Suicide disparities across urban and suburban areas in the U.S.: A comparative assessment of socio-environmental factors using a data-driven predictive approach
Sayanti Mukherjee, Zhiyuan Wei

TL;DR
This study uses a data-driven approach to analyze how socio-environmental factors influence suicide rates differently in urban and suburban areas in the U.S., revealing key vulnerabilities and environmental sensitivities.
Contribution
It introduces a holistic framework combining demographic, socioeconomic, and climate data with advanced machine learning to assess regional disparities in suicide rates.
Findings
Suburban populations are more vulnerable to suicides than urban populations.
Suicide rates are significantly influenced by unemployment and income levels in suburban areas.
Urban suicide rates are more sensitive to temperature and precipitation variations.
Abstract
Disparity in suicide rates between urban and suburban/rural areas is growing, with rural areas typically witnessing higher suicide rates in the U.S. However, previous studies often ignored the effect of socio-environmental factors on the suicide rates and its regional disparity. To address these gaps, we propose a holistic data-driven framework to model the associations of social (demographic, socioeconomic) and environmental (climate) factors on suicide rates, and study the disparities across urban and suburban areas. Leveraging the county-level suicide data from 2000--2017 along with the socio-environmental features, we trained, tested and validated a suite of advanced statistical learning algorithms to identify, assess and predict the influence of key socio-environmental factors on suicide rates. Random forest outperformed all other models in terms of goodness-of-fit and predictive…
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Taxonomy
TopicsSuicide and Self-Harm Studies · Health disparities and outcomes · Food Security and Health in Diverse Populations
