Search Results (1,165)

Background: Despite increasing opioid use in Brazil, the national epidemiological profile of opioid-related poisonings remains insufficiently characterized. Objective: To characterize opioid-notified poisonings reported in the Sistema de Informações sobre Agravos de Notificação (SINAN—Notifiable Diseases Information System), accessed through Departamento de Informática do Sistema Único de Saúde (DATASUS—Department of Informatics of the Unified Health System), between 2019 and 2025. Methods: This retrospective descriptive study used secondary national surveillance data from publicly accessible databases. Records of exogenous poisonings related to medications and drugs of abuse were screened, and notifications involving opioid analgesics were identified and standardized. Descriptive analyses were performed for demographic, clinical, and exposure-related variables. Bivariate analyses multivariable logistic regression models were conducted for selected outcomes. Incidence rates were estimated by year and federative unit, and temporal trends were assessed using generalized linear mixed models. Results: Between 2019 and 2025, 1,127,265 poisonings related to medications and drugs of abuse were reported in Brazil, of which 12,645 involved opioids. The opioids most frequently implicated in notifications were codeine (38.65%), tramadol (33.98%), and morphine (17.86%). Most cases occurred in women (70.3%), in individuals aged 26–50 years (47.8%), and in residences (85.6%). Digestive exposure predominated (92.3%), and suicide attempt was the main circumstance (73.5%). Most patients recovered without sequelae (75.1%), whereas 1.6% died due to exogenous intoxication. Co-exposure information was classifiable in 9573 records, most commonly involving opioids and medications. In multivariable analyses, suicide attempts were associated with female sex (aOR = 1.98; 95% CI: 1.68–2.34), residence-based exposure (aOR = 8.95; 95% CI: 6.29–12.72), and co-exposure (aOR = 2.17; 95% CI: 1.82–2.60). Hospitalization was less likely among females (aOR = 0.83; 95% CI: 0.75–0.91) and more likely with co-exposure (aOR = 1.14; 95% CI: 1.02–1.27). Serious outcomes were associated with older age (aOR = 1.017; 95% CI: 1.009–1.026), while residence-based exposure and suicide attempt showed lower odds. A significant increasing temporal trend was identified, with higher reported notification rates observed in the South and Southeast regions. Discussion: The predominance of suicide attempts and residential digestive exposures suggests that the notification profile captured by SINAN/DATASUS is predominantly shaped by intentional self-poisoning and household medication availability, while still representing a broader toxicovigilance scenario involving abuse, habitual use, adverse reactions, and other exposure contexts. The contrast between the most frequent notification profile and the profile associated with serious outcomes indicates that occurrence and severity may follow different epidemiological patterns. Therefore, these findings should be interpreted as a toxicovigilance signal reflecting multiple exposure contexts rather than as evidence of a single opioid-use pattern. Conclusions: Reported opioid-notified poisonings in Brazil increased over the study period and were predominantly associated with domestic exposure, suicide attempts, and co-exposure to other substances. These findings highlight the clinical and public health relevance of opioid-notified poisonings and support the need for strengthened surveillance, improved reporting quality, and preventive strategies addressing both opioid use and mental health. Limitations: Underreporting, missing data, regional reporting differences, and possible misclassification in SINAN/DATASUS records; therefore, associations, temporal increases, and projections should be interpreted as exploratory, and hypothesis generating. Full article

