Modeling the evolution of the US opioid crisis for national policy development.

The opioid crisis is a major public health challenge in the United States, killing about 70,000 people in 2020 alone. Long delays and feedbacks between policy actions and their effects on drug-use behavior create dynamic complexity, complicating policy decision-making. In 2017, the National Academies of Sciences, Engineering, and Medicine called for a quantitative systems model to help understand and address this complexity and guide policy decisions. Here, we present SOURCE (Simulation of Opioid Use, Response, Consequences, and Effects), a dynamic simulation model developed in response to that charge. SOURCE tracks the US population aged ≥12 y through the stages of prescription and illicit opioid (e.g., heroin, illicit fentanyl) misuse and use disorder, addiction treatment, remission, and overdose death. Using data spanning from 1999 to 2020, we highlight how risks of drug use initiation and overdose have evolved in response to essential endogenous feedback mechanisms, including: 1) social influence on drug use initiation and escalation among people who use opioids; 2) risk perception and response based on overdose mortality, influencing potential new initiates; and 3) capacity limits on treatment engagement; as well as other drivers, such as 4) supply-side changes in prescription opioid and heroin availability; and 5) the competing influences of illicit fentanyl and overdose death prevention efforts. Our estimates yield a more nuanced understanding of the historical trajectory of the crisis, providing a basis for projecting future scenarios and informing policy planning.

A dynamic model of the opioid drug epidemic with implications for policy.

Background: The U.S. opioid epidemic has caused substantial harm for over 20 years. Policy interventions have had limited impact and sometimes backfired. Experts recommend a systems modeling approach to address the complexities of opioid policymaking.Objectives: Develop a system dynamics simulation model that reflects the complexities and can anticipate intended and unintended intervention effects.Methods: The model was developed from literature review and data gathering. Its outputs, starting in 1990, were compared against 12 historical time series. Four illustrative interventions were simulated for 2020-2030: reducing prescription dosage by 20%, cutting diversion by 30%, increasing addiction treatment from 45% to 65%, and increasing lay naloxone use from 4% to 20%. Sensitivity testing was performed to determine effects of uncertainties. No human subjects were studied.Results: The model fits historical data well with error percentage averaging 9% across 201 data points. Interventions to reduce dosage and diversion reduce the number of persons with opioid use disorder (PWOUD) by 11% and 16%, respectively, but each of these interventions reduces overdoses by only 1%. Boosting treatment reduces overdoses by 3% but increases PWOUD by 1%. Expanding naloxone reduces overdose deaths by 12% but increases PWOUD by 2% and overdoses by 3%. Combining all four interventions reduces PWOUD by 24%, overdoses by 4%, and deaths by 18%. Uncertainties may affect these numerical results, but policy findings are unchanged.Conclusion: No single intervention significantly reduces both PWOUD and overdose deaths, but a combination strategy can do so. Entering the 2020s, only protective measures like naloxone expansion could significantly reduce overdose deaths.

System Dynamics to Investigate Opioid Use and Chiropractic Care for Chronic Musculoskeletal Pain.

OBJECTIVE: The purpose of this investigation was to create a system dynamics (SD) model, including published data and required assumptions, as a tool for future research identifying the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population. METHODS: We present an illustrative case description of how we evaluated the feasibility of conducting a large-scale clinical trial to assess the impact of chiropractic care in mitigating excessive opioid use in Canada. We applied SD modeling using current evidence and key assumptions where such evidence was lacking. Modeling outcomes were highlighted to determine which potential factors were necessary to account for compelling study designs. RESULTS: Results suggest that a future clinical study diverting patients with nonmalignant musculoskeletal pain early to the chiropractic stream of care could be most effective. System dynamics modeling also highlighted design challenges resulting from unresearched assumptions that needed to be proxied for model completion. Assumptions included changing rates in opioid-associated deaths and rates of success in treatment management of addicted patients. CONCLUSION: In this case, SD modeling identified current research gaps and strong contenders for appropriate follow-up questions in a clinical research domain, namely the role of chiropractic care in the management of chronic, nonmalignant pain in a Canadian population.

