Measuring objective and subjective well-being: dimensions and data sources

AbstractWell-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development

Measuring well-being through novel digital data

Well-being is an important value for people's lives, and it is crucial for societal progress. Considering that well-being is a vague and multi-dimensional concept, it cannot be captured as a whole but through a set of health, socio-economic, safety, environmental, and political dimensions. The current Ph.D. thesis focuses on the safety dimension, and in particular on peace, which is an emerging challenge nowadays. Peace is the way out of inequity and violence, and its measurement is crucial, considering that the world is constantly under socio-economic, political, and military instability. Novel digital data streams and AI tools foster peace studies during the last years. Following this direction, we exploit information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture the Global Peace Index (GPI), a well-known official peace index. Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a higher frequency than the offcial yearly index cost- and time-efficiently. Additionally, we conduct variable importance analysis, and we use explainable AI techniques to understand better the models' behaviour, peace, and its determinants. This in-depth analysis highlights each country's profile and explains the predictions, prediction errors, and events that drive these errors. We believe that novel digital data exploited by researchers, policymakers, and non-governmental organisations, with data science tools as powerful as machine learning, could maximize the societal benefits and minimize the risks to peace and well-being as a whole

Multiple osteolytic lesions due to Double-Expressor Primary non‑Hodgkin Lymphoma of the Bone

Primary non-Hodgkin lymphoma of the bone (PLB) is a rare type of non-Hodgkin’s lymphoma (NHL) that affects the skeletal system with or without regional lymph node involvement. We present the case of a 74-year-old female patient with pain due to multifocal osteolytic lesions. The diagnosis of diffuse large B-cells (non-GCB) phenotype was made by clinical, laboratory, histopathological examination accompanied by an extensive immunohistochemical profile of one of the skeletal lesions