Current Projects
National/International Research Projects
Heterogeneous Treatment Effects in Health Care
Background
Responses to a particular treatment can substantially differ between individual patients depending on their personal characteristics such as demographic, socio-economic, or clinical attributes. These factors can significantly influence the quality of healthcare that patients receive. Medical-decision making will become more precise if they are based on real-world evidence of treatment effect heterogeneity. Considering this information and recognizing heterogeneity of treatment effects helps to identify groups that benefit the most or least from a particular treatment, leading to personalized and effective health care.
Although we know that different patients react differently to treatments, much of the empirical evidence in medicine represents average treatment effects. While sample sizes in randomized clinical trials are suitable to estimate the average treatment effect well, they are often insufficient for estimation of treatment-covariate interactions that are crucial to precision medicine. Vast amounts of (routinely) collected real-world data together with data-driven methods has the potential to leverage these shortcomings and deliver new insights into treatment effect heterogeneities.
Objective
In this context, the following questions will be addressed: What influence does the heterogeneity arising from patient characteristics have on the assessment and measurement of quality of care? Which methods exist for this and how accurate are they?
Approach
First, a systematic literature review will be conducted to find out how frequently heterogeneous treatment effects are examined in health care research using observational data, and which methods are employed to do so. Second, unique real-world data from a community health center in socially disadvantaged neighbourhoods in the city of Hamburg will be used to investigate the presence of heterogeneities in treatment effects.
Funding
This project is part of the research training group „Managerial and Economic Dimensions of Health Care Quality” funded by the German Academic Research Foundation, Grant No. GRK 2805/1.
Predicting COVID-19 Vaccination Uptake from Public Discourse: A Machine Learning Approach
Background
Through increasing population density and the encroachment of settlements on animal habitats, human societies are increasingly vulnerable to epidemics. Newly emerging epidemics can be countered through (i) the development of a vaccine or cure and (ii) its deployment to a sufficiently large part of the population. In the case of the COVID-19 pandemic, the development of effective vaccines was initially far from assured, but ultimately much faster and more successful than its deployment. This task, in turn, encountered important but addressable logistical challenges, but ultimately failed to convince a significant minority of the population to get vaccinated.
Objective
In this project, we will explore the relationship between public discourse and COVID-19 vaccination uptake and how to use real world data from Germany and England to identify public opinion on COVID-19 vaccination, with the ultimate aim of identifying strategies to increase the uptake of COVID-19 vaccinations. The analysis will apply big data and machine learning techniques to Twitter data and will link these to data on local vaccination rates. From a policy perspective, the output of this project can be used to inform public health response in real time in future pandemics.
Approach
At the heart of the proposal is an interdisciplinary approach, combining health economics and linguistics with new methods from data science. Our project will contribute to several key areas of the University of Hamburg: The core research area Infection Research, the emerging field Health Economics and the profile initiative Linguistic Diversity.
Funding
It is funded through Excellence Strategy of the Federal and State Governments.
R2D - Ready to Discharge? Implementation, influencing factors and effects of discharge management in cardiological care
Background
Cardiovascular diseases represent a significant proportion of illnesses in Germany. Often, specialist in-hospital treatment is required. During and after an in-hospital stay, effective discharge planning is crucial to ensure patient-centered, seamless, and continuous transition from inpatient to outpatient care. Despite the introduction of the German regulatory agreement for discharge planning (“Rahmenvertrag Entlassmanagement”), its implementation varies significantly across hospitals. Often, problems arise at the intersection between actors involved in patient care. Specifically for cardiovascular diseases, there are currently no stringent guidelines for the implementation of discharge planning.
Objective
This project aims at analyzing the implementation, influencing factors, and outcomes of discharge planning in cardiological care in Germany. The evaluation focuses especially on the quality and continuity of care. Actionable recommendations will be derived based on the project’s results to sustainably improve discharge planning in German hospitals.
Approach
The project considers the perspectives of various stakeholder within the German healthcare systems (patients, hospitals, experts) by conducting interviews and surveys. In addition, data from a German statutory health insurance and other publicly available data sources (e.g., hospital quality reports) will be analyzed jointly.
