Count Response Research Articles

A Predictive Model for Treatment Effectiveness in Severe Primary Immune Thrombocytopenia during Pregnancy: A Retrospective Study in a Tertiary Critical Maternity Referral Center

Objectives: The study aimed to identify factors influencing the severity of primary immune thrombocytopenia (ITP) during pregnancy, develop a predictive model for treatment response, and report maternal and neonatal outcomes associated with severe ITP. Design: A retrospective analysis was conducted on 155 pregnancies with severe ITP between January 2018 and April 2023 at a tertiary critical maternity referral center in Shanghai, China. Participants/Materials: The study included 155 pregnancies diagnosed with severe ITP, divided into groups based on the lowest platelet count (<30 × 109/L vs. 30–50 × 109/L) and first-line treatment response (non-response vs. response). Setting: The study was conducted at Renji Hospital, Shanghai Jiao Tong University School of Medicine, a tertiary critical maternity rescue referral center. Methods: Clinical characteristics and outcomes were compared between groups. A multivariable logistic regression model was used to identify factors associated with the severity of ITP. A prediction model for treatment response was established using LASSO-logistic regression and internally validated. Results: ITP severity was found to be correlated with low maximum amplitude of thromboelastography (OR 5.43, 95% CI: 1.48–16.00, p = 0.002), bleeding events (OR 4.91, 95% CI: 1.62–14.86, p = 0.005), and low reticulocytes (OR 2.40 × 10−7, 95% CI: 1.06 × 10−13 to 0.55, p = 0.04). Of the 118 patients who received first-line therapy, 52 (44%) responded. The dataset was randomly split into a training (N = 99) and test (N = 23) set with a ratio of 8:2. A predictive nomogram was created and internally validated showing good discrimination. The model yielded an area under receiver operating characteristic curve of 0.78 (0.69–0.87) and 0.85 (0.67–1.00) in the training and validation cohort, respectively. Earlier delivery and high rate of neonatal intensive care unit admission occurred with severe ITP and treatment failure. Limitations: The study was limited by a relatively small sample size and the retrospective observational design, which imposed limitations on the assessment of treatment efficacy. Conclusions: We identified clinical predictors of ITP severity and treatment resistance during pregnancy. A nomogram predicting first-line response was validated. These findings can facilitate clinical decision-making and counseling regarding this challenging pregnancy complication.

A ridge estimation method for the Waring regression model: simulation and application

This study focuses on parameter estimation in the presence of multicollinearity for the count response that follows the Waring distribution. The Waring regression model deals with over-dispersion. So, this study proposed the Waring ridge regression (WRR) model as a solution for multicollinearity with over-dispersion. We conducted a theoretical comparison between the ridge estimator and the maximum likelihood estimators using matrix and scalar mean squared error as a performance evaluation criterion. Several ridge parameters are considered for the WRR estimator. The performance of these parameters is numerically evaluated using a Monte Carlo simulation study and a real application. The results of the simulation and application demonstrate the superiority of the WRR model with different ridge parameters over the maximum likelihood estimator.

Pantoea peritonitis in peritoneal dialysis: a report of two cases and literature review

BackgroundPantoea spp., a non-encapsulated, non-spore-forming Gram-negative rod bacterium that belongs to the Erwiniaceae family, can be found as a colonizer in humans, plants, and the environment, such as water and soil. Although it has the pathogenic potential to cause disease in humans, patients infected with this pathogen generally experience favorable outcomes. In this article, we present two cases of peritoneal dialysis (PD)-associated peritonitis caused by Pantoea spp. along with literature review.Case presentationThe first case is a 66-year-old male patient with end-stage kidney disease (ESKD) on PD, admitted for P. dispersa peritonitis. He presented with abdominal pain and cloudy dialysis effluent, responding well to intraperitoneal vancomycin and cefepime. Antibiotics were deescalated to ceftazidime monotherapy on the basis of antibiotic susceptibility testing. Despite initial recovery with a 3-week course of antibiotics, he developed recurrent peritonitis with P. dispersa, necessitating PD catheter removal and transition to hemodialysis. The second case is a 42-year-old male patient with ESKD on PD who was admitted after 6 days of bloody PD fluid without trauma or associated symptoms. With elevated PD fluid cell counts and positive PD fluid culture showing Streptococcus mitis and P. agglomerans, he was empirically treated for PD-associated peritonitis with intraperitoneal vancomycin and cefepime. Due to a suboptimal response in repeat PD fluid cell counts at day 5, the PD catheter was removed, and he was switched to hemodialysis, followed by a 3-week course of intravenous ceftriaxone.ConclusionsWe described two unique cases of Pantoea peritonitis in PD, recurrent P. dispersa peritonitis and refractory P. agglomerans peritonitis, both of which resulted in PD catheter removal. Our cases indicate the formation of bacterial biofilm as a potential reason for recurrence of infection and underscores the importance of vigilant monitoring and need for PD catheter removal in Pantoea peritonitis.

Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi

Recent research has established existence of a correlation between women’s education and fertility, suggesting that they share similar risk factors. However, in many studies, the two variables were analysed separately, which could bias the conclusions by undermining the apparent correlations of such paired outcomes. In this article, the univariate and bivariate Poisson regression models were applied to nationally representative sample of 24,562 women from the 2015-16 Malawi demographic and health survey to examine the risk factors of women’s education levels and fertility. The R software version 4.1.2 was used for the analyses. The results showed that estimates from the bivariate Poisson model were consistent with those obtained from the separate univariate Poisson models. The sizes of estimates of coefficients, their standard errors, p-values, and directions were comparable in both bivariate and univariate Poisson models. Using either the univariate or bivariate Poisson model, it was found that the age of a woman at first sexual experience, her current age, household wealth index, and contraceptive usage were significantly associated with both the woman’s schooling and fertility. The study further revealed that ethnicity, religion, and region of residence impacted education level only and not fertility. Similarly, marital status and occupation impacted fertility only and not education. The study also found that higher education levels were linked to a lower number of children, with a strong negative correlation of -0.62 between the two variables. The study recommends using bivariate Poisson regression for analysing paired count response data, when there is an apparent covariance between the outcome variables. The results suggest that efforts by policymakers to achieve the desired women’s sexual and reproductive health in sub-Saharan Africa should be intertwined with improving women’s and girls’ education attainment in the region.

Optical-acoustic responses in uniaxial compression failure progress of fibre-reinforced cemented bodies

ABSTRACT Incorporation of fibers in the cemented body material can obviously improve its mechanical properties. Digital image correlation (DIC) and acoustic emission (AE) techniques were used to monitor the “optical-acoustic” response in uniaxial compression failure progress of the fiber-free and glass fiber-reinforced cemented bodies, and the response characteristics of the two methods in the stability monitoring of the cemented body as a support structure were discussed. The results show that the glass fiber-reinforced cemented body showed a failure to the internal structure after the peak strength, but the specimen remained intact without spalling and still maintained a long ductile deformation. The transverse displacement changes curve of the fiber-free cemented body appeared a “positive-negative turning point”. The transverse displacement of the glass fiber-reinforced cemented body is negatively correlated with the DIC time. The full-field strain information obtained by the DIC technique can be used to quantify the degree of damage on the surface of the cemented bodies, and the AE ringing count and energy signal response are more sensitive to the internal friction and impact of the cemented bodies. The results of the study can provide a reference for determining the stability monitoring method of the cemented body as a support structure.

Frontline use of rituximab may prevent ADAMTS13 inhibitor boosting during caplacizumab treatment in patients with iTTP: post hoc analysis of a phase 2/3 study in Japan

