ECONOMETRIC MODELS

Econometric modeling offers a powerful and structured methodology to assess how environmental and economic factors influence public health, particularly in the context of respiratory diseases such as Chronic Obstructive Pulmonary Disease (COPD) and Cystic Fibrosis (CF). By combining economic theory and statistical data analysis, econometrics enables researchers to quantify the strength and direction of associations between variables and to test hypotheses within a macroeconomic framework. This is particularly valuable when assessing the impact of exposome factors—the totality of environmental exposures individuals face over their lifetime—on disease development and progression.

Unlike traditional epidemiological methods, which typically operate at the patient level and focus on identifying health risk factors, econometric models operate at the population or country level, using large-scale, high-quality datasets such as those from EUROSTAT. These models are especially well-suited for informing public policy, allowing decision-makers to evaluate the effectiveness of interventions, understand the broader economic and health implications, and optimize resource allocation.

The methodology begins with exploratory statistical analysis, particularly the use of the Pearson correlation coefficient, to identify significant linear relationships between potential exposome indicators and health outcomes. These correlations guide the selection of variables and inform the structure of the model. Only statistically significant relationships (typically p ≤ 0.05) are considered for further modeling, ensuring robustness and relevance.

A key strength of econometric modeling lies in its use of time series data, which allows for trend analysis and investigation of cause-effect relationships over time. Once relevant variables are identified, the model is specified and estimated, allowing researchers to quantify the influence of selected exposome factors—such as air pollutants, environmental taxes, or land use patterns—on respiratory disease indicators like mortality or morbidity rates.

The transparency, reproducibility, and interpretability of linear econometric models make them especially useful for policymakers. They provide straightforward, quantitative estimates of how a change in one variable (e.g., emission levels or taxation policy) could influence health outcomes at the population level, enabling the design of evidence-based environmental health policies.

Moreover, the integration of macroeconomic data with environmental and health statistics enables a unique perspective that complements—but does not replace—traditional epidemiological research. While epidemiology identifies risk factors and causal links at the clinical or population scale, econometrics contributes a broader understanding of policy impacts and economic dynamics, creating a more holistic framework for public health planning.

Finally, econometric analysis benefits from EUROSTAT’s harmonized and validated datasets, which ensure comparability across EU member states and enable cross-country analysis. Though it is not always possible to trace the exact data collection method for each variable, the general quality and consistency of EUROSTAT data make it a reliable foundation for robust econometric investigation.

Target users: Decision makers