Binary Variables Research Articles

US neonatal intensive care unit registered dietitian nutritionists salary description and correlates: results of a survey.

This survey described the compensation of neonatal intensive care unit (NICU) registered dietitian nutritionists (RDNs) in the United States and examined correlates of higher salaries within this group. A cross-sectional online survey was completed in 2021 by 143 NICU RDNs from 127 US hospitals who reported hourly wage in US dollars (USD). We used initial bivariate analyses to assess the relationship of selected institution-level and individual-level variables to hourly wage; the rank-sum test for binary variables; bivariate regression and Pearson correlation coefficients for continuous variables; the Kruskal-Wallis test for categorical variables. Variables with a compelling relationship to the hourly wage outcome were considered in model creation. Final model selection was based on comparisons of model fit. Median hourly compensation was USD 33.24 (interquartile range [IQR] 29.81, 38.49). Seven variables had a compelling bivariate relationship with hourly wage: cost of living, employer facility with a paediatric residency, employer facility with a neonatal fellowship, NICU bed: full-time equivalents (FTE) RDN ratio, years in neonatal nutrition, having a certification and order writing privileges. In the final adjusted model (R2 = 0.42), three variables remained associated with increased hourly wage: higher cost of living, longer length of career in neonatal nutrition and fewer NICU beds per NICU RDN FTE. US NICU RDNs earn similar or slightly higher wages than other US paediatric RDNs; they earn substantially less than other NICU healthcare team members. Employers need to improve compensation for NICU RDNs to incentivise their retention and recognise their additional non-clinical responsibilities.

The establishment of a performance evaluation model using education informatization to evaluate teacher morality construction in colleges and universities

Abstract To improve the effectiveness of the construction of teacher morality in colleges and universities, this article employs the performance evaluation model of education informatization to construct an evaluation system for the construction of teacher morality in colleges and universities. The evaluation of the construction effect of teacher morality is carried out through intelligent data analysis, and the significant error is used as the parameter of the teacher’s morality to make estimations. Moreover, a binary variable is introduced to judge whether the data have a significant error. Mixed integer linear and nonlinear programming methods are employed to detect significant errors. In addition, the idea of synchronizing teachers’ moral data is added. The effectiveness of the proposed method is verified by a simulation study using MATLAB 2017. The results show that it is necessary to establish and improve the talent flow mechanism that conforms to the requirements to promote the rational flow of teachers.

Determinants of Financial Inclusion in Kenya: A Demand-Side Perspective

This study sought to analyze the underlying financial inclusion determinants in Kenya. The study applies ordinal logit regression to examine the effect of the residential area, gender, education level, marital status, and employment type on financial inclusion. Financial inclusion is measured by developing a financial inclusion index for ten binary financial services variables. From the index, three financial inclusion levels are designed. These include low financial inclusion with scores of zero to three, medium with scores of four to six, and high level with scores of seven to ten. The estimates of the ordinal model are statistically significant for all factors considered except gender. Area of residence, age, education type, income, and marital status positively affect the log odds of financial inclusion, while employment is negatively linked. Education, employment, and marital status have interaction effects on financial inclusion. This study recommends that the Kenyan government formulate and strengthen policies to tackle challenges such as gender disparity, rural bank infrastructure development, fostering an environment conducive for entrepreneurship to address unemployment and income disparities, advocating for secondary school completion, and addressing social issues impacting family stability, including separation or the absence of marriage.

