Clustering Error Research Articles

Racial and Ethnic Disparities in Testing of Hepatitis C Virus-Exposed Children Across the United States.

Despite rising hepatitis C virus (HCV) prevalence among pregnant individuals in the United States, HCV testing among exposed infants remains low. Although recent guidelines recommend early ribonucleic acid (RNA) testing for HCV-exposed children to help improve testing rates, national studies describing factors associated with HCV testing and the type of testing completed are lacking. In this retrospective national study, we characterized HCV testing and care among HCV-exposed infants born between 2010 and 2020 captured in the electronic health record-based TriNetX Research Network. We analyzed factors associated with appropriate HCV testing completion (negative or positive HCV RNA testing or negative HCV antibody testing at any age through study end in 2022) and with RNA compared with antibody testing using univariable and multivariable logistic regression with clustered standard errors by healthcare organization. Of 8516 HCV-exposed children, 45.8% completed any HCV testing and 42.1% completed appropriate testing (25% of whom had RNA testing only). A total of 182 (5.1% of appropriately tested children) had evidence of HCV infection. Of 104 treatment-eligible children, 14.4% were treated. Black (odds ratio [OR]: 0.38, 95% confidence interval [CI]: 0.26-0.55), Asian/Pacific Islander (OR: 0.06, 95% CI: 0.03-0.11), and Hispanic/Latinx (OR: 0.56, 95% CI: 0.36-0.88) children had lower odds of appropriate testing compared with White and non-Hispanic/Latinx children. Fewer than half of HCV-exposed children in this national sample were tested for HCV, with lower testing odds among Black, Asian/Pacific Islander, and Hispanic/Latinx children. Substantial work to increase testing and treatment and decrease disparities in testing among HCV-exposed children is needed to help reach US HCV elimination goals.

Evaluating the current methodological practices and issues in existing literature in pooling complex surveys: a systematic review

BackgroundPooling data from complex survey designs is increasingly used in the health and medical sciences. However, current methodological practices are not well documented in the literature while performing the pooling strategy. We aimed to review related pooling studies and evaluate the quality of pooling within the framework of specific methodological guidelines, particularly when combining complex surveys such as Demographic & Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).MethodsWe performed a systematic literature search focusing on studies utilizing the pooling method with DHS and MICS survey data. These studies were selected from those published between 2010 and 2021 and were retrieved from electronic databases (PubMed and Scopus) in accordance with pre-defined inclusion criteria. Then, we extracted 355 studies for the final review and evaluated the reporting quality of the pooling strategy while considering some methodological issues.ResultsThe majority of studies (81.4%) reported using a pooled (one-stage) approach, while 11.8% used a separate (two-stage) approach, and 6.8% used both approaches. Approximately 63.3% of studies did not clearly describe their pooling strategy. Only 3.4% of the studies mentioned the variable harmonization process, while 66.9% addressed dealing with heterogeneity between surveys. All studies that used the separate (two-stage) approach conducted a meta-analytic procedure, while 38.1% of studies using the pooled approach employed a multilevel model. More than half of the studies (55.6%) mentioned the use of clustered standard errors. The Delta method, Bootstrap, and Taylor linearization were each applied in 11.1% of the studies for variance estimation. Survey weights, primary sampling unit (PSU) or cluster, and strata were used together in 30.5% of the studies. Survey weights were employed by 69.8%, PSU or cluster by 43.8%, and the strata variable by 31.7%. Sensitivity analysis was conducted in 16% of the studies.ConclusionsOur study revealed that fundamental methodological issues associated with pooling complex survey databases, such as the selection of pooling procedures, data harmonization, accounting for cycle effects, quality control checks, addressing heterogeneity, selecting model effects, utilizing survey design variables, and dealing with missing values, etc., were inadequately reported in the included studies. We recommend authors, readers, reviewers, and editors examine pooling studies more attentively and utilize the customized checklist developed by our study to assess the quality of future pooling studies.

