Search Results (207)

Background: Infection following total knee arthroplasty (TKA) is a challenging complication. Optimal empirical antibiotic therapy and surgical management hinge on up-to-date knowledge of local pathogen distribution and resistance patterns. However, few studies have examined whether geographical factors, specifically rural versus urban residence, influence the microbiology or clinical outcomes of periprosthetic joint infection (PJI) within integrated healthcare systems. The goal of this study was to assess the temporal evolution of bacterial species and antimicrobial resistance in knee PJI over an 11-year period. As a secondary objective, we wanted to evaluate the potential impact of patient residence on microbiological trends and treatment success. Methods: We conducted a retrospective analysis of all patients diagnosed with knee PJI who underwent surgical treatment between 2013 and 2023 at our center. Infections were classified as acute postoperative, acute hematogenous, or chronic. Patient residence was categorized as rural (<5000 inhabitants) or urban. Temporal trends were modeled using Poisson regression, and comparisons between subgroups were performed using Fisher’s exact test and Student’s t-test. Results: A total of 98 patients were analyzed, with 99 microorganisms identified. Gram-positive organisms predominated (72.3%), with Staphylococcus aureus (33.3%) and Coagulase-negative Staphylococci (CoNS) (29.3%) as the most frequent isolates. Resistance to vancomycin was not detected in S. aureus isolates. However, CoNS demonstrated high resistance to fluoroquinolones (55.2%) and rifampicin (20.7%). No significant annual shifts were observed for Gram-positive (IRR = 0.94; 95% CI: 0.86–1.03; p = 0.413) or Gram-negative cases (IRR = 0.75; 95% CI: 0.53–1.05; p = 0.086). Comparing rural versus urban populations, no differences were found in microbiological profiles (Fisher’s exact test, all p > 0.05). Furthermore, clinical treatment success rates were comparable (Rural 69.4% vs. Urban 63.0%, p = 0.500), despite a significantly higher prevalence of diabetes mellitus in rural patients (34.7% vs. 10.2%, p = 0.007). Conclusions: The microbiological landscape of knee PJI has remained stable, with no emergence of multidrug-resistant S. aureus. In our setting, standardized management protocols appeared to be equally effective regardless of patient residence. However, given the single-center nature and sample size of this study, broader multicenter validation is required before these findings can be generalized. Full article

Background/Objectives: Congenital gastrointestinal malformations (CGIMs) are important causes of neonatal surgical morbidity with potential long-term consequences. Although some can be suspected on prenatal ultrasound, data on their clinical spectrum, burden, and distribution remain limited in Eastern Europe. This study aimed to describe the diagnostic spectrum, timing of diagnosis, documented prenatal ultrasound suspicion, and the early outcomes of CGIMs managed at a Romanian tertiary referral centre between 2020 and 2024, a period overlapping the COVID-19 pandemic. Methods: We conducted a retrospective, single-centre observational study including all consecutive paediatric patients with a CGIM admitted between January 2020 and December 2024. Cases were analysed by index anatomical diagnosis, phenotypic complexity, and etiologic background. Logistic regression was used to examine factors associated with documented prenatal suspicion and in-hospital mortality, and annual hospital-based CGIM admission rates were modelled with Poisson regression, using the number of paediatric surgical admission as the offset. Results: Among the 231 children (58.9% male), the most frequent diagnoses were persistent omphalomesenteric duct remnants (16%), oesophageal atresia with or without tracheoesophageal fistula (15.6%), and anorectal malformations (13.9%). Documented prenatal ultrasound suspicion was present in 17.7% of pregnancies (41/231) and was likely underestimated because antenatal documentation was unavailable for 17.7% of the cohort. The highest proportions of documented prenatal suspicion were observed in jejuno-ileal and duodenal atresia. Foregut malformations were the most common by embryological grouping (93/231, 40.3%). Most cases were diagnosed during the neonatal period (n = 161, 69.7%). CGIM admission rates per 1000 surgical admissions ranged from 20.8 to 38.2. An exploratory Poisson model yielded a hospital-based rate ratio per calendar year of 0.88 (95% CI 0.81–0.97; p = 0.008). However, given the limited number of annual observations and increasing total surgical admissions, this finding should be considered exploratory and hypothesis-generating only. Complex cases accounted for 8.2% and associated extra-intestinal anomalies were present in 70.1%. In-hospital mortality was 13.0% and occurred predominantly in patients with complex or foregut malformations. In the primary complete-case multivariable model, prematurity remained independently associated with mortality, whereas complex CGIMs were not independently associated with mortality after adjustment. A prespecified multiple-imputation sensitivity analysis yielded a stronger estimate for complex CGIMs, but this finding was interpreted cautiously and not treated as a primary result. Conclusions: In this tertiary referral cohort, documented prenatal suspicion of CGIM was low and strongly diagnosis-dependent, while most cases were identified in the neonatal period. Mortality was concentrated in foregut and clinically complex presentations in the descriptive analysis, while prematurity remained independently associated with death in the primary multivariable model. These findings highlight the need to strengthen prenatal referral pathways and coordinated national surveillance. Full article