Reduced-order modeling on a near-term quantum computer

Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, quantum algorithms have shown promise in solving linear systems of equations as well as systems of linear ordinary differential equations (ODEs) and partial differential equations (PDEs). Reduced-order modeling (ROM) algorithms for studying fluid dynamics have shown success in identifying linear operators that can describe flowfields, where dynamic mode decomposition (DMD) is a particularly useful method in which a linear operator is identified from data. In this work, DMD is reformulated as an optimization problem to propagate the state of the linearized dynamical system on a quantum computer. This reformulation was chosen as a means of facilitating implementation on a near-term quantum computer. Quadratic unconstrained binary optimization (QUBO), a technique for optimizing quadratic polynomials in binary variables, allows for quantum annealing algorithms to be applied. A quantum circuit model (quantum approximation optimization algorithm, QAOA) is utilized to obtain predictions of the state trajectories. Results are shown for the quantum-ROM predictions for flow over a 2D cylinder at Re = 220 and flow over a NACA0009 airfoil at Re = 500 and α=15∘. The quantum-ROM predictions are found to depend on the number of bits utilized for a fixed point representation and the truncation level of the DMD model. Comparisons with DMD predictions from a classical computer algorithm are made, as well as an analysis of the computational complexity and prospects for future, more fault-tolerant quantum computers.

A Pilot Randomized Controlled Trial of Augmented Reality Just-in-Time Guidance for the Performance of Rugged Field Procedures.

Medical resuscitations in rugged prehospital settings require emergency personnel to perform high-risk procedures in low-resource conditions. Just-in-Time Guidance (JITG) utilizing augmented reality (AR) guidance may be a solution. There is little literature on the utility of AR-mediated JITG tools for facilitating the performance of emergent field care. The objective of this study was to investigate the feasibility and efficacy of a novel AR-mediated JITG tool for emergency field procedures. Emergency medical technician-basic (EMT-B) and paramedic cohorts were randomized to either video training (control) or JITG-AR guidance (intervention) groups for performing bag-valve-mask (BVM) ventilation, intraosseous (IO) line placement, and needle-decompression (Needle-d) in a medium-fidelity simulation environment. For the interventional condition, subjects used an AR technology platform to perform the tasks. The primary outcome was participant task performance; the secondary outcomes were participant-reported acceptability. Participant task score, task time, and acceptability ratings were reported descriptively and compared between the control and intervention groups using chi-square analysis for binary variables and unpaired t-testing for continuous variables. Sixty participants were enrolled (mean age 34.8 years; 72% male). In the EMT-B cohort, there was no difference in average task performance score between the control and JITG groups for the BVM and IO tasks; however, the control group had higher performance scores for the Needle-d task (mean score difference 22%; P = .01). In the paramedic cohort, there was no difference in performance scores between the control and JITG group for the BVM and Needle-d tasks, but the control group had higher task scores for the IO task (mean score difference 23%; P = .01). For all task and participant types, the control group performed tasks more quickly than in the JITG group. There was no difference in participant usability or usefulness ratings between the JITG or control conditions for any of the tasks, although paramedics reported they were less likely to use the JITG equipment again (mean difference 1.96 rating points; P = .02). This study demonstrated preliminary evidence that AR-mediated guidance for emergency medical procedures is feasible and acceptable. These observations, coupled with AR's promise for real-time interaction and on-going technological advancements, suggest the potential for this modality in training and practice that justifies future investigation.

Factor retention in ordered categorical variables: Benefits and costs of polychoric correlations in eigenvalue-based testing.

An essential step in exploratory factor analysis is to determine the optimal number of factors. The Next Eigenvalue Sufficiency Test (NEST; Achim, 2017) is a recent proposal to determine the number of factors based on significance tests of the statistical contributions of candidate factors indicated by eigenvalues of sample correlation matrices. Previous simulation studies have shown NEST to recover the optimal number of factors in simulated datasets with high accuracy. However, these studies have focused on continuous variables. The present work addresses the performance of NEST for ordinal data. It has been debated whether factor models-and thus also the optimal number of factors-for ordinal variables should be computed for Pearson correlation matrices, which are known to underestimate correlations for ordinal datasets, or for polychoric correlation matrices, which are known to be instable. The central research question is to what extent the problems associated with Pearson correlations and polychoric correlations deteriorate NEST for ordinal datasets. Implementations of NEST tailored to ordinal datasets by utilizing polychoric correlations are proposed. In a simulation, the proposed implementations were compared to the original implementation of NEST which computes Pearson correlations even for ordinal datasets. The simulation shows that substituting polychoric correlations for Pearson correlations improves the accuracy of NEST for binary variables and large sample sizes (N = 500). However, the simulation also shows that the original implementation using Pearson correlations was the most accurate implementation for Likert-type variables with four response categories when item difficulties were homogeneous.

