Demographic Research

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.

People with Down syndrome have higher age-specific mortality rates compared to the general population as well as peers with other intellectual and developmental disabilities. While a large proportion of mortality is attributable to Alzheimers disease, many die prior to Alzheimers diagnosis and some live to old ages, dying without Alzheimers. Our objectives were to use 11 years of Medicaid and Medicare data to describe characteristics and factors related to death in adults with Down syndrome and use machine learning to identify which conditions most strongly predict death in the full population and stratified by age. We identified death using Center for Medicare and Medicaid Systems reported date of death health conditions using ICD 9 and 10 codes. We used a case-control design with risk set sampling to have that controls to mimic the distribution of times of incident Alzheimers disease. We trained gradient boosted trees to identify strongest predictors. Our cohort included 137,293 adults with Down syndrome. Among those, 30,894 (22.5%) died during the study period. Mean age at death among those who died was 55 years (SD=10). Mean age of death in those with Alzheimers disease was 59 (SD=7) and those without was 52 (SD=12). The most influential predictors of mortality were any claim for dementia, any claim for pneumonia, re-occurring claim for cardiovascular disease three years before index death, and any claim for heart failure and epilepsy. Our results align with previous clinical work and highlight intervenable areas to reduce mortality in the Down syndrome population.

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.

Objectives: We tested how multi-level socioeconomic disadvantage relates to biological aging and systemic inflammation in women and men from the population-based Canadian Longitudinal Study on Aging (CLSA). Methods: We examined cross-sectional data from 8,516 CLSA participants with baseline measures on systemic inflammatory biomarkers (C-reactive protein, interleukin-6, and tumoral necrosis factor-) and biological aging (metabolomic and six DNA methylation [DNAm] age estimates). Plasma samples underwent metabolomic profiling by Metabolon, Inc. Metabolomic age was estimated separately in males and females using sex-stratified models based on age-correlated metabolite levels. DNAm data generated using the Illumina Infinium MethylationEPIC v1.0 array were used to estimate DNAm age across six established models, including Horvath, Hannum, PhenoAge, GrimAge, GrimAge2, and DunedinPACE. We used log-transformed metabolite levels to calculate metabolomic age by sex. We linked education, income, material and social deprivation to biomarkers of systemic inflammation and biological aging stratified by sex using generalized linear models. Multivariable models were adjusted by age, major behavioral risk factors, and chronic conditions. Results: Participants were aged on average of 62.6 years of age, and approximately 50% were females. In multivariable linear adjusted models, we found that in comparison to those earning [&ge;]$100K a year, women earning less <$20K were on average 1.14 (95%CI: 0.46, 1.82) year older with respect to metabolomic age; those earning [&ge;]$20K & <$50K were on average 0.90 (95%CI: 0.26, 1.53) years older; and those earning [&ge;]$50K & <$100K were on average 0.70 (95%CI: 0.05, 1.34) years older. We did not observe this dose response among men. A similar dose-response association was observed for interleukin-6 in both men and women. Discussion: These findings suggest that socioeconomic adversity influences not only inflammatory pathways but also distinct biological aging processes, including metabolomic aging.

