International Journal of Epidemiology

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.

Postnatal care (PNC) is essential for reducing preventable maternal and neonatal deaths, but its utilization remain low across sub-Saharan Africa. Intimate Partner Violence (IPV) may be an under-recognized barrier to PNC utilization, particularly in Tanzania, where direct evidence shows that IPV is linked to limited utilization of PNC. Therefore, this study assessed the association between IPV and PNC utilization within 42 days postpartum among women in Tanzania. This study conducted a secondary analysis of the 2022 Tanzania Demographic and Health Survey (TDHS), a nationally representative cross-sectional survey. The analysis included 2,674 women aged 15-49 years who had a live birth in the five years preceding the survey and were selected for the domestic violence module. IPV (any, physical, sexual, and emotional) was the primary exposure, and PNC utilization within 42 days postpartum was the outcome. Modified Poisson regression was used to estimate crude and adjusted prevalence ratios (cPR/aPR) with 95% confidence intervals (CI) because the prevalence of the outcome was common. The prevalence of PNC utilization within 42 days postpartum was 42.0%, and the overall prevalence of IPV was 33.6% (physical 26.1%, emotional 21.8% and sexual 7.3%). Women who experienced any IPV had 16% lower PNC utilization than those who did not (aPR=0.84; 95% CI: 0.74-0.96). Physical IPV (16%, aPR=0.84; 95% CI: 0.73-0.96) and sexual IPV (25%, aPR=0.75; 95% CI: 0.57-0.98) were significantly associated with lower PNC utilization, while emotional IPV was not. Maternal education, partners age, travel time to the nearest health facility, and media exposure were also other covariates associated with PNC utilization. Intimate partner violence is associated with low utilization of PNC within 42 days postpartum in Tanzania. Integrating IPV screening and survivor support into postnatal care services, alongside addressing structural barriers to access, may improve postpartum care coverage and maternal-neonatal outcomes.

Background: Despite several trials on the hematological outcomes of intravenous (IV) iron in pregnancy, only few have examined its effect on birth outcomes. We estimated the causal effect of IV-iron on moderate or severe anaemia and birth outcomes. Methods: Women presenting to routine antenatal care in Pakistan with haemoglobin <10 g/dL were eligible for treatment. We used target trial emulation (TTE) methodology to estimate the effect of IV-iron treatment within 14 days of anaemia identification, compared to no treatment, on anaemia status at follow-up. A modified TTE analysis examined birth outcomes at delivery for singleton pregnancies, including birthweight, size-for-gestational-age, and mortality. We conducted a separate TTE for each of five gestational-age periods and pooled the results of each TTE. Results: We screened 3115 pregnancies of which 1715 were eligible for IV-iron; 1043 participants were treated during pregnancy. Those who received IV-iron had half the risk of moderate or severe anaemia in pregnancy compared with no treatment (pooled relative risk (RR) 0.40; 95% confidence interval (CI): 0.27, 0.59). The pooled effect of IV-iron on stillbirth suggested an 83% risk reduction (95% CI 55-94%), and trends were similar for perinatal and neonatal mortality. Conclusion: IV-iron treatment improved haematological status in pregnant women and was associated with a large reduction in stillbirth. Given limited data from randomised trials regarding fetal death and treatment earlier in pregnancy, this study contributes important information to the potential benefit of IV-iron in contexts where anaemia and its sequelae are a major public health problem.

