Search Results (232)

Craniomaxillofacial trauma (CMFt) poses a significant burden, yet in many countries the evidence base is fragmented across single-center hospital series without specialized registry. Using Chile as a case study, we demonstrate a dual-synthesis approach to construct a national CMFt profile. Six databases were searched through February 2026 (PROSPERO: CRD420261290860). Two reviewers independently screened studies. Risk of bias was assessed with the JBI critical appraisal tool. Fracture-site proportions were pooled via random-effects meta-analysis and synthesized using GRADE. DEIS trauma discharges (2001–2024) were analyzed with negative binomial interrupted time-series. Nineteen studies were included. CMFt represented 2.6–6.1% of emergency consultations. CMFt admissions were 54.2/1000 trauma discharges; this rate dropped during 2020–2021 and rebounded post-2022. Pooled fracture-site distributions were highest for mandibular (45.3%) and zygomatic (24.2%) fractures. CMFt disproportionately affected males across both hospital series and national discharge data. According to DEIS, low-energy accidental injuries were the predominant etiology, followed by transport-related high-energy injuries and interpersonal violence, contrasting with hospital series where interpersonal violence predominated among adult surgical cohorts. Fracture admissions had longer length of stay (LOS) than soft-tissue CMFt (+0.94 days), with mean LOS ranging from 2.08 (nasal) to 8.35 days (multiple skull/facial fractures). These findings support prioritizing surgical preparedness and training in common fracture patterns, while strengthening trauma surveillance, referral pathways, and service planning in health systems without dedicated CMFt registries. Full article

For the binomial, Poisson and normal distributions, the n-th central-raw ratios $\mathbf{E}{(\xi -\mathbf{E}\xi )}^n/\mathbf{E}{\xi }^n$ are known to be infinitesimal with distinct patterns. In this paper, the higher-order moments of the negative binomial distribution $NB(r,p)$ and the Gamma distribution $Ga(\alpha ,\beta )$ are derived using combinatorial and asymptotic methods. Consequently, the asymptotic central-raw ratios of these distributions are shown to be non-zero constants: $q^{{\displaystyle \frac{q}{p}}r}$ (where $q=1-p$) for $NB(r,p)$ and $e^{-\alpha }$ for $Ga(\alpha ,\beta )$ as $n\to +\infty$. Full article

This study proposes a two-stage Hurdle machine-learning framework for Customer Lifetime Value (CLV) prediction under zero-inflated non-contractual retail settings, where conventional single-stage approaches may suffer from prediction instability and retransformation issues when zero and non-zero spending are jointly modeled. Using the UCI Online Retail II dataset, comprising 4026 customers with a 62.5% zero-spending rate, Stage 1 employs XGBoost to estimate purchase occurrence probability, while Stage 2 applies gradient- boosting regressors to predict conditional spending intensity. The inverse hyperbolic sine (arcsinh) transformation handles 59 customers with negative net spending from product returns. The Two-stage CatBoost model achieves a coefficient of determination of 0.522, outperforming the best single-stage mean-squared-error (MSE) model (0.385), the default Tweedie-loss baseline in the main 30-seed comparison (0.309), and the Beta-Geometric/Negative Binomial Distribution (BG/NBD) baseline (0.395). The contribution combines architectural innovation with a comprehensive validation protocol—including 5 × 2 CV paired t-tests, out-of-time validation, and SHAP interpretability—confirming that purchase frequency drives occurrence probability while monetary value dominates spending magnitude. A dual-dimension segmentation based on purchase probability (P) and conditional spending intensity (E) identifies 96 Dormant, High-E customers with only a 26% purchase rate despite high expected spending, demonstrating that high conditional spending does not guarantee purchase occurrence. Full article

Deforestation promotes environmental changes capable of altering regional microclimatic dynamics, intensifying wildfires, and increasing population exposure to cardiovascular risk factors. This study investigated the spatiotemporal association between deforestation and acute myocardial infarction mortality across health regions of the Brazilian Amazon between 2000 and 2023. An ecological study design was adopted using data aggregated by health region and year. Generalized additive models with a negative binomial distribution were fitted to evaluate nonlinear associations between deforestation and acute myocardial infarction mortality, including temporal lag analyses of one, two, and three years. Spatial dynamics were further investigated through Bayesian spatiotemporal modeling incorporating structured spatial effects and a smoothed temporal trend. A significant nonlinear association was identified between deforestation and acute myocardial infarction mortality, with progressive risk intensification observed in areas subjected to greater environmental degradation. Lagged models demonstrated persistence of the association over time, suggesting cumulative effects of environmental exposure. Spatial analysis revealed an expansion of areas with elevated relative risk, particularly within the Arc of Deforestation of the Amazon region. Overall, the findings indicate that deforestation may act as an important socioenvironmental determinant of cardiovascular health in the Brazilian Amazon. Full article

