Ordinal Scale Research Articles

Musculoskeletal surgeons have implicit bias towards the biomedical paradigm of human illness.

Evidence is mounting that the biopsychosocial paradigm is more accurate and useful than the biomedical paradigm of care. Habits of thought can hinder the implementation of this knowledge into daily care strategies. To understand and lessen these potential barriers, we asked: 1) What is the relative implicit and explicit attitudes of musculoskeletal surgeons towards the biomedical or biopsychosocial paradigms of medicine? 2) What surgeon factors are associated with these attitudes? An online survey-based experiment was distributed to members of the Science of Variation Group (SOVG) with a total of 163 respondents. Implicit bias towards the biomedical or biopsychosocial paradigms was measured using an Implicit Association Test (IAT) designed by our team using open-source software; explicit preferences were measured using ordinal scales. On average, surgeons demonstrated a moderate implicit bias towards the biomedical paradigm (d-score: -0.21; Interquartile range [IQR]: -0.56 to 0.19) and a moderate explicit preference towards the biopsychosocial paradigm (mean: 14; standard deviation: 14). A greater implicit bias towards the biomedical paradigm was associated with male surgeons (d-score: -0.30; IQR: -0.57 to 0.14; P = 0.005). A greater explicit preference towards the biomedical paradigm was independently associated with a European practice location (Regression coefficient: -9.1; 95% CI: -14 to -4.4; P <0.001) and trauma subspecialty (RC: -6.2; 95% CI: -11 to -1.0; P <0.001). The observation that surgeons have an implicit bias favoring the biomedical paradigm might inform strategies for implementation of care strategies based on evidence favoring the biopsychosocial paradigm.

Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.

Mobile health studies often collect multiple within-day self-reported assessments of participants' behavior and well-being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non-paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject-specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within-day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R-package.

Factors Associated With Acceptance of an Optional Diagnosis.

A sensation becomes a symptom (a concern) when a person associates it with potential illness. In the absence of objective evidence of a pathophysiological process that has important health consequences without treatment, assigning a diagnosis to the sensation is optional. This is important because labeling of benign bodily sensations as pathophysiology has potential advantages and disadvantages. We asked what patient and clinician factors are associated with willingness to accept an optional diagnosis. In a survey administered using Amazon M-Turk, 536 people anonymously completed validated measures for symptoms of anxiety and depression, intolerance of uncertainty, and skepticism regarding the healthcare system. They then viewed fictional personal medical scenarios in which they were asked to imagine they experienced certain symptoms, and were offered an optional diagnosis of a nerve problem, muscle pain syndrome, or fatigue syndrome, and were asked to rate their willingness to accept the diagnosis on an 12-point ordinal scale from 0 indicating "I do not accept it at all" to 11 indicating "I accept it with enthusiasm." The language of the scenarios was varied to attempt to reflect critical thinking, denigration of other doctors, an alternative mental health focus, or a hopeful outlook. Multilevel linear regression was used to identify factors associated with likelihood of accepting an optional diagnosis. Threshold likelihood of accepting an optional diagnosis greater than 5.5 on a 0 to 11 ordinal scale was independently associated with greater symptoms of anxiety (regression coefficient [RC]=0.38, 95% confidence interval [95% CI]=0.30-0.47, P <.001), greater skepticism regarding the healthcare system (RC=0.11, 95% CI=0.076-0.13, P <.001), and delivery tones characterized by either denigration of other doctors (RC=0.39, 95% CI=0.19-0.60, P <.001) or a hopeful outlook (RC=0.50, 95% CI=0.26-0.73, P <.001). Likelihood of accepting an optional diagnosis may be a sign of relative vulnerability from feelings of distress or distrust of medical evidence. Given this potential vulnerability, clinicians can take care to limit persuasive communication styles that can influence acceptance of optional diagnoses. III prognostic.

Associations of Wearable Activity Tracker Use With Physical Activity and Health Outcomes in Patients With Cancer: Findings from a Population-Based Survey Study.

