European Journal of Epidemiology

Background Literature investigating mediation of the association between child abuse and dementia has largely considered composite adverse childhood experience scores rather than individual adverse experiences, despite evidence that different experiences have different impacts on dementia risk. Additionally, prior studies consider mediators in isolation, despite known associations between mediators which may impact indirect pathways from child abuse to dementia. Objectives To investigate whether potentially modifiable health and lifestyle factors mediate the association between child abuse and dementia. Methods We used data from the English Longitudinal Study of Ageing to investigate associations between child abuse and dementia (N:5,448). Indirect pathways through four mediator categories (education, health behaviours, mental health and cardiovascular health) were examined. We used regression modelling to estimate associations between child abuse, mediators and dementia, and causal mediation analysis using the g-formula to estimate the joint indirect effect through the mediators. Results Individuals who experienced child abuse had, on average, an 80% higher hazard of dementia, compared to those who did not (RTE HR:1.80, 95% CI:1.21-2.39). Mental health mediators showed strong associations with both child abuse and dementia. Evidence for other mediators was weaker. Education, health behaviours, mental health and cardiovascular health mediated approximately 18% of the association. Sensitivity analysis revealed that almost all this mediation occurred through mental health. Conclusions Child abuse was associated with higher risk of dementia. Joint mediation analysis suggested that education, health behaviours, cardiovascular health, and mental health accounted for a relatively small proportion of the observed association, with most mediation occurring through mental health. Future research must focus on other potential pathways from child abuse to dementia, including biological and social mechanisms.

Background: Mortality analysis traditionally focuses on the single underlying cause of death (UCD), which obscures the wider morbidity process at the end of life. Multiple causes of death (MCoD) data, recording all conditions on the death certificate, are increasingly used as a proxy for end-of-life multimorbidity, yet how accurately they represent it remains underinvestigated. We assessed whether recorded causes reflect end-of-life health conditions or rather the chain of events leading to death. Methods: Using linked Danish registers (Population, Cause of Death, Chronic Diseases, and Cancer), we studied residents aged 50+ diagnosed with COPD, dementia, diabetes, or cancer who died in 2010-2022 (ranging from 38779 to 224330 per disease cohort). We examined how often each diagnosed disease appeared on the certificate, its location and selection as the UCD, factors associated with its appearance (logistic regression), disease-specific mortality (multiple decrement life tables), and disease associations (Cause of Death Association Indicator, CDAI). Results: Cancers appeared on the death certificate far more often than chronic diseases (around 75% versus 19-58%) and were usually recorded in Part 1 and selected as the UCD, whereas chronic diseases were rarely the UCD. The odds of a disease appearing depended on factors such as age at and time since diagnosis. When a diagnosed disease was recorded, the certificate traced a coherent path to death; when it was absent, ill-defined causes became more common. The CDAI highlighted specific association pathways between diseases. Conclusions: MCoD data capture only part of the chronic disease burden present at death and should be interpreted cautiously as a proxy for end-of-life multimorbidity. They are, however, well suited to describing the pathways leading to death.

Introduction: Multimorbidity clusters have been associated with increased dementia risk. While lifestyle factors may modify dementia risk, their role in multimorbidity clusters remains unclear. Method: Data from UK Biobank was used to identify clusters of chronic conditions using latent class analysis, assess their associations with dementia risk using Cox regression, and potential moderating effects of lifestyle factors. Results: We included 465,175 participants (mean age (SD) = 56.52 (8.01), 53.87 % female). Five clusters were identified and significantly associated with increased dementia risk, with the cardiometabolic (HR = 2.14, p < 0.001) and mental health cluster (HR =1.99, p < 0.001) exhibiting the highest risk. Only moderate physical activity lowered dementia risk in the pain-dominated multimorbidity cluster (HR = 0.77, p = 0.039). Discussion: Lifestyle factors including physical activity may protect against dementia in specific multimorbidity clusters. Future research involving objective and multiple lifestyle measures is needed.

