Seed Grants

PRC Seed Grants bieten eine ausgezeichnete Gelegenheit für Bevölkerungsforschende an der UZH. Diese Grants zielen darauf ab, Innovationen zu fördern, Zusammenarbeit zu unterstützen und Projekte in der Bevölkerungsforschung in der Frühphase zu unterstützen. Individuelle Grants von CHF 10'000 bis 20'000 stehen für verschiedene projektbezogene Ausgaben zur Verfügung

Um mehr über die Bewerbungsanforderungen und den Einreichungsprozess zu erfahren, konsultieren Sie bitte das ausführliche PDF-Dokument.

PRC Seed Grants (PDF, 178 KB)

Das PRC unterstützt seit Januar 2024 drei innovative und kooperative Forschungsprojekte im Bereich der Bevölkerungsforschung. Alle Projekte befassten sich mit Gesundheitsfragen in verschiedenen Altersgruppen (Informationen zu den einzelnen Projekten auf Englisch).

Dr. Christina Haag (Institute for Implementation Science in Health Care),
Dr. David Bürgin (Jacobs Center for Productive Youth Development)

Recent population-based research has identified a rise of mental health problems in young people, often referred to as the ‘youth mental health crisis’. Accordingly, new insights on risk and protective factors in mental health development are urgently needed to understand the perspectives, concerns, and aspirations of young people today. One aim to achieve this aim is to ask young people directly and to then use mixed-method approaches to analyze their answers.

Traditionally, population-based research has relied heavily on quantitative measures, sidelining text data due to the time-intensive nature of such analyses. However, advancements in natural language processing (NLP) have transformed language analysis, allowing for efficient extraction of themes and emotions from extensive text data. Despite these breakthroughs, the integration of text and quantitative data in longitudinal population research remains largely unexplored. Yet, this holds potential for nuanced large-scale analyses of individual narratives in conjunction with conventional quantitative measures.

This project aims to showcase how insights into youth mental health can be enhance when seamlessly incorporating text assessments into longitudinal population research (compared to relying solely on standardized quantitative assessments). We will analyze qualitative text data from the prospective-longitudinal Zurich Project on the Social Development from Childhood to Adulthood (z-proso, PIs: Prof. Manuel Eisner, Dr. Denis Ribeaud, Prof. Lilly Shanahan) which investigates psychosocial development from childhood into adulthood. Specifically, this project will examine life events that adolescents and young adults deem highly significant. In doing so, the project will assess the emotional tone of these events, and aim to understand how such events evolve from adolescence to young adulthood. Additionally, the study will examine the worries and hopes about the future that young adults report, and how these hopes and worries, in turn, are associated with current mental health.

Co-led by early career researchers Dr. David Bürgin from the 'Risk and Resilience' research group at the Jacobs Center for Productive Youth Development (Prof. Lilly Shanahan) and Dr. Christina Haag from the Digital & Mobile Health Group at the Institute for Implementation Science in Health Care (Prof. Viktor von Wyl), this PRC seed grant aims to contribute new insights into risk and protective mechanisms in mental health development by leveraging population-based text-data with natural language processing.

PD Dr. Michael von Rhein (University Children's Hospital Zurich), Prof. Boris Quednow (Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital of the University of Zurich), Dr. Holger Dressel  (Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute), Corina S. Rüegg (Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute), Prof. Oskar Jenni  (University Children's Hospital Zurich)

In recent years, the number of children with special educational needs has increased significantly - both in elementary school and in early childhood. Obviously, more children than before have a global developmental delay, a language development disorder, an autism spectrum disorder, ADHD or other behavioral problems. This can be partly explained by medical advances: Thanks to improved medical care, many more children with significant risk factors (such as children with congenital heart defects or former premature babies) survive today than 20 years ago. These children are more likely to have developmental disorders or disabilities than children without such risks. However, these factors cannot fully explain the increase in numbers. Other possible explanations include changes in biological and environmental risk factors. Recently, a number of environmental toxins and pollutants have been suspected of having negative effects on health and development. An increase in parental consumption of medication and psychoactive substances could also be considered as a possible cause. Negative influences on early childhood development have also been described through experiences of social deprivation, psychosocial trauma or social isolation. However, systematic studies on the etiology of developmental disorders are rare, particularly regarding modifiable or even avoidable causes. We therefore plan to perform an umbrella review on environmental factors, drugs and toxins as risk factors for developmental delay in children, which will lay the grounds for clinical studies targeting specific factors potentially causing developmental disorders in early childhood.

PD Dr. Cesar Hincapié (Department of Epidemiology, Epidemiology, Biostatistics and Prevention Institute [EBPI], Balgrist University Hospital – University Spine Centre Zurich [UWZH])
Prof. Thomas Friemel  (Department of Communication and Media Research)

Collaborators: Dr. Léonie Hofstetter, Nathalie Schweyckart, Dr. Christian Brand, Prof. Mazda Farshad, Prof. Milo Puhan, Prof. Laura Rosella
 
The challenge
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are standard surgical procedures in Switzerland. The Swiss National Joint Registry (SIRIS) captures data on over 95% of these surgeries annually—approximately 22,000 THAs and 18,000 TKAs per year. Despite the typical lifespan of joint replacements being 25 years on average, a small proportion requires premature revision surgery imposing significant challenges for both patients and the healthcare system. Clinical prediction modeling and effective public communication of surgical outcomes is paramount for decision-making, encompassing perspectives from patients, surgeons, hospitals, and the health system. While clinical prediction models are proliferating, the optimal practices for communicating their results to the public and other stakeholders remain unclear.
 
The opportunity
This project presents a unique opportunity to leverage clinical prediction models for improved decision-making in total joint arthroplasty. These models may offer valuable insights into the risks and probable outcomes of THA and TKA, enhancing preoperative shared decision-making for all stakeholders. The incorporation of Bayesian belief networks may hold promise in identifying causal links between presurgical factors and outcomes, potentially serving as decision support tools to enhance communication between patients and surgeons. By incorporating end-user perspectives and facilitating public communication of these decision support tools, our project aims to foster transparency, build trust, and empower individuals to actively engage in their healthcare decisions.
 
Overall aim
The overarching aim of our project is to advance understanding of prediction modeling and public health communication concerning patient-reported outcomes and revision surgery after THA and TKA, using population-based SIRIS data and qualitative approaches.
 
Specific objectives
1.   To develop and validate prediction models for patient outcomes (health-related quality of life, pain intensity, and satisfaction) one year after THA and TKA.
2.   To develop and validate prediction models for premature revision surgery within 5 years after THA and TKA.
3.   To identify predictors of patient outcomes and revision surgery after THA and TKA using Bayesian belief networks as a clinical decision support tool.
4.   To qualitatively describe and assess the effects of public health communication related to prediction modelling on patients, surgeons, hospitals, and the health system.