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The Relationship Between National Income and Mental Health: Analyzing GNI and Depressive Disorders Across Nations
- 1 Goethe University Frankfurt, Frankfurt am Main, 60629, Germany
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Abstract
This study explores the intricate relationship between economic inequality and the prevalence of depressive disorders across countries from 1990 to 2021. Gross National Income (GNI) per capita is used as an economic measure, and its association with mental health outcomes, particularly depressive disorders, is analyzed. By employing advanced predictive models, including Random Forest, ARIMA, and ETS, we estimate future mental health trends, providing a comprehensive picture of how economic factors shape mental health disparities globally. The datasets are sourced from the World Bank and WHO, ensuring robustness in the study’s conclusions. According to the forecasts generated by the ARIMA model, depressive disorder rates might escalate to exceed 6,000 cases per 100,000 people by 2030 in the absence of specific interventions. This underscores the pressing necessity for policymakers in high-income nations to tackle both income disparities and enhance mental health facilities. Both the ARIMA and ETS models delivered forecasts that closely aligned with actual rates, where the ARIMA model exhibited an RMSE of 927.97, while the ETS model demonstrated a slightly better performance with an RMSE of 573.65. These findings underscore the significance of economic factors in forecasting depressive disorder rates.
Keywords
national income, depressive disorders, GNI per capita, machine learning models, ARIMA predictions
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Cite this article
Xu,M. (2024). The Relationship Between National Income and Mental Health: Analyzing GNI and Depressive Disorders Across Nations. Advances in Economics, Management and Political Sciences,141,59-65.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of ICFTBA 2024 Workshop: Finance's Role in the Just Transition
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