Background/Objectives: Excess consumption of energy-dense foods (EDF; ultra-processed snacks, sweets, and sugar-sweetened beverages) among preschool-aged children is a public health concern, particularly in low-income families. Caregiver parenting style, psychological stress, and food-parenting practices (FPP) may shape children’s EDF consumption, yet little is known about how these factors operate in real time. This exploratory pilot study examined (1) associations between baseline characteristics and EDF feeding episodes across 1 week and (2) whether caregivers’ momentary stress during EDF episodes related to FPP used. Methods: In total, 22 caregivers of Head Start children (ages 3–5) completed baseline measures and 7 days of ecological momentary assessment (up to seven prompts/day). At each prompt, caregivers reported child EDF consumption in the past hour; if confirmed, they reported FPP used and rated momentary stress. Aim 1 used Poisson regression to model caregiver-level EDF episode counts. Aim 2 tested momentary stress–practice associations during EDF episodes using GEE, with within-person and between-person stress modeled separately. Results: Authoritarian parenting was associated with a higher weekly rate of EDF episodes (RR = 1.43, 95% CI 1.23–1.66, p < 0.001); authoritative parenting trended lower (RR = 0.90, p = 0.065). Higher baseline stress was associated with more EDF episodes (RR = 1.25, p = 0.001). Momentarily, elevated stress above a caregiver’s own average increased odds of using food as a reward (OR = 1.08 per +10 points, p = 0.011), while higher average momentary stress was associated with co-eating (OR = 1.59, p = 0.042). Domain-level FPP composites showed no association with momentary stress. Conclusions: Authoritarian parenting and higher caregiver stress were associated with increased EDF feeding, and momentary stress was linked to reward-based feeding during those episodes. These hypothesis-generating findings suggest potential behavioral targets for just-in-time adaptive intervention, pending replication in adequately powered studies. Full article

Owing to multi-target tracking in scenarios with nonlinearity, uncertain measurement model and high clutter density, the Poisson multi-Bernoulli mixture (PMBM) recursion is prone to unstable covariance propagation under nonlinear dynamics as well as uncertainty in measurement-to-target association caused by mismatched gate that causes erroneous updates from clutters. In the prediction stage, the singular value decomposition (SVD) is used in place of Cholesky factorization to construct and propagate the square-root covariance factor in the square-root cubature Kalman filter (SCKF), yielding a numerically stable square-root implementation. Then, the resulting SVD-SCKF is incorporated into the PMBM prediction step and used to propagate the Gaussian-mixture components of both the Poisson point process (PPP) intensity and the Bernoulli component in the Multi-Bernoulli mixture (MBM), yielding predicted means and covariances under nonlinear dynamics. An adaptive fading factor is determined from innovation statistics, and covariance inflation is performed to improve robustness under target maneuvers and model mismatch. In the update stage, the unknown measurement function is regressed by Gaussian process (GP) using historical state–measurement samples, yielding an equivalent measurement mapping and state-dependent uncertainty. Furthermore, the predicted measurement distribution is generated from the GP-based conditional measurement distribution with state prior approximated by SVD-SCKF cubature points. An adaptive gate is determined from the GP-based conditional measurement distribution, which is approximated by an equivalent ellipsoidal gate via fitting for screening the current measurements and filtering out clutter. Residual in-gate clutter measurements are handled via Bayesian target discrimination, where the posterior probability of measurement originated from target is employed as a weight and incorporated into association weights and update likelihoods. Simulation results further confirm the effectiveness and stability of the proposed filter in complex scenarios. Full article

