More Like this

1. Modeling approaches, challenges, and preliminary results for the opioid and heroin co-epidemic crisis

   https://doi.org/10.1109/WSC.2017.8248006  

   Benneyan, James; Garrahan, Jacqueline; Ilies, Iulian; Duan, Xiaoli (December 2017, Proc Winter Simulation Conference)

   null (Ed.)

   The U.S. is in the grips of a devastating opioid and heroin co-epidemic affecting nearly all socio-economic populations at great human (~7,800 new users/day) and financial ($78.5 billion/year) costs but with no obvious solution. We describe recent work and challenges to develop, integrate, and use several analytic multi-scale simulation models of these epidemics to develop insight into the epidemic’s complex underlying dynamics, generate causal hypotheses, and inform effective policy interventions. We developed preliminary agent-based, differential equation, network spread, and cellular automata models that reasonably replicate at multiple scales the past 17 years of this epidemic’s growth and spread at town, county, state, and national levels. Results suggest that some current approaches are unlikely to be very effective, some in fact may worsen the epidemic, and ultimately only certain combinations and sequences of policies are likely to have value, with important implications on both model architecture and policy optimization. 

   more » « less
2. Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county

   https://doi.org/10.1371/journal.pone.0250324  

   Hyder, Ayaz; Lee, Jinhyung; Dundon, Ashley; Southerland, Lauren T.; All, David; Hammond, Gretchen; Miller, Harvey J. (May 2021, PLOS ONE)

   West, Brooke (Ed.)

   Objectives An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data. Methods We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types. Results A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26–35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race. Conclusions Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis. 

   more » « less
3. Championing awareness of the opioid epidemic through a service-learning module for non-STEM biology majors

   https://doi.org/10.3389/feduc.2023.1155659  

   Fleming, Ryleigh; Adkins, Sarah J.; Esteban, Marco; Cross, Cinnamin; Chatham, Amy Hutson; Raut, Samiksha A. (December 2023, Frontiers in Education)

   Over 50,000 people die annually from opioid overdoses in the United States leading to what has become known as the “opioid epidemic.” This is of heightened concern in states like Alabama that experience higher rates of overall drug use and overdose deaths. Thus, it is increasingly important for college students in Alabama to learn about how the opioid epidemic is affecting their communities. Previous studies have demonstrated that engaging non-majors in innovative active-learning oriented pedagogies like service-learning can enhance their understanding and awareness about contemporary societal issues. Despite its pedagogical potential, the impact of opioid-related service-learning, particularly for non-majors, continues to remain unexplored. In this study, we describe the implementation of a service-learning module centered on opioid addiction. Students in a non-major biology course learned the science behind opioids, had Naloxone training, and engaged in active discussions with an opioid researcher, physician, and former illicit opioid user. Our assessment of the thematic analysis of pre- and post-reflection free-write data from 87 consenting students revealed 10 categories that students reported in the post- but not pre-reflections (essay gain), pre- and post-reflections (neutral), and pre- but not post-reflections (essay loss). We found essay gains in students humanizing addiction and awareness of the cultural context of opioid addiction and essay losses from students indicating that non-major students had a low level of awareness related to these issues. Eight one-on-one, semi-structured interviews revealed that students were personally impacted by the epidemic and valued its curricular inclusion. Our data supports that service-learning can increase non-major biology student’s awareness and contextual understanding about the opioid epidemic, enabling much-needed advocacy to further enhance its awareness among the public. 

   more » « less
4. Calculating prescription rates and addiction probabilities for the four most commonly prescribed opioids and evaluating their impact on addiction using compartment modelling

   https://doi.org/10.1093/imammb/dqab001  

   Rivas, Samantha R; Tessner, Alex C; Goldwyn, Eli E (February 2021, Mathematical Medicine and Biology: A Journal of the IMA)

   null (Ed.)

   Abstract In 2016, more than 11 million Americans abused prescription opioids. The National Institute on Drug Abuse considers the opioid crisis a national addiction epidemic, as an increasing number of people are affected each year. Using the framework developed in mathematical modelling of infectious diseases, we create and analyse a compartmental opioid-abuse model consisting of a system of ordinary differential equations. Since $$40\%$$ of opioid overdoses are caused by prescription opioids, our model includes prescription compartments for the four most commonly prescribed opioids, as well as for the susceptible, addicted and recovered populations. While existing research has focused on drug abuse models in general and opioid models with one prescription compartment, no previous work has been done comparing the roles that the most commonly prescribed opioids have had on the crisis. By combining data from the Substance Abuse and Mental Health Services Administration (which tracked the proportion of people who used or misused one of the four individual opioids) with data from the Centers of Disease Control and Prevention (which counted the total number of prescriptions), we estimate prescription rates and probabilities of addiction for the four most commonly prescribed opioids. Additionally, we perform a sensitivity analysis and reallocate prescriptions to determine which opioid has the largest impact on the epidemic. Our results indicate that oxycodone prescriptions are both the most likely to lead to addiction and have the largest impact on the size of the epidemic, while hydrocodone prescriptions had the smallest impact. 

   more » « less
5. Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors

   https://doi.org/10.1609/aaai.v38i21.30326  

   Gullapalli, Bhanu Teja; Carreiro, Stephanie; Chapman, Brittany P; Garland, Eric L; Rahman, Tauhidur (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Long-term and high-dose prescription opioid use places individuals at risk for opioid misuse, opioid use disorder (OUD), and overdose. Existing methods for monitoring opioid use and detecting misuse rely on self-reports, which are prone to reporting bias, and toxicology testing, which may be infeasible in outpatient settings. Although wearable technologies for monitoring day-to-day health metrics have gained significant traction in recent years due to their ease of use, flexibility, and advancements in sensor technology, their application within the opioid use space remains underexplored. In the current work, we demonstrate that oral opioid administrations can be detected using physiological signals collected from a wrist sensor. More importantly, we show that models informed by opioid pharmacokinetics increase reliability in predicting the timing of opioid administrations. Forty-two individuals who were prescribed opioids as a part of their medical treatment in-hospital and after discharge were enrolled. Participants wore a wrist sensor throughout the study, while opioid administrations were tracked using electronic medical records and self-reports. We collected 1,983 hours of sensor data containing 187 opioid administrations from the inpatient setting and 927 hours of sensor data containing 40 opioid administrations from the outpatient setting. We demonstrate that a self-supervised pre-trained model, capable of learning the canonical time series of plasma concentration of the drug derived from opioid pharmacokinetics, can reliably detect opioid administration in both settings. Our work suggests the potential of pharmacokinetic-informed, data-driven models to objectively detect opioid use in daily life. 

   more » « less