Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.

UbNiRF: A Hybrid Framework Based on Null Importances and Random Forest that Combines Multiple Features to Predict Ubiquitination Sites in Arabidopsis thaliana and Homo sapiens.

BACKGROUND: Ubiquitination is a crucial post-translational modification of proteins that regulates diverse cellular functions. Accurate identification of ubiquitination sites in proteins is vital for understanding fundamental biological mechanisms, such as cell cycle and DNA repair. Conventional experimental approaches are resource-intensive, whereas machine learning offers a cost-effective means of accurately identifying ubiquitination sites. The prediction of ubiquitination sites is species-specific, with many existing models being tailored for Arabidopsis thaliana (A. thaliana) and Homo sapiens (H. sapiens). However, these models have shortcomings in sequence window selection and feature extraction, leading to suboptimal performance. METHODS: This study initially employed the chi-square test to determine the optimal sequence window. Subsequently, a combination of six features was assessed: Binary Encoding (BE), Composition of K-Spaced Amino Acid Pair (CKSAAP), Enhanced Amino Acid Composition (EAAC), Position Weight Matrix (PWM), 531 Properties of Amino Acids (AA531), and Position-Specific Scoring Matrix (PSSM). Comparative evaluation involved three feature selection methods: Minimum Redundancy-Maximum Relevance (mRMR), Elastic net, and Null importances. Alongside these were four classifiers: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The Null importances combined with the RF model exhibited superior predictive performance, and was denoted as UbNiRF (A. thaliana: ArUbNiRF; H. sapiens: HoUbNiRF). RESULTS: A comprehensive assessment indicated that UbNiRF is superior to existing prediction tools across five performance metrics. It notably excelled in the Matthews Correlation Coefficient (MCC), with values of 0.827 for the A. thaliana dataset and 0.781 for the H. sapiens dataset. Feature analysis underscores the significance of integrating six features and demonstrates their critical role in enhancing model performance. CONCLUSIONS: UbNiRF is a valuable predictive tool for identifying ubiquitination sites in both A. thaliana and H. sapiens. Its robust performance and species-specific discovery capabilities make it extremely useful for elucidating biological processes and disease mechanisms associated with ubiquitination.

Hospital Readmission Rates for Patients Receiving In-Person vs. Telemedicine Discharge Follow-Up Care.

INTRODUCTION: Unplanned readmissions can be avoided by standardizing and improving the coordination of care after discharge. Telemedicine has been increasingly utilized; however, the quality of this care has not been well studied. Standardized measures can provide an objective comparison of care quality. The purpose of our study was to compare quality performance transitions of care management in the office vs telemedicine. METHODS: The Epic SlicerDicer tool was used to compare the percentage of encounters that were completed via telemedicine (video visits); or via in-person for comparison, Chi-squared tests were used. RESULTS: A total of 13,891 patients met the inclusion criteria during the study time frame. There were 12,846 patients in the office and 1,048 in the telemedicine cohort. The office readmission rate was 11.9% with 1,533 patients out of 12,846 compared with telemedicine with the rate of readmission at 12.1% with 126 patients out of 1,045 patients. The P-value for the Chi-squared test between the prepandemic and study time frame was 0.15 and 0.95, respectively. Demographic comparability was seen. DISCUSSION: Our study found a comparable readmission rate between patients seen via in-office and telemedicine for Transitions of Care Management (TCM) encounters. The findings of this study support the growing body of evidence that telemedicine augments quality performance while reducing cost and improving access without negatively impacting HEDIS performance in health care systems. CONCLUSION: Telemedicine poses little threat of negatively impacting HEDIS performance and might be as effective as posthospitalization traditional office care transitions of care management.

Machine Learning to Identify Clusters in Family Medicine Diplomate Motivations and Their Relationship to Continuing Certification Exam Outcomes: Findings and Potential Future Implications.

