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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.

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With mixed-effects regression models becoming a mainstream tool for every psycholinguist, there has become an increasing need to understand them more fully. In the last decade, most work on mixed-effects models in psycholinguistics has focused on properly specifying the random-effects structure to minimize error in evaluating the statistical significance of fixed-effects predictors. The present study examines a potential misspecification of random effects that has not been discussed in psycholinguistics: violation of the single-subject-population assumption, in the context of logistic regression. Estimated random-effects distributions in real studies often appear to be bi- or multimodal. However, there is no established way to estimate whether a random-effects distribution corresponds to more than one underlying population, especially in the more common case of a multivariate distribution of random effects. We show that violations of the single-subject-population assumption can usually be detected by assessing the (multivariate) normality of the inferred random-effects structure, unless the data show quasi-separability, i.e., many subjects or items show near-categorical behavior. In the absence of quasi-separability, several clustering methods are successful in determining which group each participant belongs to. The BIC difference between a two-cluster and a one-cluster solution can be used to determine that subjects (or items) do not come from a single population. This then allows the researcher to define and justify a new post hoc variable specifying the groups to which participants or items belong, which can be incorporated into regression analysis.

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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.

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BACKGROUND: Identifying predictors of subjective unmet need for assistance with activities of daily living (ADL) is necessary to allocate resources in social care effectively to the most vulnerable populations. In this study, we aimed at identifying population groups at risk of subjective unmet need for assistance with ADL and instrumental ADL (IADL) taking complex interaction patterns between multiple predictors into account. METHODS: We included participants aged 55 or older from the cross-sectional German Health Update Study (GEDA 2019/2020-EHIS). Subjective unmet need for assistance was defined as needing any help or more help with ADL (analysis 1) and IADL (analysis 2). Analysis 1 was restricted to participants indicating at least one limitation in ADL (N = 1,957). Similarly, analysis 2 was restricted to participants indicating at least one limitation in IADL (N = 3,801). Conditional inference trees with a Bonferroni-corrected type 1 error rate were used to build classification models of subjective unmet need for assistance with ADL and IADL, respectively. A total of 36 variables representing sociodemographics and impairments of body function were used as covariates for both analyses. In addition, the area under the receiver operating characteristics curve (AUC) was calculated for each decision tree. RESULTS: Depressive symptoms according to the PHQ-8 was the most important predictor of subjective unmet need for assistance with ADL. Further classifiers that were selected from the 36 independent variables were gender identity, employment status, severity of pain, marital status, and educational level according to ISCED-11. The AUC of this decision tree was 0.66. Similarly, depressive symptoms was the most important predictor of subjective unmet need for assistance with IADL. In this analysis, further classifiers were severity of pain, social support according to the Oslo-3 scale, self-reported prevalent asthma, and gender identity (AUC = 0.63). CONCLUSIONS: Reporting depressive symptoms was the most important predictor of subjective unmet need for assistance among participants with limitations in ADL or IADL. Our findings do not allow conclusions on causal relationships. Predictive performance of the decision trees should be further investigated before conclusions for practice can be drawn.

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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.

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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.

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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.

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Intersectionality recognizes that in the context of sociohistorically shaped structural power relations, an individual's multiple social positions or identities (e.g., gender, ethnicity) can interact to affect health-related outcomes. Despite limited methodological guidance, intersectionality frameworks have increasingly been incorporated into epidemiological studies, both to describe health disparities and to examine their causes. This study aimed to advance methods in intersectional estimation of binary outcomes in descriptive health disparities research through evaluation of 7 potentially intersectional data analysis methods: cross-classification, regression with interactions, multilevel analysis of individual heterogeneity (MAIHDA), and decision trees (CART, CTree, CHAID, random forest). Accuracy of estimated intersection-specific outcome prevalence was evaluated across 192 intersections using simulated data scenarios. For comparison we included a non-intersectional main effects regression. We additionally assessed variable selection performance amongst decision trees. Example analyses using National Health and Nutrition Examination Study data illustrated differences in results between methods. At larger sample sizes, all methods except for CART performed better than non-intersectional main effects regression. In smaller samples, MAIHDA was the most accurate method but showed no advantage over main effects regression, while random forest, cross-classification, and saturated regression were the least accurate, and CTree and CHAID performed moderately well. CART performed poorly for estimation and variable selection. Sensitivity analyses examining the bias-variance tradeoff suggest MAIHDA as the preferred unbiased method for accurate estimation of high-dimensional intersections at smaller sample sizes. Larger sample sizes are more imperative for other methods. Results support the adoption of an intersectional approach to descriptive epidemiology.

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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.

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BACKGROUND: We aimed to identify the 2001-2013 incidence trend, and characteristics associated with adolescent pregnancies reported by 20-24-year-old women. METHODS: A retrospective analysis of the Cuatro Santos Northern Nicaragua Health and Demographic Surveillance 2004-2014 data on women aged 15-19 and 20-24. To calculate adolescent birth and pregnancy rates, we used the first live birth at ages 10-14 and 15-19 years reported by women aged 15-19 and 20-24 years, respectively, along with estimates of annual incidence rates reported by women aged 20-24 years. We conducted conditional inference tree analyses using 52 variables to identify characteristics associated with adolescent pregnancies. RESULTS: The number of first live births reported by women aged 20-24 years was 361 during the study period. Adolescent pregnancies and live births decreased from 2004 to 2009 and thereafter increased up to 2014. The adolescent pregnancy incidence (persons-years) trend dropped from 2001 (75.1 per 1000) to 2007 (27.2 per 1000), followed by a steep upward trend from 2007 to 2008 (19.1 per 1000) that increased in 2013 (26.5 per 1000). Associated factors with adolescent pregnancy were living in low-education households, where most adults in the household were working, and high proportion of adolescent pregnancies in the local community. Wealth was not linked to teenage pregnancies. CONCLUSIONS: Interventions to prevent adolescent pregnancy are imperative and must bear into account the context that influences the culture of early motherhood and lead to socioeconomic and health gains in resource-poor settings.