Extended reality (XR), encompassing augmented reality (AR), virtual reality (VR), and mixed reality (MR), is a key enabling technology for virtual worlds, and XR-related patents continue to grow rapidly. However, patent-based XR technology analysis faces a fundamental challenge: document–keyword matrix (DKM) built from patent titles and abstracts are typically high dimensional, sparse, and often exhibit excess zeros, which can distort inference when conventional text mining pipelines are applied without a generative count perspective. In this study, we propose a statistically grounded XR technology analysis framework that combines likelihood-based count modeling with interpretable structure mining to map XR sub-technologies from a patent DKM. Using an XR patent–keyword matrix, we fit Poisson regression (PR), negative binomial regression (NBR), and zero-inflated negative binomial regression (ZINBR) models via maximum likelihood estimation (MLE), controlling for document-length effects. Model selection by Akaike information criterion (AIC) consistently favored NBR for both target keywords, indicating substantial overdispersion in XR patent counts. We interpret exponentiated coefficients as incidence rate ratios (IRRs) and construct a technology relatedness network from significant IRR edges, revealing a dual-axis XR structure: reality is anchored in an AR or VR experience and content axis such as virtual and augment, whereas extend is embedded in a structure and integration axis for example, surface, edge, layer, and connectivity-related terms. To show how the proposed method can be applied to real domains, we searched the XR patent documents, and analyzed them for XR technology analysis. Full article

This paper focuses on variable selection and parameter estimation for mixed-effects Poisson count regression models. To simultaneously select important variables in both fixed effects and random effects, we propose a double-penalized Poisson count regression model with the Smoothly Clipped Absolute Deviation (SCAD) penalty imposed on both components. To estimate the unknown parameters, we develop a new iterative algorithm called the Double SCAD–Local Quadratic Approximation (DSCAD-LQA) algorithm. Under regularity conditions, the consistency and Oracle property of the proposed estimator are established. Simulation studies are conducted under two types of penalty parameter selection criteria: the Schwarz Information Criterion (SIC) and the Generalized Approximate Cross-Validation (GACV). We evaluate the performance of the proposed method under different levels of correlation among explanatory variables and different covariance structures of random effects. Comparisons are also carried out with the non-penalized model, the single-penalized model, and the double LASSO-penalized model. The results demonstrate that the proposed double SCAD penalty method performs better than the other three methods in terms of important variable selection and coefficient estimation, and is especially effective for sparse models. Full article

Background/Objectives: Sabin-strain inactivated poliovirus vaccine has been increasingly incorporated into routine immunization programs as part of the global strategy to eradicate poliomyelitis. As childhood immunization schedules become more complex, concerns persist regarding the safety of concomitant vaccination. Although randomized controlled trials and regional surveillance studies have demonstrated acceptable safety profiles, additional population-based real-world evidence remains valuable for evaluating the safety of sIPV administered concomitantly with other vaccines under routine programmatic conditions. Methods: A retrospective observational study was conducted using vaccination records and adverse events following immunization surveillance data collected in Liaoning Province, China, between 1 January 2022 and 30 June 2025. All reported adverse events following immunization following Sabin-strain inactivated poliovirus vaccine administration were extracted from the Chinese National AEFI Surveillance System. The reporting rates were calculated per 100,000 administered doses. Multivariable Poisson regression models with robust variance estimation were used to estimate adjusted rate ratios and 95% confidence intervals comparing standalone and concomitant sIPV administration, adjusting for sex, age in months, dose number, and city. Interaction analyses between vaccination mode and dose number were additionally performed. Results: A total of 205,576 sIPV doses were administered, including 144,724 doses administered alone and 60,852 doses administered concomitantly with other vaccines. Fifty-six adverse events following immunization were reported, corresponding to an overall reporting rate of 27.24 per 100,000 doses. Most reported adverse events following immunization were general reactions (91.07%), and all occurred within seven days after vaccination. The reporting rates for sIPV administered alone and concomitantly were 26.26 and 29.58 per 100,000 doses, respectively, with no significant difference between groups (p = 0.7869). After adjustment, concomitant sIPV administration was not associated with an increased risk of adverse events following immunization compared with standalone administration (adjusted rate ratios = 1.13, 95% confidence intervals: 0.59–2.16). No significant interaction between vaccination mode and dose number was identified. Conclusions: Sabin-strain inactivated poliovirus vaccine demonstrated a favorable safety profile when administered either alone or concomitantly with other vaccines. These findings support the continued use of flexible and synchronized vaccination strategies involving Sabin-strain inactivated poliovirus vaccine in routine immunization programs. Full article

