Differential misclassification of overdose safety in a prominent comparative effectiveness RCT comparing buprenorphine and naltrexone for opioid dependence
Creator:
Dasgupta, Nabarun, Schwartz, Todd, Ajazi, Elizabeth, and Marshall, Stephen
Date of publication:
August 25, 2022
Abstract Tesim:
Podium/Oral presentation at ISPE's 38th International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE 2022), scheduled for August 24-28 at the Bella Center, Copenhagen, Denmark.
Resource type:
Presentation
Affiliation Label Tesim:
Injury Prevention Research Center, Gillings School of Global Public Health, and Department of Epidemiology
Conference Name:
38th International Conference on Pharmacoepidemiology and Therapeutic Risk Management (ICPE 2022)
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/p7t5-8124
Language Label:
English
ORCID:
https://orcid.org/0000-0002-4098-605X and
Other Affiliation:
Person:
Dasgupta, Nabarun, Schwartz, Todd, Ajazi, Elizabeth, and Marshall, Stephen
Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults
Creator:
Dasgupta, Nabarun, Golightly, Yvonne M., Naumann, Rebecca B., Shiue, Kristin Y., and Nelson, Amanda E.
Date of publication:
September 14, 2020
Abstract Tesim:
Background: Despite declining opioid prescribing rates in the United States, the annual prevalence of prescription opioid use in adults ≥50 years old is estimated to be 40%, higher than that of younger adults (ages 18-29 years, 36%). As the American population ages, understanding factors that contribute to overall opioid use is a necessary first step in the determination and mitigation of inappropriate prescribing and opioid-related harms. Objective: Assess predictors of prescription opioid use in an adult population with a high prevalence of chronic pain. Methods: Data were from a community-based cohort of White and African American adults aged 50-90 years residing in predominantly rural Johnston County, North Carolina. Univariable and multivariable logistic regression models were used to evaluate sociodemographic and clinical factors in non-opioid users (n=795) at baseline (2006-2010) as predictors of opioid use at follow-up (2013-2015). Variables included age, sex, race, obesity (BMI≥30kg/m2), polypharmacy (5+ medications), educational attainment (<12, ≥12 years), employment (unemployed, employed/retired), insurance (uninsured, public, private), Census block group household poverty rate (<12%, 12–24%, ≥25%), depressive symptoms (Center for Epidemiologic Studies Depression Scale ≥16 or depression diagnosis), perceived social support (moderate/poor [<19], strong [≥19]; Strong Ties Measure of Social Support, range 0-20), pain sensitivity (sensitive [<4kg], normal [≥4kg] pressure pain threshold), and pain catastrophizing (high [≥15], moderate/low [<15]; Pain Catastrophizing Helplessness Subscale, range 0-25). Results: At follow-up, 13% (n=100) of participants were using prescription opioids. In univariable models, younger age, female sex, obesity, polypharmacy, unemployment, public (vs. private) health insurance, higher poverty rate, depressive symptoms, poorer perceived social support, pain catastrophizing, and elevated pain sensitivity were independently associated (p<0.05) with opioid use. In the multivariable model, younger age (60 vs. 70 years; adjusted odds ratio, 95% confidence interval=2.52, 1.08−5.88), polypharmacy (2.16, 1.24−3.77), high pain catastrophizing (2.17, 1.33−3.56), and depressive symptoms (2.00, 1.17−3.43) remained significant independent predictors. Conclusion: The simultaneous assessment of a breadth of clinical and sociodemographic factors identified polypharmacy, pain catastrophizing, and depressive symptoms as modifiable predictors of prescription opioid use. These findings support the incorporation of pharmacological review and behavioral approaches into chronic pain management strategies. Further research is warranted to track changes in these factors as prescription opioid use declines nationwide.
Resource type:
Poster
Affiliation Label Tesim:
Injury Prevention Research Center, Department of Epidemiology, Gillings School of Global Public Health, and School of Medicine
Conference Name:
36th International Conference on Pharmacoepidemiology & Therapeutic Risk Management ICPE All Access
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/tre3-5j43
Language Label:
English
ORCID:
https://orcid.org/0000-0002-4098-605X, https://orcid.org/0000-0003-1205-2759, https://orcid.org/0000-0002-6648-0794, https://orcid.org/0000-0002-5337-6656, and https://orcid.org/0000-0002-9344-7877
Other Affiliation:
Person:
Dasgupta, Nabarun, Golightly, Yvonne M., Naumann, Rebecca B., Shiue, Kristin Y., and Nelson, Amanda E.
Changes in Buprenorphine Prescribing during the COVID-19 Pandemic in Kentucky
Creator:
Slavova, Svetla, Freeman, Patricia R, and Lei, Feitong
Date of publication:
April 8, 2021
Abstract Tesim:
Buprenorphine is a medication approved by the U.S. Food and Drug
Administration (FDA) to treat opioid use disorder (OUD). There were concerns that the COVID-19-related restrictions would interfere with the in-person prescribing of buprenorphine. In response to the COVID-19 public health emergency declaration, the Drug Enforcement Administration (DEA), partnering with the Substance Abuse and Mental Health Services Administration (SAMHSA), adopted policies to relax buprenorphine prescribing regulations [1], authorizing practitioners to prescribe buprenorphine to existing patients via telemedicine (as of Mar 16, 2020) and new patients via telephone without first having an in-person or telemedicine visit (as of Mar 31, 2020)[2].
This study evaluated the significance of the immediate changes in
buprenorphine prescribing in Kentucky after the implementation of the new federal policies on buprenorphine prescribing for OUD treatment.
