International Journal of Epidemiology

Background: Multiple observational studies have reported associations between greater parity and increased CVD risk. Whether these associations reflect causal effects or are confounded by socioeconomic factors remains unclear. Methods: We investigated associations between number of children ever born (NEB) and 16 cardiometabolic traits in up to 172,122 females and 138,390 males in the UK Biobank, and an independent sample of 53,237 UK Biobank spousal pairs. We additionally conducted sex-stratified two-sample Mendelian randomization (MR) and applied a novel spousal MR framework, in which an individual's spouse's genotype was used as the instrumental variable to estimate the causal effect of NEB on cardiometabolic health outcomes, as an approach to minimize bias from horizontal pleiotropy. Results: NEB was associated with multiple cardiometabolic traits in the multivariable regression, even after adjustment for socioeconomic status, with differences in the strength of association observed between males and females. Traditional MR provided evidence that higher NEB causally increases type 2 diabetes risk in females, body mass index (BMI) in both sexes, female basal metabolic rate (BMR) and male body fat percentage but decreases female blood pressure. Spousal MR corroborated positive effects on female BMI and BMR and additionally suggested inverse causal effects on female HDL cholesterol and ApoA1 and male blood glucose. Conclusion: These findings indicate a possible causal relationship between NEB and long-term cardiometabolic health, although causal effects are likely to be small.

Background: Reliable estimation of excess mortality is central to population health surveillance. We introduce NeMMo (New Mortality Model), an evolution of the EuroMOMO model for estimating weekly all-cause expected mortality, and assess its behaviour and performance on empirical data. Methods: NeMMo incorporates population offsets, stratifies observed deaths by age group and models seasonality using a periodic B-spline rather than a Serfling-type sinusoidal function. Baseline weeks are selected by a data-driven procedure minimizing the skewness of the residuals before refitting the model, instead of relying solely on fixed calendar windows. NeMMo enables pooling across age groups, direct age standardization and incorporation of external predictors. We applied NeMMo and EuroMOMO to mortality and population data downloaded from Eurostat for 31 countries from 2015 onwards, excluding the COVID-19 pandemic period from baseline estimation. Results: For most countries NeMMo produced a higher expected mortality baseline that better tracked observed deaths, as well as tighter prediction intervals and higher maximum Z-scores, suggesting improved discrimination of mortality excesses. Z-scores and P-scores during non-pandemic weeks were closer to zero with NeMMo than with EuroMOMO but further elevated during pandemic weeks, providing greater separation between pandemic and non-pandemic mortality. Incorporating population offsets resulted in negative linear trends across all countries, consistent with declining mortality after accounting for demographic changes. The periodic B-spline identified substantial heterogeneity in the shape and timing of seasonal mortality that was not captured by a sinusoidal function. Conclusions: NeMMo provides a flexible and parsimonious framework for all-cause mortality surveillance that improves the established EuroMOMO model and offers theoretical, empirical and practical advantages. It is thus suitable both for detecting short-term spikes and for the long-term, age-adjusted quantification and comparison of mortality excesses that has become increasingly important since the COVID-19 pandemic. The accompanying 'nemmo' package for R facilitates its widespread adoption and application.

Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) describes health inequalities through three components: (i) specific contextual effects (SCE), (ii) general contextual effects (GCE), and (iii) discriminatory accuracy of the context. We present Simple-Means MAIHDA (S-MAIHDA), which estimates each stratum directly from its observed individuals, with no distributional assumption. The observed proportions are unbiased whatever the stratum size, and their confidence intervals report the uncertainty honestly. S-MAIHDA operationalises the three components on the probability scale. The SCE are the raw and standardised stratum prevalences and the modification of the sociodemographic average differences by the area. The GCE are the variance partition coefficient (VPC) and the contextual structuring of the between-stratum inequality, expressed as the contextual clustering of inequalities, the additive sociodemographic differences, and the contextual modification of inequalities (CMI). The contextual discriminatory accuracy is expressed by the area under the ROC curve (AUC), and the sensitivity and specificity at the population prevalence as the threshold for a possible intervention. Because its estimates are the observed data themselves, S-MAIHDA is the canonical description, and the compare diagnostic quantifies how Random-Effects MAIHDA (RE-MAIHDA), the usual implementation, departs from it: RE shrinkage pulls small strata towards the overall mean and can hide the very inequalities the analysis seeks. The approach is implemented in the smaihda Stata command and reproduced in free Python code. We illustrate S-MAIHDA on register data from Malmo, Sweden (43,291 individuals; 300 area-sociodemographic strata), showing how the three components separate two contrasting outcomes: psychotropic medication use, almost purely sociodemographic, stable across areas, with weak contextual structuring (VPC {approx} 4%, CMI {approx} 0%); and choice of a private general practitioner, strongly geographical (VPC {approx} 11%, CMI {approx} 17%), with the sociodemographic differences reshaped and amplified in wealthy areas. RE-MAIHDA attenuated inequalities. For describing inequalities, S-MAIHDA preserves what the data show.