To estimate the life expectancy gains that could be realised from increases in Queenslanders physical activity (PA) levels. Design Lifetable analysis Setting, Participants We modelled the 2025 Queensland population aged [&ge;]40 years. Modelled scenarios We applied two approaches. In the first, we estimated life expectancy differences between device-measured PA quartiles, with quartile1 representing the least active and quartile 4 the most active. In the second, we compared observed device-measured PA levels in Queensland with scenarios in which all individuals moved to either [&ge;]12,000 steps/day or [&le;]2,000 steps/day. We converted the steps per day by age group and PA quartile into equivalent daily minutes of moderate-intensity walking at 4.8 km/h. Additional scenarios were explored in sensitivity analyses. Main outcomes Changes in life expectancy, and total life-years gained over the lifetime of the modelled population. Benefits were also translated into minutes of life gained per additional hour walked. Results If all Queenslanders aged [&ge;]40 years were as active as the most active quartile, life expectancy at birth could be 88.3 years, an increase of 4.8 years above the life expectancy at observed activity levels. The life expectancy differences between individuals in the least active quartile and the most active quartile was 9.7 years. Achieving the activity level of the most active quartile would require individuals in the lowest activity quartile to undertake an additional 85.9 minutes/day of moderate-intensity walking, with each extra hour of PA associated with an average gain of approximately 3 hours (177 minutes) of life. In step-based modelling, life expectancy in the most active scenario (all achieving [&ge;]12,000 steps/day) was higher by {approx}7.1 years compared with the least active scenario (all at [&le;]2,000 steps/day). Conclusions Increasing PA could yield meaningful gains in life expectancy for Queenslanders, with the largest gains seen in least active individuals. Our findings strengthen the case for prioritising investment in PA -promoting programs and environments.

The basic reproduction number R0 confounds pathogen biology with adaptive human contact behavior. Earlier epidemiological--economic theory predicted a forward-looking behavioral contact response but could not test it in the absence of appropriate behavioral data. Using directly measured mobility as an observable proxy for contact, we (i) estimate the behavioral response function directly from data; (ii) show that the biology/behavior decomposition and hence the behavioral correction to R0 is not identified from an epidemic trajectory, the apparent constant-contact R0 being one endpoint of an observational-equivalence class that fits the factual curve identically yet diverges under counterfactual; and (iii) characterize that divergence ("what R0 deletes") as state-dependent, unimodal in counterfactual severity and vanishing when behavior saturates. We then show that, across US jurisdictions, the correction is empirically bounded because risk-responsiveness and behavioral non-saturation are confounded (r=-0.57, n=51): where behavior could compensate, it was already maximal, and where it was not maximal it did not respond. What R0 deletes is thus real and structurally characterizable yet empirically modest here, for reasons the framework itself supplies.

Social relationships are an important social determinant of health. Loneliness, the perceived gap between one's actual and desired relationships, has emerged as an important mechanism through which social relationships impact health. Like other intrapersonal-level factors associated with health, loneliness is influenced by broader social and structural factors, including characteristics of one's neighborhood social environment. Although neighborhood-level protective and risk factors for loneliness and for mental health have been identified, prior studies have often focused solely on self-reported perceptions of the neighborhood environment. Further, few have considered aspects of the neighborhood social environments, such as neighborhood stability (i.e., stability of the community with long or short-term residents), independent of neighborhood socioeconomic conditions. In the current analysis, we explored longitudinal patterns of loneliness in conjunction with neighborhood stability among women with HIV (WWH) enrolled into the MACS/WIHS Combined Cohort Study (MWCCS) from 2014-2019 (N2019=1,394) to examine whether trajectories of loneliness and neighborhood stability were associated with depressive symptoms, non-prescription substance use, past-year cannabis use, number of alcoholic drinks per week, and several domains of quality of life. Loneliness at baseline (Betas = 0.24 - 0.54) and changes in loneliness over time (Betas = 0.11 - 0.26) were associated with each outcome, except for the association between changes in loneliness over time and drinks per week (Beta=0.13, p = 4.14x10-2), which did not persist after correcting for multiple comparisons. Neighborhood stability at baseline was associated with past year cannabis use (Beta=0.26, p = 1.00x10-2), depressive symptoms (Beta=-0.12, p = 1.54x10-3), and overall self-reported health (Beta=-0.08, p = 2.05x10-2). Changes in neighborhood stability across time were not associated with any outcome. Neighborhood stability moderated the association between changes in loneliness and general health perceptions. Our results demonstrate both overall loneliness and changes in loneliness over time have implications for current mental health in WWH, while changes in neighborhood stability did not.

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.

