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

Geissert, Peter; Hallvik, Sara E.; Otterloo, Joshua Van; O’Kane, Nicole; Alley, Lindsey; Carson, Jody; Leichtling, Gillian; Hildebran, Christi; Wakeland, Wayne W.; Deyo, Richard A.

doi:10.1097/j.pain.0000000000001078

Cited by 36 publications

(24 citation statements)

References 29 publications

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“…PDMPs have been used primarily to monitor and address aberrant or unsafe prescribing of opioid analgesics and benzodiazepines (Chang et al, 2016, 2018; Moyo et al, 2017; Rutkow et al, 2015). For example, recent initiatives have used prescription data linked to overdose records to help identify risk factors for overdose among patients prescribed opioid analgesics (Ferris et al, 2019; Geissert et al, 2018; Glanz et al, 2018; Oliva et al, 2017). Predictive models from these analyses can be translated into patient-specific summary overdose risk scores, which can serve as a decision support tool for clinicians and public health practitioners.…”

Section: Introductionmentioning

confidence: 99%

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

Chang

Krawczyk

Schneider

et al. 2019

Drug and Alcohol Dependence

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Background: Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences. Methods: We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphine patients (N = 25,487). We estimated a series of models that included demographics plus opioid, buprenorphine and benzodiazepine prescription variables. We applied logistic regression to generate performance measures. Results: About 3.24% of the study cohort had ≥1 nonfatal opioid overdoses. In the model with all predictors, odds of nonfatal overdoses among buprenorphine patients were higher among males (OR = 1.39, 95% CI:1.21–1.62) and those with more buprenorphine pharmacies (OR = 1.19, 95% CI:1.11–1.28), 1+ buprenorphine prescription paid by Medicaid (OR = 1.21, 95% CI:1.02–1.48), Medicare (OR = 1.93, 95% CI:1.63–2.43), or a commercial plan (OR = 1.98, 95% CI:1.30–2.89), 1+ opioid prescription paid by Medicare (OR = 1.30, 95% CI:1.03–1.68), and more benzodiazepine prescriptions (OR = 1.04, 95% CI:1.02–1.05). The odds were lower among those with longer days of buprenorphine (OR = 0.64, 95% CI:0.60–0.69) or opioid (OR = 0.79, 95% CI:0.65–0.95) supply. The model had moderate predictive ability (c-statistic = 0.69). Conclusions: Several modifiable risk factors, such as length of buprenorphine treatment, may be targets for interventions to improve clinical care and reduce harms. This model could be practically implemented with common prescription-related information and allow payers and clinical systems to better target overdose risk reduction interventions, such as naloxone distribution.

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Section: Introductionmentioning

confidence: 99%

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

Chang

Krawczyk

Schneider

et al. 2019

Drug and Alcohol Dependence

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“…7,8 Predictive risk models can accurately predict fatal and nonfatal overdose risks for these specific payers or programs, achieving area under the curve (AUC) statistics ranging from 0.75 to 0.90. [4][5][6][7][8][9] Despite their utility, current models are limited because they often exclude groups such as people without insurance, patients using specialty behavioral health programs that may be carved out of health plans, and people with criminal justice involvement. Overdose risk has substantially shifted away from people exclusively using prescription opioids to people using illicit opioids.…”

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confidence: 99%

Predictive Modeling of Opioid Overdose Using Linked Statewide Medical and Criminal Justice Data

et al. 2020

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IMPORTANCEResponding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems.OBJECTIVE To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. DESIGN, SETTING, AND PARTICIPANTSA cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019.EXPOSURES Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. MAIN OUTCOMES AND MEASURESFatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. RESULTS There were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI,), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI,). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85. CONCLUSIONS AND RELEVANCEIn this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.

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“…prescription monitoring programmes and pill mill laws) in North America which are reducing medical access to opioids. 38 – 40 However, this restriction has had devastating consequences. Opioid-dependent people without access to opioid substitution therapy, such as methadone, have moved to illicit sources of opioids, and unintended overdose deaths have rapidly increased.…”

Section: Discussionmentioning

confidence: 99%

Global, regional, and national consumption of controlled opioids: a cross-sectional study of 214 countries and non-metropolitan territories

Richards

Aronson

Mahtani

et al. 2021

British Journal of Pain

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Introduction: The consumption of opioids has increased globally since the 1990s. Previous studies of global opioid consumption have concentrated on morphine alone or a subset of opioids, with a focus on cancer pain and palliative care. In this study, we have determined the global, regional, and national consumption of all controlled opioids, including anaesthetics, analgesics, antidiarrheals, opioid substitution therapies, and cough suppressants. Methods: We conducted a cross-sectional study using data from the International Narcotics Control Board (INCB). We calculated mean opioid consumption (mg/person) globally, regionally, and nationally for 2015–2017, where consumption refers to the total amount of controlled opioids distributed for medical purposes and excludes recreational use. We ranked countries by total consumption and quantified the types of opioids consumed globally. Results: Between 2015 and 2017, 90% of the world’s population consumed only 11% of controlled opioids. An average of 32 mg/person was consumed annually, but this was not equally distributed across the world. Consumption was the highest in Germany (480 mg/person), followed by Iceland (428 mg/person), the United States (398 mg/person) and Canada (333 mg/person). Oxycodone (35%) was the most heavily consumed controlled opioid globally, followed by morphine (15.9%), methadone (15.8%) and tilidine (14%). Conclusion: Large disparities persist in most of the world in accessing essential opioid medicines. Consumption patterns should continue to be monitored, and collaborative strategies should be developed to promote access and the appropriate prescribing of opioids in all countries and non-metropolitan territories.

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

Cited by 36 publications

References 29 publications

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

Predictive Modeling of Opioid Overdose Using Linked Statewide Medical and Criminal Justice Data

Global, regional, and national consumption of controlled opioids: a cross-sectional study of 214 countries and non-metropolitan territories

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