BACKGROUND: The risk of all-cause mortality can inform decision-making for chronic disease prevention. We developed a predictive algorithm to estimate the 5-year risk of death among community-dwelling adults. METHODS: We derived and validated the Mortality Population Risk Tool (MPoRT) using data from population health surveys in Canada (the Canadian Community Health Survey) and the United States (the National Health Interview Survey), survey years 2001 to 2011, linked to vital statistics. The outcome was death within five years of the survey response. The algorithm was developed using data from Ontario respondents using a Cox proportional hazards model, then modified and re-estimated to allow cross-national assessment in Canada and the United States. Twenty-three prespecified predictors were assessed: seven sociodemographic, six behavioural, and ten general health and chronic disease. RESULTS: 527,369 respondents aged 20 to 105 years were included in the Canadian and United States development and validation cohorts, with 43,758 deaths during 3.68 million person-years follow-up. The final sex-specific MPoRT algorithms each contained 21 variables, showing strong discrimination (C-statistic: females 0.874 [0.871--0.877]; males 0.867 [0.865--0.871]) and good calibration overall and in 246 of 247 subgroups. Discrimination was modestly attenuated (0.01 decrease in C-statistic) in cross-national validation between Canada and the United States, with good calibration across all 71 subgroups. INTERPRETATION: MPoRT accurately discriminated all-cause mortality using only self-reported data, enabling broad application without clinical measures. While validation outside North America is needed to confirm broader applicability, MPoRT is designed for straightforward recalibration using routinely available national mortality data. This supports targeted chronic disease prevention strategies at both the population and individual levels, though the limitations inherent to self-reported predictors should be considered when interpreting predictions.

Early-life exposures are important to several late-life health outcomes. We sought to study the effect of an in utero nutritional environment and its interaction with Alzheimer's disease (AD) genetic risk on late-life cognitive function. We used a natural experiment created by the pellagra epidemic, a nutritional disease caused by a vitamin B3 deficiency, to evaluate the association between in utero pellagra epidemic exposure and late-life cognitive function in the Health and Retirement Study (N = 18,285). We also evaluated whether the in utero exposure could modify the AD polygenic score's (PGS) effect on cognition. In utero pellagra epidemic exposure was significantly associated with cognition ({beta} = -0.025). However, these effects were not isolated to the prenatal period as exposure during childhood periods also had an effect. The interaction between the in utero exposure and the AD PGS was significant, where the genetic effect on cognition was amplified with increasing (progressively worse) in utero exposure levels. These associations imply that the early-life nutritional environment affects late-life cognitive function and that these effects can modify genetic risk.

Background: Cardiovascular disease (CVD), cardiometabolic and neurocognitive conditions share risk factors and frequently co-occur. We evaluated whether four established CVD risk prediction models (QRISK3, PCE, SCORE2, SCORE2-OP) can be repurposed to predict 10-year risk of these conditions and their co-occurrence with CVD. Methods: The models were recalibrated using 20% of the UK Biobank (UKB) and evaluated in the remaining 80%. We performed external validation using data from Clinical Practice Research Datalink (CPRD) Aurum, assessing model discrimination (c-statistics) and calibration (intercept and slope). We used permuted feature importance to determine the influence of each individual predictor in the models. Results: Depending on the model, the c-statistics for incident CVD ranged from 0.71 to 0.74 in the UKB test set (16,137 events). Discrimination was equal to or higher than CVD when evaluated against non-traditional CVD outcomes: 0.74 to 0.77 for heart failure (3,471 events), 0.72 to 0.73 for atrial fibrillation (9,213 events), 0.73 to 0.75 for peripheral arterial disease (1,927 events) and 0.80 to 0.82 for abdominal aortic aneurysm (595 events). For the multimorbidity endpoints, model discrimination ranged from 0.74 for the composite of CVD and T2DM (SCORE2-OP) to 0.83 for the composite of CVD and dementia or Parkinson's disease (QRISK3). When considering the onset of any cardiovascular, cardiometabolic, or neurocognitive outcome discrimination ranged from 0.71 to 0.72. The repurposed models slightly underestimated the predicted risk in the CPRD compared to the UKB: average difference in calibration intercept was at most -0.64. After age and sex, smoking status and systolic blood pressure contributed most to model predictions. Conclusions: Repurposed CVD models can be used to identify 10-year risk of many CVD-related conditions and their multimorbidity. These may be used to support risk-based approaches to prevention and screening. The repurposed models have been made available at: https://repurposed-cvd-risk-models.shinyapps.io/cvd_cmd_dementia_app/ Keywords: Risk prediction; cardiovascular disease; cardiometabolic disease; dementia; disease prevention.

