The Role of Poverty-Related Social Determinants in Maternal and Perinatal Health Inequities: A cross-sectional study using the eLIXIR Born in South London, UK maternity-child data linkage

The Role of Poverty-Related Social Determinants in Maternal and Perinatal Health Inequities: A cross-sectional study using the eLIXIR Born in South London, UK maternity-child data linkage | 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 The Role of Poverty-Related Social Determinants in Maternal and Perinatal Health Inequities: A cross-sectional study using the eLIXIR Born in South London, UK maternity-child data linkage Hannah Rayment-Jones, Sam Burton, Tisha Dasgupta, Zenab Barry, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7011465/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Despite longstanding recognition of health inequities in UK maternity care, evidence remains limited on how intersecting poverty-related social determinants contribute to adverse perinatal outcomes at the population level. While individual factors such as ethnicity and deprivation have been linked to poor outcomes, few studies have examined wider determinants of heath and their cumulative effects using routinely collected data. This study addresses that gap by analysing structural and intermediary poverty-related factors and their association with adverse perinatal outcomes in a large, diverse urban cohort. Methods: We conducted a retrospective cross-sectional analysis of 67,308 pregnancies from the Early Life Cross Linkage in Research (eLIXIR) cohort, using linked electronic health records from NHS Trusts in South London. Structural and intermediary poverty-related variables were assessed using the World Health Organisation’s social determinants framework. The primary outcome was a composite of adverse perinatal events: emergency caesarean, obstetric haemorrhage, preterm birth, low birthweight, low Apgar score, stillbirth, and neonatal death. Binary logistic regression with random intercepts accounted for repeated pregnancies. Adjusted risk ratios were estimated controlling for key sociodemographic and clinical factors. Results: Women from Black (aRR 1.50, 95% CI 1.42–1.59), Asian (aRR 1.49, 95% CI 1.39–1.59), and other non-White ethnic backgrounds (aRR 1.50, 95% CI 1.42–1.59), those living in the most deprived areas (aRR 1.10, 95% CI 1.01–1.20), non-UK-born women (aRR 1.20, 95% CI 1.15–1.25), and recent migrants (aRR 1.32, 95% CI 1.14–1.53) were at significantly higher risk of adverse outcomes. Intermediary factors, e.g., lack of social support (aRR 1.21, 95% CI 1.02–1.42), unemployment (aRR 1.16, 95% CI 1.10–1.23), financial hardship (aRR 1.17, 95% CI 1.01–1.35), living in social housing (aRR 1.16, 95% CI 1.09–1.24), transfer of care between hospitals (aRR 1.27, 95% CI 1.18–1.37), missed appointments (aRR 1.19, 95% CI 1.04–1.37), and unscheduled maternity care use (aRR 1.21, 95% CI 1.14–1.29), were independently associated with increased risk. Moreover, women facing multiple overlapping social risk factors had a significantly higher likelihood of adverse outcomes (aRR 1.23, 95% CI 1.12–1.35), highlighting the cumulative impact of social vulnerability beyond clinical risk. Conclusions: Poverty-related determinants at both structural and intermediary levels substantially shape maternal and perinatal outcomes. Integrated, cross-sector approaches are needed to address these inequalities and improve outcomes for marginalised women and their infants. Figures Figure 1 Figure 2 Background Health inequities remain a persistent issue in the United Kingdom, disproportionately affecting marginalised populations—particularly in maternity care. The World Health Organization (WHO) defines health inequities as avoidable, unjust differences in health outcomes that arise from systemic social disadvantages (WHO, 2019). In the UK context, these disparities are closely tied to social determinants of health (SDOH), including socioeconomic status, housing security, education, and systemic discrimination (Marmot et al., 2020 ). While the National Health Service (NHS) offers free maternity care to those deemed “ordinarily resident,” significant barriers to equitable access and outcomes persist, especially for migrant and socioeconomically disadvantaged women (NHS England, 2019 ). Despite the NHS’s commitment to advancing health equity (NHS England, 2021 ), maternal health outcomes remain stratified by both ethnicity and socioeconomic position. Women from Black, Asian, and minority ethnic backgrounds, as well as those living in deprived areas, face markedly higher maternal mortality rates than their White and more affluent counterparts (Knight et al., 2021 ; Knight et al., 2023 ; Felker, 2024 ). While poverty is a powerful driver of adverse outcomes—through mechanisms such as food insecurity, overcrowded housing, and chronic stress—disparities persist even after adjusting for deprivation. For example, Black women experience higher risks of poor outcomes across all levels of the Index of Multiple Deprivation (IMD), suggesting that racism, discrimination, and differential treatment within services also play a critical role. Although national policy increasingly acknowledges these inequities (NHS England, 2022 ), there remains a paucity of UK-based quantitative research examining the full range of social determinants that shape maternity care access and outcomes. In particular, gaps persist in understanding the effects of structural and intermediary factors such as housing insecurity, economic precarity, immigration status, legal barriers to healthcare, and limited health literacy (Morris et al., 2020 ; Wright et al., 2020 ; Vousden, 2025). Traditional efforts to reduce health disparities in high-income countries have often focused on improving healthcare access. However, research indicates that access alone accounts for only a modest share of health outcomes. Structural determinants, including poverty, exert a more profound influence—through unstable living conditions, food insecurity, and cumulative psychosocial stress (Bambra et al., 2020 ; Taylor-Robinson et al., 2019 ). Yet, UK policy and research often adopt narrow proxies such as ethnicity or area-level deprivation, rather than directly interrogating the multi-dimensional nature of poverty and its intersection with racial and migration-related inequities (Ray-Chaudhuri et al., 2023 ; King's Fund, 2024). The WHO Commission on Social Determinants of Health (CSDH) framework provides a useful model for understanding health inequities, distinguishing between structural determinants (e.g. income, education, ethnicity), which shape the broader distribution of power, resources, and opportunity, and intermediary determinants (e.g. living conditions, behaviours, healthcare access), which represent the more immediate conditions of daily life that influence health outcomes (World Health Organisation, 2010). Structural determinants affect health indirectly by shaping exposure to intermediary determinants. Applying this framework to the UK maternity context enables a clearer understanding of how intersecting forms of social disadvantage—including but not limited to poverty—influence perinatal outcomes and where cross-sectoral interventions may be most effective. This study will investigate the association between poverty-related structural and intermediary social determinants and adverse perinatal health outcomes in an ethnically diverse, urban area of the UK. Drawing on routinely collected local data, we apply the CSDH framework to identify key drivers of inequity and inform the development of targeted, cross-sector strategies to reduce maternal and child health disparities. Methods Aim To examine the relationships between a composite adverse perinatal outcome for mother and/or baby and poverty related social determinants of health within a South London cohort. Design This study was a retrospective cross-sectional analysis of The Early Life Cross Linkage in Research (eLIXIR) cohort. The eLIXIR Partnership was developed in 2018, generating a repository of real-time, pseudonymised, structured data derived from the electronic health record systems of two acute and one Mental Health NHS Trust in South London, UK (Carson et al, 2020 ). Detailed information about the cohort and data linkage processes is available on the eLIXIR project website (King’s College London, 2025 ). Using this linked data from electronic healthcare records we analysed maternal and infant outcomes from October 2018 through until October 2023, including 56290 women (67308 pregnancies). Setting The study took place in a superdiverse urban area in South London, characterised by high levels of socioeconomic deprivation and a large proportion of residents from minority ethnic backgrounds and migrant communities. Maternity services are delivered through a network of NHS providers, with care pathways spanning community, primary, secondary, and tertiary settings. Outcome measures Adverse maternal and infant outcomes were identified based on definitions used in the National Maternity and Perinatal Audit (NMPA, 2022 ) and the English Maternal Morbidity Outcome Indicator (Webb et al., 2021 ). These were mapped to the available data within the eLIXIR cohort to ensure consistency with existing research and robustness of measurement. The primary outcome was a composite binary variable indicating the presence or absence of any of the following: emergency (unplanned) caesarean section, obstetric haemorrhage exceeding 1000 mL (either antepartum or postpartum), preterm birth occurring before 37 weeks of gestation, low birthweight defined as less than 2500 grams, an Apgar score of 7 or less at five minutes, stillbirth (defined as intrauterine death occurring at or beyond 24 weeks of gestation), or neonatal death within 28 days of birth. All outcomes were coded as binary variables, and a full list of definitions is provided in Supplementary File 1. Poverty related social determinant variables We applied the WHO Commission on Social Determinants of Health (CSDH) framework to classify and interpret poverty-related variables available in the eLIXIR dataset. The framework distinguishes between: Structural determinants , which include socioeconomic and political contexts, and individuals’ social positions (e.g. education, income, ethnicity, legal status); and Intermediary determinants , which include material circumstances, psychosocial stressors, behavioural factors, and health system interactions (WHO, 2010). Variables from the eLIXIR dataset were mapped accordingly (Table 1 ; full definitions in Supplementary File 1). This mapping can help clarify not only which factors contribute to perinatal health inequities, but also where and how targeted interventions might be developed. However, some variables span both structural and intermediary domains. For example, homelessness, criminal justice and social care involvement, are rooted in systemic disadvantage but also directly influence day-to-day stress, instability, and access to services. Likewise, learning difficulties and language barriers reflect both underlying structural inequities and practical barriers to effective healthcare engagement. These overlapping factors illustrate the complex, interconnected nature of social determinants in maternity care and the need for coordinated, multi-level responses. Table 1 Poverty-related social determinant variables available in eLIXIR, mapped to the WHO CSDH framework CSDH Domain Variables Available in eLIXIR Structural Determinants Ethnicity (as a proxy for structural racism and marginalisation); Deprivation index; Age < 20; Born outside UK; Refugee/asylum seeker; New to country; No right to work; Financial difficulties; Social care involvement; Unborn child subject to foster care/adoption; Criminal justice involvement; Female genital mutilation Intermediary Determinants Interpreter required; Feels unsupported; Housing issues or homelessness; Substance use; Domestic abuse (past or current); Mental