Modern inpatient care generates irregular streams of heterogeneous clinical events, yet most predictive models require fixed feature matrices, predefined time windows, or discretization of continuous measurements. We developed CHaRT, a decoder-only autoregressive transformer designed to jointly forecast the identity of the next clinical event and, when applicable, its associated continuous value. CHaRT was trained and internally validated on structured electronic health record data from adult acute-care encounters across a 12-hospital health system in Minnesota from 2001 to 2025. The final corpus included 4,447,625 encounters from 1,301,502 patients and 701,556,877 non-padding clinical event tokens spanning vital signs, laboratory values, medications, diagnoses, microbiology, virology, imaging, fluids, and outcomes (ICU transfer or death). Encounters were split into training, validation, and test sets before vocabulary construction, normalization, and windowing. On the held-out test set, CHaRT achieved Top-1, Top-5, and Top-10 next-event accuracies of 51.61%, 87.34%, and 93.22%, respectively, with perplexity 4.50 and expected calibration error 0.0109. For numeric prediction, z-score MSE was 0.3812 for vital signs and 0.5713 for laboratory values. Seeded examples generated clinically coherent trajectories. Using model representations, a linear probe predicted deterioration (ICU transfer or in-hospital death) at a 6 h landmark with AUROC 0.95–0.97, indicating that learned representations transfer to downstream clinical risk prediction. Full article

The human APOE gene is polymorphic with three major alleles that encode apolipoprotein (apo) E2, apoE3, and apoE4. Both apoE2 and apoE4 are associated with increased atherosclerosis risk. This study utilized human APOE2, APOE3, and APOE4 gene replacement mice and single-cell RNA sequencing approach to delineate the mechanisms underlying this association. The human APOE2, APOE3, and APOE4 mice were fed a Western-type high fat–cholesterol diet for 16 weeks. Hyperlipidemia and significant atherosclerosis were observed in APOE2 mice but not in APOE3 or APOE4 mice. However, increased vascular inflammation was observed in both APOE2 and APOE4 mice. Single-cell RNA sequencing followed by cluster analysis identified 25 major cell types in the aorta that include various immune cell types, endothelial cells, and various vascular mural cell subsets. Results showed that cells from the APOE2 mice were enriched with genes associated with intracellular lipid accumulation and inflammation, whereas cells from the APOE4 mice displayed elevated oxidative- and/or endoplasmic reticulum-stress and inflammatory response. Thus, apoE2 accelerates atherosclerosis by inducing diet-induced hyperlipidemia and inflammation, while apoE4 does not induce hyperlipidemia but enhances inflammation that may prime the vasculature for atherosclerosis development. The distinct mechanisms by which apoE2 and apoE4 promote atherosclerosis underscore the importance of including apoE genotype information in the design of therapeutics for atherosclerosis intervention. Full article

Background and Clinical Significance: Cerebral amyloid angiopathy (CAA) is a neurovascular disorder characterized by the deposition of amyloid beta (Aβ) peptides within the walls of small-to-medium-sized cerebral vessels, leading to vascular fragility and an increased risk of lobar intracerebral hemorrhage (ICH), cognitive decline, and recurrent stroke. CAA is an important cause of spontaneous ICH in elderly patients and may be underrecognized, particularly when presenting with acute neurologic symptoms that mimic ischemic stroke. Early identification has significant implications for management, prognosis, and secondary prevention. Case Presentation: An 80-year-old male presented to the emergency department with incoherent speech, rambling, and severe headache concerning for acute stroke. His medical history was notable for a prior cerebrovascular accident, hypertension, diabetes mellitus, benign prostatic hyperplasia, and recent evaluation for dementia-like symptoms. Initial neuroimaging revealed a 3.2 cm intraparenchymal hemorrhage in the left occipital lobe with surrounding edema. Subsequent MRI demonstrated a lobar hemorrhage pattern suggestive of CAA based on imaging findings and clinical context. The patient was admitted to the intensive care unit (ICU) for close neurologic monitoring. He remained hemodynamically stable with no new motor or sensory deficits. Over a three-day hospital course, his speech and visual deficits improved. Blood pressure was carefully controlled, and repeat imaging demonstrated stable hemorrhage without progression. He was diagnosed with probable CAA and discharged home with supportive services. Conclusions: This case highlights the importance of considering cerebral amyloid angiopathy in elderly patients presenting with spontaneous lobar intracerebral hemorrhage and cognitive symptoms. Prompt recognition and appropriate neuroimaging are critical for diagnosis, risk stratification, and guiding management. Full article