Opioid-Prescribing Continuity and Risky Opioid Prescriptions.

We aimed to better understand the association between opioid-prescribing continuity, risky prescribing patterns, and overdose risk. For this retrospective cohort study, we included patients with long-term opioid use, pulling data from Oregon's Prescription Drug Monitoring Program (PDMP), vital records, and hospital discharge registry. A continuity of care index (COCI) score was calculated for each patient, and we defined metrics to describe risky prescribing and overdose. As prescribing continuity increased, likelihood of filling risky opioid prescriptions and overdose hospitalization decreased. Prescribing continuity is an important factor associated with opioid harms and can be calculated using administrative pharmacy data.

Dynamic model of nonmedical opioid use trajectories and potential policy interventions.

BACKGROUND: Nonmedical use of pharmaceutical opioid analgesics (POA) increased dramatically over the past two decades and remains a major health problem in the United States, contributing to over 16 000 accidental poisoning deaths in 2010. OBJECTIVES: To create a systems-oriented theory/model to explain the historical behaviors of interest, including the various populations of nonmedical opioid users and accidental overdose mortality within those populations. To use the model to explore policy interventions including tamper-resistant drug formulations and strategies for reducing diversion of opioid medicines. METHODS: A system dynamics model was constructed to represent the population of people who initiate nonmedical POA usage. The model incorporates use trajectories including development of use disorders, transitions from reliance on informal sharing to paying for drugs, transition from oral administration to tampering to facilitate non-oral routes of administration, and transition to heroin use by some users, as well as movement into and out of the population through quitting and mortality. Empirical support was drawn from national surveys (NSDUH, TEDS, MTF, and ARCOS) and published studies. RESULTS: The model was able to replicate the patterns seen in the historical data for each user population, and the associated overdose deaths. Policy analysis showed that both tamper-resistant formulations and interventions to reduce informal sharing could significantly reduce nonmedical user populations and overdose deaths in the long term, but the modeled effect sizes require additional empirical support. CONCLUSION: Creating a theory/model that can explain system behaviors at a systems level scale is feasible and facilitates thorough evaluation of policy interventions.

Protocol for an interview-based method for mapping mental models using causal-loop diagramming and realist interviewing.

Causal-loop diagramming, a method from system dynamics, is increasingly used in evaluation to describe individuals' understanding of how policies or programs do or could work ("mental models"). The use of qualitative interviews to inform model development is common, but guidance for how to design and conduct these interviews to elicit causal information in participant mental models is scant. A key strength of semi-structured qualitative interviews is that they let participants speak freely; they are not, however, designed to elicit causal information. Moreover, much of human communication about mental models-particularly larger causal structures such as feedback loops-is implicit. In qualitative research, part of the skill and art of effective interviewing and analysis involves listening for information that is expressed implicitly. Similarly, a skilled facilitator can recognize and inquire about implied causal structures, as is commonly done in group model building. To standardize and make accessible these approaches, we have formalized a protocol for designing and conducting semi-structured interviews tailored to eliciting mental models using causal-loop diagramming. We build on qualitative research methods, system dynamics, and realist interviewing. This novel, integrative method is designed to increase transparency and rigor in the use of interviews for system dynamics and has a variety of potential applications.

System dynamics modeling for cancer prevention and control: A systematic review.