Tenant Right-to-Counsel and Adverse Birth Outcomes in New York, New York

In 2017, New York, New York, launched the United States' first right-to-counsel program, guaranteeing lawyers to low-income tenants in select zip codes, which was associated with reducing eviction risk by half. Given documented associations between evictions during pregnancy and adverse birth outcomes, the right-to-counsel program may be associated with improved birth outcomes. To measure associations between zip code-level right-to-counsel access and risk of adverse birth outcomes, including preterm birth and low birth weight, among infants born to Medicaid-insured birthing parents. This retrospective cohort study leveraged the staggered rollout of New York's right-to-counsel program from January 2016 to February 2020 as a natural experiment using a population-based sample of live births to Medicaid-insured birthing parents residing in New York, New York. Data were analyzed from February 2022 to September 2024. Zip code right-to-counsel status 9 months prior to birth. Adverse birth outcomes were measured using individual birth records from the New York Bureau of Vital Statistics. Outcomes included dichotomous indicators of low birth weight (<2500 g), preterm birth (<37 weeks' gestation), and a composite of both. Difference-in-differences linear probability models controlled for year, month, and zip code and included clustered standard errors. Among 260 493 live births (mean [SD] birthing parent age, 29 [6] years) from January 2016 to February 2020, 43 081 births (17%) were to birthing parents residing in zip codes where right-to-counsel was available during pregnancy. Exposure to right-to-counsel during pregnancy was associated with statistically significant reductions in infants' probability of adverse birth outcomes, with reductions of 0.73 (95% CI, 0.06-1.41) percentage points in low birth weight, 0.91 (95% CI, 0.10-1.71) percentage points in preterm birth, and 0.96 (95% CI, 0.09-1.84) percentage points in the composite outcome in treated vs untreated zip codes. This cohort study found that right-to-counsel was associated with reduced risk of adverse birth outcomes among Medicaid-insured birthing parents. These findings suggest that eviction prevention via right-to-counsel may have benefits that extend beyond the courtroom and across the life-course.

Trends in availability and nutritional profile of meat-based vs meat-free menu items in 75 large chain restaurants in the United States 2013-2021

Introduction: Chain restaurants are ubiquitous in the United States. While restaurants are increasingly promoting health- and climate-conscious menu options, few studies have examined whether restaurants are increasing availability of menu items with lower climate impact and whether these offerings are healthier. This study examines trends in the availability and nutritional profile of food items featuring different meat sources on menus at 75 large chain restaurants in the US from 2013-2021. MethodsLongitudinal data on menu items from 75 large US chain restaurants from 2013-2021 were obtained from MenuStat.org, an online database of menu items from the largest grossing restaurant chains in the US. Annual counts and proportions of food items featuring different meat sources were calculated overall, by food category, and by restaurant type. Differences in predicted mean calories between meat-based items and meat-free items were calculated (overall, by restaurant type, by year) using linear regression models with clustered standard errors. ResultsAvailability and calorie content of meat-based versus meat-free items were generally stable over time. Availability of chicken-containing items increased and there was an absolute reduction in the availability of beef-containing menu items (p-trends <0.001). Total calories and calories from protein, unsaturated fat, and saturated fat were lower among meat-free items versus meat-based items. However, calories from sugar were higher for meat-free items. Conclusions: While meat-free items had fewer calories and some aspects of nutritional profile were more favorable, the availability of meat-free menu items has not increased in large chain restaurants, suggesting limited improvement on reducing climate impact.

Nexus between corporate governance and FinTech disclosure: a comparative study between conventional and Islamic banks

Purpose This study investigates the impact of corporate governance on FinTech disclosure levels in Jordanian conventional and Islamic banks. It aims to determine whether governance mechanisms affect disclosure practices in the FinTech sector, exploring the interplay between governance and transparency in financial innovations. Design/methodology/approach The research methodology entails a thorough analysis of data from all 15 Jordanian conventional and Islamic banks listed on the Amman Stock Exchange, covering the period from 2015 to 2022. This study uses manual content analysis using a custom FinTech Disclosure Index (FDI) and quantitative analysis with a two-way clustered error regression model. Findings The findings show that corporate governance mechanisms, particularly board size, board meetings and “Big4” audit firms, are crucial in enhancing FinTech disclosure across conventional and Islamic banks. However, Islamic banks consistently show higher disclosure levels than their conventional counterparts, attributed to their distinct governance structures that emphasize ethical governance and transparency. These results indicate an awareness among decision-makers about the importance of business model transformation toward FinTech. Originality/value This study pioneers the introduction of FDI, using it for a novel comparative analysis of FinTech disclosure levels between Islamic and conventional banks. By exploring how various governance structures influence FinTech disclosure, this research provides fresh insights into the interplay between corporate governance and financial technologies in the banking sector.

Standard Errors for Two-Way Clustering with Serially Correlated Time Effects

Abstract We propose improved standard errors and an asymptotic theory for two-way clustered panels. Our theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods. Our asymptotic distribution theory is the first which allows for this level of inter-dependence. Under weak conditions, we demonstrate that OLS is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal. The results extend to two-way fixed-effect models; we argue that two-way clustering is still necessary even if two-way fixed effects are included. Simulation and empirical illustration are provided.