Increased Postoperative Opioid Consumption in the Presence of Co-administration of 5-HT3 Antagonists with Acetaminophen: A Hospital Registry Study.

Acetaminophen and 5-hydroxytryptamine-type-3 (5-HT3) receptor antagonists are administered as standard prophylaxes for postoperative pain, nausea, and vomiting. Preclinical studies however suggest that 5-HT3 antagonists may compromise acetaminophen's analgesic effect. This hospital registry study investigates whether 5-HT3 antagonists mitigate the analgesic effect of prophylactic acetaminophen in a perioperative setting. This study included 55,016 adult patients undergoing general anesthesia for ambulatory procedures at a tertiary healthcare center in Massachusetts, United States of America, from 2015 to 2022. Using binary exposure variables and a comprehensive selection of pre-planned patient- and procedure-related covariates for confounder control, we investigated whether intraoperative 5-HT3 antagonists affected the association between pre- or intraoperative acetaminophen and postoperative opioid consumption, gauged by opioid dose in mg oral morphine equivalents (OME) administered in the post-anesthesia care unit (PACU). A multivariable, zero-inflated negative binomial regression model was applied. 3,166 (5.8%) patients received only acetaminophen, 15,438 (28.1%) only 5-HT3 antagonists, 31,850 (57.9%) both drugs, and 4,562 (8.3%) neither drug. The median PACU opioid dose was 7.5 mg OME (interquartile range 7.5 to 14.3 mg OME) among 16,640/55,016 (30.3%) patients who received opioids and the average opioid dose was 3.2 mg OME across all patients (maximum cumulative dose: 20.4 mg OME). Acetaminophen administration was associated with a 5.5% (95%CI -9.6% to -1.4%;p=0.009; adjusted absolute difference -0.19 mg OME;95%CI -0.33 to -0.05;p=0.009) reduction in opioid consumption among patients who did not receive a 5-HT3 antagonist, while there was no effect in patients who received a 5-HT3 antagonist (adjusted absolute difference 0.00 mg OME; 95%CI -0.06 to 0.05;p=0.93,p-for-interaction=0.012). A dose-dependent association of pre- or intraoperative acetaminophen with decreased postoperative opioid consumption was not observed when 5-HT3 antagonists were co-administered, suggesting that physicians might consider reserving 5-HT3 antagonists as rescue medication for postoperative nausea or vomiting when acetaminophen is administered for pain prophylaxis.

Sensitivity Analysis of the Cost Coefficients in Multiobjective Integer Linear Optimization

This paper considers sensitivity analysis of the cost coefficients in multiobjective integer linear programming problems. We define the sensitivity region as the set of simultaneous changes to the coefficients for which the efficient set and its structure remain the same. In particular, we require that the component-wise relation between efficient solutions is preserved and that inefficient solutions remain inefficient, and we show that this is sufficient for the efficient set to be the same upon changes. For a single coefficient, we show that a subset of the inefficient solutions can be excluded from consideration. More specifically, we prove that it suffices to inspect the inefficient solutions of a q-objective problem that are efficient in one of two related q + 1-objective problems. Finally, we show that the sensitivity region is a convex set (an interval). Our approach generalizes to simultaneous changes in multiple coefficients. For illustration, we consider mean-variance capital budgeting and determine the region for which the set of efficient portfolios remains the same, despite a misspecification or a change in the net present values of the projects. Further computational experience with multiobjective binary and integer knapsack problems demonstrates the general applicability of our technique. For instance, we obtain all sensitivity intervals for changes to single coefficients of biobjective problems with 500 binary variables in less than half an hour of CPU time by excluding a large number of inefficient solutions. In fact, the number of excluded solutions is above 100 orders of magnitude larger than the number of remaining solutions. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01406 .

