Frontiers in Applied Mathematics and Statistics

Researchers must often rely on observational data to draw conclusions about the causal relationship between an exposure and an outcome. This is common in environmental epidemiology research, where the assignment to an exposure can be unethical or impossible. Consider a setting in which we seek to estimate excess mortality during heat waves, but are unable to randomize individuals to moderate or extreme heat. We propose to follow a causal framework for non-randomized time series that uses what we call historical time series matching to construct hypothetical randomized experiments, in which background covariates are balanced. We applied this framework and assessed the following causal question: Are daily mortality counts among elderly Americans higher during hot versus warm summer time periods? We addressed this causal question by drawing frequentist, Fisherian, and Bayesian inferences for five American cities. We estimated the average treatment effect of heat on three-day mortality with 95% Fisherian intervals to be 0.5 [-3.6, 4.4] deaths in Pittsburgh, 3.7 [0.7, 6.6] deaths in Seattle, 12.5 [6.1, 18.9] deaths in Chicago, 8.5 [1.4, 15.6] deaths in Los Angeles, and 20.3 [11.2, 29.5] deaths in New York.

In many natural dynamical systems, tipping points emerge where slowly changing external factors induce shifts from one state to another, known as critical transitions or bifurcations. In epidemic systems, detecting these transitions is important for outbreak preparedness. Various statistical indicators like lag-1 autocorrelation and variance have been employed as early warning signals (EWS) of critical transitions by showing how the dynamics of the system exhibit critical slowing down (CSD) near the transition. More recently, deep learning-based algorithms have been developed that attempt to more accurately detect and classify bifurcations; however, they are limited by the systems simulated in the training dataset, the length of the time series, and have not been rigorously evaluated on real-world epidemic data spanning multiple geographic regions. In this paper, we evaluate existing deep learning-based bifurcation prediction models, on a CDC-curated dataset containing weekly influenza hospital admissions across all fifty U.S. states and the nation as a whole. We develop a pipeline for data preparation, reproduction number (Rt) estimation using the EpyEstim package (a Python implementation of the EpiEstim framework [4]), and bifurcation point identification. We systematically evaluate the models under several windowing strategies, including rolling, expanding, and fixed-length windows, and report accuracy, precision, recall, F1 score, and specificity. Our best-performing configuration (expanding window, length 100) achieves an accuracy of 90.09%, precision of 72.86%, recall of 96.23%, and F1 score of 82.93%. We additionally discuss Markov Regime Switching models as an alternative framework for detecting epidemic transitions. Our findings indicate that these models can generalize to real-world, state-level influenza data and are capable of online/real-time prediction.

Background: Quantifying behavioral drivers of protective action during infectious disease outbreaks remains challenging. App-based experimental epidemic games (epigames) provide a framework for studying these decisions in naturalistic settings, but correspondence between in-game measures and real-life beliefs or behaviors requires evaluation. We examined whether an activity-based opportunity cost influenced voluntary quarantine decisions within an epigame and whether real-life health beliefs transferred into the game and predicted observed choices. Methods: We conducted a two-week randomized controlled trial from May 4 to May 17, 2026, at VinUniversity (VinUni) in Hanoi, Vietnam. Participants using the Epigames app installed in their smartphone were randomized to a low-barrier group, earning 1 point per qualifying contact with other participant when remaining active, or a high-barrier group, earning 2 points per contact. Of 439 enrolled participants, 283 were successfully randomized to one of the two groups, made at least one valid daily quarantine decision, and their data was used in the analysis. Real-life (S1) and in-game (S2) beliefs measured susceptibility, severity, quarantine self-efficacy, and perceived benefits. Belief correspondence was assessed using Holm-adjusted Spearman correlations and prospective cumulative-logit models. Quarantine choices were analyzed using participant-day binomial generalized estimating equations (GEE) and participant-level quasi-binomial models. Results: Participants made 1,823 valid daily decisions and chose quarantine on 458 participant-days (25.1%). Quarantine rates were 30.1% in the low-barrier group and 20.2% in the high-barrier group. The high barrier was associated with 41% lower population-averaged odds of quarantine (GEE odds ratio [OR] 0.59, 95% confidence interval [CI] 0.40-0.86, = 0.0059); the quasibinomial estimate was identical (OR 0.59, 95% CI 0.40-0.86, = 0.0055). Among 251 participants with matched S1-S2 data, all four corresponding beliefs were positively associated (Spearman's = 0.259-0.418; all Holm-adjusted < 0.001). Prospective ordinal models confirmed positive matched-domain associations (ORs 1.39-2.18 per one-point S1 increase). Beliefs showed little robust association with quarantine behavior, and neither S1 nor S2 beliefs jointly modified the randomized effect. Self-efficacy, network degree, and the preregistered gender main effect were also unsupported. Conclusion: A higher activity-based opportunity cost reduced voluntary quarantine within the VinUni epigame, while participants carried relative differences in stated health beliefs from real-life to in-game framing. These beliefs did not reliably predict observed quarantine choices within the game framing. Epigames can support controlled study of incentives and belief correspondence in naturalistic settings such as college campuses, but behavioral validity or real-world generalizability requires further work.

