Using prescribing and toxicology data to determine non-medical prescription drug overdose.
Ontology highlight
ABSTRACT: Background
Overdose deaths have increased dramatically in the United States and are often attributed to prescription opioids. This study presents a framework for "overdose typologies", including non-medical prescription drug use, to more accurately describe drug use patterns.Methods
This study examined linked prescription drug monitoring program (PDMP) and toxicology data (2016-2018) from accidental overdose deaths from a large metropolitan coroner's office in the Midwest (Indianapolis, Indiana).Results
In total, 1,112 accidental overdose deaths occurred and over two-thirds (68.0%; n = 756) were coded as an illicit drug user with no prescription opioid present in the toxicology. The most infrequent categories were prescription opioid users 5.5% (n = 61).Conclusion
Linked PDMP and toxicology reports are useful in identifying drug use patterns that contribute to mortality.
SUBMITTER: Huynh P
PROVIDER: S-EPMC7330872 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Using prescribing and toxicology data to determine non-medical prescription drug overdose.
Huynh Philip P Victor Grant G Ray Brad B
Addictive behaviors reports 20200608
<h4>Background</h4>Overdose deaths have increased dramatically in the United States and are often attributed to prescription opioids. This study presents a framework for "overdose typologies", including non-medical prescription drug use, to more accurately describe drug use patterns.<h4>Methods</h4>This study examined linked prescription drug monitoring program (PDMP) and toxicology data (2016-2018) from accidental overdose deaths from a large metropolitan coroner's office in the Midwest (Indian ...[more]
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Project description:IntroductionPrescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids.MethodsFrom December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination.ResultsPredictors of any opioid-related fatal overdose included male sex, age 65-80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days' supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77).ConclusionsA model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids.
| S-EPMC7996003 | biostudies-literature
Project description: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.
| S-EPMC5893429 | biostudies-literature
Project description:Drug overdose is a public health crisis in the United States, due in part to the unintended consequences of increases in prescribing of opioid analgesics. Many clinicians evaluate risk markers for opioid-related harms when prescribing opioids for chronic pain; however, more data on predictive risk markers are needed. Risk markers are attributes (modifiable and non-modifiable) that are associated with increased probability of an outcome. This review aims to identify risk markers associated with fatal and non-fatal prescription drug overdose by synthesizing findings in the existing peer-reviewed and grey literature. Eligible cohort, case-control, cross-sectional, and case-cohort studies were reviewed and data were extracted for qualitative and quantitative synthesis.Summary odds ratios (SOR) were estimated from 29 studies for six risk markers: sex, age, race, psychiatric disorders, substance use disorder (SUD), and urban/rural residence. Heterogeneity was assessed and effect estimates were stratified by study characteristics. Of the six risk markers identified, SUD had the strongest association with drug overdose death (SOR = 5.24, 95% confidence interval (CI) = 3.53 - 7.76), followed by psychiatric disorders (SOR = 3.94, 95% CI = 3.09 - 5.01), white race (SOR = 2.28, 95% CI = 1.93 - 2.70), the 35-44 year age group relative to the 25-34 year reference group (SOR = 1.52, 95% CI = 1.31 - 1.76), and male sex (SOR = 1.33, 95% CI = 1.17 - 1.51).This review highlights fatal and non-fatal prescription drug risk markers most frequently assessed in peer-reviewed and grey literature. There is a need to better understand modifiable risk markers and underlying reasons for drug misuse in order to inform interventions that may prevent future drug overdoses.
| S-EPMC5545182 | biostudies-other
Project description:Study objectivePrescription opioid use is associated with substance-related adverse outcomes among adolescents and young adults through a pathway of prescribing, diversion and misuse, and addiction and overdose. Assessing the effect of current prescription drug monitoring programs (PDMPs) on opioid prescribing and overdoses will further inform strategies to reduce opioid-related harms.MethodsWe performed interrupted time series analyses to measure the association between state-level implementation of PDMPs with annual opioid prescribing and opioid-related overdoses in adolescents (13 to 18 years) and young adults (19 to 25 years) between 2008 and 2019. We focused on PDMPs that included mandatory reviews by providers. Data were obtained from a commercial insurance company.ResultsAmong 9,344,504 adolescents and young adults, 1,405,382 (15.0%) had a dispensed opioid prescription, and 6,262 (0.1%) received treatment for an opioid-related overdose. Mandated PDMP review was associated with a 4.2% (95% CI, 1.9% to 6.4%) reduction in annual opioid dispensations among adolescents and a 7.8% (95% CI, 4.7% to 10.9%) annual reduction among young adults. For opioid-related overdoses, mandated PDMP review was associated with a 16.1% (95% CI, 3.8 to 26.7) and 15.9% (95% CI, 7.6 to 23.4) reduction in annual opioid overdoses for adolescents and young adults, respectively.ConclusionPDMPs were associated with sustained reductions in opioid prescribing and overdoses in adolescents and young adults. Although these findings support the value of mandated PDMPs as part of ongoing strategies to reduce opioid overdoses, further studies with prospective study designs are needed to characterize the effect of these programs fully.