Longitudinal household studies, combined with mathematical modeling, are widely used to characterize the drivers of respiratory pathogen transmission, including the effects of age and symptoms. In practice, household recruitment protocols vary across studies, potentially introducing biases into observed data. However, these biases are typically overlooked in statistical inference, and their impact on parameter estimates remains unknown. Here, we use synthetic household outbreak data simulated under different recruitment protocols to evaluate how recruiting through infected children affects estimates of age-specific infectiousness and susceptibility. We show that, under child-based recruitment, the standard likelihood, which accounts only for transmission dynamics, leads to underestimating child infectiousness and overestimating child susceptibility by more than 30%. We then propose a novel estimation framework that explicitly incorporates the household recruitment process into the likelihood and show that it substantially reduces these biases. Applying this new approach to a French household study conducted during the COVID-19 pandemic, we estimated that children under 6 had 49% lower infectiousness than teenagers and adults during the Alpha wave, whereas no difference was observed during the Omicron wave. This study demonstrates that ignoring recruitment protocols can bias key epidemiological parameter estimates and highlights the importance of accounting for study design.

In competing risks settings, covariate effects and group comparisons are usually assessed one event at a time - through log-rank or Cox tests on the cause-specific hazards, or Gray's test or Fine-Gray regression on a cumulative incidence function (CIF). This can obscure a clinically important quantity: the ratio between the event of interest and the competing event, since groups may differ little on the individual events yet differ sharply in their ratio. The generalized competing event (GCE) framework makes this ratio the object of inference; on the cause-specific scale the hazard ratio omega+(t) = lambda_1(t)/lambda_2(t) is estimated efficiently from a single stacked (Lunn-McNeil) model. We extend the framework to two scales that describe realized incidence. The subdistribution hazard ratio omega-tilde+(t) = lambda-tilde_1(t)/lambda-tilde_2(t) is estimated by a stacked, risk-set-weighted extension of the Lunn-McNeil construction; the cumulative-incidence ratio rho(t) = F_1(t)/F_2(t) - the odds that a subject's realized event by time t is the event of interest - by jackknife pseudo-observation regression of the Aalen-Johansen estimator. We relate the three contrasts: rho equals omega+ exactly under proportional cause-specific hazards, and equals omega-tilde+ only in the small-time limit under proportional subdistribution hazards, drifting toward 1 thereafter. The orthogonality that makes omega+ efficient is lost on both cumulative-incidence scales - omega tilde+ through overlapping weighted risk sets and shared censoring weights, rho through the shared all-cause survivor - so each carries a covariance term that must be handled and that bounds efficiency relative to the hazard-scale test. We derive the corresponding variances, study operating characteristics by simulation, illustrate on hypothetical prostate and head-and-neck cohorts, and provide an implementation in the gcemod R package.

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

Mendelian randomization (MR) analysis is widely used to estimate causal relationships between risk factors and outcomes of interest. Two-sample MR approaches have gained increasing attention in genetic epidemiology due to the growing availability of Genome-Wide Association Study (GWAS) summary statistics from public databases. A critical step in two-sample MR is the selection of genetic variants as instrumental variables (IVs). Although genome-wide significant variants are typically preferred, the inclusion of variants with weaker association p-values is considered, as they may potentially improve power through an increased instrument number of instruments, while they may introduce weak instrument bias and attenuate effect estimates towards the null. Our simulation results show that even modest levels of pleiotropy substantially increase the variability of causal effect estimates, while the inclusion of weak IVs does not substantially affect the direction and variability of causal effect estimates in most cases. In real data analyses, we used two released versions of FinnGen GWAS summary statistics with different sample sizes as exposure GWASs to assess the influence of weak IVs. Here, the inclusion of IVs with higher exposure-association p-values resulted in weakened estimated effect sizes, particularly when the exposure GWAS sample size was small. These findings suggest that incorporating weak IVs is reasonable when the exposure GWAS sample size is large, but it poses a risk of falsely concluding null associations when the exposure GWAS sample size is small.

