality of inequalities in revascularization and outcomes for acute coronary syndrome in England: nationwide linked cohort study. Eur Heart J Qual Care Clin Outcomes. 2025 Jan 30;11(6):773–82. Ailshire JA, House JS. The Unequal Burden of Weight Gain: An Intersectional Approach to Understanding Social Disparities in BMI Trajectories from 1986 to 2001/2002. Soc Forces. 2011 Dec 1;90(2):397–423. Bauer GR, Churchill SM, Mahendran M, Walwyn C, Lizotte D, Villa-Rueda AA. Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM - Population Health. 2021 Jun 1;14:100798. Gallant MP. The Influence of Social Support on Chronic Illness Self-Management: A Review and Directions for Research. Health Educ Behav. 2003 Apr 1;30(2):170–95. Junghans C, Antonacci G, Williams A, Harris M. Learning from the universal, proactive outreach of the Brazilian Community Health Worker model: impact of a Community Health and Wellbeing Worker initiative on vaccination, cancer screening and NHS health check uptake in a deprived community in the UK. BMC Health Serv Res. 2023 Oct 12;23(1):1092. Joo JY, Liu MF. Effectiveness of Culturally Tailored Interventions for Chronic Illnesses among Ethnic Minorities. West J Nurs Res. 2020 Jan;43(1):73–84. Okasako-Schmucker DL, Peng Y, Cobb J, Buchanan LR, Xiong KZ, Mercer SL, et al. Community Health Workers to Increase Cancer Screening: 3 Community Guide Systematic Reviews. Am J Prev Med. 2023 Apr;64(4):579–94. Tian L, Huang L, Liu J, Li X, Ajmal A, Ajmal M, et al. Impact of Patient Navigation on Population-Based Breast Screening: a Systematic Review and Meta-analysis of Randomized Clinical Trials. J Gen Intern Med. 2022 Aug;37(11):2811–20. Additional Declarations Competing interest reported. ZP has received honoraria for consultancy from GlaxoSmithKline, Lyric Pharmaceuticals, Faraday Pharmaceuticals and Fresenius-Kabi, educational support from Baxter and Nestle Health Science and speaker fees from Orion, Baxter, Sedana, Fresenius-Kabi and Nestle. RP undertakes paid consultancy work for Mode Sensors. SN, AJF, AA, TS, JS, JP and YW have no competing interests to declare. Supplementary Files SupplementaryMaterialsSairaNazeer.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Editor invited by journal 03 Mar, 2026 Submission checks completed at journal 27 Feb, 2026 First submitted to journal 27 Feb, 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. We do this by developing innovative software and high quality services for the global research community. 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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-8927799","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612904626,"identity":"45784ded-7d88-454b-93d5-b6c274fc35ff","order_by":0,"name":"Saira Nazeer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFACxgYEO6GCIYFELQ/OQLUcINrCh21EaJFvP9z24QfDHXn59uZjHxLn3cmTb+A9+PgDHi0GZxKbZ/YwPDNs7DmWPCNx27NigwN8yQb4bDFgSGxm4GE4zNgskWPMkLjtcOIGBh4zCbwO63/YzPiH4bB9m/z7zwyJcw4nzm/gMf+BTwvDjcRmZqAtiT0SPMwMiQ2HExsO8Jjh9b7BjYfNzDIGh5Nn8KQZMyQcAzrsMI+xxBm8Dkt/zPim4rDt/PbDjxl/1AAd1t5j+KECn8MgdiFzmAkqHwWjYBSMglFACAAAS/xSkcH/6fYAAAAASUVORK5CYII=","orcid":"","institution":"Adult Critical Care Unit, The Royal London Hospital, London, England, UK","correspondingAuthor":true,"prefix":"","firstName":"Saira","middleName":"","lastName":"Nazeer","suffix":""},{"id":612904627,"identity":"04d497bf-7a8e-414c-a5ac-3e0c5b43db4a","order_by":1,"name":"Alexander J Fowler","email":"","orcid":"","institution":"William Harvey Research Institute, Barts and The London School of Medicine \u0026 Dentistry, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"J","lastName":"Fowler","suffix":""},{"id":612904628,"identity":"5a342ed7-3449-4e12-93ac-f26ef0dba6ee","order_by":2,"name":"Amar Ahmad","email":"","orcid":"","institution":"Cancer Research UK","correspondingAuthor":false,"prefix":"","firstName":"Amar","middleName":"","lastName":"Ahmad","suffix":""},{"id":612904629,"identity":"d6fed696-c5c6-41ea-b37a-c7220e136806","order_by":3,"name":"Timothy J Stephens","email":"","orcid":"","institution":"Adult Critical Care Unit, The Royal London Hospital, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"Timothy","middleName":"J","lastName":"Stephens","suffix":""},{"id":612904630,"identity":"27437f9f-9ed0-4643-be69-321d181ae44d","order_by":4,"name":"Yize I Wan","email":"","orcid":"","institution":"Adult Critical Care Unit, The Royal London Hospital, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"Yize","middleName":"I","lastName":"Wan","suffix":""},{"id":612904631,"identity":"a9f03b67-88fc-4c2c-980a-f6ee6ae22fe2","order_by":5,"name":"Julie Sanders","email":"","orcid":"","institution":"Faculty of Nursing, Midwifery \u0026 Palliative Care, King's College London, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"","lastName":"Sanders","suffix":""},{"id":612904632,"identity":"bd7660e9-67f1-4ded-a033-67e1ecb68f7f","order_by":6,"name":"John Prowle","email":"","orcid":"","institution":"Adult Critical Care Unit, The Royal London Hospital, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Prowle","suffix":""},{"id":612904633,"identity":"92462930-6d49-4b1d-85c3-f139b276c447","order_by":7,"name":"Rupert Pearse","email":"","orcid":"","institution":"Adult Critical Care Unit, The Royal London Hospital, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"Rupert","middleName":"","lastName":"Pearse","suffix":""},{"id":612904635,"identity":"9172f721-77fc-4f99-9315-5a853b44a460","order_by":8,"name":"Zudin Puthucheary","email":"","orcid":"","institution":"Adult Critical Care Unit, The Royal London Hospital, London, England, UK","correspondingAuthor":false,"prefix":"","firstName":"Zudin","middleName":"","lastName":"Puthucheary","suffix":""}],"badges":[],"createdAt":"2026-02-20 16:40:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105641500,"identity":"a7409e1b-d5ba-4f1e-9758-cf9a7e93e150","added_by":"auto","created_at":"2026-03-28 16:30:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram describing those we excluded from subsequent data analysis. Summary of incomplete data sets can be found in supplementary material.