Distributionally robust disaster relief planning under the Wasserstein set

We study a two-stage natural disaster management problem modeled as a stochastic program, where the first stage consists of a facility location problem, deciding where to open facilities and pre-allocate resources such as medical and food kits, and the second stage is a fixed-charge transportation problem, routing resources to affected areas after observing a disaster. Our model has binary variables present in both stages. Due to the lack of data, classical stochastic programming approaches may be ill-suited, and we propose a two-stage distributionally robust formulation with a Wasserstein ambiguity set, where we consider distributions consistent with historical data and a tunable parameter to control the level of risk aversion. We develop a tailored column-and-constraint generation (CCG) algorithm to solve an extensive reformulation, where scenarios are iteratively generated. We handle the presence of binary variables in the second stage by leveraging the structure of our support set and second-stage problem, and provide conditions under which the optimal value of the latter is concave with respect to the intensity of the disaster, leading to an efficient scenario generation procedure. We also show that our results extend to the case where the second stage is a fixed-charge network flow problem. We perform extensive computational experiments demonstrating the computational advantage of our method over classical CCG implementations on synthetic instances, and illustrate the benefits of our approach on a popular case study from the literature of hurricane threats on the Gulf of Mexico in the United States.

Systematic Review of Gender and Sex Terminology Use in Arthroplasty Research: There is Room for Improvement

There is increasing appreciation of the distinction between gender and sex as well as the importance of accurately reporting these constructs. Given recent attention regarding transgender and gender nonconforming (TGNC) and intersex identities, it is more necessary than ever to understand how to describe these identities in research. This study sought to investigate the use of gender- and sex-based terminology in arthroplasty research. The five leading orthopaedic journals publishing arthroplasty research were reviewed to identify the first twenty primary clinical research articles on an arthroplasty topic published after January 1, 2022. Use of gender- or sex-based terminology, whether use was discriminate, and whether stratification or adjustment based on gender or sex was performed, were recorded. There were 98 of 100 articles that measured a construct of gender or sex. Of these, 15 articles used gender-based terminology, 45 used sex-based terminology, and 38 used a combination of gender- and sex-based terminology. Of the 38 articles using a combination of terminology, none did so discriminately. All articles presented gender and sex as binary variables, and two attempted to explicitly define how gender or sex were defined. Of the 98 articles, 31 used these variables for statistical adjustments, though only six reported stratified results. Arthroplasty articles infrequently describe how gender or sex was measured, and frequently use this terminology interchangeably. Additionally, these articles rarely offer more than two options for capturing variation in sex and gender. Future research should be more precise in the treatment of these variables to improve the quality of results and ensure findings are patient-centered and inclusive.

Reducing the Chvátal rank through binarization

In a classical paper, Chvátal introduced a rounding procedure for strengthening the polyhedral relaxation P of an integer program; applied recursively, the number of iterations needed to obtain the convex hull of the integer solutions in P is known as the Chvátal rank. Chvátal showed that this rank can be exponential in the input size L needed to describe P. We give a compact extended formulation of P, described by introducing binary variables, whose rank is polynomial in L.

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets.