In this study, we develop a mathematical model of the hypothalamic pituitary adrenal (HPA) axis and systematically investigate the model complexity required to reproduce observed hormone dynamics. The model is fitted to ACTH and cortisol measurements from five healthy volunteers reported in the literature. We show that simultaneous reproduction of both hormone profiles requires a delay differential equation model with at least five state variables among the model architectures considered. Following fitting the volunteer data, we investigate the response of the model to sustained stress using bifurcation analysis. The coupled system exhibits bifurcations over a range of stress intensities. This analysis reveals bifurcations whose critical stress thresholds depend strongly on the modulatory state of the system. These results provide a mathematical framework for investigating transitions between physiological and dysregulated HPA axis dynamics, which may be related to states of health and disease.

Automated surveillance systems can prove to be extremely useful in detecting outbreaks of respiratory diseases that need further epidemiological investigation. In order for the system to be effective, careful attention has to be paid to the baseline used, absence of the observations, and the use of proper statistical methods. As a solution, the paper describes EpiAlert, a weekly surveillance system based on CDC FluView ILINet. The system is able to transform the number of ILI visits and the total number of visits into a ILI prevalence for the region, create the first reference from the three previous years using a +/- 2-week interval, transform the rate value, and apply two different detectors - CUSUM and EWMA. CUSUM is set at k=0.5 and h=5, whereas EWMA uses {lambda}=0.2 and H=2.7 values. Among the 55 districts, the system gives 45,138 pieces of information about the disease by transforming 44,297 pieces of data into alerts. It is found that there are 2,820 alerts in total (out of which 2,711 alerts used CUSUM, 402 used EWMA, and 293 used both detectors). Additionally, the experiments show that the sensitivity of the device depends greatly on the detector. The latest statistics can be tracked in the report for week 37 of 2026 that recently concluded in West.

Non-exponential residence times give rise to history-dependent or memory effects in epidemic dynamics, yet their mathematical formulations remain fragmented. Here we develop a unified framework for a history-dependent SEIR model with general latent- and infectious-period distributions. We first establish the equivalence between the stage-age and renewal formulations and show that both admit a unified transition-flow representation. We then introduce the concept of a transition-flow multiplier, which provides a direct link between residence-time distributions and transition flows. When the multiplier is a weighted sum of shifted power functions, the model admits an exact representation in terms of Riemann-Liouville or tempered Riemann-Liouville fractional operators, providing a first-principles foundation for fractional epidemic models. Finally, using Euler-Lotka equations and stochastic-order arguments, we characterize how the latent- and infectious-period distributions shape epidemic growth and decay. A longer latent period slows both early growth and late decay, while, at fixed means, greater variability in the latent period accelerates early growth but slows late decay, whereas greater variability in the infectious period slows both.

