Regressive Model Research Articles

Impact of the use of active methodology on the performance of undergraduate radiology students

Rationale and objectivesAlthough PBL is widely used in several countries, especially in medicine courses, its application in teaching other higher education courses, which involve fundamentals applied to radiology, is still little explored. Therefore, we aim to evaluate the implementation of Problem-Based Learning (PBL) in a higher education institution's radiology and biomedicine technologist course, focusing on specific radiology-related disciplines. Materials and methodsAn interventional study was developed with 78 students. An active methodology model was created and implemented for one of the groups of participants. At the beginning of each semester, students of both groups were evaluated with pre-tests. At the end of the semesters, the students performed a post-test and a validated evaluation of the discipline methodology. Repeated measures generalized linear regressive models with robust error estimators were used to evaluate test outcomes. ResultsA significant interaction among the methodologies was found (p=0.020), with better results from students exposed to the active methodology (initial and final grades were 7.18 and 7.57 in the active methodology, respectively, and 7.45 and 6.89 in the traditional methodology, respectively). In addition, students' evaluation regarding the quality of the methodology was favorable to the active methodology with statistical significance (p<0.05) in 16 of the 22 items evaluated. ConclusionsThe students' positive response and performance were attributed to the interaction and innovation of the methodology compared to conventional methods, highlighting the effectiveness of PBL in higher education in radiology and its potential for more participatory and contextualized learning.

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

Long-term outcome of adjuvant radiotherapy upon postoperative relapse of centrally located hepatocellular carcinoma: a real-world study

Despite that surgical resection is widely regarded as the most effective approach to the treatment of liver cancer, its safety and efficacy upon centrally located hepatocellular carcinoma (HCC) remain unsatisfactory. In consequence, seeking an integrated treatment, like combined with adjuvant radiotherapy, to enhance the prognosis of patients is of critical importance. By recruiting patients undergoing surgical resection for centrally located HCC ranging from June 2015 to 2020, they were divided into liver resection combined with adjuvant radiotherapy (LR + RT) and mere liver resection (LR) groups. The calculation of propensity score and model of Cox proportional hazards regression were utilized. 193 patients were recruited in aggregation, containing 88 ones undergoing LR + RT, while 105 handled with LR. RT was verified to be an independent factor of prognosis for relapse (HR 0.60). In propensity-score analyses, significant association existed between adjuvant radiotherapy and better disease-free survival (DFS) (Matched, HR 0.60; Adjustment of propensity score, HR 0.60; Inverse probability weighting, HR 0.63). The difference of DFS was apparent within two groups (p value = 0.022), and RT significantly down-regulated early relapse (p value < 0.05) in subgroup analysis. The calculation of E-value revealed robustness of unmeasured confounding. The combination of liver surgical resection with RT is safe and effective towards patients with centrally located HCC, which would notably enhance the prognosis and decrease the early relapse of HCC.

Examining confidential wholesale margin estimates in European countries for the price negotiation of patented drugs in Germany: a statistical model

BackgroundBased on the legal framework laid down in section 130b (9) of Book V of the German Social Code, various criteria are relevant for the negotiated price for new patented drugs in Germany. European reference prices (ERPs) are one criterion. The ERP is based on the ex-factory prices (EFPs) of the countries included in the European country basket. However, in some of these countries, the EFP is not published due to confidential wholesale margins. Wholesale margins must therefore be estimated and deducted from purchase prices. In this context literature-based estimates to date do not assume regressive margins with higher pharmaceutical prices. This assumption is questionable and can lead to systematically underestimated country prices, especially for high-priced drugs. Percentage wholesale margins in the majority of European countries develop to a comparable extent regressively with increasing prices. It should therefore be examined (1) whether statistical models can predict the margins of individual countries, in principle and especially for countries where margins are unknown and regressive trends are likely, and (2) to what extent the estimation of margins improves when regressive statistical models are used to estimate margins instead of cross-price averages published in the literature.MethodsQualitative preliminary research explores the basic wholesale pricing mechanisms in countries with confidential wholesale margins. Wholesale margins for reimbursable drugs were then modeled for regulated European countries. Estimation quality and impact of the model was compared to estimations based on average margins.ResultsIn both regulated countries and in countries with confidential wholesale margins, percentage margins of wholesalers develop regressively as drug prices rise. Regressive courses of margins can be resiliently modeled for the regulated countries using a power distribution with significantly lower mean squared errors in a linear mixed model in comparison to literature-based estimations with country-specific cross-price averages.ConclusionIf there is reason to believe that margins are regressive, confidential wholesale margins are expected to be better estimated by the power function based on margins of regulated countries than by the published country-specific average margins, reducing significantly inaccurate effects on margin estimations of high-price drugs.