Background Lower respiratory infections (LRI) remain the leading infectious cause of death in children, and survival once ill is a direct tracer of health-system quality. Whether countries are converging toward the best survival performance achieved within their own region has never been tested at national level. We measured each country's distance to an empirical episode-fatality-ratio (EFR) frontier in 204 countries from 1990 to 2023. Methods For each country and year we computed EFR = LRI deaths/incident episodes using Global Burden of Disease (GBD) 2023 estimates for ages 0-19 years. Deaths span the full 1990-2023 series; episodes are observed for 1990, 2019 and 2023, with intermediate years linearly interpolated. The frontier was the 10th-percentile country EFR within each GBD super-region and year (sensitivity: 5th and 25th percentiles); the gap = EFR_country/EFR_frontier. We classified 33-year gap trajectories into catch-up phenotypes, ranked COVID-window (2019-2023) movers, cross-tabulated gap against avoidable deaths to build a priority list, and benchmarked upper respiratory infections (URI) at three time points as a near-zero-fatality contrast. Findings The median country's gap was 1.86 in 1990, 1.80 in 2019 and 1.86 in 2023; the share of countries more than twice their regional frontier was 44.6% in 1990 and 46.6% in 2023. Of 137 eligible countries, 67 narrowed and 69 widened their gap, with one unchanged. Nineteen countries achieved sustained catch-up, concentrated in North Africa and the Middle East (7) and Latin America (5), with China closing from 2.43 to 0.50, below its regional frontier; 28 countries regressed, led by Central Asia (Uzbekistan x3.5) and including the United States (x2.0). Over the COVID-19 window the median gap peaked at 2.00 in 2021 (+10.8% versus 2019, from unrounded medians) before returning to 1.86. Combining gap with avoidable deaths identifies two distinct policy problems: high-burden, moderate-gap giants (Nigeria 67,490 avoidable deaths, gap 2.4; India 54,109, gap 1.6) and extreme-gap outliers (Uzbekistan, gap 28.6). The Sub-Saharan Africa frontier fell further behind the High-income frontier (ratio 4.2 in 1990, 9.5 in 2023); the median Sub-Saharan African country sits 11.0 times the global 10th-percentile frontier but only 1.78 times its own regional frontier, so within-region benchmarking understates the region's true distance. URI gaps likewise did not converge (median 4.15 to 4.60). Interpretation Convergence toward the survival frontier is not the default national trajectory: over three decades the typical country made no net progress toward the best decile of its own region, and pandemic-era divergence was only partly reversed. National gap trajectories separate system-wide quality shortfalls from extreme outliers warranting audit, and expose a measurement trap in which regions whose frontiers stagnate appear closer to best practice than they are.

Large genomically annotated acute myeloid leukaemia (AML) datasets exist, but population-based contemporary cohorts remain scarce. Here we report clinicopathological, genomic, and outcome data from Danish AML patients. 2,512 AML patients were identified between 2015-2022, of whom 33.8% had available NGS data (NGS+). In patients [&le;]70 years, baseline characteristics and outcomes were comparable between NGS+ and NGS- groups. In patients >70 years, more NGS+ patients received intensive treatment, but survival was similar among intensively treated patients. The distribution of mutations varied significantly by age and sex, with older age and male sex exhibiting higher frequencies of adverse-risk gene mutations. In intensively treated NGS+ patients, ELN2017 stratified 5-year OS: 58.4% (favorable), 43.4% (intermediate), and 28.2% (adverse), with hazard ratios (HRs) of 0.63 (favorable) and 1.45 (adverse) relative to intermediate. ELN2022 yielded corresponding OS rates of 56.9%, 51.8%, and 29.7%, with HRs of 0.78 and 1.86. The two models had comparable predictive performance for OS in a time-dependent model. In conclusion, outcomes of intensively treated AML patients were comparable irrespective of NGS status, underscoring the representativeness of the REFORM-AML database for the Danish AML population. Age and male sex correlated with adverse-risk mutations, and both ELN2017 and ELN2022 robustly predicted survival.

