Alcohol Consumption and Closed Borders - How COVID-19 Restrictions have Impacted Alcohol Sales and Consumption in Europe

BackgroundThe closing of bars, restaurants and international borders during the COVID-19 pandemic led to significant changes in alcohol availability. The study provides a first systematic overview of the monthly development of alcohol sales in Europe during the pandemic in order to determine the effect of closed borders on the sales and consumption of alcohol.MethodsThe study covers 60 months from January 2015 to December 2020 in 14 northern-European countries with excise revenue data for beer, wine, spirits separately and summed, converted into litres of pure alcohol per capita 15+ as a proxy for alcohol sales. March-December 2020 is seen as the pandemic period. The analyses consist of (1) descriptive trends of sales before and during the pandemic, (2) assessment of the pandemic impact on sales by time-series analyses and (3) case studies of countries with substantial cross-border inflow or outflow of alcohol.ResultsThe result shows an overall reduction in alcohol sales during the pandemic. Nevertheless, the results differ based on the level of cross-border purchasing flows pre-pandemic, as countries with high cross-border inflow saw an increase in domestic sales as the pandemic hit. ConclusionThe closing of intra-European borders had a significant redistributing effect on alcohol sales. While noting sales increases, cross-border inflow countries generally saw a decrease in total alcohol consumption as not all cross-border purchases were replaced by domestic sales. This has important policy implications as large volumes of cross-border inflow of alcohol can negatively affect excise revenue as well as public health outcomes. The methodology can be used to further explore the reliance of different purchasing streams in a domestic alcohol market.

Diversity and time-series analyses of Caribbean deep-sea coral and sponge assemblages on the tropical island slope of Isla de Roatán, Honduras

Shore-based submersible operations, from 2006 to 2020, have allowed us to examine megabenthic assemblages along the island margin of Isla de Roatán from depths of about 150 to 750 m, including repeated observations of the same organisms. These dives were used to photo-document a diverse benthic assemblage and observe the health and condition of the sessile fauna in a well-explored but relatively undocumented area of the Mesoamerican Reef. Samples were collected by dip net, and some dives profiled the water column chemistry in the year 2011. The deep-sea coral assemblage observed off Roatan exhibits high abundance and diversity. The sessile habitat-forming taxa consist primarily of at least 20 different octocorals (e.g., Plexauridae, Primnoidae, Coralliidae, Isididae, and Ellisellidae) and 20 different sponges each (Demospongiae and Hexactinellida), with several known and unknown taxa of Zoantharia, Antipatharia (Bathypathes spp), and Scleractinia (e.g., Desmophyllum pertusum, Dendrophyllia alternata, Madracis myriaster, and solitary taxa). Crinoidea were also abundant and diverse, represented by at least nine species. Epifaunal assemblages associated with corals include at least 24 macroinvertebrate species dominated by Asteroschema laeve (Ophiuroidea) and Chirostylus spp. (Decapoda: Anomura). Repeated observations of a few large octocoral colonies over many years illustrate patterns of predation, recolonization, and epibiont host fidelity, including a 14-year record of decline in a plexaurid octocoral (putatively Paramuricea sp.) and loss of its resident ophiuroids. The shore-based submersible provides a practical and relatively inexpensive platform from which to study coral and sponge assemblages on a deep tropical island slope. The deep-sea coral gardens are likely to harbor new species and new discoveries if more samples can be acquired and made available for taxonomic research.

A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables—A Case Study for Indo-Gangetic River Basins

The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.

Selfee: Self-supervised Features Extraction of animal behaviors

Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in the laboratories for behavior analysis. However, it has not been achieved to use a fully unsupervised method to extract comprehensive and discriminative features directly from raw behavior video frames for annotation and analysis purposes. Here, we report a self supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end to end way. Visualization and classification of the extracted features (Meta representations) validate that Selfee processes animal behaviors in a comparable way of human understanding. We demonstrate that Meta representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in depth analysis. Furthermore, time series analyses of Meta representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.