Measuring error in the Demographic Index – steps towards a future population statistics system in England and Wales

ObjectiveWe are developing methods to measure the quality of the Demographic Index at a National Statistics Institute. The Demographic Index is the foundational dataset for a future, transformed, population statistics system for England and Wales. It clusters records for individuals across many years of health, education, and tax data, and assigns them a unique ID. Previous research has helped us to conceptualise three types of error in the Demographic Index: clustering error, coverage error, and data measurement error. We are currently working on estimating one type of clustering error, whereby records for two individuals are mistakenly assigned the same ID (“false positive clusters”). ApproachPrevious work has allowed us to identify variables associated with false positive clusters. We are now working on a stratification method, whereby each ID in the Demographic Index is scored according to how likely it is to contain this error. We plan to measure uncertainty using bootstrapping. Results and ConclusionsThis work is ongoing, and we hope to obtain results by summer 2024. ImplicationsIt is vital for us to understand and measure error in the Demographic Index because this is necessary for measuring error in any statistics derived from the Index. And it is crucial that population statistics made by any future system should have measures of error. Assuming that our work shows promise, we will continue by measuring other types of error, and by creating use cases to test how these measures can be fed forward into secondary analyses (e.g. population estimation).

Consonant Cluster Acquisition in Toddlers: An Explorative Study

The acquisition of consonant clusters is considered as one of the final stages of speech development in typically developing children. Nonetheless, the phonological development in children is now more advanced compared to the previously established norms. The current study aimed to determine how consonantal clusters, in particular non-geminate clusters, emerged throughout the early phases of phonological acquisition and to understand the positional variations in the cluster acquisition. The study involved 40 Malayalam speaking children aged between two to three years, divided into four groups with an age interval of 3 months. These children were administered 30 non-geminate consonant clusters from the Malayalam Articulation Test – Revised. Positional effect of the cluster acquisition was examined. In addition, the type of articulatory errors on consonant production were also determined. Findings from the present study revealed that children achieve the mastery of many consonant clusters by two to three years of age. Positional effect of cluster acquisition revealed medial clusters were acquired earlier than initial clusters. Cluster reduction was the dominant cluster error type, decreasing with age. Thus, compared with the previous established norms, the current findings demonstrate a much earlier acquisition of nongeminate clusters in Malayalam. The revised norms for cluster acquisition proposed from the findings of this study could benefit Speech-Language Pathologists to conduct age-appropriate assessment and treatment of speech sound disorders.

Relationship between reasons for intermittent missing patient-reported outcomes data and missing data mechanisms

PurposeNon-response (NR) to patient-reported outcome (PRO) questionnaires may cause bias if not handled appropriately. Collecting reasons for NR is recommended, but how reasons for NR are related to missing data mechanisms remains unexplored. We aimed to explore this relationship for intermittent NRs.MethodsPatients with multiple myeloma completed validated PRO questionnaires at enrolment and 12 follow-up time-points. NR was defined as non-completion of a follow-up assessment within seven days, which triggered contact with the patient, recording the reason for missingness and an invitation to complete the questionnaire (denoted “salvage response”). Mean differences between salvage and previous on-time scores were estimated for groups defined by reasons for NR using linear regression with clustered standard errors. Statistically significant mean differences larger than minimal important difference thresholds were interpreted as “missing not at random” (MNAR) mechanism (i.e. assumed to be related to declining health), and the remainder interpreted as aligned with “missing completely at random” (MCAR) mechanism (i.e. assumed unrelated to changes in health).ResultsMost (7228/7534 (96%)) follow-up questionnaires were completed; 11% (802/7534) were salvage responses. Mean salvage scores were compared to previous on-time scores by reason: those due to hospital admission, mental or physical reasons were worse in 10/22 PRO domains; those due to technical difficulties/procedural errors were no different in 21/22 PRO domains; and those due to overlooked/forgotten or other/unspecified reasons were no different in any domains.ConclusionIntermittent NRs due to hospital admission, mental or physical reasons were aligned with MNAR mechanism for nearly half of PRO domains, while intermittent NRs due to technical difficulties/procedural errors or other/unspecified reasons generally were aligned with MCAR mechanism.