Background Childhood respiratory infectious deaths are partitioned across four Global Burden of Disease cause modules-26 etiological attributions within lower respiratory infections, tuberculosis, COVID-19, and whooping cough-never jointly reported. Whether the structure of this combined mortality spectrum has changed over time, and with what implications for intervention design, has not been quantified. We assembled and analyzed the integrated spectrum for children and adolescents aged 0-19 years, 1990-2023. Methods We integrated Global Burden of Disease Study 2023 (release v8352) estimates into a 29-node spectrum-26 lower respiratory infection etiologies plus tuberculosis, COVID-19, and pertussis-globally and across seven super-regions, with uncertainty propagated by summing bounds. We computed Shannon diversity, Herfindahl concentration, and effective cause counts; phenotyped pandemic-window collapse and rebound per cause; linked pathogen shares to WHO/UNICEF vaccine coverage; and mapped geographic concentration in sub-Saharan Africa and South Asia. Reporting follows GATHER. Results In 2023 the 29 causes jointly accounted for 965,330 deaths (95% uncertainty interval [UI] 680,096-1,342,437). Shannon diversity rose from 2.336 to 2.711 (+16.1%) between 1990 and 2023; the effective number of causes nearly doubled (5.57 to 9.94), inversely coupled to total deaths (Spearman rho = -0.997). Whooping cough ranked second (112,954 deaths; 95% UI 64,576-185,708; 11.7%) and showed the spectrum's only rebound above 100% (-57.4% collapse, +111.0% rebound). Tuberculosis ranked third (87,764; 57,779-124,912; 9.1%) with the highest concentration in sub-Saharan Africa and South Asia (87.1%). COVID-19 entered at rank five (52,899; 47,275-59,183; 5.5%). Nineteen of 29 causes exceeded the poverty-lock threshold (>80.59% of deaths in sub-Saharan Africa plus South Asia). Conclusions Childhood respiratory infectious mortality has become more diverse and more concentrated in poverty as it has declined. Single-pathogen interventions now address a shrinking share; the spectrum's structure argues for platform interventions-oxygen, antimicrobial access, referral-tailored jointly by age and geography, implying that pathogen-specific strategies alone cannot finish the remaining mortality agenda.

Background Childhood respiratory mortality in China has fallen by over 90% in three decades alongside sustained national warming, yet national long-run evidence on temperature and child respiratory mortality is lacking. Methods We linked Global Burden of Disease (GBD) 2021 mortality estimates for China - lower respiratory infections (LRI), ages 0-19, and asthma, ages 0-24, 1990-2021 - with C-LSAT 0.5 deg gridded temperature data (1990-2019), aggregated nationally and to five climate zones. Four annual indicators (mean temperature, diurnal temperature range, seasonal amplitude, interannual variability) entered regressions of log mortality rates with Newey-West standard errors. A bootstrapped (500 resamples) quadratic model probed the minimum mortality temperature (MMT), with PM2.5-adjusted analyses and future-exposure, permutation, and detrended falsification tests. Results LRI deaths fell by 96.3% (330,194 in 1990 to 12,098 in 2021; 95% uncertainty interval 9,669-14,891) and asthma deaths by 94.9% (3,287 to 167), while mean temperature rose 0.364 deg C per decade and diurnal temperature range narrowed 0.092 deg C per decade. Baseline coefficients were large (mean temperature -1.696, SE 0.174; diurnal temperature range +2.408, SE 0.336; seasonal amplitude -0.162, SE 0.082; interannual variability +2.924, SE 1.514, per 1 deg C in log rate), but the future-exposure test failed and detrending nullified every coefficient: the associations are trend-level, and short-cycle causal effects are not identifiable. Nor was the national MMT identifiable - observed temperature support spans only 6.66-8.13 deg C, and the nominal turning point of 35.84 deg C is an extrapolation artifact (quadratic term p = 0.963). Within the observed range, warming and declining mortality moved in the same direction. Conclusions The 96% decline in childhood respiratory mortality cannot be attributed to warming. China sits on the low-temperature side of the optimum, and the marginal direction of future warming requires stronger designs to establish. The falsification framework offers a discipline for climate-health inference in China.