health problems; Learning difficulties; Late booking for maternity care; Transfer of care from another hospital; Missed > 2 antenatal appointments; Inadequate antenatal care; Unscheduled maternity care use; A&E visits during pregnancy Both Structural and Intermediary Multiple risk factors composite variable (indicating co-occurrence of multiple poverty-related indicators) Analysis All analyses were conducted using the R (version 4.3.1) programming environment to support transparency and reproducibility. Pregnancies with duplicate records or involving multiples (e.g. twins) were excluded. Only completed pregnancies for which data were available from the first antenatal (booking) appointment were retained for inclusion. We employed binary logistic regression models with a random intercept for the individual woman’s ID, accounting for the possibility of multiple pregnancies per woman. The models examined associations between each poverty-related social determinant and the composite adverse perinatal outcome. Stepwise regression models were used, adjusting for relevant maternal sociodemographic and clinical characteristics, including socioeconomic deprivation (measured using the Index of Multiple Deprivation), maternal age, parity, smoking status, body mass index over 30 kg/m², pre-existing medical risk factors, and previous caesarean section (Villar et al., 2022 ; Knight et al., 2023 ). Risk ratios (RRs) with corresponding 95% confidence intervals (CIs) were calculated. Statistical significance was defined as a p-value of ≤ 0.05 for all models. Results A total of 15,633 (35.24%) adverse outcomes were recorded across 44,634 completed pregnancies, encompassing both maternal and infant outcomes. See Table 2 for a breakdown of the adverse outcomes. Participant characteristics Table 2 outlines the maternal baseline characteristics at the time of the booking appointment. The majority of the sample identified their ethnicity as White (53.41%), followed by Black (19.92%), Asian British (10.08%), Mixed/Multiple ethnic groups (5.21%), and other ethnic groups (6.53%), with 4.85% of ethnicity data missing. Nearly three quarters (74.42%) of participants were classified as having a high medical risk status at booking, and 46.21% were primiparous. The mean maternal age at booking was 32.86 years (SD = 5.42), the average BMI was 24.39 (SD = 6.63), and 3.77% of women reported smoking at the time of booking. Table 2 Maternal baseline characteristics and disaggregated outcomes Demographic n(%) Age at booking (years) mean ± SD 32.86 (5.42) Primiparous 23313 (52.23%) High medical risk status at booking 33219 (74.42%) BMI 24.39 (SD 6.63) Smoker at booking 1684 (3.77%) Outcome Emergency (unplanned) caesarean section, 10688 (24.09%) Obstetric haemorrhage > 1000ml 4970 (11.2%) Preterm birth < 37/40 4009 (9.04%) Low birthweight < 2500 grams 4152 (9.36%) Low Apgar score (< 7 at 5 minutes) 777 (1.75%) Stillbirth or Neonatal Death 502 (1.13%) Structural determinants Table three presents the risk ratios (RR) and adjusted risk ratios (aRR) for the composite measure of adverse outcomes, stratified by structural, intermediary, and intersectional determinants of health, and figure two presents a forest plot of any adverse outcome by social determinant. Compared to White women, those from Black, Asian, Mixed, and Other ethnic backgrounds experienced significantly higher risk of adverse outcomes, both before and after adjustment (p < .05 for all). Women living in the most deprived IMD quintile had significantly increased risk of adverse outcomes compared to those in the least deprived quintile in both unadjusted and adjusted models (p < .05). Women aged under 20 were not at significantly lower risk in the unadjusted analysis (p = .161) but were significantly less likely to experience an adverse outcome following adjustment (p < .001). Women born outside the UK and those recently arrived in the country had significantly elevated risks in both unadjusted and adjusted models (p < .001). Financial hardship was also associated with increased risk (p < .05). Women who missed more than three antenatal appointments were not at elevated risk in the unadjusted model (p = .173), but this association became significant after adjustment (p < .01). Those who accessed unscheduled maternity care had increased risk of adverse outcomes in both unadjusted and adjusted models (p < .001). In contrast, inadequate antenatal care (< 10 appointments for primiparous women and < 7 for multiparous women) was associated with increased risk in the unadjusted model only (p < .001), with no significant association after adjustment (p = .146). Intermediary determinants Women who felt unsupported (p < .05), were unemployed (p < .001), or had experienced domestic abuse (p < .05) were at increased risk of adverse outcomes in both unadjusted and adjusted models. Substance use was associated with increased risk in the unadjusted model (p < .001), but not after adjustment (p = .445). Living in social housing was significantly associated with higher risk after adjustment (p < .001). Having a learning disability increased risk in the unadjusted model (p < .01), but this association was not statistically significant after adjustment (p = .085). Other factors such as homelessness or housing insecurity, use of an interpreter, referral to mental health services during pregnancy, previous mental health inpatient admission, and late booking (both > 13 and > 20 weeks’ gestation) were not significantly associated with adverse outcomes after adjustment. Women who transferred care from another hospital were at significantly increased risk in both unadjusted and adjusted analyses (p < .001). Similarly, unscheduled access to maternity services (e.g. maternity triage) remained a significant risk factor after adjustment (p < .001). Intersectional determinants Women who had more than three social or health-related risk factors—excluding ethnicity and deprivation—were at increased risk of experiencing an adverse event. This association remained significant in both unadjusted and adjusted models (p < .001). Discussion This study highlights the significant impact of poverty-related social determinants on maternal and perinatal outcomes in a large, ethnically diverse urban UK cohort. By mapping routinely recorded social risk factors onto the WHO Commission on Social Determinants of Health (CSDH) framework (WHO, 2010), we demonstrate how both structural determinants—such as deprivation, minoritisation, and immigration status—and intermediary determinants—including housing instability, social isolation, and service access—contribute to elevated risks. These findings align with a growing body of evidence suggesting that perinatal inequalities are shaped by intersecting social, economic, and systemic drivers beyond biomedical factors alone (Bambra et al., 2020 ; Marmot et al., 2020 ; Vousden et al., 2025 ). Importantly, the study found that associations between social complexity and adverse perinatal outcomes persisted even after adjusting for pre-existing clinical risks. Women experiencing multiple intersecting disadvantages, particularly those facing three or more social risk factors, had significantly poorer outcomes, underscoring the cumulative burden of social adversity on maternal and infant health (Knight & Burrows, 2024 ; Public Health England, 2021 ). Our findings highlight several specific risk factors for adverse perinatal outcomes that warrant targeted attention within NHS maternity services. Women from Black, Asian, and minority ethnic backgrounds continue to experience disproportionately poor outcomes, even after adjusting for area-level deprivation, echoing prior research suggesting that ethnicity-related inequities are not fully explained by socioeconomic status alone (Knight et al., 2021 ; Ray-Chaudhuri et al., 2023 ). Similarly, women who are new to the UK face significantly elevated risk, potentially reflecting barriers related to immigration status, limited entitlement to care, language proficiency, and unfamiliarity with the healthcare system—all of which are modifiable through more inclusive policy and service design (Fellmeth et al., 2018 ). One of the most striking associations was between domestic abuse and adverse outcomes. Despite the existence of clear NHS guidelines on identifying and managing domestic abuse in pregnancy (NICE, 2014 ), our findings support wider research showing persistent gaps in implementation (Hildersley et al, 2022 ). This indicates an urgent need for system-wide accountability, improved staff training, and integration of specialist advocacy services in maternity settings (McGarry & Nair, 2019 ). Interestingly, younger maternal age was associated with lower odds of adverse outcomes in our study. While younger age is often considered a risk factor in clinical and social care contexts (Gupta et al., 2008 ), this finding may reflect a relative lack of chronic conditions and medical complexity among younger women in this population, the influence of targeted support services for teenage or first-time mothers in urban areas(Office for Health Improvement and Disparities, 2022), or other protective factors such as family support. Further research is needed to explore these mechanisms and avoid assumptions that may lead to misdirected resource allocation. A key contribution of this work is its focus on intermediary determinants, which represent the social conditions most immediately experienced by women during pregnancy and are often identifiable within clinical care settings. Unlike structural determinants, intermediary factors such as unstable housing, lack of social support, or difficulty accessing services are more amenable to intervention through support, signposting, advocacy, and trauma-informed care. This clinical relevance highlights opportunities for maternity professionals—particularly during pre-conception and antenatal periods—to engage meaningfully with women’s social circumstances, potentially mitigating risk and improving outcomes (Rayment-Jones et al., 2019 ; 2021 ). Nevertheless, routinely collected data have inherent limitations in capturing the full spectrum of social determinants. Certain complex and sensitive factors—such as nuanced social class differences, acculturation, experiences of discrimination or racism, domestic abuse, and histories of trauma, are often under-recorded or absent (Wright et al., 2020 ; Taylor-Robinson et al., 2019 ; Byford et al., 2024). Underreporting can stem from ambiguous definition, stigma, time constraints, provider discomfort, and challenges in asking and documenting sensitive information. As a result, administrative data may underestimate the prevalence and impact of social vulnerabilities, particularly among marginalized groups, warranting cautious interpretation of findings and further qualitative and participatory research to explore lived experiences and barriers to care. Alongside identifying risk factors, it is essential to recognise and integrate the strengths and resilience within individuals and their communities. Social capital, informal support networks, faith groups, and culturally rooted knowledge systems often play vital roles in sustaining maternal mental health and parenting under adversity. Embedding these assets into care models—not as peripheral elements but as core components—can enhance the equity and cultural responsiveness of maternity services (Belsky et al., 2022 ; Hadebe et al, 2021 ). International evidence further supports integrated, cross-sector approaches to reducing health inequalities. In Denmark and Sweden, municipal-level coordination of maternity, housing, and social services facilitates early risk identification and personalised, continuous care (Thorsen et al., 2022 ; Lindqvist et al., 2023). Singapore similarly demonstrates the value of systemic integration across health, housing, and social domains to promote health equity (Ng et al., 2023 ). While the UK context differs, community-based initiatives