Diagnostic Contribution of the DSM-5 Criteria for Internet Gaming Disorder

Background: Internet gaming disorder (IGD) can have long-term severe consequences in affected individuals, especially adolescents and young people. Empirical studies of IGD using the DSM-5 criteria are still lacking. This study aimed to evaluate the contribution of specific criteria to the diagnosis of IGD based on the DSM-5 in the context of Chinese culture.Methods: The Chinese version of the Internet Gaming Disorder Scale–Short Form (IGDS9-SF) was applied to investigate the prevalence of IGD in a general sample of 28,689 middle school students aged 12–19 years from two cities in China.Results: The prevalence of IGD was 4.6% among this adolescent sample. The group of IGD students reported longer weekly gaming times and worse academic performance than the group of non-IGD students. Although “preoccupation” and “playing to escape” were the most frequently reported criteria, the conditional inference trees showed that “give up other activities,” ‘negative consequences,” and “continue despite problems” contributed most to the diagnosis of IGD based on the DSM-5.Conclusions: The prevalence of IGD among Chinese adolescents (ages 12–19) was 4.6%. This study provides evidence for retaining or deleting specific diagnostic criteria by the DSM framework in the future.

Epi-MEIF, a flexible and efficient method for detection of high order epistatic interactions from complex phenotypic traits

Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for high-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The epiMEIF model is fitted on a group of potential causal SNPs and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify high order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture of complex phenotypes.

Health Status Assessment of a Population of Asylum Seekers in Northern Italy

BackgroundSince 2011 Italy has faced an extraordinary increase in migrants arrivals, mainly from the Mediterranean route, one of the world’s most dangerous journeys. The purpose of the present article is to provide a comprehensive picture of the migrants' health status in the centre "T.Fenoglio", Settimo Torinese (Turin, Italy).MethodsA retrospective cross-sectional study was conducted using data collected from June 2016 to May 2018 on adult migrants (>18 years) from Africa, East and Middle East. Data was collected through the migrants' medical records. Descriptive statistics were performed on socio-demographic variables. The diagnosed diseases were anonymously registered and classified according to the International Classification of Primary Care (ICPC-2).Conditional Inference Trees were used to perform a descriptive analysis of the sample and to detect the covariates with the strongest association with the outcome variables Disease on Arrival and ICPC-2 for diseases on arrival.ResultsAnalyzed observations were 9857. 81.8% were men, median age was 23 (Interquartile range= 20.0-27.4). 70.3% of the sample came from Sub-Saharan Africa. 2365 individuals (24%) arrived at the center with at least one disease. On arrival, skin (27.71%), respiratory (14.46%), digestive (14.73%) and generic diseases (20.88%) were the most frequent. During the stay respiratory diseases were the most common (25.70%). The highest probability of arriving with a disease occurred in 2018 and in the period September-November 2016, in particular for people from the Horn of Africa. During this period and also in the first half of 2017, skin diseases were the most reported. In quarters with lower prevalence of diseases on arrival the most common disease code was generic for both men and women.ConclusionsHorn of Africa was the most troubled area with severe conditions frequently reported regarding skin diseases, in particular scabies. 2018 was the most critical year, especially for migrants from Horn of Africa and Sub-Saharan Africa. A better understanding of the health status of asylum seekers is an important factor to determine a more efficient reception and integration process and the better allocation of economic resources in the context of migrants' health care.

Live Crown Ratio as an Indicator for Tree Vigor and Stability of Turkey Oak (Quercus cerris L.): A Case Study

Stability of forests represents a significant objective for climate change mitigation. As stand stability is influenced by the stability of individual trees, promoting stable trees is vital for a sustainable forest management. However, inside stands, trees experience intense competition. As a result, the crown recedes and diameter growth is affected, the trees becoming slender and more susceptible to biotic and abiotic disturbances. Finding effective indicators for tree vigor and stability is therefore important. This study aimed to assess the performance of the live crown ratio as an indicator in deciding the timing of tending operations for obtaining and maintaining vigorous Turkey oak trees. Live crown ratio (LCR) and height to diameter ratio (HDR) were determined for 80 sampled Turkey oak trees. A threshold of 100 for HDR was chosen to classify trees as slender or not slender. Next, conditional inference tree and logistic regression were used to determine the LCR threshold value where trees become slender. As the sample included small trees, using breast height to measure diameter may have affected the results. Therefore, small and large trees were also analyzed separately. For the entire dataset, the methods reached quite different results (LCR threshold of 0.371 for conditional inference tree and of 0.434 for the logistic regression), and relatively high values compared to the literature. For tall trees (height > 12.5 m), the methods reached similar results: 0.386 for the conditional inference tree and 0.382 for the logistic regression. For small trees (height < 12.5 m), the conditional inference tree method could not calculate any LCR threshold estimate, while the one from the logistic regression was unrealistically large (0.628). This confirms that using DBH for small trees to compute slenderness brings systematic errors. The live crown ratio was a good indicator of growth vigor for Turkey oak trees. Therefore, for stable trees (HDR < 100), a LCR of 0.36–0.39 must be maintained and could be used to decide the timing for thinning in Turkey oak stands.

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.

Trends and factors related to adolescent pregnancies: an incidence trend and conditional inference trees analysis of northern Nicaragua demographic surveillance data

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.