Physical inactivity is a global issue for cancer survivors. Wearable activity trackers are promising to address physical inactivity by providing real-time feedback on physical activity and offering opportunities for self-monitoring and goal setting. Meta-analysis has reported the effects of interventions that incorporate wearable activity trackers on improved physical inactivity and related health outcomes (eg, BMI, anxiety and depression, and self-rated health status). However, wearable activity trackers were often used as an adjunct to physical activity interventions, and the effectiveness of wearable activity trackers alone is unknown. This study aims to determine the association of wearable activity trackers with physical activity and health outcomes in patients with cancer. Data from 957 cancer survivors from the Health Information National Trends Survey-Surveillance, Epidemiology, and End Results (HINTS-SEER) were analyzed. The outcome variables examined were time spent in moderate to vigorous physical activity, weekly frequency of strength training, BMI, anxiety and depression levels, and self-assessed health status. The primary independent variable was whether cancer survivors had used wearable activity trackers within the past 12 months. Design-based linear regression for continuous outcome variables and ordinal logistic regression for ordinal outcome variables were conducted to determine the associations after controlling for sociodemographic, cancer-related, and health-related factors. All data analyses accounted for the complex survey design and sample weights. Only 29% of cancer survivors reported wearable activity tracker use. Bivariate analyses showed that younger age (P<.001), higher education (P=.04), higher income (P<.001), and an employed status (P<.001) were significantly associated with wearable activity tracker use. Wearable activity tracker use was significantly associated with higher time spent in moderate to vigorous physical activity (adjusted =37.94, 95% CI 8.38-67.5; P=.01), more frequent strength training per week (adjusted odds ratio [OR] 1.50, 95% CI 1.09-2.06; P=.01), and better self-rated health status (adjusted OR 1.58, 95% CI 1.09-2.29; P=.01), but not with BMI or anxiety and depression. This study suggests that the uptake of wearable activity trackers is low and highlights the digital divide among patients with cancer. This study has confirmed the associations of wearable activity tracker use with physical activity and self-rated health, supporting using wearable activity trackers as a promising tool to facilitate physical activity promotion.

Is Anonymization Through Discretization Reliable? Modeling Latent Probability Distributions for Ordinal Data as a Solution to the Small Sample Size Problem

Is Anonymization Through Discretization Reliable? Modeling Latent Probability Distributions for Ordinal Data as a Solution to the Small Sample Size Problem

Research of the dynamics of wholesale segment concentration level in the regional pharmaceutical market

Introduction: The study of the competitive environment in the pharmaceutical market is an element of the analysis of the drug supply system. The purpose of the study is to analyze the dynamics of concentration levels in the wholesale segment of the regional pharmaceutical market. Materials and methods: Data on market shares of key players were collected and summarized based on the executed contracts (461 contracts in total) for the supply of pharmaceutical products in the model region. For the competitive analysis of pharmaceutical distributors, the Herfindahl-Hirschman Index (HHIn) calculation method was used. We also used the method of constructing the SV matrix, which has the form of a graph consisting of four quadrants divided on the abscissa scale by the obtained values of market concentration ratios (CRSV) and on the ordinate scale by the values of Hall-Teidman indices (HTSV). Results: Market concentration indicators in the wholesale segment of the regional pharmaceutical market indicate the formation of oligopoly. The constructed SV matrix demonstrated the dynamics of market concentration levels corresponding to the changing economic situation. Based on the results of the analysis, proposals to improve the competitiveness of regional pharmaceutical distributors were formed. Conclusion: The study of the dynamics of market concentration indicators allowed to analyze their state, establish the vectors of their drift, and propose measures to optimize the wholesale supply of pharmaceutical goods.

Factors Influencing the Association of 24-hour National Institutes of Health Stroke Scale & 90-day Modified Rankin Score.

