Leibniz-Institut für Präventionsforschung und Epidemiologie BIPS GmbH

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Use of digital technology to enhance participation in population-based studies for Public Health & Epidemiology
(2026-06-05)
Herrera Espejel, Paula Sofia
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Rach, Stefan
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Brannath, Werner
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Rach, Stefan
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Ahrens, Wolfgang
Advances in digital technologies represent alternative or complementary solution for generating, collecting, storing, processing, and exchanging health information in Public Health and Epidemiological (PHE) research settings. The expanding availability, affordability and accessibility of these tools has enabled the adoption and use of a wide range of hardware, software, and networking services throughout the entire research continuum. Among other capabilities, researchers are now able to collect personal and self-reported health data directly from the research subjects and their own environments. In particular, new information and communication technologies may be practically integrated as part of pre-analytical research stages, such as recruitment and data collection, with the purpose of improving the external and internal validity of sampled data used for statistical analyses and outcome generation. That is, digital technologies are integrated into the routine procedures of population-based studies (e.g., interviewing, invitation and consent material) with the double aim of increasing confidence over findings from available data and increasing the representativeness of such findings to the wider population under study to other settings beyond the study parameters. While these technologies certainly offer promising solutions to the current limitations of procedural stages in population-based research, their use is, however, not without caveats. For example, while technologies such as Machine Translation (MT) may support outreach to certain specific linguistically diverse communities, these technologies might exclude or offer less quality communication services to other specific communities due to differential accuracy across language translations. Moreover, while the availability of online survey technologies offers solutions to increase participation and reduce nonresponse (and, as such, to increase study generalizability and representativeness), these solutions may also generate measurement errors and reduce internal reliability of the data collected. For such reasons, continuous assessments of the technical, socio-economic, and ethical-legal maturity of digital technologies are essential to align their effective and efficient development, adoption and deployment with PHE objectives, such as the World Health Organization (WHO) Essential Public Health Operations. Moreover, the research on their use may also help to clarify their limitations and shed light on their actual potential (e.g., as to not misuse them and further health outcome divides among already disenfranchised communities). Against this background, this dissertation investigates how different digital technologies may serve to increase response in population-based studies from their research onset, namely at the points of initial study invitation and data collection. The cumulative dissertation presents four peer-reviewed manuscripts: three published prior to submission, and one published after, in March 2026. The dissertation addresses the use of digital technology to enhance participation in population-based studies for public health & epidemiology. Section I of the dissertation consists of three introductory chapter. In Chapter One, the dissertation introduces its reader to the current debate on the use of digital technologies in public health and epidemiological research, presenting the working rationale and objectives of each submitted manuscript. In Chapter Two, the dissertation offers an overview of the research concepts, definitions and working frameworks used throughout these manuscripts. Additionally, this chapter offers an overview and mapping of available literature on the use of digital technology throughout ten general research stages for developing population-based studies. Chapter Three summarizes the methods and results of the four submitted manuscripts, while presenting short commentaries and ancillary analyses. Section II presents the four manuscripts submitted as part of the cumulative dissertation. Chapter Four presents a scoping review of peer-reviewed literature published after 2016 on the use of use of MT technologies for disseminating PHE material to specific target audiences in population-based research and surveillance efforts (Herrera-Espejel and Rach 2023). Culturally and linguistically diverse (CALD) populations are often underrepresented in PHE studies. Such underrepresentation may lead to biased study results, limiting their generalizability in practice. Modern MT tools might offer a cost-effective solution to this problem. The review identifies 46 articles addressing the use of MT to facilitate dissemination of public health information to CALD communities. The review indicates that current commercial MT solutions, such as Google Translate and DeepL, may provide sufficiently accurate translations, when used along with pre- and post-editing efforts, namely for non-legally or ethically sensitive materials. As such, the Chapter discusses the limitations of current MT solutions to one-way communication between public health staff and addressed audiences. Questions for future research include: assessing how machine-translated texts are received by different segments of target populations; and understanding the effectiveness of MT for specific two-way communication material, such as participant enrollment, informed consent, and response materials in general. Chapter Five exhibits a cluster randomized control