The Poisson–Three-Parameter Lindley (PTPL) distribution constitutes a flexible Poisson mixture model for overdispersed count data, encompassing several classical count distributions as special or limiting cases. Despite its growing use in applied contexts, no formal goodness-of-fit test specifically designed for this distribution is currently available. In this paper, we propose and study a new goodness-of-fit test for the PTPL model based on a Cramér–von Mises type distance between the empirical and theoretical probability generating functions (PGFs). For polynomial weight functions, the test statistic admits an explicit closed-form representation; in practice, it is computed efficiently via numerical quadrature. The null distribution of the statistic is approximated via parametric bootstrap. We establish theoretical properties of the proposed procedure, including consistency against fixed alternatives and the validity of the bootstrap approximation. Monte Carlo simulations with sample sizes $n\in \{50, 100, 150, 200, 500\}$ for size evaluation and $n\in \{100, 250, 500\}$ for power comparisons, as well as weight exponents $a\in \{0, 1, 2\}$, show that the empirical size is well controlled at both the 5% and 10% nominal levels, and that the test exhibits competitive power against Poisson, Negative Binomial, COM-Poisson, and Zero-Inflated Poisson alternatives. A real data application to five overdispersed count datasets further illustrates the practical utility of the method. The empirical size is further verified across twelve parameter configurations spanning dispersion indices from 1.37 to 59.33, confirming bootstrap validity under strong overdispersion. Full article

Microplastic contamination is a growing environmental concern in coastal ecosystems, particularly on recreational beaches where human activities may influence plastic inputs. This study investigated microplastic abundance and particle characteristics across five recreational zones along Hatwanakorn Beach in the Gulf of Thailand, focusing on fine-scale variability within a spatially continuous beach system and across management regimes. Supratidal sediments were collected using a quadrat-based approach, and polymer types were identified using Fourier Transform Infrared spectroscopy (FTIR). Fibers were the predominant particle type, followed by fragments, and most particles were classified as large microplastics (1–5 mm). Significant spatial differences in abundance were observed among recreational zones (Kruskal–Wallis test, χ² = 13.37, p = 0.0096). At the management regime scale, a negative binomial generalized linear model also indicated significant differences (χ² = 30.58, p < 0.001), with higher abundance in the Hatwanakorn Forestry Research and Student Training Station (HWK Station) and Community regimes than in the National Park regime. These results indicate that microplastic distribution can be spatially heterogeneous even within a continuous recreational beach system, underscoring the importance of accounting for fine-scale spatial variability when assessing microplastic contamination in coastal environments. Full article

This study proposes a transformed two-part bootstrap confidence interval (TTB-CI) for zero-inflated count data. The method combines a standard zero-inflated mixture formulation, parametric bootstrap, and monotone transformations to improve inference for practically meaningful estimands, including the marginal mean, zero probability, and positive-part mean. Simulation studies under zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) data-generating processes show that the proposed method maintains nominal or near-nominal coverage while reducing interval width, particularly for the positive-part mean. Compared with conventional Poisson- and negative binomial-based confidence intervals, the proposed TTB-CI provides a more favorable coverage and width tradeoff and yields more informative intervals for positive count inference. These results indicate that the proposed method offers a practical and efficient confidence interval framework for zero-inflated count data. Full article

