Introduction

For close to two decades now, the number of drug overdose fatality cases has steadily risen each year. By studying and analyzing the patterns of these drug-related fatalities, researchers from the University of California San Diego School of Medicine, San Diego State University (SDSU), and international collaborators have created and authorized a Predictive Analysis Model to warn counties that are at risk of future overdose fatalities. The aim of the open-source tool is to forecast and stop deaths by the quick and early deployment of public health resources. The researchers have used the Centers for Disease Control and Prevention (CDC) data that was collected between the years  2013 and 2018 to create this model.

To Prevent the Future Overdose Outbreaks

Charlie Marks, MPH, one of the study’s authors and a graduate research assistant at SDSU-UC San Diego’s joint doctoral program, states that “this study offers a novel, thoroughly tested tool to inform policy planning in the context of overdose epidemics driven by evolving and emerging drugs and forms a new standard for the development of a data-driven response to drug use epidemics”.

“A big challenge for public health experts is figuring out which parts of the country are at greatest risk of future overdose outbreaks. If such outbreaks can be predicted, then we can intervene and prevent deaths from occurring,” states senior author Annick Borquez, PhD, an epidemiologist and assistant professor in the Division of Infectious Disease and Global Public Health at UC San Diego School of Medicine.

Drug overdose deaths have multiplied since the year 1999, based on a report published by CDC. Over 70 percent of 2019 drug overdose deaths have been due to opioids. Illegally made fentanyl, a synthetic opioid, gave rise to the third wave of the opioid epidemic in the year 2013. The CDC states that fentanyl is 50 to 100 times more powerful than morphine and since the year 2013, there has been a substantial spike in overdose deaths in the U.S.

The CDC does not specify and report county-level data concerning the number of overdose deaths to the public if the county’s total was less than 10. However, the study’s authors were provided access to CDC’s full database with specific county rates to create the prediction model. With the final model, researchers analyzed each event, ranging from high school graduation and unemployment rates to opioid prescription rates and emergency care facility access. The model was created to recognize patterns between each county characteristics and death rates.

Borquez and Marks both agree that all predictive models are restricted by the data sets used to inform them and have stated that more timely and available information concerning the deaths, prescription data, and drug markets and seizures at both county and state levels is urgently required.

While the overall approach can be effective, the tool also needs the fatal overdose data from all the counties in the U.S. to be available and accessible for the current year, which is not yet the standard practice being followed. The model will only be beneficial in forecasting and stopping deaths if there is no delay in receiving data from both local and national agencies. Borquez estimates that the tool will require a few more years to be able to make real-time predictions.

Conclusion

While the tool is being finalized, the opioid epidemic is continuing to cause devastation across the country. However, new developments offer hope for reducing overdose rates. CareSource has announced a partnership that offers a medication disposal powder to counteract active ingredients in unused medications to prevent opioid misuse.