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAPACHE II \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAcute Physiology and Chronic Health Evaluation II, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIMD \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eIndices of Multiple Deprivation, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSOFA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Sequential Organ Failure Assessment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8927799/v1/4b33d73d1f2bfe1b798d8d0b.png"},{"id":105728480,"identity":"b987c8bf-9bbb-4109-9b1c-ff301f499e56","added_by":"auto","created_at":"2026-03-30 11:11:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot depicting the odds ratios for factors associated with non-engagement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAPACHE II \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eAcute Physiology and Chronic Health Evaluation II, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIMD \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eIndices of Multiple Deprivation, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eLOS \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eLength of Stay\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8927799/v1/d39dd286e4a966a172cc4a84.png"},{"id":105641501,"identity":"cc62b8b9-a510-48a6-9429-25365e542574","added_by":"auto","created_at":"2026-03-28 16:30:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA visualisation of the intersectionality of engagement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis figure heat maps the percentage of non-engagement with the follow-up service in our cohort by ethnicity and socioeconomic status (represented by IMD Quintile).\u003c/strong\u003e \u003cstrong\u003en= number of people in each group, followed by % rate of non-engagement, and the colour represents the non-engagement rate in the critical care follow up service (see scale).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8927799/v1/39faed7739fcbe95cc1cc7ce.png"},{"id":105903804,"identity":"7353dfc6-1b50-49d1-ae9e-701210b17c84","added_by":"auto","created_at":"2026-04-01 09:53:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530762,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927799/v1/fc15142a-4d1f-4739-8095-b10c88ce8d9c.pdf"},{"id":105641504,"identity":"81fd4939-e7db-433e-8e68-42ccf30ea1ec","added_by":"auto","created_at":"2026-03-28 16:30:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32577,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsSairaNazeer.docx","url":"https://assets-eu.researchsquare.com/files/rs-8927799/v1/34204e00a87d1da6defa9f6e.docx"}],"financialInterests":"Competing interest reported. ZP has received honoraria for consultancy from GlaxoSmithKline, Lyric Pharmaceuticals, Faraday Pharmaceuticals and Fresenius-Kabi, educational support from Baxter and Nestle Health Science and speaker fees from Orion, Baxter, Sedana, Fresenius-Kabi and Nestle. RP undertakes paid consultancy work for Mode Sensors. SN, AJF, AA, TS, JS, JP and YW have no competing interests to declare.","formattedTitle":"Intersectional Influences on Non-Engagement Among Critical Care Survivors: A Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver 200,000 people are admitted to intensive care units (ICU) in the United Kingdom annually (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Seventy percent survive to discharge, but over half experience hospital readmission within a year and many develop new chronic conditions (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Alongside physical recovery, psychological and cognitive challenges can occur, collectively termed Post Intensive Care Syndrome (PICS) and can lead to long-term reliance on health and social care services (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). National guidelines recommend follow-up care to address these complex needs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Despite this, follow-up service engagement is variable (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Failure to address patient engagement contributes to persistent health inequalities, poorer health outcomes, and increased demand on health and social care services (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHealthcare policies and quality improvement initiatives increasingly recognise a patient\u0026rsquo;s ability to engage as a critical factor influencing healthcare utilisation and outcomes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Policy makers have highlighted the importance of supporting patient engagement to improve service uptake and adherence and to reduce the impact of social determinants of health (\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Patient engagement relies on the coordinated partnerships between patients and healthcare providers, but is not shaped by a single factor (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Rather there is a dynamic interplay of multiple social identities and structural constructs - a concept known as intersectionality (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Ethnicity and socioeconomic status have emerged as important factors influencing patient engagement, with ethnic minority (term used for consistency with existing literature, though \u0026ldquo;global majority\u0026rdquo; is acknowledged as more accurate and inclusive) and socioeconomically deprived groups often facing greater barriers to engagement, higher admission rates and worse health outcomes (\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Conventional determinants of causal and associative factors may be too simplistic, as intersectionality is a framework determined by the overlap of identity factors (such as race, gender, socioeconomic status and disability) with systems of power (such as racism, classism and ableism), interacting to shape experiences (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Social determinants of health and service accessibility are deeply intertwined with these intersectional identities and are key drivers of health disparities and engagement barriers (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Critical care follow-up services design traditionally assumes a uniform capacity to engage across survivor populations. This risks systematically excluding groups whose recovery trajectories and social systems differ substantially. While some studies have examined characteristics of patients who engage with follow-up services compared with those who do not, this literature remains limited and has largely focused on clinical factors such as illness severity, length of stay, or comorbidity (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Socio-demographic characteristics are less consistently examined, and are often treated as isolated variables rather than considered jointly. To date, no studies have explicitly explored patterns of engagement with critical care follow-up services through an intersectional lens, potentially obscuring groups at heightened risk of non-engagement.