BACKGROUND: The potential for machine learning (ML) to enhance the efficiency of medical specialty boards has not been explored. We applied unsupervised ML to identify archetypes among American Board of Family Medicine (ABFM) Diplomates regarding their practice characteristics and motivations for participating in continuing certification, then examined associations between motivation patterns and key recertification outcomes. METHODS: Diplomates responding to the 2017 to 2021 ABFM Family Medicine continuing certification examination surveys selected motivations for choosing to continue certification. We used Chi-squared tests to examine difference proportions of Diplomates failing their first recertification examination attempt who endorsed different motivations for maintaining certification. Unsupervised ML techniques were applied to generate clusters of physicians with similar practice characteristics and motivations for recertifying. Controlling for physician demographic variables, we used logistic regression to examine the effect of motivation clusters on recertification examination success and validated the ML clusters by comparison with a previously created classification schema developed by experts. RESULTS: ML clusters largely recapitulated the intrinsic/extrinsic framework devised by experts previously. However, the identified clusters achieved a more equal partitioning of Diplomates into homogenous groups. In both ML and human clusters, physicians with mainly extrinsic or mixed motivations had lower rates of examination failure than those who were intrinsically motivated. DISCUSSION: This study demonstrates the feasibility of using ML to supplement and enhance human interpretation of board certification data. We discuss implications of this demonstration study for the interaction between specialty boards and physician Diplomates.

Diabetes and the social, biologic, and behavioral determinants of endometrial cancer in the United States.

BACKGROUND: Endometrial cancer is one of the most common types of cancer that affects women's reproductive system. The risk of endometrial cancer is associated with biologic, behavioral and social determinants of health (SDOH). The focus of the work is to investigate the cumulative effect of this cluster of covariates on the odds of endometrial cancer that heretofore have only been considered individually. METHODS: We conducted a quantitative study using the Behavioral Risk Factor Surveillance System (BRFSS) national data collected in 2020. Data analysis using weighted Chi-square test and weighted logistic regression were carried out on 84,118 female study participants from the United States. RESULTS: Women with diabetes mellitus were approximately twice as likely to have endometrial cancer compared to women without diabetes (OR 1.54; 95%CI: 1.01-2.34). Biologic factors that included obesity (OR 3.10; 95% CI: 1.96-4.90) and older age (with ORs ranging from 2.75 to 7.21) had a significant increase in the odds of endometrial cancer compared to women of normal weight and younger age group of 18 to 44. Among the SDOH, attending college (OR 1.83; 95% CI: 1.12-3.00) was associated with increased odds of endometrial cancer, while renting a home (OR 0.50; 95% CI: 0.28-0.88), having other arrangements (OR 0.05; 95% CI: 0.02-0.16), being divorced (OR 0.55; 95% CI: 0.30-0.99), and having higher incomes ranging from $35,000 to $50,000 (OR 0.35; 95% CI: 0.16-0.78), and above $50,000 (OR 0.29; 95% CI: 0.14-0.62), were all associated with decreased odds of endometrial cancer. As for race, Black women (OR 0.24; 95% CI: 0.07-0.84) and women of other races (OR 0.37; 95% CI: 0.15-0.88) were shown to have lower odds of endometrial cancer compared to White women. CONCLUSION: Our results revealed the importance of adopting a comprehensive approach to the study of the associated factors of endometrial cancer by including social, biologic, and behavioral determinants of health. The observed social inequity in endometrial cancer among women needs to be addressed through effective policies and changes in social structures to advocate for a standardized healthcare system that ensures equitable access to preventive measures and quality of care.

Cooling Performance Prediction of Particle-Based Radiative Cooling Film Considering Particle Size Distribution.

Here, we present a novel protocol concept for quantifying the cooling performance of particle-based radiative cooling (PBRC). PBRC, known for its high flexibility and scalability, emerges as a promising method for practical applications. The cooling power, one of the cooling performance indexes, is the typical quantitative performance index, representing its cooling capability at the surface. One of the primary obstacles to predicting cooling power is the difficulty of simulating the non-uniform size and shape of micro-nanoparticles in the PBRC film. The present work aims to develop an accurate protocol for predicting the cooling power of PBRC film using image processing and regression analysis techniques. Specifically, the protocol considers the particle size distribution through circle object detection on SEM images and determines the probability density function based on a chi-square test. To validate the proposed protocol, a PBRC structure with PDMS/Al2O3 micro-nanoparticles is fabricated, and the proposed protocol precisely predicts the measured cooling power with a 7.8% error. Through this validation, the proposed protocol proves its potential and reliability for the design of PBRC.

Clinical evaluation and treatment outcomes of caustic ingestion injuries in Syrian pediatric patients: A retrospective study: Short title: High-dose steroids for pediatric caustic ingestions in Syria.