Background: Observational research reports positive associations between type 2 diabetes mellitus (T2DM) and obesity-related cancers (ORCs), but causality remains unclear due to confounding (namely the shared risk factor of obesity, commonly approximated as body mass index, BMI), immortal time bias, and detection-time bias. Here, we aimed to use causal inference methods to minimise the above problems and estimate causal associations between new-onset T2DM and incident cancer. Methods: We performed a cohort study within UK Biobank, comparing new-onset T2DM with unexposed individuals matched 1 to 3 on BMI, age, and sex using a sequential longitudinal approach. The primary outcomes were total incident cancer, divided into ORCs and non-obesity-related cancers (NORCs). The secondary outcomes were site-specific cancers. We developed Cox models to estimate time-split hazard ratios (tsHRs) and 95% confidence intervals (CIs) stratified by sex. Findings: 23,771 participants with new-onset T2DM were matched with 71,170 unexposed participants. During a median follow-up of 5 years, there were 7694 (T2DM: 2432; unexposed: 5262) incident cancers. In men, there was evidence for an effect of T2DM on obesity-related cancer (tsHR 1.39, 95% CI 1.21-1.59), particularly on hepatocellular carcinoma (tsHR 3.97, 95% CI 2.38-6.65), pancreatic (tsHR 1.77, 95% CI 1.15-2.72) and kidney (tsHR 1.62, 95% CI 1.13-2.32) cancers. In women, there was evidence for an effect on obesity-related cancers (tsHR 1.33, 95% CI 1.16-1.52). Importantly, there were no associations with post-menopausal breast and endometrial cancers, two cancer types consistently associated with elevated BMI. There was no effect of new-onset T2DM on incidence of NORCs. There was evidence of detection-time bias, particularly in men. Interpretation: This is the first large-scale study to demonstrate evidence of a BMI-independent associations between new-onset T2DM and incident cancer. In men, this was primarily driven by hepatocellular carcinoma, pancreatic cancer, and kidney cancer. In women, the underlying cancers driving this relationship were less clearly defined. Funding: This study was funded by Cancer Research UK and administered through the Manchester Cancer Research Centre MB-PhD scheme (SEBCATP-2023/100010).

Abstract Background Persistent pain in later life is associated with functional decline. Identifying resources associated with preserved daily function may broaden pain research beyond symptom burden alone. Objective To examine six prespecified resource indicators-physical activity, depressive symptoms, recall performance, current partner status, education and wealth-in relation to subsequent maintenance of independence in activities of daily living (ADL). Methods This longitudinal observational study analysed adults aged 50 years or older with pain at both the initial and baseline assessments in the Health and Retirement Study (HRS), the English Longitudinal Study of Ageing (ELSA) and the Survey of Health, Ageing and Retirement in Europe (SHARE). ADL maintenance was defined as no difficulty in all five prespecified ADL activities at baseline and no difficulty in all five at follow-up. Indicator-specific risk ratios were estimated using modified Poisson regression with robust variance and pooled using random-effects meta-analysis. The six meta-analysis P values were adjusted using the Holm procedure. Each indicator used a separate complete-case sample; no common six- indicators sample was constructed. Results The descriptive samples comprised 8,279 HRS person-windows contributed by 4,688 unique participants, 1,200 ELSA participants and 6,995 SHARE participants; indicator-specific denominators varied. Physical activity was associated with a higher probability of ADL maintenance across all three cohorts (pooled risk ratio, 1.145; 95% CI, 1.097-1.195; P = .005; Holm-adjusted P = .032) and was the only prespecified hypothesis to meet the Holm-adjusted criterion. The other five hypotheses did not meet this criterion; failure to do so should not be interpreted as evidence that the corresponding associations were absent. Several of these pooled estimates showed substantial heterogeneity. Conclusions Among older adults with persistent pain, physical activity was associated with a higher probability of ADL maintenance, and the physical activity hypothesis was the only one of the six prespecified hypotheses to meet the Holm-adjusted criterion in the three-cohort meta-analysis. These observational findings support further study of physical activity and functional maintenance but do not establish causal benefit.