The modified Rankin Scale (mRS) at 90days is the primary outcome in most acute stroke studies, but the long follow-up period has disadvantages. The National Institutes of Health Stroke Scale (NIHSS) at 24 h shows astrong, but imperfect, association with 90-day mRS. This study examines the association between 24-hour NIHSS and 90-day mRS and reasons for discrepancies. Data are from the ESCAPE-NA1 thrombectomy patients. To address the non-normality distribution of the NIHSS and include deceased patients, a7-point ordinal score was generated by grouping 24-hour NIHSS. The association of ordinal 24-hour NIHSS and 90-day mRS was assessed with adjusted ordinal logistic regression. Differences in baseline and treatment/post-treatment variables were compared between patients with discordant and concordant outcomes. One-thousand-seventy-six patients with available 24-hour NIHSS and 90-day mRS were included (median 24-hour NIHSS6[IQR: 2-14], median 90-day mRS2[IQR: 1-4]). Ordinal 24-hour NIHSS was associated with 90-day mRS (adjusted cOR 2.53 [95%CI 2.33-2.74]). Forty-eight (4.5%) patients had discordant outcomes. Of those, 19(1.8%) had 24-hour NIHSS < 6 and 90-day mRS5-6; all of which had ≥ 1 severe adverse event, most commonly pneumonia (6[31.6%]) or recurrent stroke (4[21.1%]). Twenty-nine patients (2.7%) had 24-hour NIHSS > 14 and 90-day mRS 0-2. In these patients, baseline NIHSS and ASPECTS was lower, and collateral status was worse. An ordinal NIHSS score that includes death at 24 h shows astrong association with 90-day mRS, suggesting that it could be used as an alternative outcome. Patients with discrepant outcomes differed from the remaining patients regarding their baseline NIHSS, ASPECTS, collateral status, and post-stroke complications.

Using Deep Learning Neural Networks to Improve Dementia Detection: Automating Coding of the Clock-Drawing Test.

Alzheimer's disease and related dementias (ADRD) is a growing public health concern. The clock-drawing test (CDT), where subjects draw a clock, typically with hands showing 11:10, has been widely used for ADRD-screening. A limitation of including CDT in large-scale studies is that the CDT requires manual coding, which could result in biases if coders interpret and implement coding rules differently. This study created and evaluated an intelligent CDT Clock Scoring system built with Deep Learning Neural Networks (DLNN) to automatically code CDT images. We used a large, publicly available repository of CDT images from the 2011-2019 National Health and Aging Trends Study (NHATS) and compared three advanced DLNN methods - ResNet101, EfficientNet and Vision Transformers (ViT) in coding CDT into binary and ordinal (0 to 5) scores. We extended beyond the traditional nominal classification approach (which does not recognize order) by introducing structured ordering into the coding system and compared DLNN-coded CDT images with manual coding. Results suggest that ViT outperforms ResNet101 and EfficientNet, as well as manual coding. The ordinal coding system has the ability to allow researchers to minimize either under- or over-estimation errors. Starting in 2022, our developed ViT-coding system has been used in NHATS' annual CDT-coding.

Vertical back movement of cows during locomotion: detecting lameness with a simple image processing technique.

This research paper proposes a simple image processing technique for automatic lameness detection in dairy cows under farm conditions. Seventy-five cows were selected from a dairy farm and visually assessed for a reference/real lameness score (RLS) as they left the milking parlor, while simultaneously being video-captured. The method employed a designated walking path and video recordings processed through image analysis to derive a new computerized automatic lameness score (ALDS) based on calculated factors from back arch posture. The proposed automatic lameness detection system was calibrated using 12 cows, and the remaining 63 were used to evaluate the diagnostic characteristics of the ALDS. The agreement and correlation between ALDS and RLS were investigated. ALDS demonstrated high diagnostic accuracy with 100% sensitivity and specificity and was found to be 100% accurate with a perfect agreement (ρc = 1) and strong correlation (r = 1, P < 0.001) for lameness detection in binary scores (lame/non-lame). Moreover, the ALDS had a strong agreement (ρc = 0.885) and was highly correlated (r = 0.840; 0.796-1.000 95% confidence interval, P < 0.001) with RLS in ordinal scores (lameness severity; LS1 to LS5). Our findings suggest that the proposed method has the potential to compete with vision-based lameness detection methods in dairy cows in farm conditions.