Introduction: Accidental ingestion of caustic agents poses a significant concern in pediatric emergency departments globally. It is a growing public health concern in low-to-middle income countries, which often lack comprehensive data reporting. This study examines high doses of corticosteroid treatment outcomes of caustic ingestion injuries in Syrian pediatric patients, addressing clinical features, and associated variables. Methods and materials: A retrospective observational study was conducted at University Pediatric Hospital from January 2016 to January 2019. Medical records were reviewed for patients aged <10 years with esophagoscopy-confirmed grade IIa, IIb, or III burns. Data collected included sociodemographics, esophagoscopy results, treatment details, and outcomes. Results: Among 114 pediatric patients, 76 (67%) were males and 38 (33%) were females. Age groups included <1 year (11%), 1-3 years (39%), 3-5 years (29%), 5-7 years (11%), and >7 years (11%). Alkaline burns accounted for 54% of injuries, acidic for 32%, and other substances for 13%. Complications included bleeding (19%) and psychomotor disability (7%). The most common burn site was the entire esophagus (62%), with 81% having grade II burns. Healing was achieved in 71% of patients with high doses of corticosteroids treatment, and 29% required dilation, with final 92% healing rate. Conclusion: The use of corticosteroids for esophageal strictures remains inconclusive, demanding further robust research with larger sample sizes and control groups. While our study revealed that high doses of corticosteroids treatment followed by esophageal dilation had a 92% success rate. However, our study demonstrates promising results, methodological limitations and absence of a control group underscore the need for more definitive evidence. Both alkali and acidic ingestion contribute to stricture development.

Power analyses for measurement model misspecification and response shift detection with structural equation modeling.

PURPOSE: Statistical power for response shift detection with structural equation modeling (SEM) is currently underreported. The present paper addresses this issue by providing worked-out examples and syntaxes of power calculations relevant for the statistical tests associated with the SEM approach for response shift detection. METHODS: Power calculations and related sample-size requirements are illustrated for two modelling goals: (1) to detect misspecification in the measurement model, and (2) to detect response shift. Power analyses for hypotheses regarding (exact) overall model fit and the presence of response shift are demonstrated in a step-by-step manner. The freely available and user-friendly R-package lavaan and shiny-app 'power4SEM' are used for the calculations. RESULTS: Using the SF-36 as an example, we illustrate the specification of null-hypothesis (H0) and alternative hypothesis (H1) models to calculate chi-square based power for the test on overall model fit, the omnibus test on response shift, and the specific test on response shift. For example, we show that a sample size of 506 is needed to reject an incorrectly specified measurement model, when the actual model has two-medium sized cross loadings. We also illustrate power calculation based on the RMSEA index for approximate fit, where H0 and H1 are defined in terms of RMSEA-values. CONCLUSION: By providing accessible resources to perform power analyses and emphasizing the different power analyses associated with different modeling goals, we hope to facilitate the uptake of power analyses for response shift detection with SEM and thereby enhance the stringency of response shift research.

Association of Facial Wrinkles With Different Facial Forms in the Population of Maharashtra: A Prospective Observational Study.

INTRODUCTION: Wrinkles commonly manifest in various areas of the face as individuals age. This study aimed to assess the association between facial wrinkles and different facial forms. MATERIALS AND METHODS: An observational, prospective study was conducted on the facial photographs of 400 subjects aged 40-60 years, which were divided into four groups of 100 subjects each: Group 1, square facial form; Group 2, ovoid facial form; Group 3, square tapered facial form; and Group 4, tapered facial form. All groups had almost equal distributions of males and females. Six types of facial wrinkles were studied, namely, forehead, glabellar, canthal, nasolabial, wrinkles at the corner of the mouth, and perioral wrinkles. Analysis of variance (ANOVA) was used for intergroup comparison, and an independent Student's t-test was used to assess gender differences in facial wrinkles. RESULTS: Significant gender differences were observed for forehead wrinkles in Groups 1 and 3, canthal wrinkles in Groups 1 and 2, and right perioral wrinkles in Group 1 (p<0.05). There were non-significant gender differences between right and left-side facial wrinkles (p>0.05). Significant differences between the groups were observed for all facial wrinkles between the right and left sides of the face (p<0.05). There was a significant difference between the groups for the presence of glabellar, corner of the mouth, and perioral wrinkles, with the presence of higher wrinkles in ovoid and tapered facial forms compared to square and square tapered facial forms (p<0.05). CONCLUSIONS: Females had more facial wrinkles than males, predominantly in the forehead region. The least prominent wrinkles were observed in the perioral region of the face. Glabellar, corner of the mouth, and perioral wrinkles were predominantly observed in ovoid and tapered facial forms.

Fault-Tolerant SINS/Doppler Radar/Odometer Integrated Navigation Method Based on Two-Stage Fault Detection Structure.