Background/Objectives: Discharge letters are essential for continuity of care but are often time-consuming to prepare and variable in quality. Large language models (LLMs) may help standardize and support this process, yet evidence in ophthalmology remains limited. This study compared the quality of resident-written and GPT-5.2-generated ophthalmology discharge letters derived from the same de-identified clinical data. Methods: This retrospective blinded study was conducted at a tertiary hospital in Croatia. For 146 consecutive inpatient discharges, original resident-written letters were paired with GPT-5.2-generated letters created using a standardized prompt; 142 complete pairs were available for the primary analysis. Three board-certified ophthalmologists evaluated anonymized letters using a structured assessment of accuracy, completeness, clarity/structure, tone/professional phrasing, conciseness, global quality, errors, omissions, and key content elements. Results: In the primary paired analysis, GPT-5.2-generated letters performed similarly to resident-written letters across accuracy, completeness, clarity/structure, errors, omissions, and overall quality. GPT-5.2-generated letters received higher ratings for tone/professional phrasing, whereas resident-written letters were rated as more concise, although inter-rater agreement was poor on these stylistic domains (at or below chance for conciseness) and these findings should therefore be interpreted as exploratory. Resident-written letters more often documented operations, while GPT-5.2-generated letters more consistently included findings. Reviewer-adjusted sensitivity analyses were less favorable to GPT-5.2 for several domains. Conclusions: GPT-5.2-generated ophthalmology discharge letters showed similar performance to resident-written letters in several evaluated domains in the primary paired analysis, but differences in specific content elements and less favorable sensitivity analyses indicate that clinician oversight remains necessary to ensure accuracy, procedural completeness, and clinical usability. Full article

This study investigates the use of interpretable machine learning techniques to identify behavioral and demographic patterns associated with diabetes, based on structured population survey data from the Canadian Community Health Survey (CCHS). A decision tree classifier was applied to a dataset comprising $16,824$ respondents and 38 preprocessed features covering lifestyle, well-being, and sociodemographic factors. The model was optimized through grid search with five-fold stratified cross-validation, achieving a test accuracy of $61.3$% (mean $62.6$% ±$0.6$% across a $10\times 5$ repeated stratified cross-validation). Feature importance analysis revealed that age, alcohol consumption patterns, daily energy expenditure, and physical activity were the most influential factors associated with diabetes status, with the top three features exhibiting stable importance across all cross-validation folds. The model produced a set of 32 human-readable decision rules; a sensitivity analysis confirmed that these rules are stable across encoding choices and cross-validation folds. Several model variants were evaluated: a class-weighted decision tree, a logistic regression baseline, an age-only decision tree, and an age and sex logistic regression. The class-weighted model improved minority-class recall (from $0.25$ to $0.53$) at the cost of overall accuracy. A one-hot encoding sensitivity analysis showed that replacing ordinal label encoding of nominal variables with one-hot encoding produces virtually identical results (accuracy: $61.4$% vs. $61.3$%), confirming that the main rules are not artifacts of the encoding choice. Although the classification accuracy is moderate and not significantly better than a majority-class baseline (McNemar’s test, $p=0.455$), the extracted rules confirmed several known associations and revealed interactions between social and lifestyle variables. These rules are intended as hypothesis-generating population-level descriptors rather than validated clinical decision tools, and no causal inference is claimed. This approach demonstrates the value of rule-based models for exploratory public health research. Full article

