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1. 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. 

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2. Co-prescription network reveals social dynamics of opioid doctor shopping

   https://doi.org/10.1371/journal.pone.0223849  

   Perry, BL; Yang, KC; Kaminski, P; Odabas, M; Park, J; Martel, M; Oser, C; Talbert, J; Freeman, P; Ahn, YY. (October 2019, PloS one)

   This paper examines network prominence in a co-prescription network as an indicator of opioid doctor shopping (i.e., fraudulent solicitation of opioids from multiple prescribers). Using longitudinal data from a large commercially insured population, we construct a network where a tie between patients is weighted by the number of shared opioid prescribers. Given prior research suggesting that doctor shopping may be a social process, we hypothesize that active doctor shoppers will occupy central structural positions in this network. We show that network prominence, operationalized using PageRank, is associated with more opioid prescriptions, higher predicted risk for dangerous morphine dosage, opioid overdose, and opioid use disorder, controlling for number of prescribers and other variables. Moreover, as a patient’s prominence increases over time, so does their risk for these outcomes, compared to their own average level of risk. Results highlight the importance of co-prescription networks in characterizing high-risk social dynamics. 

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3. Conceptualizing the Gut Thanatomicrobiota in Substance Abuse Disorders

   Javan, GT; Finley, SJ; Scott, L; Visonà, S. (February 2021, Proceedings American Academy of Forensic Sciences)

   null (Ed.)

   In this century, drug abuse continues to be a national crisis. Since 1999, the number of opioid-induced overdoses has increased four-fold to more than 500,000 deaths. The microbiota gut-brain-axis is a bidirectional circuit that links the neural, endocrine, and immunological systems with gut microbial communities. Gut microbiota play significant roles in human mind and behavior, specifically pain perception, learning capacity and memory, mood, and emotion, and anxiolytic effects and temperament. Also, disruptions in the gut microbiome have been associated with substance use disorders. While much research still needs to be performed, elucidating the interplay of gut microbiota in substance abuse disorders may produce promising avenues for future forensic development. The goal of the current study was to determine gut microbiome composition in substance abuse disorder cases using transverse colon tissues of 21 overdose criminal cases versus 19 non-overdose-related cases. The hypothesis was that postmortem samples of the same origin will reveal similar taxonomic relationships. Using weighted UniFrac analysis, drug abuse was found to be a significant factor in determining microbiome similarity (F = 1.93; df = 1, 35; p < 0.048; R2 = 0.05) indicating that there are detectable differences in composition that are attributable to substance abuse. Using unweighted UniFrac, however, sex was instead found to be a significant predictor of microbiome similarity (F = 1.88; df = 1, 30; p = 0.028; R2 = 0.05). A heatmap was generated of the relative abundances of the 30 most prevalent bacteria per case and their associated substance profile. The results revealed that samples of the same origin cluster together, showing a high degree of similarity between samples and a low degree of similarity among samples of different origin. This examination of human transverse colon microflora in decomposing cadavers expands the emerging literature on postmortem microbial communities, which will ultimately contribute to advanced knowledge of putrefaction. 

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4. “I don’t go to funerals anymore”: how people who use opioids grieve drug-related death in the US overdose epidemic

   https://doi.org/10.1186/s12954-022-00693-7  

   Schlosser, Allison V.; Hoffer, Lee D. (December 2022, Harm Reduction Journal)

   Abstract Background Opioid-related overdose death is a public health epidemic in much of the USA, yet little is known about how people who use opioids (PWUO) experience overdose deaths in their social networks. We explore these experiences through a qualitative study of opioid-related overdose death bereavement among PWUO. Methods We recruited 30 adults who inject opioids from a syringe service program in the Midwestern USA and interviewed them using a semi-structured guide that addressed experiences of opioid use, opioid-related overdose, and overdose reversal via the medication naloxone. Interviews were transcribed verbatim and analyzed thematically. Findings Participants described overdose death as ever-present in their social worlds. Most (approximately 75%) reported at least one overdose death in their social network, and many came to consider death an inevitable end of opioid use. Participants described grief shaped by complex social relations and mourning that was interrupted due to involvement with social services and criminal legal systems. They also reported several ways that overdose deaths influenced their drug use, with some increasing their use and others adopting safer drug use practices. Despite the high prevalence of overdose deaths in their social networks, only one participant reported receiving grief support services. Discussion Findings underscore the need for interventions that not only maintain life, such as naloxone distribution, but also improve quality of life by attending to grief related to overdose death bereavement. We discuss policies and practices with the potential to address the unique psychological, social, and structural challenges of grief for this population. 

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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. 

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