COVID‐19 and Anticoagulation for Atrial Fibrillation: An Analysis of US Nationwide Pharmacy Claims Data

Background Adherence to oral anticoagulation (OAC) is critical for stroke prevention in atrial fibrillation. However, the COVID‐19 pandemic may have disrupted access to such therapy. We hypothesized that our analysis of a US nationally representative pharmacy claims database would identify increased incidence of lapses in OAC refills during the COVID‐19 pandemic. Methods and Results We identified individuals with atrial fibrillation prescribed OAC in 2018. We used pharmacy dispensing records to determine the incidence of 7‐day OAC gaps and 15‐day excess supply for each 30‐day interval from January 1, 2019 to July 8, 2020. We constructed interrupted time series analyses to test changes in gaps and supply around the pandemic declaration by the World Health Organization (March 11, 2020), and whether such changes differed by medication (warfarin or direct OAC), prescription payment type, or prescriber specialty. We identified 1 301 074 individuals (47.5% women; 54% age ≥75 years). Immediately following the COVID‐19 pandemic declaration, we observed a 14% decrease in 7‐day OAC gaps and 56% increase in 15‐day excess supply (both P <0.001). The increase in 15‐day excess supply was more marked for direct OAC (69% increase) than warfarin users (35%; P <0.001); Medicare beneficiaries (62%) than those with commercial insurance (43%; P <0.001); and those prescribed OAC by a cardiologist (64%) rather than a primary care provider (48%; P <0.001). Conclusions Our analysis of nationwide claims data demonstrated increased OAC possession after the onset of the COVID‐19 pandemic. Our findings may have been driven by waivers of early refill limits and patients’ tendency to stockpile medications in the first weeks of the pandemic.

Impact of Focused Deterrence on Lived Experiences With Gangs and Gun Violence: Extending Effects Beyond Officially Recorded Crime

The study evaluates a geographically based focused deterrence (FD) intervention, extending knowledge about FD impact beyond crime data to also examine residents’ lived experiences with gangs and gun violence via a two-wave household survey. We employ a quasi-experimental design and utilize time-series analyses, coefficient difference tests, and mixed-effects ordinal logistic regression. The results show a significant reduction in shots fired incidents in the target area relative to comparison areas. Shots fired calls for service trended downward citywide, but the magnitude was doubled in the target area. Survey data showed substantive declines in the target area on all six gang and gun violence outcomes, significantly exceeding changes experienced in comparison areas. We conclude that focusing geographically as well as on repeat offenders is an effective FD approach, and evaluating community surveys provides an improved understanding of the community impact.

THE LIGHT-DOSIMETER – A NEW DEVICE TO HELP ADVANCE RESEARCH ON THE NON-VISUAL RESPONSES TO LIGHT

Research on the non-visual responses to light under real-world conditions has been hindered by the lack of suitable measuring devices. Here, we present a novel, portable and miniaturised light-dosimeter attached to a spectacle frame, taking measurements in the near-corneal plane. The recorded data is processed with the help of the custom-made software package Lido Studio. In addition to commonly used metrics such as illuminance and correlated colour temperature (CCT), it also provides metrics standardised in CIE S 026:2018. Data can be analysed directly in Lido Studio or exported as a PDF report or a comma-separated values (CSV) file for further in-depth time-series analyses. The Federal Institute of Metrology (METAS) optics laboratory (Bern-Wabern, Switzerland) assessed the light-dosimeter’s spectral and geometric properties. Subsequentially, the team at the Centre for Chronobiology (Basel, Switzerland) confirmed that measurements performed with a light-dosimeter were comparable to those from a commercial spectroradiometer.

Did States’ Adoption of More Rigorous Standards Lead to Improved Student Achievement? Evidence From a Comparative Interrupted Time Series Study of Standards-Based Reform

This study was designed to assess the effects of states’ adoption of more rigorous standards as part of the current wave of standards-based reform on student achievement using comparative interrupted time series analyses based on state-level NAEP data from 1990 to 2017. Results show that the effects of adopting more rigorous standards on students’ mathematics achievement were generally small and not significant. The effects on students’ reading achievement were also generally small, but negative and statistically significant for Grade 4. The study also revealed that the effects of states’ adoption of more rigorous standards varied across NAEP subscales and student subgroups.