Objective: Preanalytical errors account for the vast majority of preanalytical incidents and remain a fundamental threat to the reliability of test results. Although the types and frequencies of these errors have been extensively studied in the literature, their time-dependent variability has received comparatively little attention. This study aimed to evaluate how preanalytical specimen rejection rates vary across intraday time intervals and to assess the independent influence of time on preanalytical quality. Methods: This retrospective observational study included a total of 579,845 specimens accepted by the central laboratory of Istanbul Atlas University Hospital between January 2024 and December 2025. Specimens were analyzed with respect to preanalytical rejection reasons, the distribution and rate of these reasons across clinical units, and time of day. Each day was divided into six equal four-hour intervals: Z1 (00:00–04:00), Z2 (04:00–08:00), Z3 (08:00–12:00), Z4 (12:00–16:00), Z5 (16:00–20:00), and Z6 (20:00–24:00). Statistical analyses were performed using the Pearson chi-square test, and effect sizes were quantified using Cramér’s V coefficient. Results: Of the 579,845 specimens examined, 4365 were rejected, yielding an overall rejection rate of 0.79%. Rejection rates were found to be non-uniformly distributed across the day (p < 0.001). The highest rejection rate was observed during the Z2 interval (04:00–08:00) at 1.98%, whereas the lowest was recorded during Z3 (08:00–12:00) at 0.45%. Negative binomial regression analysis identified the Z2 interval as the only time period independently associated with an increased rejection risk Incidence Rate Ratio (IRR) = 1.63; 95% Confidence Interval (CI): 1.22–2.19. Among clinical units, the highest rejection rate was recorded in the emergency department (1.92%). Analysis of error types revealed that the majority of rejections were attributable to hemolysis (47.5%) and clotted specimens (26.3%). Hemolysis rates peaked in the emergency department, while clotted specimens occurred more frequently within intensive care units. Analysis of time and error interactions revealed that clotted specimens peaked during Z1 and Z2, whereas hemolysis became the primary cause of rejection during Z3 and Z4. Conclusions: Preanalytical specimen rejection rates exhibited significant variation according to time of day, clinical unit, and error type, with time emerging as a factor independently associated with preanalytical quality. The coexistence of elevated rejection risk during Z2 (04:00–08:00) and markedly low rejection rates during Z3 (08:00–12:00) indicates that the relationship between workload and error frequency is not linear. Although hemolysis and clotted specimens constituted the dominant error types, their distribution followed distinct patterns depending on the clinical unit and time interval. These results underscore the necessity of time-based monitoring to pinpoint unit-specific risks, providing a clear roadmap for targeted quality improvement interventions. Full article

We propose a quantile-based survival model that accounts for a cure fraction. By modeling unobserved event causes with a negative binomial distribution and time-to-event with the generalized log-logistic Weibull distribution, we estimate parameters using a frequentist framework. We validated our model and applied it to prostate cancer data to prove its practical use. Comparing it to a Random Survival Forest model, we highlight how different approaches handle long-term survival, ultimately weighing the balance between flexibility, interpretability, and predictive performance. Full article

Grassland ecosystems are among the most threatened habitats in North America, and their degradation has contributed to widespread population declines of grassland-dependent birds. The Burrowing Owl (Athene cunicularia) is a grassland specialist whose populations have shown sustained declines at a continental scale; however, quantitative data on relative abundance remain limited in northern Mexico. We estimated a relative abundance index for the Burrowing Owl in the grasslands of the southern Chihuahuan Desert, Mexico, using vehicle-based line transects expressed as the number of individuals per linear kilometer (ind/km). Additionally, we evaluated the relationship between human disturbance and owl records using a standardized Human Disturbance Index (HDI) based on field indicators of grazing pressure and solid waste. A total of 18 transects (1 km each) yielded 83 detections, with a mean relative abundance of 4.61 ± 5.93 standard deviation (SD) ind/km. A Generalized Linear Model with a Negative Binomial distribution revealed a significant negative effect of the HDI on owl abundance (β = −1.27, z = −3.81, p = 0.0001; incidence rate ratio (IRR) = 0.28, 95% confidence interval (CI): 0.14–0.51). Our results provide a baseline abundance estimate for the Burrowing Owl in the southern Chihuahuan Desert and highlight the importance of habitat disturbance metrics to assess population status in fragmented and human-impacted grassland landscapes. Full article