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. "Binary" stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and -1, with the probability of being in either level determined by an external bias. "Analog" stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence "analog") and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g., adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Wasserstein Robust Classification with Fairness Constraints

Problem definition: Data analytics models and machine learning algorithms are increasingly deployed to support consequential decision-making processes, from deciding which applicants will receive job offers and loans to university enrollments and medical interventions. However, recent studies show these models may unintentionally amplify human bias and yield significant unfavorable decisions to specific groups. Methodology/results: We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in the equality of opportunity criterion. We use a type-[Formula: see text] Wasserstein ambiguity set centered at the empirical distribution to represent distributional uncertainty and derive a conservative reformulation for the worst-case equal opportunity unfairness measure. We show that the model is equivalent to a mixed binary conic optimization problem, which standard off-the-shelf solvers can solve. We propose a convex, hinge-loss-based model for large problem instances whose reformulation does not incur binary variables to improve scalability. Moreover, we also consider the distributionally robust learning problem with a generic ground transportation cost to hedge against the label and sensitive attribute uncertainties. We numerically examine the performance of our proposed models on five real-world data sets related to individual analysis. Compared with the state-of-the-art methods, our proposed approaches significantly improve fairness with negligible loss of predictive accuracy in the testing data set. Managerial implications: Our paper raises awareness that bias may arise when predictive models are used in service and operations. It generally comes from human bias, for example, imbalanced data collection or low sample sizes, and is further amplified by algorithms. Incorporating fairness constraints and the distributionally robust optimization (DRO) scheme is a powerful way to alleviate algorithmic biases. Funding: This work was supported by the National Science Foundation [Grants 2342505 and 2343869] and the Chinese University of Hong Kong [Grant 4055191]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0230 .

Partitioning QUBO with two-way one-hot conditions on traveling salesman problems for city distributions with multiple clusters

We introduce a method for solving a quadratic unconstrained binary optimization (QUBO) with the two-way one-hot constraints by dividing the QUBO into parts and solving it with an Ising machine. The one-hot constraint is a constraint condition in that only one binary variable takes the value one for a set of multiple binary variables. The two-way one-hot constraint imposes one-hot constraint conditions on every row and every column of a two-dimensional array of binary variables with equal numbers of rows and columns. For example, traveling salesman problems (TSPs) have two-way one-hot constraints. We propose two methods to solve a TSP by dividing the cities into clusters based on information in the QUBO matrix. The proposed methods decompose cities into clusters and solve TSPs for each cluster. We solve TSPs such that the cities are distributed in clusters and compare the results of the two proposed methods with the results of solving the problem without partitioning. The results show that the proposed methods are robust to the coefficient parameters in the Hamiltonian of QUBO and can obtain a solution closer to the optimal solution when solving the problem without partitioning is hard. We also discuss the application of the methods proposed in the TSPs to the quadratic assignment problems and to the problems of ordering things.

Disparities in clinical trial enrollment at a Canadian comprehensive cancer center: A 15-year retrospective study.

Disparities in clinical trials (CTs) enrollment perpetuate inequities in treatment access and outcomes, but there is a paucity of Canadian data. The objective of this study was to examine disparities in cancer CT enrollment at a large Canadian comprehensive cancer center. Retrospective study of CT enrollment among new patient consultations from 2006 to 2019, with follow-up to 2021 (N=154,880), with the primary outcome of enrollment as a binary variable. Factors associated with CT enrollment were evaluated using multivariable Bayesian hierarchical logistic regression with random effects for most responsible physician (MRP) and geography, adjusted for patient characteristics (sex, age, language, geography, and primary care provider [PCP]), area-level marginalization (residential instability, material deprivation, dependency, and ethnic concentration), disease (cancer site and stage), and MRP (department, sex, language, and training). A sensitivity analysis of the cumulative incidence of enrollment was conducted to account for differences in disease type and follow-up length. CT enrollment was 11.2% overall, with a 15-year cumulative incidence of 18%. Lower odds of enrollment were observed in patients who were female (adjusted odds ratio [AOR], 0.82; 95% confidence interval [CI], 0.78-0.86), ≥65 years (AOR vs.<40, 0.61; 95% CI, 0.56-0.66), non-English speakers (0.72; 95% CI, 0.67-0.77), living ≥250 km away (AOR vs.<15 km, 0.71; 95% CI, 0.62-0.80), and without a PCP. Disease characteristics accounted for the largest proportion of observed variation (20.8%), with significantly greater odds of enrollment in patients with genitourinary cancers and late-stage disease. Significant sociodemographic disparities were observed, suggesting the need for targeted strategies to increase diversity in access to cancer CTs in Canada.