| S-EPMC10091852 | biostudies-literature
Project description:Background and aimsIn the US, benzodiazepine overdose deaths increased at an alarming rate in the past two decades. Benzodiazepines were also the most common drugs involved in prescription opioid overdose deaths. Benzodiazepine prescribing has been monitored by Prescription Drug Monitoring Programs (PDMPs), but little was known about whether PDMPs reduced drug overdose deaths involving benzodiazepines.Design and methodsThis study used a difference-in-difference design with state-quarter aggregate data on drug overdose deaths. The primary data source was Mortality Multiple Cause Files in 1999-2016. Three age-adjusted rates of drug overdose deaths were examined, including those involving benzodiazepines, those involving prescription opioids, and those involving both benzodiazepines and prescription opioids. The policy variables included PDMP data access for benzodiazepines and mandatory use of PDMP data for benzodiazepines. Linear multivariable regressions were used to assess the associations of PDMP policies specific to benzodiazepines with drug overdose death rates, controlling for other state-level policy and socioeconomic factors, state and time fixed effects, and state-specific time trends.ResultsNo significant associations were found between PDMP data access for benzodiazepines and changes in drug overdose death rates involving benzodiazepines and/or prescription opioids. Similarly, no significant associations were found between mandatory use of PDMP data for benzodiazepines and changes in drug overdose death outcomes.Discussion and conclusionsThis study suggested no evidence that PDMP policies specific to benzodiazepines were associated with reduction in benzodiazepine overdose death rates. Future research is warranted to examine detailed features of PDMPs and continuously monitor the impacts of PDMP policies on benzodiazepine-related consequences.
| S-EPMC6641857 | biostudies-literature
Project description:BackgroundCommunity pharmacists are well-positioned to identify patients engaged in non-medical prescription opioid use (NMPOU) through Prescription Drug Monitoring Program (PDMP) databases. Integrating patient-reported outcomes with PDMP data may improve the interpretability of PDMP information to support clinical decision-making.ObjectiveThis study linked patient-reported clinical measures of substance use with PDMP data to examine relationships between average daily opioid dose in morphine milligram equivalents (MME) and visits to multiple pharmacies/prescribers with self-reported NMPOU.MethodsData from a cross-sectional health assessment given to patients aged ≥18 years filling opioid prescriptions were linked to PDMP records. NMPOU in the past three months was assessed on a continuous scale (range 0-39) using an adapted version of the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). PDMP measures included average daily MME and number of distinct pharmacies/prescribers visited in the past 180 days. Univariable and multivariable zero-inflated negative binomial models estimated associations between PDMP measures and any NMPOU and severity of use.ResultsThe sample included 1421 participants. In multivariable models adjusted for sociodemographic, mental health, and physical health characteristics, any NMPOU was associated with higher average daily MME (adjusted OR = 1.22, 95% CI = 1.05-1.39) and number of distinct prescribers visited (adjusted OR = 1.15, 95% CI = 1.01-1.30). Higher average daily MME (adjusted mean ratio (MR) = 1.12, 95% CI = 1.08-1.15), number of distinct pharmacies visited (adjusted MR = 1.11, 95% CI = 1.04-1.18), and number of distinct prescribers visited (adjusted MR = 1.07, 95% CI = 1.02-1.11) were associated with increased NMPOU severity.ConclusionsWe observed significant, positive associations between average daily MME and visits to multiple pharmacies/prescribers with any NMPOU and severity of use. This study demonstrates self-report clinical measures of substance use can be cross-walked to PDMP data and translated into clinically interpretable information.
| S-EPMC10523937 | biostudies-literature
Project description:INTRODUCTION:Non-medical prescription opioid (NMPO) use is a critical public health problem in the United States, with 2.1 million new initiates annually. Young adult NMPO users are at high risk for initiating injection drug use. We assessed correlates of injection drug use among young adult NMPO users in Rhode Island, a state heavily impacted by opioid overdose. METHODS:We used data from the Rhode Island Young Adult Prescription Drug Study (RAPiDS), which recruited 199 residents aged 18-29 who reported past-30-day NMPO use (65.3% male). We compared individuals who reported ever having injected with individuals who reported never injecting, using logistic regression to identify independent correlates of injection. RESULTS:Among eligible participants, the mean age was 24.6years and 61.3% were white. Over one-quarter (n=59, 29.6%) of the sample had ever injected drugs. The majority (n=46, 78.0%) of participants who had ever injected drugs reported injecting heroin as her/his first drug; the majority also reported previously snorting her/his first drug that was injected (n=46, 78.0%). In multivariable analyses, white race, older age, lifetime homelessness, and ever having overdosed or seen someone overdose were independently associated with an increased likelihood of ever injecting drugs. CONCLUSIONS:These findings demonstrate a high prevalence of lifetime injection drug use among young adults who use prescription opioids non-medically. Given the observed associations between injection drug use and witnessing as well as experiencing overdose, interventions are urgently needed to improve overdose education and naloxone distribution to young adult NMPO users who inject drugs.