such as Sure Start and the Lambeth Early Action Partnership have shown promise in addressing perinatal and wider inequalities by delivering multiagency support and building social capital within communities (Melhuish et al., 2008 ; Bertram & Pascal, 2010 ; Hadebe, 2021). Specialist midwifery models in the UK, particularly those emphasising continuity of carer—a form of relational care characterised by sustained, trust-based relationships between women and their maternity providers—show promise in mitigating social risk. Continuity of carer fosters earlier disclosure of psychosocial needs, supports help-seeking, and is associated with improved clinical outcomes in socially high-risk populations (Rayment-Jones et al., 2021 ; 2023 ). Nonetheless, these models cannot fully counterbalance entrenched structural inequalities. Without wider social reforms addressing housing insecurity, immigration barriers, and income precarity, the impact of relational care models will be limited. Future evaluations should explore the dynamic interplay between clinical care models and structural determinants, ensuring responsibility for engagement is not unfairly placed solely on women. To facilitate systematic identification of social risk factors, structured tools such as the Maternity Disadvantage Assessment Tool (MATDAT) have been developed (Patient Safety Learning Hub, 2024 ). However, evidence regarding their effectiveness in real-world clinical settings remains limited, and further research is needed to assess implementation and impact. Additionally, expanding data collection to capture underrepresented domains—including housing precarity, language barriers, and cumulative trauma—through mixed-methods and intersectional research designs is vital for advancing understanding and improving care. Strengths and Limitations This study benefits from a large, diverse urban cohort with comprehensive linkage of clinical, social, and demographic data, enabling detailed exploration of multiple intersecting social determinants of perinatal outcomes. The use of routinely collected data enhances generalisability to comparable high-income urban settings and supports applicability to clinical and policy contexts. Moreover, the application of the WHO CSDH framework provides a robust conceptual basis for interpreting mechanisms linking social adversity to health outcomes. However, important limitations must be acknowledged. Health record data inevitably omit more nuanced or unmeasured social determinants—such as experiences of discrimination, social class distinctions, acculturation processes, and trauma histories—that are challenging to quantify but likely influential. This limitation introduces residual confounding and may lead to underestimation of the true burden of social complexity. Another potential limitation of this study is the inclusion of emergency caesarean section (CS) and preterm birth (PTB) within the composite adverse outcome, without distinguishing between their underlying causes. While both can reflect clinical complications, emergency CS may be a life-saving intervention rather than an adverse outcome per se. Similarly, PTB includes both spontaneous births, often linked to adverse conditions, and iatrogenic births, which may result from necessary clinical decisions to protect the health of the mother or baby. Future analyses could benefit from disaggregating these subtypes to better understand the underlying drivers and implications. The observational study design constrains causal inference, and despite adjustment for multiple confounders, selection bias and unmeasured confounding remain possible. The focus on a single urban area may also limit generalisability to rural or less ethnically diverse populations. Lastly, while the dataset supports robust quantitative analysis, it cannot capture the lived experiences of women and families—highlighting the need for future qualitative and participatory research to explore barriers, facilitators, and culturally responsive care. Conclusion This study illuminates the complex and multifaceted impact of poverty-related social determinants on maternal and perinatal outcomes in a diverse UK urban population. Both structural factors—such as deprivation, immigration status, and ethnicity—and intermediary determinants—including housing instability, social support, and service access—interact to shape health risks. Less measurable influences like social class, acculturation, and prior traumatic experiences also likely contribute and warrant further investigation. Recognising and integrating community strengths and resilience—such as social networks and cultural resources—into care models is essential for equitable and effective interventions. Our findings support the need for early, multisectoral collaboration and the systematic use of tools to identify social risks. Adopting holistic, life-course approaches and learning from international best practices can help shift maternity care beyond a narrow biomedical focus to comprehensively address social determinants, with the ultimate goal of reducing perinatal health inequalities. Declarations Ethics approval and consent to participate The Early Life Cross Linkage in Research, Born in South London (eLIXIR-BiSL) Partnership has received ethical approval from the Oxfordshire Research Ethics Committee C (23/SC/0116) as an anonymised dataset for medical research. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form. Clinical trial number: not applicable. Availability of data and materials The data accessed by eLIXIR remain within an NHS firewall and governance is provided by the eLIXIR Oversight Committee reporting to relevant information governance clinical leads. Subject to these conditions, data access is encouraged and those interested should contact the eLIXIR Chief Investigator (Professor Lucilla Poston; [email protected] ). Competing interests We declare no competing interests. Funding This project is funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust. It was supported by the Early Life Cross Linkage in Research, Born in South London (eLIXIR-BiSL) Partnership developed by an MRC Partnership Grant [MR/P003060/1] and the MRC Longitudinal Population Study Grant [MR/X009742/1]. The eLIXIR platform is also part-supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King’s College London. Dr Hannah Rayment-Jones is funded by a NIHR Advanced Fellowship (NIHR 303183). The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. The funders had no role in data collection, analysis, interpretation, report writing, or the decision to submit this report for publication. Authors' contributions SB*, HRJ*, JS and AE conceived the study. SB and HRJ developed the research question and methodology. SB conducted data analysis and HRJ wrote the first draft. All authors contributed to interpretation of results and revisions of the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors have seen and approved the final manuscript. *These two authors contributed equally to this work. Acknowledgements We wish to thank the women, their infants, and families from all participating sites for sharing their data and supporting this programme. Members of the eLIXIR-BiSL Partnership Professor Lucilla Poston, Professor of Maternal & Fetal Health, Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London. Professor Laura A Magee, Professor of Women’s Health, Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London. Professor Robert Stewart, Professor of Psychiatric Epidemiology & Clinical Informatics, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London and NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London. Consultant Psychiatrist at South London and Maudsley NHS Foundation Trust, London. Professor David Edwards, Chair in Paediatrics & Neonatal Medicine, Department of Perinatal Imaging and Health, King’s College London. Neonatal Consultant at Guy’s and St. Thomas’ NHS Foundation Trust. Professor Mark Ashworth, Professor of Primary Care, Department of Population Health Sciences, School of Life Course and Population Sciences, King’s College London.* Professor Jane Sandall, Professor of Social Science & Women’s Health, Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London. Dr Ingrid Wolfe, Clinical Senior Lecturer, Department of Women and Children’s Health, School of Life Course and Population Sciences, King’s College London and Consultant in Children's Public Health Medicine and Director of the Evelina London Children’s Healthcare. Dr Cheryl Gillett, Head of Tissue Banking, Department of Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, King’s College London. Dr Michael Absoud, Paediatric Consultant at Evelina London Children’s Healthcare. Dr Lucy Pickard, Consultant Paediatrician, King’s College Hospital NHS Foundation Trust. Ms Amanda Grey, Lay member of the eLIXIR Oversight Committee. Ms Sarah Spring, Lay member of the eLIXIR Oversight Committee. Ms Toyin Kazeem, Information Governance Operations Lead, South London and Maudsley NHS Foundation Trust, London. Ms Amelia Jewell, Clinical Data Linkage Service Lead, NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust. Mr Matthew Broadbent, CRIS Clinical Informatics Lead, NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London. Ms Finola Higgins Research Informatics Programme Manager, Guy’s and St Thomas’ NHS Foundation Trust. Mr Leonardo de Jongh, Data Warehouse Manager, Guy’s and St. Thomas’s Hospital NHS Foundation Trust. Ms Tisha Dasgupta, Research Associate and eLIXIR Coordinator, Department of Women & Children’s Health, School of Life Course and Population Sciences, King’s College London. Dr Carolyn Gill, School Bioresource Manager, School of Life Course and Population Sciences, King’s College London. *Deceased References Bambra, C., Taylor-Robinson, D., & Pearce, J. (2020). The impact of poverty and socioeconomic factors on maternal and perinatal health. Public Health Reviews, 41(1), 20. https://doi.org/10.1186/s40985-020-00122-1 Belsky, J., Melhuish, E., Barnes, J., Leyland, A. H., Romaniuk, H., Fearon, P., & the National Evaluation of Sure Start Research Team. (2022). The effects of Sure Start local programmes on children and families: a long-term evaluation. Lancet Public Health, 7(1), e25–e34. https://doi.org/10.1016/S2468-2667(21)00246-7 Bertram, T., & Pascal, C. (2010). Sure Start Children’s Centres: What Works for Children and Families? National Evaluation of Sure Start. Department for Children, Schools and Families. https://dera.ioe.ac.uk/13755/1/DCSF-RR215.pdf Carson, L. E., Azmi, B., Jewell, A., Taylor, C. L., Flynn, A., Gill, C., Broadbent, M., Howard, L., Stewart, R., & Poston, L. (2020). Cohort profile: the eLIXIR Partnership—a maternity–child data linkage for life course research in South London, UK. BMJ Open, 10(10), e039583. https://doi.org/10.1136/bmjopen-2020-039583 Felker, A. (2024). Ethnic disparities in UK maternal health: A systematic review. Maternal Health Journal, 12(2), 98–110. Felker, A., Patel, R., Kotnis, R., Kenyon, S. & Knight, M. October 2024 Maternal, Newborn and Infant Clinical Outcome Review Programme Saving Lives, Improving Mothers’ Care. (2024). Fellmeth G, Fazel M, Plugge E. (2018). Migration and perinatal mental health in women from low‐ and middle‐income countries: A systematic review and meta‐analysis. BJOG , 124(5), 742–752. Gupta N, Kiran U, Bhal K. (2008). Teenage pregnancies: Obstetric characteristics and outcome. European Journal of Obstetrics & Gynecology and Reproductive Biology , 137(2), 165–171. Hadebe, R., Seed, P.T., Essien, D., Headen, K., Mahmud, S., Owasil, S., Turienzo, C.F., Stanke, C., Sandall, J., Bruno, M. and Khazaezadeh, N., 2021. Can birth outcome inequality be reduced using targeted caseload midwifery in a deprived diverse inner city population? A retrospective cohort study, London, UK. BMJ open , 11 (11), p.e049991. Hildersley R, Easter A, Bakolis I, Carson L, Howard LM. Changes in the identification and management of mental health