Use of a prescription opioid registry to examine opioid misuse and overdose in an integrated health system (original) (raw)
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Cureus, 2021
To explore the demographic patterns of hospitalizations related to prescription opioid overdose (POD) and evaluate the mortality risk of association in POD inpatients. Methodology We conducted a cross-sectional study using the Nationwide Inpatient Sample of 184,711 POD inpatients. A binomial logistic regression model was used to evaluate the odds ratio (OR) of association for mortality risk due to comorbidities (substance use disorders (SUD) and medical complications) in POD inpatients. Results POD inpatients were majorly females (54.1%), older adults aged 51-75 years (48.5%), whites (81.5%), and from lower household income quartet (32.8%). The most prevalent comorbid SUD among POD inpatients was alcohol (15.7%), followed by cannabis (5.7%), cocaine (4.2%), and amphetamine (1.8%). Comorbid alcohol use disorders had a minimally increased association with mortality but were not statistically significant (OR = 1.036; P = 0.438). POD in patients with cardiac arrest had the highest risk of mortality (OR = 103.423; P < 0.001), followed by shock (OR = 15.367; P < 0.001), coma (OR = 13.427; P < 0.001), and respiratory failure (OR = 12.051; P < 0.001). Conclusions Our study indicates that the hospitalizations related to POD were more prevalent among females, elders between 51 and 75 years of age, whites, and those in the lower household income quartet. The prevalence of prescription opioid use and the hospitalization related to POD remains a significant public health issue. POD inpatients with medical complications were at a higher risk of mortality than with comorbid SUD.
Potentially Inappropriate Opioid Prescribing, Overdose, and Mortality in Massachusetts, 2011-2015
Journal of general internal medicine, 2018
Potentially inappropriate prescribing (PIP) may contribute to opioid overdose. To examine the association between PIP and adverse events. Cohort study. Three million seventy-eight thousand thirty-four individuals age ≥ 18, without disseminated cancer, who received prescription opioids between 2011 and 2015. We defined PIP as (a) morphine equivalent dose ≥ 100 mg/day in ≥ 3 months; (b) overlapping opioid and benzodiazepine prescriptions in ≥ 3 months; (c) ≥ 4 opioid prescribers in any quarter; (d) ≥ 4 opioid-dispensing pharmacies in any quarter; (e) cash purchase of prescription opioids on ≥ 3 occasions; and (f) receipt of opioids in 3 consecutive months without a documented pain diagnosis. We used Cox proportional hazards models to identify PIP practices associated with non-fatal opioid overdose, fatal opioid overdose, and all-cause mortality, controlling for covariates. All six types of PIP were associated with higher adjusted hazard for all-cause mortality, four of six with non-fa...
PLoS ONE, 2011
Background: As a population, non-medical prescription opioid users are not well-defined. We aimed to derive and describe typologies of prescription opioid use and nonmedical use using latent class analysis in an adult population being assessed for substance abuse treatment. Methods: Latent class analysis was applied to data from 26,314 unique respondents, aged 18-70, self-reporting past month use of a prescription opioid out of a total of 138,928 cases (18.9%) collected by the Addiction Severity Index-Multimedia Version (ASI-MVH), a national database for near real-time prescription opioid abuse surveillance. Data were obtained from November 2005 through December 2009. Substance abuse treatment, criminal justice, and public assistance programs in the United States submitted data to the ASI-MV database (n = 538). Six indicators of the latent classes derived from responses to the ASI-MV, a version of the ASI modified to collect prescription opioid abuse and chronic pain experience. The latent class analysis included respondent home ZIP code random effects to account for nesting of respondents within ZIP code. Results: A four-class adjusted latent class model fit best and defined clinically interpretable and relevant subgroups: Use as prescribed, Prescribed misusers, Medically healthy abusers, and Illicit users. Classes varied on key variables, including race/ ethnicity, gender, concurrent substance abuse, duration of prescription opioid abuse, mental health problems, and ASI composite scores. Three of the four classes (81% of respondents) exhibited high potential risk for fatal opioid overdose; 18.4% exhibited risk factors for blood-borne infections. Conclusions: Multiple and distinct profiles of prescription opioid use were detected, suggesting a range of use typologies at differing risk for adverse events. Results may help clinicians and policy makers better focus overdose and blood-borne infection prevention efforts and intervention strategies for prescription opioid abuse reduction.
Medical Use and Misuse of Prescription Opioids in the US Adult Population: 2016–2017
American Journal of Public Health, 2019
Objectives. To characterize prescription opioid medical users and misusers among US adults.Methods. We used the 2016–2017 National Surveys on Drug Use and Health to compare medical prescription opioid users with misusers without prescriptions, misusers of own prescriptions, and misusers with both types of misuse. Multinomial logistic regressions identified substance use characteristics and mental and physical health characteristics that distinguished the groups.Results. Among prescription opioid users, 12% were misusers; 58% of misusers misused their own prescriptions. Misusers had higher rates of substance use than did medical users. Compared with with-prescription-only misusers, without-and-with-prescription misusers and without-prescription-only misusers had higher rates of marijuana use and benzodiazepine misuse; without-and-with-prescription misusers had higher rates of heroin use. Compared with without-prescription-only misusers, without-and-with-prescription and with-prescription-only misusers had higher rates of prescription opioid use disorder. Most misusers, especially with-prescription-only misusers, used prescription opioids to relieve pain. Misusers were more likely to be depressed than medical users.Conclusions. Prescription opioid misusers who misused both their own prescriptions and prescription opioid drugs not prescribed to them may be most at risk for overdose. Prescription opioid misuse is a polysubstance use problem.
