Drug overdose mortality is a growing problem in the United States. In 2017 alone over 72,000 deaths were attributed to drug overdose, most of which were caused by fentanyl and fentanyl analogs (synthetic opioids). While nearly every community has seen an increase in drug overdose, there is considerable variation in the degree of increase in specific communities. The Harris County community, which includes the City of Houston, has not seen the massive spikes observed in some communities, such as West Virginia, Kentucky, and Ohio.

As a Centers for Disease Control and Prevention Enhanced State Opioid Overdose Surveillance (ESOOS) funded state, Kentucky started utilizing Emergency Medical Services (EMS) data to increase timeliness of state data on drug overdose events in late 2016. Using developed definitions of heroin overdose for EMS emergency runs, Kentucky analyzed the patterns of refused/transported EMS runs for both statewide and local jurisdictions.

Using death certificates alone to identify contributing substances in drug overdose deaths may result in misclassification and underestimation of the burden of illicit and prescription opioids and other drugs in drug-related deaths. To enable timely and targeted prevention in Tennessee (TN), the identification and monitoring of new drugs and trends in use should utilize toxicology and medicolegal death investigation data directly, as recommended by others 1-3. These data can inform mortality outcome definitions for improved surveillance and risk factor identification 4-7.

Timely and accurate measurement of overdose morbidity using emergency department (ED) data is necessary to inform an effective public health response given the dynamic nature of opioid overdose epidemic in the United States. However, from jurisdiction to jurisdiction, differing sources and types of ED data vary in their quality and comprehensiveness. Many jurisdictions collect timely emergency department data through syndromic surveillance (SyS) systems, while others may have access to more complete, but slower emergency department discharge datasets.