Andreas Edel

Andreas Edel forscht am Einstein Center for Population Diversity zu den Auswirkungen wachsender Bevölkerungsvielfalt auf soziale Ungleichheit und Gesundheitsdisparitäten. Sein Fokus liegt auf dem Zusammenhang zwischen sich wandelnden Familienstrukturen, demografischen Veränderungen und der Entstehung von Ungleichheiten über Generationen hinweg. Für Unternehmen und öffentliche Institutionen ist diese Forschung relevant, um evidenzbasierte Politiken und Maßnahmen zur Reduktion von Gesundheits- und Wohlfahrtsungleichheiten zu entwickeln — etwa in den Bereichen Sozialplanung, Gesundheitswesen und Familienförderung. Die Arbeit verbindet Demografie, Soziologie, Politikwissenschaft und Datenwissenschaft, um Mechanismen zu verstehen, durch die Vielfalt und Ungleichheit miteinander verflochten sind.

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• Einstein Center for Population Diversity

  Quelle ↗

  The Einstein Center for Population Diversity will bring together leading scholars in demography, sociology, political science, psychology / health sciences, and data science to study the consequences of growing population diversity for socioeconomic inequality and to examine how diversity and socioeconomic inequality relate to health disparities.

• Einstein Center Population Diversity

  Quelle ↗

  The Einstein Center for Population Diversity (ECPD) will study the consequences of increasing population diversity for social inequality and health disparities by focusing on the growing diversity of families, including changing conceptions and boundaries of the family itself. The family is a crucial, if not the primary, arena where inequalities are (re-)produced within and across generations, in and through the continuous interaction with social policy, the labor market, and educational institutions. Thus, changing family patterns and behavior are both a source of growing population diversity on the societal level and a driver of social inequality and wellbeing on the individual and household level. For example, when people get married, have children and divorce, they define the population structure. Union formation, marriage and union dissolution also have, however, immediate consequences for socialinequality, poverty, wellbeing and health of individuals and households. The strong relation between family patterns or family behavior and social inequality is maybe most obvious in the case of the large fraction of women who transit into poverty and welfare dependence after separation and divorce. It was also very evident during the COVID-19 pandemic, when families took over many tasks that are usually performed by the welfare state including care for children and care-dependent older adults. While this development was instrumental in maintaining key societal functions, it also put many families under intense pressure and strain, depending on the individual family constellation and its resources. The pandemic thus illustrates how families become the “place” where causes and consequences of population diversity and societal challenges play out. The ECPD will transcend disciplinary silos by linking biomedical sciences and social sciences to conduct collaborative research on the interrelations between family diversity, health, education, and social inequalities in aging societies. This will be done by a group of leading scholars in demography, sociology, medicine, psychology, and health sciences. The ECPD will be thus uniquely situated to investigate the biological, psychological, social, and environmental pathways and mechanisms as well as their interrelations operating at the family level. To unravel the longitudinal nature of the intra- and intergenerational effects of diversity in family trajectories and patterns, the ECPD will be committed to a holistic life course approach. Further, as a cross-cutting theme, the ECPD will investigate the role of global and regional crises and their multiple relations with population and family diversity. We will combine household panel data and register-based information with biomarker and genetic data to better understand biosocial pathways along the life course.

• Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

  2021

  Critical Care · DOI

  BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

• Effectiveness of an intensive care telehealth programme to improve process quality (ERIC): a multicentre stepped wedge cluster randomised controlled trial