Socioeconomic hardship,uncertainty about the future,and adolescent mental wellbeing over a year during the COVID-19 pandemic

Socioeconomic vulnerabilities put adolescents at risk for mental wellbeing issues, also in times of a pandemic. In the present longitudinal online survey study, we explored changes in mental wellbeing (i.e., mood and life satisfaction) during the COVID-19 pandemic. Second, we examined how socioeconomic hardship in online home schooling predicted adolescents’ mental wellbeing 1 year later. Third, we tested whether this relation was mediated by feelings of uncertainty about the future. Fourth, we tested whether this relation was moderated (in terms of a protective factor) by self-efficacy. In total, 177 Dutch-speaking adolescents aged 10–18 years (M_age = 15.64, SD_age = 1.72, 79% females) participated in all three 6-month separated waves (T1 = May 2020, T2 = November 2020, and T3 = May 2021). Mood results demonstrated that feelings of vigor decreased between T1 and T2, and feelings of tension and depression increased between T1, T2, and T3. Socioeconomic hardship in online home schooling in the early phase of the pandemic was negatively associated with both baseline mental wellbeing and 1 year later. Socioeconomic hardship at T1 predicted higher feelings of future uncertainty at T2, and higher levels of future uncertainty were associated with lower feelings of vigor, and higher feelings of tension and depression at T3. However, we found no evidence for mediation or moderation effects. In conclusion, the present findings illustrate the complexity of disentangling the adverse effects of socioeconomic hardship on adolescent mental wellbeing.     

The effect of artificial light on bat richness and nocturnal soundscapes along an urbanization gradient in an arid landscape of central Peru

Urbanization usually reduces bat richness; however, the presence of green areas within cities and peripheral rural areas in arid ecosystems may provide microhabitats for some species. Light pollution is a major feature of urbanization, but its impact on bat behavior appears to be species-specific and previous studies have documented contrasting responses. Moreover, the effect of urbanization on bat species has been poorly studied in arid regions. We assessed the effect of artificial night light intensity (as a proxy of urbanization) on both bat occupancy and the acoustic space used (ASU) in an urbanization gradient in Peruvian central coast, based on passive acoustic recorders. We collected 26,169 recordings from 19 sites which resulted in 579 independent detections of 15 bat species. Variation in both ASU and species richness was best explained by artificial night light intensity. Species-specific effects of the artificial night light intensity based on a multi-species occupancy modeling showed that this covariate had a negative effect on occupancy for most of the bat species (12 species). ASU and both observed and posterior bat species richness were positively correlated, suggesting that ASU can be used as a proxy of bat richness. This study provides evidence that both bat richness and occupancy decrease with artificial light intensity; nevertheless, eight species used urban areas, similar to results found in other cities around the world.

     

Natural cubic splines for the analysis of Alzheimer's clinical trials

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.