Background/Objectives: Chronic lower limb ulcers represent a significant clinical challenge, with conventional therapies achieving healing in only 30–40% of complex cases. This study evaluated the comparative effectiveness of autologous blood clot therapy (ActiGraft, delivering platelet- and leukocyte-derived growth factors) and autologous micrograft therapy (Rigenera, containing viable progenitor cells) versus advanced wound dressings for refractory chronic wounds. Methods: This retrospective analysis of a prospectively collected, non-randomized clinical cohort included 132 patients with chronic lower limb ulcers refractory to prior therapy, who were treated between 2019 and 2024 at a single wound care center. The patients received ActiGraft (n = 32), Rigenera (n = 33), or advanced wound dressings (n = 67) based on their choice after informed discussion. The primary outcome was complete wound closure at 52 weeks. Multivariable Poisson regression with robust variance was performed, adjusting for baseline wound area (log-transformed), chronic renal failure, age, and peripheral vascular disease. Cox proportional hazards was used to model time to closure. Bonferroni correction (threshold p < 0.0167) was applied for three pairwise comparisons. This study was not pre-registered, and the results should be considered hypothesis-generating. Results: Unadjusted wound closure rates were 68.8% (ActiGraft; RR = 1.71, 95% CI: 1.17–2.48, p = 0.015), 60.6% (Rigenera; RR = 1.50, 95% CI: 1.01–2.25, p = 0.089), and 40.3% (advanced dressings). After multivariable adjustment, ActiGraft showed attenuated benefit (adjusted RR = 1.38, 95% CI: 0.86–2.21, p = 0.179), while the beneficial effect of Rigenera became non-significant (adjusted RR = 1.19, 95% CI: 0.73–1.94, p = 0.488). However, the adjusted Cox regression revealed significantly faster healing for ActiGraft (HR = 10.67, 95% CI: 4.17–27.30, p < 0.001) and Rigenera (HR = 4.12, 95% CI: 1.75–9.73, p = 0.001). Sensitivity analyses restricted to comparable wound sizes (≤10 cm²) showed a consistent direction of effect (ActiGraft 71.4% vs. Advanced 37.5%). Infection rates were lower in the autologous therapy groups (0–3.0% vs. 11.9%; Fisher’s exact p = 0.006). Conclusions: ActiGraft autologous blood clot therapy showed trends toward superior wound closure and demonstrated significantly faster healing compared to advanced dressings in patients with refractory chronic lower limb ulcers, with autologous micrograft therapy (Rigenera) showing intermediate results. Significant baseline imbalances in wound size limit causal inference from the closure rate comparisons. These hypothesis-generating findings from a non-randomized cohort warrant confirmation in adequately powered randomized controlled trials with stratification by wound characteristics. Full article

With the deepening of population ageing, the spatial planning of an elderly care service system faces unprecedented challenges. Building an elderly care service network that aligns with the pace of population ageing has become increasingly important and urgent. Based on annual longitudinal data on older adults’ health status and care service utilization from Japan’s Long-Term Care Insurance (LTCI) system, this study quantifies the relationship between changes in health status and elderly care service demand using a discrete time homogeneous Markov model and Poisson regression analysis. Subsequently, Geographic Information System (GIS) techniques are applied to conduct spatial analysis of the urban built environment to identify living service centres for older adults. Indicators including distance, supply–demand balance, and service capacity are then integrated through multi-objective clustering optimization to construct a multi-level elderly care service network system, achieving a quantitative linkage between elderly health status and spatial demand-oriented planning. Finally, the proposed integrated framework, which combines health status transitions, service demand estimation, and spatial allocation, is applied to Qinhuai district in Nanjing, China, generating practical policy recommendations that promote the integration of healthy ageing and precision service delivery. Full article

