Search Results (39)

Background/Objectives: Analyses of the 2023 Korea National Health and Nutrition Examination Survey described health-literacy distributions by smoking status but did not model current conventional cigarette smoking across continuous Health Literacy Index for the Community (HLIC) scores or report a standardized absolute difference. Methods: We analyzed 5792 adults aged ≥19 years. Survey-weighted quasi-Poisson models estimated prevalence ratios (PRs) per 5-point-lower HLIC score, adjusting for age, sex, education, and household income. Standardized prevalences were compared for scores below versus at or above 30. Exploratory analyses separated former smokers from adults with fewer than 100 lifetime cigarettes and tested interaction by sex. Results: The adjusted PR per 5-point-lower score was 1.103 (95% confidence interval (CI), 1.020–1.192). Standardized smoking prevalence was 18.08% below 30 and 14.74% at or above 30; the difference was 3.34 percentage points (95% CI, 1.08–5.60). There was no clear evidence of sex-related heterogeneity on the PR scale (interaction p = 0.541), although the estimate among women was imprecise. Conclusions: Lower self-reported HLIC was associated with modestly higher smoking prevalence. The cross-sectional design precludes temporal inference, but the association may be relevant to the design and evaluation of accessible smoking-prevention and cessation services. Longitudinal and intervention studies are needed. Full article

The Clopper–Pearson method of constructing a confidence interval for the probability of success in a binary population that follows a Bernoulli distribution is well known. This paper pedagogically justifies the Clopper–Pearson method and extends the method to all distributions with one parameter whose cumulative probability distribution functions are monotonically continuous with respect to their single parameter. The conservativeness of Clopper–Pearson confidence intervals is proved analytically. Clopper–Pearson confidence intervals are constructed for Poisson, geometric, non-central hyper-geometric, and exponential, etc. It turns out that such extensions result in various well-known confidence intervals in the literature. 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

Background/Objectives: While the descriptive trajectory of COVID-19 is well-documented, there is a methodological gap in quantifying the precise magnitude of risk reduction across multi-year pandemic phases in Brazilian subnational units. This study aimed to fill this gap by applying Generalized Linear Models (GLMs) to quantify the temporal transition of epidemiological risks (Incidence, Mortality, and Case Fatality) in Ceará (2020–2023), using the first year of the pandemic as a statistical baseline. Methods: Ecological time-series study was conducted using official surveillance data. We employed GLMs with Poisson distribution to calculate Rate Ratios (RRs) and 95% Confidence Intervals, allowing for a robust comparative risk modeling between 2020 (reference) and subsequent years (2021–2023). Results: Modeling revealed a significant epidemiological dissociation between transmission and severity. While the risk of incidence remained high through 2022 (RR = 1.42), the mortality risk showed an earlier and more drastic decline, with a 68% reduction as early as 2022 (RR = 0.32) and 99% in 2023 (RR = 0.01). The Case Fatality Rate (CFR) risk decreased consistently from 2021 onwards, reaching its lowest point in 2023 (RR = 0.09; 91% reduction). Conclusions: Between 2020 and 2023, Ceará transitioned to reduced COVID-19 severity. Despite ecological design and data limitations, these findings underscore the importance of resilient health systems and equitable immunization. Full article