and domestic abuse among pregnant women during the COVID-19 lockdown: regression discontinuity study. BJPsych open. 2022 Jul;8(4):e96. King’s College London. (2025). The Early Life Cross Linkage in Research (eLIXIR) project. https://www.kcl.ac.uk/elixir King’s Fund. (2024). Poverty and health in the UK: Current challenges and future directions. https://www.kingsfund.org.uk/publications/poverty-and-health-uk Knight, M., Bunch, K., Vousden, N., & Burrows, R. (2021). Ethnic disparities in maternal mortality in the UK. BMJ, 373, n1186. https://doi.org/10.1136/bmj.n1186 Knight, M., Vousden, N., & Burrows, R. (2023). Integrated models for maternity care: Addressing social complexity to improve outcomes. Public Health England. https://www.gov.uk/government/publications/integrated-maternity-care-models Knight, M., & Burrows, R. (2024). Social determinants and maternity outcomes in urban UK populations: A review. Journal of Maternal and Child Health, 28(3), 215–230. Marmot, M., Allen, J., Boyce, T., Goldblatt, P., & Morrison, J. (2020). Health equity in England: The Marmot Review 10 years on. Institute of Health Equity. https://www.instituteofhealthequity.org McGarry J, Nair N. (2019). The role of the midwife in recognising and responding to domestic violence and abuse. British Journal of Midwifery , 27(2), 91–97. Melhuish, E., Belsky, J., Leyland, A., & Barnes, J. (2008). Effects of Sure Start Local Programmes on Child Development and Health at Age 3 Years in England: A Quasi- Experimental Evaluation. The Lancet, 372(9650), 1641–1647. https://doi.org/10.1016/S0140-6736(08)61695-4 Morris, M., Wright, J., & Jones, S. (2020). Barriers to healthcare access for migrant women in the UK. Health & Social Care in the Community, 28(5), 1760–1767. https://doi.org/10.1111/hsc.12910 NHS England. (2019). NHS maternity services: A guide for migrants and refugees. https://www.england.nhs.uk/maternity-services NHS England. (2021). NHS Long Term Plan: Health equity and inclusion. https://www.england.nhs.uk/long-term-plan NHS England. (2022). Core20PLUS5 – An approach to reducing health inequalities. NHS England. https://www.england.nhs.uk/about/equality/equality-hub/core20plus5/ NICE. (2014). Domestic violence and abuse: multi-agency working. NICE guideline [PH50] Ng, W. L., Lee, M. Y., & Tan, S. L. (2023). Housing policy and public health integration in Singapore: A model for cross-sector collaboration. International Journal of Public Health, 68, 1604511. https://doi.org/10.3389/ijph.2023.1604511\ NMPA, 2022. National Maternity and Perinatal Audit: Clinical Report 2022. Royal College of Obstetricians and Gynaecologists. Available at: https://maternityaudit.org.uk Office for Health Improvement and Disparities (OHID). (2022). Teenage Pregnancy Prevention Framework: Supporting young people to prevent unplanned pregnancy and develop healthy relationships. Patient Safety Learning Hub. (2024). Maternity Disadvantage Assessment Tool (MATDAT) Implementation Guide. https://www.patientsafetylearninghub.org.uk Public Health England. (2021). Social determinants of health and maternity outcomes: Evidence briefing. https://www.gov.uk/government/publications/social-determinants-and-maternity-outcomes Rayment-Jones, H., Murrells, T., et al. (2019). Complex maternity: exploring candidacy, surveillance, and trust in maternity care. BMC Pregnancy and Childbirth, 19, 281. https://doi.org/10.1186/s12884-019-2425-y Rayment-Jones, H., Silverio, S. A., Harden, A., & Sandall, J. (2021). Improving maternal and neonatal outcomes through specialist models of care for women with social risk factors. Midwifery, 92, 102877. https://doi.org/10.1016/j.midw.2020.102877 Rayment-Jones, H., Rennie, T., Viney, R., & Sandall, J. (2023). Mechanisms of continuity of care: A realist evaluation for women with social risk factors. BMJ Open, 13(4), e065354. https://doi.org/10.1136/bmjopen-2022-065354 Ray-Chaudhuri, A., Patel, S., & Kumar, R. (2023). Poverty, ethnicity, and maternal health: Broadening the UK policy agenda. Health Policy and Planning, 38(4), 405–414. Taylor-Robinson, D. C., Lai, E. T. C., Wickham, S., Rose, T., & Whitehead, M. (2019). The impact of poverty on health outcomes in early childhood. Archives of Disease in Childhood, 104(10), 1008–1013. https://doi.org/10.1136/archdischild-2018-315483 Thorsen, A., Lindqvist, P., & Karlsson, H. (2022). Coordinated municipal care for families during pregnancy and postpartum in Denmark. Nordic Journal of Social Work, 12(1), 45–60. Vousden, N., Burrows, R., & Knight, M. (2025). Social determinants and health inequities in perinatal care: Emerging evidence from the UK. Journal of Public Health Research, 14(1), 70–82. Villar, J., Cheikh Ismail, L., Victora, C., Ohuma, E. O., Ismail, L. C., Barros, F. C., et al. (2022). Maternal and neonatal outcomes in global health: A new composite index for measuring adverse perinatal events. The Lancet Global Health, 10(6), e853–e863. https://doi.org/10.1016/S2214-109X(22)00035-1 Webb, R.T., Kontopantelis, E., Doran, T., et al., 2021. Development of an English Maternal Morbidity Outcome Indicator (EMMOI) : A composite measure using routine hospital data. BMJ Open, 11(4):e046694. doi:10.1136/bmjopen-2020-046694 World Health Organization (WHO). (2010). A conceptual framework for action on the social determinants of health. WHO Commission on Social Determinants of Health. https://www.who.int/publications/i/item/9789241500852 World Health Organization. (2019). Equity, social determinants and public health programmes. https://www.who.int/publications-detail-redirect/9789241515559 World Health Organization. (2023). Life-course approach to maternal and child health. WHO Policy Brief. https://www.who.int/publications/i/item/life-course-approach-maternal-child-health Wright, J., Small, N., Raynor, P., & Pickett, K. (2020). Social determinants and inequalities in perinatal health: A review of evidence in the UK. Health & Place, 65, 102409. https://doi.org/10.1016/j.healthplace.2020.102409 Tables Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1Definitions.docx Table3.docx Cite Share Download PDF Status: Posted Version 1 posted 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-7011465","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481332817,"identity":"48ac801a-ed26-4881-a90c-cd10a0bb6734","order_by":0,"name":"Hannah 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14:08:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7011465/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7011465/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86141512,"identity":"3a1b17b0-8da3-4b2d-8aa8-9acb2ff1aa5d","added_by":"auto","created_at":"2025-07-07 08:26:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115493,"visible":true,"origin":"","legend":"\u003cp\u003eA conceptual framework for action on the social determinants of health (World Health Organisation (WHO), 2010)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011465/v1/47c21db97c72f8c14b38a5ca.jpg"},{"id":86141513,"identity":"fe7989e9-2566-4695-bedc-2d05d6c3b326","added_by":"auto","created_at":"2025-07-07 08:26:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70141,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of any adverse outcome by social determinant\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7011465/v1/0de18dd8f0a39f9637842f61.jpg"},{"id":88075146,"identity":"80bd89c1-9783-45bb-bec3-c68f884e799c","added_by":"auto","created_at":"2025-08-01 06:53:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":821945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7011465/v1/1d2be44d-2efc-47bc-acd1-3c674aee61a7.pdf"},{"id":86141515,"identity":"046b4ba1-8333-4d34-9662-e2ec04d31416","added_by":"auto","created_at":"2025-07-07 08:26:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1Definitions.docx","url":"https://assets-eu.researchsquare.com/files/rs-7011465/v1/39841c6802f83f13a9d71629.docx"},{"id":86141511,"identity":"2528f667-fed3-4fee-9d74-4b8f883b1cdf","added_by":"auto","created_at":"2025-07-07 08:26:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24564,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7011465/v1/6de24e7146354d5d9aac1994.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Poverty-Related Social Determinants in Maternal and Perinatal Health Inequities: A cross-sectional study using the eLIXIR Born in South London, UK maternity-child data linkage","fulltext":[{"header":"Background","content":"\u003cp\u003eHealth inequities remain a persistent issue in the United Kingdom, disproportionately affecting marginalised populations\u0026mdash;particularly in maternity care. The World Health Organization (WHO) defines health inequities as avoidable, unjust differences in health outcomes that arise from systemic social disadvantages (WHO, 2019). In the UK context, these disparities are closely tied to social determinants of health (SDOH), including socioeconomic status, housing security, education, and systemic discrimination (Marmot et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While the National Health Service (NHS) offers free maternity care to those deemed \u0026ldquo;ordinarily resident,\u0026rdquo; significant barriers to equitable access and outcomes persist, especially for migrant and socioeconomically disadvantaged women (NHS England, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the NHS\u0026rsquo;s commitment to advancing health equity (NHS England, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), maternal health outcomes remain stratified by both ethnicity and socioeconomic position. Women from Black, Asian, and minority ethnic backgrounds, as well as those living in deprived areas, face markedly higher maternal mortality rates than their White and more affluent counterparts (Knight et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Knight et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Felker, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While poverty is a powerful driver of adverse outcomes\u0026mdash;through mechanisms such as food insecurity, overcrowded housing, and chronic stress\u0026mdash;disparities persist even after adjusting for deprivation. For example, Black women experience higher risks of poor outcomes across all levels of the Index of Multiple Deprivation (IMD), suggesting that racism, discrimination, and differential treatment within services also play a critical role.\u003c/p\u003e\u003cp\u003eAlthough national policy increasingly acknowledges these inequities (NHS England, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), there remains a paucity of UK-based quantitative research examining the full range of social determinants that shape maternity care access and outcomes. In particular, gaps persist in understanding the effects of structural and intermediary factors such as housing insecurity, economic precarity, immigration status, legal barriers to healthcare, and limited health literacy (Morris et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wright et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vousden, 2025). Traditional efforts to reduce health disparities in high-income countries have often focused on improving healthcare access. However, research indicates that access alone accounts for only a modest share of health outcomes. Structural determinants, including poverty, exert a more profound influence\u0026mdash;through unstable living conditions, food insecurity, and cumulative psychosocial stress (Bambra et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taylor-Robinson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet, UK policy and research often adopt narrow proxies such as ethnicity or area-level deprivation, rather than directly interrogating the multi-dimensional nature of poverty and its intersection with racial and migration-related inequities (Ray-Chaudhuri et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; King's Fund, 2024).