Evaluation of Knowledge, Attitudes and Practices for Hepatitis B Virus Infection Among Primary Healthcare Physicians in Georgia.

A nationwide serosurvey among adults in 2021 showed a 2.7% (95% confidence interval [CI]: 2.3%-3.4%) prevalence of hepatitis B. Our analysis evaluates knowledge, attitudes and practices (KAP) for hepatitis B virus (HBV) infection among primary healthcare physicians (PHPs) in Georgia. We randomly selected 550 PHPs from medical facilities in Georgia's six largest cities. Using bivariate ordinal regression, we assessed the association of socio-demographic factors with an ordinal knowledge score (low/middle/high). Multivariable logistic regression was performed to calculate adjusted odds ratios (aOR) and 95% CI to determine associations between HBV knowledge score and practices. Of 550 selected PHPs, 506 (92.0%) agreed to participate. Among them, 62.8% scored in the medium or high knowledge tertiles, 72.7% were confident in diagnosing HBV infection, 37.3% were confident in managing patients with hepatitis B; 47.4% reported being screened for and 26.2% reported being vaccinated against HBV infection. Compared to those with low knowledge scores, PHPs with a high score were less likely to recommend activities not supported by evidence, such as: the use of 'hepatoprotective' medications (aOR 0.43, 95% CI 0.25-0.73), caesarean sections (aOR 0.47, 95% CI 0.27-0.82) and withholding breastfeeding (aOR 0.57, 95% CI 0.34-0.96) to prevent HBV transmission. The majority of PHPs were confident in diagnosing HBV infection, but only one in three were confident in managing patients with hepatitis B. PHPs with higher HBV knowledge were less likely to provide inaccurate instructions to their patients. These findings will help to develop awareness and education campaigns supporting HBV elimination in Georgia.

Reconstruction of the Temperature Index Series of China in 1368–1911 based on REACHES database

This study reports the methodology for reconstructing anomalous temperature index series of China in 1368–1911 based on the REACHES database which digitizes the Chinese records quoted in the Compendium of Meteorological Records of China in the Last 3000 Years. The reconstruction adopts an ordinal scale index approach ranging from −2 (very cold) to 1 (warm). Based on the grading criteria, a total of 12,871 records were retrieved through a standard coding system established at REACHES. Sensitivity experiments were performed to test robustness of the index system and a reasonability test was conducted to develop an appropriate method for deriving areal mean temperature index. The reconstructed series were validated through comparison with early instrumental data from Global Historical Climatology Network which shows good correlations and reliability of the REACHES reconstructed index data. Annual and semi-annual (winter and summer) temperature index data series were released for the whole domain as well as the 3- and 15-subregion geographical domains in China.

Application of Paired Correlation Algorithms for the Distance Matrices Between DNA Chains

