Self-interest bias in the COVID-19 pandemic: A cross-cultural comparison between the United States and China

In the global crisis of the COVID-19 pandemic, many countries attempt to enforce new social norms to prevent the further spread of the coronavirus. A key to the success of these measures is the individual adherence to norms that are collectively beneficial to contain the spread of the pandemic. However, individuals’ self-interest bias (i.e., the prevalent tendency to license own but not others’ self-serving acts or norm violations) can pose a challenge to the success of such measures. The current research examines COVID-19-related self-interest bias from a cross-cultural perspective. Two studies (N = 1,558) sampled from the United States and China consistently revealed that participants from the United States evaluated their own self-serving acts (exploiting test kits in Study 1; social gathering and sneezing without covering the mouth in public in Study 2) as more acceptable than identical deeds of others, while such self-interest bias did not emerge among Chinese participants. Cultural underpinnings of independent versus interdependent self-construal may influence the extent to which individuals apply self-interest bias to justifications of their own self-serving behaviors during the pandemic

The Terneuzen Birth Cohort:BMI change between 2 and 6 years is most predictive of adult cardiometabolic risk

BACKGROUND: We recently reported the age interval 2–6y being the earliest and most critical for adult overweight. We now aim to determine which age intervals are predictive of cardiometabolic risk at young adulthood. METHODS AND FINDINGS: We analyzed data from 642 18–28 years olds from the Terneuzen Birth Cohort. Individual BMI SDS trajectories were fitted by a piecewise linear model. By multiple regression analyses relationships were assessed between subsequent conditional BMI SDS changes and components of the metabolic syndrome (MetS), skinfold thickness and hsCRP at young adulthood. Results were adjusted for gender and age, and other confounders. Gender was studied as an effect modifier. All BMI SDS changes throughout childhood were related to waist circumference and skinfold thickness. No other significant relationship was found before the age of 2 years, except between the BMI SDS change 0–1y and hsCRP. Fasting blood glucose was not predicted by any BMI SDS change. BMI SDS change 2–6y was strongly related to most outcome variables, especially to waist circumference (ß 0.47, SE 0.02), systolic and diastolic blood pressure (ß 0.20 SE 0.04 and ß 0.19 SE 0.03), and hsCRP (ß 0.16 SE 0.04). The BMI SDS change 10–18y was most strongly related to HDL cholesterol (ß -0.10, SE 0.03), and triglycerides (ß 0.21, SE 0.03). To a lesser degree, the BMI SDS change 6–10y was related to most outcome variables. BMI SDS changes 2–6y and 10–18y were significantly related to MetS: the OR was respectively 3.39 (95%CI 2.33–4.94) and 2.84 (95%CI 1.94–4.15). CONCLUSION: BMI SDS changes from 2y onwards were related to cardiometabolic risk at young adulthood, the age interval 2–6y being the most predictive. Monitoring and stabilizing the BMI SDS of children as young as 2–6y may not only reverse the progression towards adult overweight, but it may also safeguard cardiometabolic status

Has primary care antimicrobial use really been increasing? Comparison of changes in different prescribing measures for a complete geographic population 1995-2014

Objectives To elucidate how population trends in total antimicrobials dispensed in the community translate into individual exposure. Methods Retrospective, population-based observational study of all antimicrobial prescribing in a Scottish region in financial years 1995, 2000 and 2005–14. Analysis of temporal changes in all antimicrobials and specific antimicrobials measured in: WHO DDD per 1000 population; prescriptions per 1000 population; proportion of population with ≥1 prescription; mean number of prescriptions per person receiving any; mean DDD per prescription. Results Antimicrobial DDD increased between 1995 and 2014, from 5651 to 6987 per 1000 population [difference 1336 (95% CI 1309–1363)]. Prescriptions per 1000 fell (from 821 to 667, difference –154, –151 to –157), as did the proportion prescribed any antimicrobial [from 39.3% to 30.8% (–8.5, –8.4 to –8.6)]. Rising mean DDD per prescription, from 6.88 in 1995 to 10.47 in 2014 (3.59, 3.55–3.63), drove rising total DDD. In the under-5s, every measure fell over time (68.2% fall in DDD per 1000; 60.7% fall in prescriptions per 1000). Among 5–64 year olds, prescriptions per 1000 were lowest in 2014 but among older people, despite a reduction since 2010, the 2014 rate was still higher than in 2000. Trends in individual antimicrobials provide some explanation for overall trends. Conclusions Rising antimicrobial volumes up to 2011 were mainly due to rising DDD per prescription. Trends in dispensed drug volumes do not readily translate into information on individual exposure, which is more relevant for adverse consequences including emergence of resistance.PostprintPeer reviewe