\u003c/p\u003e\u003cp\u003eThe WHO Commission on Social Determinants of Health (CSDH) framework provides a useful model for understanding health inequities, distinguishing between structural determinants (e.g. income, education, ethnicity), which shape the broader distribution of power, resources, and opportunity, and intermediary determinants (e.g. living conditions, behaviours, healthcare access), which represent the more immediate conditions of daily life that influence health outcomes (World Health Organisation, 2010). Structural determinants affect health indirectly by shaping exposure to intermediary determinants. Applying this framework to the UK maternity context enables a clearer understanding of how intersecting forms of social disadvantage\u0026mdash;including but not limited to poverty\u0026mdash;influence perinatal outcomes and where cross-sectoral interventions may be most effective.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study will investigate the association between poverty-related structural and intermediary social determinants and adverse perinatal health outcomes in an ethnically diverse, urban area of the UK. Drawing on routinely collected local data, we apply the CSDH framework to identify key drivers of inequity and inform the development of targeted, cross-sector strategies to reduce maternal and child health disparities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAim\u003c/p\u003e\u003cp\u003eTo examine the relationships between a composite adverse perinatal outcome for mother and/or baby and poverty related social determinants of health within a South London cohort.\u003c/p\u003e\u003cp\u003eDesign\u003c/p\u003e\u003cp\u003eThis study was a retrospective cross-sectional analysis of The Early Life Cross Linkage in Research (eLIXIR) cohort. The eLIXIR Partnership was developed in 2018, generating a repository of real-time, pseudonymised, structured data derived from the electronic health record systems of two acute and one Mental Health NHS Trust in South London, UK (Carson et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Detailed information about the cohort and data linkage processes is available on the eLIXIR project website (King\u0026rsquo;s College London, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Using this linked data from electronic healthcare records we analysed maternal and infant outcomes from October 2018 through until October 2023, including 56290 women (67308 pregnancies).\u003c/p\u003e\u003cp\u003eSetting\u003c/p\u003e\u003cp\u003eThe study took place in a superdiverse urban area in South London, characterised by high levels of socioeconomic deprivation and a large proportion of residents from minority ethnic backgrounds and migrant communities. Maternity services are delivered through a network of NHS providers, with care pathways spanning community, primary, secondary, and tertiary settings.\u003c/p\u003e\u003cp\u003eOutcome measures\u003c/p\u003e\u003cp\u003eAdverse maternal and infant outcomes were identified based on definitions used in the National Maternity and Perinatal Audit (NMPA, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the English Maternal Morbidity Outcome Indicator (Webb et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These were mapped to the available data within the eLIXIR cohort to ensure consistency with existing research and robustness of measurement. The primary outcome was a composite binary variable indicating the presence or absence of any of the following: emergency (unplanned) caesarean section, obstetric haemorrhage exceeding 1000 mL (either antepartum or postpartum), preterm birth occurring before 37 weeks of gestation, low birthweight defined as less than 2500 grams, an Apgar score of 7 or less at five minutes, stillbirth (defined as intrauterine death occurring at or beyond 24 weeks of gestation), or neonatal death within 28 days of birth.\u003c/p\u003e\u003cp\u003eAll outcomes were coded as binary variables, and a full list of definitions is provided in Supplementary File 1.\u003c/p\u003e\u003cp\u003ePoverty related social determinant variables\u003c/p\u003e\u003cp\u003eWe applied the WHO Commission on Social Determinants of Health (CSDH) framework to classify and interpret poverty-related variables available in the eLIXIR dataset. The framework distinguishes between:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStructural determinants\u003c/b\u003e, which include socioeconomic and political contexts, and individuals\u0026rsquo; social positions (e.g. education, income, ethnicity, legal status); and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntermediary determinants\u003c/b\u003e, which include material circumstances, psychosocial stressors, behavioural factors, and health system interactions (WHO, 2010).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eVariables from the eLIXIR dataset were mapped accordingly (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; full definitions in Supplementary File 1). This mapping can help clarify not only which factors contribute to perinatal health inequities, but also where and how targeted interventions might be developed. However, some variables span both structural and intermediary domains. For example, homelessness, criminal justice and social care involvement, are rooted in systemic disadvantage but also directly influence day-to-day stress, instability, and access to services. Likewise, learning difficulties and language barriers reflect both underlying structural inequities and practical barriers to effective healthcare engagement. These overlapping factors illustrate the complex, interconnected nature of social determinants in maternity care and the need for coordinated, multi-level responses.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePoverty-related social determinant variables available in eLIXIR, mapped to the WHO CSDH framework\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSDH Domain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariables Available in eLIXIR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStructural Determinants\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEthnicity (as a proxy for structural racism and marginalisation); Deprivation index; Age\u0026thinsp;\u0026lt;\u0026thinsp;20; Born outside UK; Refugee/asylum seeker; New to country; No right to work; Financial difficulties; Social care involvement; Unborn child subject to foster care/adoption; Criminal justice involvement; Female genital mutilation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntermediary Determinants\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterpreter required; Feels unsupported; Housing issues or homelessness; Substance use; Domestic abuse (past or current); Mental health problems; Learning difficulties; Late booking for maternity care; Transfer of care from another hospital; Missed\u0026thinsp;\u0026gt;\u0026thinsp;2 antenatal appointments; Inadequate antenatal care; Unscheduled maternity care use; A\u0026amp;E visits during pregnancy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBoth Structural and Intermediary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMultiple risk factors composite variable (indicating co-occurrence of multiple poverty-related indicators)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAnalysis\u003c/p\u003e\u003cp\u003eAll analyses were conducted using the R (version 4.3.1) programming environment to support transparency and reproducibility. Pregnancies with duplicate records or involving multiples (e.g. twins) were excluded. Only completed pregnancies for which data were available from the first antenatal (booking) appointment were retained for inclusion.\u003c/p\u003e\u003cp\u003eWe employed binary logistic regression models with a random intercept for the individual woman\u0026rsquo;s ID, accounting for the possibility of multiple pregnancies per woman. The models examined associations between each poverty-related social determinant and the composite adverse perinatal outcome. Stepwise regression models were used, adjusting for relevant maternal sociodemographic and clinical characteristics, including socioeconomic deprivation (measured using the Index of Multiple Deprivation), maternal age, parity, smoking status, body mass index over 30 kg/m\u0026sup2;, pre-existing medical risk factors, and previous caesarean section (Villar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Knight et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Risk ratios (RRs) with corresponding 95% confidence intervals (CIs) were calculated. Statistical significance was defined as a p-value of \u0026le;\u0026thinsp;0.05 for all models.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 15,633 (35.24%) adverse outcomes were recorded across 44,634 completed pregnancies, encompassing both maternal and infant outcomes. See Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for a breakdown of the adverse outcomes.\u003c/p\u003e\u003cp\u003eParticipant characteristics\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlines the maternal baseline characteristics at the time of the booking appointment. The majority of the sample identified their ethnicity as White (53.41%), followed by Black (19.92%), Asian British (10.08%), Mixed/Multiple ethnic groups (5.21%), and other ethnic groups (6.53%), with 4.85% of ethnicity data missing. Nearly three quarters (74.42%) of participants were classified as having a high medical risk status at booking, and 46.21% were primiparous. The mean maternal age at booking was 32.86 years (SD\u0026thinsp;=\u0026thinsp;5.42), the average BMI was 24.39 (SD\u0026thinsp;=\u0026thinsp;6.63), and 3.77% of women reported smoking at the time of booking.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMaternal baseline characteristics and disaggregated outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at booking (years) mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.86 (5.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimiparous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23313 (52.23%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh medical risk status at booking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33219 (74.42%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.39 (SD 6.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoker at booking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1684 (3.77%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmergency (unplanned) caesarean section,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10688 (24.09%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObstetric haemorrhage\u0026thinsp;\u0026gt;\u0026thinsp;1000ml\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4970 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePreterm birth\u0026thinsp;\u0026lt;\u0026thinsp;37/40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4009 (9.04%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow birthweight\u0026thinsp;\u0026lt;\u0026thinsp;2500 grams\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4152 (9.36%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Apgar score (\u0026lt;\u0026thinsp;7 at 5 minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e777 (1.75%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStillbirth or Neonatal Death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e502 (1.13%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStructural determinants\u003c/p\u003e\u003cp\u003eTable three presents the risk ratios (RR) and adjusted risk ratios (aRR) for the composite measure of adverse outcomes, stratified by structural, intermediary, and intersectional determinants of health, and figure two presents a forest plot of any adverse outcome by social determinant.