Authors:This paper is a continuation of some previous works by the authors. We consider various algorithms for calculating distances between genomes of similar species (we use primarily mitochondrial DNA, mtDNA) and various distance matrices between the same genomes obtained on the basis of these algorithms. We can say, just to simplify the situation a little, that all our publications on the subject of DNA analysis are associated with various applications of metrics set on such matrices. The paper also has a second subject, i.e., the study of the obtained distance matrices using special statistical characteristics. We consider two matrices obtained for the mtDNA of 32 species of monkeys; the species were selected so that they all belong to different genera. For them, we have obtained 2 matrices of distances between genomes corresponding to the Jaro –Winkler’s and Needleman –Wunsch’s algorithms. Next, we considered all the triangles obtained in these matrices, and for each of them we used a specially calculated badness. It is actually a measure of the deviation of the resulting triangle from some acuteangled isosceles one. For two sequences of such badness, we have considered variants of paired correlation. At the same time, in addition to the two standard pair correlation algorithms (Spearman’s and Kendall’s ones), we also considered a new algorithm proposed by us. The reason for considering this new algorithm is as follows. In the usual way of calculating the correlation, we consider only the set of pairwise values of two random variables, without taking into account the pairs themselves. Vice versa, in both of the mentioned pair correlation algorithms, despite their slight difference, we consider only the order of the elements in these pairs, not paying attention to the values themselves; we specifically note that this also applies to Spearman’s criterion, which is usually written about as being more suitable for measurements made on an ordinal scale. In our proposed algorithm, we tried to take into account both the value of both random variables and their order in pairs. The results obtained are of interest. Thus, the “pole” variants (i.e., the usual correlation formula and standard pair correlation algorithms) show some (though very small) correlation between two sequences of 4960 pairs of triangles: from 0.1 to 0.4, depending on the specific algorithm, on whether preliminary normalization was carried out, etc. And the “intermediate” variant (taking into account both the order of pairs and the values of random variables) showed a complete lack of connection: the absolute value of the correlation coefficient did not exceed 0.006. Even more interesting is another result obtained in the work, which can be called a small connection between two well-known algorithms for determining the distances between genomes, namely, algorithms of Jaro –Winkler and Needleman –Wunsch.

Incomplete ordinal panel responses with initial condition and skew-normal random effect

This paper presents a model for the ordinal categorical panel data with skew-normal random effects, endogenous initial condition, and non ignorable (NI) non monotone missing data. We assumed three separate models for the initial condition, subsequent responses, and missing procedure. The baseline or initial response variable and subsequent response variables are modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process where the maximum likelihood estimates are obtained using Monte Carlo methods. The results show that in the presence of missing data, the skew-normal random effects produce better results compared to the normal one, and considering the endogenous initial conditions is effective in modeling the results of the observed and missing responses such that the patients whose severity of schizophrenia is higher at the beginning of the process have a higher chance of missing on the subsequence times. In this research, schizophrenia data and then simulation studies have been used to check the performance of the proposed model. The results show that the skew-normal random effect using the NI non monotone missing mechanism by increasing the skewness value produces better results in the analysis of simulation studies.

A reliable and valid assessment of upper limb movement quality after stroke: the observational Drinking Task Assessment.

To develop and evaluate the reliability and validity of a new observational Drinking Task Assessment (DTA) designed to assess quality of movement in task performance after stroke. Reliability and validity. The DTA measures movement time and movement quality (smoothness, trunk, shoulder, elbow, and grasp movements) on a 4-level ordinal scale. Thirty participants with chronic stroke were assessed independently by 2 therapists. Intra-class correlation (ICC), standard error of measurement (SEM) and minimal real difference (MRD), weighted kappa, percentage of agreement, and Svensson method were used for reliability assessment. Motion capture-based kinematics and established clinical scales were used to evaluate validity. The absolute SEM and MRD for movement time were 0.4 and 1 s (11%), respectively. The ICC (≥ 0.93) and weighted kappa (0.71-1.0) showed good to excellent agreement for intra- and inter-rater reliability. DTA showed strong correlations with Fugl-Meyer Assessment (0.74), Action Research Arm Test (0.93), and kinematic measures of smoothness (0.93), trunk displacement (0.91), elbow extension (0.73), and shoulder movements (0.56), indicating good construct validity. The new DTA proved to be a reliable and valid tool for assessment of movement quality during task performance after stroke.

Managing perceptions on the linguistic terms of qualitative scales

In this paper, we consider the problem of how individuals perceive the ordinal proximities between the linguistic terms of an ordered qualitative scale. We address the challenge of accurately measuring these ordinal proximities in ordered qualitative scales used in surveys. To tackle this issue, we have devised a visual procedure, managed through sliders, and an appropriate software that generates the metrizable ordinal measure which represents the ordinal arrangement of the proximities between the linguistic terms of an ordered qualitative scale. The procedure has been applied to real-world survey data to validate its effectiveness. The results indicate that the new proposal significantly outperforms traditional techniques in terms of accuracy and reliability, providing a robust tool for researchers in fields requiring precise ordinal data analysis.