Abstract Purpose: Haemochromatosis due to HFE-C282Y homozygosity can lead to excess iron absorption and is typically associated with liver malignancy, plus widespread arthritis. Recent evidence suggests that limb fractures are more common, but little is known about vertebral effects. This study investigated the association of vertebral compression fractures, assessed with intelligent dual-energy X-ray absorptiometry (iDXA), and HFE genotype in a large community cohort. Methods: UK Biobank data from 227 European genetic ancestry C282Y homozygotes (mean 64.6 years) and 234 age, sex, and BMI-matched controls without common HFE haemochromatosis variants were included. Lateral vertebral assessment scans (iDXA, GE-Lunar) were acquired at imaging reassessment (2014-2020) and reviewed, blind to genotype, for radiological evidence of vertebral fracture. Matched logistic regression models assessed associations between C282Y homozygosity and vertebral fractures. Results: 78 vertebral fractures (16.9%) were identified within 461 participants. Male C282Y homozygotes had increased odds of vertebral fracture (n=22/89, 24.7%) compared to participants without HFE alleles (n=9/90, 10.0%); Odds Ratio [OR]: 2.95, 95%CI: 1.28-6.85, p=0.01. The association persisted after excluding individuals with a diagnosis of haemochromatosis (OR: 3.37, 95% CI: 1.41-8.10, p=0.007). No excess fracture risk was observed in female C282Y homozygotes (n=23/138, 16.7%) vs those without HFE alleles (n=24/144, 16.7%); OR: 0.99, 95%CI: 0.53-1.87, p=1.00. Conclusion: In this community-based imaging study, male HFE C282Y homozygotes had a markedly higher likelihood of vertebral fractures than those without HFE variants. These findings support further evaluation of vertebral fracture assessment in C282Y homozygous men to ensure prompt treatment to prevent future fracture if appropriate.

Background Loss to follow-up (LTFU) during tuberculosis treatment is a major programmatic gap, but its causal effect on mortality has been difficult to quantify. We estimated this effect using a sequential landmark cohort analysis. Methods and Findings We constructed a retrospective cohort of individuals aged [≥]15 years initiating their first tuberculosis treatment in Sao Paulo State, Brazil (2013-2023), using the State registry (TBweb) linked to the national mortality system (SIM). To account for immortal-time bias, we compared mortality between LTFU and continued-treatment patients in time-aligned monthly cohorts, applying a symmetric 30-day grace period to align eligibility. Cause-specific Cox models estimated adjusted hazard ratios for late (6-24 month) mortality, with non-TB mortality as a within-cohort negative control. We also computed standardized (g-formula) absolute mortality risk differences over the same window. Effect modification was assessed across pre-specified subgroups (age, sex, HIV, homelessness, drug-resistance status, calendar period). Of 171,048 individuals initiating tuberculosis therapy, 20,830 (12.2%) experienced LTFU. LTFU at any month substantially increased late mortality (adjusted hazard ratios [aHR] 1.83 [95% CI 1.27-2.64] to 2.85 [2.34-3.48] by month of LTFU), corresponding to standardized late-window mortality risk differences of up to 1.6 percentage points. The excess was concentrated in TB-attributable deaths (aHR 1.60-4.36) and was essentially null for non-TB mortality (0.96-1.48). Relative effects were largest in younger, stably housed individuals with low baseline mortality, whereas the largest absolute excess fell on people living with HIV (risk difference 3.0 percentage points). Conclusions LTFU at any point in tuberculosis therapy substantially increased late TB-attributable mortality, consistent with a causal pathway through interrupted treatment. Preventing LTFU should be a programmatic priority.