Background. Given uncertainty about whether later-life health at similar ages is improving over time, we examined trends across multiple health domains. Methods. We analysed data from community-dwelling adults aged 50 and older in the English Longitudinal Study of Ageing in 2004/05, 2012/13, and 2023/24 (main survey: N=8389, 8549, and 6090, respectively). Outcomes included self-rated health, limiting long-standing illness, pain, mobility limitations, cardiometabolic and chronic conditions, obesity, inflammation, mental health, quality of life and memory. Weighted pooled modified Poisson and linear regressions compared outcomes over time, overall, and by age group and education, with additional adjustment for sex and wealth. Results. Adjusted estimates showed divergent trends. Fair/poor self-rated health increased from 27% to 34%, and any pain from 37% to 47%, whereas mobility impairments declined from 58% to 52%. Self-reported high cholesterol increased from 19% to 39%, while biomarker-defined high cholesterol declined from 78% to 54%; diabetes increased on both measures. Psychiatric problems increased from 6% to 10%, quality of life declined, and memory improved. However, trends differed by age and education, particularly for limiting long-standing illness, mobility limitations, cholesterol biomarkers, and mental health, indicating that aggregate trends masked unevenly distributed changes. Conclusion. Later-life health in England has not improved uniformly. Gains in functioning, biomarkers, and cognition coexist with rising pain and poorer mental health. Trends were also socially and age patterned, producing increasingly multidimensional and socially patterned health outcomes. Multidomain health monitoring is essential for interpreting population health trends and planning healthy ageing, prevention, long-term care, and work policies.

Background: Smoking inequalities by socioeconomic status have widened consistently in Germany, but sex-specific trends after 2013 and inequalities in daily cigarette consumption among smokers (intensity) are unknown. We analyzed trends in absolute and relative socioeconomic inequalities in smoking prevalence and intensity among German adults across three decades. Methods: We used 14 waves (1998-2024) of population-representative cross-sectional data from the German Socio-Economic Panel to estimate sex-specific trends in smoking prevalence and intensity in adults aged 25-64. Inequalities were quantified across strata of education, occupation, and equivalized household income using the absolute and relative concentration index with 95% bootstrap confidence intervals. Results: Overall smoking prevalence declined from 35.05% (CI: [33.90%, 36.20%] in 1998 to 22.19% (CI: [21.15%, 23.24%]) in 2024, and mean intensity from 17.49 (CI: [17.09,17.90]) to 13.33 (CI: [12.88, 13.79]) cigarettes/day. Over this period sex-differences in both outcomes narrowed almost completely. Absolute and relative inequalities in smoking prevalence widened across all SES dimensions, particularly for education and occupation. By 2024, inequalities were larger among women than men driven by a stagnating or rising smoking prevalence among low-SES women at least until 2018 alongside continued declines in higher-SES women and for men. Inequalities in smoking intensity, particularly related to income, were generally smaller than those in prevalence. Conclusion: Socioeconomic smoking inequalities in Germany widened from 1998 to 2024 primarily driven by reductions among higher-SES groups and increases in low-SES women. However, recent reductions in low-SES women may indicate a new phase in the smoking epidemic. Health equity considerations should be integrated into a targeted German tobacco control strategy.