Cancer prevention and control requires consideration of complex interactions between multilevel factors. System dynamics modeling, which consists of diagramming and simulation approaches for understanding and managing such complexity, is being increasingly applied to cancer prevention and control, but the breadth, characteristics, and quality of these studies is not known. We searched PubMed, Scopus, APA PsycInfo, and eight peer-reviewed journals to identify cancer-related studies that used system dynamics modeling. A dual review process was used to determine eligibility. Included studies were assessed using quality criteria adapted from prior literature and mapped onto the cancer control continuum. Characteristics of studies and models were abstracted and qualitatively synthesized. 32 studies met our inclusion criteria. A mix of simulation and diagramming approaches were used to address diverse topics, including chemotherapy treatments (16%), interventions to reduce tobacco or e-cigarettes use (16%), and cancer risk from environmental contamination (13%). Models spanned all focus areas of the cancer control continuum, with treatment (44%), prevention (34%), and detection (31%) being the most common. The quality assessment of studies was low, particularly for simulation approaches. Diagramming-only studies more often used participatory approaches. Involvement of participants, description of model development processes, and proper calibration and validation of models showed the greatest room for improvement. System dynamics modeling can illustrate complex interactions and help identify potential interventions across the cancer control continuum. Prior efforts have been hampered by a lack of rigor and transparency regarding model development and testing. Supportive infrastructure for increasing awareness, accessibility, and further development of best practices of system dynamics for multidisciplinary cancer research is needed.

System dynamics modeling for traumatic brain injury: Mini-review of applications.

Traumatic brain injury (TBI) is a highly complex phenomenon involving a cascade of disruptions across biomechanical, neurochemical, neurological, cognitive, emotional, and social systems. Researchers and clinicians urgently need a rigorous conceptualization of brain injury that encompasses nonlinear and mutually causal relations among the factors involved, as well as sources of individual variation in recovery trajectories. System dynamics, an approach from systems science, has been used for decades in fields such as management and ecology to model nonlinear feedback dynamics in complex systems. In this mini-review, we summarize some recent uses of this approach to better understand acute injury mechanisms, recovery dynamics, and care delivery for TBI. We conclude that diagram-based approaches like causal-loop diagramming have the potential to support the development of a shared paradigm of TBI that incorporates social support aspects of recovery. When developed using adequate data from large-scale studies, simulation modeling presents opportunities for improving individualized treatment and care delivery.

Reducing opioid use disorder and overdose deaths in the United States: A dynamic modeling analysis.

Opioid overdose deaths remain a major public health crisis. We used a system dynamics simulation model of the U.S. opioid-using population age 12 and older to explore the impacts of 11 strategies on the prevalence of opioid use disorder (OUD) and fatal opioid overdoses from 2022 to 2032. These strategies spanned opioid misuse and OUD prevention, buprenorphine capacity, recovery support, and overdose harm reduction. By 2032, three strategies saved the most lives: (i) reducing the risk of opioid overdose involving fentanyl use, which may be achieved through fentanyl-focused harm reduction services; (ii) increasing naloxone distribution to people who use opioids; and (iii) recovery support for people in remission, which reduced deaths by reducing OUD. Increasing buprenorphine providers' capacity to treat more people decreased fatal overdose, but only in the short term. Our analysis provides insight into the kinds of multifaceted approaches needed to save lives.

System dynamics modeling as a potentially useful tool in analyzing mitigation strategies to reduce overdose deaths associated with pharmaceutical opioid treatment of chronic pain.

OBJECTIVE: To illustrate a system-level, simulation-based approach for evaluating mitigation strategies to address the dramatic rise in abuse, addiction, and overdose deaths associated with the use of pharmaceutical opioid analgesics to treat chronic pain. SIMULATED INTERVENTIONS: Making available drug formulations with increased tamper-resistance, prescriber education programs, and programs that reduce rates of medical user-related abuse and addiction. SIMULATED OUTCOME MEASURE: Number of overdose deaths of medical users of pharmaceutical opioid analgesics, including those who abuse or have become addicted. METHODS: A demonstration system dynamics model is developed, tested, and used to evaluate the impact of candidate mitigation strategies on the outcome measures. RESULTS: Tamper-resistant drug products will likely reduce overdose death rates but may not reduce overall deaths if there is increased prescribing. Prescriber education would likely reduce deaths through a reduction in patient access to pharmaceutical opioid analgesics. CONCLUSIONS: The system dynamics approach may have potential for opioid-related policy evaluation. However, metrics must be carefully selected, and trade-offs may be involved. For example, it may be difficult to limit negative outcomes associated with pharmaceutical opioids without adversely affecting chronic pain patients' access to pharmaceutical treatment. Ultimately, a combination of metrics and value judgments will be needed to properly evaluate mitigation strategies.