experiment (Herrera-Espejel et al. 2025), which was embedded in the World Health Organization European Childhood Obesity Surveillance Initiative in second-and third-grade classrooms in the cities of Bremen and Bremerhaven, Germany. The study offers an evaluation of the effectiveness of a co-developed physical-digital multilingual outreach solution, namely, flyer with a QR-code linking to multiple language translation of the original study invitation. Based on multilevel mixed-effects logistic regression models, the study measures the extent to which the inclusion of the flyer in the study invitation was positively associated with the response and eventual study participation proportions of linguistically diverse eligible participants, supporting more equitable participation. While the presence of the flyer was associated with increased participation and response of linguistically diverse households from medium-level socioeconomic backgrounds, its effect was not significant for children studying in schools in low- nor high-levels. In view of these results, the study in Chapter Five underscores the importance of tailored approaches to increase engagement among underrepresented communities in population-based health research, as well as the importance of using appropriate statistical methods to investigate the differential outcomes of digitally-based interventions across socio-economic and linguistic subgroups within heterogenous populations. Chapter Six offers an exploratory analysis and evaluation of the effect of using two concurrent interviewing modes on the collection of self-reported symptoms in population-based studies. As was the case of the CoVerlauf study (Rach et al. 2023), two inherently different interviewing modes were used to collect participant data on post-Covid symptoms at their time of infection and interview: Computer-Assisted Website Interviewing and Computer-Assisted Telephone Interviewing. The Chapter explores and discusses the extent of selection effects and measurements effects cause by the use of two modes. The use of the two modes was in itself advantageous to reduce nonresponse, as well broaden the coverage and response of eligible study participants which would have otherwise (e.g., in a single-mode design) not engaged in the study activities. However, due to the inherent characteristics of each mode, the mixed-mode design potentially introduced parallel mode-specific measurement errors, blurring the comparability of items collected through the different information exchange methods at the point of data collection. [This chapter presents the submitted manuscript prior to its publication. The published version is available online (DOI: 10.2196/80631)]. Chapter Seven explores how digital tools might unintentionally increase health inequalities, especially among different socio-economic, gender, and underserved groups. It highlights how the unequal access, use and effectiveness of digital technologies may widen differential health outcomes. The chapter underscores the importance of involving targeted subgroups in the design and development of health interventions to reduce nonresponse or differential response due to the use of digital technology. Section III consists of three conclusive chapters. In Chapter Eight, the dissertation discusses the overall research contributions and limitations for each of the previous chapters. In Chapter Nine, the dissertation offers overall research reflections after completion of the writing of the manuscripts included. Specifically, the dissertation shares final reflections on the importance of linguistic inclusiveness and fairness in PHE participant material (e.g., invitations, consent forms, questionnaires, etc.). It also discusses insights that might be gained from language demographics in Germany based on available data from the Statistische Bundesamt from August 2024, and the foreseeable paradoxes of using digital technology for inclusion. The final Chapter Ten summarizes the overall research conclusions of the dissertation, concluding that, while digital technologies may significantly enhance participation and response in population-based studies, they also risk exacerbating existing inequalities and creating new forms of divides due to limitations in their technological readiness. The dissertation argues that the evaluation and continuous investment in training PHE researchers to understand intersectionality theory and adopt quantitative methods that reflect the superdiversity of populations under study is crucial to improve both generalizability and the reliability of findings in the field. A last section IV organizes the appendixes from Chapter Four, Five and Six.
Dissertation
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Characterizing treatment of lung cancer patients in Germany based on real-world data with a special focus on novel therapies
(2026-07-03)
Nikolaj Rischke
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Griesinger, Frank
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Berger-Höger, Birte
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Haug, Ulrike
Many treatment options are available for the treatment of lung cancer, such as surgery, radiotherapy and systemic therapy. In the last two decades in particular, the field of available drugs has been steadily expanded hrough the development and approval of targeted therapies and immunotherapeutics. Historical cohort studies based on insurance data were conducted to characterize therapy patterns of lung cancer patients in Germany. In the first study, patients were described with regard to all relevant therapy options, with a focus on age-related differences. The second study analyzed the utilization of targeted therapies (EGFR, ALK and BRAF inhibitors) and determines whether there are regional differences in the utilization of these drugs within Germany. The third study examined the utilization of immune checkpoint inhibitors and describes the occurrence of serious immune-related adverse events.