Polygenic scores are imperfect measures of the additive genetic effects of common genetic variants. The resulting measurement error biases estimates of quantities of interest in epidemiological analyses integrating polygenic scores. For example, how much of an exposure-outcome association is genetically confounded can be substantially underestimated when using polygenic scores alone. Here we present extensions to Gsens, a genetic sensitivity analysis, which aims to correct for such measurement error using both polygenic scores and heritability estimates. Gsens now allows for multiple exposures and estimates several quantities of interest, i.e. genetic confounding, adjusted residual association (net of genetic confounding), genetic overlap and environmentally mediated genetic effects. We present derivations and simulations showing how Gsens accounts for measurement error in the polygenic score; we also show how estimation may be affected by misspecifications of the causal structure between exposures. Applying Gsens in the Norwegian Mother, Father and Child Cohort Study (MoBa), we uncover, among other results, substantial genetic confounding in the associations between multiple known risk factors for attention deficit hyperactivity disorder (ADHD), such as low birth weight and temperament, and measures of ADHD in childhood. The updated Gsens R package offers multiple options, including for missing data handling and customisable syntax. Our extended version of Gsens is applicable to a broad range of substantive questions in multiple disciplines.

Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal mediation framework that integrates socioeconomic, psychosocial, clinical, laboratory, behavioral, and genomic data from the All of Us Research Program. Modality-specific variational autoencoders were used to derive latent representations of each data domain, and mediation analyses were subsequently performed in latent space to evaluate indirect associations between socioeconomic disadvantage, psychosocial factors, and multimorbidity. The final analytic cohort included 20,804 participants with complete multimodal data. Across 800 exposure--mediator--outcome combinations, mediation signals were concentrated within a small number of latent dimensions. The strongest indirect association linked a socioeconomic disadvantage dimension, a psychosocial vulnerability dimension, and a cardiometabolic multimorbidity dimension (NIE = 0.002517). The psychosocial dimension was characterized by poorer mental health, greater loneliness, lower social well-being, and lower health literacy, whereas the outcome dimension was associated with hypertension, diabetes, hyperlipidemia, obesity, chronic kidney disease, and heart disease. Bootstrap analyses supported the stability of the leading pathway. These findings suggest that psychosocial vulnerability may contribute to the association between socioeconomic disadvantage and cardiometabolic multimorbidity. More broadly, the proposed framework illustrates how AI-based representation learning can be used to investigate complex relationships across high-dimensional multimodal health data.

Background: The heart-brain axis links cardiovascular and neurodegenerative disease through shared vascular and inflammatory mechanisms. Although low-density lipoprotein cholesterol (LDL-C) is an established causal factor in atherosclerotic cardiovascular disease (ASCVD), its relationship with dementia remains uncertain, with midlife elevations associated with increased risk but late-life associations often appearing null or inverse. To address this cholesterol paradox, we integrated mendelian randomization (MR) with an active-comparator new-user target trial emulation. Methods: We applied a triangulated causal inference framework integrating two-sample MR with observational target trial emulation. Genetic variants associated with LDL-C were used as instrumental variables to evaluate Alzheimer disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and any dementia (AnyDem), with causal estimates derived using inverse-variance weighted models and sensitivity analyses for heterogeneity and pleiotropy. In parallel, an active-comparator new-user design compared statin versus ezetimibe initiation among adults aged 60 years or older using propensity score (PS) overlap weighting and Cox proportional hazards models to evaluate cardiovascular and dementia outcomes. Results: Genetically predicted LDL-C was associated with increased risk of DLB (OR 1.65, 95% CI 1.30-2.10; p<0.001), but not AD or AnyDem; FTD estimates were inconsistent. Sensitivity analyses suggested heterogeneity and possible pleiotropy for DLB. In the observational analysis (n=6,977), statin initiation was associated with higher risks of ASCVD (HR 1.26, 95% CI 1.11-1.45) and AnyDem (HR 1.66, 95% CI 1.16-2.38), although estimates attenuated after lipid adjustment and lagged analyses, suggesting residual confounding, treatment selection, and reverse causation in late-life observational associations. Conclusions: These findings suggest that LDL-C reflects accumulated vascular and metabolic risk rather than a direct causal driver of AD or overall dementia, although a subtype-specific association was observed for DLB. Late-life associations appeared influenced by timing, reverse causation, and treatment selection, warranting cautious interpretation. Keywords: Heart-brain axis, dementia, cardiovascular disease, low-density lipoprotein cholesterol, causal inference