Measuring the impact of enterprise risk management on performance, value, and risk indicators of Borsa Istanbul XBANK companies with data mining prediction models

Risk management (RM) is viewed by organizations from a holistic perspective instead of a silo-based point of view and a change in basic assumptions has occurred in recent years. The comprehensive approach to RM that is developed based on a holistic perspective and is adopted by companies is called enterprise risk management (ERM). From the general point of view, it is expected that companies will have sustainable operations, improved performance and value, and controlled risks by using the ERM concept. In this article, the relations of ERM adaptation with firm performance, firm value, and risks were researched through ordinal variables such as the implementation level of ERM and the sophistication level of the organizational structure of ERM in regression models based on panel data (PD). Empirical evidence depending on a sample of ten banking companies listed on the Borsa Istanbul (BIST) Banks index (XBANK) for the period from 2019 to 2022 confirms the above basic argument for firm performance, firm value, and the insolvency risk of banks in general terms. In addition, the prediction accuracy of PD models is calculated for the performance, value, and risk indicators of banks, and the partial least squares regression model is proposed as the alternative prediction model of data mining.

Mixed fuzzy C-means clustering

Clustering analysis becomes challenging when the dataset has mixed data types comprising categorical (nominal or ordinal scale) and numerical (interval scale) features. Mainstream distance metrics cannot handle the information in categorical data about the similarity between the observations and cluster centers, leading to performance loss. Various methods are introduced in the literature to handle the mixed data types in clustering. However, each method has disadvantages in capturing categorical information about similarity, adjusting the contribution of categorical information to clustering, and computational or implementation inefficiency. This study proposes a mixed fuzzy C-means clustering method for mixed data types. Two new distance metrics are developed to handle binary and multi-class nominal features. The scaled entropy of each data type is used to adjust the weight of each data type in the overall similarity metric, providing a lower bias since no user-specified weight is required. A comparative numerical study is conducted with twenty real datasets and seven benchmark methods using five cluster validation statistics. The mixed fuzzy C-means clustering performs better than the benchmark methods and is computationally efficient in practice. Since all the computer codes for implementing mixed fuzzy C-means clustering are given, the proposed method is readily applicable to practical problems.

6479 Acute Estradiol and Progesterone Therapy In Hospitalized Adults With Moderate COVID-19: A Randomized Control Trial

Abstract Disclosure: D. Lovre: Research Investigator; Self; Novo Nordisk, Corcept Therapeutics. K.M. Bateman: None. L.Y. Saltzman,: None. M. Qadir: None. F. Mauvais-Jarvis: None. Background and Objective: COVID-19 outcomes are less severe in women vs men. Experimental studies show that estradiol (E2) and progesterone (P4) produce anti-inflammatory immune responses and immune tolerance and enhance antibody production. The effect of E2 and P4 combination on COVID-19 severity is unknown. The aim of the study was to evaluate the efficacy of a short systemic E2/P4 combination in mitigating COVID-19 severity in hospitalized men and women. Methods: Phase 2, double blind, placebo-equivalent controlled, single center trial of ten participants (men and women) who were hospitalized for COVID-19, with scores 3 to 5 on the 9-point World Health Organization (WHO) ordinal scale for clinical improvement. Participants were randomized into two arms and treated for five days: 1) E2 Cypionate (5 mg IM once) and micronized P4 (200mg, PO daily); and 2) Placebo-equivalent (normal saline (1ml, IM once) and folate (400mcgs, PO daily), in addition to standard of care (SOC). The primary outcome was the proportion of patients improving to scores 1 or 2 on the WHO scale on the day of discharge. Secondary outcomes included length of hospital stay (LOS), days on oxygen therapy (DOT), readmission rates (RR), adverse events (AEs) and change in inflammatory biomarkers assessed by untargeted proteomics. Findings: There were no significant differences between the treatment groups in COVID-19 severity by WHO score, LOS, DOT, RR or AEs. Patients in the E2/P4 arm exhibited a decrease in inflammatory biomarkers of respiratory and gastrointestinal disease (GI) and significant change in immune and inflammatory responses. Conclusions: Use of short-term systemic E2/P4 is associated with decrease in biomarkers of respiratory and GI disease inflammation and change in biomarkers of immune and inflammatory response. Presentation: 6/3/2024