\u003c/p\u003e\u003cp\u003eCompared to White women, those from Black, Asian, Mixed, and Other ethnic backgrounds experienced significantly higher risk of adverse outcomes, both before and after adjustment (p\u0026thinsp;\u0026lt;\u0026thinsp;.05 for all). Women living in the most deprived IMD quintile had significantly increased risk of adverse outcomes compared to those in the least deprived quintile in both unadjusted and adjusted models (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). Women aged under 20 were not at significantly lower risk in the unadjusted analysis (p\u0026thinsp;=\u0026thinsp;.161) but were significantly less likely to experience an adverse outcome following adjustment (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Women born outside the UK and those recently arrived in the country had significantly elevated risks in both unadjusted and adjusted models (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Financial hardship was also associated with increased risk (p\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eWomen who missed more than three antenatal appointments were not at elevated risk in the unadjusted model (p\u0026thinsp;=\u0026thinsp;.173), but this association became significant after adjustment (p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Those who accessed unscheduled maternity care had increased risk of adverse outcomes in both unadjusted and adjusted models (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). In contrast, inadequate antenatal care (\u0026lt;\u0026thinsp;10 appointments for primiparous women and \u0026lt;\u0026thinsp;7 for multiparous women) was associated with increased risk in the unadjusted model only (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with no significant association after adjustment (p\u0026thinsp;=\u0026thinsp;.146).\u003c/p\u003e\u003cp\u003eIntermediary determinants\u003c/p\u003e\u003cp\u003eWomen who felt unsupported (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), were unemployed (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), or had experienced domestic abuse (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) were at increased risk of adverse outcomes in both unadjusted and adjusted models. Substance use was associated with increased risk in the unadjusted model (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), but not after adjustment (p\u0026thinsp;=\u0026thinsp;.445). Living in social housing was significantly associated with higher risk after adjustment (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Having a learning disability increased risk in the unadjusted model (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), but this association was not statistically significant after adjustment (p\u0026thinsp;=\u0026thinsp;.085).\u003c/p\u003e\u003cp\u003eOther factors such as homelessness or housing insecurity, use of an interpreter, referral to mental health services during pregnancy, previous mental health inpatient admission, and late booking (both \u0026gt;\u0026thinsp;13 and \u0026gt;\u0026thinsp;20 weeks\u0026rsquo; gestation) were not significantly associated with adverse outcomes after adjustment. Women who transferred care from another hospital were at significantly increased risk in both unadjusted and adjusted analyses (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Similarly, unscheduled access to maternity services (e.g. maternity triage) remained a significant risk factor after adjustment (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eIntersectional determinants\u003c/p\u003e\u003cp\u003eWomen who had more than three social or health-related risk factors\u0026mdash;excluding ethnicity and deprivation\u0026mdash;were at increased risk of experiencing an adverse event. This association remained significant in both unadjusted and adjusted models (p\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the significant impact of poverty-related social determinants on maternal and perinatal outcomes in a large, ethnically diverse urban UK cohort. By mapping routinely recorded social risk factors onto the WHO Commission on Social Determinants of Health (CSDH) framework (WHO, 2010), we demonstrate how both structural determinants\u0026mdash;such as deprivation, minoritisation, and immigration status\u0026mdash;and intermediary determinants\u0026mdash;including housing instability, social isolation, and service access\u0026mdash;contribute to elevated risks.\u003c/p\u003e\u003cp\u003eThese findings align with a growing body of evidence suggesting that perinatal inequalities are shaped by intersecting social, economic, and systemic drivers beyond biomedical factors alone (Bambra et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marmot et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vousden et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Importantly, the study found that associations between social complexity and adverse perinatal outcomes persisted even after adjusting for pre-existing clinical risks. Women experiencing multiple intersecting disadvantages, particularly those facing three or more social risk factors, had significantly poorer outcomes, underscoring the cumulative burden of social adversity on maternal and infant health (Knight \u0026amp; Burrows, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Public Health England, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur findings highlight several specific risk factors for adverse perinatal outcomes that warrant targeted attention within NHS maternity services. Women from Black, Asian, and minority ethnic backgrounds continue to experience disproportionately poor outcomes, even after adjusting for area-level deprivation, echoing prior research suggesting that ethnicity-related inequities are not fully explained by socioeconomic status alone (Knight et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ray-Chaudhuri et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, women who are new to the UK face significantly elevated risk, potentially reflecting barriers related to immigration status, limited entitlement to care, language proficiency, and unfamiliarity with the healthcare system\u0026mdash;all of which are modifiable through more inclusive policy and service design (Fellmeth et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). One of the most striking associations was between domestic abuse and adverse outcomes. Despite the existence of clear NHS guidelines on identifying and managing domestic abuse in pregnancy (NICE, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), our findings support wider research showing persistent gaps in implementation (Hildersley et al, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This indicates an urgent need for system-wide accountability, improved staff training, and integration of specialist advocacy services in maternity settings (McGarry \u0026amp; Nair, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Interestingly, younger maternal age was associated with \u003cem\u003elower\u003c/em\u003e odds of adverse outcomes in our study. While younger age is often considered a risk factor in clinical and social care contexts (Gupta et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), this finding may reflect a relative lack of chronic conditions and medical complexity among younger women in this population, the influence of targeted support services for teenage or first-time mothers in urban areas(Office for Health Improvement and Disparities, 2022), or other protective factors such as family support. Further research is needed to explore these mechanisms and avoid assumptions that may lead to misdirected resource allocation.\u003c/p\u003e\u003cp\u003eA key contribution of this work is its focus on intermediary determinants, which represent the social conditions most immediately experienced by women during pregnancy and are often identifiable within clinical care settings. Unlike structural determinants, intermediary factors such as unstable housing, lack of social support, or difficulty accessing services are more amenable to intervention through support, signposting, advocacy, and trauma-informed care. This clinical relevance highlights opportunities for maternity professionals\u0026mdash;particularly during pre-conception and antenatal periods\u0026mdash;to engage meaningfully with women\u0026rsquo;s social circumstances, potentially mitigating risk and improving outcomes (Rayment-Jones et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNevertheless, routinely collected data have inherent limitations in capturing the full spectrum of social determinants. Certain complex and sensitive factors\u0026mdash;such as nuanced social class differences, acculturation, experiences of discrimination or racism, domestic abuse, and histories of trauma, are often under-recorded or absent (Wright et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taylor-Robinson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Byford et al., 2024). Underreporting can stem from ambiguous definition, stigma, time constraints, provider discomfort, and challenges in asking and documenting sensitive information. As a result, administrative data may underestimate the prevalence and impact of social vulnerabilities, particularly among marginalized groups, warranting cautious interpretation of findings and further qualitative and participatory research to explore lived experiences and barriers to care.\u003c/p\u003e\u003cp\u003eAlongside identifying risk factors, it is essential to recognise and integrate the strengths and resilience within individuals and their communities. Social capital, informal support networks, faith groups, and culturally rooted knowledge systems often play vital roles in sustaining maternal mental health and parenting under adversity. Embedding these assets into care models\u0026mdash;not as peripheral elements but as core components\u0026mdash;can enhance the equity and cultural responsiveness of maternity services (Belsky et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hadebe et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). International evidence further supports integrated, cross-sector approaches to reducing health inequalities. In Denmark and Sweden, municipal-level coordination of maternity, housing, and social services facilitates early risk identification and personalised, continuous care (Thorsen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lindqvist et al., 2023). Singapore similarly demonstrates the value of systemic integration across health, housing, and social domains to promote health equity (Ng et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the UK context differs, community-based initiatives such as Sure Start and the Lambeth Early Action Partnership have shown promise in addressing perinatal and wider inequalities by delivering multiagency support and building social capital within communities (Melhuish et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bertram \u0026amp; Pascal, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hadebe, 2021).\u003c/p\u003e\u003cp\u003eSpecialist midwifery models in the UK, particularly those emphasising continuity of carer\u0026mdash;a form of relational care characterised by sustained, trust-based relationships between women and their maternity providers\u0026mdash;show promise in mitigating social risk. Continuity of carer fosters earlier disclosure of psychosocial needs, supports help-seeking, and is associated with improved clinical outcomes in socially high-risk populations (Rayment-Jones et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nonetheless, these models cannot fully counterbalance entrenched structural inequalities. Without wider social reforms addressing housing insecurity, immigration barriers, and income precarity, the impact of relational care models will be limited. Future evaluations should explore the dynamic interplay between clinical care models and structural determinants, ensuring responsibility for engagement is not unfairly placed solely on women.