Translating Cultural Safety to the UK

Disproportional morbidity and mortality experienced by ethnic minorities in the UK have been highlighted by the COVID-19 pandemic. The ‘Black Lives Matter’ movement has exposed structural racism’s contribution to these health inequities. ‘Cultural Safety’, an antiracist, decolonising and educational innovation originating in New Zealand, has been adopted in Australia. Cultural Safety aims to dismantle barriers faced by colonised Indigenous peoples in mainstream healthcare by addressing systemic racism. This paper explores what it means to be ‘culturally safe’. The ways in which New Zealand and Australia are incorporating Cultural Safety into educating healthcare professionals and in day-to-day practice in medicine are highlighted. We consider the ‘nuts and bolts’ of translating Cultural Safety into the UK to reduce racism within healthcare. Listening to the voices of black, Asian and minority ethnic National Health Service (NHS) consumers, education in reflexivity, both personal and organisational within the NHS are key. By listening to Indigenous colonised peoples, the ex-Empire may find solutions to health inequity. A decolonising feedback loop is required; however, we should take care not to culturally appropriate this valuable reverse innovation

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data

[EN] Advanced statistical models can help industry to design more economical and rational investment plans. Fault detection and diagnosis is an important problem in continuous hot dip galvanizing. Increasingly stringent quality requirements in the automotive industry also require ongoing efforts in process control to make processes more robust. Robust methods for estimating the quality of galvanized steel coils are an important tool for the comprehensive monitoring of the performance of the manufacturing process. This study applies different statistical regression models: generalized linear models, generalized additive models and classification trees to estimate the quality of galvanized steel coils on the basis of short time histories. The data, consisting of 48 galvanized steel coils, was divided into sets of conforming and nonconforming coils. Five variables were selected for monitoring the process: steel strip velocity and four bath temperatures. The present paper reports a comparative evaluation of statistical models for binary data using Receiver Operating Characteristic (ROC) curves. A ROC curve is a graph or a technique for visualizing, organizing and selecting classifiers based on their performance. The purpose of this paper is to examine their use in research to obtain the best model to predict defective steel coil probability. In relation to the work of other authors who only propose goodness of fit statistics, we should highlight one distinctive feature of the methodology presented here, which is the possibility of comparing the different models with ROC graphs which are based on model classification performance. Finally, the results are validated by bootstrap procedures.The authors are indebted to the anonymous referees whose suggestions improved the original manuscript. This work was supported by a grant from PAID-06-08 (Programa de Apoyo a la Investigacion y Desarrollo) of the Universitat Politecnica de Valencia.Debón Aucejo, AM.; García-Díaz, JC. (2012). Fault diagnosis and comparing risk for the steel coil manufacturing process using statistical models for binary data. Reliability Engineering and System Safety. 100:102-114. https://doi.org/10.1016/j.ress.2011.12.022S10211410

A decision support tool for predicting patients at risk of readmission : a comparison of classification trees, logistic regression, generalized additive models, and multivariate adaptive regression splines

This is the peer reviewed version of the following article: Eren Demir, “Classification Trees, Logistic Regression, Generalized Additive Models, and Multivariate Adaptive Regression Splines” Decision Sciences, Vol 45(5): 849-880, October 2014, which has been published in final form at doi: 10.1111/deci.12094. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. © 2014 Decision Sciences InstituteThe number of emergency (or unplanned) readmissions in the United Kingdom National Health Service (NHS) has been rising for many years. This trend, which is possibly related to poor patient care, places financial pressures on hospitals and on national healthcare budgets. As a result, clinicians and key decision makers (e.g. managers and commissioners) are interested in predicting patients at high risk of readmission. Logistic regression is the most popular method of predicting patient-specific probabilities. However, these studies have produced conflicting results with poor prediction accuracies. We compared the predictive accuracy of logistic regression with that of regression trees for predicting emergency readmissions within forty five days after been discharged from hospital. We also examined the predictive ability of two other types of data-driven models: generalized additive models (GAMs) and multivariate adaptive regression splines (MARS). We used data on 963 patients readmitted to hospitals with chronic obstructive pulmonary disease and asthma. We used repeated split-sample validation: the data were divided into derivation and validation samples. Predictive models were estimated using the derivation sample and the predictive accuracy of the resultant model was assessed using a number of performance measures, such as area under the receiver operating characteristic (ROC) curve in the validation sample. This process was repeated 1000 times—the initial data set was divided into derivation and validation samples 1000 times, and the predictive accuracy of each method was assessed each time. The mean ROC curve area for the regression tree models in the 1000 derivation samples was 0.928, while the mean ROC curve area of a logistic regression model was 0.924. Our study shows that logistic regression model and regression trees had performance comparable to that of more flexible, data-driven models such as GAMs and MARS. Given that the models have produced excellent predictive accuracies, this could be a valuable decision support tool for clinicians (health care managers, policy makers, etc.) for informed decision making in the management of diseases, which ultimately contributes to improved measures for hospital performance management.Peer reviewedFinal Accepted Versio

