Research Lander Willem

Abstract

This project proposes a methodological framework in the context of respiratory pathogens with pandemic potential, based on historical data of SARS-CoV-2. Clustered social contact patterns have been pivotal in combination with stochasticity to explain disease spread and heterogeneous behaviour. Therefore, we focus on mathematical models that accommodate heterogeneity in infection acquisition and additional randomness at the individual level. However, estimation of key epidemiological parameters based on stochastic and computationally intensive individual-based models is challenging. Especially when we focus on multiple outcomes, which is required when evaluating the health economic impact of preventive measures. A coarse-grained cost-effectiveness analysis is possible through individual-based modelling, yet complicated by a cascade of uncertainties and stochasticity in the underlying disease process. The availability of options to define the most (cost-)effective scenario requires multi-criteria selection techniques. Machine learning methods have been proven useful for this, however, this complex modelling context requires progressive algorithms. Informing the decision making process is particularly challenging in an epidemic setting with unexpected events such as the emergence of new variants of concern. We aim to accelerate decision making in the next pandemic with a public health framework grounded in advanced statistics, health economics and computer sciences.

Researcher(s)

• Promoter: Willem Lander

Research team(s)

• Primary and interdisciplinary care Antwerp (ELIZA)

Project website

Project type(s)

• Research Project

Abstract

Most traditional youth mental health interventions fail to achieve sustainable impact at scale because they overly rely on individualized, medical illness-focused models and treat youth as passive beneficiaries. citiesRISE, a multi-stakeholder initiative founded in 2017 to address these gaps, has worked with youth, communities, and professionals across five cities, as well as social innovators in over twenty countries, to develop a set of evidence-based, scalable youth mental health interventions and implementation models. The YiPEE project aims to provide robust evidence on the feasibility, adaptability, effectiveness, and cost-effectiveness of a multi-component intervention targeting the inner, social, and environmental dimensions that underpin mental health and broader NCD risk reduction outcomes, when implemented using a youth-informed and -activated approach. This will be achieved through a a mixed-methods approach, conducting a realist evaluation across the four sites (Chennai, India; Nairobi, Kenya; Cape Town, South Africa; Stockholm, Sweden) to study key implementation outcomes following the Practical Robust Implementation and Sustainability Model (PRISM) as well as the mechanisms underlying why the intervention works, for whom, and under what real-world conditions. In Chennai, YiPEE will conduct a randomized controlled trial for more robust evaluation of effectiveness and cost-effectiveness in achieving key mental health and other NCD related lifestyle behavioral outcomes. YiPEE focuses on the combination of a school-based multicomponent intervention targeting positive disruption in the inner, social, and environmental dimensions of adolescents' mental health and a youth-informed and -activated implementation model that puts young people at the centre of transformation, working collaboratively with a range of other stakeholders.

Researcher(s)

• Promoter: van Olmen Josefien
• Co-promoter: Willem Lander
• Co-promoter: Wouters Edwin

Research team(s)

• Primary and interdisciplinary care Antwerp (ELIZA)

Project type(s)

• Research Project

Abstract

Healthcare services such as acute care hospitals are confronted with challenges to attract and retain nurses in order provide high quality of care and a safe environment for patients. Currently, the nursing profession is considered as a bottleneck profession. Balance Nursing Teams or BNuT is a software-as-a-service (SAAS) which allows informed strategic, tactical, and operational decision-making by using relevant and available data on nursing teams and their patients. The aim of BNuT is to align team composition (numbers, skill mix, qualifications, and competencies) with components of team capacity, reliability, and efficiency to guarantee desirable team and patient outcomes. This PhD-project will examine how nursing team should be composed and has three research objectives: the relationships between team composition on the one hand and the other key capacity and efficient and reliable performance factors and how do they jointly impact nursing care team outcomes; identify the key parameters within the BNuT database to explain differences in team and patient outcomes and how to utilize knowledge on key concepts and parameters to improve nursing teams over time. The study sample contains 12 acute care hospitals that are using the BNuT system. For each hospital 8 nursing units have been selected such as medical or surgical nursing units, combined medical and surgical nursing units and geriatric nursing units. BNuT implementation started in four hospitals in February 2021. This means the PhD applicant will use the existing dataset together collected data up to October 30th, 2024 to achieve a dataset with a duration of at least two years. To our knowledge, the proposed project's aim is new, innovative and neither studied nor implemented in practice nationally or internationally. Our findings will provide a better insight for decision makers in healthcare organizations as well as policy makers to understand how to put in place resources to achieve desirable outcomes in healthcare. By examining a wide variety of compositional characteristics as well as key team-level context factors using data mining and scenario analyses, we can create a better theoretical understanding of what constitutes an optimal nursing team composition.