Background: Maternal mortality remains uneven globally, and the COVID-19 pandemic disrupted maternal health services through direct infection-related risks and indirect health-system pathways. We estimated country-level deviations in maternal deaths and maternal mortality ratios (MMR) during 2020-2023 relative to pre-pandemic trends. Methods: We used WHO/UN MMEIG model-based country-level estimates of maternal deaths and MMR from 2000-2019 to fit an ensemble n-sub-epidemic forecasting model. We generated no-pandemic counterfactual projections for 2020-2023 and compared them with WHO/UN MMEIG estimates for the same years. Excess was defined as the positive difference between the WHO/UN MMEIG estimate and the counterfactual prediction; uncertainty was quantified using bootstrap-based prediction intervals. Results: Globally, estimated cumulative excess maternal deaths were 68,489 (95% UI 34,706-147,118) during 2020-2023, and the aggregate excess MMR was 10,154 (95% UI 4,568-23,744). The largest regional excess death burdens were observed in the South-East Asia Region, Eastern Mediterranean Region, and African Region. Among the eight illustrative high-burden countries, Afghanistan and Somalia had statistically detectable excess maternal deaths, with totals of 2,335 (95% UI 1,148-4,350) and 1,815 (639-3,401), respectively. Liberia had a positive median estimate of 265 excess maternal deaths, but its interval included zero (0-990). Nigeria, Chad, and South Sudan had median totals of zero, although uncertainty intervals indicated that nonzero excess could not be excluded in Chad and South Sudan. Conclusion: Pandemic-period WHO/UN MMEIG estimates deviated heterogeneously from pre-pandemic counterfactual trends. These findings should be interpreted as modeled excess relative to a no-pandemic baseline and may reflect pandemic-related disruptions together with other contemporaneous health-system, political, and social shocks, rather than directly observed deaths or causal effects attributable solely to COVID-19.

Importance: Oral healthcare is often financed outside universal medical coverage, leaving working-age adults exposed to out-of-pocket costs. People with long-term disability may face added financial, physical, and service barriers to care, but longitudinal evidence on disability and oral healthcare unaffordability is limited. Objective: To estimate the effect of time-varying long-term disability on oral healthcare unaffordability among working-age adults in Australia. Design: Longitudinal cohort study using Household, Income and Labour Dynamics in Australia survey data from waves 18 to 22 (2018-2022), analysed with targeted maximum likelihood estimation for longitudinal modified treatment policies. Setting: Nationally representative household panel survey in Australia. Participants: Adults aged 25 to 65 years at wave 18 who could validly contribute to the longitudinal analysis (identified using HILDA longitudinal weights) and had complete baseline covariate data. Exposure: Time-varying self-reported disability at waves 18 to 21 (2018-2022), defined as any long-term health condition, impairment, or disability restricting everyday activities and lasting, or likely to last, for at least 6 months. Hypothetical interventions comprised 50% and 25% reductions in the odds of disability at each wave, and deterministic sustained disability and no disability regimes. Main Outcome and Measure: Self-reported avoidance of dental treatment because of cost at wave 22 (2022). Results: The analytic sample included 9635 adults; 4901 (50.9%) were female, mean age was 44 (SD=12) years, and 2419 (25.1%) reported disability at baseline. A total of 399 participants (4.1%) reported oral healthcare unaffordable at wave 22 follow-up. Compared with the natural course, sustained disability increased the risk of unaffordability (risk ratio [RR], 1.60; 95% CI, 1.15-2.22). No disability at any time point reduced the risk (RR, 0.59; 95% CI, 0.45-0.77). Reducing the odds of disability by 50% and 25% also reduced the risk of oral healthcare unaffordability by 28% (RR, 0.72; 95% CI, 0.65-0.81) and 17% (RR, 0.83; 95% CI, 0.78-0.89), respectively. Conclusions and Relevance: Under the study assumptions, long-term disability was estimated to increase experienced unaffordability of oral healthcare among working-age Australians. Population level policy responses should address both the upstream conditions that shape disability trajectories and the downstream exclusion of adult dental care from routine financial protection.