Real-Time Gas Identification by Analyzing the Transient Response of Capillary-Attached Conductive Gas Sensor

In this study, the ability of the Capillary-attached conductive gas sensor (CGS) in real-time gas identification was investigated. The structure of the prototype fabricated CGS is presented. Portions were selected from the beginning of the CGS transient response including the first 11 samples to the first 100 samples. Different feature extraction and classification methods were applied on the selected portions. Validation of methods was evaluated to study the ability of an early portion of the CGS transient response in target gas (TG) identification. Experimental results proved that applying extracted features from an early part of the CGS transient response along with a classifier can distinguish short-chain alcohols from each other perfectly. Decreasing time of exposition in the interaction between target gas and sensing element improved the reliability of the sensor. Classification rate was also improved and time of identification was decreased. Moreover, the results indicated the optimum interval of the early transient response of the CGS for selecting portions to achieve the best classification rates

Factors influencing the normalization of CD4+ T-cell count, percentage and CD4+/CD8+ T-cell ratio in HIV-infected patients on long-term suppressive antiretroviral therapy

AbstractWe evaluated factors associated with normalization of the absolute CD4+ T-cell counts, per cent CD4+ T cells and CD4+/CD8+ T-cell ratio. A multicentre observational study was carried out in patients with sustained HIV-RNA <50 copies/mL. Outcomes were: CD4-count >500/mm3 and multiple T-cell marker recovery (MTMR), defined as CD4+ T cells >500/mm3 plus %CD4 T cells >29% plus CD4+/CD8+ T-cell ratio >1. Kaplan-Meier survival analysis and Cox regression analyses to predict odds for achieving outcomes were performed. Three hundred and fifty-two patients were included and followed-up for a median of 4.1 (IQR 2.1–5.9) years, 270 (76.7%) achieving a CD4+ T-cell count >500 cells/mm3 and 197 (56%) achieving MTMR. Using three separate Cox models for both outcomes we demonstrated that independent predictors were: both absolute CD4+ and CD8+ T-cell counts, %CD4+ T cells, a higher CD4+/CD8+ T-cell ratio, and age. A likelihood-ratio test showed significant improvements in fitness for the prediction of either CD4+ >500/mm3 or MTMR by multivariable analysis when the other immune markers at baseline, besides the absolute CD4+ count alone, were considered. In addition to baseline absolute CD4+ T-cell counts, pretreatment %CD4+ T cells and the CD4+/CD8+ T-cell ratio influence recovery of T-cell markers, and their consideration should influence the decision to start antiretroviral therapy. However, owing to the small sample size, further studies are needed to confirm these results in relation to clinical endpoints

AIDS-Related Tuberculosis in Rio de Janeiro, Brazil

BACKGROUND: We studied the incidence of tuberculosis, AIDS, AIDS deaths and AIDS-TB co-infection at the population level in Rio de Janeiro, Brazil where universal and free access to combination antiretroviral therapy has been available since 1997. METHODOLOGY/PRINCIPAL FINDINGS: This was a retrospective surveillance database match of Rio de Janeiro databases from 1995-2004. Proportions of tuberculosis occurring within 30 days and between 30 days and 1 year after AIDS diagnosis were determined. Generalized additive models fitted with cubic splines with appropriate estimating methods were used to describe rates and proportions over time. Overall, 90,806 tuberculosis cases and 16,891 AIDS cases were reported; 3,125 tuberculosis cases within 1 year of AIDS diagnosis were detected. Tuberculosis notification rates decreased after 1997 from a fitted rate (fR per 100,000) of 166.5 to 138.8 in 2004. AIDS incidence rates increased 26% between 1995 and 1998 (30.7 to 38.7) followed by a 33.3% decrease to 25.8 in 2004. AIDS mortality rates decreased dramatically after antiretroviral therapy was introduced between 1995 (27.5) and 1999 (13.4). The fitted proportion (fP) of patients with tuberculosis diagnosed within one year of AIDS decreased from 1995 (24.4%) to 1998 (15.2%), remaining stable since. Seventy-five percent of tuberculosis diagnoses after an AIDS diagnosis occurred within 30 days of AIDS diagnosis. CONCLUSIONS/SIGNIFICANCE: Our results suggest that while combination ART should be considered an essential component of the response to the HIV and HIV/tuberculosis epidemics, it may not be sufficient alone to prevent progression from latent TB to active disease among HIV-infected populations. When tuberculosis is diagnosed prior to or at the same time as AIDS and ART has not yet been initiated, then ART is ineffective as a tuberculosis prevention strategy for these patients. Earlier HIV/AIDS diagnosis and ART initiation may reduce TB incidence in HIV/AIDS patients. More specific interventions will be required if HIV-related tuberculosis incidence is to continue to decline