Background: The UK Government aims to reduce smoking rates by implementing new, and investing in existing, tobacco control strategies including increased funding for Stop Smoking Services (SSS) in England. This study examined whether the additional funding starting in April 2024 was associated with a detectable increase in quit attempts supported by SSS and whether it was cost-effective. Methods: We used data from the Smoking Toolkit Study, a repeat cross-sectional survey conducted in 2021 to 2025. Adults aged [&ge;]18 years who smoked cigarettes and had made a quit attempt in the past year were included (weighted n=5,076). The outcome was monthly prevalence of past-year quit attempts supported by SSS. We fitted general additive models with a step change in April 2024 to represent the start of the increased funding. We adjusted for tobacco tax increases, the Swap-to-Stop scheme, age, gender, and a measure of socioeconomic position. In an unplanned analysis, we extended the time series back to 2006. For the cost-effectiveness, we estimated incremental cost-effectiveness ratios for the total population and age groups, accounting for future lifetime cessation. Results: In the primary model, the April 2024 step change was not statistically significant (adjusted odds ratio: 1.13; 95% CI: 0.52, 2.49). The cost-effectiveness analysis ranged from cost-effective to extremely ineffective (incremental cost-effectiveness ratio (ICER): GBP 104,126, 95% CI: 939,398 to 8,293). When using the extended time series, the adjusted odds ratio for the step change was 2.70 (95% CI: 2.03, 3.60) and the intervention was cost-effective (ICER: GBP 13,857; 21,393 to 9,620). Conclusions: Compared with the long-term trend, increased funding to SSS in England in 2024 appeared to lead to an increase in quit attempts supported by SSS at the population level. This result is somewhat uncertain because our primary pre-planned analyses assessing the impact relative to a more recent trend were insensitive.

Background: A central concern in global health priority-setting is whether the supply of scientific knowledge aligns with health needs and demands. This alignment is usually assessed by comparing research effort with disease burden, overlooking other type of "social demand" of disease, in particular whether diseases are socially visible and generate public attention. We develop an analytical framework that treats public attention and epidemiological burden as complementary dimensions of health demand and examines their alignment with knowledge supply. Methods: We combine data on publications indexed in OpenAlex, disability-adjusted life years from the Global Burden of Disease, and Wikipedia pageviews for 2016 to 2023, as indicators of research effort, disease burden, and public attention, respectively. We map 19 disease groups and 138 specific diseases across these three dimensions. Ternary plots are used to position diseases according to their relative balance across dimensions and to identify diseases that are over- or under-represented in research effort relative to epidemiological burden and public attention. We compare Global North-South patterns using German, Persian, Swahili, and Vietnamese language areas to assess how these relationships vary across territories. Results: The three dimensions show limited alignment. At the disease group level, cardiovascular diseases account for the largest share of disease burden, mental disorders attract the largest share of public attention, and neoplasms concentrate the largest share of research effort. Public attention and disease burden are weakly correlated at both group and specific disease levels, indicating that Wikipedia pageviews and DALYs capture distinct dimensions of health demand. Ternary plots reveal different forms of misalignment, with some diseases showing plots dominated by burden, others by research effort, and others by public attention. Territorial analyses add a further layer by showing that diseases follow disparate patterns of supply-demand (mis)alignment across different linguistic territories. Conclusions: Public attention provides a complementary dimension for mapping global health needs and demands. Our approach identifies where scientific knowledge supply fails to match epidemiological and/or public attention, supporting more nuanced global health analysis that may be useful for priority-setting.