BackgroundA recent Phase 2/3 study in Japanese patients showed that caplacizumab was effective in treating immune-mediated thrombotic thrombocytopenic purpura (iTTP), with a low rate of iTTP recurrence. ADAMTS13 activity is monitored weekly during caplacizumab treatment to guide discontinuation of caplacizumab and consequently avoid exacerbations or relapse. The aim of this study was to assess changes in ADAMTS13 activity/inhibitor levels during caplacizumab treatment in this patient population.MethodsA post hoc analysis of the Phase 2/3 study in Japanese patients was conducted. Patients ≥ 18 years old with confirmed iTTP received 10 mg of caplacizumab daily in conjunction with therapeutic plasma exchange (TPE) and immunosuppression for 30 days post-TPE. Outcomes included time to recovery of ADAMTS13 activity, ADAMTS13 activity level at treatment end, incidence of ADAMTS13 inhibitor re-elevation (ie, inhibitor boosting) during treatment, time to platelet count recovery, number of days of TPE, and safety. Outcomes according to presence of inhibitor boosting were also assessed.ResultsNineteen patients had confirmed iTTP and were included in this analysis. Median (95% confidence interval) time to recovery of ADAMTS13 activity to ≥ 10%, ≥ 20%, and ≥ 60% was 14.6 (5.9–24.8), 18.5 (5.9–31.8), and 47.5 (18.5–60.9) days, respectively. Median (range) ADAMTS13 activity level at caplacizumab treatment end was 62.0% (29.0–101.0). Nine patients had ADAMTS13 inhibitor boosting. Delayed response of ADAMTS13 activity was observed in patients with inhibitor boosting. The median time to platelet count response and median number of TPE days were shorter in patients with inhibitor boosting compared with patients without inhibitor boosting. Rituximab was administered to almost all patients with inhibitor boosting (88.9%), after completion of TPE. Patients without inhibitor boosting who were treated with rituximab received it prior to completion of TPE. Only one patient experienced a recurrence, which occurred shortly after caplacizumab discontinuation due to an adverse event.ConclusionsIn patients with iTTP, caplacizumab with TPE and immunosuppression may reduce the risk of ADAMTS13 inhibitor boosting if rituximab is administered early in the iTTP treatment period. Early administration of rituximab in addition to caplacizumab may prevent iTTP recurrence with inhibitor boosting.Trial registrationNCT04074187.

A robust regression model for bounded count health data.

Bounded count response data arise naturally in health applications. In general, the well-known beta-binomial regression model form the basis for analyzing this data, specially when we have overdispersed data. Little attention, however, has been given to the literature on the possibility of having extreme observations and overdispersed data. We propose in this work an extension of the beta-binomial regression model, named the beta-2-binomial regression model, which provides a rather flexible approach for fitting a regression model with a wide spectrum of bounded count response data sets under the presence of overdispersion, outliers, or excess of extreme observations. This distribution possesses more skewness and kurtosis than the beta-binomial model but preserves the same mean and variance form of the beta-binomial model. Additional properties of the beta-2-binomial distribution are derived including its behavior on the limits of its parametric space. A penalized maximum likelihood approach is considered to estimate parameters of this model and a residual analysis is included to assess departures from model assumptions as well as to detect outlier observations. Simulation studies, considering the robustness to outliers, are presented confirming that the beta-2-binomial regression model is a better robust alternative, in comparison with the binomial and beta-binomial regression models. We also found that the beta-2-binomial regression model outperformed the binomial and beta-binomial regression models in our applications of predicting liver cancer development in mice and the number of inappropriate days a patient spent in a hospital.

WHOLE-CLASS AUTOMATIC PROMOTION IN THE PRIMARY SCHOOL AND PUPILS’ LITERACY ACHIEVEMENT

The present study had two research questions to investigate whole-class promotion in primary school and pupils’ achievement in literacy. The study adopted the cross-sectional survey research design. The target population was composed of all teachers and pupils in primary schools in Fako Division, in Cameroon, while the accessible population was made up of all Classes Four (Grade 4), Five and Six pupils, and 60 teachers, selected using purposive sampling techniques. The instruments for data collection were a structured questionnaire and an interview guide. The descriptive statistical tools used were frequency count, percentages, and multiple response sets while Spearman’s rho test was used in testing the study's hypotheses. The findings of the study indicated that teachers’ use of alternative resources has a significant effect on pupils’ literacy achievement (P= 0.030, &lt; 0.05). Findings also indicated that remedial teaching has a significant effect on pupils’ literacy achievement (P= 0.036, &lt; 0.05). Some recommendations included both trainees and qualified teachers to receive more training regarding alternative resources and remedial teaching.

Enhancing estimation efficiency with proposed estimator: A comparative analysis of Poisson regression-based mean estimators

In many studies, the Poisson regression model is mostly intended for modelling count responses. Recently, it was shown that the exploitation of the Poisson regression coefficient within the Koç (2021) ratio estimator increases the efficiency of the estimator. This study uses a new Poisson regression-based regression-type mean estimator with simple random sampling and finds its related mean square error formula. Essentially, we contrast the suggested estimators' mean square errors with those of previously published estimators. For the real data study, estimators were calculated for three real populations and the superior performance of the proposed estimator was observed. Similar results were obtained from the simulation study. As an outcome of these estimations, the proposed estimators are more effective than existing estimators. The empirical results verified the theoretical results to be remarkable.

