Summary
Overweight and obesity account for one eighth of the disease burden and five percent of health care expenditures in Europe and cause large societal productivity losses. Thus, cost-effective strategies for the prevention of overweight and obesity are urgently needed to assure the financial sustainability of health and social security systems. Population-based interventions (PBIs) that change the social and environmental context are promising strategies, however, robust evidence on their health and economic impact is lacking. This uncertainty might lead to inefficient resource allocation and harm for patients and society. In EcIMPACT, I aim to 1) quantify the real-world causal effect of two policy relevant PBIs on body weight, 2) estimate the causal effect of excess body weight on health and economic outcomes, and 3) model the long-term health and economic impact of PBIs by combining results from Aims 1 and 2. I will do so by combining innovative methods from economics, public health, and epidemiology. Specifically, I will pool data from large cohort studies, surveillance initiatives, and household panels across Europe and use econometric methods that exploit natural policy experiments (Aim 1), link primary and secondary data sources that comprise granular genetic, phenotypic, and socio-economic information and apply cutting-edge Mendelian Randomization techniques (Aim 2), and build a simulation model that integrates those estimated parameters in a novel model type (Aim 3). Synthesizing cutting-edge methods from different academic disciplines, EcIMPACT will provide urgently needed evidence for decision makers on the value of PBIs that will ultimately improve population health. EcIMPACT will advance public health research substantially by showing that combinations of innovative quasi-experimental and model-based techniques, which leverage the full potential of modern data infrastructures, can guide health policy in an efficient and timely manner.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101162962 |
| Start date: | 01-01-2026 |
| End date: | 31-12-2030 |
| Total budget - Public funding: | 1 496 598,00 Euro - 1 496 598,00 Euro |
Cordis data
Original description
Overweight and obesity account for one eighth of the disease burden and five percent of health care expenditures in Europe and cause large societal productivity losses. Thus, cost-effective strategies for the prevention of overweight and obesity are urgently needed to assure the financial sustainability of health and social security systems. Population-based interventions (PBIs) that change the social and environmental context are promising strategies, however, robust evidence on their health and economic impact is lacking. This uncertainty might lead to inefficient resource allocation and harm for patients and society. In EcIMPACT, I aim to 1) quantify the real-world causal effect of two policy relevant PBIs on body weight, 2) estimate the causal effect of excess body weight on health and economic outcomes, and 3) model the long-term health and economic impact of PBIs by combining results from Aims 1 and 2. I will do so by combining innovative methods from economics, public health, and epidemiology. Specifically, I will pool data from large cohort studies, surveillance initiatives, and household panels across Europe and use econometric methods that exploit natural policy experiments (Aim 1), link primary and secondary data sources that comprise granular genetic, phenotypic, and socio-economic information and apply cutting-edge Mendelian Randomization techniques (Aim 2), and build a simulation model that integrates those estimated parameters in a novel model type (Aim 3). Synthesizing cutting-edge methods from different academic disciplines, EcIMPACT will provide urgently needed evidence for decision makers on the value of PBIs that will ultimately improve population health. EcIMPACT will advance public health research substantially by showing that combinations of innovative quasi-experimental and model-based techniques, which leverage the full potential of modern data infrastructures, can guide health policy in an efficient and timely manner.Status
SIGNEDCall topic
ERC-2024-STGUpdate Date
03-12-2024
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