Dissertation
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Anwendung von direkten oralen Antikoagulantien im realen Versorgungsgeschehen unter besonderer Berücksichtigung von Risikoprofilen
(2026-07-17)
Voss, Annemarie
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Hoffmann, Falk
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Schmiemann, Guido
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Haug, Ulrike
Orale Antikoagulantien (OAKs) werden zur Schlaganfallprophylaxe und Prävention systemischer Embolien bei erwachsenen Patienten mit nicht-valvulärem Vorhofflimmern (nvVHF) verwendet. Zur medikamentösen Therapie stehen zwei OAK-Gruppen zur Verfügung: Vitamin-K-Antagonisten (VKAs, z. B. Phenprocoumon) und direkte orale Antikoagulantien (DOAKs). In dieser Doktorarbeit wird analysiert, wie sich die Risikoprofile und Wechselmuster von OAK-Neunutzern über die Jahre von 2011 bis 2019 verändern. Angesichts des anhaltenden Bedarfs an Real-World-Daten zum Vergleich von DOAKs und Phenprocoumon sind diese Erkenntnisse essentiell für die Interpretation aktueller Studien, sowie die Konzipierung neuer Studien. Hierzu wurden drei Kohortenstudien basierend auf Versichertendaten durchgeführt. Es wird deutlich, dass DOAKs zunehmend den Standard der Erstverordnung darstellen, während Phenprocoumon an Bedeutung verliert (2012: 64 % vs. 2019: 5 %). Gleichzeitig verändern sich die Grundcharakteristika der Patientengruppen hinsichtlich Alter, Komorbiditäten, Schlaganfall- und Blutungsrisiko je nach Wirkstoff und Jahr. Es zeigten sich Unterschiede in den Risikoprofilen zwischen neuen Nutzern verschiedener DOAKs und erhebliche Veränderungen bei neuen Phenprocoumon-Nutzern. Speziell für das DOAK Rivaroxaban konnten Unterschiede zwischen Deutschland und den Niederlanden als Teil der Rivaroxaban Post-Approval Safety-Studie festgestellt werden. Die Häufigkeit von Wirkstoffwechseln unterscheidet sich deutlich zwischen den DOAKs: von 7 % bei Apixaban bis 21 % bei Dabigatran, wobei fast die Hälfte der Wechsel hin zu Apixaban erfolgte. Auch hier lassen sich unterschiedliche Risikoprofile zwischen Wechslern und kontinuierlichen Nutzern beobachten. Die beobachteten Unterschiede müssen in Beobachtungsstudien zwingend methodisch berücksichtigt werden, um Verzerrungen wie zum Beispiel Confounding, Channeling Bias, oder Selektionsbias und damit Fehlschlüsse zu vermeiden.
Dissertation
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Missing Data in Machine Learning -- Tree-based generative imputation, uncertainty in explanation and data augmentation in healthcare application
(2026-06-12)
Golchian, Pegah
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Wright, Marvin N.
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Laabs Björn-hergen
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Wright, Marvin N.