Researcher(s)

• Promoter: Van Bogaert Peter
• Co-promoter: De Meulenaere Kim
• Co-promoter: Willem Lander
• Fellow: Vleminckx Senne

Research team(s)

• Centre for Research and Innovation in Care (CRIC)

Project type(s)

• Research Project

Abstract

The current state-of-the art advocates for a biopsychosocial rehabilitation approach for persistent pain after breast cancer treatment. Within this approach pain science education is combined with promotion of an active lifestyle based on self-regulation techniques. We argue for testing an innovative eHealth self-management support program for this purpose. The assumption is that this delivery mode reduces barriers to pain self-management support, through bringing timely support near to people, creating a safe environment as opposed to hospital settings, providing a multidimensional support model taking into account the biopsychosocial needs of patients, and lowering costs. This program can provide patients with the knowledge, proactive, cognitive and self-management skills to master their situation and journey towards less pain and pain-related disability and participation in normal life again. Therefore, the general aim of the proposed project is to investigate the effectiveness of an eHealth self-management support program for pain-related disability (I) in breast cancer survivors with persistent pain (P). The program makes use of an innovative chatbot format for delivering pain science education and motivating and monitoring physical activity. The eHealth program is automated and personalized using comprehensive decision-tree-based algorithms in order to promote pain self-management support. The primary scientific objective of the study is to determine the effectiveness of this eHealth self-management support program for persistent pain after breast cancer treatment compared to 1) usual care (i.e. superiority of the eHealth self-management support program) (C1) and 2) a comprehensive pain rehabilitation program delivered face-to-face in a physical therapy setting (i.e. non-inferiority of the eHealth self-management support program) (C2) on pain-related disability (O).

Researcher(s)

• Promoter: De Groef An
• Co-promoter: Meeus Mira
• Co-promoter: van Olmen Josefien
• Co-promoter: Wens Johan
• Co-promoter: Willem Lander

Research team(s)

• Movement Antwerp (MOVANT)

Project type(s)

• Research Project

Abstract

Health economic evaluations based on mathematical modelling provide many opportunities in the fields of epidemiology, health care management and quality of care. The aim of this research project is to apply and extend model-based health economic research to primary care practices, nursing teams, infectious disease management and multi-morbidity. Part 1 targets out-of-hours care in Belgium, which is increasingly organized in general practitioner cooperatives. They provide a valuable addition to more expensive emergency departments within the hospital setting. Part 2 targets nursing teams in the acute hospital setting and the cost-effectiveness of interventions based on the decision support instrument BNuT. The aim is to evaluate the costs of different nursing team compositions and interventions to improve the team performance and patient outcomes. Part 3 aims to improve the use of stochastic individual-based modelling for health economic evaluations. Structural issues on the interplay between uncertainty and stochasticity will be investigated with case studies for COVID-19. Part 4 targets economic evaluations of non-communicable and chronic diseases in ageing populations.

Researcher(s)

• Promoter: Willem Lander
• Fellow: Willem Lander

Research team(s)

• Primary and interdisciplinary care Antwerp (ELIZA)

Project type(s)

• Research Project

Abstract

The Phd project will focus on the organization and cost-effectiveness of out-of-hours and withinhospital care. For out-of-hours care, patients can choose in Belgium between general practitioner (GP) services and the hospital's emergency department (ED) without former contact or referral. Every ED in Belgium needs to give appropriate care to anyone entering the service and overuse of ED is of concern. The organization of out-of-hours care within GP cooperatives has improved accessibility, sustainability and safety of primary care as trustworthy alternative for EDs. An important difference for the patient is that there is no direct payment at the ED as compared to GP services. Although medical care is largely reimbursed by medical insurance in Belgium, out of pocket payment accounts for approximately 25% of health expenses. In 2014, a database infrastructure iCAREdata was established to link data from GP Cooperatives, ERs and pharmacies during out-of-hours care. Within the doctoral project, the aim is to extent iCAREdata with costing data and extend this valuable initiative with health economic opportunities. Nursing shortage is one of the greatest challenges in global health care. Nurses from the baby boom generation will soon leave the labor market and many younger nurses consider leaving the occupation. In addition, the need for individualized care is increasing and requires additional qualifications of the nursing staff. The computerized decision support instrument "BNuT" aims to support nursing team management based on hard and soft data on nursing teams and patient care. The aim within this Phd project is to evaluate the cost-effectiveness of nursing team interventions in terms of staff numbers, qualifications, competencies, and demographics. The PhD candidate will contribute to the BNuT project by the introduction of cost data. There is already hospital data available from the prototype, though more data will become available soon with the valorization process of the tool.

