Grim headlines about the ongoing U.S. opioid epidemic have become dismayingly familiar. Recently released data from the Centers for Disease Control and Prevention show deaths related to drug overdose – a category substantially driven by opioid abuse – are growing at an alarming rate. A total of 63,600 people died from drug overdose in the U.S. in 2016—a 21% increase from 2015, itself a record year for overdose deaths. It’s difficult to overstate the enormity of this crisis: for the second year in a row, U.S. life expectancy has fallen, in part due to deaths caused by the misuse of powerful, highly addictive painkillers. Beyond the epidemic’s toll on individuals and families, it is also hollowing out communities and placing unprecedented strains on social services.
As the trajectory of the crisis grows steeper, the need for effective action grows increasingly urgent. Getting a handle on the problem, however, is both complex and difficult. The impact of the opioid epidemic is not uniform across the country, but is concentrated in particular geographic regions and correlated with specific socioeconomic and demographic characteristics. For these reasons, strategies for tackling the crisis need to be fine-tuned to meet different circumstances. Caution is called for: responses to drug-abuse epidemics have often created unintended consequences as attempts to curb one problem give rise to others, and for many people, opioids represent one of the few effective options for managing severe pain.
Amid these complexities, how should healthcare providers and policymakers respond? This question was at the heart of the Department of Health and Human Services’ (HHS) recent Opioid Code-a-Thon. The event, which took place in Washington, D.C. on December 6-7, 2017, was aimed at “jump-starting” innovative, data-driven approaches to understanding and addressing different facets of the opioid epidemic. Teams of informaticists, public-health experts, physicians, and programmers offered proposals for one of three tracks: treatment (devoted to improving access to treatment and recovery services), usage (focused on identifying at-risk populations and individual risk characteristics), and prevention (dedicated to accurately describing patterns of drug supply and use to guide effective interventions).
Duke Forge successfully fielded two teams for the grueling event, where roughly 50 groups from around the country were provided with unprecedented access to data resources and then tasked with developing—through 24 hours of nonstop effort—a workable proof-of-concept digital tool capable of supporting one of the key aims represented in the Code-a-Thon’s three tracks.
In coming posts, we’ll be taking a closer look at the experiences of the two Duke Forge teams at this remarkable event, and dig deeper into the role that data science can play in addressing major public health challenges.