Leveraging Machine Learning to Predict High-Risk Opioid Overdose Cases in Massachusetts: A Jurisdiction-Level Analysis

Awele Okolie; Dumebi Okolie; Callistus Obunadike; Darlington Ekweli; Bello Abdul-Waliyyu; Paschal Alumona

doi:10.18535/ijsrm/v13i10.ec01

Abstract

The opioid crisis remains a significant public health challenge in the United States, characterized by evolving drug supply dynamics and significant geographic and demographic disparities. This study leverages machine learning to analyze jurisdiction-level data from Massachusetts to identify the primary drivers of opioid overdose deaths. Using a descriptive quantitative design, this research employs a supervised machine learning framework, comparing Random Forest, Gradient Boosting, and Ridge Regression models to predict state-level opioid overdose rates. The analysis focuses on model interpretability to determine the relative importance of various substance use rates and demographic factors. The Gradient Boosting Regressor demonstrated exceptional predictive accuracy, achieving a Mean Absolute Error (MAE) of 0.81 and a coefficient of determination (R²) of 0.9930. The feature importance analysis revealed a singular, dominant predictor: the stimulant rate. This finding indicates that the co-use of stimulants is the most critical factor driving opioid-related fatalities, overshadowing the predictive power of other variables, including the fentanyl dominance ratio, heroin rate, and demographic characteristics. The results strongly suggest that the opioid epidemic has transitioned into a polysubstance crisis. Consequently, effective public health interventions must shift from an opioid-centric focus to integrated strategies that address the concurrent use of opioids and stimulants. This study underscores the power of machine learning to provide clear, actionable insights for tackling complex public health crises and recommends the reallocation of resources toward programs that target polysubstance use.

Keywords

Opioid CrisisMachine LearningOverdose PredictionPolysubstance UseStimulantsPublic

References

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Wind, K. S. (2021). What Causes Health? Revisiting the Social Determinants of Health (SDH) Through a Salutogenic Lens and Self-Reported Health (SRH) as the Main Outcome: A Realist Evaluation (Doctoral dissertation, University of Toronto (Canada)).Google Scholar ↗

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[refR-3] Cesare, N. L., Chandler, R., Gibson, E. B., Vickers-Smith, R., Jackson, R., & Oga, E. (2024). Development and validation of a community-level social determinants of health index for drug overdose deaths in the Healing Communities Study. Journal of substance use and addiction treatment.Google Scholar ↗

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[refR-15] Liu, J., Wu, J., Wang, J., Chen, S., Yin, X., & Gong, Y. (2024). Prevalence and associated factors for depressive symptoms among the general population from 31 provinces in China: The utility of social determinants of health theory. . Journal of Affective Disorder.Google Scholar ↗

[refR-16] Liu, Y. S., Kiyang, L., Hayward, J., Zhang, Y., Metes, D., Wang, M., & Cao, B. (2023). Individualized prospective prediction of opioid use disorder. The Canadian Journal of Psychiatry, 54-63.Google Scholar ↗

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[refR-24] Schell, R. C., Allen, B., G. W., Hallowell, B. D., Scagos, R., Li, Y., & Ahern, J. (2022). Identifying predictors of opioid overdose death at a neighborhood level with machine learning. American Journal of Epidemiology, 526-533.Google Scholar ↗

[refR-25] Schell, R. C., Allen, B., Goedel, W. C., Hallowell, B. D., Scagos, R., Li, Y., & Ahern, J. (2022). Identifying predictors of opioid overdose death at a neighborhood level with machine learning. American Journal of Epidemiology, 191(3), 526-533.Google Scholar ↗

[refR-26] Shojaati, N. (2023). Systems Science Approaches to the Opioid Crisis: Exploring its Multifaceted Nature through Agent-Based Model Simulations (Doctoral dissertation).Google Scholar ↗

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[refR-28] Smith, M. K., Planalp, C., Bennis, S. L., Stately, A., Nelson, I., Martin, J., & Evans, P. (2025). Widening racial disparities in the U.S. overdose epidemic. American journal of preventive medicine, 68(4), 745-753.Google Scholar ↗

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