This study focuses on the relationship between access to Advanced Neonatal Care (ANC) and fertility across the regions in Ghana between 1988 and 2022. It builds on previous studies focused on inequity in maternal health across subnational levels and incorporates spatial analytics, machine learning, and a welfare-adjusted fertility care metric. Nine waves of the Ghana Demographic and Health Survey (DHS) were analyzed, with 94 region-by-year units across 8 to 16 regions in each survey wave in the 16 Ghana administrative regions. Skilled ANC along with the Total Fertility Rate (TFR) and demographic control variables were extracted for the analysis. The methodologies employed include decomposition of the Gini coefficient of inequality, bivariate z-score risk stratification, Random Forest (RF), and Decision Tree (DT) regression, partial dependence, Local Indicators of Spatial Association (LISA), global Moran's I with permutation inference and a novel Care Efficiency Index (CEI = ANC% / TFR). Care for the outcomes employed region aggregations along with district boundary geometries for the display of the choropleth maps. National skilled ANC coverage increased from 83.1% (1988) to 97.7% (2022), with inter-regional Gini declining 87.9% (0.070 to 0.008). The North-South gap narrowed from 32.4 to 0.9 percentage points. Northern region showed the greatest absolute gain (+43.0pp). Machine learning identified an exploratory RF partial-dependence inflection near TFR=5.90, above which predicted ANC coverage declined in the historical data. Survey year was the dominant RF predictor (43.7%), followed by TFR (38.8%). TFR spatial clustering intensified by 2022 (Moran's I=0.606, p=0.001). Greater Accra led the Care Efficiency Index (CEI=31.9); Northern Belt regions lagged (CEI=14.5-16.5). Risk stratification classified 23 observations as Critical (Low ANC/High TFR), predominantly from Northern Belt regions in earlier survey waves. ANC coverage converged substantially, yet fertility-related spatial inequities persisted, especially in the Northern Belt. The Care Efficiency Index and exploratory TFR inflection provide hypothesis-generating tools for targeting health-system investment. They should not be interpreted as causal thresholds.

In this paper, we propose a new model to estimate the impact of an intervention on human hosts of a vector-borne infection, such as dengue, which occurs in yearly outbreaks of different magnitudes. The model applies to these outbreaks and, in fact, is independent of their intensity, that is, it does not require the steady-state assumption. The model takes as input the officially reported age-dependent number of cases of a vector-borne infection. It is deterministic and does not account for stochasticity. Our objective is to estimate the impact of the intervention (the efficacy), and we rely on the observed fact that the age distribution of the proportion of cases of the infections transmitted by the same vector is independent of both the intensity of transmission and the geographic area studied, at least for Brazilian regions. This finding is highlighted in the main text and forms the basis of our calculations. A hypothetical intervention is simulated using a dengue vaccine, which allows the determination of the optimal strategy for a vaccination campaign.

An epidemic's expected course is determined by the magnitude and timing of a typical person's infectiousness --- captured, in turn, by the basic reproduction number and the generation-time distribution. These fundamental, population-average quantities can mask individual-level variation that shapes how an epidemic actually unfolds: for example, individual variation in the magnitude of infectiousness (overdispersion) creates superspreading, a key feature of the SARS-CoV-1 and SARS-CoV-2 epidemics. However, the impact of individual variation in infectiousness timing is less well understood. Here, we demonstrate that individual infectiousness timing varies substantially and to different degrees across pathogens. For some common pathogens, including influenza, measles, and SARS-CoV-2, infectiousness is "bursty", or highly concentrated and variably-timed across individuals: for example, the window of appreciable infectiousness for SARS-CoV-2 may last for roughly a day, vs. the 9--12 days usually quoted. We show that bursty infectiousness creates superspreading without inherent superspreaders, makes epidemic timing more variable, amplifies the time-sensitivity of common interventions, and complicates inference of key epidemiological parameters. Together with the reproduction number, the generation-time distribution, and overdispersion, burstiness completes a family of basic parameters that govern how epidemics unfold.