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system.

Understanding professional development challenges of Chinese public health professionals: association and prediction analyses with data validity screening.

Background: Little is known about the public health professionals engaged in educating and training new or future researchers in public health. Research in this direction identifies their issues, concerns, challenges, and needs. This study focused on the professional development challenges of Chinese public health professionals. Methods: Snowball sampling was utilized. A total of 265 public health professionals participated. An instrument of 6 dimensions (burnout, sleep issue, mood issue, friends' support, exercise, and challenges) was developed, revised, and administered online. Two different approaches, the conventional and data screening approaches, were applied. The former started with item quality analyses, whereas the latter began with data quality checks. The chi-square tests of associations and logistic regressions were performed on both approaches. Results and discussion: 19.25% of the participants were detected and deleted as careless respondents. Using both approaches, six professional development challenges except one ("Multidisciplinary learning") were significantly associated with various demographic features. The two approaches produced different models though they converged sometimes. The latent variables of exercise predicted professional development challenges more frequently than other latent variables. Regarding correct classification rates, results from the data screening approach were comparable to those from the conventional approach. Conclusion: The latent variables of exercise, such as "Exercise effects," "Expectations of exercise," and "Belief in exercise," might be understudied. More research is necessary for professional development challenges using exercise as a multidimensional construct. Based on the current study, screening and deleting careless responses in survey research is necessary.

Fitting and Testing Log-Linear Subpopulation Models with Known Support.

In this paper, the support of the joint probability distribution of categorical variables in the total population is treated as unknown. From a general total population model with unknown support, a general subpopulation model with its support equal to the set of all observed score patterns is derived. In maximum likelihood estimation of the parameters of any such subpopulation model, the evaluation of the log-likelihood function only requires the summation over a number of terms equal to at most the sample size. It is made clear that the parameters of a hypothesized total population model are consistently and asymptotically efficiently estimated by the values that maximize the log-likelihood function of the corresponding subpopulation model. Next, new likelihood ratio goodness-of-fit tests are proposed as alternatives to the Pearson chi-square goodness-of-fit test and the likelihood ratio test against the saturated model. In a simulation study, the asymptotic bias and efficiency of maximum likelihood estimators and the asymptotic performance of the goodness-of-fit tests are investigated.

A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques.

In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson's correlation coefficient on WEKA for features' importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).

Food insecurity, body mass index, socio-economic status, and food intake in lactating and non-lactating mothers with children under two years.

BACKGROUND: food insecurity (FI) is considered as an essential public health problem which may have detrimental effects on people's health. The aim of present study was to evaluate FI, body mass index, quantity and quality of food intake in lactating and non-lactating mothers with children under two years. METHODS: in this cross-sectional study 307 mothers (237 lactating and 70 non-lactating) were participated. Socio-economic and demographic information were gathered by questionnaires. FI of families was evaluated by the questionnaire of the United States Department of Agriculture (USDA) Household Food Security. For assessing quality and quantity of food intake of mothers, dietary diversity score (DDS), diet quality index-international (DQI-I) and nutrient adequacy ratio (NAR) were calculated. Weight and height of participants were measured and body mass index (BMI) was calculated. Finally, Chi-squared test, analysis of variance (ANOVA) and linear regression were used for statistical analysis. RESULTS: in this study the rate of underweight, normal weight, overweight, and obesity in mothers was 0.3%, 39.2%, 42.3%, and 18.2%, respectively. Among the determinants of BMI, household food security status had the greatest effect (Beta=-1.584, P < 0.001) and mother age had the least effect (Beta = 0.101, P = 0.013). Mother's occupational and educational status, having facilities, physiological status of mother, and house size had significant correlation with NAR. Mother's occupational and educational status, and having facilities had significant relationship with DDS, too. Also, the significant correlation of Mother's education, having facilities, and physiological status of mother with DQI-I were found. CONCLUSIONS: we found that Household food security status had the most effect on BMI of mothers. In this study, the best nutrient adequacy and dietary diversity were found in the obese group and the most diet quality was found in the normal weight group.

Evaluating the waste management situation and attitudes of residents in Bandung, Indonesia.