Multiview data analysis has become increasingly important for integrating heterogeneous information from multiple data sources. Cooperative learning provides a flexible framework for multiview integration by bridging early and late fusion strategies. However, its use of squared loss makes it sensitive to outliers. In this study, we propose robust cooperative learning based on the soft-IPOD framework. The proposed method introduces an observation-specific outlier absorption vector into the cooperative learning objective, thereby reducing the influence of outlying observations while preserving information sharing across views. The resulting optimization problem can be formulated as a weighted Lasso-type problem using an augmented design matrix and can be naturally extended to more than two data views. Numerical simulations and a real-data application to labor onset prediction demonstrate the predictive performance of the proposed method in the presence of outliers.

Nitrogen dioxide (NO2) is a widespread air pollutant associated with the risk and prevalence of various respiratory infectious diseases. Epidemiological studies have reported associations between NO2 exposure and COVID-19 incidence, and biological evidence indicates that NO2 exposure increases vulnerability to infection. Motivated by these findings and by the scarcity of mechanistic epidemic models that incorporate air pollutant data, here we introduce an NO2- and mobility-informed metapopulation model and study the first COVID-19 wave in Oklahoma. County-level NO2 concentrations enter the infection rates through a first-order parameterization, alongside high-resolution mobility patterns capturing heterogeneous inter-county connectivity. Using numerical simulations, we demonstrate that higher environmental sensitivity produces earlier and more synchronized outbreaks across counties, while lower sensitivity yields delayed, asynchronous trajectories. When fitted to county-level COVID-19 incidence data, our model outperformed the classic SIR model (fitted independently in each county) in over 75% of counties in Oklahoma across all standard error metrics. The improvements were even more pronounced in forecasting tasks across epidemic stages, with county-level win rates against the SIR model of 59.74% (surge), 87.01% (peak), 80.52% (post-peak), and 88.31% (decline) by nRMSE. To isolate the contribution of NO2, we compared our model against a mobility-only model with NO2 removed and homogeneous infection rates across counties. The NO2 term improved county-level forecasts during the surge (94.81% of counties by nRMSE), post-peak (88.31%), and peak (62.34%) stages, though not during the early and decline stages, suggesting that NO2 information can generate better forecasts than mobility alone during periods of high incidence. Our results demonstrate that environmental exposure can enter the transmission structure of a mechanistic epidemic model directly and that doing so yields measurable forecasting gains.

Pterocarpus erinaceus Poir. (Fabaceae), the barwood or vene, is at the heart of one of the most severe conservation crises currently affecting tropical timber species. It has become the most heavily traded timber species in the world by volume, at the centre of an illegal rosewood trade to Asia that has driven the species to near-total disappearance in several countries despite its listing on CITES Appendix II since 2016. Faced with this urgency, ex situ conservation and restoration programmes often rest on the untested assumption that selecting seed trees by size improves the quality of the offspring produced in the nursery. This study rigorously tests this hypothesis against the competing one of a determinism dominated by the planting site, using six complementary analytical approaches (continuous regression, linear mixed models with a site x seed-tree interaction, seed-mass mediation analysis, non-linear growth modelling of height and diameter, between-site phenotypic plasticity, and multi-predictor allometric equations), across two contrasting bioclimatic sites in COte d'Ivoire (Korhogo: dry zone; Daloa: humid zone) and six seed-tree categories. Site exerts a decisive influence on growth at all ages (ANOVA, p<0.001 for height, diameter and biomass), whereas germination does not differ significantly between sites (70.3% at Daloa versus 61.6% at Korhogo; X2=1.95; p=0.162). Seed-tree DBH does not affect growth in any of the tests (continuous regression, p>0.30; mixed model, p>0.07), and the seed-mass mediation analysis remains inconclusive, seed mass varying only slightly among seed trees in this species (0.07-0.08 g). The exponential growth model provides the best fit for height and diameter at both sites, and two-predictor allometric equations explain 96.2% of biomass variance compared with 89.9% for the single-predictor model. These results indicate that ex situ conservation and compensatory reforestation programmes for this endangered species would benefit from prioritising the choice of planting site over the dendrometric selection of seed trees. A recommendation directly applicable to conservation nurseries established in response to the rosewood crisis.