Relationship of external genitalia measurement with antral follicular count and estrus induction response in pre-pubertal Murrah buffalo heifers

Relationship of external genitalia measurement with antral follicular count and estrus induction response in pre-pubertal Murrah buffalo heifers

A New Regression Model for Over-Dispersed Count Responses Based on Poisson and Geometric Convolution

A New Regression Model for Over-Dispersed Count Responses Based on Poisson and Geometric Convolution

Comparison of Non-Convex Variable Selection Criteria in High-dimensional Data with Count Response

Comparison of Non-Convex Variable Selection Criteria in High-dimensional Data with Count Response

Approximate Gibbs sampler for efficient inference of hierarchical Bayesian models for grouped count data

Hierarchical Bayesian Poisson regression models (HBPRMs) provide a flexible modelling approach of the relationship between predictors and count response variables. The applications of HBPRMs to large-scale datasets require efficient inference algorithms due to the high computational cost of inferring many model parameters based on random sampling. Although Markov Chain Monte Carlo (MCMC) algorithms have been widely used for Bayesian inference, sampling using this class of algorithms is time-consuming for applications with large-scale data and time-sensitive decision-making, partially due to the non-conjugacy of many models. To overcome this limitation, this research develops an approximate Gibbs sampler (AGS) to efficiently learn the HBPRMs while maintaining the inference accuracy. In the proposed sampler, the data likelihood is approximated with Gaussian distribution such that the conditional posterior of the coefficients has a closed-form solution. Numerical experiments using real and synthetic datasets with small and large counts demonstrate the superior performance of AGS in comparison to the state-of-the-art sampling algorithm, especially for large datasets.

A term infant with severe hypereosinophilia secondary to CMV infection and the STAT1 gene mutation: a case report

Hypereosinophilia is a rare presentation in all age groups, particularly when it is severe, persistent, and progressive. We describe the clinical characteristics and course of severe hypereosinophilia in a full-term Saudi female neonate. A febrile respiratory illness evolved with a progressive increase in peripheral blood leukocyte and eosinophil counts, reaching 44.9% of leukocytes and an absolute value of 57,000 cells/µl. Different etiological examinations (for viral, bacterial, immunodeficiency, hyper IgE syndrome, gene mutations) revealed extremely high CMV antigenemia and a homozygous mutation in the STAT1 gene. Anhelation was relieved by oxygen and anti-viral treatment. Steroids brought a dramatic response in peripheral blood counts within 24 h. After a 6-week course of antiviral and steroid treatment at home, she had an excellent general condition. Conclusion: Although a rare pathology, it is important to consider genetic disorders when there is an atypical immune response to viral infections.

Copula deep learning control chart for multivariate zero inflated count response variables

With the increasing popularity of big data analysis, research on zero-inflated count data with the copula method has garnered significant attention because zero-inflated count data do not follow a normal distribution and have high correlation among variables. Within the domain of quality control, there has been limited emphasis on multivariate statistical process control (SPC) techniques that specifically address the challenge of multicollinearity within regression models for multivariate zero-inflated count responses. In this paper, we explain a computational challenge in handling big data with parametric generalized linear models, such as the zero-inflated Poisson model. This challenge motivates us to introduce a copula-based deep learning and neural network model involving multivariate zero-inflated count response variables with highly correlated explanatory variables. This approach builds upon existing deep learning and neural network models designed for univariate count responses, expanding them to encompass multivariate count response models through the integration of a copula regression framework. To evaluate the performance of our proposed methodology, we conduct a comparative analysis of accuracies using the zero-inflated Poisson model, univariate deep learning and neural network models, multivariate count response deep learning and neural network models, and our proposed copula deep learning and neural network models. This assessment involves employing metrics such as root mean square error (RMSE), weighted mean absolute percentage error (WMAPE), and mean absolute deviation (MAD). We include both copula-based asymmetrical zero-inflated simulated data and real-world data. We also propose a temporal dependence control chart for assessing the temporal dependence between bivariate zero-inflated count response variables. Our proposed copula deep learning and neural network temporal control charts can check time-varying dependence and outliers by leveraging the Shewhart statistical process control chart and t-copula ARMA-GARCH dynamic control correlation.

A flexible count regression model with varying precision

In this paper, we formulate mean and dispersion submodels for the class of Bell-Touchard regression models, and so we introduce the Bell-Touchard regression model with varying precision for count response variables. This regression model is, in some aspects, similar to the double generalized linear model framework, allowing for parameter interpretation in terms of the response variable in its original scale. We also provide an iterative process to estimate the regression coefficients in the mean and dispersion submodels, and develop diagnostic measures for this class of regression models. The methods developed are applied in a real data set for illustrative purposes.