Funding
The project is funded by the Innovation Fund of the Joint Federal Committee for a duration of three years (EUR 1.2 million).
European COvid Survey
Objective
The objective of the European Covid Survey (ECOS) is to assess the acceptance of the measures introduced and how pandemic-related concerns and problems are dealt with in European society. The overarching themes of ECOS are vaccination preparedness, individual, financial and economic concerns, perception and acceptance of regulations, and information policy and trust in information sources.
Approach
Under the direction of the Hamburg Center for Health Economics (HCHE) and in cooperation with the Nova School of Business and Economics (Portugal), Bocconi University (Italy) and Erasmus University Rotterdam (Netherlands), waves of surveys have been conducted for this representative survey in eight European countries at intervals of about two months since April 2020.
Together with cooperation partners, questionnaires are being developed that cover current issues and issues relevant to decision-makers with a view to the dynamic development of the pandemic. The questionnaires are translated by partnersfrom the respective countries into their native languages and made available via an online platform. With the help of the market research company Dynata, it will be ensured that the samples from the different countries are representative in terms of age structure, regional distribution, gender and education.
Funding
The project is funded by the Excellence Initiative of the University of Hamburg and by the German Research Foundation (KO 6492/1-1, STA 1311/5-1) and it received funding from the EU Framework Programme of the European Union for Research and Innovation "Horizon 2020" under Grant No. 721402.
Political Consulting
ESV - Uniform, sector-equivalent remuneration
Background
Patients should be treated where they receive the most medically appropriate treatment. However, the current reimbursement structure stands in the way of this. This also affects "sector-equivalent" services that can be treated both as outpatient and inpatient care. Outpatient and inpatient service provision are reimbursed from different budgets which cements sector boundaries and prevents an understanding of joint service provision. Outpatient services are predominantly reimbursed according to individual services, while inpatient treatments are reimbursed via per-case flat rates. This includes financial incentives, whereby economic considerations can compete with the medical needs of the patient.
Objective
The objective is to develop a viable concept that shows how uniform, sector-equivalent reimbursement can be structured. It is intended to enable political decision-makers to initiate cross-sectoral reimbursement that is supported by service providers and health insurers. In this way, the German healthcare system can be aligned more closely to needs, made more efficient and the quality of service provision improved.
Procedure
Based on the experience of other OECD countries, a comparative literature review will identify which service areas are suitable for sectoral treatment and reimbursement. Subsequently, the status quo of sector-equivalent service provision will be surveyed and it will be investigated how comparable the patient groups in both sectors are in practice. A survey of service providers and health insurers will be used to determine how suitable the identified service areas are.
Funding
Innovation Committee of the Joint Federal Committee (Innovation Fund for the Promotion of Health Services Research (§§ 92a and 92b SGB V)
The Impact of Public Discourse on Health Care Utilization during the COVID-19 Pandemic
Background
To counteract potential long-term public health problems and better prepare for future pandemics, it is critical to develop effective public policy responses to pandemics. Estimating the effects of public discourse and measures taken during the current pandemic will be crucial to mitigate adverse effects on public health.
Objective
The primary objective of this study is to examine the relationship between public discourse on the COVID-19 pandemic and responses by the population considering policy measures taken to mitigate the pandemic, socioeconomic factors and political orientation. These responses include health care utilization and adherence to social distancing. This project is intended to provide essential lessons for public policy actors on how public policies are perceived by the population and to what extent they comply or respond to them.
Approach
The project focuses on a comparative analysis between England and Germany, as both countries are similar in many ways, but exhibit different policy responses and public discourses during the pandemic. An AI approach will be used to analyze newspaper and social media data in both countries while incorporating additional datasets. Modern analytic techniques will be used to examine the impact of public discourse, taking into account pandemic containment policies, socioeconomic factors, and political orientation, on health care utilization and adherence to social distancing.
Funding
German Research Foundation (DFG)
Cooperations and Projects in the Practice
There are currently no projects with practical cooperation.