Prediction analysis of TBI 24-h survival outcome based on machine learning

BackgroundTraumatic brain injury (TBI) is the major reason for the death of young people and is well known for its high mortality and morbidity. This paper aim to predict the 24h survival of patients with TBI. MethodsA total of 1224 samples were involved in this analysis, and the clinical indicators involved included age, gender, blood pressure, MGAP and other fields, among which the target variable was “outcome”, which was a binary variable. The methods mainly involved in this paper include data visualization analysis, single factor analysis, feature engineering analysis, random forest model (RF), K-Nearst Neighbors (KNN) model, and so on. Logistic regression model (LR) and deep neural network model (DNN). We will oversample the training set using the SMOTE method because of the very unbalanced labeling of the sample itself. ResultsAlthough the accuracy of all models is very high, the recall rate is relatively low. The DNN model with the best performance only reaches 0.17, and the corresponding AUC is 0.80. After resampling, we find that the recall rate of positive samples of all models has increased a lot, but the AUC of some models has decreased. Finally, the optimal model is LR, whose positive sample recall rate is 0.67 and AUC is 0.82. ConclusionThrough resampling, we obtained that the best model is the RF model, whose recall rate and AUC are the best, and the AUC level is about 0.87, indicating that the accuracy performance of the model is still good.

The use of voluntary health insurance in the access to specialist care: Evidence from the Italian NHS

We examine whether the endorsement of voluntary health insurance (VHI) in a Beveridge-oriented healthcare system may increase the probability of visiting a specialist. We use data from the 2019 European Health Interview Survey, which includes information on demographics, socioeconomic characteristics, health status, and healthcare access of over 30,000 Italian households. We also apply data from the Health for All dataset to measure the supply of physicians at regional level.In order to account for endogeneity and unobserved heterogeneity, data is modeled through a simultaneous equation model for binary variables, specifically a trivariate recursive probit.Findings show a positive impact of VHI on the probability of accessing specialist care, after controlling for physician distribution. A socioeconomic gradient is also detected in the probability of both holding VHI and visiting a specialist, with wealthier and better educated individuals showing more likelihood to do so; these results raise concern on the equity of access granted by a Beveridge-oriented system. The role of the GP in filtering the need for care and referring patients to specialists is also recognized. Both supplier inducement effects and macroregional fixed effects are detected in the access to either category of physicians.

P165 Sustained improvement in clinical outcomes and long-term safety of bimekizumab in patients across the spectrum of axial spondyloarthritis up to week 52: results from two phase 3 studies