\u003c/p\u003e\u003cp\u003eTo facilitate systematic identification of social risk factors, structured tools such as the Maternity Disadvantage Assessment Tool (MATDAT) have been developed (Patient Safety Learning Hub, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, evidence regarding their effectiveness in real-world clinical settings remains limited, and further research is needed to assess implementation and impact. Additionally, expanding data collection to capture underrepresented domains\u0026mdash;including housing precarity, language barriers, and cumulative trauma\u0026mdash;through mixed-methods and intersectional research designs is vital for advancing understanding and improving care.\u003c/p\u003e\u003cp\u003eStrengths and Limitations\u003c/p\u003e\u003cp\u003eThis study benefits from a large, diverse urban cohort with comprehensive linkage of clinical, social, and demographic data, enabling detailed exploration of multiple intersecting social determinants of perinatal outcomes. The use of routinely collected data enhances generalisability to comparable high-income urban settings and supports applicability to clinical and policy contexts. Moreover, the application of the WHO CSDH framework provides a robust conceptual basis for interpreting mechanisms linking social adversity to health outcomes.\u003c/p\u003e\u003cp\u003eHowever, important limitations must be acknowledged. Health record data inevitably omit more nuanced or unmeasured social determinants\u0026mdash;such as experiences of discrimination, social class distinctions, acculturation processes, and trauma histories\u0026mdash;that are challenging to quantify but likely influential. This limitation introduces residual confounding and may lead to underestimation of the true burden of social complexity. Another potential limitation of this study is the inclusion of emergency caesarean section (CS) and preterm birth (PTB) within the composite adverse outcome, without distinguishing between their underlying causes. While both can reflect clinical complications, emergency CS may be a life-saving intervention rather than an adverse outcome per se. Similarly, PTB includes both spontaneous births, often linked to adverse conditions, and iatrogenic births, which may result from necessary clinical decisions to protect the health of the mother or baby. Future analyses could benefit from disaggregating these subtypes to better understand the underlying drivers and implications.\u003c/p\u003e\u003cp\u003eThe observational study design constrains causal inference, and despite adjustment for multiple confounders, selection bias and unmeasured confounding remain possible. The focus on a single urban area may also limit generalisability to rural or less ethnically diverse populations. Lastly, while the dataset supports robust quantitative analysis, it cannot capture the lived experiences of women and families\u0026mdash;highlighting the need for future qualitative and participatory research to explore barriers, facilitators, and culturally responsive care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study illuminates the complex and multifaceted impact of poverty-related social determinants on maternal and perinatal outcomes in a diverse UK urban population. Both structural factors\u0026mdash;such as deprivation, immigration status, and ethnicity\u0026mdash;and intermediary determinants\u0026mdash;including housing instability, social support, and service access\u0026mdash;interact to shape health risks. Less measurable influences like social class, acculturation, and prior traumatic experiences also likely contribute and warrant further investigation.\u003c/p\u003e\u003cp\u003eRecognising and integrating community strengths and resilience\u0026mdash;such as social networks and cultural resources\u0026mdash;into care models is essential for equitable and effective interventions. Our findings support the need for early, multisectoral collaboration and the systematic use of tools to identify social risks. Adopting holistic, life-course approaches and learning from international best practices can help shift maternity care beyond a narrow biomedical focus to comprehensively address social determinants, with the ultimate goal of reducing perinatal health inequalities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Early Life Cross Linkage in Research, Born in South London (eLIXIR-BiSL) Partnership has received ethical approval from the Oxfordshire Research Ethics Committee C (23/SC/0116) as an anonymised dataset for medical research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data accessed by eLIXIR remain within an NHS firewall and governance is provided by the eLIXIR Oversight Committee reporting to relevant information governance clinical leads. Subject to these conditions, data access is encouraged and those interested should contact the eLIXIR Chief Investigator (Professor Lucilla Poston;\u0026nbsp;[email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is funded by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King\u0026rsquo;s College Hospital NHS Foundation Trust. It was supported by the Early Life Cross Linkage in Research, Born in South London (eLIXIR-BiSL) Partnership developed by an MRC Partnership Grant [MR/P003060/1] and the MRC Longitudinal Population Study Grant [MR/X009742/1]. The eLIXIR platform is also part-supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King\u0026rsquo;s College London. Dr Hannah Rayment-Jones is funded by a NIHR Advanced Fellowship (NIHR 303183). The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. The funders had no role in data collection, analysis, interpretation, report writing, or the decision to submit this report for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSB*, HRJ*, JS and AE conceived the study. SB and HRJ developed the research question and methodology. SB conducted data analysis and HRJ wrote the first draft. All authors contributed to interpretation of results and revisions of the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. All authors have seen and approved the final manuscript. *These two authors contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank the women, their infants, and families from all participating sites for sharing their data and supporting this programme.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMembers of the eLIXIR-BiSL Partnership\u003c/p\u003e\n\u003cp\u003eProfessor Lucilla Poston, Professor of Maternal \u0026amp; Fetal Health, Department of Women and Children\u0026rsquo;s Health, School of Life Course and Population Sciences, King\u0026rsquo;s College London.\u003c/p\u003e\n\u003cp\u003eProfessor Laura A Magee, Professor of Women\u0026rsquo;s Health, Department of Women and Children\u0026rsquo;s Health, School of Life Course and Population Sciences, King\u0026rsquo;s College London.\u003c/p\u003e\n\u003cp\u003eProfessor Robert Stewart, Professor of Psychiatric Epidemiology \u0026amp; Clinical Informatics,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King\u0026rsquo;s College London and NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London. Consultant Psychiatrist at South London and Maudsley NHS Foundation Trust, London.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eProfessor David Edwards, Chair in Paediatrics \u0026amp; Neonatal Medicine, Department of Perinatal Imaging and Health, King\u0026rsquo;s College London. Neonatal Consultant at Guy\u0026rsquo;s and St. Thomas\u0026rsquo; NHS Foundation Trust.\u003c/p\u003e\n\u003cp\u003eProfessor Mark Ashworth, Professor of Primary Care, Department of Population Health Sciences, School of Life Course and Population Sciences, King\u0026rsquo;s College London.*\u003c/p\u003e\n\u003cp\u003eProfessor Jane Sandall, Professor of Social Science \u0026amp; Women\u0026rsquo;s Health, Department of Women and Children\u0026rsquo;s Health, School of Life Course and Population Sciences, King\u0026rsquo;s College London.\u003c/p\u003e\n\u003cp\u003eDr Ingrid Wolfe, Clinical Senior Lecturer, Department of Women and Children\u0026rsquo;s Health, School of Life Course and Population Sciences, King\u0026rsquo;s College London and Consultant in Children\u0026apos;s Public Health Medicine and Director of the Evelina London Children\u0026rsquo;s Healthcare. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr Cheryl Gillett, Head of Tissue Banking, Department of Comprehensive Cancer Centre, School of Cancer \u0026amp; Pharmaceutical Sciences, King\u0026rsquo;s College London.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr Michael Absoud, Paediatric Consultant at Evelina London Children\u0026rsquo;s Healthcare. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDr Lucy Pickard, Consultant Paediatrician, King\u0026rsquo;s College Hospital NHS Foundation Trust.\u003c/p\u003e\n\u003cp\u003eMs Amanda Grey, Lay member of the eLIXIR Oversight Committee.\u003c/p\u003e\n\u003cp\u003eMs Sarah Spring, Lay member of the eLIXIR Oversight Committee.\u003c/p\u003e\n\u003cp\u003eMs Toyin Kazeem, Information Governance Operations Lead, South London and Maudsley NHS Foundation Trust, London.\u003c/p\u003e\n\u003cp\u003eMs Amelia Jewell, Clinical Data Linkage Service Lead, NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust.\u003c/p\u003e\n\u003cp\u003eMr Matthew Broadbent, CRIS Clinical Informatics Lead, NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London.\u003c/p\u003e\n\u003cp\u003eMs Finola Higgins Research Informatics Programme Manager, Guy\u0026rsquo;s and St Thomas\u0026rsquo; NHS Foundation Trust.\u003c/p\u003e\n\u003cp\u003eMr Leonardo de Jongh, Data Warehouse Manager, Guy\u0026rsquo;s and St. Thomas\u0026rsquo;s Hospital NHS Foundation Trust.\u003c/p\u003e\n\u003cp\u003eMs Tisha Dasgupta, Research Associate and eLIXIR Coordinator, Department of Women \u0026amp; Children\u0026rsquo;s Health, School of Life Course and Population Sciences, King\u0026rsquo;s College London.\u003c/p\u003e\n\u003cp\u003eDr Carolyn Gill, School Bioresource Manager, School of Life Course and Population Sciences, King\u0026rsquo;s College London.\u003c/p\u003e\n\u003cp\u003e*Deceased\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBambra, C., Taylor-Robinson, D., \u0026amp; Pearce, J. (2020). The impact of poverty and socioeconomic factors on maternal and perinatal health. Public Health Reviews, 41(1), 20. https://doi.org/10.1186/s40985-020-00122-1\u003c/li\u003e\n\u003cli\u003eBelsky, J., Melhuish, E., Barnes, J., Leyland, A. H., Romaniuk, H., Fearon, P., \u0026amp; the National Evaluation of Sure Start Research Team. (2022). The effects of Sure Start local programmes on children and families: a long-term evaluation. Lancet Public Health, 7(1), e25\u0026ndash;e34. https://doi.org/10.1016/S2468-2667(21)00246-7\u003c/li\u003e\n\u003cli\u003eBertram, T., \u0026amp; Pascal, C. (2010). Sure Start Children\u0026rsquo;s Centres: What Works for Children and Families? National Evaluation of Sure Start. Department for Children, Schools and Families. https://dera.ioe.ac.uk/13755/1/DCSF-RR215.pdf\u003c/li\u003e\n\u003cli\u003eCarson, L. E., Azmi, B., Jewell, A., Taylor, C. L., Flynn, A., Gill, C., Broadbent, M., Howard, L., Stewart, R., \u0026amp; Poston, L. (2020). Cohort profile: the eLIXIR Partnership\u0026mdash;a maternity\u0026ndash;child data linkage for life course research in South London, UK. BMJ Open, 10(10), e039583. https://doi.org/10.1136/bmjopen-2020-039583\u003c/li\u003e\n\u003cli\u003eFelker, A. (2024). Ethnic disparities in UK maternal health: A systematic review. Maternal Health Journal, 12(2), 98\u0026ndash;110.\u003c/li\u003e\n\u003cli\u003eFelker, A., Patel, R., Kotnis, R., Kenyon, S. \u0026amp; Knight, M. October 2024 Maternal, Newborn and Infant Clinical Outcome Review Programme Saving Lives, Improving Mothers\u0026rsquo; Care. (2024).\u003c/li\u003e\n\u003cli\u003eFellmeth G, Fazel M, Plugge E. (2018). Migration and perinatal mental health in women from low‐ and middle‐income countries: A systematic review and meta‐analysis. \u003cem\u003eBJOG\u003c/em\u003e, 124(5), 742\u0026ndash;752.