The increasing amount of waste is becoming a crucial topic in developing countries, causing health-related and environmental issues. In past decades, the Indonesian government implemented regulations dedicated to municipal solid waste management (MSWM); unfortunately, these have not been well communicated to the public. The set tax level is inadequate for the MSWM financial needs in Bandung, Indonesia. Previous studies determined the public interest in MSWM as the critical factor of sustainable MSWM. However, this aspect is not well considered. This study investigated public perception and involvement in MSWM in Bandung. These investigations were conducted through two leading indicators: public interest in MSWM in environmental issues and willingness of inhabitants to pay for sufficient MSWM services. Using a binary probit model, it was found that age, educational level, locality, and satisfaction with MSWM practices play a significant role in the respondents' interest in MSWM, which is crucial to the public perception of MSWM. The results also demonstrate that more than 97% of respondents are not satisfied with MSWM practices, and more than 83% of respondents consider MSWM services insufficient. Awareness of essential operations such as waste handling and recycling is at a low level. On the other hand, more than 67% of respondents are interested in MSWM in connection with environmental issues and are familiar with the 3R concept (reducing waste, reusing, and recycling resources and products). Our MSWM research provides new perspectives on the public perception and involvement in Bandung that could be considered in the planning and implementation of sustainable MSWM. The results indicate that the public's perception and approach to MSWM in Bandung city should be enhanced by better education, providing sufficient awareness, and offering workshops.  Integr Environ Assess Manag 2023;19:114-125. © 2022 SETAC.

Assessing psychometric challenges and fatigue during the COVID-19 pandemic.

Environmental hazards and/or pandemics may push humans to use different protective methods to maintain their well-being. This study aimed to identify populations vulnerable to psychometric challenges and fatigue during the Coronavirus Disease (COVID-19) pandemic in Kazakhstan. A total of 1,592 participants were recruited and asked to complete the Multidimensional Fatigue Inventory-20 (MFI-20). Participants were classified according to gender and age. Data were analyzed using the Chi-square test (x2) and MedCalc to detect the odds of MFI-scales with a score ≥12 in women compared to men. Young women had significantly higher odds (OR) of reduced activity (OR 2.4, p<0.0001), physical (OR 2.5, p<0.0001), and mental fatigue (OR 3.4, p<0.0001) than young men. Middle-aged women had significantly higher odds of general fatigue (OR 2.1; p<0.0001), reduced motivation (OR 2.1, p<0.0001), physical (OR 2.1, p<0.0001), and mental fatigue (OR 1.9, p<0.0001) than did middle-aged men. Elderly women had significantly higher odds of general fatigue (OR 3.6, p<0.0001), reduced motivation (OR 3.5, p<0.0001), and physical fatigue (OR 3.5, p<0.0001) than men in the same age category. The study highlights that women were more susceptible, with significantly increased odds of experiencing various aspects of fatigue syndrome compared to men during the COVID-19 pandemic in Kazakhstan. Therefore, it is necessary to investigate individual behavioral changes to help identify vulnerable populations and provide relevant evidence for developing protocols and guidelines during pandemics and/or outbreaks.

Factors Associated with Intention to Vaccinate Children 0-11 Years of Age Against COVID-19.

BACKGROUND: Millions of children have tested positive for SARS-CoV-2, and over 1000 children have died in the US. However, vaccination rates for children 5 to 11 years old are low. METHODS: Starting in August 2020, we conducted a prospective SARS-CoV-2 household surveillance study in Spanish and English-speaking households in New York City and Utah. From October 21 to 25, 2021, we asked caregivers about their likelihood of getting COVID-19 vaccine for their child, and reasons that they might or might not vaccinate that child. We compared intent to vaccinate by site, demographic characteristics, SARS-CoV-2 infection detected by study surveillance, and parents' COVID-19 vaccination status using Chi-square tests and a multivariable logistic regression model, accounting for within-household clustering. RESULTS: Among parents or caregivers of 309 children (0 to 11 years) in 172 households, 87% were very or somewhat likely to intend to vaccinate their child. The most prevalent reasons for intending to vaccinate were to protect family and friends and the community; individual prevention was mentioned less often. The most prevalent reasons for not intending to vaccinate were side effect concerns and wanting to wait and see.In multivariable analysis, parents had much lower odds of intending to vaccinate if someone in the household had tested SARS-CoV-2-positive during the study (adjusted odds ratio = 0.09; 95% confidence interval, 0.03-0.3). CONCLUSION: This study highlighted several themes for clinicians and public health officials to consider including the importance and safety of vaccination for this age-group even if infected previously, and the benefits of vaccination to protect family, friends, and community.

Rank correlation inferences for clustered data with small sample size.