Researcher(s)

• Promoter: Willem Lander
• Fellow: Phiri Jane

Research team(s)

• Primary and interdisciplinary care Antwerp (ELIZA)

Project type(s)

• Research Project

Abstract

Multiple Sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease leading to focal and diffuse damage of myelin sheath and axons in the central nervous system (CNS). Pathophysiologically, the adaptive and innate immune system are involved in the inflammatory process, while mitochondrial dysfunction, oxidative stress and failure of remyelination are the main mechanisms in chronic neurodegeneration. Despite currently available disease modifying treatments (DMTs) that target the immune system, patients continue to accumulate disability leading to progression. Unfortunately, no neuroprotective or remyelinating agents are available as therapy for progressive MS. Hence, drugs to tackle disease progression in MS represent a major unmet need. In this respect, metformin is a very interesting drug to investigate in MS patients as a neuroprotective and remyelinating therapy. Several preclinical studies in animal models of MS have shown that metformin has both anti-inflammatory, neuroprotective and remyelinating properties. A clinical study with metformin in a limited sample of MS patients did not demonstrate significant adverse events. As metformin is available as generic drug and the price is low (0.10 eurocent per tablet), pharmaceutical companies have no interest is sponsoring clinical trials with this agent. However, major gains for patients and society may be reached if metformin proves to be a neuroprotective and remyelinating agent. In this research proposal we aim to provide evidence for the neuroprotective and remyelinating effects of metformin (I) in MS patients (P) via measurement of clinical and MRI outcome measures (O), via a multicentre randomized placebo-controlled (C) clinical trial.

Researcher(s)

• Promoter: Bilcke Joke
• Promoter: Willem Lander

Research team(s)

• Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)
• Primary and interdisciplinary care Antwerp (ELIZA)

Project type(s)

• Research Project

Abstract

Background – Infectious diseases have substantial impact on society and model-based health economic evaluation has acquired prominence for policy making. Stochastic individual-based models, in which each individual is modelled separately, are highly relevant to capture heterogeneities in social contacts patterns and transmission dynamics. Although they are suitable to predict disease burden and medical costs in detail, they are not fully exploited yet due to model complexity and computational burden. Aim – To improve health economic evaluations with stochastic individual-based disease transmission models while accounting for uncertainty. Methods – To advance from a C++ simulator towards a new platform for health economic evaluation, "rStride" in the widely used R software. This open-source package will integrate high-performant transmission modelling and state-of-the-art uncertainty analysis. Expected results – (1) New and improved individual-based modelling methods on demography and transmission dynamics. (2) Insights on the effect of social mixing assumptions on the estimated burden of disease. (3) The integration of stochastic effects from individual-based transmission models within uncertainty analysis in economic evaluations. (4) Long-term model predictions, including household dynamics, to explore between- and within-host dynamics. All methods will be applied to case studies on Respiratory Syncytial Virus (RSV) and Varicella-Zoster Virus (VZV).

Researcher(s)

• Promoter: Beutels Philippe
• Fellow: Willem Lander

Research team(s)

• Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)

Project type(s)

• Research Project

Abstract

Low incidence of vaccine-preventable diseases and rising concerns about adverse events increasingly lead to the delay or refusal of vaccinations. This threatens the high immunization coverage, established through decades of prevention, which is important at the community level to protect risk groups who cannot be vaccinated due to age or medical reasons (e.g., very young children or immunocompromised individuals). The availability of options to control and prevent the emergence of pathogens warrants continuous evaluation. Mathematical models provide a powerful set of tools in this process, as timely, budgetary or ethically feasible alternatives are often lacking. Measles is highly transmissible so vaccination coverage of 90-95% is required in order to achieve herd immunity. According to the WHO, measles immunisation in South Africa is below 80% and every year, outbreaks occur. In addition, the country faces the most severe HIV epidemic in the world, and measles in children with HIV infection is more often severe and results in higher mortality. In this project, we will survey and quantify preferences through a discrete choice experiment by forcing individuals to choose between competing profiles. We aim to discover the drivers of the decision-making process regarding immunization and to assess prevention, control – and eventually eradication – programs. We will calibrate our open-source individual-based model "Stride" to reproduce measles outbreaks in South Africa and perform scenario analyses for measles outbreaks.

Researcher(s)

• Promoter: Willem Lander

Research team(s)

Project type(s)

• Research Project