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Health Economic Policy Analysis with Real World Data

Project description

Rethinking health policy to tackle inequality

People today may be living longer than previous generations, but health inequalities persist among different regions and socio-economic groups. This is why it’s important for researchers to consider local context and social factors. Supported by the Marie Skłodowska-Curie Actions programme, the HEPARD project is training early-stage researchers in advanced data analytics, causal inference and machine learning. Specifically, it is training them to evaluate real-world policy impacts. Focusing on lifestyle interventions, family support and insurance systems, the project combines economics, public health and data science to produce evidence that reflects Europe’s diversity. It will also help design fairer, more effective health policies for all.

Objective

The HEPARD (Health Economic Policy Advice with Real World Data) Doctoral Network addresses persistent health inequalities across Europe through a multidisciplinary approach uniting health economics, public health, and advanced data analytics. Despite positive trends in general health and life expectancy, disparities—particularly among socioeconomic groups and regions—continue to challenge policy efforts. HEPARD aims to train researchers in causal inference methods, using observational data to evaluate the real-world impacts of health policies and interventions.

HEPARD focuses on key drivers of health disparities. Through interdisciplinary research clusters, it investigates the effects of lifestyle interventions, family-centered policies, and health insurance systems on health equity. Traditional studies often overlook socioeconomic and contextual factors shaping policy outcomes. HEPARD instead uses quasi-experimental and machine learning methods to analyze real-world data, yielding insights more relevant to Europe’s diverse conditions.

The network’s research program includes four main work packages (WPs):
- Real Data Innovation Analytics: Applies machine learning and causal inference to assess lifestyle health interventions in reducing chronic disease.
- Family Futures: Studies health policies’ impacts on parents and children, focusing on policies like postpartum support and childcare across diverse European context.
- Incentives and Impacts: Examines how various health insurance systems affect equity and access across Europe.
- Methodological Cross-Breeding for Enhanced Causal Inference: Develops new causal inference tools by integrating economic and epidemiological approaches.

Together, these WPs create a unified framework for understanding and addressing health inequities, combining methodological innovation with policy relevance to equip early-stage researchers with the tools to design impactful, evidence-based solutions that enhance well-being across Europe.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-DN-01

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Coordinator

UNIVERSITAET DUISBURG-ESSEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 580 544,64
Address
UNIVERSITATSSTRASSE 2
45141 ESSEN
Germany

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Region
Nordrhein-Westfalen Düsseldorf Essen, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

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Participants (5)

Partners (7)

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