Background: Femoral arteriotomy closure after peripheral angiography and intervention is commonly achieved using vascular closure devices (VCDs) or compression-based strategies; however, comparative randomised data in contemporary peripheral endovascular practice remain limited. Methods: In this prospective randomised trial, adults undergoing femoral-access diagnostic angiography or peripheral endovascular intervention were assigned in a 1:1 ratio to haemostasis with the FemoStop™ II Gold pneumatic compression system or a contemporary VCD strategy. The primary endpoint was a composite of major or minor groin-site complications immediately after sheath removal. Secondary endpoints included composite complications at recovery, discharge, and 30 days, with separate analyses of major and minor complications. Patient-reported pain was assessed using the Verbal Numerical Rating Scale (VNRS). Efficacy and safety analyses were performed according to the intention-to-treat and as-treated principles, respectively. Risk ratios were estimated using modified Poisson regression with robust variance, with prespecified adjustment for sex, systolic blood pressure before sheath removal, and sheath size. Results: A total of 130 participants underwent randomisation, including 66 assigned to FemoStop™ II Gold and 64 assigned to VCDs. The primary composite endpoint occurred in 23/66 participants (34.9%) in the FemoStop™ II Gold group and 16/64 (25.0%) in the VCD group (absolute difference, 9.9 percentage points; 95% confidence interval [CI], −6.1 to 25.7; p = 0.25), with the numerical difference driven predominantly by minor-only events (28.8% versus 15.6%; p = 0.09). At 30 days, the composite endpoint occurred in 17/66 participants (25.8%) and 12/64 participants (18.8%), respectively (absolute difference, 7.0 percentage points; 95% CI, −13.3 to 26.4; p = 0.40). Serious access-site events remained infrequent both immediately post-procedure (6.1% versus 9.4%; p = 0.53) and at 30 days (6.1% versus 4.7%; p = 0.72). The adjusted risk ratios were 1.28 (95% CI, 0.74 to 2.21) for the primary composite endpoint and 1.23 (95% CI, 0.63 to 2.40) for the 30-day composite endpoint. Ordinal VNRS pain distributions did not differ significantly at any timepoint, although “any pain” immediately post-procedure was less frequent with FemoStop™ II Gold (22.7% versus 40.6%; unadjusted risk ratio, 0.56; 95% CI, 0.33 to 0.93); this association was attenuated after adjustment (adjusted risk ratio, 0.63; 95% CI, 0.38 to 1.03). Prespecified interaction testing suggested that the effect of treatment on composite complications varied according to sheath size both immediately post-procedure and at 30 days (p < 0.001 for both interactions). Conclusions: In patients undergoing femoral-access diagnostic angiography or peripheral endovascular intervention, haemostasis with FemoStop™ II Gold resulted in 30-day groin-site complication rates that did not differ significantly from those observed with contemporary VCD strategies. Serious access-site events remained infrequent in both groups, and the apparent early reduction in patient-reported pain with FemoStop™ II Gold was not definitive after adjustment. Larger, adequately powered multicentre studies are warranted to clarify sheath size-dependent effects and uncommon clinically consequential vascular events. Full article

Schizophrenia is a chronic mental disorder affecting approximately 1% of the global population and imposing a significant economic and social burden. In Ecuador, comprehensive data on its incidence, burden, and spatial distribution are scarce. This study aims to estimate the hospital-diagnosed incidence, disease burden, and spatial patterns of schizophrenia in Ecuador using national hospital discharge records from 2010 to 2021. A retrospective observational study was conducted using publicly available hospital discharge records from the Instituto Nacional de Estadística y Censos (INEC). Schizophrenia cases were identified using ICD-10 codes F20–F29. Incidence rates per 100,000 population were estimated with 95% Poisson confidence intervals. Disability-Adjusted Life Years (DALYs) were calculated under three scenarios: no discounting or age weighting, 3%-time discounting, and both. Spatial clusters were identified using SATSCAN software v10.1.2. A total of 10,542 schizophrenia cases were recorded between 2010 and 2021, with an overall incidence rate of 5.36 per 100,000 population (95% CI: 5.26–5.46). Incidence significantly decreased over time (p = 0.029). The estimated burden ranged from 153.05 to 289.78 DALYs per 100,000. High-incidence clusters were identified in Guayas and Pichincha provinces. This study provides the first nationwide assessment of schizophrenia in Ecuador, offering critical insights for health policy development, resource allocation, and improved care strategies. Full article