Surgical counting interruptions in operating rooms

BackgroundOperating rooms are complex working environments with high workloads and high levels of cognitive demand. The first surgical count which occurs during the chaotic preoperative stage and is considered a critical phase, is a routine task in ORs. Interruptions often occur during the first surgical count; however, little is known about the first surgical counting interruptions. This study aimed to observe and analyse the sources, outcomes, frequency of the first surgical counting interruptions and responses to interruptions.MethodsA retrospective observational study was carried out to examine the occurrence of the first surgical counting interruptions between 1st August 2023 and 30th September 2023. The data were collected using the “Surgical Counting Interruption Event Form”, which was developed by the researchers specifically for this study.ResultsA total of 66 circulating nurses (CNs) and scrub nurses (SNs) were observed across 1015 surgeries, with 4927.8 min of surgical count. The mean duration of the first surgical count was 4.85 min, with a range of 1.03 min to 9.51 min. In addition, 697 interruptions were identified, with full-term interruptions occurring an average of 8.7 times per hour. The most frequent source of interruption during the first surgical counts was instruments (N = 144, 20.7%). The first surgical counting interruptions mostly affected the CN (336 times; 48.2%), followed by the ORNs (including CNs and SNs) (243 times; 34.9%) and the SN (118 times; 16.9%). Most of the outcomes of interruptions were negative, and the majority of the nurses responded immediately to interruptions.ConclusionsThe frequency of the first surgical counting interruption is high. Managers should develop interventions for interruptions based on different surgical specialties and different nursing roles.

Estimation and selection for spatial zero‐inflated count models

AbstractThe count data arise in many scientific areas. Our concerns here focus on spatial count responses with an excessive number of zeros and a set of available covariates. Estimating model parameters and selecting important covariates for spatial zero‐inflated count models are both essential. Importantly, to alleviate deviations from model assumptions, we propose a spatial zero‐inflated Poisson‐like methodology to model this type of data, which relies only on assumptions for the first two moments of spatial count responses. We then design an effective iterative estimation procedure between the generalized estimating equation and the weighted least squares method to respectively estimate the regression coefficients and the variogram of the data model. Moreover, the stabilization of estimators is evaluated via a block jackknife technique. Furthermore, a distribution‐free model selection criterion based on an estimate of the mean squared error of the estimated mean structure is proposed to select the best subset of covariates. The effectiveness of the proposed methodology is demonstrated by simulation studies under various scenarios, and a real dataset regarding the number of maternal deaths in Mozambique is analyzed for illustration.

Indirect Questioning Technique Related to Sensitive Quantitative Variables with Options for Direct, Randomized and Item Count Responses

Randomized Response (RR) Technique (RRT) pioneered by Warner (1965) is a useful tool to elicit responses on sensitive characteristics, such as induced abortions, drug abuse, drunken driving, total amount of counterfeit notes of a particular denomination held by individuals in the population, etc. There exists a huge literature on Randomized Response (RR) devices for estimation of finite population mean of quantitative variables, sensitive in nature mostly based on Eichhorn and Hayre (1983). Device-I and Device-II vide Chaudhuri and Christofides (2013) allow estimation of population mean of sensitive quantitative variables using sample chosen by a general sampling design. On the other hand, Item Count Technique (ICT), described elaborately in Chaudhuri and Christofides (2013), is an alternative to RRT for respondents who do not choose to provide RRs. While some respondents may find a variable as sensitive, others may find it innocuous enough to provide a direct response (DR) about his/her true value. In such a case, Optional Randomized Response (ORR) Technique (ORRT) with options for DR and RR was introduced by Chaudhuri and Mukherjee (1985). Pal (2007) proposed an ORR device which offers choices for RR and ICT to the respondents for giving their answers. A new ORRT with options for DR, RR and ICT was provided by Shaw and Pal (2021) for eliciting indirect responses on sensitive characteristics. As this device relates to estimation of population proportion of sensitive characteristics, an attempt has been made to extend it for sensitive quantitative variables. Further, to take care of individuals’ varying choices for DR, RR and ICT and to protect the privacy of the respondents’ choices, this paper develops an ORR device allowing the respondents chosen by a general sampling design, to choose any one of the three options according to their choices. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 155-170

High-dimensional covariate-augmented overdispersed poisson factor model.

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.