Background In adults, cold-attributable mortality exceeds heat-attributable mortality roughly 17-fold. Child-specific evidence has begun to emerge only recently - a nationwide Brazilian case-crossover study located the minimum mortality temperature (MMT) for under-five deaths, and a 56-country survey-based analysis linked monthly temperature anomalies to under-five mortality - but no multi-country, climate-zone-resolved estimate of the childhood respiratory-infection MMT exists, and whether temperature variability is independently associated with childhood respiratory mortality at the global scale is unknown. We quantified both. Methods We combined Global Burden of Disease 2023 mortality estimates, lower respiratory infection (LRI) deaths at ages 0-19 years and asthma deaths at ages 0-24 years, 171 countries, 1990-2023 - with 0.5 deg monthly land temperature and diurnal temperature range (DTR) fields from C-LSAT/C-LDTR (1901-2023). Four exposure dimensions (annual mean, DTR, seasonal amplitude, interannual variability) entered two-way fixed-effects models with Driscoll-Kraay standard errors. A quadratic term in mean temperature located the MMT, with percentile confidence intervals from a 300-replication country-cluster bootstrap. Future-exposure leads, country-level detrending, and permutation tests assessed contemporaneous causality, applied to both the linear coefficients and the quadratic term generating the MMT; national pneumococcal conjugate vaccine (PCV3) coverage and ambient PM2.5 exposure series were added as time-varying mechanistic covariates. Results The childhood LRI MMT was 17.1 C (95% CI 14.7-19.8), the 36th percentile of the annual-temperature distribution; zone estimates were 24.7 C in tropical and 15.8 C in subtropical countries, with weak temperate and no subarctic identification. The quadratic term underpinning the MMT, however, failed both falsification checks - future temperatures reproduced the U-shape and country-level detrending erased it - so these MMT values describe a trend-level geographic pattern of the annual construct rather than a contemporaneous dose-response. Interannual temperature variability was positively associated with LRI (+0.278, 95% CI 0.102-0.454; p = 0.002) and asthma mortality (+0.836, 95% CI 0.447-1.226; p = 2.6 x 10^-5) per 1 C, but future-exposure models returned nearly identical significant coefficients and detrending erased significance, supporting only a trend-level association; adjustment for national PCV3 coverage and PM2.5 exposure left these estimates essentially unchanged. Annual mean temperature was likewise inversely associated with both outcomes at the trend level; DTR and seasonal amplitude showed no independent within-country effects. Conclusions This study provides the first multi-country, climate-zone-resolved geography of the optimal temperature for childhood respiratory survival, spanning 171 countries; because the underlying quadratic association is trend-level, the estimates are directional. The observed variability-mortality associations are trend-level signals rather than contemporaneous causal evidence; daily-scale, child-specific designs are required to determine whether short-term thermal variability affects paediatric respiratory mortality.

Wearable (accelerometer) data and machine-learning allow objective assessment of the amount of daily physical activity. However, wearable-derived human activity is subject to measurement error. No studies have corrected the dose-response association between physical activity and survival time to chronic diseases, including cardiovascular disease (CVD). The objective is to estimate the measurement error-corrected association between CVD events and multiple measures of daily duration of light and total physical activity, derived from machine-learning and conventional accelerometer-processing methods. Our method combined an accelerated failure time model, spline, and simulation-extrapolation (SIMEX). The method recovered the true dose-response non-linear association in simulated data, while the naive model failed to capture it due to substantial attenuation. Application to the UK Biobank accelerometer cohort also showed an increased protective association of total physical activity after SIMEX correction (Time Ratio [TR] = 1.56, 95% CI: 1.28-1.82 vs. TR = 1.38, 95% CI: 1.24-1.54 for SIMEX-corrected vs. uncorrected dose-response association between the 95th and 5th percentiles of total activity), with a similar increase for light physical activity. Sensitivity analysis indicates that the female population experiences a substantially larger protective association after SIMEX correction than males. Dose-response survival analysis is a widely used analytical method in physical activity epidemiology and benefits from measurement error correction.