An analysis of the root causes for opioid-related overdose deaths in the United States.

OBJECTIVE: A panel of experts in pain medicine and public policy convened to examine root causes and risk factors for opioid-related poisoning deaths and to propose recommendations to reduce death rates. METHODS: Panelists reviewed results from a search of PubMed and state and federal government sources to assess frequency, demographics, and risk factors for opioid-related overdose deaths over the past decade. They also reviewed results from a Utah Department of Health study and a summary of malpractice lawsuits involving opioid-related deaths. RESULTS: National data demonstrate a pattern of increasing opioid-related overdose deaths beginning in the early 2000s. A high proportion of methadone-related deaths was noted. Although methadone represented less than 5% of opioid prescriptions dispensed, one third of opioid-related deaths nationwide implicated methadone. Root causes identified by the panel were physician error due to knowledge deficits, patient non-adherence to the prescribed medication regimen, unanticipated medical and mental health comorbidities, including substance use disorders, and payer policies that mandate methadone as first-line therapy. Other likely contributors to all opioid-related deaths were the presence of additional central nervous system-depressant drugs (e.g., alcohol, benzodiazepines, and antidepressants) and sleep-disordered breathing. CONCLUSIONS: Causes of opioid-related deaths are multifactorial, so solutions must address prescriber behaviors, patient contributory factors, nonmedical use patterns, and systemic failures. Clinical strategies to reduce opioid-related mortality should be empirically tested, should not reduce access to needed therapies, should address risk from methadone as well as other opioids, and should be incorporated into any risk evaluation and mitigation strategies enacted by regulators.

Validation of a spatial agent-based model for Taenia solium transmission ("CystiAgent") against a large prospective trial of control strategies in northern Peru.

BACKGROUND: The pork tapeworm (Taenia solium) is a parasitic helminth that imposes a major health and economic burden on poor rural populations around the world. As recognized by the World Health Organization, a key barrier for achieving control of T. solium is the lack of an accurate and validated simulation model with which to study transmission and evaluate available control and elimination strategies. CystiAgent is a spatially-explicit agent based model for T. solium that is unique among T. solium models in its ability to represent key spatial and environmental features of transmission and simulate spatially targeted interventions, such as ring strategy. METHODS/PRINCIPAL FINDINGS: We validated CystiAgent against results from the Ring Strategy Trial (RST)-a large cluster-randomized trial conducted in northern Peru that evaluated six unique interventions for T. solium control in 23 villages. For the validation, each intervention strategy was replicated in CystiAgent, and the simulated prevalences of human taeniasis, porcine cysticercosis, and porcine seroincidence were compared against prevalence estimates from the trial. Results showed that CystiAgent produced declines in transmission in response to each of the six intervention strategies, but overestimated the effect of interventions in the majority of villages; simulated prevalences for human taenasis and porcine cysticercosis at the end of the trial were a median of 0.53 and 5.0 percentages points less than prevalence observed at the end of the trial, respectively. CONCLUSIONS/SIGNIFICANCE: The validation of CystiAgent represented an important step towards developing an accurate and reliable T. solium transmission model that can be deployed to fill critical gaps in our understanding of T. solium transmission and control. To improve model accuracy, future versions would benefit from improved data on pig immunity and resistance, field effectiveness of anti-helminthic treatment, and factors driving spatial clustering of T. solium infections including dispersion and contact with T. solium eggs in the environment.

Diverse Autonomic Nervous System Stress Response Patterns in Childhood Sensory Modulation.