Aims: The COVID-19 pandemic has significantly impacted global mental health. At the onset of the pandemic (2020), Belgians experienced increased anxiety, depression, and psychological distress compared to 2018 due to the outbreak and the associated public health measures. Understanding the drivers of this distress is crucial for mitigating mental health effects in future crises. This study examines determinants of psychological distress in Belgium during the March 2020 lockdown, using data from the Great Corona Study (GCS). Methods: Data were drawn from the second wave of the GCS, a citizen science initiative conducted in Belgium on March 24, 2020, with 332,169 respondents. Psychological distress was measured using the General Health Questionnaire-12 (GHQ-12), applying a 2/3 cutoff to classify distress levels. To identify predictor variables, a random forest algorithm and literature review reduced 207 initial variables to 16. A generalized linear model was then used to examine associations between predictors and psychological distress Results: Psychological distress was significantly associated with various demographic, social, occupational, and health-related factors. Younger individuals, women, and residents of Wallonia or Brussels exhibited higher odds of distress. Household composition, and the frequency of real-life social interactions significantly influenced distress levels. Occupational status played a key role, with part-time employees and working students exhibiting higher levels of distress. At the same time retired individuals with no current occupation showed lower odds. Perceived workplace safety and compliance with public health measures also significantly impacted distress levels. Lastly, individuals experiencing influenza-like or COVID-19 symptoms had substantially higher odds of psychological distress. Conclusions: Our findings highlight significant sociodemographic, occupational, and health-related predictors of psychological distress during the initial COVID-19 lockdown in Belgium. Young adults, women, individuals with limited in-person interactions, and those experiencing influenza-like illness or COVID-19 symptoms were particularly vulnerable. Additionally, perceptions of others' adherence to preventive measures played a crucial role in mental well-being. These results highlight the complex interplay between individual and environmental factors in shaping psychological distress, providing valuable insights for future public health policies and mental health interventions during crises.

Controlled human infection models (CHIMs) are increasingly used in infectious disease research to study pathogen dynamics and evaluate interventions under controlled conditions. However, these studies are resource-intensive and involve ethical and safety constraints, making efficient study design critical. Dose-finding is a key early component in CHIMs, where the aim is to identify a challenge dose that achieves a target infection probability. Traditional rule-based designs are commonly used but can be inefficient, motivating the use of model-based adaptive approaches such as the Bayesian Continual Reassessment Method (CRM). Although CRM has been extensively studied and widely adopted in Phase I oncology trials for identifying the maximum tolerated dose of therapeutics, its application in CHIM settings remains limited, particularly when the endpoint of interest is infection. This tutorial provides step-by-step guidance for implementing a Bayesian CRM in dose-finding CHIMs, using an oropharyngeal Neisseria gonorrhoeae challenge as a motivating case study. The framework outlines key design components, including dose-grid specification, dose-response model, prior elicitation, Bayesian updating, decision rules, and stopping criteria, with particular emphasis on a clinically interpretable parameterisation. Trial operating characteristics are evaluated through simulation studies under multiple dose-response scenarios and prior-predictive analyses, and compared with a commonly used '3+3' type rule-based design. This work highlights the advantages of Bayesian model-based designs for dose-finding in CHIMs over classic rule-based designs and provides a structured, reproducible framework for implementing CRM, supporting their application in future CHIM studies.