Objectives To analyse the long-term effects of a cruciate ligament (CL) injury on health and socioeconomic outcomes. Methods We used a comprehensive national injury insurance database to identify CL injuries occurring in New Zealand between 2009 and 2022, and employed a doubly robust staggered difference-in-differences research design to identify the effects of these injuries on outcomes up to 10 years after injury. The outcomes of interest were healthcare use (hospitalisations, emergency department visits, medications, knee replacement surgery for osteoarthritis), associated healthcare costs, and labour market outcomes (employment rates, income, and government benefit payments). Results We identified 61 344 CL injuries for inclusion in the analysis. Over 10-year follow-up, a CL injury resulted in increased healthcare use (0.6 more hospitalizations [95%CI 0.4 to 0.7], 1.7 more days spent in hospital [95%CI 1.3 to 2.1], 0.4 more emergency department visits [95%CI 0.3 to 0.6], 2.5 more outpatient visits [95%CI 1.8 to 3.2], and 4.7 more medications dispensed [95%CI -1.8 to 11.2]) and public healthcare costs ($7 537; 95%CI 5 888 to 9 186), reduced income (-$6 060; 95%CI -11 644 to -475), and increased benefit payments ($1 152; 95%CI 542 to 1 761). Conclusion CL injuries have long-term impacts on healthcare use and socioeconomic outcomes. Strategies to reduce the incidence of CL injuries have the potential to realise large health and economic benefits.

Obesity is a significant public health concern. Early-onset obesity in the context of rare disease can reflect genetically-mediated pathology or elevated susceptibility through indirect mechanisms. Mapping the diverse characteristics and needs of young people with obesity in the rare disease population is a first step toward mechanistic and translational research. We carried out a retrospective comparative analysis of demographic, genotypic, phenotypic and health service utilisation data for young people with obesity (cases: n=500) and without obesity (controls: n=11,444) from the UK 100,000 Genomes Project rare disease cohort. Cases and controls were recruited prior to genomic diagnosis, across clinical disorder categories. We observed significant association between socioeconomic deprivation and obesity risk. Young people with obesity had significantly higher utilisations of acute care and mental health services, indicating an overall higher health burden. A curated panel of 519 candidate obesity-associated genes demonstrated aggregate association with obesity, although no single gene reached significance. Phenotypic comparison between cases and controls highlighted increased multi-organ and neurological system involvement, highlighting the overlap between neurodevelopmental and obesity risks. Within the case group, we conducted cluster analysis to identify early-onset obesity groups with different phenotypic profiles, potentially arising from different causal pathways - this identified six obesity subgroups of interest, with differing involvement of neurodevelopmental and other systems. Our study confirms that obesity co-occurs with a wide range of factors within the rare disease population, and is associated with significant physical and mental health needs, requiring holistic lifelong care.

Urban air pollution remains a significant public health concern, contributing to premature deaths and adverse health outcomes. However, there is little causal research evaluating the effectiveness of policies designed to improve air quality. This study assesses the impact of all three stages of London's Ultra Low Emission Zone (ULEZ) on air pollution, via PM2.5 levels, and respiratory health, via prescription records for bronchodilator and respiratory corticosteroid medications. Analyses are at general practice level, using a generalised synthetic control method to estimate causal impacts. Stage 1 was associated with a statistically significant but negligible 0.77% reduction in PM2.5 levels, with no corresponding change in prescribing. Stage 2 produced a paradoxical 2.69% increase in PM2.5, alongside a 4.44% decrease in inhaled corticosteroid quantity but a 12.51% increase in average daily quantity (ADQ) usage, suggesting a worsening of disease severity among existing patients. Stage 3 yielded a 2.69% PM2.5 reduction and a modest 2.18% decrease in bronchodilator ADQ usage. Spillover effects beyond the ULEZ boundary were statistically significant, but negligible. We find overall that the ULEZ had minimal effects on both air quality and respiratory prescribing across all three stages. These findings provide new insights into the effectiveness of ULEZ policies in reducing air pollution and its associated health impacts, suggesting the zone's effects are considerably smaller than previously reported, and that integration with broader policy measures may be necessary to achieve meaningful public health gains.