Mobile health (mHealth) systems in rural communities require rigorous software engineering methodology and empirical validation of end-user acceptance. A gap exists in applying structured System Development Life Cycle (SDLC) frameworks to community-facing mHealth platforms with embedded technology acceptance evaluation. This study presents the design, architecture, and iterative development of the “Smart Daily Life Care” cross-platform mobile application using a six-phase SDLC framework, targeting rural elderly communities in Thailand. The system architecture employed a microservices design with age-friendly UI engineering, conforming to WCAG 2.1 AA. Technology acceptance was evaluated post-deployment using the Technology Acceptance Model (TAM) with 200 participants (elderly users, caregivers, and health personnel). System efficiency was rated at $\overline{\mathrm{x}}$ = 4.58 and user satisfaction at $\overline{\mathrm{x}}$ = 4.64. TAM regression identified perceived usefulness as the dominant predictor of behavioral intention (β = 0.412), followed by perceived ease of use (β = 0.318) and social influence (β = 0.268), with R² = 0.682. Integrating TAM evaluation within SDLC phases enables iterative remediation of acceptance barriers before deployment. Village Health Volunteer networks function as indispensable sociotechnical enablers of adoption. The SDLC–TAM integration provides a structured methodological approach suitable for replication in age-sensitive health information systems in low-resource settings. Full article

Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges, including within-subject correlation, time-varying covariates, irregular observation times, informative dropout, and high dimensionality. While traditional statistical methods, such as linear mixed-effects models and generalized estimating equations, remain foundational, they often struggle with complex nonlinear dynamics, ultra-high-dimensional feature spaces, and very large sample sizes. Over the past two decades, machine learning (ML) and artificial intelligence (AI) methods have emerged as powerful complementary approaches to address these limitations. This review provides a comprehensive survey of mathematical and computational methods for longitudinal data analysis. We cover classical statistical models, penalized regression techniques, tree-based ensemble methods, kernel machines, Bayesian hierarchical models, and modern deep learning architectures, including recurrent neural networks, temporal convolutional networks, attention-based Transformers, neural ordinary differential equations, and generative models. We propose a unified taxonomy that organizes existing methods along two primary axes: the underlying mathematical framework and the analytical objective. For each category, we present detailed mathematical formulations, discuss key theoretical properties, examine computational considerations, and summarize representative reported applications drawn from the published literature. To increase the practical value of this review, we provide a cross-cutting comparison of method families against five key challenges (within-subject correlation, irregular sampling, missing data, high dimensionality, and scalability) and offer concrete guidance on method selection according to sample size, dimensionality, and analytical objective. Finally, we critically evaluate the strengths and limitations of these approaches, with particular emphasis on interpretability, scalability, handling of missing data, robustness to covariance misspecification, and uncertainty quantification. Full article

Background/Objectives: Strategies to improve the Social Health Authority (SHA)’s equity can be identified by analyzing the Kenya Demographic and Health Survey (KDHS) 2022. This study reports evidence of determinants of health insurance coverage in Kenya. Methods: Household- and individual-level datasets from the Kenya Demographic and Health Survey conducted between February and July 2022 were combined to form the analyzed dataset. Proportions of individuals with and without health insurance were estimated. The associations between potential determinants and health insurance status were calculated using the Rao–Scott chi-square. Logistic regression was used to analyze the determinants of health insurance coverage. Results: Most of the 14,232 participants were literate (75%), relatively poor (56%), in good health (79%), connected to electricity (55%), and radio listeners (61%). About 34% had health insurance, with 93% of the insured covered by the NHIF. Twenty predictors (Adjusted F = 4.2–434.1, p < 0.0001) were included in the complex sample logistic regression model, but only nine were statistically significant predictors of health insurance coverage. The key predictors were education level; wealth index; ownership of a solar panel, television, smartphone, and computer; age; and recent outpatient care (11–80% differences in odds). Conclusions: Health insurance coverage remains low in Kenya due to low education levels, poor economic status, and disparities in access to media. The SHA can emphasize media campaigns in the informal sector to increase premium payments. Accelerating socioeconomic advancement and adopting tax-based funding could speed up Kenya’s progress towards UHC. Full article