Measuring the Maturity of Integrated Care in Singapore with the SCIROCCO Exchange Tool.

How have we progressed and where are the gaps of integrated care in Singapore? Social-health care provision in the context of an ageing population is critical in the city-state's management of the unprecedented demand as the proportion of seniors with multiple complex medical needs have almost doubled in the past decade. This study measures the maturity level of Singapore's integrated care, identifies key gaps and discusses their implications using the SCIROCCO Exchange tool, an online self-assessment tool consisting of the 12 dimensions necessary for the provision of integrated care. A three-step mixed method Delphi study was used to derive expert consensus. Participants across the social-healthcare sector as well as representatives from all three public healthcare delivery networks with at least five years of experience were included. Participants rated each of the twelve dimensions of the SCIROCCO Exchange tool on a six-point ordinal scale and provided justifications for each rating. Criteria from the RAND UCLA appropriateness method and thematic analysis were adopted for the analysis. All participants completed the study. The study found five dimensions in the "Initial" maturity level and five dimensions in the "Progressing" maturity level. There were two dimensions which were "Uncertain" because of split responses, possibly due to their differing vantage points and conceptualisations of integrated care. The overall medians were plotted on a spider diagram. The absence of a systematic approach for integrated care was the most common subtheme across all dimensions. This is foundational for integrated care as this would enable stakeholders across health and social care to identify with a common goal. The findings emphasise the imperative to reshape social-health care delivery by focusing on foundational dimensions (such as structure, governance and citizen empowerment) to enable progress in other dimensions. Following the conclusion of this study, Singapore initiated a primary care reform with the launch of Healthier SG in July 2023. Future research may wish to explore the impact of Healthier SG on maturity of integrated care in Singapore.

Use of the Win Ratio for Analysis of Stroke Trials: Description, Illustration, and Planned Use in the Second European Carotid Surgery Trial (ECST-2).

Randomized trials in stroke often focus on outcomes beyond a single clinical event. Trials of stroke prevention commonly use composite outcomes that include multiple components (eg, death, stroke, or myocardial infarction). A major limitation is that all events count equally but may differ markedly in terms of clinical severity. Trials in acute stroke often use ordinal outcomes or scale scores. Limitations include the requirement for statistical assumptions and the difficulty of handling the competing risk of death. We introduce the win ratio as an alternative method. It works by placing components of a composite into a hierarchy, whereby clinically more important outcomes take priority over less important ones. We illustrate how it works using data from 2 major stroke trials: the ICSS (International Carotid Stenting Study, a trial in stroke prevention) and the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). Potential benefits of the win ratio approach include the possibility to (1) emphasize the clinically most important outcomes, (2) combine components of different outcome types into a composite (eg, a mixture of time-to-event, continuous, and categorical), and (3) naturally handle the competing risk of death in analyses of quantitative outcomes. The win ratio will be used in the upcoming analysis of the ECST-2 (Second European Carotid Surgery Trial), which has a hierarchical primary outcome of (1) time to perioperative death, fatal stroke, or fatal myocardial infarction (most important); (2) time to nonfatal stroke; (3) time to nonfatal myocardial infarction (excluding silent infarcts); and (4) new silent cerebral infarct on brain imaging (least important). The win ratio provides a useful clinically relevant method for analyzing trial outcomes. It has some advantages over conventional methods, and we recommend its wider application in future stroke trials.