The Effect of Preoperative Carbohydrate-containing Fluid Consumption on Maternal Oxidative Stress Markers During Elective Cesarean Delivery

PurposeThe purpose of this study was to evaluate the effect of carbohydrate loading prior to the cesarean surgery under spinal anesthesia on thiols and ischemia-modified albumin (IMA) levels. DesignProspective, randomized placebo-controlled study. MethodsSeventy-nine pregnant women planned for cesarean sections under spinal anesthesia at Karaman Training and Research Hospital were randomized into a control group (group C) (n = 42), and an oral carbohydrate preloading group (group OCH) (n = 37). OCH loading requires consuming 400 mL the night before surgery and 200 mL up to 2 hours before anesthesia. Group OCH consumed an oral carbohydrate-rich beverage (Nutricia-Fantomalt), and group C consumed an equal volume of water. This study investigated thiol-disulfide homeostasis after preoperative carbohydrate consumption. Preoperative gastric fluid, volume, antral cross-sectional area, hypotension following the birth, and fetal blood gas parameters were compared across groups. FindingsThiols and IMA levels did not differ across groups before and after surgery (P > .05). Gastric ultrasonography showed similar antral cross-sectional area and stomach volume between groups (P = .172, P = .128, respectively). When surgery caused hypotension, group OCH received more ephedrine for surgery-induced hypotension, although this difference is not statistically significant (P = .704). A clustered error bar (95% confidence interval) plot with an interpolation line was used for a time-based comparison of mean differences in heart rate and mean arterial pressure between the groups. ConclusionsThis study supports that mothers' thiols and IMA levels were unaffected by preoperative OCH loading before cesarean surgery. We did not examine thiol and its derivatives in umbilical cord blood; hence, we can not comment on thiol/disulfide homeostasis levels in neonates.

Drought risk and audit pricing: a mixed-methods study

PurposeClimate change has led to a rise in the frequency, intensity and scope of droughts, posing significant implications for businesses. This study examines the impact of local community drought levels on audit pricing. Additionally, it explores the moderating effects of high-tech industries, auditor busyness and the level of local community concern regarding the drought crisis.Design/methodology/approachThis study employs a mixed-methods approach to rigorously test the research hypotheses. The quantitative phase of the study utilizes a sample of 1,278 firm-year observations from Iran’s capital market. For the analysis of the quantitative data, ordinary least squares regression with clustered robust standard errors is used. Additionally, this research supplements its quantitative findings with qualitative evidence obtained through semi-structured interviews with 19 Iranian audit partners.FindingsThe results suggest that firms operating in provinces facing severe droughts experience notably higher audit fees. Furthermore, the positive relationship between drought and audit fees is weakened when auditors are busy, local community concern regarding the drought crisis is high or the firm operates within high-tech industries. These findings are supported by a range of robustness checks and qualitative evidence gathered from the field.Originality/valueThis research contributes to the growing literature on climate change by examining the influence of local community drought levels on audit pricing within an Iranian context. Additionally, our study sheds light on how high-tech industries, auditor workload and the level of local community concern regarding the drought crisis moderate the relationship between drought and audit fees. Importantly, our study pioneers in providing mixed-methods evidence of the association between drought severity and audit fees.

Contrastive visual clustering for improving instance-level contrastive learning as a plugin

Contrastive learning has achieved remarkable success in computer vision, however it is built on instance-level discrimination which leaves the valuable intra-class correlation in dataset unexploited. Current semantic clustering methods are proven to be helpful but they would suffer from the error accumulated in the iteration process without ground-truth guidance. In an attempt to remedy the clustering error accumulation when utilizing intra-class correlation for contrastive learning, we propose an online Contrastive Visual Clustering (CVC) method with two actions: gathering instances with highly similar feature embeddings, and penalizing instances being clustered with low confidence. CVC can integrate with not only contrastive learning but also arbitrary self-supervised learning frameworks simply as a plugin. Under various experiment settings, we show that CVC improves the linear classification performance by a large margin for models pre-trained with self-supervised representation learning, in both image and video scenarios. The code is available at https://github.com/yliu1229/CVC.