Healthcare datasets are often affected by missing data and data scarcity, which require careful handling to ensure reliable analysis. Missing data is typically addressed with imputation methods, which fill the missing values with point estimates. The literature distinguishes between single imputation, which outputs one complete dataset and analyzes the dataset once, and multiple imputation, which outputs several imputed datasets, analyzes each dataset, and pools the results into a point estimate and a standard error. Novel imputation methods based on machine learning are usually only introduced for single imputation, which is convenient but does not account for imputation uncertainty as multiple imputation does. Data scarcity is usually addressed by data augmentation, for example, by synthesizing new data. Both missing data imputation and synthesizing data can be solved with generative models, which have recently seen great success in generating text and images. Most of the proposed methods, however, are deep-learning-based, which is not always the best approach for tabular data, which this work focuses on. Here, tree-based machine learning methods traditionally perform well, and there are also some generative models that offer promising solutions for data synthesis and density estimation, such as adversarial random forests (ARF). This thesis discusses handling missing values in a machine learning pipeline, from prediction to explanation. I examine the role of missing data, how it is addressed in machine learning models, and the effects of missing data on model explanations. I focus on the application of generative modeling in this field and connect imputation and generative modeling. My contribution to the field is a novel imputation method missing value imputation with adversarial random forests (MissARF), based on ARF, which offers competitive imputation performance in both single and multiple imputation at no additional computational cost in multiple imputation. Second, I discuss imputation uncertainty in post-hoc global explanations and show that only multiple imputation achieves good coverage. Lastly, I examine a real data example from the field of geriatrics, where data is scarce, and the aim is to improve the predictive performance through data augmentation. I compare two approaches, synthesizing the data and combining the dataset with a related dataset with imputation.
Dissertation
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Development and application of methods to assess potentially harmful drug use during pregnancy and in childbearing age based on the German Pharmacoepidemiological Research Database (GePaRD)
(2025-04-24)
Wentzell, Nadine
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Zeeb, Hajo
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Hoffmann, Falk
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Haug, Ulrike
Ziel dieser kumulativen Dissertation war es, zur Entwicklung von Methoden zur Untersuchung potentiell schädlichen Arzneimittgebrauchs in der Schwangerschaft und bei Mädchen und Frauen im gebärfähigen Alter basierend auf der pharmakoepidemiologischen Forschungsdatenbank (GePaRD) beizutragen und diese exemplarisch auf die Beschreibung der Anwendung der bekannten Teratogene Valproat und Methotrexat anzuwenden. Im Rahmen der Dissertation wurde ein bestehender Algorithmus zur Identifizierung des Schwangerschaftsausgangs in Hinblick auf Lebendgeburten optimiert und validiert. Ein Algorithmus zur Schätzung des Schwangerschaftsbeginns basierend auf dem erwarteten Entbindungsdatum wurde für Lebendgeburten entwickelt und auch intern validiert. Die Anwendung der Methoden lieferte wichtige Informationen bezüglich der Anwendung von Valproat, d.h. einen rückläufigen Trend bei valproatexponierten Schwangerschaften und eine Zunahme der Verwendung alternativer und sichererer Arzneimittel bei Frauen mit Epilepsie, eine abnehmende Verordnungsprävalenz von Valproat bei Mädchen und Frauen im gebärfähigen Alter und Potenzial zur weiteren Verringerung der Anwendung bei anderen Indikationen als Epilepsie. Für Methotrexat wurde eine beträchtliche Anzahl exponierter Schwangerschaften identifiziert und es wurden lebend geborene Kinder mit schwerwiegenden angeborenen Fehlbildungen beobachtet. Die in dieser Dissertation entwickelten, exemplarisch angewendeten und weiter diskutierten Methoden bilden die Grundlage, um GePaRD für die Untersuchung potentiell schädlicher Arzneimittelanwendung in der Schwangerschaft und im gebärfähigen Alter zu etablieren. Weiterführende Studien basierend auf diesen Methoden werden wichtige Erkenntnisse zu diesem public health relevanten Thema beitragen.
Dissertation
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Applied Machine Learning in Epidemiology: Feature Selection, Benchmarks, and Software
(2026-06-17)
Burk, Lukas
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Wright, Marvin N.
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Boulesteix Anne-laure
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Wright, Marvin N.
