Zan M Dodson1, Eun-Hye Enki Yoo1, Christian Martin-Gill1, Ronald Roth1. 1. Zan M. Dodson is with the Public Health Dynamics Laboratory, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA. Eun-Hye Enki Yoo is with the Department of Geography, State University of New York at Buffalo. Christian Martin-Gill and Ronald Roth are with the Department of Emergency Medicine, University of Pittsburgh.
Abstract
OBJECTIVES: To improve public health surveillance and response by using spatial optimization. METHODS: We identified cases of suspected nonfatal opioid overdose events in which naloxone was administered from April 2013 through December 2016 treated by the city of Pittsburgh, Pennsylvania, Bureau of Emergency Medical Services. We used spatial modeling to identify areas hardest hit to spatially optimize naloxone distribution among pharmacies in Pittsburgh. RESULTS: We identified 3182 opioid overdose events with our classification approach, which generated spatial patterns of opioid overdoses within Pittsburgh. We then used overdose location to spatially optimize accessibility to naloxone via pharmacies in the city. Only 24 pharmacies offered naloxone at the time, and only 3 matched with our optimized solution. CONCLUSIONS: Our methodology rapidly identified communities hardest hit by the opioid epidemic with standard public health data. Naloxone accessibility can be optimized with established location-allocation approaches. Public Health Implications. Our methodology can be easily implemented by public health departments for automated surveillance of the opioid epidemic and has the flexibility to optimize a variety of intervention strategies.
OBJECTIVES: To improve public health surveillance and response by using spatial optimization. METHODS: We identified cases of suspected nonfatal opioid overdose events in which naloxone was administered from April 2013 through December 2016 treated by the city of Pittsburgh, Pennsylvania, Bureau of Emergency Medical Services. We used spatial modeling to identify areas hardest hit to spatially optimize naloxone distribution among pharmacies in Pittsburgh. RESULTS: We identified 3182 opioid overdose events with our classification approach, which generated spatial patterns of opioid overdoses within Pittsburgh. We then used overdose location to spatially optimize accessibility to naloxone via pharmacies in the city. Only 24 pharmacies offered naloxone at the time, and only 3 matched with our optimized solution. CONCLUSIONS: Our methodology rapidly identified communities hardest hit by the opioid epidemic with standard public health data. Naloxone accessibility can be optimized with established location-allocation approaches. Public Health Implications. Our methodology can be easily implemented by public health departments for automated surveillance of the opioid epidemic and has the flexibility to optimize a variety of intervention strategies.
Authors: Ziad Faramand; Mohammad Alrawashdeh; Stephanie Helman; Zeineb Bouzid; Christian Martin-Gill; Clifton Callaway; Salah Al-Zaiti Journal: Res Nurs Health Date: 2021-11-24 Impact factor: 2.228
Authors: Xiao Zang; Alexandria Macmadu; Maxwell S Krieger; Czarina N Behrends; Traci C Green; Jake R Morgan; Sean M Murphy; Shayla Nolen; Alexander Y Walley; Bruce R Schackman; Brandon Dl Marshall Journal: Int J Drug Policy Date: 2021-09-03