Poisson Modeling Predicts Acute Telestroke Patient Call Volume.

Background: Predicting the frequency of calls for telestroke and emergency teleneurology consultation is essential to prepare staffing for the immediate management of time-sensitive strokes. In this study, we evaluate Poisson distribution count data using a generalized linear model that predicts the volume of hourly telestroke calls over a 24-h period. Methods: We performed an Institutional Review Board approved retrospective cohort review of patients (January 2019-December 2022) from an institutional telestroke database at a large nonprofit multihospital system in the United States. All patients ≥18 years with a telestroke activation were included. Telestroke calls were quantified in frequency per day and analyzed by multiple time and date intervals. Poisson probability mass function (PMF) and cumulative distribution function (CDF) were used to predict call probabilities. A univariable Poisson regression model was fit to predict call volumes. Results: A total of 8,499 patients at 21 hospitals met inclusion criteria, the mean calls/day were 5.82 ± 2.54, and mean calls/day within each hour increment ranged from a minimum of 0.07 from 5 a.m. to 6 a.m. to a maximum of 0.45 from 7 p.m. to 8 p.m. The Poisson distribution was the most appropriate parametric probability model for these data, confirmed by the fit of the data to the expected distributions corresponding to the calculated means. The predicted probabilities of call frequencies by hour were calculated using the Poisson PMF and CDF; the probability of two or fewer calls/day by hour ranged from 98.9% to 99.9%. Univariable Poisson regression modeled an increase of future calls/day from 6.7 calls/day in July 2023 to 7.6 calls/day in October 2025. Conclusion: Poisson modeling closely fits telestroke call volumes, predicts the future volumes, and can be applied to any health system in which the mean call volume is known, which may inform the number of physicians needed to cover calls in real-time.

Risk factors for head and neck squamous cell carcinoma in disease pathogenesis and prognosis: the importance of smoking cessation and the concept of harm reduction