The construction industry is recognised globally as one of the most hazardous sectors. Effective hazard management necessitates identifying and communicating these risks early in the project lifecycle. Construction Hazard Prevention through Design (CHPtD) addresses this by incorporating safety information into the design phase that is often cumbersome and heavily reliant on reviewer expertise. The present work enhances hazard recognition and visualisation by automating the process using computational intelligence and building information modelling, aligning with the theoretical framework of CHPtD. The proposed tool provides detailed hazard information, including the nature of the hazard, its causes, and potential resolutions, empowering designers to make informed decisions and mitigate risks proactively. The tool’s performance is evaluated using a confusion matrix, demonstrating promising results with an overall accuracy of 84.77% and a Kappa coefficient of 0.83. While the tool shows strong performance in identifying several hazard classes, further refinement is needed to improve its ability to detect catastrophic events and manage traffic-related hazards. Full article

Polysomnography monitors sleep health with non-linear physiological time-series data, consequently making interpretability a challenge. This study explores the feasibility of applying control charts, a statistical process control method, to cardio-respiratory signals derived from polysomnography studies to provide transparent and interpretable analysis of sleep-related physiological variability. Cardio-respiratory signals from a publicly available polysomnography dataset were preprocessed, transformed, and analyzed using univariate control charts. Sleep stage annotations were used as reference information to contextualize physiological variability across wake and non-REM sleep stages. Phase-level control chart rule violations were examined relative to annotated sleep-state transitions and summarized quantitatively. The results indicate that control chart rule violations occur more frequently during wakefulness and at wake–non-REM sleep transitions, while remaining relatively stable during sustained non-REM sleep. These findings indicate structural correspondence between SPC-based variability flags and annotated sleep stage dynamics. This exploratory, feasibility-focused study does not evaluate diagnostic performance or detection accuracy. Instead, it provides evidence that SPC control charts can serve as a transparent and interpretable analytical framework for exploring physiological variability in sleep data and for supporting future research on sleep-state analysis and explainable data-driven methods. Full article

With the rapid development of e-commerce, data-driven models have significantly enhanced service experience. We can obtain the optimal values for the price but have also intensified consumer privacy concerns. Among various privacy protection policies, which are more effective? Is there a governance framework that balances commercial efficiency with privacy safety? To address this, we develop a duopoly game-theory model that analyzes consumer behavior characterized by heterogeneous privacy costs and preferences, aiming to evaluate the impact of differentiated privacy protection policies within digital ecosystems. We analyze whether opt-in requirement or inference regulation is more advantageous for consumer and firm competition. We find that, in a competitive environment, imposing opt-in requirement on one party can yield competitive advantages and profit increases, whereas imposing inference regulation on the other may result in a competitive disadvantage. Such differentiated policies create an asymmetric competitive landscape, effectively avoiding a prisoner’s dilemma and, under certain conditions, increasing both consumer and total surplus. Furthermore, our study reveals significant differences in the impact of these policies on data-driven and usage-driven firms. Based on these findings, we recommend that regulators carefully tailor privacy protection policies according to industry-specific data characteristics, adopting differentiated regulatory strategies when appropriate and providing compensation mechanisms for disadvantaged firms to optimize total welfare. Full article

Food security in middle-income countries is a growing phenomenon, becoming more relevant than ever before. This study examines the effects of government expenditure, nutrition, and sustainability on health and food security in middle-income countries, with a focus on child stunting under macroeconomic constraints. It measures the impact on the empirical environment, accounting for relevant macroeconomic constraints that affect child stunting. Using the System Generalized Method of Moments (System–GMM) model to control for endogeneity and persistence in food security, a panel data set of 35 middle-income countries over the period 2000–2023 is employed. The results reveal strong persistence in food security dynamics (β = 0.642, p < 0.01). Government health expenditure significantly improves food security (β = −0.481, p < 0.01), whereas inflation (β = 0.074), public debt (β = 0.028), and exchange rate depreciation (β = 0.516) increase food insecurity. Child stunting was positively associated with food insecurity (β = 0.219, p < 0.01), whereas sustainability was associated with improved food security outcomes (β = −0.273, p < 0.05). The long-run effect of government health expenditure (−1.344) substantially exceeds its short-run impact, highlighting the importance of sustained investment. The findings underscore the need for integrated policies that combine public health investment, macroeconomic stability, and sustainability-oriented development to strengthen food security and reduce chronic malnutrition in middle-income countries. Full article