Background and Aims The optimal treatment for first episodes and first recurrences of Clostridioides difficile infections (CDI) is unknown and there is emerging evidence for pulse and taper (P-T) regimens. Therefore, we sought to estimate the relative efficacy of treatment options. Methods MEDLINE and CENTRAL were searched from database inception to May 21, 2025 and unpublished conference abstracts were searched from recent infectious disease conferences. RCTs on the treatment of first episodes or first recurrences of CDI comparing fixed-dose or P-T regimens of fidaxomicin or vancomycin were included. The primary and secondary outcomes were 40- and 56-day CDI recurrence, respectively. A random-effects network meta-analysis on the risk ratio (RR) scale was conducted using a standard regimen (10-14 days) of vancomycin as the comparator. Treatments were ranked using the surface under the cumulative ranking curve (SUCRA). Results 8 RCTs were included comprising a total of 2181 patients. For 40-day recurrence, fidaxomicin P-T had the highest probability of ranking best (RR=0.10, 95%Confidence Interval [95%CI]=0.10-0.49, SUCRA=1.00), followed by vancomycin P-T (RR=0.49, 95%CI=0.32-0.76, SUCRA=0.61), fixed-dose fidaxomicin (RR=0.61, 95%CI=0.49-0.76, SUCRA=0.39), and, finally, fixed-dose of vancomycin (SUCRA=0.00). The treatments ranked in the same order for 56-day recurrence, though only 3 RCTs reported on this timepoint. Conclusion Vancomycin P-T, fidaxomicin P-T, and fixed-dose fidaxomicin were all superior to a fixed-dose vancomycin. Head-to-head comparative effectiveness RCTs are needed to quantify their relative effect sizes of and impact on long-term prevention of recurrent CDI.

Background The 2022-2023 global mpox outbreak highlighted the critical need for robust genomic surveillance capabilities to track mpox virus (MPXV) evolution and transmission dynamics. Methods Building upon our established SARS-CoV-2 sequencing infrastructure, we implemented a Molecular Loop probe-based long-read sequencing approach using Pacific Biosciences Sequel II technology for comprehensive MPXV genomic surveillance across the United States (US). From August 2024 to June 2025, we generated 326 high-quality whole genome sequences from residual mpox-positive clinical specimens collected by Labcorp across all 10 US Department of Health and Human Services regions. Results Our analysis identified two samples containing clade Ib MPXV in January and June 2025 and captured shifting trends in clade IIb diversity, with 13 distinct lineages observed. We also identified multiple instances of large (~1.6-17.6kb) deletions proximal to the inverted terminal repeats in clade IIb genomes. APOBEC3 mutation analysis indicated substantial evidence of human-to-human transmission among both clades. Further, we observed significantly higher APOBEC3-associated SNPs per kilobase (P<0.001) in clade IIb genomic variable regions relative to their central conserved region. Our assay exhibited strong reproducibility across biological replicates from individual patients and accuracy was confirmed via parallel sequencing of select specimens by US Centers for Disease Control and Prevention (CDC) using metagenomic sequencing. We also demonstrated via custom simulation that our assay discriminates all known MPXV clades and lineages, including those we have not observed in the US. Conclusions Our integrated nationwide surveillance system facilitates real-time genomic tracking of outbreak evolution, with demonstrated capacity across SARS-CoV-2 and MPXV, positioning this platform for rapid deployment during future pathogen emergence.

Background: Monoclonal antibodies have emerged as a prophylactic strategy to prevent symptomatic SARS-CoV-2 infection in immunocompromised individuals. However, the evolutionary and clinical implications of breakthrough infections under this regime remain unclear. Methods: A male in their 80s with a haematological/oncological diagnosis received a 2000 mg intravenous infusion of sotrovimab in March 2023 and was diagnosed with COVID-19 by RT-qPCR from a nasopharyngeal swab in August 2023. Weekly samples (n=24) were collected through February 2024 (171 days). All samples underwent whole-genome sequencing, with select mutations subjected to functional assessment. Findings: Sequencing identified the GE.1 lineage at all timepoints. An intra-host recombination event in ORF1ab (positions 8942-12458) was detected prior to 23 weeks post-detection, followed by a 14-fold increase in viral load (7.42e+06 to 1.00e+08 RNA copies/mL) and a marked shift in the viral population. E340D, a sotrovimab resistance mutation, was detected at low abundance (46%) within the first week post-infection, fluctuated over time, and was nearly fixed by week 15 (107 days) post-detection. We assessed five spike mutations - V36M, S98F, and V213G in the N-terminal domain, Y505P in the receptor-binding domain, and P681Q near the S1/S2 cleavage site - and additionally evaluated the impact of E340D. V36M conferred the highest infectivity across all cell lines, with the most significant effect in low-TMPRSS2 cells. While all mutations showed enhanced infectivity with the addition of E340D, the effect was most pronounced in mutations with lower baseline infectivity. The addition of E340D significantly decreased relative neutralizing titres for V36M, S98F, and V213G, enabling escape from neutralizing antibodies in XBB-responsive individuals, illustrating an enhanced phenotypic advantage. Patient neutralizing activity was absent pre-sotrovimab, and sotrovimab-induced neutralization was further compromised by selection of E340D. Interpretation: Sotrovimab pre-exposure prophylaxis in an immunocompromised patient did not prevent SARS-CoV-2 infection, and selected for resistant mutation E340D, with unexpected fitness consequences across non-receptor binding domain spike regions.