Introduction. Cigarette smoking along with alcohol consumption, infection with human papilloma (HPV) and the Epstein–Barr viruses, is one of the main risk factors for head and neck squamous cell carcinoma. It can also contribute to virus-associated carcinogenesis and affect the clinical and prognostic features of the tumor. Continued smoking at diagnosis of head and neck squamous cell carcinoma is a negative prognostic factor and contributes to reduced patient survival rates. Aim. To analyze the smoking status and the effect of etiopathogenesis on molecular and clinical features in patients with oropharyngeal squamous cell carcinoma (OpSCC). Materials and methods. In patients with OPSCC, the smoking status and the level of motivation to its cessation were clarified. Immunohistochemical analysis of tumor sections from paraffin blocks from the ThermoScentific immunohis-tosteiner was performed using monoclonal antibodies to estrogen receptors α (ERα), programmed death-ligand 1 (pD-L1), Bcl-2, p53 and p16. The percentage of tumor-infiltrating lymphocytes (TILs) in the stromal component of the tumor (hematoxylin and eosin staining) was also analyzed. Results. Of 113 patients with OpSCC, 80 % of patients were current smokers, including 75 % of patients with Hpv-positive tumor status. men with OpSCC were statistically significantly more likely to smoke than women (p = 0.001), as were men with Hpv-positive tumor (p = 0.015). Smoking patients were characterized by a larger size of the primary tumor (T3–4), including those with Hpv (p = 0.015). positive Hpv status of the tumor is associated with autoimmune diseases (autoimmune thyroiditis, psoriasis) (p ˂ 0.05). A high level of TILs is characteristic of smokers, including those with p16+-status (p ˂0.05), but not for all patients with this status. The positive expression of ERα is associated with p16+-status, and the latter is associated with the absence of p53 expression (p ˂0.05). During conservative therapy, tumor regression is associated with the primary tumor size T1–2, p16 + status, female sex, age less than 55 years, TILs level ≥ 57.5 %, pD-L1 expression levels on tumor cells ≥2.2 %, Bcl-2 ≥5 % and ERα ≥65 % (p ˂ 0.05). At the same time, quality of good predictive model for tumor regression showed a TILs level of ≥57.5 % (area under curve, AuC) 0.796; sensitivity 78.6 %; specificity 75 %) and ERα (AuC 0.725; sensitivity 65 %; specificity 83 %). The level of motivation to quit smoking ranged from 1 to 7 points according the 10-point motivation scale in 89 % of respondents. Only 10 patients were ready to quit smoking immediately (10 points on the motivation scale). Conclusion. Molecular heterogeneity of OPSCC was revealed, including in the group of p16+-tumors. Correlation of clinical and molecular parameters with response to conservative therapy can be used in planning of primary therapy and earlier use of surgical treatment. The level of motivation to quit smoking in patients with squamous cell carcinoma of the head and neck may be increased by drawing attention to the problem at each doctor’s appointment as well as by informing about how to quit smoking and therapeutic approaches. In unmotivated patients, the concept of reducing the harm of smoking can be used involving the replacement of cigarettes with electronic tobacco heating systems, eliminating the carcinogenic effect of its combustion products.

Coupling Different Road Traffic Noise Models with a Multilinear Regressive Model: A Measurements-Independent Technique for Urban Road Traffic Noise Prediction.

Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model-NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source-receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models' application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors.

Hypothesis testing for varying coefficient models in tail index regression

This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model is flexible, but leaner and simpler models are preferred for applications. Therefore, it is important to evaluate whether the estimated coefficient function varies significantly with covariates. If the effect of the non-linearity of the model is weak, the varying coefficient structure is reduced to a simpler model, such as a constant or zero. Accordingly, the hypothesis test for model assessment in the varying coefficient model has been discussed in mean and quantile regression. However, there are no results in tail index regression. In this study, we investigate the asymptotic properties of an estimator and provide a hypothesis testing method for varying coefficient models for tail index regression.

A NONLINEAR REGRESSION MODEL FOR EARLY LOC ESTIMATION OF OPEN-SOURCE KOTLIN-BASED APPLICATIONS

Context. The early lines of code (LOC) estimation in software projects holds significant importance, as it directly influences the prediction of development effort, covering a spectrum of different programming languages, and open-source Kotlin-based applications in particular. The object of the study is the process of early LOC estimation of open-source Kotlin-based apps. The subject of the study is the nonlinear regression models for early LOC estimation of open-source Kotlin-based apps. Objective. The goal of the work is to build the nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps based on the Box-Cox four-variate normalizing transformation to increase the confidence in early LOC estimation of these apps. Method. For early LOC estimation in open-source Kotlin-based apps, the model, confidence, and prediction intervals of nonlinear regression were constructed using the Box-Cox four-variate normalizing transformation and specialized techniques. These techniques, relying on multiple nonlinear regression analyses incorporating multivariate normalizing transformations, account for the dependencies between variables in non-Gaussian data scenarios. As a result, this method tends to reduce the mean magnitude of relative error (MMRE) and narrow confidence and prediction intervals compared to models utilizing univariate normalizing transformations. Results. An analysis has been carried out to compare the constructed model with nonlinear regression models employing decimal logarithm and Box-Cox univariate transformation. Conclusions. The nonlinear regression model with three predictors for early LOC estimation of open-source Kotlin-based apps is constructed using the Box-Cox four-variate transformation. Compared to the other nonlinear regression models, this model demonstrates a larger multiple coefficient of determination, a smaller value of the MMRE, and narrower confidence and prediction intervals. The prospects for further research may include the application of other data sets to construct the nonlinear regression model for early LOC estimation of open-source Kotlin-based apps for other restrictions on predictors.