The Poisson distribution is a discrete probability model, widely used in science and engineering to describe various natural and man-made phenomena. It possesses an important feature, namely being inherently asymmetric, but as its parameter becomes large, the distribution becomes approximately symmetric. To broaden its use, multiple extensions and variations have been developed. Determining whether a data set follows a Poisson distribution involves hypothesis testing at a chosen significance level. When sampling from a Poisson distribution, confidence intervals provide an estimated range instead of a single value. Due to the discrete nature of the Poisson distribution, confidence intervals cannot be derived from a simple formula, and are therefore computed using specialized algorithms. In this paper, three alternatives are given and discussed. Full article

We propose a novel deep learning framework for counterfactual inference on the COMPAS dataset, utilizing a multi-task CNN-LSTM architecture. The model jointly predicts multiple outcome types: (i) count outcomes with zero inflation, modeled using zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and negative binomial (NB) distributions; (ii) time-to-event outcomes, modeled via the Cox proportional hazards model. To effectively leverage the structure in high-dimensional tabular data, we integrate functional data analysis (FDA) techniques by transforming covariates into smooth functional representations using B-spline basis expansions. Specifically, we construct a pseudo-temporal index over predictor variables and fit basis expansions to each subject’s feature vector, yielding a low-dimensional set of coefficients that preserve smooth variation while reducing noise. This functional representation enables the CNN-LSTM model to capture both local and global temporal patterns in the data, including treatment-covariate interactions. Our approach estimates both population-average and individual-level treatment effects (ATE and CATE) for each outcome and evaluates predictive performance using metrics such as Poisson deviance, root mean squared error (RMSE), and the concordance index (C-index). Statistical inference on treatment effects is supported via bootstrap-based confidence intervals and hypothesis testing. Overall, this comprehensive framework facilitates flexible modeling of heterogeneous treatment effects in structured, high-dimensional data, advancing causal inference methodologies in criminal justice and related domains. Full article

In this paper, the stress–strength reliability of complex systems with diverse component types is investigated based on the theoretical framework of survival signatures. Assuming that both the strength and stress of components of the same type follow the Lomax distribution, the maximum likelihood estimation (MLE), maximum spacing estimation (MSPE), and Bayesian estimation for the stress–strength model are derived under the condition that components of the same type have common scale parameters. Subsequently, the 95% Bootstrap-p and Highest Posterior Density confidence intervals for the stress–strength reliability were derived using Monte Carlo simulation. Additionally, since stress cycles are represented by a Poisson process, a dynamic stress–strength model for the system subjected to periodic stresses over the interval $(0,t]$ was developed, together with an approximate computational algorithm for this model. Finally, a simulation experiment was conducted using a system consisting of a total of nine components of three different types to analyze these estimation methods. The findings reveal that MLE exhibits the lowest estimation error, registering merely 0.001 in the case of small-sized samples. Compared with the previous two methods, Bayesian estimation has a relatively larger error. However, in the case of large samples, the error is 0.0112. In addition, the performance and accuracy of the dynamic model were verified through the proposed algorithm. The results indicate that compared with the static model at $t=0$, the error of the algorithm is 0.0464. Overall, the model evaluation results are satisfactory. Full article

Modeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with COVID-19 cases. The forecasted model with both Poisson and negative binomial marginal distributions yielded trends in COVID-19 cases that closely paralleled the reported cases, with 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported data. However, the model did not effectively forecast the trends and the rising cases of hospital admissions and deaths. The forecasting model was validated for predicting clinical cases and trends with a non-normal distribution in a time series manner. Additionally, the model showed potential for using wastewater SARS-CoV-2 viral load as a predictor for forecasting COVID-19 cases. Full article

In addition to the detrimental health consequences, the early stages of the COVID-19 pandemic have yielded unforeseen benefits in terms of reducing air pollution emissions. This study investigated air pollution changes in Novi Sad, Serbia, during the COVID-19 lockdown (March–June 2020) and their correlation with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) hospitalizations. Using quasi-Poisson generalized linear models (GLM) and distributed lag non-linear models (DLNM), we examined the relationship between the number of AECOPD hospitalizations and the concentrations of selected air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) from March to June of 2019, 2020, and 2021. During the COVID-19 lockdown, significant reductions in most air pollutant concentrations and the number of AECOPD hospitalizations were observed. However, neither the study year nor its interaction with air pollutant concentration significantly predicted AECOPD hospitalizations (p > 0.05). The 95% confidence intervals of the relative risks for the occurrence of AECOPD hospitalizations at each increase in the examined air pollutant by 10 μg/m³ overlapped across years, suggesting consistent effects of air pollution on the risk of AECOPD hospitalizations pre-pandemic and during lockdown. In conclusion, reduced air pollution emissions during the COVID-19 lockdown did not lead to a statistically significant change in the number of AECOPD hospitalizations. Full article