Classification tree analysis for an intersectionality-informed identification of population groups with non-daily vegetable intake

Background Daily vegetable intake is considered an important behavioural health resource associated with improved immune function and lower incidence of non-communicable disease. Analyses of population-based data show that being female and having a high educational status is most strongly associated with increased vegetable intake. In contrast, men and individuals with a low educational status seem to be most affected by non-daily vegetable intake (non-DVI). From an intersectionality perspective, health inequalities are seen as a consequence of an unequal balance of power such as persisting gender inequality. Unravelling intersections of socially driven aspects underlying inequalities might be achieved by not relying exclusively on the male/female binary, but by considering different facets of gender roles as well. This study aims to analyse possible interactions of sex/gender or sex/gender related aspects with a variety of different socio-cultural, socio-demographic and socio-economic variables with regard to non-DVI as the health-related outcome. Method Comparative classification tree analyses with classification and regression tree (CART) and conditional inference tree (CIT) as quantitative, non-parametric, exploratory methods for the detection of subgroups with high prevalence of non-DVI were performed. Complete-case analyses (n = 19,512) were based on cross-sectional data from a National Health Telephone Interview Survey conducted in Germany. Results The CART-algorithm constructed overall smaller trees when compared to CIT, but the subgroups detected by CART were also detected by CIT. The most strongly differentiating factor for non-DVI, when not considering any further sex/gender related aspects, was the male/female binary with a non-DVI prevalence of 61.7% in men and 42.7% in women. However, the inclusion of further sex/gender related aspects revealed a more heterogenous distribution of non-DVI across the sample, bringing gendered differences in main earner status and being a blue-collar worker to the foreground. In blue-collar workers who do not live with a partner on whom they can rely on financially, the non-DVI prevalence was 69.6% in men and 57.4% in women respectively. Conclusions Public health monitoring and reporting with an intersectionality-informed and gender-equitable perspective might benefit from an integration of further sex/gender related aspects into quantitative analyses in order to detect population subgroups most affected by non-DVI.

Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm

In vitro fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceive a baby successfully. Nevertheless, IVF-ET does not guarantee success. Frozen embryo transfer (FET) is an important supplement to IVF-ET. Many factors are correlated with the outcome of FET which is unpredictable. Machine learning is a field of study that predict various outcomes by defining data attributes and using relevant data and calculation algorithms. Machine learning algorithm has been widely used in clinical research. The present study focuses on making predictions of early pregnancy outcomes in FET through clinical characters, including age, body mass index (BMI), endometrial thickness (EMT) on the day of progesterone treatment, good-quality embryo rate (GQR), and type of infertility (primary or secondary), serum estradiol level (E2) on the day of embryo transfer, and serum progesterone level (P) on the day of embryo transfer. We applied four representative machine learning algorithms, including logistic regression (LR), conditional inference tree, random forest (RF) and support vector machine (SVM) to build prediction models and identify the predictive factors. We found no significant difference among the models in the sensitivity, specificity, positive predictive rate, negative predictive rate or accuracy in predicting the pregnancy outcome of FET. For example, the positive/negative predictive rate of the SVM (gamma = 1, cost = 100, 10-fold cross validation) is 0.56 and 0.55. This approach could provide a reference for couples considering FET. The prediction accuracy of the present study is limited, which suggests that there may be some other more effective predictors to be developed in future work.

Compound Endoscopic Morphological Features for Identifying Non-Pedunculated Lesions ≥20 mm with Intramucosal Neoplasia

Background: The major limitation of piecemeal endoscopic mucosal resection (EMR) is the inaccurate histological assessment of the resected specimen, especially in cases of submucosal invasion. Objective: To classify non-pedunculated lesions ≥20 mm based on endoscopic morphological features, in order to identify those that present intramucosal neoplasia (includes low-grade neoplasia and high-grade neoplasia) and are suitable for piecemeal EMR. Design: A post-hoc analysis from an observational prospective multicentre study conducted by 58 endoscopists at 17 academic and community hospitals was performed. Unbiased conditional inference trees (CTREE) were fitted to analyse the association between intramucosal neoplasia and the lesions’ endoscopic characteristics. Result: 542 lesions from 517 patients were included in the analysis. Intramucosal neoplasia was present in 484 of 542 (89.3%) lesions. A conditional inference tree including all lesions’ characteristics assessed with white light imaging and narrow-band imaging (NBI) found that ulceration, pseudodepressed type and sessile morphology changed the accuracy for predicting intramucosal neoplasia. In ulcerated lesions, the probability of intramucosal neoplasia was 25% (95%CI: 8.3–52.6%; p < 0.001). In non-ulcerated lesions, its probability in lateral spreading lesions (LST) non-granular (NG) pseudodepressed-type lesions rose to 64.0% (95%CI: 42.6–81.3%; p < 0.001). Sessile morphology also raised the probability of intramucosal neoplasia to 86.3% (95%CI: 80.2–90.7%; p < 0.001). In the remaining 319 (58.9%) non-ulcerated lesions that were of the LST-granular (G) homogeneous type, LST-G nodular-mixed type, and LST-NG flat elevated morphology, the probability of intramucosal neoplasia was 96.2% (95%CI: 93.5–97.8%; p < 0.001). Conclusion: Non-ulcerated LST-G type and LST-NG flat elevated lesions are the most common non-pedunculated lesions ≥20 mm and are associated with a high probability of intramucosal neoplasia. This means that they are good candidates for piecemeal EMR. In the remaining lesions, further diagnostic techniques like magnification or diagnostic +/− therapeutic endoscopic submucosal dissection should be considered.