The Mental Health Index Across the Italian Regions in the ESG Context

Speaker(s)

ABSTRACT WITHDRAWN

OBJECTIVES: The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023.

METHODS: In the following section we analyze with econometric techniques the relationship between the mental health index and each of the three components of the ESG model, namely Environment, Social and Governance. The econometric models used are panel data, focusing mainly on fixed effects and random effects. The data analyzed refers to the 20 Italian regions from 2004 to 2023.

RESULTS: The results suggest that:

  • there is a positive relationship between the value of the mental health index and the E-Environment component within the ESG model in the Italian regions. That is, the value of the mental health index tends to increase with the improvement of the environment.
  • There is a positive relationship between the value of the mental health index and the value of the analyzed variables attributable to the S-Social dimension within the ESG model. That is, an improvement in the social condition of the population found in the Italian regions tends to be positively associated with an improvement in the mental health index.
  • regions that have better outcomes in terms of Governance also have higher levels of Mental Health Index-MHI.

CONCLUSIONS: By underscoring the complex yet tangible ways in which ESG factors influence mental health across different Italian regions, the article provides a foundational blueprint for crafting more resilient, inclusive, and mentally healthy communities. As Italy continues to navigate its regional disparities, the insights from this research will be instrumental in guiding targeted health interventions and fostering an environment where mental well-being is a shared priority, ultimately leading to a more equitable distribution of health resources and better overall public health outcomes.

Code

EE222

Topic

Clinical Outcomes, Epidemiology & Public Health, Health Policy & Regulatory, Methodological & Statistical Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Comparative Effectiveness or Efficacy, Health Disparities & Equity, Public Health

Disease

Mental Health (including addition), Neurological Disorders