The specific role of the autonomic nervous system (ANS) in emotional and behavioral regulation-particularly in relation to automatic processes-has gained increased attention in the sensory modulation literature. This mini-review article summarizes current knowledge about the role of the ANS in sensory modulation, with a focus on the integrated functions of the ANS and the hypothalamic-pituitary-adrenal (HPA) axis and their measurement. Research from the past decade illustrates that sympathetic and parasympathetic interactions are more complex than previously assumed. Patterns of ANS activation vary across individuals, with distinct physiological response profiles influencing the reactivity underlying automatic behavioral responses. This review article advances a deeper understanding of stress and the complex stress patterns within the ANS and HPA axis that contribute to allostatic load (AL). We argue that using multiple physiological measurements to capture individual ANS response variation is critical for effectively treating children with sensory modulation disorder (SMD) and sensory differences. We consider the relative contributions of automatic vs. deliberately controlled processes across large-scale neural networks in the development of sensorimotor function and their associated links with arousal patterns and sensory over- and under-responsivity.

Understanding transmission and control of the pork tapeworm with CystiAgent: a spatially explicit agent-based model.

BACKGROUND: The pork tapeworm, Taenia solium, is a serious public health problem in rural low-resource areas of Latin America, Africa and Asia, where the associated conditions of nuerocysticercosis (NCC) and porcine cysticercosis cause substantial health and economic harms. An accurate and validated transmission model for T. solium would serve as an important new tool for control and elimination, as it would allow for comparison of available intervention strategies, and prioritization of the most effective strategies for control and elimination efforts. METHODS: We developed a spatially-explicit agent-based model (ABM) for T. solium ("CystiAgent") that differs from prior T. solium models by including a spatial framework and behavioral parameters such as pig roaming, open human defecation, and human travel. In this article, we introduce the structure and function of the model, describe the data sources used to parameterize the model, and apply sensitivity analyses (Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC)) to evaluate model parameters. RESULTS: LHS-PRCC analysis of CystiAgent found that the parameters with the greatest impact on model uncertainty were the roaming range of pigs, the infectious duration of human taeniasis, use of latrines, and the set of "tuning" parameters defining the probabilities of infection in humans and pigs given exposure to T. solium. CONCLUSIONS: CystiAgent is a novel ABM that has the ability to model spatial and behavioral features of T. solium transmission not available in other models. There is a small set of impactful model parameters that contribute uncertainty to the model and may impact the accuracy of model projections. Field and laboratory studies to better understand these key components of transmission may help reduce uncertainty, while current applications of CystiAgent may consider calibration of these parameters to improve model performance. These results will ultimately allow for improved interpretation of model validation results, and usage of the model to compare available control and elimination strategies for T. solium.

Assessing the prediction potential of an in silico computer model of intracranial pressure dynamics.

OBJECTIVE: Traumatic brain injury (TBI) frequently results in poor outcome, suggesting that new approaches are needed. We hypothesized that a patient-specific in silico computer model of intracranial pressure (ICP) dynamics may predict the ICP response to therapy. DESIGN: In silico model analysis of prospectively collected data. SETTING: Twenty-three and 16-bed pediatric intensive care units in two tertiary care academic hospitals. PATIENTS: Nine subjects with severe TBI undergoing ICP monitoring (7 M/2 F, age range 3-17 years). INTERVENTIONS: Random changes in head-of-bed (HOB) (0 degrees , 10 degrees , 20 degrees , 30 degrees , 40 degrees ) elevation and respiratory rate (to achieve a DeltaETco2 = +/-3-4 mm Hg) were performed daily according to a study protocol as long as an intracerebral monitoring device was in place. METHODS AND MAIN OUTCOME MEASURES: A six-compartment dynamic ICP model was developed based on published equations and parametric data (baseline model parameter values). For each of 24 physiologic challenge sessions, patient-specific model parameter values were estimated that minimized the model fitness error, the difference between model-calculated ICP and observed ICP, both for baseline parameters and patient-specific parameter. Next, model prediction error was measured using two analyses. First, a "within" session analysis estimated parameter values using data from an initial Segment A, and then used those parameter values to predict the ICP during a later Segment B. The predicted ICP for B was compared with the observed ICP for B. Second, a "between" session analysis was performed. This analysis used parameter values estimated from earlier sessions to predict the ICP in later sessions. Fitness and prediction errors were measured in terms of mean absolute error (MAE). To normalize the errors, MAE was divided by the mean absolute deviation (MAD) for the associated segment or session, yielding a measure for both model fitness error and model prediction error that is favorable when <1. RESULTS: For baseline parameter values, MAE/MAD was <1 in 2 of 24 (8%) sessions. For session-specific parameter values, MAE/MAD was <1 in 21 of 24 (88%) sessions and <0.5 in 9 of 24 (38%) sessions. Sessions with low (<12 mm Hg) (n = 8; 33%) or high (>18 mm Hg) (n = 6; 25%) ICP had lower error than moderate ICP (12-18 mm Hg) (n = 10; 42%). MAE/MAD was <1 for 6 of 22 (27%) for within-session predictions and 3 of 31 (10%) for between-session predictions. CONCLUSIONS: The protocol for collecting physiologic data in subjects with severe TBI was feasible. The in silico ICP model with session-specific parameters accurately reproduced observed ICP response to changes in head-of-bed and respiration rate. We demonstrated modest success at predicting future ICP within a session and to a lesser extent between sessions.