We propose the Bivariate Poisson–X–Exponential Distribution (BPXED), a flexible bivariate count model obtained by compounding Poisson variables with a shared X–Exponential latent mixing distribution. The model extends the Poisson–X–Exponential (PXED) distribution and includes several bivariate Poisson-type models as special or limiting cases. Closed-form expressions are derived for the joint probability mass function, probability generating function, moments, and covariance structure, showing that dependence arises from shared latent heterogeneity and is restricted to positive correlation. Parameter estimation is developed using maximum likelihood, regression-based, and Bayesian approaches, and a Monte Carlo simulation study demonstrates a good finite-sample performance. Applications to soccer scores, reliability failures, and correlated photon counts illustrate improved goodness-of-fit over classical and recent competing models. Overall, BPXED provides an analytically tractable and interpretable framework for modeling positively dependent and overdispersed bivariate count data. Full article

We present an advanced statistical framework for estimating the relative intensity of astrophysical event distributions (e.g., Gamma-Ray Bursts, GRBs) on the sky tofacilitate population studies and large-scale structure analysis. In contrast to the traditional approach based on the ratio of Kernel Density Estimation (KDE), which is characterized by numerical instability and bandwidth sensitivity, this work applies a logistic regression embedded in a Bayesian framework to directly model selection effects. It reformulates the problem as a logistic regression task within a Generalized Additive Model (GAM) framework, utilizing isotropic Splines on the Sphere (SOS) to map the conditional probability of redshift measurement. The model complexity and smoothness are objectively optimized using Restricted Maximum Likelihood (REML) and the Akaike Information Criterion (AIC), ensuring a data-driven bias-variance trade-off. We benchmark this approach against an Adaptive Kernel Density Estimator (AKDE) using von Mises–Fisher kernels and Abramson’s square root law. The comparative analysis reveals strong statistical evidence in favor of this Preconditioned (Precon) Estimator, yielding a log-likelihood improvement of $\Delta \mathcal{L}\approx 74.3$ (Bayes factor $>{10}^{30}$) over the adaptive method. We show that this Precon Estimator acts as a spectral bandwidth extender, effectively decoupling the wideband exposure map from the narrowband selection efficiency. This provides a tool for cosmologists to recover high-frequency structural features—such as the sharp cutoffs—that are mathematically irresolvable by direct density estimators due to the bandwidth limitation inherent in sparse samples. The methodology ensures that reconstructions of the cosmic web are stable against Poisson noise and consistent with observational constraints. Full article

The New Polynomial Exponential Distribution (NPED), introduced by Beghriche et al. (2022), provides a flexible one-parameter family capable of representing diverse hazard shapes and heavy-tailed behavior. Regression frameworks based on the NPED, however, have not yet been established. This paper introduces two methodological extensions: (i) a generalized linear model (NPED-GLM) in which the distribution parameter depends on covariates, and (ii) a Poisson–NPED count regression model suitable for overdispersed and heavy-tailed count data. Likelihood-based inference, asymptotic properties, and simulation studies are developed to investigate the performance of the estimators. Applications to engineering failure-count data and insurance claim frequencies illustrate the advantages of the proposed models relative to classical Poisson, negative binomial, and Poisson–Lindley regressions. These developments substantially broaden the applicability of the NPED in actuarial science, reliability engineering, and applied statistics. Full article