Background Severe acute respiratory infections (SARI) strain healthcare systems. Sentinel surveillance remains central to SARI monitoring, but routinely collected hospital discharge data offer a scalable, population-wide complement. In Finland, national registers now enable register-based surveillance, yet SARI case definitions remain unevaluated. Aim To evaluate whether routinely collected electronic health records can support register-based SARI surveillance and establish a national case definition. Methods We conducted a retrospective register-based study linking inpatient discharge data from the Finnish Care Register for Health Care (Hilmo) and laboratory-confirmed pathogen notifications from the National Infectious Diseases Register (NIDR). Admissions were aggregated into hospitalisation episodes using generic and pathogen-specific respiratory ICD-10 codes and linked to laboratory-confirmed respiratory pathogens within an admission-centred window. We assessed the impact of diagnostic coding position, laboratory linkage windows and alternative case definitions on age distribution, seasonality and epidemic trend detection. Results We included 145,435 respiratory hospitalisation episodes. Laboratory confirmations clustered around admission, and a -7-to-+3-day window was selected; 51,498 (35.4%) had a linked laboratory confirmation. Specific primary-position diagnoses preserved clear seasonality and age distributions consistent with SARI epidemiology, whereas secondary-position diagnoses showed attenuated seasonality. A combined case definition incorporating specific primary diagnoses and laboratory-supported syndromic episodes produced stable epidemic curves while improving sensitivity over laboratory confirmation alone. Conclusion National discharge and laboratory registers can support robust SARI surveillance in Finland when case definitions are carefully designed. A combined register-based definition balances specificity, sensitivity and feasibility, complementing sentinel surveillance and integrated respiratory monitoring. Keywords Severe acute respiratory infection (SARI); surveillance; electronic health records; ICD-10; case definition; Finland

Standard polygenic risk scores (PRSs) are constructed based on additive genome-wide association study (GWAS) summary statistics. Nonlinear machine learning methods have been increasingly applied to construct PRSs directly from individual-level data, with the aim of improving predictive performance over standard PRSs through their ability to model non-additive genetic effects. However, their superiority across studies has been inconsistent, and the conditions under which they provide meaningful improvements remain unclear. We combined theoretical analysis, simulations and a real-world application to investigate when two widely used nonlinear machine learning methods, random forest and XGBoost, outperform standard PRSs. Theoretical analysis showed that standard PRSs can implicitly capture part of the genetic variance attributable to nonadditive genetic effects through their contributions to marginal SNP effects, thereby losing less information than commonly assumed. Although nonlinear models have a higher theoretical potential, their greater flexibility incurs a bias-variance trade-off that can limit predictive gains at finite sample sizes. Simulations showed that XGBoost outperformed the standard PRS only when the genetic architecture involves a sufficiently large proportion of interaction genetic variance concentrated across relatively few interaction effects and large training samples were available. Random forest consistently underperformed the standard PRS. In an application to ischemic heart disease prediction using UK Biobank data, XGBoost showed no meaningful improvement in predictive performance over the standard PRS, whereas random forest again performed worse. Together, these findings suggest that nonlinear machine learning do not uniformly outperform standard PRSs; rather, their relative performance depends jointly on genetic architecture and training sample size. Our study helps to reconcile the inconsistent results reported across previous studies and provides a framework for identifying settings in which more complex PRS models are likely to be beneficial.

Communities exposed to flooding can experience markedly different mental health outcomes, yet conventional resilience indicators capture only part of the social and contextual conditions that may explain this variation. This study develops a multilevel and predictive framework for examining community resilience and depressive symptoms following flood exposure in Indonesia. Data were drawn from 20,303 respondents aged 15 years and older nested within 312 communities in the Indonesia Family Life Survey (IFLS-5). Depressive symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale (CES-D-10), with Rasch Partial Credit Model calibration used to examine measurement properties. Bayesian multilevel models quantified between-community heterogeneity and assessed how far observable structural resources accounted for this variation. Community resilience was represented through two complementary constructs: structural resilience, based on observable socioeconomic and social-capital resources, and Latent Community Protective Capacity (LCPC), a model-derived proxy for residual contextual variation in depressive-symptom risk. Approximately 6 percent of variation was attributable to between-community differences, while observable structural resources explained only part of this heterogeneity. Structural resilience and LCPC were weakly correlated (r = 0.155). Moderation analyses provided no clear evidence that structural resilience altered the flood-depression association, while LCPC showed a directionally consistent but uncertain buffering pattern. Predictive models incorporating community-level information improved discrimination, with the best-performing model reaching an ROC-AUC of approximately 0.71. The findings suggest that observable resource-based indices provide an incomplete account of community-level mental health vulnerability and that residual contextual measures may provide complementary information, while requiring cautious interpretation and independent validation.

