Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach

Abstract

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

A Statistical Learning Approach to Evaluate Factors Associated With Post-Traumatic Stress Symptoms in Physicians: Insights From the COVID-19 Pandemic

“…Despite their considerable predictive power and increasing popularity in public health [47], [49]- [51], [95], [131], to the best of our knowledge, this is the first study to leverage stateof-the-art, statistical learning algorithms to predict and evaluate the factors associated with risks for experiencing PTSS in frontline physicians. Our results demonstrate the value of nonparametric, nonlinear statistical learning algorithms to reveal complex relationships between predictor variables and PTSS risk, outperforming more conventional linear logistic regression in sophistication and precision [59], [132].…”