Predictive modeling is central to epidemiology, biostatistics, and adjacent fields. The number of available algorithms keeps growing, alongside related techniques to make sense of them, leaving practitioners with three common questions: Which algorithm should they choose, which features are actually relevant, and if so, how relevant are they, in which way? This cumulative dissertation addresses these questions with three main contributions focusing on empirical evaluation and open-source software. The first contribution presents the most comprehensive large-scale neutral comparison study of survival prediction algorithms on low-dimensional data to date, evaluating 19 algorithms on 34 datasets with two distinct tuning measures and a total of six evaluation measures. After extensive hyperparameter tuning via Bayesian optimization, the study does not find any method to statistically significantly outperform the Cox proportional hazards model in aggregate. Sensitivity analyses using the Plackett-Luce model indicate that violation of the proportional hazards assumption, or generally misspecification of the Cox model, changes this picture. Model-based boosting and oblique random survival forests are among the methods that gain an advantage when these assumptions are violated. The second contribution introduces a novel method for feature selection in competing event settings, named cooperative penalized regression (CooPeR). The method builds on the feature-weighted elastic net, which is used to fit cause-specific penalized Cox models in a manner that reduces penalization for features with large effect on the respective other event, while also amplifying the penalization of noise features. The method's effectiveness is demonstrated in a simulation study mimicking gene-sequencing data and in a real-data application. The third contribution presents \xplainfi, an \rstats package for feature importance analysis with a unified and modular interface. The package supports perturbation-based, refitting-based, and Shapley-based importance measures while supporting both model- and learner-importance. The package also supports multiple statistical inference approaches, including the conditional predictive impact with either model-X knockoffs or adversarial random forests. Empirical benchmarks demonstrate correctness of the results alongside reduced runtime compared to selected reference implementations. Five secondary contributions complement the three primary research areas, covering survival analysis with an overview of reduction techniques, work on performance evaluation, and algorithmic fairness. Other contributions address conditional feature importance, and community-driven machine learning algorithm integration in the mlr ecosystem
Dissertation
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Exploring community participation in digital health tool implementation to enhance health equity in low and middle-income countries and humanitarian contexts
(2026-03-10)
Benson, Jennifer
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Zeeb, Hajo
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Böckmann, Melanie
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Fuhr, Daniela
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Gerhardus, Ansgar
Health inequities, arising from socio-economic, political, environmental, and technological determinants, are the unfair differences between population groups. These inequities are widespread within low and middle-income countries and humanitarian contexts and can result in uneven health outcomes. Digital health offers potential to overcome complex health challenges in these contexts. Community participation involves collaborating with communities to address real-world needs. However, superficial participation and digital exclusion can undermine digital health benefits and exacerbate inequities. This research explored how community participation has been adopted as an implementation mechanism for digital health tool development in low and middle-income countries (LMIC) and humanitarian contexts. This research comprised three methodological phases: A systematic scoping review synthesised relevant scholarly literature on this topic. Qualitative interviews with digital health experts and humanitarian practitioners outlined contemporary digital health implementation practices in relation to participation and responded to knowledge gaps identified within the literature. This was followed by an in-depth examination of a unique and intentional community participatory method to develop the ‘Oky’, a smartphone digital health tool to support adolescent girls with menstruation to indicate what is feasible in this regard. The literature indicated a dearth of attention to community participation in digital health tools. Authorship and implementation were largely led by Global North authors, indicating international power dynamics over low-and-middle-income challenges. The interviews revealed that community participation did occur in various forms in practice; however, it tended to be ad hoc, superficial and uneven, due to contextual and organisational factors. The unique community participatory method highlighted the feasibility of community participation in the implementation of digital health tools in diverse low and middle-income and humanitarian contexts. However, its success depended upon contextual, implementation and collaborative conditions. This research found that community participation implementation norms vary in intensity, inclusion, and influence in the development of digital health tools. Current practice highlights that, in general, community participation in digital health implementation is undervalued, under-supported, and underreported. However, when intentionally undertaken and appropriately resourced, community participation is possible in LMIC and humanitarian contexts and can contribute towards health equity gains.