Frontiers in Pain Research
Background“As part of the U.S. government's urgent response to the epidemic of overdose deaths (1)” the United States Centers for Disease Control and Prevention (CDC) issued the “CDC Guideline for Prescribing Opioids for Chronic Pain-United States, 2016 (2)” (guideline) followed by the “CDC Clinical Practice Guideline for Prescribing Opioids–United States, 2022 (3) (guideline update). ” The guideline and guideline update cite a direct correlation between prescription opioids sales (POS) and opioid treatment admissions (OTA) and prescription opioid deaths (POD), which was based on data from 1999 to 2010. This paper updates those relationships and includes the correlations between prescription opioid sales (POS) and any opioid deaths (AOD) and total overdose deaths (TOD) from 2010 to 2019.MethodsLinear regression models were fit to each response separately. Opioid sales (measured as MME (morphine milligram equivalent) per capita) was the independent variable. Total overdose deaths...
Risk factors of prescription opioid overdose among Colorado Medicaid beneficiaries
The journal of pain : official journal of the American Pain Society, 2015
This study aims to determine risk factors of opioid overdose among the Colorado Medicaid population. A retrospective nested case-control study was undertaken. Medicaid beneficiaries who had a medical claim(s) for an emergency department visit or a hospitalization associated with an opioid overdose from July 2009 to June 2014 were defined as cases. Controls were selected using a nearest neighbor matching without replacement. The matched controls were selected based on age, gender, and opioid prescription. One case was matched with three controls. Multivariate conditional logistic regression was used to compare risk factors. A total of 816 cases with 2,448 controls were included. Six factors were associated with opioid overdose: mean morphine dose equivalent (>50 mg/day) [Odds ratio (OR) 1.986 (1.509; 2.614), methadone use (switching opioid to methadone vs. no methadone use) [OR 7.230 (2.346 - 22.286)], drug/alcohol abuse [OR 3.104 (2.195; 4.388)], other psychiatric illness [OR 1.7...
Academic Emergency Medicine, 2017
Background-Despite increasing reliance on Prescription Drug Monitoring Programs (PDMPs) as a response to the opioid epidemic, the relationship between aberrant drug-related behaviors captured by the PDMP and opioid use disorder is incompletely understood. How PDMP data should guide Emergency Department (ED) assessment has not been studied. Study Objective-To evaluate a relationship between PDMP opioid prescription records and self-reported non-medical opioid use of prescription opioids in a cohort of opioid dependent ED patients enrolled in a treatment trial. Methods-PDMP opioid prescription records during one year prior to study enrollment on 329 adults meeting Diagnostic and Statistical Manual IV criteria for opioid dependence entering a randomized clinical trial (RCT) in a large, urban ED were cross tabulated with data on 30-day non-medical prescription opioid use self-report. The association among these two types of data was assessed by the Goodman and Kruskal's Gamma; a logistic regression was used to explore characteristics of participants who had PDMP record of opioid prescriptions. Results-During one year prior to study enrollment,118/329 (36%) patients had ≥ 1 opioid prescriptions (range 1-51) in our states' PDMP. Patients who reported ≥15 out of 30 days of nonmedical prescription opioid use were more likely to have ≥4 PDMP opioid prescriptions (20/38; 53%) than patients reporting 1-14 days (14/38, 37%) or zero days of non-medical prescription opioid use (4/38,11%); p=0.002. Female gender and having health insurance were significantly more represented in the PDMP (p<0.05 for both). Conclusion-PDMPs may be helpful in identifying patients with certain aberrant drug-related behavior, but are unable to detect many patients with OUD. The majority of ED patients with
Background: There has been a significant increase in opioid prescriptions and the prevalence of opioid nonmedical use. Nonmedical use may lead to opioid abuse/dependence, a serious public health concern. The aim of this paper was to determine the mental and physical health predictors of incident nonmedical prescription opioid use (NMPOU) and abuse/dependence, and the impact of comorbidity in a longitudinal, nationally representative sample. Methods: Data come from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (N=34,653; ≥20 years old). Mental disorders were assessed using the Alcohol Use Dis- order and Associated Disabilities Interview Schedule-DSM-IV edition. Physical conditions were based on self-reports of physician-diagnoses. Multiple logistic regression models examined the associations between mental and physical health predictors at Wave 1 and their association to incident NMPOU and abuse/dependence disorders at Wave 2. Results: After adjusting for sociodemographics, Axis I and II mental disorders and physical conditions, the presence of mental disorders (i.e., mood, personality disorders and substance use disorders), phys- ical conditions (i.e., increasing number of physical conditions, any physical condition, arteriosclerosis or hypertension, cardiovascular disease and arthritis) and sociodemographic factors (i.e., sex and mar- ital status) at Wave 1 positively predicted incident abuse/dependence at Wave 2. Comorbid disorders increased the risk of NMPOU and abuse/dependence. Conclusion: These results suggest the importance of mental and physical comorbidity as a risk for NMPOU and abuse/dependence, emphasizing the need for careful screening practices when prescribing opioids.