Significant Turning Point: Common Buzzard (Buteo buteo) Exposure to Second-Generation Anticoagulant Rodenticides in the United Kingdom

Second-generation anticoagulant rodenticides (SGARs) are widely used to control rodent populations, resulting in the serious secondary exposure of predators to these contaminants. In the United Kingdom (UK), professional use and purchase of SGARs were revised in the 2010s. Certain highly toxic SGARs have been authorized since then to be used outdoors around buildings as resistance-breaking chemicals under risk mitigation procedures. However, it is still uncertain whether and how these regulatory changes have influenced the secondary exposure of birds of prey to SGARs. Based on biomonitoring of the UK Common Buzzard (Buteo buteo) collected from 2001 to 2019, we assessed the temporal trend of exposure to SGARs and statistically determined potential turning points. The magnitude of difenacoum decreased over time with a seasonal fluctuation, while the magnitude and prevalence of more toxic brodifacoum, authorized to be used outdoors around buildings after the regulatory changes, increased. The summer of 2016 was statistically identified as a turning point for exposure to brodifacoum and summed SGARs that increased after this point. This time point coincided with the aforementioned regulatory changes. Our findings suggest a possible shift in SGAR use to brodifacoum from difenacoum over the decades, which may pose higher risks of impacts on wildlife.

More Than Identity: An Intersectional Approach to Understanding Mental-Emotional Well-Being of Emerging Adults by Centering Lived Experiences of Marginalization

Understanding social determinants that shape pertinent developmental shifts during emerging adulthood (i.e., ages 18-25 years) and their associations with psychological health requires a nuanced approach. In our exploratory study, we investigated how multiple social identities and lived experiences generated by systems of marginalization and power (e.g., racism, classism, sexism) intersect in connection to the mental-emotional well-being of emerging adults (EAs). Eating and Activity Over Time (EAT, 2010-2018) data were collected from 1,568 EAs (mean age = 22.2 (standard deviation, 2.0) years) recruited initially in 2010 from Minneapolis/St. Paul schools. Conditional inference tree analyses were employed to treat "social location" and systems of marginalization and power as interdependent social factors influencing EAs' mental-emotional well-being outcomes: depressive symptoms, stress, self-esteem, and self-compassion. Conditional inference tree analyses identified EAs' subgroups with differing mean levels of mental-emotional well-being outcomes, distinguished primarily by marginalized social experiences (e.g., discrimination, financial difficulties) rather than social identities themselves. The relative positioning of EAs' experiences of social marginalization (e.g., discrimination) to their social identities (e.g., race/ethnicity) suggests that the social experiences generated by systems of privilege and oppression (e.g., racism) are more adjacent social determinants of mental-emotional well-being than the social identities used in public health research to proxy the oppressive systems that give them social meaning.

Predicting Survival for Veno-Arterial ECMO Using Conditional Inference Trees-A Multicenter Study

BACKGROUND

Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion.

METHODS

This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019.

RESULTS

837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets.

CONCLUSIONS

Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables.

A tree-based modeling approach for matched case-control studies

Conditional logistic regression (CLR) is the indisputable standard method for the analysis of matched case-control studies. However, CLR is strongly restricted with respect to the inclusion of non-linear effects and interactions of confounding variables. A novel tree-based modeling method is proposed which accounts for this issue and provides a flexible framework allowing for a more complex confounding structure. The proposed machine learning model is fitted within the framework of CLR and, therefore, allows to account for the matched strata in the data. A simulation study demonstrates the efficacy of the method. Furthermore, for illustration the method is applied to a matched case-control study on cervical cancer.

Exploring the intersection of multiple social determinants of health and disordered eating behaviors in a population-based sample in the United States

OBJECTIVE

Disordered eating behaviors (DEBs) have long-term, deleterious effects on health and are more prevalent among socially marginalized groups, likely as a result of systemic inequities across social determinants of health (SDoH). This exploratory study aimed to identify subgroups of emerging adults characterized by main and interactive associations between SDoH and two forms of DEB (binge eating, extreme unhealthy weight control behaviors).

METHOD

Participants (n = 1568; age 22.2 ± 2.1 years) from the United States were drawn from the EAT 2010-2018 longitudinal study. Conditional inference tree (CIT) analyses derived main and intersecting SDoH related to DEB across 33 input variables collected during adolescence and emerging adulthood.