Mechanistic models of infectious disease transmission provide a framework for understanding and predicting disease dynamics, but statistical inference is often computationally prohibitive. This challenge is especially pronounced for models with high-dimensional structures and many unknown parameters, where trajectory matching may require thousands of evaluations of an expensive likelihood function. We developed a surrogate-assisted optimization framework that uses Bayesian optimization to reduce the number of evaluations of the underlying mechanistic model required for parameter inference. We evaluated the approach using simulated fitting problems based on age-structured susceptible-exposed-infected-recovered models with 4, 8, and 16 age classes and a 210-compartment age-structured rotavirus transmission model. Applying surrogate-assisted optimization substantially reduced the number of likelihood evaluations required to identify good parameter estimates, with larger improvements as model dimensionality increased. In the rotavirus model, the surrogate Bayesian optimization approach reached the benchmark after approximately 0.7 hours, compared with approximately 9.4 hours using traditional Nelder-Mead optimization. These results demonstrate that surrogate-assisted optimization can substantially reduce the computational burden of trajectory matching for complex mechanistic transmission models, particularly for higher-dimensional inference problems.

In this work, we investigate the homogeneity of performance in binary classification between the Rotterdam phenotypes and phenotype prediction. For this purpose, a multi-task neural network architecture is designed to improve the prediction accuracy of phenotypes while maintaining the accuracy of binary classification. Using a clinical PCOS dataset which contains unlabeled samples of patients that cannot be attributed to any of the known phenotypes, three models are evaluated: optimized binary Random Forest (RF), six-class weighted class RF, and multi-task neural network (with a common hidden layer and two heads for binary and six-class classifications) optimized by a genetic algorithm. Techniques used for evaluation include ten-fold out-of-fold predictions, paired McNemar tests, bootstrap tests, ablation study, and clustering (Adjusted Rand Index, ARI). Binary RF model reached 90.2% accuracy; however, recall of different phenotypes was significantly unbalanced - from 75.0% of phenotype B to 97.8% of phenotype A. This difference of 22.8 % points was observed (23.3 points) even after excluding phenotype-specific features. While direct phenotype prediction improved recall for common phenotypes, it decreased recall for phenotype B to 43.8% and unclassified cases to 31.5%. Our multi-task model preserved 90.2% binary accuracy and significantly recovered phenotype B recall to 87.5% and unclassified cases to 79.6% (p<0.05). The cluster analysis confirmed the weak correlation between the results and labels (ARI=0.045). The ablation analysis demonstrates that feature selection and class weighting drive this improvement, isolating the specific components responsible for performance gains. We establish that aggregate binary accuracy does not reflect the heterogeneous detection of PCOS phenotypes. Direct phenotype prediction alone redistributes the classification errors, but the multi-task architecture successfully improves the detection of rare phenotypes without compromising binary accuracy. Phenotype-disaggregated evaluations, supported by significance testing and ablation, are essential for clinical classifier research.