Sex-differential PTSD symptom trajectories across one year following suspected serious injury

Background Recent years have shown an increased application of prospective trajectory-oriented approaches to posttraumatic stress disorder (PTSD). Although women are generally considered at increased PTSD risk, sex and gender differences in PTSD symptom trajectories have not yet been extensively studied. Objective To perform an in-depth investigation of differences in PTSD symptom trajectories across one-year post-trauma between men and women, by interpreting the general trends of trajectories observed in sex-disaggregated samples, and comparing within-trajectory symptom course and prevalence rates. Method We included N = 554 participants (62.5% men, 37.5% women) from a multi-centre prospective cohort of emergency department patients with suspected severe injury. PTSD symptom severity was assessed at 1, 3, 6, and 12 months post-trauma, using the Clinician-Administered PTSD Scale for DSM-IV. Latent growth mixture modelling on longitudinal PTSD symptoms was performed within the sex-disaggregated and whole samples. Bayesian modelling with informative priors was applied for reliable model estimation, considering the imbalanced prevalence of the expected latent trajectories. Results In terms of general trends, the same trajectories were observed for men and women, i.e. resilient, recovery, chronic symptoms and delayed onset. Within-trajectory symptom courses were largely comparable, but resilient women had higher symptoms than resilient men. Sex differences in prevalence rates were observed for the recovery (higher in women) and delayed onset (higher in men) trajectories. Model fit for the sex-disaggregated samples was better than for the whole sample, indicating preferred application of sex-disaggregation. Analyses within the whole sample led to biased estimates of overall and sex-specific trajectory prevalence rates. Conclusions Sex-disaggregated trajectory analyses revealed limited sex differences in PTSD symptom trajectories within one-year post-trauma in terms of general trends, courses and prevalence rates. The observed biased trajectory prevalence rates in the whole sample emphasize the necessity to apply appropriate statistical techniques when conducting sex-sensitive research

Comorbidity between depression and anxiety:assessing the role of bridge mental states in dynamic psychological networks

Background: Comorbidity between depressive and anxiety disorders is common. A hypothesis of the network perspective on psychopathology is that comorbidity arises due to the interplay of symptoms shared by both disorders, with overlapping symptoms acting as so-called bridges, funneling symptom activation between symptom clusters of each disorder. This study investigated this hypothesis by testing whether (i) two overlapping mental states "worrying"and "feeling irritated"functioned as bridges in dynamic mental state networks of individuals with both depression and anxiety as compared to individuals with either disorder alone, and (ii) overlapping or non-overlapping mental states functioned as stronger bridges. Methods: Data come from the Netherlands Study of Depression and Anxiety (NESDA). A total of 143 participants met criteria for comorbid depression and anxiety (65%), 40 participants for depression-only (18.2%), and 37 for anxiety-only (16.8%) during any NESDA wave. Participants completed momentary assessments of symptoms (i.e., mental states) of depression and anxiety, five times a day, for 2 weeks (14,185 assessments). First, dynamics between mental states were modeled with a multilevel vector autoregressive model, using Bayesian estimation. Summed average lagged indirect effects through the hypothesized bridge mental states were compared between groups. Second, we evaluated the role of all mental states as potential bridge mental states. Results: While the summed indirect effect for the bridge mental state "worrying"was larger in the comorbid group compared to the single disorder groups, differences between groups were not statistically significant. The difference between groups became more pronounced when only examining individuals with recent diagnoses (< 6 months). However, the credible intervals of the difference scores remained wide. In the second analysis, a non-overlapping item ("feeling down") acted as the strongest bridge mental state in both the comorbid and anxiety-only groups. Conclusions: This study empirically examined a prominent network-approach hypothesis for the first time using longitudinal data. No support was found for overlapping mental states "worrying"and "feeling irritable"functioning as bridge mental states in individuals vulnerable for comorbid depression and anxiety. Potentially, bridge mental state activity can only be observed during acute symptomatology. If so, these may present as interesting targets in treatment, but not prevention. This requires further investigation

