A need exists to accurately estimate overdose risk and improve understanding of how to deliver treatments and interventions in people with opioid use disorder in a way that reduces such risk. We consider opportunities for predictive analytics and routinely collected administrative data to evaluate how overdose could be reduced among people with opioid use disorder. Specifically, we summarise global trends in opioid use and overdoses; describe the use of big data in research into opioid overdose; consider the potential for predictive modelling, including machine learning, for prevention and monitoring of opioid overdoses; and outline the challenges and risks relating to the use of big data and machine learning in reducing harms that are related to opioid use. Future research for improving the coverage and provision of existing interventions, treatments, and resources for opioid use disorder requires collaboration of multiple agencies. Predictive modelling could transport the concept of stratified medicine to public health through novel methods, such as predictive modelling and emulated trials for evaluating diagnoses and prognoses of opioid use disorder, predicting treatment response, and providing targeted treatment recommendations.
Bibliographical noteFunding Information:
LD has received untied educational funding from Reckitt Benckiser, Indivior, Mundipharma, and Seqirus, outside the submitted work. All other authors declare no competing interests.
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license
- Analgesics, Opioid/administration & dosage
- Big Data
- Drug Overdose/etiology
- Machine Learning
- Models, Statistical
- Opioid Epidemic
- Opioid-Related Disorders/mortality