BACKGROUND: Prescription drug abuse is a critical problem in the USA and has been linked to more deaths than automobile accidents. Despite this growing epidemic, the USA lacks a timely early warning system. Poison centers (PCs) have the potential to act as sentinel reporting entities for prescription drug abuse and misuse due to near-real-time data reporting and abundant coverage in the USA. METHODS: Data from the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS®) System PC program were compared with data from the Drug Abuse Warning Network (DAWN) from 2004 through 2010. Population rates of PC call mentions regarding abuse and misuse of prescription opioids were compared with population rates of emergency department visit mentions of the same using linear regression. Products included in the analysis were the following: buprenorphine, fentanyl, hydrocodone, hydromorphone, methadone, morphine, and oxycodone. RESULTS: The strength of association between RADARS System PC data and DAWN emergency department visits regarding all opioids in aggregate was strong (R² = 0.81, p < 0.001). The correlations between the two programs at the drug class level also were strong for buprenorphine, hydrocodone, hydromorphone, methadone, and oxycodone (all R² > 0.70, all p < 0.01), significant for fentanyl (p = 0.05), and moderate for morphine (p = 0.09). CONCLUSIONS: Data on prescription opioid drug abuse from the RADARS System PC program correlates well with emergency room data from DAWN. Due to timeliness of data, geographic coverage and strong associations with other warning systems, PC data can be used for sentinel reporting on prescription drug abuse and misuse in the USA.
BACKGROUND: Prescription drug abuse is a critical problem in the USA and has been linked to more deaths than automobile accidents. Despite this growing epidemic, the USA lacks a timely early warning system. Poison centers (PCs) have the potential to act as sentinel reporting entities for prescription drug abuse and misuse due to near-real-time data reporting and abundant coverage in the USA. METHODS: Data from the Researched Abuse, Diversion and Addiction-Related Surveillance (RADARS®) System PC program were compared with data from the Drug Abuse Warning Network (DAWN) from 2004 through 2010. Population rates of PC call mentions regarding abuse and misuse of prescription opioids were compared with population rates of emergency department visit mentions of the same using linear regression. Products included in the analysis were the following: buprenorphine, fentanyl, hydrocodone, hydromorphone, methadone, morphine, and oxycodone. RESULTS: The strength of association between RADARS System PC data and DAWN emergency department visits regarding all opioids in aggregate was strong (R² = 0.81, p < 0.001). The correlations between the two programs at the drug class level also were strong for buprenorphine, hydrocodone, hydromorphone, methadone, and oxycodone (all R² > 0.70, all p < 0.01), significant for fentanyl (p = 0.05), and moderate for morphine (p = 0.09). CONCLUSIONS: Data on prescription opioid drug abuse from the RADARS System PC program correlates well with emergency room data from DAWN. Due to timeliness of data, geographic coverage and strong associations with other warning systems, PC data can be used for sentinel reporting on prescription drug abuse and misuse in the USA.
Authors: Janetta L Iwanicki; S Geoff Severtson; Zachary Margolin; Nabarun Dasgupta; Jody L Green; Richard C Dart Journal: Am J Public Health Date: 2018-12 Impact factor: 9.308
Authors: Celeste A Mallama; James P Trinidad; Richard S Swain; Yueqin Zhao; Corinne Woods; Jana K McAninch Journal: Pharmacoepidemiol Drug Saf Date: 2019-08-11 Impact factor: 2.890
Authors: João Mauricio Castaldelli-Maia; Laura H Andrade; Katherine M Keyes; Magdalena Cerdá; Daniel J Pilowsky; Silvia S Martins Journal: J Psychiatr Res Date: 2016-05-24 Impact factor: 4.791
Authors: Jonathan M Davis; Bruce Mendelson; Jay J Berkes; Katie Suleta; Karen F Corsi; Robert E Booth Journal: Am J Prev Med Date: 2015-09-16 Impact factor: 5.043
Authors: Janetta L Iwanicki; S Geoff Severtson; Heather McDaniel; Andrew Rosenblum; Chunki Fong; Theodore J Cicero; Matthew S Ellis; Steven P Kurtz; Mance E Buttram; Richard C Dart Journal: PLoS One Date: 2016-12-09 Impact factor: 3.240
Authors: Richard C Dart; Hilary L Surratt; Marie-Claire Le Lait; Yami Stivers; Vikhyat S Bebarta; Clark C Freifeld; John S Brownstein; John J Burke; Steven P Kurtz; Nabarun Dasgupta Journal: Pain Med Date: 2015-12-14 Impact factor: 3.750