\u003c/p\u003e \u003cp\u003eIdentifying factors of non-engagement therefore requires acknowledgement of these intersecting factors and careful consideration when selecting analysis methodologies. Doing so ensures we are investigating the complex intersecting nature of non-engagement, rather than simply revealing single factor relationships. We examined our population of critical care survivors for features of non-engagement. We hypothesised that there might be a complex interaction between an individual\u0026rsquo;s socioeconomic status and ethnicity, and their ability and inclination to engage with a critical care follow up service. Accordingly we aimed to identify intersectionality in subgroups of patients who did not engage.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe performed a prospective cohort study of adult patients admitted to the Adult Critical Care Unit at the Royal London Hospital, UK, between April 2023 and December 2024. Research ethics approval was obtained from Yorkshire and The Humber - Leeds West Research Ethics Committee (reference number 23/YH/0146, 22.08.2023). This study is reported in line with the STROBE checklist for cohort studies and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (37).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSetting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Royal London Hospital is a large tertiary centre located in North-East London, which is one of the most\u0026nbsp;ethnically diverse and socioeconomically deprived regions in England (38). The unit serves a mixed patient population, including trauma and neurocritical care patients. Patients can be discharged to locations across London and neighbouring regions, either being discharged directly home or via their local hospital, a rehabilitation unit, a specialist hospital or indeed discharged to supported living facilities. Data collection occurred continuously between April 2023 and December 2024.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants - Eligibility criteria and selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePatients aged \u0026ge;16 years who survived to hospital discharge and for whom complete data sets were available were included in the analysis (see section below \u003cem\u003eExcluded Datasets\u0026nbsp;\u003c/em\u003efor more detail).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients were sent an electronic questionnaire via text message and/or email, 72 hours post-discharge from critical care, with two reminder messages sent in the following 48 hours and a further invitation at 4 weeks post discharge from critical care. It was available in two additional languages (Polish and Bengali) selected to reflect the local population. A care navigator (a healthcare professional involved in the recovery pathway for critical care survivors) reviewed the questionnaires and referred to appropriate services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuestionnaire\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe electronic questionnaire consisted of four existing quality-of-life patient-reported outcome measures, chosen to reflect the breadth of symptoms experienced after critical illness: the EuroQol Five Dimensions (EQ5D), a widely used generic health-related quality of life instrument, demonstrating good validity in critically ill cohorts; the Post-ICU Presentation Screen - Community Version (PICUPS-Community), which in itself has limited psychometric data but is related to the PICUPS tool and is the only tool specifically designed for ICU population; the World Health Organisation Disability Assessment Schedule 2.0 (WHODAS 2.0), a disability measure shown to be reliable and valid in the ICU population; and the 36-Item Short Form Health Survey (SF-36), which has demonstrated good reliability and validity in this population (39\u0026ndash;42). These four existing measures were included in entirety, back to back with no adaptations made by the study team.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExposures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were collected as part of our Intensive Care National Audit and Research Centre (ICNARC) routine data collection. Demographic data included age on admission in whole years (owing to the small number of patients at either end of the age range, age was grouped into \u0026lsquo;\u0026le;17 years\u0026rsquo; and \u0026lsquo;\u0026ge; 90 years\u0026rsquo;), sex (at birth, as recorded in medical records), and ethnicity (grouped by Office of National Statistics census categories into Black, Asian, Mixed/Other, White and Not Stated). Socioeconomic status was determined from the recorded postcode, which was then linked to Office for National Statistics lower super output area to obtain the index of multiple deprivation (IMD). IMD was grouped into quintiles based on the IMD rank. Clinical data included: level of frailty (Clinical Frailty Scale (CFS) in conjunction with clinician based assessment informed by the subjective history documented in the medical notes \u0026ndash; owing to the small number of patients classified as having a \u0026lsquo;stable long term disability\u0026rsquo;, affecting their independence with activity of daily living, this category was combined with the \u0026lsquo;frail\u0026rsquo; category for analytical purposes), admitting speciality (categorised into medical, surgical (this was mainly emergency as elective surgical cases are treated in a different location post-operatively), neurosurgical and trauma), critical care length of stay (LOS), maximum organ support, APACHE II and the SOFA score. The primary exposures of interest were ethnicity and IMD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome measures\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was engagement. Engagement was defined as completion of part or all of the questionnaire at any time. Completion of the questionnaire represents the minimum threshold for contact with the critical care follow-up pathway and therefore serves as a pragmatic indicator of service reach and accessibility rather than patient motivation alone. For the purpose of this study, \u0026lsquo;non-engagement\u0026rsquo; refers to when a patient did not complete any of the questionnaire.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Sources and Measurement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll demographic and clinical data were obtained from routine ICNARC data collection and hospital records. IMD scores were derived from patient postcode linked to the Office for National Statistics lower super output areas.