Bayesian statistics and modelling

Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade

Prevalence of responsible research practices among academics in The Netherlands

Background: Traditionally, research integrity studies have focused on research misbehaviors and their explanations. Over time, attention has shifted towards preventing questionable research practices and promoting responsible ones. However, data on the prevalence of responsible research practices, especially open methods, open codes and open data and their underlying associative factors, remains scarce. Methods: We conducted a web-based anonymized questionnaire, targeting all academic researchers working at or affiliated to a university or university medical center in The Netherlands, to investigate the prevalence and potential explanatory factors of 11 responsible research practices. Results: A total of 6,813 academics completed the survey, the results of which show that prevalence of responsible practices differs substantially across disciplines and ranks, with 99 percent avoiding plagiarism in their work but less than 50 percent pre-registering a research protocol. Arts and humanities scholars as well as PhD candidates and junior researchers engaged less often in responsible research practices. Publication pressure negatively affected responsible practices, while mentoring, scientific norms subscription and funding pressure stimulated them. Conclusions: Understanding the prevalence of responsible research practices across disciplines and ranks, as well as their associated explanatory factors, can help to systematically address disciplinary- and academic rank-specific obstacles, and thereby facilitate responsible conduct of research

IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R

The analysis of transcriptomic experiments with ordered covariates, such as dose-response data, has become a central topic in bioinformatics, in particular in omics studies. Consequently, multiple R packages on CRAN and Bioconductor are designed to analyse microarray data from various perspectives under the assumption of order restriction. We introduce the new R package IsoGene Graphical User Interface (IsoGeneGUI), an extension of the original IsoGene package that includes methods from most of available R packages designed for the analysis of order restricted microarray data, namely orQA, ORIClust, goric and ORCME. The methods included in the new IsoGeneGUI range from inference and estimation to model selection and clustering tools. The IsoGeneGUI is not only the most complete tool for the analysis of order restricted microarray experiments available in R but also it can be used to analyse other types of dose-response data. The package provides all the methods in a user friendly fashion, so analyses can be implemented by users with limited knowledge of R programming

Plurality in the Measurement of Social Media Use and Mental Health: An Exploratory Study Among Adolescents and Young Adults

On a daily basis, individuals between 12 and 25 years of age engage with their mobile devices for many hours. Social Media Use (SMU) has important implications for the social life of younger individuals in particular. However, measuring SMU and its effects often poses challenges to researchers. In this exploratory study, we focus on some of these challenges, by addressing how plurality in the measurement and age-specific characteristics of SMU can influence its relationship with measures of subjective mental health (MH). We conducted a survey among a nationally representative sample of Dutch adolescents and young adults (N=3,669). Using these data, we show that measures of SMU show little similarity with each other, and that age-group differences underlie SMU. Similar to the small associations previously shown in social media-effects research, we also find some evidence that greater SMU associates to drops and to increases in MH. Albeit nuanced, associations between SMU and MH were found to be characterized by both linear and quadratic functions. These findings bear implications for the level of association between different measures of SMU and its theorized relationship with other dependent variables of interest in media-effects research

Effects of the Dating Matters® comprehensive prevention model on health- and delinquency-related risk behaviors in middle school youth:A cluster-randomized controlled trial

Teen dating violence (TDV) is associated with a variety of delinquent behaviors, such as theft, and health- and delinquency-related risk behaviors, including alcohol use, substance abuse, and weapon carrying. These behaviors may co-occur due to shared risk factors. Thus, comprehensive TDV-focused prevention programs may also impact these other risk behaviors. This study examined the effectiveness of CDC's Dating Matters (R): Strategies to Promote Healthy Teen Relationships (Dating Matters) comprehensive TDV prevention model compared to a standard-of-care condition on health- and delinquency-related risk behaviors among middle school students. Students (N = 3301; 53% female; 50% black, non-Hispanic; and 31% Hispanic) in 46 middle schools in four sites across the USA were surveyed twice yearly in 6th, 7th, and 8th grades. A structural equation modeling framework with multiple imputation to account for missing data was utilized. On average over time, students receiving Dating Matters scored 9% lower on a measure of weapon carrying, 9% lower on a measure of alcohol and substance abuse, and 8% lower on a measure of delinquency by the end of middle school than students receiving an evidence-based standard-of-care TDV prevention program. Dating Matters demonstrated protective effects for most groups of students through the end of middle school. These results suggest that this comprehensive model is successful at preventing risk behaviors associated with TDV. clinicaltrials.gov Identifier: NCT01672541