Background: Opioid use disorder (OUD) is heritable, yet most genome-wide association studies (GWAS) have focused on European populations, leaving the genetic architecture of OUD in non-European populations underexplored. Methods: We conducted GWAS of OUD across three ancestries using electronic health records and genomic data from 52,357 All of Us Research Program participants (8,912 cases; 43,445 matched opioid-exposed controls; 48.5% female). Participants were stratified into European (EUR), African (AFR), and Admixed American (AMR) ancestry groups for logistic regression GWAS, with independent replication in the Million Veteran Program. We then applied the deep-learning model AlphaGenome to predict the tissue-specific transcriptomic and splicing consequences of top risk variants across 13 reward-pathway brain regions. Results: We identified and replicated a novel DDX6 risk locus, alongside established OPRM1 and FURIN signals. AlphaGenome predicted the DDX6 regulatory allele downregulates the stress-resistance gene FOXR1 in the nucleus accumbens, while the protective OPRM1 variant (rs1799971) upregulates OPRM1 expression across reward networks. Other signals of interest included IL6R and SHISA9 (EUR); GHR (AFR); and ASTN2 (AMR). Conclusions: This study identifies DDX6 as a novel OUD risk locus, replicates associations with OPRM1 and FURIN, and highlights biologically plausible ancestry-specific signals in AFR and AMR populations. We also replicated top variants in an independent population. Finally, integrating GWAS with deep-learning annotations provides specific, localized biological hypotheses to guide future experimental validation and targeted therapeutics.

The World Health Organization (WHO) validated Sri Lanka had eliminated lymphatic filariasis as a public health problem in 2016, the second country in Southeast Asia to attain this status. However, post-validation surveillance has identified sporadic cases of brugian filariasis. The reemergence of Brugia malayi infections in Sri Lanka warrants urgent investigations. Recent studies have shown that the parasite responsible for the reemergence is a novel zoonotic Brugia sp. maintained among dogs that is closely related but distinct to the human-infecting B. malayi species. The current study employed morphological and morphometric assessments, revealing that this novel zoonotic Brugia sp. is within the B. malayi morphological range. Molecular characterization of three genomic regions, the nuclear genomic region SLXI, the non-coding region HhaI, and the mitochondrial genomic region COXI confirmed it as a genetic variant more closely related to B. malayi than to B. pahangi. Phylogenetic analysis further indicated it as a distinct genomic variant, closely related to a B. malayi-like parasite reported from India. Notably, that same parasite was identified in infected humans, animals, and potential vector mosquitoes. This, together with the detection of both human and animal blood within the same brugian infective mosquitoes, and delineating the canine origin of the parasites in human infections, provides compelling evidence supporting zoonotic transmission of this parasite. To our knowledge, this is the first report demonstrating the presence of the same brugian parasite in humans, domestic animals, and potentially infective mosquitoes in Sri Lanka, supported by multi-genomic evidence. The recent identification of multiple potential mosquito vector species suggests that this parasite may have undergone adaptive changes, facilitating its ability to overcome the species barrier. These findings substantiate the long-held hypothesis of zoonotic transmission of the reemerged brugian parasite, highlighting significant implications for ongoing surveillance and control strategies.

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.