Abstract Background/Aims Bimekizumab (BKZ), a monoclonal IgG1 antibody that selectively inhibits IL-17F in addition to IL-17A, has shown rapid and clinically meaningful improvements in disease activity across the axial spondyloarthritis spectrum (axSpA; active non-radiographic axSpA [nr-axSpA; BE MOBILE 1, NCT03928704] and ankylosing spondylitis [AS] / radiographic axSpA [r-axSpA; BE MOBILE 2, NCT03928743]). Here we present outcomes from these two phase 3 studies up to Week (Wk)52. Methods BE MOBILE 1 and 2 have similar study designs: 16-Wk double-blind period followed by 36-Wk maintenance period. Patients were randomised (1:1 in BE MOBILE 1; 2:1 in BE MOBILE 2) to BKZ 160 mg every 4Wks (Q4W) or placebo (PBO). All patients received BKZ 160 mg Q4W from Wk16 up to Wk52. Sustained improvements in ASAS40, BASDAI50 responses, BASDAI, BASFI and ASDAS-CRP scores were assessed up to Wk52. Missing data were imputed using non-responder or multiple imputation for binary and quantitative variables, respectively. Results 254 nr-axSpA (BKZ: 128; PBO: 126) and 332 r-axSpA (BKZ: 221; PBO: 111) patients were randomised; 86.6% and 89.8% completed Wk52, respectively. ASAS40 responses increased from Wk16 to Wk52 with similar efficacy across TNF-naïve/IR patients (Table). Approximately 47% of patients on BKZ achieved BASDAI50 at Wk16, increasing to ∼54% at Wk52 (NRI, Table). All improvements at Wk16 in BASDAI, BASFI and ASDAS were sustained up to Wk52 (Table). Efficacy was similar between treatment arms at Wk52. During the overall study period, 75% (183/244, nr-axSpA) and 75.5% (249/330, r-axSpA) patients reported ≥1 treatment-emergent adverse events (TEAEs). Most common TEAEs included nasopharyngitis, upper respiratory tract infection and oral candidiasis. Incidence of serious TEAEs were low (3.7% [nr-axSpA] and 6.1% [r-axSpA]); no incidence of systemic candidiasis, adjudicated MACE or deaths were reported. ‘Any definite or probable adjudicated IBD TEAE’ was reported in 0.8% (2/240, nr-axSpA) and 0.6% (2/326, r-axSpA) patients with no history of IBD and 25% (1/4, r-axSpA) patients with prior history of IBD. Incidence of uveitis (1.2% [3/244, nr-axSpA]; 2.1% [7/330, r-axSpA]) was low. Conclusion Across the axSpA disease spectrum, bimekizumab demonstrated sustained improvements in clinical outcomes up to Wk52 with no new safety signals. Disclosure K. Gaffney: Shareholder/stock ownership; KG is a shareholder of Rheumatology events. Grants/research support; KG received grant/research support from NASS, Versus Arthritis, AbbVie, Pfizer, UCB Pharma, Novartis, Lilly, Cellgene, Celltrion, Janssen, Gilead and Biogen. Other; KG has received honoraria/ consultation fees from Novartis, AbbVie, UCB Pharma, Lilly and Pfizer, KG has received speaker’s bureau from Novartis, UCB Pharma, AbbVie and Lilly; meeting expenses from AbbVie, Lilly, Roche, Novartis, Pfizer and UCB Pharma. N.D. McKay: Other; NDM received travel fees from UCB Pharma and Gilead, and Speaker’s bureau from Gilead. R. Sengupta: Grants/research support; RS received grants/ research support from AbbVie, Celgene, Novartis and UCB Pharma. Other; RS recevied honoraria or consultancy fees from Abbvie, Biogen, Novartis, Celgene, Lilly, Chugai, MSD and UCB Pharma, RS is on the advisory boards for AbbVie, Biogen, Chugai, Lilly, Novartis and UCB Pharma. C. Fleurinck: Other; CF is an employee of UCB Pharma. C. de la Loge: Consultancies; CdlL is a consultant to UCB Pharma, Brussels, Belgium. U. Massow: Other; UM is an employee of UCB Pharma. V. Taieb: Other; VT is an employee of UCB Pharma. S. Zhao: Honoraria; SSZ received honoraria from UCB Pharma. H. Marzo-Ortega: Grants/research support; HMO received grants/research support from Janssen, Novartis and UCB Pharma. Other; HMO received honoraria and/or consultancy and/or speaker fees from Abbvie, Biogen, Celgene, Janssen, Eli-Lilly, Moonlake, Novartis, Pfizer and UCB Pharma.

Quality of life and quality-adjusted life years after stroke in Sierra Leone.