RESULTS

The binge eating CIT revealed five subgroups (prevalence: 6.3-23.2%) shaped by variables collected during emerging adulthood: appearance-based teasing (p < .001), financial difficulty (p = .003), gender (p < .001), and everyday discrimination (p = .008). The CIT results for extreme unhealthy weight control behaviors derived six subgroups (prevalence: 2.3-45.5%) shaped by weight teasing (p < .001) and gender (p < .001) during emerging adulthood and public assistance (p = .008) and neighborhood safety (p = .007) in adolescence.

DISCUSSION

This exploratory study revealed distinct subgroups of emerging adults with varying DEB prevalence, suggesting that variability in DEB prevalence may be partially explained by intersecting SDoH during adolescence and emerging adulthood. Hypothesis-driven research and replication studies are needed to further explore the associations between SDoH and DEB during emerging adulthood.

PUBLIC SIGNIFICANCE STATEMENT

Disordered eating behaviors are common among young people in the United States and have long-term health consequences. This exploratory study identified subgroups of young people, characterized by combinations of social inequities (e.g., financial difficulties, teasing). Results highlight high-risk subgroups of emerging adults that should be examined further in hypothesis-driven research.

Assessing effect heterogeneity of a randomized treatment using conditional inference trees

Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and conditional inference trees to characterize effect heterogeneity of a randomized binary treatment. One key feature that distinguishes our method from alternative approaches is that it controls the Type I error rate, that is, the probability of identifying effect heterogeneity if none exists and retains the underlying subgroups. This feature makes our technique particularly appealing in the context of clinical trials, where there may be significant costs associated with erroneously declaring that effects differ across population subgroups. Treatment effect heterogeneity trees are able to identify heterogeneous subgroups, characterize the relevant subgroups and estimate the associated treatment effects. We demonstrate the efficacy of the proposed method using a comprehensive simulation study and illustrate our method using a nutrition trial dataset to evaluate effect heterogeneity within a patient population.

The Impact of the COVID-19 Pandemic on Nursing Care: A Cross-Sectional Survey-Based Study

The COVID-19 pandemic has had a severe impact on nursing care. This cross-sectional survey-based study compared aspects of nursing care and nurses’ satisfaction with care provided before and during the first wave of the COVID-19 pandemic. A total of 936 registered nurses (RNs) rated the frequency with which they performed fundamental care, nursing techniques, patient education, symptom management, and nurse–patient relationships before and during the pandemic. A recursive partitioning for ordered multivariate response in a conditional inference framework approach was applied. More frequent fundamental cares were associated with their frequency before the pandemic (p < 0.001), caring for COVID-19 patients (p < 0.001), and workplace reassignment (p = 0.004). Caring for COVID-19 patients (p < 0.001), workplace reassignment (p = 0.030), and caring for ≤7.4 COVID-19 patients (p = 0.014) increased nursing techniques. RNs in high-intensity COVID-19 units (p = 0.002) who educated patients before the pandemic, stopped this task. RNs caring for COVID-19 patients reported increased symptom management (p < 0.001), as did RNs caring for more non-COVID-19 patients (p = 0.037). Less frequent nurse–patient relationships before the pandemic and working in high-intensity COVID-19 units decreased nurse–patient relationships (p = 0.002). Despite enormous challenges, nurses continued to provide a high level of care. Ensuring the appropriate deployment and education of nurses is crucial to personalize care and to maintain nurses’ satisfaction with the care provided.

What Machine Learning Can Tell Us About the Role of Language Dominance in the Diagnostic Accuracy of German LITMUS Non-word and Sentence Repetition Tasks