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBias\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe attempted to minimise bias by excluding individuals with incomplete key clinical or demographic variables to reduce misclassification (see below section \u003cem\u003eExcluded Datasets\u003c/em\u003e and figure 1). Measurement bias was minimised by using routinely collected ICNARC data, which are subject to standardised definitions and audit processes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy Size\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cohort defined by all eligible admissions during the study period; no formal sample size calculation was performed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarise characteristics of the cohort. Associations between characteristics and engagement status were assessed using Chi-squared tests for categorical variables and Mann-Whitney-Wilcoxon tests for continuous variables. We used logistic regression for univariate analyses with engagement as the dependent variable. All variables apart from the SOFA score were included in a multivariable logistic regression. We present odds ratios with associated 95% confidence intervals.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eVariance inflation factors (VIFs) were calculated for all predictors in the final multivariable logistic regression model to assess multi-collinearity. We included an interaction term between ethnicity and IMD quintiles in a multivariable logistic regression model as a statistical test for intersectionality. An intersectional visualisation technique was used to explore the interaction between IMD quintiles and ethnicity. We examined patterns across these intersectional groups using a heat map, allowing visual interpretation of engagement rates and group counts. All analyses were performed using R software and associated packages ggplot2 (R software foundation, Vienna, version 4.4.3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExcluded Datasets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom the total 3058 patients discharged from critical care over the data collection period, we excluded a total of 867 from the analysis either because they had no opportunity to engage with the follow-up service or because missing data could have introduced misclassification or bias (see figure 1). We excluded 604 datasets as the patients did not survive to critical care or hospital discharge and therefore would not have had the opportunity to engage with the service. We excluded 137 datasets as they did not have at least one form of valid contact detail and therefore would not have been given the opportunity to engage in the electronic format of this service. We excluded a further 126 datasets due to incomplete data to minimise the risk of misclassification, including 80 datasets missing severity of illness (APACHE II and SOFA) scores, 40 missing postcodes (precluding assignment of IMD), and 6 with missing frailty data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom a total of 3058 critical care survivors, 2191 (72%) had complete data sets and survived to hospital discharge (Figure 1). Patient characteristics are summarised in Table 1. In brief, ICU survivors, had an average age of 53 years (range 39-67), were predominantly male (1382/2191, 63%), half were of White ethnicity (1101/2191, 50%), living in areas of high socioeconomic deprivation (1216/2191, 56% from the lowest 3 IMD deciles) and were admitted under medical (800/2191, 36%), neurosurgical (301/2191, 14%), surgical (652/2191, 30%) and trauma specialities (438/2191, 20%). We describe the characteristics and outcomes of patients with missing data (supplementary material table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Patient characteristics for overall cohort (n=2191), stratified for engagement.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNumbers are presented as n (% of total) unless otherwise stated. To calculate p values, Mann-Whitney U was used for continuous data and chi-squared tests for categorical data. Data regarding residence prior to admission has been omitted for data integrity concerns. IMD quintiles based on IMD rank within the cohort.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"543\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngagement Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Engaged (n=1552)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngaged \u0026nbsp; \u0026nbsp; \u0026nbsp; (n=639)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e56 (39 to 68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e54 (39 to 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eETHNICITY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e360 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e136 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e266 (17.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e74 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMixed/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e69 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e33 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eNot stated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e103 (6.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e49 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e754 (48.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e347 (54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e565 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e244 (38.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e987 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e395 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMD QUINTILES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e1 - most deprived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e326 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e112 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e327 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e111 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e316 (20.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e122 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e293 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e145 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e5 - least deprived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e289 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e149 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRAILTY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e272 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e76 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eNot frail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1280 (82.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e563 (88.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAX ORGAN SUPPORT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e29 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e12 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e816 (52.