Seasonal patterns in risk factors for Taenia solium transmission: a GPS tracking study of pigs and open human defecation in northern Peru.

BACKGROUND: Taenia solium (cysticercosis) is a parasitic cestode that is endemic in rural populations where open defecation is common and free-roaming pigs have access to human feces. The purpose of this study was to examine the roaming patterns of free-range pigs, and identify areas where T. solium transmission could occur via contact with human feces. We did this by using GPS trackers to log the movement of 108 pigs in three villages of northern Peru. Pigs were tracked for approximately six days each and tracking was repeated in the rainy and dry seasons. Maps of pig ranges were analyzed for size, distance from home, land type and contact with human defecation sites, which were assessed in a community-wide defecation survey. RESULTS: Consistent with prior GPS studies and spatial analyses, we found that the majority of pigs remained close to home during the tracking period and had contact with human feces in their home areas: pigs spent a median of 79% (IQR: 61-90%) of their active roaming time within 50 m of their homes and a median of 60% of their contact with open defecation within 100 m of home. Extended away-from-home roaming was predominately observed during the rainy season; overall, home range areas were 61% larger during the rainy season compared to the dry season (95% CI: 41-73%). Both home range size and contact with open defecation sites showed substantial variation between villages, and contact with open defecation sites was more frequent among pigs with larger home ranges and pigs living in higher density areas of their village. CONCLUSIONS: Our study builds upon prior work showing that pigs predominately roam and have contact with human feces within 50-100 m of the home, and that T. solium transmission is most likely to occur in these concentrated areas of contact. This finding, therefore, supports control strategies that target treatment resources to these areas of increased transmission. Our finding of a seasonal trend in roaming ranges may be useful for control programs relying on pig interventions, and in the field of transmission modeling, which require precise estimates of pig behavior and risk.

A review of physiological simulation models of intracranial pressure dynamics.

This paper reviews the literature regarding the development, testing, and application of physiology-based computer simulation models of intracranial pressure dynamics. Detailed comparative information is provided in tabular format about the model variables and logic, any data collected, model testing and validation methods, and model results. Several syntheses are given that summarize the research carried out by influential research teams and researchers, review important findings, and discuss the methods employed, limitations, and opportunities for further research.

Teen driver system modeling: a tool for policy analysis.