| S-EPMC5614848 | biostudies-literature
Project description:PurposePrescription drug overdose (PDO) deaths are a critical public health problem in the United States. This study aims to assess the association between emergency department (ED) utilization patterns in a cohort of ED patients and the risk of subsequent unintentional PDO mortality.MethodsUsing data from the New York Statewide Planning and Research Cooperative System for 2006-2010, a nested case-control design was used to examine the relationship between ED utilization patterns in New York State residents of age 18-64 years and subsequent PDO death.ResultsThe study sample consisted of 2732 case patients who died of PDO and 2732 control ED patients who were selected through incidence density sampling. With adjustment for demographic characteristics, and diagnoses of pain, substance abuse, and psychiatric disorders, the estimated odds ratios of PDO death relative to one ED visit or less in the previous year were 4.90 (95% confidence interval [CI]: 4.50-5.34) for those with two ED visits, 16.61 (95% CI: 14.72-18.75) for those with three ED visits, and 48.24 (95% CI: 43.23-53.83) for those with four ED visits or more.ConclusionsFrequency of ED visits is strongly associated with the risk of subsequent PDO death. Intervention programs targeting frequent ED users are warranted to reduce PDO mortality.
| S-EPMC4675463 | biostudies-literature
Project description:ImportanceSafe opioid prescribing practices are critical to mitigate the risk of prescription opioid overdose in adolescents and young adults. However, studies that examine opioid prescribing patterns associated with prescription opioid overdose have mostly focused on older adults. The generalizability of these studies to adolescents and young adults is unclear.ObjectiveTo identify opioid prescribing patterns associated with prescription opioid overdose in adolescents and young adults.Design, setting, and participantsThis retrospective cohort study assessed privately insured patients aged 12 to 21 years with opioid prescription claims in the IBM MarketScan Commercial Claims and Encounters database between July 1, 2009, and October 1, 2017, and no cancer diagnosis. Data analysis was performed from January 1 to April 30, 2019.Main outcomes and measuresThe outcome was a treated opioid overdose as indicated by diagnosis codes. On the basis of days supplied, opioid prescription claims were converted to person-days (the unit of analysis) on which opioid exposure would occur if patients took medications as prescribed. Logistic regression with clustered SEs at the patient level was used to model the occurrence of overdose on a person-day as a function of daily opioid dosage category (<30, 30-59, 60-89, 90-119, or ≥120 morphine milligram equivalents), concurrent benzodiazepine use, and extended-release or long-acting opioid use. Regressions controlled for demographic characteristics, year, opioid use within 180 days, and comorbidities (mental health disorder, substance use disorder, and other chronic condition).ResultsA total of 2 752 612 patients (mean [SD] age at cohort entry, 17.2 [2.5] years; 1 451 918 [52.8%] female) participated in the study. Patients had 4 686 355 opioid prescription claims, corresponding to 21 605 444 person-days. Overdose occurred on 255 person-days among 249 patients (0.01% of the sample). Each increase in daily opioid dosage category was associated with higher overdose risk (adjusted odds ratio [AOR], 1.18; 95% CI, 1.05-1.31). Compared with no use, both concurrent benzodiazepine use (AOR, 1.83; 95% CI, 1.24-2.71) and extended-release or long-acting opioid use (AOR, 2.01; 95% CI, 1.16-3.46) were associated with increased overdose risk.Conclusions and relevanceThe findings suggest that when prescribing opioids to adolescents and young adults, practitioners could potentially mitigate overdose risk by using the lowest effective daily dosage, avoiding concurrent opioid and benzodiazepine prescribing, and relying on short-acting opioids. Findings are broadly consistent with prior opioid safety studies focused on older adults.
| S-EPMC6990690 | biostudies-literature
Project description:ObjectiveTo develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.Materials and methodsStudy data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric "weak learner" models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.ResultsWithin 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79-0.83; Spiegelhalter P value: 0-.12). Risk concentration captured 47-52% of cases in the top quantiles of predicted probabilities.DiscussionPartitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.ConclusionPredicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.
| S-EPMC8714265 | biostudies-literature