Background/Objectives: Medication discrepancies at hospital discharge are common and may contribute to adverse drug events and avoidable healthcare use. Patients with mental health conditions may be at increased risk because of greater clinical complexity, polypharmacy, and fragmented care, but comparative evidence during general hospital admissions is limited. Our primary objective was to determine whether adults with mental health conditions were more likely than those without such conditions to experience unintended medication discrepancies at hospital discharge. Secondary objectives were to examine discrepancy subtypes, assess whether associations differed for serious mental illness versus other mental health conditions, and explore whether associations varied by reconciliation arm. Methods: We conducted a retrospective cohort study using linked data from the RightRx cluster-randomized trial at the McGill University Health Centre (2014–2016) and Quebec administrative databases. Adults with continuous provincial drug coverage for at least 12 months before admission who met study eligibility criteria were included. The primary exposure was any documented mental health condition; secondary analyses distinguished serious mental illness (SMI) from other mental health conditions. The primary outcome was any unintended medication discrepancy at discharge; subtype analyses examined omissions, therapeutic duplications, and unintended dose changes. Results: Among 3567 patients, 877 (24.6%) had a mental health condition. Crude discrepancy prevalence was similar between groups. In the prespecified primary analysis, mental health condition status was associated with lower observed odds of any unintended discrepancy at discharge. This unexpected inverse association should not be interpreted as evidence of a protective effect and may reflect differences in documentation, residual confounding, selection, or other unmeasured processes. Secondary and supplementary analyses, including omission and SMI subgroup comparisons, did not remain statistically significant after Holm correction. Conclusions: These findings suggest that documentation-based discrepancy measures may relate to mental health status in heterogeneous ways, but they require confirmation in independent settings. Full article

Introduction: Prelacteal feeding, the practice of giving newborns substances other than breast milk within the first few days of life, remains a common yet harmful practice in many low- and middle-income countries, including Ethiopia. No evidence in Ethiopia indicates that mHealth can help improve prelacteal feeding. This study aimed to evaluate the effect of mobile health (mHealth) intervention on reducing prelacteal feeding practices and improving antenatal care (ANC) and postnatal care (PNC) utilization among mothers in South Ethiopia. Methods: A cluster-randomized controlled trial (CRT) was conducted in rural areas of South Ethiopia. A total of 20 clusters were selected using simple random sampling for intervention (mHealth) and control groups, each containing 340 women. Mothers in the intervention group received automated weekly SMS messages and reminders on exclusive breastfeeding, prelacteal feeding risks, ANC, and PNC. Mothers were only selected if they could read, write, and use mobile phones. Results: The mHealth intervention significantly reduced prelacteal feeding practice (AOR = 0.19, 95% CI: 0.06–0.58); p < 0.05). Higher ANC visits related to decreased prelacteal feeding (AOR = 0.28, 95% CI: 0.21–0.39; p < 0.001). The log count of ANC visit increased by 0.14 among intervention groups (IRR = 1.15, 95% CI: 1.06–1.25; p < 0.001). The PNC time was delayed 2.05 days among controls (β = −2.05, 95% CI: −2.66–−1.42; p < 0.001). Maternal and partner education, postnatal time, and ANC visits influenced prelacteal feeding. Conclusions: This finding might suggest that mHealth can reduce prelacteal feeding practices and improve maternal healthcare behaviors such as ANC attendance and timely PNC. These findings highlight the potential of mobile health interventions in promoting healthy maternal and infant practices in rural settings, where healthcare access is limited. Further research is needed to explore the long-term impacts of such interventions on maternal and child health outcomes. Multi-level analysis reduced variability. However, an unexplained variance could be reduced by including more cluster-level variables. Full article