Background: Research consistently shows that colorectal cancer (CRC) screening uptake is socially patterned; however, sociodemographic determinants are usually analysed separately, overlooking how multiple social conditions jointly shape inequalities. This also applies to policy research, where heterogeneity in screening programme effects remains underexplored. Methods: Using data from the European Health Interview Survey (2014 and 2019; n=201,214; 24 countries), we applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to analyse CRC screening uptake across 72 subgroups defined by sex, education, living arrangement and employment. To assess heterogeneity in screening programme effects, we combined MAIHDA with difference-in-differences (MAIHDA-DiD). Results: MAIHDA revealed inequalities in uptake: lower- and middle-educated men, whether employed or unemployed, had the lowest uptake, whereas men and women not living alone, retired or living with disability, had the highest uptake. Lower-educated homemaker women were the only female group with below-average uptake. MAIHDA-DiD showed that programmes increased overall uptake but did not produce larger gains among groups with lower pre-intervention uptake, and therefore did not reduce inequalities. Instead, programmes generated above-average increases among groups with higher pre-intervention uptake, particularly lower- and middle-educated men and women not living alone and retired. Living arrangement explained more variation in programme effects than other factors, with individuals living alone benefiting less from the programmes. Conclusion: CRC programmes did not reduce (and may have widened) inequalities, underscoring the need for equity-focused strategies in population-based screening. By extending MAIHDA with difference-in-differences, this study introduces a novel approach for evaluating heterogeneous policy effects in public health.

ABSTRACT Background: Onset of cancer is linked to psychological distress and cancer is prevalent in older adults. Yet, the association to suicide is scarcely examined. The aim of this study was to assess whether cancer diagnosed in older adults is associated with suicide incidence. Methods: All older adults (65+ years) who lived in Austria in the years 2014-2021 (n=2,175,134) were followed. Of these, 223,932 were diagnosed with a new cancer. We used non-parametric survival models with inverse-probability-treatment weights to compare risk ratios (relative risk) and risk differences (absolute risk) of older adults with and without cancer. Results: Out of 2,158 suicide deaths, 442 (20.5%; 83.7% males) occurred among older adults with a new cancer diagnosis. The incidence rate was 74 among those with a new cancer diagnosis versus 23 per 100,000 person-years among those with no new cancer. One year after being diagnosed, older adults with a new cancer had a 4 times higher relative risk of dying by suicide compared to those without. The risk was highest within the first three months after diagnosis and for cancers with a poor prognosis (disseminated disease; lung, oesophagus, stomach, liver, pancreas, and brain cancers). The absolute risk of dying by suicide within 5 years after cancer diagnosis was 0.18% versus to 0.11% among those with no new cancer. Discussion: Older adults who received a new cancer diagnosis had elevated suicide risks. Provision of support to cope with mental distress should be considered at cancer diagnosis, especially for older adults with a poor prognosis.

Background: Epigenetic clocks are markers of biological aging that may vary in their sensitivity to environmental stressors and lifestyle modifiers. To evaluate the utility of these biomarkers as sensors of the human exposome, we investigated how they respond to two powerful and opposing exposures: smoking, a source of oxidative stress, and the antioxidant-rich Mediterranean diet. Objectives: We assessed the sensitivity of eleven epigenetic clocks to diet and smoking and evaluated whether Mediterranean diet adherence modifies associations between smoking and epigenetic aging. Methods: We analysed 928 participants (mean age 41 years, 59% male) from the Airwave Health Monitoring Study. Linear regression models assessed associations between Mediterranean Diet Score (MDS) and epigenetic age acceleration (EAA), alongside smoking status and blood cotinine. Interaction terms between smoking status and MDS were included to detect dietary attenuation of smoking-related EAA. Models were adjusted for demographic, socioeconomic, lifestyle, and psychological covariates. Results: Higher MDS was associated with lower EAA for GrimAge ({beta} = -0.07 SD; 95% CI: -0.13, -0.01) and Bernabeu ({beta} = -0.08 SD; 95% CI: -0.14, -0.02) after false discovery rate correction. Smoking was strongly associated with increased EAA, particularly for GrimAge, Bernabeu, and DunedinPACE. Among current smokers, effect sizes were greater in those with lower dietary adherence (e.g. GrimAge: 1.79 SD, 95% CI: 1.54, 2.04) compared with those with higher adherence (1.35 SD, 95% CI: 1.01, 1.68; P_interaction < 0.001). Similar attenuation patterns were observed for Bernabeu. Higher intake of fruits, vegetables, and whole grains contributed most to the attenuation of smoking-related EAA. Conclusions: Our findings indicate that certain epigenetic clocks effectively capture the tension between harmful and protective exposures within the exposome. Rather than suggesting that diet neutralises the risks of tobacco, these results demonstrate that specific clocks are sensitive enough to monitor how lifestyle factors modify molecular responses to environmental toxins. This highlights the value of second-generation clocks in quantifying biological resilience.