The green-belly stink bug (GBB), Diceraeus melacanthus (Dallas, 1851) (Heteroptera: Pentatomidae) is a key South American maize (Zea mays L.) pest, feeding on seedlings and causing physiological disorders. Understanding D. melacanthus population distribution and establishing sampling plans is essential to manage this species. Hence, the objective was to determine a distribution pattern, recommend a sampling unit size and develop sampling plans for the GBB, covering maize pre-sow period up to the maize V4 stage. Assessments were carried out in an experimental field and nine crop fields in northern Paraná State. In the experimental field, quadrants of n, 2n, 4n, 8n and 16n (n = 0.25 × 0.25 m²) were tested, thus determining an aggregated distribution with a recommended sampling unit size of 0.5 × 0.5 m². After the nine crop field samplings, a negative binomial distribution was deemed fit to represent GBB in field conditions. Two sampling plans were developed, highlighted is the sequential presence–absence plan, which recommends a maximum of 60 sample points, and a minimum of 25, with at least six presences to make control decisions. For a more assertive sampling, divide the evaluated area into glebes with distinct natural characteristics and employ the sampling plan to each glebe. These sampling plans must be validated before IPM recommendation. Full article

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies. Full article

Cetacean mortality serves as a critical indicator of marine ecosystem health, reflecting the cumulative impacts of climate-driven environmental shifts and anthropogenic pressures. However, long-term national-scale assessments remain limited in Thailand. This study aimed to assess population status, analyse spatiotemporal mortality patterns, and evaluate the influence of environmental drivers and anthropogenic stressors in Thai waters over the past two decades. Secondary data from multiple sources were analysed using generalised linear models with a negative binomial distribution. A total of 29 cetacean species were recorded, with an estimated population of approximately 3000 individuals. Mortality was documented in 24 species and showed an increasing trend over time. Coastal species, particularly Irrawaddy dolphins and finless porpoises, accounted for the majority of deaths (56%). Mortality patterns varied significantly by region (p < 0.05) but not by season, with the highest levels observed in the Upper Gulf of Thailand. Environmental factors were significantly associated (p < 0.05) with mortality, including wind speed in the Andaman Sea and extreme conditions (drought and heavy rainfall) in the Upper Gulf. In the Lower Gulf of Thailand, mortality was significantly associated with a combination of environmental (sea surface temperature and wind speed) and anthropogenic factors (fishery production). Overall, environmental variability appeared to exert a stronger influence than anthropogenic stressors. These findings highlight the requirement for targeted monitoring in high-risk regions and periods, alongside improved investigation of mortality causes to support effective conservation strategies. Full article

We developed a class of multivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson and negative binomial marginals. Cross-sectional dependence is modeled via a trivariate Gaussian or a “t-copula”, allowing for both positive and negative correlations and providing a flexible dependence structure. Model parameters are estimated using likelihood-based inference, where the trivariate Gaussian or t-copula integrals are evaluated through standard randomized Monte Carlo methods. Simulation results, along with an analysis of annual counts of major hurricanes (Category 3+) across the North Atlantic, Eastern North Pacific, and Western North Pacific basins, demonstrate the effectiveness of the proposed model. Full article

Background/Objectives: Oral behaviors are increasingly considered relevant within the biopsychosocial framework of temporomandibular disorders, yet their relationship with emotional symptoms remains insufficiently characterized in general adult populations. This study investigated the association between the frequency of oral behaviors and the severity of anxiety and depression symptoms in adults. Methods: This observational, cross-sectional, multicenter study included 460 adults recruited from private dental practices. Oral behaviors were assessed using the Oral Behaviors Checklist (OBC-21), while anxiety and depression were evaluated using Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Associations were examined using Spearman correlations and generalized linear models with negative binomial distributions, adjusted for age, sex, and area of residence. Results: OBC-21 scores were positively associated with GAD-7 (R = 0.469, p < 0.001) and PHQ-9 (R = 0.432, p < 0.001). In adjusted models, OBC-21 remained significantly associated with anxiety symptoms (IRR = 1.0292, 95% CI: 1.0187–1.0399, p < 0.001) and depressive symptoms (IRR = 1.0293, 95% CI: 1.0187–1.0400, p < 0.001). Male sex was associated with lower anxiety scores, while age and area of residence were not significant. GAD-7 and PHQ-9 scores were strongly correlated. Conclusions: In this sample of adults attending private dental practices, a higher frequency of oral behaviors was associated with increased anxiety and depression symptoms, independently of age, sex, and area of residence. These findings support the clinical relevance of assessing oral behaviors as part of a biopsychosocial evaluation in dental practice. Full article