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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.

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The cadmium (Cd) activity in soil has been widely studied. However, the interactive effects of soil properties (e.g. soil pH, CEC, and SOM) on Cd transfer from soil to grain are generally overlooked. In total 325 datasets including soil pH, CEC, SOM, and soil Cd content were used in this study. The descriptive statistics indicated that Cd content in wheat and maize soils ranged from 0.05 to 10.31 mg/kg and 0.02-13.68 mg/kg, with mean values of 0.87 and 1.14 mg/kg, respectively. Cd contents in wheat and maize grains were 0.01-1.36 mg/kg and 0.001-1.08 mg/kg with average values of 0.15 and 0.10 mg/kg, respectively. The results of SEM demonstrated that the interactive effects of soil properties contributed more to Cd transfer from soil to wheat grain than the soil Cd content. Subsequently, CITs-MLR indicated that the critical factors, including soil pH and total soil Cd content, could mask the contribution of other soil properties on Cd accumulation in grain; soil CEC may prevent Cd from leaching and therefore improve grain Cd level of wheat especially at acidic soil condition. The result of derived Cd thresholds revealed that current Cd thresholds are not completely suitable to wheat and maize grain at different soil conditions. This study provides a new model for further investigation on relationships between soil properties, soil Cd content and grain Cd level.

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High adolescent alcohol consumption is predictive for alcohol problems later in life. To tailor interventions, early identification of risk groups for adolescent alcohol consumption is important. The IMAGEN dataset was utilized to investigate predictors for problematic alcohol consumption at age 18-20 years as a function self and parental personality and drug-related measures as well as life-events and cognitive variables all assessed at age 14 years (N = 1404). For this purpose the binary partitioning algorithm ctree was used in an explorative analysis. The algorithm recursively selects significant input variables and splits the outcome variable based on these, yielding a conditional inference tree. Four significant split variables, namely Place of residence, the Disorganization subscale of the Temperament and Character Inventory, Sex, and the Sexuality subscale of the life-events questionnaire were found to distinguish between adolescents scoring high or low on the Alcohol Use Disorders Identification Test about five years later (all p < 0.001). The analyis adds to the literature on predictors of adolescent drinking problems using a large European sample. The identified split variables could easily be collected in community samples. If their validity is proven in independent samples, they could facilitate intervention studies in the field of adolescent alcohol prevention.

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BACKGROUND: In order to further identify the needed interventions for continued poverty reduction in our study area Cuatro Santos, northern Nicaragua, we aimed to elucidate what predicts poverty, measured by the Unsatisfied Basic Need index. This analysis was done by using decision tree methodology applied to the Cuatro Santos health and demographic surveillance databases. METHODS: Using variables derived from the health and demographic surveillance update 2014, transferring individual data to the household level we used the decision tree framework Conditional Inference trees to predict the outcome "poverty" defined as two to four unsatisfied basic needs using the Unsatisfied Basic Need Index. We further validated the trees by applying Conditional random forest analyses in order to assess and rank the importance of predictors about their ability to explain the variation of the outcome "poverty." The majority of the Cuatro Santos households provided information and the included variables measured housing conditions, assets, and demographic experiences since the last update (5 yrs), earlier participation in interventions and food security during the last 4 weeks. RESULTS: Poverty was rare in households that have some assets and someone in the household that has a higher education than primary school. For these households participating in the intervention that installed piped water with water meter was most important, but also when excluding this variable, the resulting tree showed the same results. When assets were not taken into consideration, the importance of education was pronounced as a predictor for welfare. The results were further strengthened by the validation using Conditional random forest modeling showing the same variables being important as predicting the outcome in the CI tree analysis. As assets can be a result, rather than a predictor of more affluence our results in summary point specifically to the importance of education and participation in the water installation intervention as predictors for more affluence. CONCLUSION: Predictors of poverty are useful for directing interventions and in the Cuatro Santos area education seems most important to prioritize. Hopefully, the lessons learned can continue to develop the Cuatro Santos communities as well as development in similar poor rural settings around the world.

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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.

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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.

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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.

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BACKGROUND: Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction performance are not unique or there is not even a clear definition, as in the case of multivariate response random forests. METHODS: A new alternative importance measure, called Intervention in Prediction Measure, is investigated. It depends on the structure of the trees, without depending on performance measures. It is compared with other well-known variable importance measures in different contexts, such as a classification problem with variables of different types, another classification problem with correlated predictor variables, and problems with multivariate responses and predictors of different types. RESULTS: Several simulation studies are carried out, showing the new measure to be very competitive. In addition, it is applied in two well-known bioinformatics applications previously used in other papers. Improvements in performance are also provided for these applications by the use of this new measure. CONCLUSIONS: This new measure is expressed as a percentage, which makes it attractive in terms of interpretability. It can be used with new observations. It can be defined globally, for each class (in a classification problem) and case-wise. It can easily be computed for any kind of response, including multivariate responses. Furthermore, it can be used with any algorithm employed to grow each individual tree. It can be used in place of (or in addition to) other variable importance measures.

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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.

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