Early life adversity is a recognised risk factor for poor health in later life, but its relationship with allostatic load (AL; a measure of cumulative physiological stress) remains underexplored across populations around the world. This study builds evidence to test a life course approach to AL using harmonised data from six longitudinal ageing cohorts: Health and Retirement Study (US, N=2,427), English Longitudinal Study of Ageing (England, N=2,038), The Irish Longitudinal Study on Ageing (Ireland, N=8,184), Survey of Health Ageing and Retirement in Europe (Europe, N=4,079), China Health and Retirement Longitudinal Study (China, N=5,220), and Indonesia Family Life Survey (Indonesia, N=1,243). Childhood information at aged ten to sixteen was collected retrospectively from adults aged 65 on average. An AL score is constructed using biomarker data and a latent construct of early life adversity constructed to address recall bias. Results show that early life poverty is significantly associated with raised AL in Ireland, China, and Indonesia, supporting the impact of early life experiences on older adulthood. However, studies from other countries do not confirm statistical significance. These findings document that experiencing poverty during development phase in some settings is associated with higher AL in later life, however further analyses are required to explore possible variations across different populations and their social and economic context. These results add further evidence that social determinants of health, including child poverty, in some settings, contribute to cumulative physiological stress across the life course, and that policies and actions to reduce child poverty can have beneficial effects not only as children, but also as older adults.

Abstract Purpose This study introduces a novel approach to address unmeasured confounding in terminal event studies using the prior event rate ratio (PERR) method. The proposed approach PERR_{proxy} used a proxy event to replace the original terminal event in the pre-exposure period, enabling the application of PERR in terminal event settings. Additionally, we also applied difference in difference (DID) regression, which is conceptually analogous to PERR to estimate the standard errors and confidence intervals of PERR_{proxy}. Methods We conducted numeric simulations to evaluate the validity of PERR_{proxy} approach and assessed its performance under varying levels of unmeasured confounding effects, baseline hazard ratios, and the correlation between the proxy and terminal events. To demonstrate its practical applicability, we also performed an empirical analysis to investigate the impact of severe hospitalized COVID-19 on circulatory system disease mortality using the PERR_{proxy}. Results In simulation studies, PERR_{proxy} effectively reduced the unmeasured confounding effects compared to the conventional methods. The performance of PERR_{proxy} was influenced by the strength of unmeasured confounding, baseline hazard ratios, and the correlation between the proxy and terminal outcomes. In addition, difference in difference (DID) regression had much faster computational speed for estimating standard errors and confidence intervals compared to bootstrap. In the empirical analysis, PERR_{proxy} identified that severe hospitalized COVID-19 as a significant risk factor for the circulatory system disease mortality and reduced the unmeasured confounding effects. Conclusions The PERR_{proxy} approach extends the applicability of the original PERR method to terminal event studies, offering a promising solution for addressing unmeasured confounding. Additionally, the DID regression framework provides a computationally efficient alternative for parameter estimation in PERR-based studies. However, careful consideration is still required in PERR_{proxy} for proxy events selection and other underlying assumptions of the PERR method to ensure valid results. Keywords: prior event rate ratio, unmeasured confounding, proxy event, terminal event study, observational study, electronic health records