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e346 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e489 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e200 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e201 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e79 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e17 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADMITTING SPECIALITY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e582 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e218 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eNeurosurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e219 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e82 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eSurgical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e427 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e225 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e324 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e114 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA SCORE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e4 (2 to 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3 (2 to 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPACHE II SCORE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e13 (10 to 17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e12 (9 to 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRITICAL CARE LOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e6 (4 to 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e6 (4 to 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAPACHEII\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eAcute Physiology and Chronic Health Evaluation II, \u003cem\u003eIMD\u0026nbsp;\u003c/em\u003eIndices of Multiple Deprivation, \u003cem\u003eLOS\u0026nbsp;\u003c/em\u003eLength of Stay, \u003cem\u003eSOFA\u003c/em\u003e Sequential Organ Failure Assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient outcomes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA large proportion of the patients (1558/2191, 71%) did not engage with the follow up service (Table 1). Median age and sex distribution did not differ significantly between groups, however there were significant differences in ethnicity and socioeconomic status (measured by IMD). In univariate logistic regression patients of Black ethnicity had significantly lower odds of engagement compared to those of White ethnicity (OR = 1.65, CI = 1.25-2.22). This association remained significant after adjusting for age, sex, level of frailty, admission speciality, and illness severity in the multivariable logistic regression model (aOR = 1.69, CI = 1.25-2.28). Similarly in univariate analysis, patients from more socioeconomically deprived areas had lower odds of engagement. Compared to those in the least deprived IMD quintile, individuals in more deprived quintiles had significantly lower odds of engagement, noticeably in IMD Quintile 1 (OR = 1.5, CI = 1.12-2.01). and 2 (OR = 1.52, CI = 1.13-2.04). This relationship also remained significant in the multivariable model (IMD 1 aOR = 1.39, CI = 1.03-1.88\u0026nbsp;and IMD 2\u0026nbsp;aOR = 1.46, CI = 1.08-1.98) (Table 2, Figure 2).\u0026nbsp;Scaled generalised VIFs ranged from 1.01 to 1.22, indicating no evidence of multicollinearity among the included variables.\u0026nbsp;The introduction of an interaction term to the logistic model (measured with ANOVA), resulted in no significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Logistic regression analysis:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNumbers are presented as OR (CI) or the p value. For categorical data, the reference category is stated in the table.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUNIVARIATE ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMULTIVARIABLE ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVARIABLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted OR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.04 (0.99-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.04 (0.98-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.08 (0.89-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.2 (0.99-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eReference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;Reference category \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eETHNICITY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.22 (0.96-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.24 (0.97-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.65 (1.25-2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.69 (1.25-2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMixed / Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.96 (0.63-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.99 (0.64-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNot stated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.97 (0.68-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.98 (0.68-1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eReference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp; Reference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMD QUINTILE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e1 \u0026ndash; most deprived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.5 (1.12-2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.39 (1.03-1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.52 (1.13-2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.46 (1.08-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.34 (1-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.26 (0.93-1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.04 (0.79-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1 (0.75-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e5 \u0026ndash; least deprived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eReference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp; Reference category \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAX ORGAN SUPPORT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.02 (0.53-2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.82 (0.41-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;Reference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eReference category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.04 (0.84-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.05 (0.84-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.08 (0.81-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.13 (0.8-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e3.6 (1.03-22.