\u003c/li\u003e\n\u003cli\u003eGupta N, Kiran U, Bhal K. (2008). Teenage pregnancies: Obstetric characteristics and outcome. \u003cem\u003eEuropean Journal of Obstetrics \u0026amp; Gynecology and Reproductive Biology\u003c/em\u003e, 137(2), 165\u0026ndash;171.\u003c/li\u003e\n\u003cli\u003eHadebe, R., Seed, P.T., Essien, D., Headen, K., Mahmud, S., Owasil, S., Turienzo, C.F., Stanke, C., Sandall, J., Bruno, M. and Khazaezadeh, N., 2021. Can birth outcome inequality be reduced using targeted caseload midwifery in a deprived diverse inner city population? A retrospective cohort study, London, UK. \u003cem\u003eBMJ open\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), p.e049991.\u003c/li\u003e\n\u003cli\u003eHildersley R, Easter A, Bakolis I, Carson L, Howard LM. Changes in the identification and management of mental health and domestic abuse among pregnant women during the COVID-19 lockdown: regression discontinuity study. BJPsych open. 2022 Jul;8(4):e96. \u003c/li\u003e\n\u003cli\u003eKing\u0026rsquo;s College London. (2025). The Early Life Cross Linkage in Research (eLIXIR) project. https://www.kcl.ac.uk/elixir\u003c/li\u003e\n\u003cli\u003eKing\u0026rsquo;s Fund. (2024). Poverty and health in the UK: Current challenges and future directions. https://www.kingsfund.org.uk/publications/poverty-and-health-uk\u003c/li\u003e\n\u003cli\u003eKnight, M., Bunch, K., Vousden, N., \u0026amp; Burrows, R. (2021). Ethnic disparities in maternal mortality in the UK. BMJ, 373, n1186. https://doi.org/10.1136/bmj.n1186\u003c/li\u003e\n\u003cli\u003eKnight, M., Vousden, N., \u0026amp; Burrows, R. (2023). Integrated models for maternity care: Addressing social complexity to improve outcomes. Public Health England. https://www.gov.uk/government/publications/integrated-maternity-care-models\u003c/li\u003e\n\u003cli\u003eKnight, M., \u0026amp; Burrows, R. (2024). Social determinants and maternity outcomes in urban UK populations: A review. Journal of Maternal and Child Health, 28(3), 215\u0026ndash;230.\u003c/li\u003e\n\u003cli\u003eMarmot, M., Allen, J., Boyce, T., Goldblatt, P., \u0026amp; Morrison, J. (2020). Health equity in England: The Marmot Review 10 years on. Institute of Health Equity. https://www.instituteofhealthequity.org\u003c/li\u003e\n\u003cli\u003eMcGarry J, Nair N. (2019). The role of the midwife in recognising and responding to domestic violence and abuse. \u003cem\u003eBritish Journal of Midwifery\u003c/em\u003e, 27(2), 91\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eMelhuish, E., Belsky, J., Leyland, A., \u0026amp; Barnes, J. (2008). Effects of Sure Start Local Programmes on Child Development and Health at Age 3 Years in England: A Quasi- Experimental Evaluation. The Lancet, 372(9650), 1641\u0026ndash;1647. https://doi.org/10.1016/S0140-6736(08)61695-4\u003c/li\u003e\n\u003cli\u003eMorris, M., Wright, J., \u0026amp; Jones, S. (2020). Barriers to healthcare access for migrant women in the UK. Health \u0026amp; Social Care in the Community, 28(5), 1760\u0026ndash;1767. https://doi.org/10.1111/hsc.12910\u003c/li\u003e\n\u003cli\u003eNHS England. (2019). NHS maternity services: A guide for migrants and refugees. https://www.england.nhs.uk/maternity-services\u003c/li\u003e\n\u003cli\u003eNHS England. (2021). NHS Long Term Plan: Health equity and inclusion. https://www.england.nhs.uk/long-term-plan\u003c/li\u003e\n\u003cli\u003eNHS England. (2022). \u003cem\u003eCore20PLUS5 \u0026ndash; An approach to reducing health inequalities.\u003c/em\u003e NHS England. https://www.england.nhs.uk/about/equality/equality-hub/core20plus5/\u003c/li\u003e\n\u003cli\u003eNICE. (2014). Domestic violence and abuse: multi-agency working. NICE guideline [PH50]\u003c/li\u003e\n\u003cli\u003eNg, W. L., Lee, M. Y., \u0026amp; Tan, S. L. (2023). Housing policy and public health integration in Singapore: A model for cross-sector collaboration. International Journal of Public Health, 68, 1604511. https://doi.org/10.3389/ijph.2023.1604511\\\u003c/li\u003e\n\u003cli\u003eNMPA, 2022. National Maternity and Perinatal Audit: Clinical Report 2022. Royal College of Obstetricians and Gynaecologists. Available at: https://maternityaudit.org.uk\u003c/li\u003e\n\u003cli\u003eOffice for Health Improvement and Disparities (OHID). (2022). Teenage Pregnancy Prevention Framework: Supporting young people to prevent unplanned pregnancy and develop healthy relationships.\u003c/li\u003e\n\u003cli\u003ePatient Safety Learning Hub. (2024). Maternity Disadvantage Assessment Tool (MATDAT) Implementation Guide. https://www.patientsafetylearninghub.org.uk\u003c/li\u003e\n\u003cli\u003ePublic Health England. (2021). Social determinants of health and maternity outcomes: Evidence briefing. https://www.gov.uk/government/publications/social-determinants-and-maternity-outcomes\u003c/li\u003e\n\u003cli\u003eRayment-Jones, H., Murrells, T., et al. (2019). Complex maternity: exploring candidacy, surveillance, and trust in maternity care. BMC Pregnancy and Childbirth, 19, 281. https://doi.org/10.1186/s12884-019-2425-y\u003c/li\u003e\n\u003cli\u003eRayment-Jones, H., Silverio, S. A., Harden, A., \u0026amp; Sandall, J. (2021). Improving maternal and neonatal outcomes through specialist models of care for women with social risk factors. Midwifery, 92, 102877. https://doi.org/10.1016/j.midw.2020.102877\u003c/li\u003e\n\u003cli\u003eRayment-Jones, H., Rennie, T., Viney, R., \u0026amp; Sandall, J. (2023). Mechanisms of continuity of care: A realist evaluation for women with social risk factors. BMJ Open, 13(4), e065354. https://doi.org/10.1136/bmjopen-2022-065354\u003c/li\u003e\n\u003cli\u003eRay-Chaudhuri, A., Patel, S., \u0026amp; Kumar, R. (2023). Poverty, ethnicity, and maternal health: Broadening the UK policy agenda. Health Policy and Planning, 38(4), 405\u0026ndash;414.\u003c/li\u003e\n\u003cli\u003eTaylor-Robinson, D. C., Lai, E. T. C., Wickham, S., Rose, T., \u0026amp; Whitehead, M. (2019). The impact of poverty on health outcomes in early childhood. Archives of Disease in Childhood, 104(10), 1008\u0026ndash;1013. https://doi.org/10.1136/archdischild-2018-315483\u003c/li\u003e\n\u003cli\u003eThorsen, A., Lindqvist, P., \u0026amp; Karlsson, H. (2022). Coordinated municipal care for families during pregnancy and postpartum in Denmark. Nordic Journal of Social Work, 12(1), 45\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eVousden, N., Burrows, R., \u0026amp; Knight, M. (2025). Social determinants and health inequities in perinatal care: Emerging evidence from the UK. Journal of Public Health Research, 14(1), 70\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eVillar, J., Cheikh Ismail, L., Victora, C., Ohuma, E. O., Ismail, L. C., Barros, F. C., et al. (2022). Maternal and neonatal outcomes in global health: A new composite index for measuring adverse perinatal events. The Lancet Global Health, 10(6), e853\u0026ndash;e863. https://doi.org/10.1016/S2214-109X(22)00035-1\u003c/li\u003e\n\u003cli\u003eWebb, R.T., Kontopantelis, E., Doran, T., et al., 2021. \u003cem\u003eDevelopment of an English Maternal Morbidity Outcome Indicator (EMMOI)\u003c/em\u003e: A composite measure using routine hospital data. BMJ Open, 11(4):e046694. doi:10.1136/bmjopen-2020-046694\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2010). A conceptual framework for action on the social determinants of health. WHO Commission on Social Determinants of Health. https://www.who.int/publications/i/item/9789241500852\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2019). Equity, social determinants and public health programmes. https://www.who.int/publications-detail-redirect/9789241515559\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2023). Life-course approach to maternal and child health. WHO Policy Brief. https://www.who.int/publications/i/item/life-course-approach-maternal-child-health\u003c/li\u003e\n\u003cli\u003eWright, J., Small, N., Raynor, P., \u0026amp; Pickett, K. (2020). Social determinants and inequalities in perinatal health: A review of evidence in the UK. Health \u0026amp; Place, 65, 102409. https://doi.org/10.1016/j.healthplace.2020.102409\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7011465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7011465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eDespite longstanding recognition of health inequities in UK maternity care, evidence remains limited on how intersecting poverty-related social determinants contribute to adverse perinatal outcomes at the population level. While individual factors such as ethnicity and deprivation have been linked to poor outcomes, few studies have examined wider determinants of heath and their cumulative effects using routinely collected data. This study addresses that gap by analysing structural and intermediary poverty-related factors and their association with adverse perinatal outcomes in a large, diverse urban cohort.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cross-sectional analysis of 67,308 pregnancies from the Early Life Cross Linkage in Research (eLIXIR) cohort, using linked electronic health records from NHS Trusts in South London. Structural and intermediary poverty-related variables were assessed using the World Health Organisation\u0026rsquo;s social determinants framework. The primary outcome was a composite of adverse perinatal events: emergency caesarean, obstetric haemorrhage, preterm birth, low birthweight, low Apgar score, stillbirth, and neonatal death. Binary logistic regression with random intercepts accounted for repeated pregnancies. Adjusted risk ratios were estimated controlling for key sociodemographic and clinical factors.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eWomen from Black (aRR 1.50, 95% CI 1.42\u0026ndash;1.59), Asian (aRR 1.49, 95% CI 1.39\u0026ndash;1.59), and other non-White ethnic backgrounds (aRR 1.50, 95% CI 1.42\u0026ndash;1.59), those living in the most deprived areas (aRR 1.10, 95% CI 1.01\u0026ndash;1.20), non-UK-born women (aRR 1.20, 95% CI 1.15\u0026ndash;1.25), and recent migrants (aRR 1.32, 95% CI 1.14\u0026ndash;1.53) were at significantly higher risk of adverse outcomes. Intermediary factors, e.g., lack of social support (aRR 1.21, 95% CI 1.02\u0026ndash;1.42), unemployment (aRR 1.16, 95% CI 1.10\u0026ndash;1.23), financial hardship (aRR 1.17, 95% CI 1.01\u0026ndash;1.35), living in social housing (aRR 1.16, 95% CI 1.09\u0026ndash;1.24), transfer of care between hospitals (aRR 1.27, 95% CI 1.18\u0026ndash;1.37), missed appointments (aRR 1.19, 95% CI 1.04\u0026ndash;1.37), and unscheduled maternity care use (aRR 1.21, 95% CI 1.14\u0026ndash;1.29), were independently associated with increased risk. Moreover, women facing multiple overlapping social risk factors had a significantly higher likelihood of adverse outcomes (aRR 1.23, 95% CI 1.12\u0026ndash;1.35), highlighting the cumulative impact of social vulnerability beyond clinical risk.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003ePoverty-related determinants at both structural and intermediary levels substantially shape maternal and perinatal outcomes. Integrated, cross-sector approaches are needed to address these inequalities and improve outcomes for marginalised women and their infants.\u003c/p\u003e","manuscriptTitle":"The Role of Poverty-Related Social Determinants in Maternal and Perinatal Health Inequities: A cross-sectional study using the eLIXIR Born in South London, UK maternity-child data linkage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 08:26:26","doi":"10.21203/rs.3.rs-7011465/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64218111-4de0-4f39-8bac-ecb7ea997ab2","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-01T06:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-07 08:26:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7011465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7011465","identity":"rs-7011465","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}