The chick embryo is a widely used vertebrate model for developmental biology, cardiovascular, and bioengineering research. We present an automated laser speckle contrast imaging (LSCI) platform for non-invasive, longitudinal, label-free imaging of the developing heart and extraembryonic vasculature of the chick embryo through the intact eggshell. The platform can detect blood vessels as small as 70 +/- 17 um in diameter and enable repeated imaging from day 0 to day 8 of incubation without disturbing normal embryonic development. It provides both structural (a map of growing extraembryonic blood vessels) and functional (blood flow dynamics and heart rate) information. Using this system, we generated the first continuous, non-invasive, time-lapse recordings of the early functioning chick heart and its extraembryonic vessels. Longitudinal imaging was performed on 15 embryos from different chicken breeds with eggshell colors white, brown and green. Analysis revealed slight variability in early cardiac and vascular morphogenesis across breeds and shell types and shows its potential for automated developmental staging. Finally, for the first time, the growing vasculature of the yolk sac and the chorioallantoic (CAM) membrane was imaged from day 4 to 8. The heart rate and growth of the extraembryonic blood vessels were quantified automatically during imaging.

Abstract Background: Cervical cancer is a leading malignancy and mortality cause among Ethiopian women. Despite screening expansions coverage remains low. Traditional models miss complex determinants. This study aimed to develop machine learning models to predict cervical cancer screening utilization and identify drivers using the 2024 to 2025 Ethiopian Demographic and Health Survey. Methods: A secondary analysis of the 2024 to 2025 Ethiopian Demographic and Health Survey was conducted among women aged 15 to 49. Six algorithms Logistic Regression, Decision Tree, Support Vector Machine, Random Forest, Gradient Boosting Machine and Extreme Gradient Boosting were trained. Synthetic Minority Over-sampling Technique handled class imbalance with a 4.2 percent screening prevalence. Models were evaluated on a holdout set using Area Under the Receiver Operating Characteristic curve, Area Under the Precision-Recall Curve, Brier score and Decision Curve Analysis. SHapley Additive exPlanations explained model predictions. Results: Extreme Gradient Boosting achieved superior discrimination with an Area Under the Receiver Operating Characteristic curve of 0.896 and Area Under the Precision-Recall Curve of 0.781 outperforming baseline Logistic Regression at 0.772. Isotonic calibration optimized prediction loss with a Brier score of 0.038. Decision Curve Analysis confirmed clinical net benefit across thresholds from 5 percent to 55 percent. Explanatory analysis identified cancer awareness, education, autonomy, wealth quintile, recent health visits and urban residence as primary screening drivers. Conclusions: Probability-calibrated Extreme Gradient Boosting with explainability provides an accurate tool for identifying un-screened women in Ethiopia. Public health programs can deploy these tools in primary care applications to target outreach, optimize community engagement and accelerate World Health Organization elimination targets.