Revisiting openness-growth nexus: Panel data estimates for selected developing Asian countries

Trade openness is viewed as a way for Asian countries to progress economically and socially. Therefore, determining how trade affects economic growth is crucial for society, particularly for emerging economies. This paper provides empirical evidence on the issue of how openness to trade affects economic growth. A sample of 22 selected Asian countries has been taken as a balanced panel. To carry out the analysis, Auto Regressive Distributive Lag Model (ARDL) on annual data from 1990 to 2020 is used. To select between Pooled Mean Group (PMG) Mean Group (MG) and Dynamic Fixed Effect (DFE) estimators of ARDL, the Hausman test is used. PMG turns out as a better estimator. PMG estimation confirms that trade openness and economic growth exhibit a positive and statistically significant relation in the long run, however, short-run results lack significance. The study concluded with the recommendation that policymakers should adopt and implement an optimal trade policy. The optimal trade policy suggests the sample countries be more open to trade because it may result in higher long-run economic growth.

Association Between Patellar Tendon Abnormality and Land-Jump Biomechanics in Male Collegiate Basketball Players During the Preseason.

Patellar tendinopathy is a degenerative condition that predominantly affects jumping athletes. Symptoms may be subtle or nonexistent at preseason, but structural abnormalities may be present. Assessing patellar tendon abnormality (PTA) through magnetic resonance imaging (MRI) and ultrasound (US) and classifying symptoms using the Victorian Institute for Sport Assessment-Patellar tendon (VISA-P) may provide useful insights if combined with biomechanics measurements. To (1) assess whether land-jump biomechanical patterns are associated with clinically pertinent PTA as seen on imaging and through VISA-P scores and (2) model the contributing risk and accuracy of biomechanics to classify PTA and symptomatic observations. Cross-sectional study; Level of evidence, 3. A total of 26 National Collegiate Athletic Association Division I and II male basketball players (n = 52 limbs) were recruited during the preseason. We collected VISA-P scores, bilateral PTA through US and MRI morphology measurements, and bilateral 3-dimensional lower extremity kinematics and kinetics measurements from a land-jump test from an 18-inch-high (45.7-cm-high) box. Statistically, each limb was treated independently. The association of biomechanics with PTA and symptoms (VISA-P score <80) was tested with multivariate models and post hoc tests. Logistic regression modeled relative risk and accuracy of biomechanical variables to classify PTA and symptomatic limbs. There were 19 to 24 limbs with PTA depending on US and MRI measurements. Differences in hip and knee kinematic strategies and ground-reaction loads were associated with PTA and symptomatic limbs. Peak landing vertical ground-reaction force was significantly decreased (169 ± 26 vs 195 ± 29 %body weight; P = .001), and maximum hip flexion velocity was significantly increased (416 ± 74 vs 343 ± 94 deg/s; P = .005) in limbs with versus without PTA on imaging. Knee flexion at the initial contact was decreased in symptomatic versus healthy limbs (17°± 5° vs 21°± 5°, respectively; P = .045). Regression models classified PTA limbs and symptomatic limbs with 71.2% to 86.5% accuracy. Hip and knee maximum flexion velocity and vertical ground-reaction force variables were most common across models observing clinically pertinent PTA. Our findings suggested that functional kinematic and kinetic biomechanical strategies at the hip and knee were associated with PTA, identified on imaging, and symptomatic limbs.