Background: Evidence suggests chronic distress influences ovarian cancer (OC) etiology and metabolomic profiles. Here, we evaluated the association of a metabolite-based distress score (MDS) and OC risk. Methods: We included two matched case-control studies nested within the Nurses' Health Studies (N=584) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N=348). Metabolites were measured 3-27 years before diagnosis using liquid-chromatography tandem mass spectrometry. We examined the association of quintiles of MDS and 19 constituent metabolites with OC risk using unconditional logistic regression and stratified by tumor histotype, menopausal status, and age at diagnosis. Results: We observed women in the highest versus lowest quintile of MDS had an increased OC risk (OR=1.62,95%CI=1.03-2.54,ptrend=0.07), and type 2 tumors (OR=1.71,95%CI=1.03-2.83,ptrend=0.11). Associations were suggestively stronger for premenopausal and <69-year-old women, and driven by pseudouridine, and N2,N2-dimethylguanosine. Conclusion: Our findings suggest chronic distress-associated metabolic dysregulation may represent a novel OC risk factor, especially among younger women.

Background Phenotypic age acceleration (PhenoAgeAccel), derived from PhenoAge, and MetaboHealth are composite exposures of biological ageing and metabolic health associated with dementia-related outcomes. Whether these associations are causal and reflect the exposures, constituent biomarkers, or both remains unclear. Methods This study included UK Biobank participants of White British genetic ancestry. MetaboHealth was derived from nuclear magnetic resonance (NMR) metabolomics and PhenoAgeAccel from clinical biomarkers and chronological age. Genome-wide association studies (GWAS) were conducted for MetaboHealth (n=272,568) and PhenoAgeAccel (n=274,077). Independent genome-wide significant variants were used as genetic instruments in two-sample Mendelian randomisation (MR) with FinnGen all-cause dementia summary statistics. Inverse-variance weighting was the primary MR method. Causal network analysis estimated relationships among constituent biomarkers and dementia. Findings GWAS identified 126 and 141 independent genome-wide significant variants for MetaboHealth and PhenoAgeAccel, of which 109 and 141 were retained as genetic instruments. MR found no evidence of a causal effect of genetically predicted MetaboHealth (per unit: OR 0.83, 95% CI 0.49-1.42; p=0.51) or PhenoAgeAccel (per year: OR 0.99, 95% CI 0.95-1.02; p=0.44) on all-cause dementia, with consistent findings across sensitivity analyses and robust MR methods. Lower lymphocyte percentage and higher NMR-derived glucose had direct relationships with dementia in the joint constituent-biomarker network. Interpretation MR provided no evidence that either composite exposure causally influenced dementia. The network prioritised lymphocyte percentage and NMR-derived glucose, supporting examination of composite exposures alongside their constituent biomarkers. Funding NIHR, UKRI, MRC, UK Dementia Research Institute, Innovate UK, and European Union. Full funding details are provided in the acknowledgements.

Confounding is a central challenge in observational studies. Here, we propose a framework for identifying confounders of two non-causally related traits by employing cross-trait pleiotropy analysis to detect genetic loci that affect both traits and multi-trait colocalisation to identify molecular phenotypes mediating these effects. We apply this approach to the analysis of C-reactive protein (CRP) - a non-specific marker of inflammation - and 10 inflammation-related cancers. In UK Biobank, higher pre-diagnostic CRP levels are associated with increased risk of multiple cancers, but bidirectional Mendelian randomization provides little evidence for a causal relationship. Cross-trait genetic analyses identify 92 loci with shared CRP-cancer effects including those with established roles in cancer and 50 novel loci such as RSPO3 (breast cancer) and GCKR (colorectal cancer). Integration with proteomic and single-cell transcriptomic data identified putative molecular mediators at 24 loci including plasma TLR1 levels in breast cancer and CD4+ T cell IRF5 expression in kidney cancer. Notably, 15 candidate effector genes encode targets of approved or investigational medications, including IL6, PDE4D, and CASP8, indicating potential opportunities for their repurposing for cancer prevention. The proposed approach provides a generalisable framework for leveraging non-causal phenotypic relationships to yield insights into disease mechanisms and therapeutic targets for disease prevention.