This paper develops methods to test for associations between two variables with clustered data using a U-Statistic approach with a second-order approximation to the variance of the parameter estimate for the test statistic. The tests that are presented are for clustered versions of: Pearsons χ 2 test, the Spearman rank correlation and Kendall's τ for continuous data or ordinal data and for alternative measures of Kendall's τ that allow for ties in the data. Shih and Fay use the U-Statistic approach but only consider a first-order approximation. The first-order approximation has inflated significance level in scenarios with small sample sizes. We derive the test statistics using the second-order approximations aiming to improve the type I error rates. The method applies to data where clusters have the same number of measurements for each variable or where one of the variables may be measured once per cluster while the other variable may be measured multiple times. We evaluate the performance of the test statistics through simulation with small sample sizes. The methods are all available in the R package cluscor.

Evaluating the Uptake of Antiracism Training, Policies, and Practices in Departments of Family Medicine.

BACKGROUND: Recent attention and focus on, antiracism training in health care has potential to accelerate our path to social justice and achieve health equity on a national scale. However, theoretical frameworks and empirical data have yet to emerge that explain the uptake of antiracism trainings and their efficacy. OBJECTIVE: This goal of this study was to test hypotheses regarding uptake of antiracism training in Family Medicine departments using Diffusion of Innovation Theory. METHODS: In 2021, we incorporated 10 survey items in the Council of Academic Family Medicine Educational Research Alliance's national omnibus survey for Department of Family Medicine Chairs (n = 104). We used DOI (Diffusion of Innovation) attributes (ie, relative advantage, compatibility, complexity, trialability, and observability) as a guiding framework to assess perceived innovation of antiracism training. We also evaluated the mode of training (eg, didactic, experiential) and whether any subsequent policy or practice-level antiracist actions occurred. We used c2 tests to examine associations between DOI attributes and antiracist actions; and logistic regression to determine odds of association. RESULTS: Ninety-two percent of respondents indicated antiracism training was happening in their department. Relative advantage, compatibility and observability were positively associated with antiracist actions, P < .05. Perceived relative advantage was associated with implementation of antiracist action (OR 1.94, 1.27-2.99). Complexity and trialability were not statistically significantly associated with action. CONCLUSION: Our findings provide evidence of DOIs influence on antiracism uptake in Departments of Family Medicine. We believe our findings can facilitate the future implementation of antiracism training activities and actionable antiracist policies and practices.

Genetic control of earliness in cowpea (Vigna unguiculata (L) Walp).

Global climate change is expected to further intensify the already harsh conditions in the dry savannah ecological zones of sub-Saharan Africa, posing serious threats to food and income security of millions of smallholder farmers. Breeding cowpea for improved earliness could help minimize this risk, by ensuring that the crops complete their lifecycle before the cessation of rainfall. In this study, we crossed two sets of cowpea lines showing contrasting phenotypes for earliness in terms of days to 50% flowering (DFF). One set of the lines comprised three extra-early parents (viz.: Sanzi-Nya, Tobonaa and CB27, 30-35 DFF), and the other set consisted of three early-to-medium maturity lines (viz.: Kirkhouse-Benga, Wang-Kae and Padi-Tuya, 42-45 DFF). The derived crosses and their parents were evaluated for key earliness-related traits at Nyankpala and Manga sites of CSIR-Savanna Agricultural Research Institute (SARI), Ghana. To unravel the genetic control of measured traits, we compared the appropriateness of Chi-square goodness of fit tests using classical Mendelian ratios, and frequency distribution (histogram)-related statistics such as skewness and kurtosis. The Chi-square test suggested a single dominant gene mode of inheritance for earliness, whereas the quantitative methods implicated duplicate epistasis and complementary epistatic gene actions. Our results show that coercing segregating lines to fit into classical Mendelian ratios to determine the genetic control of earliness could be misleading, due to its subjectivity. Thus, the genetic control of earliness in cowpea is governed by complementary and duplicate epistasis. The most applicable breeding approach for traits influenced by duplicate epitasis is selection of desirable recombinants from segregating populations developed from bi-parental crosses. Complementary epitasis, as found in the Wang-Kae × CB27 cross, could be exploited in developing improved extra-early lines through backcrossing. Heritability and genetic advance estimates were high for days to first flower appearance (DFFA) and days to 95 % pod maturity (DNPM) in the Padi-Tuya × CB27 and Kirkhouse-Benga x CB27 crosses, indicating that breeding for extra-earliness is feasible. CB27 could be a good donor for introgression of earliness into medium to late maturing improved cowpea varieties, because crosses developed from it had high heritability and genetic advance estimates.