Health Economic Impact of Population-based Interventions for Prevention of Overweight and Obesity

Introduction

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.

Need for Evidence

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.

Objectives of EcIMPACT

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.
3. Model the long-term health and economic impact of PBIs by combining results from Aims 1 and 2.

Methodology

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).
• Build a simulation model that integrates those estimated parameters in a novel model type (Aim 3).

Expected Outcomes

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.

Contribution to Public Health Research

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.