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.39 (0.09-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPACHE II SCORE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.02 (1.01-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.02 (1-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRAILTY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eFrail\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.57 (1.2-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.5 (1.12-2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNot frail\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eReference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp; Reference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRITICAL CARE LOS (days)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.01 (1-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.01 (1-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADMITTING SPECIALITY\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eNeurosurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1 (0.74-1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.15 (0.83-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eSurgical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.71 (0.57-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.75 (0.59-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eTrauma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.06 (0.82-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 116px;\"\u003e\n \u003cp\u003e1.27 (0.96-1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 160px;\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eReference category\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp;Reference category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAPACHE II\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eAcute Physiology and Chronic Health Evaluation, \u003cem\u003eIMD\u0026nbsp;\u003c/em\u003eIndices of Multiple Deprivation, \u003cem\u003eLOS\u0026nbsp;\u003c/em\u003eLength of Stay\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted odds ratios and 95% confidence intervals for all variables used in multivariate logistic regression analysis. Reference categories same as shown in Table 2. An odds ratio of \u0026gt;1 shows factors associated with non-engagement (i.e. that make you more at risk of non-engagement), whereas an odds ratio of \u0026lt;1 shows factors associated with engagement. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther analysis of our data was conducted to understand the intersecting impact of socioeconomic status and ethnicity on an individual\u0026rsquo;s ability to engage. A heat map (Figure 3) allowed us to distinguish patterns of non-engagement, including the groups revealed by the traditional single-factor analyses: IMD quintiles 1 and 2 and Black ethnicity groups. Obscured at-risk groups were revealed, including a large group of White individuals living in areas of high deprivation and a small group of individuals from Black ethnic backgrounds living in the least deprived areas with the highest rate of non-engagement. Although not statisticallysignificant, this analysis revealed hidden populations outside of the categories defined by the single-factor analyses; including a large group of Asian ethnicity individuals from mid to low socioeconomic deprivation (IMD quintiles 3 and 4) with poor engagement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e We found distinct social and demographic patterning in non-engagement with the critical care follow-up service. Patients from more socioeconomically deprived areas and those from Black ethnic backgrounds had consistently higher odds of non-engagement, even after adjusting for confounders. Intersectional visualisation further demonstrated substantial variation in non-engagement rates across combined ethnicity and IMD strata, alongside marked differences in the size of the populations represented within each stratum (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo understand these patterns more comprehensively, we applied two analytical approaches. Using traditional single-factor logistic regression, we identified individuals from IMD quintiles 1 and 2 and those of Black ethnicity as having higher odds of non-engagement. However, when we applied an intersectional approach, visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we uncovered a sizeable additional group of individuals with high rates of non-engagement that was not visible through single-factor methods. Although these groups did not always exhibit the highest relative odds of non-engagement, they collectively represented a substantial proportion of the ICU survivor population.\u003c/p\u003e \u003cp\u003eAn important methodological distinction between these approaches is that odds ratios derived from logistic regression quantify relative risk but do not account for the absolute size of the populations affected. Consequently, groups with moderately elevated non-engagement rates but large population sizes may represent a substantial proportion of unmet need, despite appearing less prominent in regression-based analyses. By jointly considering non-engagement rates and population size, the intersectional approach revealed additional at-risk groups, including individuals of Black ethnicity in less deprived areas and Asian individuals from IMD quintiles 3 and 4, patterns not evident using single-factor methods alone.\u003c/p\u003e \u003cp\u003eTaken together, these findings demonstrate that reliance on conventional regression approaches alone may obscure important population-level patterns of non-engagement, and may underestimate the true scale and distribution of unmet need, potentially overlooking groups who may benefit from targeted or tailored follow-up interventions. This may have important implications for policy development. Our results underscore the added value of intersectional approaches in uncovering both visible and obscured dimensions of inequity in access and engagement with healthcare services.