Introduction: The Mississippi River Basin—a model for studying the Anthropocene in situ

Is it possible to trace ongoing transitions in the Earth system back to the regional scales at which they are produced and where their effects can be directly experienced? This editorial introduces two special issues of The Anthropocene Review that document a two-year, transdisciplinary experiment: a collaborative investigation of the Mississippi River Basin (MRB) as a model region for studying the Anthropocene condition in situ. Coordinated by the Anthropocene Curriculum, an initiative led by the Haus der Kulturen der Welt and the Max Planck Institute for the History of Science,1 the project Mississippi: An Anthropocene River involved a large consortium of institutions and more than three hundred researchers, artists, activists, and local community members. Together, participants learned about, questioned, and experienced the Anthropocene at a level meaningful to people, a level at which historical legacies and future commitments play out amid concrete infrastructures and socio-ecological formations, and alongside existing inequalities and life’s everyday struggles. The introduction summarizes eleven scientific and creative research outputs that were selected from this wide-ranging experiment, contextualizes the river’s history, and explains the regional approach the project undertook.

HPV vaccination of girls in the German model region Saarland: Insurance data-based analysis and identification of starting points for improving vaccination rates

In Germany, the incidence of cervical cancer, a disease caused by human papillomaviruses (HPV), is higher than in neighboring European countries. HPV vaccination has been recommended for girls since 2007. However, it continues to be significantly less well received than other childhood vaccines, so its potential for cancer prevention is not fully realized. To find new starting points for improving vaccination rates, we analyzed pseudonymized routine billing data from statutory health insurers in the PRAEZIS study in the federal state Saarland serving as a model region. We show that lowering the HPV vaccination age to 9 years led to more completed HPV vaccinations already in 2015. Since then, HPV vaccination rates and the proportion of 9- to 11-year-old girls among HPV-vaccinated females have steadily increased. However, HPV vaccination rates among 15-year-old girls in Saarland remained well below 50% in 2019. Pediatricians vaccinated the most girls overall, with a particularly high proportion at the recommended vaccination age of 9-14 years, while gynecologists provided more HPV catch-up vaccinations among 15-17-year-old girls, and general practitioners compensated for HPV vaccination in Saarland communities with fewer pediatricians or gynecologists. We also provide evidence for a significant association between attendance at the U11 or J1 medical check-ups and HPV vaccination. In particular, participation in HPV vaccination is high on the day of U11. However, obstacles are that U11 is currently not funded by all statutory health insurers and there is a lack of invitation procedures for both U11 and J1, resulting in significantly lower participation rates than for the earlier U8 or U9 screenings, which are conducted exclusively with invitations and reminders. Based on our data, we propose to restructure U11 and J1 screening in Germany, with mandatory funding for U11 and organized invitations for HPV vaccination at U11 or J1 for both boys and girls.

INTEGRAL ASSESSMENT OF THE BIOPSYCHOSOCIAL STATUS OF OLDER CITIZENS AND IMPLEMENTATION OF THE SYSTEMIC SUPPORT COMPONENTS

В статье рассмотрены проблемы определения нуждаемости граждан старших возрастных групп в комплексной медико-социально-психологической помощи в рамках реализации персонифицированного подхода к предоставлению услуг. Вводится понятие интегральной оценки биопсихосоциального статуса пожилых граждан и предлагается алгоритм ее проведения, основанный на ранжировании обследуемых по степени снижения функций организма вследствие старения и развития ассоциированных с возрастом заболеваний. Обоснована возможность и показаны особенности использования этого алгоритма на примере Санкт-Петербурга как модельного региона с учетом его технического, кадрового и организационного потенциала. Сформулированы требования по реализации составляющих системной поддержки оказания комплексной медико-социально-психологической помощи пожилым гражданам в процессе организационно-технического обеспечения интегральной оценки их биопсихосоциального статуса. The article discusses the problems of determining the need of citizens of older age groups in complex medical, social and psychological assistance in the framework of the implementation of a personalized approach to the service provision. The concept of an integral assessment of the biopsychosocial status of older citizens has been introduced. Based on the ranking of subjects according to the degree of decrease in body functions due to ageing and the development of age-associated diseases, an algorithm for the implementation of the assessment has been proposed. The possibility of using this algorithm has been substantiated, and its features have been shown on the example of St. Petersburg as a model region, taking into account its technical, personnel and organizational potential. The requirements for the implementation of the systemic support components for the provision of comprehensive medical, social and psychological assistance to senior citizens in the process of organizational and technical support for an integrated assessment of their biopsychosocial status have been defined.

Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Australia, West Africa, India, and China are blanketed by cover (intensely weathered surface material and/or later sediment deposition, both up to hundreds of metres thick). Mineral exploration through cover poses a significant technological challenge worldwide. Classifying and understanding landscape types and their variability is of key importance for mineral exploration in covered regions. Landscape variability expresses how near-surface geochemistry is linked to underlying lithologies. Therefore, landscape variability mapping should inform surface geochemical sampling strategies for mineral exploration. Advances in satellite imaging and computing power have enabled the creation of large geospatial data sets, the sheer size of which necessitates automated processing. In this study, we describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available digital elevation model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve approximately 98% classification accuracy with a reasonable runtime of 48 minutes on a single Intel® Core™ i7-8550U CPU core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, whereas smaller tiles result in a more detailed and, at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region. Mapping landscape variability at a large scale can be used globally as a fundamental tool for guiding more efficient mineral exploration programs in regions under cover.

Impact of COVID-19 pandemic lockdown on myocardial infarction care

The aim of this study was to evaluate the impact of the COVID-19 pandemic lockdown on acute myocardial infarction (AMI) care, and to identify underlying stressors in the German model region for complete AMI registration. The analysis was based on data from the population-based KORA Myocardial Infarction Registry located in the region of Augsburg, Germany. All cases of AMI (n = 210) admitted to one of four hospitals in the city of Augsburg or the county of Augsburg from February 10th, 2020, to May 19, 2020, were included. Patients were divided into three groups, namely pre-lockdown, strict lockdown, and attenuated lockdown period. An additional survey was conducted asking the patients for stress and fears in the 4 weeks prior to their AMI. The AMI rate declined by 44% in the strict lockdown period; in the attenuated lockdown period the rate was 17% lower compared to the pre-lockdown period. The downward trend in AMI rates during lockdown was seen in STEMI and NSTEMI patients, and independent of sex and age. The door-to-device time decreased by 70–80% in the lockdown-periods. In the time prior to the infarction, patients felt stressed mainly due to fear of infection with Sars-CoV-2 and less because of the restrictions and consequences of the lockdown. A strict lockdown due to the Covid-19 pandemic had a marked impact on AMI care even in a non-hot-spot region with relatively few cases of COVID-19. Fear of infection with the virus is presumably the main reason for the drop in hospitalizations due to AMI.

Prioritizing structural reform areas: Methodological aspects

Subject. This article examines the theoretical basis for prioritizing structural reforms in countries and regions. Objectives. The article aims to develop a methodology that helps identify and rank the areas of structural policy and structural reforms by their priority. Methods. For the study, we used a regression analysis, gap analysis, benchmarking, cross-country and regional comparison, and graphical presentation of data. Results. The article proposes a methodology of quantitative assessment and ordered estimate of the priorities of structural reforms. It shows maps of the priorities of the target region, namely the Chelyabinsk Oblast, and the group of regions that have a development pattern similar to the model region, or competitor regions. Conclusions. The problem of increasing the rates of economic growth and well-being standard in the regions can be tackled through structural policies based on prioritizing the economy areas and concentrating resources in the selected ones.

Prioritizing structural reform areas: Methodological aspects

Subject. This article examines the theoretical basis for prioritizing structural reforms in countries and regions. Objectives. The article aims to develop a methodology that helps identify and rank the areas of structural policy and structural reforms by their priority. Methods. For the study, we used a regression analysis, gap analysis, benchmarking, cross-country and regional comparison, and graphical presentation of data. Results. The article proposes a methodology of quantitative assessment and ordered estimate of the priorities of structural reforms. It shows maps of the priorities of the target region, namely the Chelyabinsk Oblast, and the group of regions that have a development pattern similar to the model region, or competitor regions. Conclusions. The problem of increasing the rates of economic growth and well-being standard in the regions can be tackled through structural policies based on prioritizing the economy areas and concentrating resources in the selected ones.

Using Machine Learning to Map Western Australian Landscapes

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.