Dissertation
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Constraint-based causal discovery with tiered background knowledge
(2025-03-11)
Bang, Christine
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Magliacane, Sara
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Didelez, Vanessa
This thesis explores how information about temporal structures can improve causal discovery methods. I consider extensions of existing constraint-based causal discovery algorithms: The PC, FCI, and IOD. These algorithms estimate graphs based on (conditional) independence testing, and are originally purely data-driven. However, often we have more information available than only the dependence structure. This I refer to as background knowledge, and this can be used to extend and improve the existing algorithms. In this thesis, I focus on information given by a temporal order, e.g. in which order variables are measured. This entails a special kind of background knowledge, which I refer to as temporal, or tiered, background knowledge. The fact that the use of tiered background knowledge improves causal discovery methods appears evident. The novel contribution of this thesis is a thorough investigation of the ways in which the algorithms are improved. This includes formal results and examples, as well as empirical results using both simulated and real data. The improvements due to tiered background knowledge fall into one of two categories: Informativeness and accuracy. Constraint-based causal discovery suffer from an under-identifiability of the causal structure, since multiple graphs may encode the same dependence structure. An improvement in informativeness means an increased certainty of the causal connections. In practice, the output graphs are often incorrect due to errors arising from independence testing using finite sample data. An improvement in accuracy means that the estimated graphs are less prone to errors. This thesis presents a formalisation of (tiered) background knowledge, and results on how it improves constraint-based causal discovery in different aspects under different assumptions: First, I assume that all relevant variables are observed, and that we are given the correct (conditional) independencies among the observed variables. I give a criterion for when an increase in informativeness is obtained by adding tiered background knowledge. This criterion suggests that adding background knowledge of early tiers yields the largest increase in informativeness, with the overall largest increase for sparse graphs. This is supported by the results of a simulation study. Moreover, I show that the graphical output has some desirable interpretational and computational properties. Second, I relax the assumption of knowing the correct (conditional) independencies among the observed variables. However, I still assume that all relevant variables are observed. I consider the properties of the existing tiered PC (tPC) algorithm. This is an extension of the original PC, which skips some conditional independence tests and orients some (additional) edges, both based on tiered background knowledge. I show how this improves the accuracy. Third, I again assume knowledge of (conditional) independencies among the observed variables. However, I allow for unobserved variables, and I combine multiple overlapping datasets. I describe the existing tiered FCI (tFCI) and introduce the novel tiered IOD (tIOD), which both extend the existing algorithms with tiered background knowledge, similar to the tPC. The output of the IOD algorithm consists of multiple graphs, which implies an additional level of under-identifiability. I show that tiered background knowledge can decrease this number of graphs. Lastly, I discuss how the results presented here relate to similar work, as well as some open problems and possible extensions. All algorithms are provided as pseudo-algorithms, and are accompanied by proofs of soundness, and often also completeness.
Dissertation
79 222
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Survival after childhood cancer: patterns, temporal trends and inequalities with a focus on Germany
(2026-03-13)
Wellbrock, Maike
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Haug, Ulrike
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Dalton Susanne Oksbjerg
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Erdmann, Friederike
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Zeeb, Hajo
Background: Although the incidence of cancer in children is low, it constitutes a matter of considerable public health relevance. Childhood cancer is the leading disease-related cause of death among children in high-income countries (HICs). As primary preventive measures are not available, improving survival probabilities remains the primary goal. Although survival has increased markedly over the past decades, prognosis still differs across diagnoses and is influenced by a range of clinical factors. Moreover, a growing body of studies observed poorer survival among children from families of lower socioeconomic position (SEP), even within HICs. Since evidence from Germany is largely lacking, this PhD project sought to comprehensively assess childhood cancer survival patterns in Germany and, as a complement and for comparison purposes, childhood leukaemia survival patterns in Sweden. The objectives of this PhD project were addressed through four independent, yet content-related studies, which together form the basis of this cumulative dissertation. Methods: We used data from the German Childhood Cancer Registry to analyse temporal patterns of overall survival (OS) from childhood cancer as well as to assess potential survival inequalities in association to area-based SEP, we assigned calendar-year-specific values of the German Index of