Analysis of the effectiveness of clustering algorithms for multimodal samples using computer simulation of an educational experiment

Introduction. The article is devoted to the problem of primary data processing of pedagogical experiments having a multimodal character. The purpose of the study is to identify the most effective and universal clustering algorithms for pedagogical experiments. Materials and Methods. The study used the method of modeling a pedagogical experiment. The analysis of 5 clustering algorithms is conducted. The effectiveness of clustering algorithms was evaluated based on the proportion of observations with clustering errors at various tolerance levels and the Jacquard similarity coefficient. Regression analysis was used to assess the influence of modeling parameters of a pedagogical experiment and indicators of descriptive statistics on the effectiveness of clustering algorithms. Results. The assessment of the effectiveness of various data clustering algorithms is provided, as well as a correlation and regression analysis of factors affecting clustering efficiency indicators was carried out. Conclusions. The most effective clustering algorithms for multimodal samples include the K-means algorithm and the agglomerative hierarchical algorithm. The results obtained in this research can be used for statistical analysis of pedagogical, psychological, sociological, biological and medical research data.

Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm

Massive multi-input multi-output (MIMO) is realized as the principal technology in the emerging fifth generation communication network system. Hybrid structure uplink communication is considered for the MIMO Non-orthogonal multiple access (MIMO-NOMA) system’s beam forming and power efficiency improvement through the novel three-layer user grouping. In the three-layer user grouping, the K-means algorithm is adopted in the initial layer for grouping users among different clusters and rectifying clustering errors in the third layer. The second layer used the agglomerative nesting (AGNES) algorithm for merging smaller clusters based on the channel correlation and angles of arrival similarity. The beam selection is carried out to minimize the intrusion of defined beam elements and to overcome beam overlapping problems. The non-convex optimization of the power allocating problem is modified as a convex problem by introducing a Quadratic transform (QT) to minimize each user’s data rate requirement. The algorithm of coati optimization is proposed to iteratively optimize the power allocation problem. The simulation results show that our proposed methodology goes beyond the existing schemes in terms of energy efficiency beyond the maximum power and achievable sum rate can be achieved.

Self-Supervised Deep Clustering Method for Detecting Abnormal Data of Wastewater Treatment Process

In wastewater treatment process (WWTP), abnormal data seriously reduce data quality rendering the application techniques impractical. The implementation of abnormal data detection is challenging due to the nonlinear nature of WWTP. Typically, constructing an accurate anomaly detector requires large amounts of labeled data, which is difficult in practice. Thus, a self-supervised memory enhanced deep clustering method (SMEL) is proposed to detect abnormal data without using any labels. First, a self-supervised deep clustering network, combining stacked autoencoders and the clustering algorithm, leverages unlabeled data to extract nonlinear features and capture the normal pattern. Second, an adaptive weight objective function, jointly optimizing the reconstruct error and clustering error, is designed to obtain a robust clustering structure. Third, double memory enhanced modules, consisting of a centroid memory and a score memory, are presented to enhance training stability and detection accuracy. Finally, experiments on three WWTP datasets show that SMEL achieves the highest detection accuracy.

Analysis of the agricultural economic value of a weather forecasting service based on a survey of peasant households in Chinese provinces

The enhancement of weather forecasting capabilities plays a pivotal role in agricultural production, storage, and transportation. This paper conducts a comprehensive survey among agricultural producers across 31 provinces and over 1000 counties in China using household questionnaires. The collected data is analyzed utilizing a multiple linear regression model that incorporates clustering and robust standard errors to ensure accurate results. The findings reveal a positive correlation between the frequency at which agricultural production entities pay attention to meteorological forecast information and the subsequent incremental increase in their income. Moreover, it is observed that both the number of access channels to meteorological information and their frequency significantly impact the rise in agricultural income attributed to such forecasts. Specifically, each additional type of access channel leads to an average income increase of 11.11 yuan. Furthermore, through regression analysis, this study demonstrates statistically significant effects of meteorological forecasts on cost savings as well as income growth within the realm of agricultural production.

Classification of dementia from spoken speech using feature selection and the bag of acoustic words model

&lt;p&gt;Memory disorders and dementia are a central factor in the decline of functioning and daily activities in older individuals. The workload related to standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken speech. This study presented a bag of acoustic words approach for distinguishing dementia patients from control individuals based on audio speech recordings. In this approach, each individual's speech was segmented into voiced periods, and these segments were characterized by acoustic features using the open-source openSMILE library. Word histogram representations were formed from the characterized speech segments of each speaker, which were used for classifying subjects. The formation of word histograms involved a clustering phase where feature vectors were quantized. It is well-known that partitional clustering involves instability in clustering results due to the selection of starting points, which can cause variability in classification outcomes. This study aimed to address instability by utilizing robust K-spatial-medians clustering, efficient K-means$ ++ $ clustering initialization, and selecting the smallest clustering error from repeated clusterings. Additionally, the study employed feature selection based on the Wilcoxon signed-rank test to achieve computational efficiency in the methods. The results showed that it is possible to achieve a consistent 75% classification accuracy using only twenty-five features, both with the external ADReSS 2020 test data and through leave-one-subject-out cross-validation of the entire dataset. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.&lt;/p&gt;