Background: High levels of sedentary behaviour are an emerging global public health concern, but its impact on cancer risk remains unclear. Methods: Within the Global Cancer Update Programme (CUP Global), we systematically searched the literature in PubMed and Embase until September 2024 for observational cohort studies on sedentary behaviour and adult cancer risk. Using dose-response meta-analyses, we investigated sedentary behaviour domains (total, occupational, recreational, transportation, and/or other) and dimensions (duration, frequency), and additionally pooled across domains. The quality of evidence was graded by the CUP Global Expert Panel (protocol registration: https://osf.io/7utbm/). Findings: We identified 62 publications from 27 cohorts comprising 162,902 incident cancer cases across 19 anatomical sites. There was evidence for a probable causal positive association between sedentary time (mixed definitions) and breast (RRper 2 hours/day=1.03; 95%CI=1.02-1.05; I2=10%; n=11 studies) and colon (RR=1.05; 95%CI=1.03-1.07; I2=0%; n=9) cancer risk, and between television watching time and colon cancer risk (RRper 2 hours/day=1.08; 95%CI=1.05-1.11; I2=0%; n=6). Limited suggestive evidence supported positive associations between sedentary time (mixed definitions) and lung (RR=1.04; 95%CI=1.00-1.09; I2=69%; n=7), ovarian (RR=1.06; 95%CI=1.01-1.10; I2=0%; n=7), premenopausal (RR=1.03; 95%CI=0.99-1.08; I2=20%; n=7) and postmenopausal breast (RR=1.02; 95%CI=1.00-1.04; I2=7%; n=11), and colorectal (RR=1.02; 95%CI=1.00-1.04; I2=47%; n=11) cancers, and between occupational sitting time and breast (RRper 2 hours/day=1.05; 95%CI=1.01-1.09; I2=0%; n=4) and colon (RR=1.08; 95%CI=1.02-1.15; I2=28%; n=2) cancers. An interactive evidence platform is available at: https://teacup.cc.ic.ac.uk/sedentary-behaviour-cancer.html. Interpretation: Evidence supports that prolonged sedentary behaviour is probably a cause of breast and colon cancers, while limited suggestive evidence supports positive associations for several other exposure-cancer pairs, including lung and ovarian cancers. This evidence should lead to revised cancer prevention recommendations. Future research should focus on device-based exposure assessments, repeated measurements, exposure substitution models, inclusion of diverse populations and investigation of biological mechanisms.

Abstract Preventable neonatal deaths remain a major public health problem in Zimbabwe, where near-universal antenatal and facility-delivery coverage coexist with a rising neonatal mortality rate. This study evaluated whether institutionalising three core "vital signs" of the community health system (a trained village health worker (VHW) workforce, functional community governance structures, and modified women's and men's participatory learning and action groups) reduces preventable neonatal deaths in Mashonaland West Province. An embedded QUAN (qual) mixed-methods design was used, with a two-arm, parallel-group cluster-randomised controlled trial as the dominant strand. Fifty-two ward-level clusters were randomised 1:1 to the institutionalised community health system package or to standard Ministry of Health and Child Care community services, and 984 pregnant women were enrolled between 1 September 2020 and 31 October 2021, with each mother-infant pair followed to 28 days after delivery, yielding 973 mother-infant pairs for intention-to-treat analysis. The primary outcome was neonatal death within 28 days of life, expressed per 1,000 live births. The primary analysis used a three-level mixed-effects log-binomial regression model with cluster and community-health-worker random intercepts, adjusted for pre-specified covariates. Supervised machine-learning classifiers with leave-one-cluster-out cross-validation, Cox proportional-hazards regression, and multilevel logistic models were fitted as supplementary analyses. An embedded longitudinal process evaluation used key informant interviews and focus group discussions, which were analysed thematically and integrated with the quantitative findings. The neonatal mortality rate was 44.8 per 1,000 live births in the intervention arm versus 110.1 per 1,000 in the control arm. The adjusted risk ratio for neonatal death was 0.43 (95% CI 0.26-0.70; p < 0.001), a 57% relative reduction, with a number needed to treat of 16 mother-infant pairs (95% CI 11-29). Low birthweight (<2,500 g), birth interval under two years, and low community women's literacy were the strongest risk factors, while trained VHWs, functional community governance, early antenatal care, and sustained participatory group attendance were independently protective. The women's and men's groups were protective in a dose-dependent manner, becoming significant at four or more cycles (about 14 meetings) (adjusted odds ratio 0.71; 95% CI 0.60-0.85; p = 0.001). A random forest classifier discriminated against neonatal deaths with a cross-validated area under the curve of 0.842 and a sensitivity of 0.912. Qualitative findings converged with the trial results, identifying male engagement, earlier care-seeking, danger-sign literacy, social-network activation, and community death audits as the behavioural and structural mechanisms of change. Institutionalising the community health system package (trained VHWs, functional governance, early antenatal engagement, and sustained participatory groups) was associated with a substantial reduction in preventable neonatal deaths. The findings suggest that in high-coverage, high-mortality settings, the binding constraint is structural rather than clinical, and that scaling functional community governance and workforce infrastructure in the most disadvantaged communities may accelerate progress toward neonatal survival targets. The principal limitations are a one-year follow-up period, the rarity of neonatal death, and concurrent national programming that only partially reached the control clusters. Trial registration: Pan African Clinical Trials Registry, PACTR202607591142118 (https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=PACTR202607591142118); registered retrospectively on 7 July 2026.