Stroke is a leading cause of mortality and negatively affects health-related quality of life (HRQoL). HRQoL after stroke is understudied in Africa and there are no reports of quality-adjusted life years after stroke (QALYs) in African countries. We determined the impact of stroke on HRQoL after stroke in Sierra Leone. We calculated QALYs at 1 year post-stroke and determined sociodemographic and clinical variables associated with HRQoL and QALYs in this population. A prospective stroke register was established at the two-principal adult tertiary government hospitals in Freetown, Sierra Leone. Participants were followed up at 7, 90 days, and 1 year post-stroke to capture all-cause mortality and EQ-5D-3L data. QALYs were calculated at the patient level using EQ-5D-3L utility values and survival data from the register, following the area under the curve method. Utilities were based on the UK and Zimbabwe (as a sensitivity analysis) EQ-5D value sets, as there is no Sierra Leonean or West African value set. Explanatory models were developed based on previous literature to assess variables associated with HRQoL and QALYs at 1 year after stroke. To address missing values, Multiple Imputation by Chained Equations (MICE), with linear and logistic regression models for continuous and binary variables, respectively, were used. EQ-5D-3L data were available for 373/460 (81.1%), 360/367 (98.1%), and 299/308 (97.1%) participants at 7, 90 days, and 1 year after stroke. For stroke survivors, median EQ-5D-3L utility increased from 0.20 (95% CI: -0.16 to 0.59) at 7 days post-stroke to 0.76 (0.47 to 1.0) at 90 days and remained stable at 1 year 0.76 (0.49 to 1.0). Mean QALYs at 1 year after stroke were 0.28 (SD: 0.35) and closely associated with stroke severity. Older age, lower educational attainment, patients with subarachnoid hemorrhage and undetermined stroke types all had lower QALYs and lower HRQoL, while being the primary breadwinner was associated with higher HRQoL. Sensitivity analysis with the Zimbabwe value set did not significantly change regression results but did influence the absolute values with Zimbabwe utility values being higher, with fewer utility values less than 0. We generated QALYs after stroke for the first time in an African country. QALYs were significantly lower than studies from outside Africa, partially explained by the high mortality rate in our cohort. Further research is needed to develop appropriate value sets for West African countries and to examine QALYs lost due to stroke over longer time periods. The Stroke in Sierra Leone anonymized dataset is available on request to researchers, see data access section.

Sustainable business models in SMEs: The customer’s perspective

&#x0D; &#x0D; &#x0D; The main purpose of the study is to identify the relationship between sustainability goals and measures of the degree of sustainable policy implementation, as well as the customer perspective as determined by the business model (BM) template of A. Osterwalder and Y. Pigneur. In the conducted research, the customer perspective takes into account five elements of the BM and each element was examined by several characteristics. The elements of the BM from the customer perspective are: customer segment, value proposition, distribution channels, customer relationships, and revenue. The study used the authors’ survey questionnaire, in which respondents, who were enterprises in the SME sector, answered questions about their own BM divided into three layers – business, social, and environmental. The results were statistically analysed using SPSS STATISTIC 28 software and a logit model was developed to verify the significance of the relationship between the binary variables studied. 303 enterprises participated in the survey, 59% of which were enterprises guided by sustainability goals (SGs) in their BMs. However, only 27% of those surveyed had measures of the degree of sustainable policy implementation.&#x0D; &#x0D; &#x0D; &#x0D; The conducted research confirmed that acting in accordance with SGs is strongly related to customer-oriented elements of the business model, such as (1) customer segregation by sex or by level of environmental awareness, (2) value proposition – an environmental orientation, (3) distri- bution channels – direct sales online, (4) customer relationships through dedicated personal assistance or automated service based on personal customer profiles, (5) revenue – sales of products and services. Slightly different results were obtained for using measures of the degree of the SD policy implementation, where the highest correlation occurred for: (1) main customers – manufacturing entrepreneurs, (2) customer segregation by end-user experience or by level of involvement in socially important issues, (3) value proposition – strengthening democracy, (4) distribution channels – wholesale stores are run by partners, (5) customer relationships through self-service or automated service based on personal customer profiles or involving customers in the product development process, (6) revenue – rental/lease/leasing or licenses.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;