Relationship between perceived body weight and body mass index based on self- reported height and weight among university students: a cross-sectional study in seven European countries

Mikolajczyk RT, Maxwell AE, El Ansari W, Stock C, Petkeviciene J, Guillen-Grima F. Relationship between perceived body weight and body mass index based on self- reported height and weight among university students: a cross-sectional study in seven European countries. BMC Public Health. 2010;10(1): 40.Background Despite low rates of obesity, many university students perceive themselves as overweight, especially women. This is of concern, because inappropriate weight perceptions can lead to unhealthy behaviours including eating disorders. Methods We used the database from the Cross National Student Health Survey (CNSHS), consisting of 5,900 records of university students from Bulgaria, Denmark, Germany, Lithuania, Poland, Spain and Turkey to analyse differences in perceived weight status based on the question: "Do you consider yourself much too thin, a little too thin, just right, a little too fat or much too fat?". The association between perceived weight and body mass index (BMI) calculated from self-reported weight and height was assessed with generalized non-parametric regression in R library gam. Results Although the majority of students reported a normal BMI (72-84% of males, 65-83% of females), only 32% to 68% of students considered their weight "just right". Around 20% of females with BMI of 20 kg/m2 considered themselves "a little too fat" or "too fat", and the percentages increased to 60% for a BMI of 22.5 kg/m2. Male students rarely felt "a little too fat" or "too fat" below BMI of 22.5 kg/m2, but most felt too thin with a BMI of 20 kg/m2. Conclusions Weight ideals are rather uniform across the European countries, with female students being more likely to perceive themselves as "too fat" at a normal BMI, while male students being more likely to perceive themselves as "too thin". Programs to prevent unhealthy behaviours to achieve ill-advised weight ideals may benefit students

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.J Crossa, P Pérez, J Hickey, J Burgueño, L Ornella, J Cerón-Rojas, X Zhang, S Dreisigacker, R Babu, Y Li, D Bonnett and K Mathew

Assessing and mapping language, attention and executive multidimensional deficits in stroke aphasia.

There is growing awareness that aphasia following a stroke can include deficits in other cognitive functions and that these are predictive of certain aspects of language function, recovery and rehabilitation. However, data on attentional and executive (dys)functions in individuals with stroke aphasia are still scarce and the relationship to underlying lesions is rarely explored. Accordingly in this investigation, an extensive selection of standardized non-verbal neuropsychological tests was administered to 38 individuals with chronic post-stroke aphasia, in addition to detailed language testing and MRI. To establish the core components underlying the variable patients' performance, behavioural data were explored with rotated principal component analyses, first separately for the non-verbal and language tests, then in a combined analysis including all tests. Three orthogonal components for the non-verbal tests were extracted, which were interpreted as shift-update, inhibit-generate and speed. Three components were also extracted for the language tests, representing phonology, semantics and speech quanta. Individual continuous scores on each component were then included in a voxel-based correlational methodology analysis, yielding significant clusters for all components. The shift-update component was associated with a posterior left temporo-occipital and bilateral medial parietal cluster, the inhibit-generate component was mainly associated with left frontal and bilateral medial frontal regions, and the speed component with several small right-sided fronto-parieto-occipital clusters. Two complementary multivariate brain-behaviour mapping methods were also used, which showed converging results. Together the results suggest that a range of brain regions are involved in attention and executive functioning, and that these non-language domains play a role in the abilities of patients with chronic aphasia. In conclusion, our findings confirm and extend our understanding of the multidimensionality of stroke aphasia, emphasize the importance of assessing non-verbal cognition in this patient group and provide directions for future research and clinical practice. We also briefly compare and discuss univariate and multivariate methods for brain-behaviour mapping