Unmeasured confounding threatens causal inference and replicability in observational multi-omic studies across variable environments. Genetic instrumental variables (Mendelian randomization) and negative-control calibration each address complementary sources of unmeasured confounding, yet no existing framework unifies them for omics-scale mediation analysis. We introduce ICONIC, an R package that embeds genetic instruments and negative controls within a proximal causal inference framework for total-effect and mediation analysis. ICONIC implements eight estimators spanning five confounding-control strategies, supports continuous, binary, and time-to-event outcomes, and provides extensive diagnostics including sensitivity analyses that map estimator performance across plausible assumptions. Ground-truth benchmarks are calibrated to real-omics covariance structures via a hybrid generative model (GAN + feature-level Gaussian copula) rather than parametric simulation, and a companion planning tool predicts performance gains from collecting additional omic data. We demonstrate ICONIC in two case studies: identifying placental transcriptomic mediators of gestational diabetes on birth weight (n = 164), and tumor-expression mediators of smoking intensity on lung cancer survival (n = 494). Notably, ICONIC's diagnostics recommended different estimation strategies across the two scenarios, reflecting differences in the likely influence of unmeasured confounding. ICONIC is freely available at https://github.com/sbresnahan/iconic/.

Background Respiratory infections remain the leading infectious cause of death among children and adolescents, yet the share of these deaths that could be averted with currently feasible care is not routinely quantified. Existing amenable-mortality frameworks rely on cause lists and population-level mortality benchmarks and do not exploit information on how many episodes occur. We propose an episode-fatality-ratio (EFR) frontier approach and apply it to lower respiratory infections (LRI), whooping cough (pertussis) and upper respiratory infections (URI) in 204 countries, 1990-2023. Methods For each cause, country and year we computed EFR = deaths/incident episodes using Global Burden of Disease (GBD) 2023 estimates for ages 0-19 years. The frontier was defined as the 10th-percentile country EFR within each GBD super-region, cause and year; avoidable deaths = max(0, deaths - episodes x frontier EFR). Primary estimates are deterministic; 95% uncertainty intervals (UIs) come from 2,000 Monte Carlo draws. Sensitivity analyses varied the frontier percentile, applied an aspirational global frontier, constructed pertussis counterfactuals, and recomputed all estimates within the single under-5 age band. Results In 2023, 333,803 childhood deaths from lower respiratory infections (95% UI 289,123-417,460; 46.9% of LRI deaths) were avoidable. Summing the three causes deterministically gives 391,034 avoidable deaths (46.5% of 840,444); the combined figure is a deterministic sum, and a UI is available for the LRI component only. The pertussis (43,958; 39.0%) and URI (13,273; 81.0%) estimates are secondary: their deterministic point values fall below their own Monte Carlo intervals and the underlying death estimates carry very wide uncertainty (global pertussis UI 12,545-321,874). Avoidable deaths fell from 1,050,468 (44.9%) in 1990, but between 2019 and 2023 the avoidable share for LRI+URI barely moved (48.7% to 47.7%) while absolute avoidable deaths fell 14.5%, a pattern consistent with stalled convergence to the frontier. Sub-Saharan Africa plus South Asia held 73.1% of avoidable deaths in 2023 versus 41.8% in 1990; ten countries accounted for 59.1%. Conclusion Nearly half of childhood respiratory-infection deaths remain avoidable relative to within-region best practice, and the residual burden is increasingly concentrated in low-income settings. In the pertussis counterfactual, most countries kept pace with their regional frontier, so further gains require advancing the frontier itself through quality-of-care improvements.