\u003c/p\u003e \u003cp\u003eOur findings largely align with existing literature, which describes lower engagement in ethnic minority groups due to barriers such as language, lower health literacy, and cultural mismatch (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Lower engagement is associated with worse health outcomes, including higher readmission rates, increased morbidity and reduced quality of life (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). There is also a well-established association between socioeconomic disadvantage and poorer healthcare access and outcomes among critical care survivors (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Additionally, broader healthcare utilisation disparities across ethnic groups have been documented in national English datasets (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, much of the evidence has examined ethnicity and socioeconomic status as separate determinants, or has focused on attendance versus non-attendence without explicitly considering how these factors interact. While some studies describe characteristics of patients who do not engage with follow-up services, few have applied quantitative approaches to identify intersecting patterns of risk across social identities (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). To that end, the additional groups identified through our intersectional analysis - specifically individuals of Black ethnicity in the least deprived areas and the large group of Asian individuals from IMD quintiles 3 and 4 - are less well described in existing literature. As these patterns do not emerge in single-factor approaches, such groups may be systematically overlooked in existing evidence, underscoring the need for further work to understand the mechanisms driving their lower engagement. Although intersectionality has been widely applied in qualitative research, it\u0026rsquo;s use in quantitative research is more recent, with growing evidence of it\u0026rsquo;s analytical value (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). However, intersectional analysis risks being applied superficially, with one systematic review highlighting that the theory is frequently misunderstood with only simplistic descriptive analysis of demographic data being conducted with no consideration of wider social context (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eA key strength of this study is the use of intersectional visualisation techniques, which allowed for a more detailed exploration of non-engagement patterns across both demographic factors and social dimensions that are often examined in isolation. This approach helped reveal both small but high risk groups and large populations with unmet need, offering novel insights to inform policy and practice. The diversity of the study population facilitated this comprehensive analysis, revealing inequities that may not have been apparent in less heterogeneous cohorts. Although those without valid contact details were excluded from data analysis, reliance on electronic communication may have disadvantaged individuals experiencing digital poverty and/or poor digital literacy. Digital poverty itself was unlikely to be a major driver of non-engagement given the small number excluded for having no telephone or email contact details (137/3058, 4% of the total population), however digital literacy remains a potentially important and underexplored barrier in this population. Comparable services within the same NHS Trust have achieved engagement rates of up to 60\u0026ndash;80%, suggesting that digital exclusion alone does not fully explain the lower response rate observed in our population. Data integrity and inaccuracies may have introduced a source of bias and influenced the findings. Additionally, as this questionnaire was distributed via an automated service to a population which is heterogeneous in it\u0026rsquo;s recovery trajectory and timeline, it is likely that for some it did not coincide with a point at which they were clinically able to complete it or considered it relevant. To mitigate this, reminder messages were sent including a second distribution four weeks later. Finally, as this study was conducted in a single large inner-city trauma centre, the results may not be fully generalisable to other settings with different population profiles or service structures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImplications for future practice\u003c/h2\u003e \u003cp\u003eOur findings emphasise the need for tailored engagement strategies that account for intersecting identities and structural barriers, rather than focussing on single factors in isolation. Intervention developers must recognise that disadvantage rarely exists in a vacuum, but rather through the compounding intersecting effects of multiple factors. Embedding intersectionality into intervention design is therefore essential to ensure that engagement strategies meet the complex needs of critical care survivors and reduce inequities in access.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImplications for future research\u003c/h2\u003e \u003cp\u003eRoutinely collected hospital data fail to capture many key structural determinants of non-engagement. Evidence suggests that greater social support is associated with improved engagement (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Components of social support such as informal care arrangements and insecure housing will affect an individual\u0026rsquo;s ability to engage, however they are poorly recorded or absent from hospital datasets. Linkage to social care records will provide some of the answers, however a comprehensive understanding of engagement in this population will require prospective and considered data collection to ensure all relevant determinants are considered. Future research will need to employ codesign methodologies to ensure relevance and responsiveness to the needs of critical care survivors and their communities. A caveat is that co-design processes often attract individuals who are already engaged, which risks overlooking the perspectives of those less likely to participate. Using pre-existing trusted community pathways and networks to reach and involve groups who might otherwise remain disengaged may mitigate this. Local faith groups, community centres, peer support networks, or voluntary organisations with established relationships can help build trust and facilitate access to underrepresented groups. It is also important to note, that studies evaluating tailored engagement interventions in other areas of healthcare rarely achieve universal engagement (\u003cspan additionalcitationids=\"CR53 CR54\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). This suggests that a more nuanced approach may be required when designing engagement strategies, one that considers multiple intersecting factors and the frequency of a variable alongside its relative odds.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe method of analysing disparities and risk factors for non-engagement can significantly affect which populations are revealed to be at risk. Failure to account for intersectionality risks designing interventions that are ineffective or inequitable, ultimately perpetuating existing disparities in recovery outcomes for critical care survivors. Incorporating intersectional approaches into the evaluation of critical care follow-up services can improve service design, resource allocation, and equity of recovery after critical illness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch ethics approval was obtained from Yorkshire and The Humber - Leeds West Research Ethics Committee (reference number 23/YH/0146, 22.08.2023). This study is reported in line with the STROBE checklist for cohort studies and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained by continuation to complete questionnaire.