Background and Aims: MASLD has become the most prevalent chronic liver disease globally. Although MVPA and plasma fatty acids have been individually studied in relation to metabolic health, their independent and combined associations with MASLD incidence remain unclear. We aimed to investigate these associations. Methods: This study included 51,717 UK Biobank participants free of liver disease at baseline, with MVPA measured using wrist-worn accelerometers and plasma fatty acids quantified via NMR. Multivariable-adjusted Cox models and restricted cubic splines were used. Results: Over a median follow-up of 7.8 years, 472 incident cases were identified. In fully adjusted models, meeting recommended MVPA levels together with higher n-6 PUFA concentrations was associated with a 71% lower risk (HR 0.29, 95% CI 0.18-0.45). The MVPA-MASLD association was nonlinear, with risk reduction plateauing at approximately 189 minutes per week. Higher n-6 PUFA was associated with reduced risk, whereas n-3 PUFA showed no significant association. Conclusions: These findings suggest that behavioral and metabolic factors may jointly influence MASLD risk. Further studies in diverse populations are needed to confirm these associations.

Introduction: Cardiovascular disease (CVD) is a leading non-cancer cause of morbidity among breast cancer (BC) survivors. Among them, women carrying germline BRCA1 or BRCA2 mutations (BRCA-BC) may be at particular risk of CVD, but evidence is inconsistent. The objective of this study is to determine whether BRCA-BC independently influences the risk for CVD after BC diagnosis in the Stockholm-Gotland region in Sweden (2008-2019). Methods: In this registry-based cohort study, we used exact matching on age at diagnosis, tumor stage, laterality, and pre-existing CVD or risk factors to construct 32 matched (1:1) subgroups. Multi-state Cox proportional hazards models estimated hazard ratios (HRs) for transitions from BC diagnosis to first cardiovascular event, while accounting for competing risks of distant metastasis or non-cardiovascular death. Results: In matched subgroups, BRCA-BC experienced fewer CVD (6.4% vs. 11.2% (IQR 9.4%-12.2%), but significantly more competing events (22.3% vs. 10.1% (IQR 8.9%-11.3%); p<0.05. Multi-state Cox models revealed an inverse association between BRCA-BC status and the first cardiovascular event (HR<1), but a higher hazard of the competing risk. Cardiovascular events clustered in the first year after BC diagnosis, especially among BRCA-BC, suggesting truncated time at risk. Conclusion: BRCA-BC did not demonstrate increased cardiovascular risk after BC diagnosis. The apparent inverse association with CVD likely reflects the high incidence of competing risks, which limit the window for CVD to manifest. A small subgroup of long-term BRCA-BC survivors may represent biologically distinct individuals with different cardiovascular susceptibility, warranting further investigation.

Mendelian randomization (MR) is widely used to infer causal relationships using genetic variants as instrumental variables, yet the selection of genetic instruments is not always given sufficient attention. Many MR studies rely on default linkage disequilibrium (LD) clumping parameters (r2 <0.001, 10,000 kb), as implemented in commonly used tools, without assessment of their suitability for specific exposures. We investigated whether this approach yields optimal instruments or whether a more pragmatic strategy yields stronger instruments. Using UK Biobank data, we examined three distinct exposure types-circulating amino acids, body mass index (BMI), and major depressive disorder (MDD). For each phenotype, we systematically varied LD clumping thresholds (r2 and genomic distance) and evaluated each instrument via both their average strength (F-statistic) and total strength (R2). Across all phenotypes, optimal instruments differed from default parameters and varied by exposure. For amino acids and BMI, more stringent LD thresholds (r2=0.00001) combined with larger clumping windows improved instrument strength, whereas for MDD, a highly polygenic, binary trait, smaller windows with stringent r2 maximized variance explained while maintaining F-statistics above the desired threshold (>10). Notably, increasing the number of SNPs did not consistently improve instrument quality, highlighting a trade-off between instrument strength and potential pleiotropy. We demonstrate that universal reliance on default LD clumping parameters can lead to suboptimal instruments. We propose a pragmatic framework for instrument selection based on empirical evaluation of strength metrics, improving the robustness and transparency of MR analyses across different exposure types.