Socioeconomic Deprivation (GISD), developed and published by the Robert-Koch Institute, to the cancer diagnoses via municipality code. The association between SEP and survival from childhood leukaemia in Sweden was assessed at both the individual and area-based level. Individually linked data on leukaemia cases and parental SEP were obtained through the use of the Swedish civil register infrastructure. Using Joinpoint Regression Program we performed time trend analyses of OS estimates. Univariable and multivariable Cox proportional hazard models were fitted to assess potential survival inequalities in both Germany and Sweden. For Sweden in particular, we considered individual-level and area-based covariates separately to reveal their individual contribution to survival in multivariable models as well as period-specific patterns. Results: For Germany, we observed increasing five-year OS for all cancer types combined reaching 86.5 % in 2011-2016, with strongest survival improvement for acute myeloid leukaemia (AML). Still, five-year OS from malignant CNS tumours (among others) showed only modest improvements and recently plateaued at below 80 %. We found little evidence for social inequalities in childhood cancer survival in Germany. However, while our findings indicated survival disadvantages for children with AML residing in more deprived areas, we found the opposite for children with CNS tumours. Results from Sweden revealed survival inequalities for lymphoblastic leukaemia in relation to both, individual and area-based SEP. Notably, most associations were only evident in more recent years. Conclusions: Substantial enhancements, including the development of highly standardised treatment protocols for most cancer types, have likely contributed to overall improvements in survival. The observed null association between area-based SEP and childhood cancer survival in Germany was contrary to our initial expectations. This finding may, in part, reflect the dense network of treating hospitals across Germany, which could compensate for any socioeconomic inequalities. Moreover, the use of the GISD may have contributed to inconclusive results, as this composite score is limited by the municipalities’ comparatively coarse and disproportionate geographical resolution. German municipalities vary considerably in size, extending from small villages to major cities such as Berlin or Hamburg, which have populations in the millions and exhibit extensive social diversity. On the contrary, we observed survival inequalities among children with leukaemia in Sweden, where analyses were based on both individual-level and high-resolution area-based SEP information. In order to identify potential survival differences that may also exist in Germany, access to individual data for epidemiological research continues to be urgently required. The observed period-specific pattern of more pronounced survival inequalities in relation to SEP may be related to the increasing social and cultural diversity in both Sweden and Germany, which clearly underscores that healthcare provision for children with cancer will remain a matter of high public health relevance in the future.
Dissertation
29 34
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Radiation response in fibroblasts of long-term survivors of childhood cancer with and without second primary neoplasms
(2025-07-07)
Grandt, Caine Lucas
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Zeeb, Hajo
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Stopsack Konrad H.
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Aleksandrova, Krasimira
The past decade has brought an increase in long-term survival of childhood cancer, accompanied by a rise in second primary cancers later in life. Despite active research efforts the causes for secondary primary neoplasms in childhood cancer survivors remain unclear. High penetrance mutations and anti-cancer cytostatics can explain some, but not all cases. A dysfunctional cellular radiation response may explain further cases as a response to radiation therapy against the first cancer entity. Therefore, the aim of this dissertation was to expand the role of the radiation response in second neoplasms of childhood cancer survivors. Thus, we measured radiation-induced transcriptomic changes in a large (N=156, n=52 per group) collective of sampled human fibroblasts as part of the nested case-control study KiKme. Here, the transcriptome in fibroblasts of cancer-free controls, long-term survivors of childhood cancer with and without second primary neoplasms was measured using RNASeq and compared after exposure to a low (0.05 Gray) or a high (2 Gray) radiation dose. The groupwise differences and variability in expression, as well as subsequent functional implications were identified using established and novel bioinformatic pipelines. There were two key findings after exposure to 0.05 Gray. First, the degree of p53 signaling pathway-activity ranged from most in controls to least active in survivors with second primary neoplasms. Second, the latter showed abnormalities in the cell fate decision, potentially indicating an impaired functioning. Genomic damages may not be removed by the cell, accumulate, and drive carcinogenesis. Another highlight of this work was the identification of the lncRNAs AL109976.1 and AL158206.1 to be markedly upregulated and functionally involved in the radiation response post-2 Gray. In future efforts, these may be potent targets for modulating tumor radiation sensitivity. Additionally, the interplay of the identified key players with further OMICs levels needs to be investigated next. Such efforts may help identifying patients at risk for immediate adverse reactions to ionizing radiation.
Dissertation
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