Residential exposure associations with ALS risk, survival, and phenotype: a Michigan-based case-control study

Background Environmental exposures impact amyotrophic lateral sclerosis (ALS) risk and progression, a fatal and progressive neurodegenerative disease. Better characterization of these exposures is needed to decrease disease burden. Objective To identify exposures in the residential setting that associate with ALS risk, survival, and onset segment. Methods ALS and control participants recruited from University of Michigan completed a survey that ascertained exposure risks in the residential setting. ALS risk was assessed using logistic regression models followed by latent profile analysis to consider exposure profiles. A case-only analysis considered the contribution of the residential exposure variables via a Cox proportional hazards model for survival outcomes and multinomial logistic regression for onset segment, a polytomous outcome. Results This study included 367 ALS and 255 control participants. Twelve residential variables were associated with ALS risk after correcting for multiple comparison testing, with storage in an attached garage of chemical products including gasoline or kerosene (odds ratio (OR) = 1.14, p adjusted < 0.001), gasoline-powered equipment (OR = 1.16, p adjusted < 0.001), and lawn care products (OR = 1.15, p adjusted < 0.001) representing the top three risk factors sorted by p adjusted. Latent profile analysis indicated that storage of these chemical products in both attached and detached garages increased ALS risk. Although residential variables were not associated with poorer ALS survival following multiple testing corrections, storing pesticides, lawn care products, and woodworking supplies in the home were associated with shorter ALS survival using nominal p values. No exposures were associated with ALS onset segment. Conclusion Residential exposures may be important modifiable components of the ALS susceptibility and prognosis exposome.

IMAR in Frequency Domain for Separating Speech Signals by STFT

Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

Association Between FEV₁ Decline Rate and Mortality in Long-Term Follow-Up of a 21-Patient Pilot Clinical Trial of Inhaled Liposomal Cyclosporine Plus Standard-of-Care Versus Standard-of-Care Alone for Bronchiolitis Obliterans Syndrome After Lung Transplantation.

BACKGROUND The association between forced expiratory volume in 1 second (FEV1) trajectory and mortality in bronchiolitis obliterans syndrome (BOS) is not well defined. Using long-term data from a prior clinical trial of inhaled liposomal cyclosporine A (L-CsA-I) for lung transplant patients with BOS, this study examined the association between longitudinal FEV₁ change and mortality. MATERIAL AND METHODS We analyzed long-term data from a clinical trial which randomized 21 patients with BOS (³20% decrease in FEV1 from personal maximum) to receive L-CsA-I plus standard-of-care (n=11) or standard-of-care (SOC) alone (n=10) for 24 weeks. A joint statistical model, combining a linear mixed model for FEV₁ change and Cox regression for mortality, was utilized to examine the overall association between FEV₁ trajectory and mortality during follow-up. RESULTS The 21 trial participants (10 single, 11 double lung recipients) had a mean FEV₁ of 1.7±0.6 Liters at randomization. Median follow-up post-randomization was 35 months. In joint model analysis, 1 percent FEV₁ decline predicted 1.076-fold increased mortality risk (95% confidence interval: -0.998 to 1.160, p=0.058). FEV₁ decline was reduced by 2.6% per year in L-CsA-I patients compared to SOC (p=0.210), and overall survival at 1/3/5 years was 91%/64%/27% vs 90%/20%/0% for L-CsA-I versus SOC, respectively (p=0.164). CONCLUSIONS In BOS patients, greater longitudinal FEV₁ decline predicts increased mortality. Trends towards prolonged stabilization of FEV₁ and improved survival were observed with L-CsA-I receipt. Further analyses will aid in evaluating the utility of FEV₁ change as a survival predictor, having implications in BOS management and future trial design.

Structured Probabilistic Coding

This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input related to the target task. SPC is an encoder-only probabilistic coding technology with a structured regularization from the target space. It can enhance the generalization ability of pre-trained language models for better language understanding. Specifically, our probabilistic coding simultaneously performs information encoding and task prediction in one module to more fully utilize the effective information from input data. It uses variational inference in the output space to reduce randomness and uncertainty. Besides, to better control the learning process of probabilistic representations, a structured regularization is proposed to promote uniformity across classes in the latent space. With the regularization term, SPC can preserve the Gaussian structure of the latent code and achieve better coverage of the hidden space with class uniformly. Experimental results on 12 natural language understanding tasks demonstrate that our SPC effectively improves the performance of pre-trained language models for classification and regression. Extensive experiments show that SPC can enhance the generalization capability, robustness to label noise, and clustering quality of output representations.