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable as using data in an anonymised manner in publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource data from this study is not publicly available as GDPR compliant de-identification within a geographically limited dataset addressing protected characteristics and sensitive healthcare data cannot be ensured. The lead author is happy to consider reasonable requests for aggregated results for additional analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZP has received honoraria for consultancy from GlaxoSmithKline, Lyric Pharmaceuticals, Faraday Pharmaceuticals and Fresenius-Kabi, educational support from Baxter and Nestle Health Science and speaker fees from Orion, Baxter, Sedana, Fresenius-Kabi and Nestle. RP undertakes paid consultancy work for Mode Sensors. SN, AJF, AA, TS, JS, JP and YW have no competing interests to declare. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Barts Charity (IRAS number 318066). They had no role in the design, collection, analysis or interpretation of the data, nor in writing or decision to submit the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSN, TS, JS and ZP conceived and designed the study.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSN, AA and AJF performed the analyses with input from JP, YW and RP.\u003c/p\u003e\n\u003cp\u003eSN, AJF and ZP drafted the initial manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the critical care audit team at The Royal London Hospital and all patients whose data contributed to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eICNARC [Internet]. 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Learning from the universal, proactive outreach of the Brazilian Community Health Worker model: impact of a Community Health and Wellbeing Worker initiative on vaccination, cancer screening and NHS health check uptake in a deprived community in the UK. BMC Health Serv Res. 2023 Oct 12;23(1):1092. \u003c/li\u003e\n\u003cli\u003eJoo JY, Liu MF. Effectiveness of Culturally Tailored Interventions for Chronic Illnesses among Ethnic Minorities. West J Nurs Res. 2020 Jan;43(1):73\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eOkasako-Schmucker DL, Peng Y, Cobb J, Buchanan LR, Xiong KZ, Mercer SL, et al. Community Health Workers to Increase Cancer Screening: 3 Community Guide Systematic Reviews. Am J Prev Med. 2023 Apr;64(4):579\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eTian L, Huang L, Liu J, Li X, Ajmal A, Ajmal M, et al. Impact of Patient Navigation on Population-Based Breast Screening: a Systematic Review and Meta-analysis of Randomized Clinical Trials. J Gen Intern Med. 2022 Aug;37(11):2811\u0026ndash;20. \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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Critical care recovery, service access and engagement, intersectionality","lastPublishedDoi":"10.21203/rs.3.rs-8927799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCritical care survivors face long-term health and social care needs. Barriers to engagement with essential follow-up services contribute to worse outcomes. Understanding these barriers is crucial for equitable healthcare provision and service design.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a prospective cohort study of adult patients admitted to a critical care unit, between April 2023 and December 2024. Eligible patients with valid contact details were sent a questionnaire electronically, designed to identify needs, 72 hours post critical care discharge. The primary outcome was service engagement, defined as completion of the electronic questionnaire. Demographic and clinical data were obtained from the local audit team. Primary exposures were index of multiple deprivation (IMD) and ethnicity. Associations were determined with multivariable logistic regression (adjusted odds ratios, aOR) and intersectional analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 3058 patients discharged, 2191 (72%) were eligible for analysis. The majority (1558/2191,71%) did not engage with the follow-up service. Higher non-engagement was observed in patients from more deprived areas (IMD Quintiles 1[aOR\u0026thinsp;=\u0026thinsp;1.39, CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.88] and 2 [aOR\u0026thinsp;=\u0026thinsp;1.46, CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;1.98]) and of Black ethnic background (aOR\u0026thinsp;=\u0026thinsp;1.69, CI\u0026thinsp;=\u0026thinsp;1.25\u0026ndash;2.28). Intersectional analysis revealed further at-risk populations (915/2191, 42%), including a large group of White individuals in areas of high deprivation and a small group of Black individuals in the least deprived areas.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIntersectional analysis identified groups at-risk of non-engagement overlooked by single-factor approaches. Considering both population size and relative risk is vital for designing critical care follow-up support services to maximise impact, allocate resources efficiently, and reduce existing disparities in recovery outcomes for critical care survivors.\u003c/p\u003e","manuscriptTitle":"Intersectional Influences on Non-Engagement Among Critical Care Survivors: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-28 16:30:47","doi":"10.21203/rs.3.rs-8927799/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-13T13:53:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164803666493338033978061575339892148778","date":"2026-04-02T17:26:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87637072255481237786566315061875908895","date":"2026-04-02T16:33:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94416912034061536765340993188178832624","date":"2026-03-26T21:14:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T06:56:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T09:21:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T23:28:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T17:55:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-02-27T10:46:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9055788b-9f8f-4f6a-9840-93bdd159c5f4","owner":[],"postedDate":"March 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-28T16:30:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-28 16:30:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8927799","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8927799","identity":"rs-8927799","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}