BACKGROUND: Motor vehicle crashes remain the leading cause of teen deaths in spite of preventive efforts. Prevention strategies could be advanced through new analytic approaches that allow us to better conceptualize the complex processes underlying teen crash risk. This may help policymakers design appropriate interventions and evaluate their impacts. METHODS: System Dynamics methodology was used as a new way of representing factors involved in the underlying process of teen crash risk. Systems dynamics modeling is relatively new to public health analytics and is a promising tool to examine relative influence of multiple interacting factors in predicting a health outcome. Dynamics models use explicit statements about the process being studied and depict how the elements within the system interact; this usually leads to discussion and improved insight. A Teen Driver System Model was developed by following an iterative process where causal hypotheses were translated into systems of differential equations. These equations were then simulated to test whether they can reproduce historical teen driving data. The Teen Driver System Model that we developed was calibrated on 47 newly-licensed teen drivers. These teens were recruited and followed over a period of 5-months. A video recording system was used to gather data on their driving events (elevated g-force, near-crash, and crash events) and miles traveled. RESULTS: The analysis suggests that natural risky driving improvement curve follows a course of a slow improvement, then a faster improvement, and finally a plateau: that is, an S-shaped decline in driving events. Individual risky driving behavior depends on initial risk and driving exposure. Our analysis also suggests that teen risky driving improvement curve is created endogenously by several feedback mechanisms. A feedback mechanism is a chain of variables interacting with each other in such a way they form a closed path of cause and effect relationships. CONCLUSIONS: Teen risky driving improvement process is created endogenously by several feedback mechanisms. The model proposed in the present article to reflect this improvement process can spark discussion, which may pinpoint to additional processes that can benefit from further empirical research and result in improved insight.

Use of prescription opioids before and after an operation for chronic pain (lumbar fusion surgery).

Lumbar fusion surgery is usually prompted by chronic back pain, and many patients receive long-term preoperative opioid analgesics. Many expect surgery to eliminate the need for opioids. We sought to determine what fraction of long-term preoperative opioid users discontinue or reduce dosage postoperatively; what fraction of patients with little preoperative use initiate long-term use; and what predicts long-term postoperative use. This retrospective cohort study included 2491 adults undergoing lumbar fusion surgery for degenerative conditions, using Oregon's prescription drug monitoring program to quantify opioid use before and after hospitalization. We defined long-term postoperative use as ≥4 prescriptions filled in the 7 months after hospitalization, with at least 3 occurring >30 days after hospitalization. Overall, 1045 patients received long-term opioids preoperatively, and 1094 postoperatively. Among long-term preoperative users, 77.1% continued long-term postoperative use, and 13.8% had episodic use. Only 9.1% discontinued or had short-term postoperative use. Among preoperative users, 34.4% received a lower dose postoperatively, but 44.8% received a higher long-term dose. Among patients with no preoperative opioids, 12.8% became long-term users. In multivariable models, the strongest predictor of long-term postoperative use was cumulative preoperative opioid dose (odds ratio of 15.47 [95% confidence interval 8.53-28.06] in the highest quartile). Cumulative dose and number of opioid prescribers in the 30-day postoperative period were also associated with long-term use. Thus, lumbar fusion surgery infrequently eliminated long-term opioid use. Opioid-naive patients had a substantial risk of initiating long-term use. Patients should have realistic expectations regarding opioid use after lumbar fusion surgery.

High-risk prescribing and opioid overdose: prospects for prescription drug monitoring program-based proactive alerts.

To develop a simple, valid model to identify patients at high risk of opioid overdose-related hospitalization and mortality, Oregon prescription drug monitoring program, Vital Records, and Hospital Discharge data were linked to estimate 2 logistic models; a first model that included a broad range of risk factors from the literature and a second simplified model. Receiver operating characteristic curves, sensitivity, and specificity of the models were analyzed. Variables retained in the final model were categories such as older than 35 years, number of prescribers, number of pharmacies, and prescriptions for long-acting opioids, benzodiazepines or sedatives, or carisoprodol. The ability of the model to discriminate between patients who did and did not overdose was reasonably good (area under the receiver operating characteristic curve = 0.82, Nagelkerke R = 0.11). The positive predictive value of the model was low. Computationally simple models can identify high-risk patients based on prescription history alone, but improvement of the predictive value of models may require information from outside the prescription drug monitoring program. Patient or prescription features that predict opioid overdose may differ from those that predict diversion.