Impact of living with an adult with depressive symptoms among households in the United States

BackgroundThe effect of depressive symptoms on individuals has been widely studied but their impact on households remains less explored. This study assessed the humanistic and economic impact of living with an adult with depressive symptoms on adults without depressive symptoms among households in the United States (US). MethodsThe Medical Expenditure Panel Survey (MEPS) Household Component database was used to identify adults without depressive symptoms living in households with ≥1 adult with depressive symptoms (depression household) and adults without depressive symptoms living in households without an adult with depressive symptoms (no-depression household). Weighted generalized linear models with clustered standard errors were used to compare total income (USD 2020), employment status, workdays missed, quality of life (QoL), and healthcare resource utilization (HRU) between cohorts. ResultsAdults without depressive symptoms living in a depression household (n = 1699) earned $4720 less in total annual income (representing 11.3% lower than the average income of $41,634 in MEPS), were less likely to be employed, missed more workdays per year, and had lower QoL than adults without depressive symptoms living in a no-depression household (n = 15,286). Differences in total annual healthcare costs and for most types of HRU, except for increased outpatient mental health-related visits, were not significant. LimitationsData is subject to reporting bias, misclassification, and other inaccuracies. Causal inferences could not be established. ConclusionThe economic and humanistic consequences of depressive symptoms may extend beyond the affected adults and impact other adult members of the household.

Youth Preseason Performance on the Sport Concussion Assessment Tool 5 Across Multiple Sports.

To examine preseason Sport Concussion Assessment Tool 5 (SCAT5) performance of adolescent sport participants by environment (in-person/virtual), sex, age, concussion history, collision/noncollision sport participation, and self-reported medical diagnoses. Cross-sectional. Canadian community and high-school sport settings. Three thousand eight hundred five adolescent (2493 male, 1275 female, and 37 did not disclose; 11- to 19-year-old) sport participants. Sport Concussion Assessment Tool 5 administration method (in-person/virtual), sex (male/female/unreported), age (years), concussion history (0/1/2/3+), collision/noncollision sport participant, and self-reported medical diagnoses [attention deficit disorder or attention-deficit/hyperactivity disorder, headache/migraine, learning disability, and psychiatric disorder (ie, anxiety/depression/other)]. Preseason SCAT5 outcomes including total number of symptoms (TNS; /22), symptom severity score (SSS; /132), Standardized Assessment of Concussion (SAC; /50), and modified Balance Error Scoring System (mBESS; /30). Multiple multilevel linear or Poisson regression complete case analyses adjusting for clustering and robust standard errors, with β-coefficients (95% CI) back-transformed to indicate an increase/decrease in SCAT5 subdomains when relevant for clinical interpretation. Virtual (V) performance was associated with fewer symptoms reported [TNS Difference V-IP = -1.53 (95% CI, -2.22 to -0.85)], lower SSS [-2.49 (95% CI, -4.41 to -0.58)], and fewer mBESS errors (IP) [-0.52 (95% CI, -0.77 to -0.27)] compared with in-person. For every one-year increase in age, more symptoms [TNS = 0.22 (95% CI, 0.01-0.44)], higher SSS [0.52 (95% CI, 0.01-1.06)], higher SAC [0.27 (95% CI, 0.15-0.38), and poorer balance [mBESS = -0.19 (-0.28 to -0.09)] were observed. Differences between males and females were also seen across all SCAT5 outcomes. Individuals reporting any medical diagnosis or 3+ concussion history also reported more symptoms (TNS) and higher SSS than those who did not. Administration environment, sex, age, concussion history, and medical diagnoses were associated with SCAT5 subdomains and are important considerations when interpreting the SCAT5 results.

The Role of Gender in Asylum Migration to Europe: Analyzing Country-Level Factors of Gendered Selection of Asylum Seekers to Europe

While 50% of displaced individuals worldwide are female, women comprised only 31% of the people seeking asylum in Europe between 2008 and 2018. This study utilizes data from Eurostat on 5.6 million asylum-seekers between 2008 and 2018 to identify the country-level factors that drive this gendered selection. The effects are calculated using both a random effects panel model and a fixed effects panel model with clustered standard errors. The results suggest that country-level differences in the degree of gendered selection are largely driven by socio-demographic factors related to women’s social position within their country of origin.