Background: Randomized controlled trials have shown that computed tomographic (CT) screening reduces lung cancer mortality. Improved identification of at-risk groups, by leveraging non-smoking risk factors, could help refine screening selection. Aim: To evaluate polygenic risk scores (PRSs) and ambient air pollution (AAP) exposure for risk stratification in the NELSON lung cancer screening cohort. Methods: Two PRSs (PRS-McKay/PRS-Byun) and several AAPs (including nitrogen dioxide, ozone, and particulate matter [PM]) were assessed in the NELSON lung cancer screening trial (N=7,364). PRSs were validated in the Rotterdam Study (N=11,493). Associations with lung cancer, mortality, screening results, and discriminative ability to distinguish lung cancer were evaluated. Results: PRS-McKay and PRS-Byun were associated with lung cancer (odds ratio [OR] per SD [95%CI]: 1.22 [1.08-1.37] and 1.28 [1.13-1.44], respectively) and lung cancer-specific mortality (OR [95%CI]: 1.24 [1.05-1.47], for both), but not with non-lung cancer mortality (OR [95%CI]: 1.01 [0.94-1.10] and 1.03 [0.95-1.12], respectively). Exposure to PM2.5 was associated with lung cancer (OR [95%CI]: 1.11 [1.01-1.22]). PM constituents were associated with adenocarcinoma, particularly PM10 (OR [95%CI]: 1.16 [1.01-1.32]) and ultra-fine particles (OR [95%CI]: 1.16 [1.04-1.30]). PRS and AAP added modestly to the discriminative ability for lung cancer on top of pack-years, age, and sex (area under the curve [95%CI]: 0.659 [0.624-0.695] vs. 0.643 [0.608-0.679]). Conclusions: PRSs and exposure to PM were associated with lung cancer in a high-risk screening population. The primary potential of PRSs may reside in refining lung cancer screening selection toward individuals at higher risk of dying from lung cancer specifically.

Abstract Background: The dietary inflammatory index (DII) has been associated with metabolic syndrome (MetS), but evidence for sex-specific effects with causal support and cross-national validation remains limited. Methods: We harmonised individual-participant data from 56,475 adults across US NHANES, Korean KNHANES, and Mexican ENSANUT using survey-weighted sex-stratified logistic regression. Three complementary approaches assessed the inflammatory pathway: CRP-based Mendelian randomization (primary: 84 female-matched instruments; sensitivity: 56-SNP sex-overlap subset), exploratory direct diet MR, and proxy gap decomposition. Country-specific population attributable fractions evaluated cross- cultural DII construct validity. Results: Observational analyses revealed a robust female-specific DII-MetS association (OR=1.15, P<0.0001; Q5 vs Q1 OR=1.56) across three countries, with null associations in men (sex interaction P<0.0001; RERI P>0.05). CRP-based MR provided conservative support: the primary 84-SNP set yielded an attenuated estimate (IVW OR=1.03, P=0.31), while the pleiotropy-robust MR-RAPS method indicated a modest signal (OR=1.03-1.05, P<=0.01). Exploratory direct diet MR showed directionally consistent effects (female OR=0.91, P=0.10). Country-specific PAFs exposed a construct validity gradient: US women 20.2% versus negative PAFs in Korea (-8.0%) and Mexico (-33.1%). Formal triangulation, noting partial evidence-line dependency, yielded Fisher combined P=4.4e-8. Conclusions: Pro-inflammatory diet shows a consistent female-specific association with MetS across three nations. CRP-based MR provides conservative genetic support for the inflammatory pathway hypothesis (MR-RAPS OR=1.03-1.05). A critical finding is the construct validity failure of the 20-item DII in non-Western dietary contexts, underscoring the need for population-specific inflammation metrics. The multiplicative sex interaction was significant while the additive interaction was not, suggesting population-wide dietary guidance may capture the potential benefit. Trials are needed to establish causality. Keywords: Dietary Inflammatory Index; Metabolic syndrome; Mendelian randomization; Sex differences; Cross-national; Construct validity