Host immunity acquired through prior infections is a key factor shaping the spread of emerging variants. However, accounting for complete infection histories in mathematical models is challenging because the number of possible histories grows exponentially with the number of variants. Here, we present a flexible framework for modeling the co-circulation of multiple variants emerging at arbitrary times while incorporating cross-immunity and variant-specific characteristics. We introduce three tractable approximations: truncation, which retains only infection histories up to a given number of strains; one variant at a time, which is exact when variants circulation does not overlap; and two variants at a time, which is exact when at most two strains co-circulate. We systematically evaluate these approximations across a range of cross-immunity levels and inter-variant emergence delays. Truncation approximations perform best under short emergence delays and strong cross-immunity, whereas the other approaches outperform them at longer delays. When applied to SARS-CoV-2 variant dynamics, the second-order truncation and the two variants at a time approximation accurately capture incidence patterns, whereas the other approaches perform well only for specific subsets of variants. These results identify the parameter regimes in which simplifying assumptions remain valid, provide guidance for model selection, and highlight the key role of infection history in shaping epidemic trajectories.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by persistent symptoms of inattention, hyperactivity, and impulsivity that significantly affect cognitive, academic, and social functioning. Although electroencephalography (EEG) has emerged as a promising non-invasive tool for identifying objective neurophysiological biomarkers of ADHD because of its high temporal resolution and cost-effectiveness, many existing machine learning studies rely on single-domain EEG features, which may not adequately capture the complex functional organization of the brain. Methods: This study proposes a subject-level EEG classification framework that integrates three complementary feature domains: spectral characteristics, weighted Phase Lag Index (wPLI)-based functional connectivity, and topological descriptors derived from persistent homology. EEG recordings from 121 subjects were preprocessed and segmented into overlapping windows for feature extraction. Window-level features were aggregated to generate subject-level representations. Recursive Feature Elimination (RFE) was employed to select the 110 most informative features from an initial set of 1,043 descriptors. Five machine learning classifiers, including Support Vector Machine (SVM), Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron (MLP), were evaluated using stratified five-fold cross-validation. An ablation study was further conducted to assess the contribution of each feature domain. Model interpretability was investigated using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), two widely adopted Explainable Artificial Intelligence techniques for interpreting machine learning models. Results: Among the evaluated classifiers, the SVM achieved the highest classification performance, yielding an accuracy of 80.17%, an F1-score of 80.95%, a balanced accuracy of 80.14%, a receiver operating characteristic area under the curve (ROC-AUC) of 86.72%, and a precision recall area under the curve (PR-AUC) of 87.34%. The ablation study demonstrated that combining spectral, functional connectivity, and topological features consistently outperformed individual feature domains, highlighting the complementary nature of these representations. Explainable AI analyses further identified functional connectivity features as the primary contributors to classification performance while confirming the additional value of spectral and topological descriptors. Conclusions: The proposed framework demonstrates that integrating spectral analysis, functional connectivity, and topological data analysis provides an effective and interpretable approach for subject-level ADHD classification from EEG recordings. The combination of recursive feature selection, multi-domain feature fusion, ablation analysis, and Explainable Artificial Intelligence establishes a transparent and reproducible pipeline that may facilitate the development of reliable EEG-based clinical decision-support systems for ADHD.

We propose a minimal mathematical framework to describe the go-or-grow dynamics of tumor cells comprising two phenotypically distinct populations. One population is migratory and undergoes constant diffusion, while the other proliferates in an oxygen-dependent manner. The local oxygen concentration governs transitions between these phenotypes. We then ask whether the time scale of cyclic hypoxia, where oxygen is spatially homogeneous but fluctuates between normoxic and hypoxic levels over time, alters the dynamics of the reduced generalist model in the fast phenotype-switching regime compared with the full go-or-grow model. We observe that the reduced generalist phenotype model fails to capture the go-or-grow dynamics under rapid oxygen fluctuations. Our results predict that the tumor growth rate increases in the generalist-phenotype model under rapid oxygen fluctuations compared with tumors of the proposed go-or-grow model. We then explore whether oxygen consumption, which leads to spatial heterogeneity in oxygen concentration, plays a role in distinguishing the dynamics of the reduced model from the proposed go-or-grow model. We demonstrate that the tumor in the proposed go-or-grow model exhibits a higher growth rate than the tumor in the reduced generalist-phenotype model under a slow oxygen consumption rate.

Moment-closure methods provide a widely used approach for approximating low-order statistics of stochastic biochemical reaction networks, but the resulting closed equations may exhibit physically meaningless dynamics. Here, we systematically investigate the stability of second-order moment-closure approximations for a bursty gene expression model with nonlinear protein degradation. We consider the Gaussian, log-normal, and negative-binomial closures. By analyzing the resulting dynamical systems, we show that the Gaussian closure fails the proposed stability criterion for all parameter values, whereas both the log-normal and negative-binomial closures possess a unique physically accessible fixed point and are stable if and only if kB(1 + 2B) [&ge;] 1, where B is the mean burst size and k is the dimensionless protein synthesis rate. Moreover, this condition is automatically satisfied when the steady-state mean protein copy number is at least one, suggesting that it is mild in biologically relevant regimes. Numerical comparisons over a broad parameter range further show that the negative-binomial closure is the most accurate, while the Gaussian closure is the least accurate. We further develop higher-order negative-binomial moment-closure schemes and demonstrate that increasing the closure order substantially improves predictive accuracy. These results highlight the importance of considering dynamical stability, in addition to numerical accuracy, when assessing moment-closure approximations.

