Iris Pigeot

Digitalization has changed virtually every aspect of the health sector. Enormous amounts of health data are now generated almost by default and stored continuously by various organizations and institutions, which can be very useful for health research. In addition, the sharing and re-use of data is playing an ever-increasing role in science, which is also evident in the recognition of the FAIR principles. These state that scientifically usable data should be FAIR ( f indable, a ccessible, i nteroperable, and r eusable). Although some secondary and registry data sources are findable and accessible, full implementation of the FAIR principles requires more effort. At the same time, the international landscape of such data is very diverse and difficult to survey. Moreover, the advantages and potential of using these data have not yet been fully exploited. In this chapter, we will give an overview of important secondary data sources and registries available in Germany, Europe, and worldwide, highlight the advantages of using secondary and registry data for public health research, and provide an overview of methodological requirements and possibilities using record linkage. Finally, we provide perspectives on the use of secondary and registry data for digital public health to enable a widespread FAIRification of health data.

This chapter provides a summary overview and an outlook towards different pathways which the future of digital public health may take. We discuss three somewhat distinct potential scenarios, including a dystopian view, a slow transformation scenario and comprehensively digitalized public health landscape, while also summarizing core insights from different chapters of this book.

The global increase of overweight and obesity in children and adults is one of the most prominent public health threats, often accompanied by insulin resistance, hypertension, and dyslipidemia. The simultaneous occurrence of these health problems is referred to as metabolic syndrome. Various criteria have been proposed to define this syndrome, but no general consensus on the specific markers and the respective cut-offs has been achieved yet. As a consequence, it is difficult to assess regional variations and temporal trends and to obtain a comprehensive picture of the global burden of this major health threat. This limitation is most striking in childhood and adolescence, when metabolic parameters change with developmental stage. Obesity and related metabolic disorders develop early in life and then track into adulthood, i.e., the metabolic syndrome seems to originate in the early life course. Thus, it would be important to monitor the trajectories of cardio-metabolic parameters from early on. We will summarize selected key studies to provide a narrative overview of the global epidemiology of the metabolic syndrome while considering the limitations that hinder us to provide a comprehensive full picture of the problem. A particular focus will be given to the situation in children and adolescents and the risk factors impacting on their cardio-metabolic health. This summary will be complemented by key findings of a pan-European children cohort and first results of a large German adult cohort.

FAIRification of personal health data is of utmost importance to improve health research and political as well as medical decision-making, which ultimately contributes to a better health of the general population. Despite the many advances in information technology, several obstacles such as interoperability problems remain and relevant research on the health topic of interest is likely to be missed out due to time-consuming search and access processes. A recent example is the COVID-19 pandemic, where a better understanding of the virus’ transmission dynamics as well as preventive and therapeutic options would have improved public health and medical decision-making. Consequently, the NFDI4Health Task Force COVID-19 was established to foster the FAIRification of German COVID-19 studies.This paper describes the various steps that have been taken to create low barrier workflows for scientists in finding and accessing German COVID-19 research. It provides an overview on the building blocks for FAIR health research within the Task Force COVID-19 and how this initial work was subsequently expanded by the German consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health) to cover a wider range of studies and research areas in epidemiological, public health and clinical research. Lessons learned from the Task Force helped to improve the respective tasks of NFDI4Health.

Background In Germany, all citizens must purchase health insurance, in either statutory (SHI) or private health insurance (PHI). Because of the division into SHI and PHI, person insurance’s status is an important variable for studies in the context of public health research. In the German National Cohort (NAKO), the variable on self-reported health insurance status of the participants has a high proportion of missing values (55.4%). The aim of our study was to develop and internally validate models to predict the health insurance status of NAKO baseline survey participants in order to replace missing values. In this respect, our research interest was focused on the question to which extent socio-demographic characteristics are suitable for predicting health insurance status. Methods We developed two prediction models including 53,796 participants to estimate the probability that a participant is either member of a SHI (model 1) or PHI (model 2). We identified eight predictors by literature research: occupation, income, education, sex, age, employment status, residential area, and marital status. The predictive performance was determined in the internal validation considering discrimination and calibration. Discrimination was assessed based on the Area Under the Curve (AUC) and the Receiver Operating Characteristic (ROC) curve and calibration was assessed based on the calibration slope and calibration plot. Results In model 1, the AUC was 0.91 (95% CI: 0.91-0.92) and the calibration slope was 0.97 (95% CI: 0.97-0.97). Model 2 had an AUC of 0.91 (95% CI: 0.90-0.91) and a calibration slope of 0.97 (95% CI: 0.97-0.97). Based on the calculated performance parameters both models turned out to show an almost ideal discrimination and calibration. Employment status and household income and to a lesser extent educational level, age, sex, marital status, and residential area are suitable for predicting health insurance status. Conclusions Socio-demographic characteristics especially employment status and household income assessed at NAKO’s baseline were suitable for predicting the statutory and private health insurance status. However, before applying the prediction models in other studies, an external validation in population-based studies is recommended.

COVID-19 posed one of the greatest challenges to individuals and societies worldwide in recent decades. Public health research, epidemiological and clinical studies were essential to track the spread of SARS-CoV-2 responsible for the pandemic and its variants, to better understand the consequences for health and social life, and to identify effective treatment and vaccination methods. Such studies provided policy makers, industry, health care providers, and society with an empirical basis for containing and managing the pandemic and for making decisions that were based on the most recent data. Therefore, the COVID-19 pandemic excellently illustrates the relevance of data sharing and the importance of providing an effective infrastructure. From the researchers’ perspective, there were significant challenges associated with this request. In a very short time, numerous projects, studies, and networks had emerged to investigate the pandemic, making it increasingly difficult to maintain an overview. Such an overview would have been essential to coordinate research activities, avoid unplanned duplication of research, and to implement studies in a harmonized manner.

Modern research data management in biomedicine requires data literacy skills. The NFDI4Health consortium, the national research data infrastructure for personal health data, addresses this need with several different training concepts for clinical and epidemiological scientists. Both the institutional anchorage of the training and the thematic focus vary and can be assembled from a modular system as required. The aim is to enable multipliers to adapt and use the training modules through open educational resources. In addition, a competency profile for data stewards is under development to support the choice of required training in research institutions. The results will be fed into the NFDI “Education & Training” Section and will contribute to the Data Literacy Alliance in the future.