Background: Metal implants have been preferentially used in THA due to its biocompatibility, mechanical stability and durability. Yet concerns have emerged regarding their potential to release metallic ions, leading to long-term adverse effects, including carcinogenicity. This study aimed to investigate the risk of cancer development in patients with orthopaedic metal implants in total hip arthroplasty (THA). Methods: Patients with THA conducted at a local tertiary implant centre from 2001–2008 were linked to the local cancer registry and followed up to the end of 2023. Standardized incidence ratios (SIRs) for cancer incidence and its confidence interval by Poisson distribution were calculated. Survival analysis was depicted using the Kaplan–Meier method, and the log-rank test was used to assess the differences across groups. Results: The study cohort included 388 patients and 53 cancers diagnosed during follow-up, at least 5 years post THA. All-site cancer risks were increased in patients with THA (SIR: 1.97; 95% CI: 1.48–2.46), validated with chi-square analysis (chi-square = 15.2551, N = 100,388, p < 0.01). A statistically significant increase in multiple site-specific cancers including haematological cancers were identified. Conclusions: Patients with THA were found to have an increased risk for cancer compared to the general population during a mean follow-up of 16 years. Full article

One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss–Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss–Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region. The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management. Full article

Surface-checking is a significant quality issue of veneer and sliced lamellae-based wood products. This study explores how surface-checking in sliced lamellae-based engineered wood Flooring (EWF) is influenced by two key structure parameters: core type and top-layer thickness. The core types assessed were a standard solid wood lamellae with a veneer back-end layer (S), a standard solid wood lamellae core with veneer back-end layers on the two sides (DS), and a single-layer oriented strand board (OS) core. The EWF element’s top-layer lamellae were plain sliced at nominal dimensions of 1.5, 2.5, 3.5, and 4.5 mm from freshly sawn slabs of European oak (Quercus spp.). The surface-checking of EWF specimens was quantified based on a digital image correlation (DIC) method, which outputs a surface-checking index. The surface-checking results were evaluated using a Tweedie compound Poisson data distribution to fit a general linear model. The model evaluated the impact of individual factors, sliced lamellae thickness and core type, and their interaction. The checking index confidence intervals were estimated using a bootstrapping technique. Findings reveal a significant interaction between studied factors and provide insight into optimizing top-layer thickness and core construction to diminish surface-checking. A low sliced lamella thickness on standard solid wood lamellae core resulted in low surface-checking, deemed relevant for further research. Full article

To determine the association between women’s autonomy and the presence of childhood anemia in children under five years of age in Peru, a cross-sectional study utilizing data from the 2019 Demographic and Family Health Survey was carried out. The study employed generalized linear models with a Poisson distribution and log link function. Crude and adjusted prevalence ratios (aPR) were calculated, along with their corresponding 95% confidence intervals (CI), to assess the association of interest. A total of 15,815 women and their children under five years of age were analyzed. The prevalence of childhood anemia was 30.4% (95%CI: 29.5–31.3%), while the proportions of low, moderate and high autonomy of the mothers were 44.5%, 38.4% and 17.1%, respectively. Children under five years of age of women with a low level of autonomy were more likely to have anemia (aPR: 1.10; 95%CI: 1.00–1.21). Three out of ten children under five years of age suffer from anemia, and four out of ten mothers have a low level of autonomy. A low level of women’s autonomy was associated with a higher probability of anemia in children under 5 years of age. Full article