Dynamic systems often evolve faster than reliable measurements can be collected. More frequent indirect observations may fill these gaps, but their noise and differing scales complicate estimation of the underlying state. We developed Bayesian Anchored Latent Learning (BALL) to combine sparse reference measurements with denser records on an interpretable scale. A bidirectional transformer teacher reconstructs trajectories from complete training histories, and a forward-only transformer student learns from those reconstructions and observed measurements. Across 30 simulations, BALL reduced composite trajectory error by 7% relative to the same transformer trained directly from measurements, achieving lower error across all datasets. In an applied example using psychiatric records from 1,609 patients, BALL improved the prediction of withheld questionnaire scores compared with direct transformer training at 21- and 28-day measurement schedules. Requests guided by estimated uncertainty further improved prediction. BALL connects retrospective learning with prospective state estimation and provides a framework for directing additional measurement.

Background: Long-term mortality data among Brazilian police officers and firefighters are scarce. This study investigated the leading causes of death in Brazilian male military police officers and firefighters over three decades, comparing their mortality and mean age at death with those of the male population. Methods: We retrospectively analyzed death records of male military police officers and firefighters from Espirito Santo (ES), Brazil, from 1988 to 2019. Mortality was analyzed across three decades and the standardized mortality ratios (SMRs) for the three leading causes were calculated using the male populations of ES and Brazil as references. Results: Overall, 2,198 deaths were identified, with a mean age at death of 59 {+/-} 18.5 years, compared with 60.5 {+/-} 23.9 years among men in ES and 63.4 {+/-} 23.7 years among Brazilian men. Mean age at death was 36 {+/-} 9 years among active-duty and 68 {+/-} 13 years among inactive personnel. Across the study population, the leading causes of death were circulatory system diseases (32%), external causes (26%) and neoplasms (15%). Circulatory disease mortality was higher in the second and third decades compared with both reference populations, whereas external-cause mortality shifted from higher, in the first two decades, to lower than expected in the last decade. Neoplasm mortality was higher relative to Brazil only in the last decade. Conclusion: Mortality patterns differed by cause and over time. The younger mean age at death among active-duty personnel, together with cause-specific excess mortality, highlights the importance of long-term occupational health surveillance and preventive strategies.

Lifestyle behaviors influence cardiometabolic and cognitive health through multiple interconnected processes, involving metabolic, cardiovascular, behavioral and cognitive factors. This study aimed to assess associations between multivariate plasma and physiological profiles, and behavioral parameters, and their potential association with reaction time (RT) in active young adults. Data on physical activity levels were collected from wearable devices and GPAQ questionnaire, along with anthropometric and clinical assessments, cognitive tests, and blood analyses. Principal component analysis was performed separately for physiological and plasma parameters, retaining the first two principal components (PCs) from each analysis. Multiple linear regression models were used to evaluate the associations between plasma, sex, physical activity levels (METs), daily step count, and alcohol/marijuana use on physiological PC1/PC2 scores, as well as RT. Continuous independent variables were previously categorized, and combined variables were created to represent specific combinations between plasma/physiological PC1 categories and sex. Physiological PC1 (blood pressure) was associated with combined plasma PC1 (insulin/HOMA-IR)-sex variable, total METs and alcohol consumption (adjusted R2 = 0.30). Physiological PC2 (handgrip strength) was mainly explained by combined plasma PC1-sex variable (adjusted R2 = 0.54), with minimal contribution from behavioral factors. RT was associated with physiological PC1 variable and sex (adjusted R2 = 0.35), with slower responses observed among women with lower physiological PC1 scores or hypotension; these associations were supported by RT-physiological PC1 and RT-blood pressure correlations. These exploratory findings suggest sex-dependent associations between physiological, metabolic and lifestyle-related factors, supporting a holistic approach in future research, particularly among young adults.