Background: Potential drug–drug interactions (pDDIs) present substantial challenges to medication safety during care transitions. Warfarin, with its narrow therapeutic index and extensive interaction profile, provides a strategic model for examining pDDIs at discharge. This study aimed to characterize the burden and determinants of major warfarin pDDIs among patients discharged from a tertiary-care hospital. Methods: This retrospective cross-sectional study analyzed electronic health records of 1667 patients discharged home on warfarin. Major pDDIs were identified using the Micromedex^® Drug Interaction database. Log-binomial regression was used to assess predictors of ≥1 major pDDIs, and generalized Poisson regression was used to model the number of pDDIs per patient. Results: Major warfarin pDDIs were identified in 81.6% (95% CI: 79.6–83.4%) of patients at hospital discharge. The burden was considerable: 35.1% (95% CI: 32.8–37.4%) of patients had one major pDDI, while 46.5% (95% CI: 44.1–48.9%) had two or more. Polypharmacy (≥5 concurrent medications) was the strongest predictor, associated with a higher risk of any major pDDI (adjusted risk ratio 1.72, 95% CI: 1.46–2.02) and nearly three times the burden of interactions per patient (adjusted incidence rate ratio (IRR) 2.87, 95% CI: 2.36–3.49). When modeled as a continuous variable, each additional discharge medication was associated with a 9% increase in predicted pDDI burden (IRR 1.09, 95% CI: 1.08–1.10). Conclusions: Using warfarin as a model for high-risk medication safety, major pDDIs were highly prevalent at hospital discharge, with polypharmacy as a significant predictor of both the presence and burden of interactions. These findings emphasize the importance of identifying polypharmacy-related pDDIs to reduce potential drug interaction risk during care transitions. Full article

Background and Objectives: Cardiometabolic diseases are rising in Latin America, yet rural Amazonian populations remain understudied. We aimed to characterize the prevalence and factors associated with a simple composite cardiometabolic risk in rural Amazonian adults. Materials and Methods: We conducted an analytical cross-sectional study during community screenings in San Martín, Peru, in 2025, enrolling adults aged ≥ 18 years. The outcome was present when ≥2 biological/anthropometric alterations were identified at the same visit (hypertension, dyslipidemia, hyperglycemia, hyperuricemia, general obesity, abdominal obesity, or elevated waist-to-hip ratio). Behaviors included current tobacco use, alcohol risk (AUDIT), and physical activity (IPAQ). We summarized variables (univariate), compared groups (bivariate: chi-square; Fisher for alcohol), and fitted modified Poisson regression with robust errors to estimate prevalence ratios (PRs); variables with p ≤ 0.20 in bivariate analysis entered multivariable models. Results: We enrolled 205 adults; 70.2% met the composite outcome. In multivariable models, abdominal obesity (adjusted PR [aPR] 1.70; 95% CI 1.40–2.10), hyperglycemia (1.65; 1.25–2.17), hyperuricemia (1.38; 1.19–1.61), dyslipidemia (1.25; 1.07–1.46), and general obesity (1.21; 1.04–1.40) were independently associated with cardiometabolic risk. Hypertension (1.06; 0.88–1.29) and elevated waist-to-hip ratio (1.20; 0.88–1.63) were not. Physical activity differed crudely but showed no independent association; tobacco and alcohol were not associated. Conclusions: In this rural Amazonian population, we observed a high prevalence of composite cardiometabolic risk and found that central adiposity and metabolic derangements, not blood pressure or self-reported behaviors, were the main correlates. Simple measures such as waist circumference, fasting glucose or HbA1c, a basic lipid panel, and serum urate may help flag adults at higher cardiometabolic risk in similar low-resource primary-care settings, but prospective studies are needed to evaluate their predictive value and screening performance. Full article

Overdispersion is a major problem in the context of count data analysis, and the classical Poisson regression estimators are, in general, unreliable since they imply that the mean equals its variance. In this article, an event-driven class of average estimators, which is based on the Poisson–inverse Gaussian (P-IG) regression model, is formulated to overcome this shortcoming. P-IG regression is a mixture of Poisson and inverse Gaussian regression that is modeled to deal with the overdispersion that is often found in real data. It approximates such count data by a compound distribution with a heavy-tailed inverse Gaussian component. Suggested estimators are more effective in estimating the population means in situations of overdispersion using auxiliary data in the form of covariates. The design-based framework specifies the statistical properties of proposed estimators with respect to their bias and mean squared error (MSE). To confirm the effectiveness and the strength of the suggested methodology, a reasonable amount of simulations and real-data applications are carried out, contrasting it with customary Poisson-based estimators. The results indicate that the P-IG-based estimators are superior over their counterparts. The study provides a statistically valid and practically useful breakthrough in survey sampling and count data regression that can provide researchers and practitioners with a strong alternative to classical Poisson-regression-based mean estimator procedures. Full article