Assessing Regional Weather's Impact on Spinal Cord Injury Survivors, Caregivers, and General Public in Miami, Florida.

(1) Background: Climate change is increasing the already frequent diverse extreme weather events (EWE) across geographic locations, directly and indirectly impacting human health. However, current ongoing research fails to address the magnitude of these indirect impacts, including healthcare access. Vulnerable populations such as persons with spinal cord injury (pSCI) face added physiologic burden such as thermoregulation or mobility challenges like closure of public transportation. Our exploratory research assessed commute and transport to healthcare facilities as well as the knowledge, attitudes and behaviors (KAB) of pSCI regarding EWE and climate change when compared to pSCI caregivers (CG) and the general public (GP). (2) Methods: A KAB survey was employed to conduct a cross-sectional assessment of pSCI, CG, and GP in Miami from October through November 2019 using snowball sampling. Descriptive and logistic regression statistical analyses were used. (3) Results: Of 65 eligible survey respondents, 27 (41.5%) were pSCI, 11 (17%) CG, and 27 (41.5%) GP. Overall, pSCI reported EWE, particularly flooding and heavy rain, affecting their daily activities including healthcare appointments, more frequently than CG or GP. The overall models for logistic regression looking at commute to and attendance of healthcare appointments were statistically significant. pSCI self-report being less vulnerable than others, and a large proportion of each group was not fully convinced climate change is happening. (4) Conclusions: This study provided insight to the KAB of 3 population subgroups in Miami, Florida. pSCI are significantly more vulnerable to the effects of regional weather events yet exhibit disproportionate self-perception of their vulnerability. Continued and more comprehensive research is needed to characterize the barriers that vulnerable populations face during weather events.

Analysing the nexus between income inequality and economic growth in Ethiopia: using the non linear auto regressive distributed lag model

Analysing the nexus between income inequality and economic growth in Ethiopia: using the non linear auto regressive distributed lag model

Assessment of preventive practices towards hepatitis B infection among nursing students in Bangladesh: role of knowledge, attitudes and sociodemographic factors

BackgroundGlobally, hepatitis B infection (HBI) poses a substantial public health concern and healthcare workers, including nursing students, are at a higher risk of contracting this disease. Thus, the study aimed to assess how knowledge, attitudes, and sociodemographic factors are associated with HBI prevention among a sample of Bangladeshi nursing students.MethodsA cross-sectional survey was performed among 737 nursing students from the nursing institutes of Khulna and Barishal divisions in Bangladesh from January to April 2023. The data were collected by providing questionnaires (structured questionnaire) in the classroom, following a stratified random sampling process. A model of multinomial logistic regression was used to evaluate the factors linked to HBI prevention practices.ResultThe mean (SD) scores were 11.42 (± 2.88) for knowledge, 4.33 (± 1.91) for attitude and 4.27 (± 2.056) for practice respectively. Participants’ low knowledge (adjusted odds ratio, aOR = 2.562, 95% CI: 1.29–5.07) and poor attitude (aOR = 5.730, 95% CI: 3.19–10.28) regarding HBI were significantly associated with higher likelihood of poor practice towards HBI prevention. Moreover, being 2nd year of nursing students (aOR = 2.147, 95% CI: 1.19–3.86), being aged 19–20 years (aOR = 3.038, 95% CI: 1.30–7.09), being married (aOR = 0.320, 95%CI: 0.13–0.82) and having a family history of HBI (aOR = 0.134, 95%CI: 0.05–0.36) were significantly associated with poor practices of HBI prevention among study participants.ConclusionThe knowledge, attitude and practice scores of the nursing students on HBI prevention were suboptimal. We advocate for implementing regular HBI prevention education and policies, free or subsidized services, skill development, proper HBI prevention enforcement and strict professional ethics within nursing colleges. Such efforts should predominantly focus on second-year, aged 19–20 and unmarried nursing students.