The present study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6-9;0 on German LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and non-word-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed in accordance with the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate phonologically and syntactically complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of language impairment across language combinations (see Marinis and Armon-Lotem, 2015; for sentence-repetition; Chiat, 2015 for non-word-repetition). Given the great variability in bilingual language exposure and the potential effect of language experience on language performance in bilingual children, we examined whether background variables related to bilingualism, particularly, the degree language dominance as measured by relative amount of use and exposure, could compromise the diagnostic accuracy of the German LITMUS-SRT and NWRT. We further investigated whether a combination of the two tasks provides better diagnostic accuracy and helps avoid cases of misdiagnosis. To address this, we used an unsupervised machine learning algorithm, the Partitioning-Around-Medoids (PAM, Kaufman and Rousseeuw, 2009), for deriving a clinical category for the children as ± language-impaired based on their performance scores on SRT and NWRT (in isolation and combined) while withholding information about their clinical status based on standardized assessment in their first (home language, L1) and second language (societal language, L2). Subsequently, we calculated diagnostic accuracy and used regression analysis to investigate which background variables (age of onset, length of exposure, degree of language dominance, socio-economic-status, and risk factors for SLI) best explained clinical-group-membership yielded from the PAM-analysis based on the children's NWRT and SRT performance scores. Results show that although language-dominance clearly influences the performance of bilingual typically developing children, especially in the SRT, the diagnostic accuracy of the tools is not compromised by language dominance: while risk factors for SLI were significant predictors for clinical group membership in all models, language dominance did not contribute at all to explaining clinical cluster membership as typically developing or SLI based on any of the combinations of the SRT and NWRT variables. Additionally, results confirm that a combination of SRT scored by correct target structure and the structurally more complex language-dependent part of the NWRT yields better diagnostic accuracy than single measures and is only sensitive to risk factors for SLI and not to dominance levels or SES.

Sexual Violence against Women in Germany: Prevalence and Risk Markers

Previous research has repeatedly shown that gender-based violence affects a considerable proportion of women in any given population. Apart from providing current estimates of the prevalence of sexual violence against women in Germany, we identified specific risk markers applying an advanced statistical method. We analyzed data from a survey of N = 4450 women representative of the German population, conducted by the Criminological Research Institute of Lower Saxony in 2011. Lifetime prevalence for experiencing sexual violence was 5.4% for women aged 21–40 years (five-year prevalence: 2.5%). Non-parametric conditional inference tree (C-Tree) analyses revealed that physical and sexual abuse during childhood as well as being divorced, separated, or widowed was the most informative constellation of risk markers, increasing the five-year prevalence rate of experienced sexual violence victimizations up to 17.0%. Furthermore, knowing about the official penalization of marital rape was related to a lower victimization risk for women without a history of parental violence. Possible explanations for these findings as well as implications for future research are critically discussed.

Survival and Prognostic Factors in Pediatric Patients with Medulloblastoma in Southern Thailand

BACKGROUND

The current prognosis of medulloblastoma in children is better because of technological advancements and improvements in treatment strategies and genetic investigations. However, there is a lack of studies that focus on medulloblastoma in Thailand. The aims of our study were to conduct a survival analysis and to identify the prognostic factors of pediatric medulloblastoma.

MATERIALS AND METHODS

Fifty-five children, with medulloblastoma, were eligible for analysis between 1991 and 2015. We retrospectively reviewed both the clinical and the histological data. Survival curves were constructed using the Kaplan-Meier method. For comparisons of dichotomous factors, between groups, the log-rank test was used to determine survival. The Cox proportional hazard regression model was used to identify the univariate and multivariate survival predictors.

RESULTS

The mortality rate was 49.1% in this study. The median follow-up time was 68.8 months (range: 1-294 months). The 5-year overall survival rate and median survival time were 53.8% (95% CI 38.7-66.7) and 80 months (95% CI 23-230), respectively. Univariate analysis revealed children <3 years of age, hemispheric tumor location, high risk according to risk stratification, and patients who did not receive radiation therapy affected the prognosis. In multivariable analysis, hemispheric tumors (hazard ratio [HR] 2.54 [95% CI 1.11-5.80]; P = 0.01)and high risk groups (HR 3.86 [95% CI 1.28-11.60]; P = 0.01) influenced death. Finally, using conditional inference trees, the study showed that hemispheric tumor locations are truly aggressive in behavior, whereas risk stratification is associated with the prognosis of midline tumors.

CONCLUSIONS

Hemispheric medulloblastoma and high-risk groups according to risk stratification were associated with poor prognosis.

Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests

In an interesting and quite exhaustive review on Random Forests (RF) methodology in bioinformatics Touw et al. address—among other topics—the problem of the detection of interactions between variables based on RF methodology. We feel that some important statistical concepts, such as ‘interaction’, ‘conditional dependence’ or ‘correlation’, are sometimes employed inconsistently in the bioinformatics literature in general and in the literature on RF in particular. In this letter to the Editor, we aim to clarify some of the central statistical concepts and point out some confusing interpretations concerning RF given by Touw et al. and other authors.