Background During the COVID-19 pandemic, public health agencies used near real-time observational data to answer questions regarding vaccine effectiveness. However, traditional observational methods do not allow conclusions regarding counterfactual scenarios to be drawn from clinical data. Counterfactuals, which are outcomes that would have occurred under alternative interventions, can be used to formally assess the causal effects of public health interventions on health outcomes while accounting for the effects of confounding. Ideally individual patient data is used for the development of counterfactuals. Low-fidelity synthetic data may be useful for advancing methodological development where governance and privacy constraints prohibit access to sensitive personal data. Methods We simulated synthetic datasets based on the EAVE-II COVID-19 platform which has been limited to use for surveillance purposes. EAVE-II includes almost all resident people in Scotland registered with qualified general medical practitioners. Patient characteristics were simulated to reflect the known distribution of the Scottish population, accounting for dependencies between variables. Each synthetic dataset was encoded to different realistic scenarios for EAVEII 'ground truth' vaccine rollout and effectiveness results, explicitly stating the causal and confounding mechanisms, using a statistically sound method based on marginal structural models. Synthetic datasets of 100,000 individuals were then generated across five confounding scenarios and five severe outcome types. Results In scenarios with weak confounding, both unweighted and inverse probability of treatment weighted (IPTW) logistic regression recovered the true causal parameters. As confounding strength increased, only weighted models recovered the true mechanism. Conclusions Low-fidelity synthetic datasets simulated from EAVE-II data analysts to build and test causal inference pipelines, develop novel analysis pipelines, and train new researchers while awaiting access to real data. We showed how to generate synthetic datasets from a marginal structural model under different confounding scenarios.

Background: previous research has demonstrated a relationship between chronically elevated postabsorptive amino acid (AA) levels and the pathophysiology of neurological disorders such as dementia. It is unclear if some of these associations are causal or secondary to other pathologies. Amino acid metabolism also has a complex relationship with glycaemia, which itself may be implicated in brain health and the aetiology of dementia. Study design: we implemented a two-sample Mendelian randomisation (MR) in the UK Biobank (maximum N=375,078 participants) utilising exposure data from a recent genome-wide association study (GWAS) of circulating metabolites. Both univariate and multivariable MR methods were implemented, complemented by standard sensitivity analyses using the weighted median estimator, Egger regression, and MR-PRESSO. Our exposures were genetic instruments proxying elevated levels of the branched chain AAs (BCAAs; isoleucine, leucine, and valine), aromatic AAs (AAAs; phenylalanine, tyrosine, and histidine), and glycated haemoglobin A1c (HbA1c). Our outcomes were subcortical volume measures (of the accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus); total brain and white matter hyperintensity (WMH) volumes; and all-cause dementia (ACD), vascular dementia (VaD), and Alzheimer's dementia (AD). Results: we found evidence of direct associations independent of glycaemia between genetically-proxied elevated circulating tyrosine and multiple subcortical volumes of the accumbens ( {beta} = 11.0 95%CI [4.2, 17.8] mm3 / mmol L-1), caudate ({beta} = 30 95% CI [2.9, 5.71] mm3 / mmol L-1), hippocampus ({beta} = 43.1 95%CI [14.1, 72.1] mm3 / mmol L-1), and thalamus ({beta}= 81.4 95%CI [8.54, 154.3] mm3 / mmol L-1). For dementia outcomes, elevated valine and histidine were associated with a reduced (OR = 0.45 95% CI [0.21, 0.96] / mmol L-1) and increased (OR = 1.47 95% CI [1.03, 2.09] / mmol L-1) vascular dementia risk, respectively. Conclusions: elevated circulating tyrosine was associated with increases in several subcortical volumes, and these effects were found to be independent of glycaemia. This warrants further investigation as to the effects and possible benefits of tyrosine modification or supplementation in the diet to protect against brain atrophy and the development of neurological disorders. On the other hand, associations between circulating AAs and vascular dementia may indicate mechanistic effects of chronically elevated AA levels on dementia.