The Dysmorphic Concern Questionnaire: Is It Possible to Predict a Satisfactory Result After Cosmetic Eyelid Surgery?

As oculofacial surgeons performing esthetic surgery, the most valuable result is a satisfied patient. It can be extremely frustrating to have an unhappy patient with, what we believe is, a satisfactory result. Therefore, the ability to predict satisfaction is of extreme importance. We studied our cosmetic eyelid surgery patients to try to find factors that can predict satisfaction. We retrospectively studied 454 consecutive patients, who underwent any combination of 7 cosmetic surgical procedures. Patients answered the Dysmorphic Concern Questionnaire (DCQ) before surgery. We studied age, gender, type and number of procedures performed, DCQ score, presence of complications, and history of previous fillers. At the 3-month follow-up visit, patients were given an outcome report survey to graduate their surgical results on a scale from 1 to 5. Results were evaluated using a model of binary logistic regression in which the outcome variable was divided into 2 groups. Three hundred eighty-six patients (85%) were female and the median age was 54.8 years old. The median for the number of procedures was 2 (range, 1-5). Two hundred two of the 454 patients (44.5%) had a history of fillers. Twenty-five patients had complications. DCQ score was significantly lower in the satisfied group (median score, 3) versus the unsatisfied group (median, 6), p = 0.007. In the multiple binary logistical model, the variables age, male gender, DCQ, presence of complications, and upper eyelid blepharoplasty resulted as significant. We found a significant positive association with upper lid blepharoplasty and a negative association with increasing age, male gender, occurrence of complications, and DCQ score, as predictors of patient satisfaction. We routinely administer the DCQ to all cosmetic patients: It is a brief, sensitive, and specific screening instrument for body dysmorphic disorder.

Abstract P108: Stroke Incidence and High Sensitivity C-reactive Protein Among African Americans: The Jackson Heart Study

In the United States, strokes are a leading cause of death and disabilities, especially for older adults; however, effective biological markers to predict stroke remain elusive. The objective of this study was to investigate the association between inflammation, as measured by high-sensitivity c-reactive protein (hs-CRP) and stroke incidence among African Americans (AA) enrolled in a community-based longitudinal observational study, the Jackson Heart Study.Baseline hs-CRP levels were categorized in quintiles: Quintile 1 (0.0084 mg/L); Quintile 2 (0.0085 - 0.0189 mg/L); Quintile 3 (0.0190 - 0.036 mg/L); Quintile 4 (0.037 - 0.0675 mg/L); Quintile 5 (≥ 0.0676 mg/L). Stroke risk factors/covariates were compared across hs-CRP tertiles. Continuous variables were compared using a one-way ANOVA, and a Chi-Square test was used for categorical variables. To determine whether baseline hs-CRP levels were associated with stroke incidence, Cox regression analyses were conducted to estimate hazard ratios (HRs) and 95% confidence intervals (CI).Results from the unadjusted model of the Cox regression indicates hs-CRP levels in quintile 2 (HR 1.48; 95% CI 0.96-2.29), quintile 3 (HR 1.44; 95% CI 0.93-2.24), and quintile 4 (HR 1.09; 95% CI 0.68-1.74) were not associated with stroke incidence when compared to quintile 1 (reference). Participants with the highest hs-CRP levels, quintile 5, were at an increased risk for stroke (HR 1.78; 95% CI 1.17-2.72) compared to the reference quintile. In a fully adjusted model including risk factors and mediators for stroke/hs-CRP (demographics, anthropometrics, health condition covariates, health behavioral risk factors, and cardiovascular disease history), the increased stroke risk incidence remained present for quintile 5 (HR 1.87; 95% CI 1.17-2.98) compared to reference quintile 1.Overall, these results corroborate previous studies’ findings in Black Americans that hs-CRP is associated with stroke incidence only at most severe levels. Although several studies have demonstrated higher hs-CRP in AAs, there have been a lesser association of hs-CRP and cardiovascular disease; hence, these findings provide new insights that hs-CRP l may not be a reliable predictor for stroke incidence among AAs.