Resource type:
Poster
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/zxae-3z78
Language Label:
English
ORCID:
Other Affiliation:
University of Kentucky Department of Biostatistics and University of Kentucky Department of Pharmacy Practice and Science
Person:
Slavova, Svetla, Freeman, Patricia R, and Lei, Feitong
Overview of Challenges of Evaluating ADFs in the Community
Creator:
Dasgupta, Nabarun
Date of publication:
September 10, 2020
Abstract Tesim:
Dr. Nabarun Dasgupta presents as the invited Guest Speaker at the US Food and Drug Administration Advisory Committee on the 10 year evaluation of OxyContin reformulation. Epidemiologic considerations on evaluation of abuse deterrent formulations are presented. Presented June 10, 2020 at the Joint Meeting of the Drug Safety and Risk Management Advisory Committee (DSARM) and the Anesthetic and Analgesic Drug Products Advisory Committee (AADPAC) https://www.fda.gov/advisory-committees/advisory-committee-calendar/september-10-11-2020-joint-meeting-drug-safety-and-risk-management-advisory-committee-and-anesthetic
Resource type:
Presentation
Affiliation Label Tesim:
Injury Prevention Research Center
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/tcva-qx06
Identifier:
https://doi.org/10.17615/q38n-db05
Keyword:
OxyContin , Advisory Committee , overdose, pharmacoepidemiology , drug safety , science, graphic design , regulation, drug abuse, Food and Drug Administration, epidemiology, and opioid
Language Label:
English
ORCID:
0000-0002-4098-605X
Other Affiliation:
Person:
Dasgupta, Nabarun
Rights Statement Label:
In Copyright
Subject:
Opioids, Drug abuse, Chronic pain, Racism, Drugs, Injections, Intravenous Drug addiction, Epidemiology, and Databases
Prescribing and Dispensing Abuse-Deterrent Opioids: A Survey of Physicians and Pharmacists
Creator:
Slavova, Svetla, Dasgupta, Nabarun, Brown, John R, and Freeman. Patricia R
Date of publication:
July 28, 2022
Abstract Tesim:
Research Objective: To inform factors influencing the prescribing and
dispensing of abuse-deterrent formulation opioid analgesics (ADFs) and to enhance understanding of the context of ADF utilization in clinical practice.
Affiliation Label Tesim:
Injury Prevention Research Center
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/12rh-wb60
Language Label:
English
ORCID:
and 0000-0002-4098-605X
Other Affiliation:
University Of Kentucky College of Public Health, , and University Of Kentucky College of Pharmacy
Person:
Slavova, Svetla, Dasgupta, Nabarun, Brown, John R, and Freeman. Patricia R
Off-Label Day-of-Surgery Gabapentinoids and Prolonged Opioid Use: A Retrospective Cohort Study of Medicare Patients using Electronic Health Records
Creator:
Pate, Virginia, Jonsson-Funk, Michele, Hudgens, Michael, Young, Jessica, Chidgey, Brooke, Dasgupta, Nabarun, and Sturmer, Til
Date of publication:
August 23, 2021
Abstract Tesim:
Background: Gabapentinoids are increasingly incorporated into multimodal analgesia protocols for surgery. However, these drugs are not approved by the US FDA for use in managing surgical pain , and little is known regarding the effects of this off-label use on prolonged post-surgical opioid use following surgery.
Objectives: We estimate the association between preoperative day-of-surgery gabapentinoid administration on the risk of prolonged postsurgical opioid use.
Methods: We identified older adults (65+ years) undergoing surgery using electronic health records (EHR) from a large integrated healthcare system in the southeast (2016-2019) . Exposure to preoperative gabapentinoids was measured using inpatient medication administration records on the day of surgery, and the outcome of prolonged opioid use was measured using outpatient medication order s. We used stabilized inverse probability of treatment weights with 1% asymmetric trimming to adjust for patient demographics, baseline health indicators, preoperative pain score, and surgical details. We used log-binomial regression to estimate risk ratios and 95% confidence intervals (Cl) of prolonged opioid use comparing patients who received preoperative gabapentinoids to those who did not.
Results: Overall, 17,435 surgical patients met inclusion criteria, of whom 17.2% received preoperative gabapentinoids. The overall observed 90-day risk of prolonged opioid use following surgery was 1.20% (95% Cl: 1.05, 1.38) patients. The adjusted risk ratio of prolonged opioid use comparing patients who received a dose of preoperative gabapentinoids on the day of surgery to those who did not was 1.67 (95% Cl: 0.85,3.23).
Conclusions: Using data from a large integrated health system, we did not find evidence that preoperative gabapentinoids reduced the risk of prolonged opioid use in patients undergoing a broad range of surgeries. Off-label use of these medications to manage surgical pain should be carefully evaluated.
Affiliation Label Tesim:
Gillings School of Global Public Health, University of North Carolina at Chapel Hill, and Injury Prevention Research Center
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/y3xd-0f27
Language Label:
English
ORCID:
Other Affiliation:
Person:
Pate, Virginia, Jonsson-Funk, Michele, Hudgens, Michael, Young, Jessica, Chidgey, Brooke, Dasgupta, Nabarun, and Sturmer, Til
Background: The inability to identify dates of death in insurance claims data is a major limitation to retrospective claims based research. If not an outcome, death is a competing risk and poses a threat to validity when treated as non-informative right censoring.
Objectives: We aim to develop a user-friendly public algorithm to predict death within the year of disenrollment using an administrative claims database.
Methods: We identified adults (18+ years) with at least 2 years of continuous enrollment prior to disenrollment between 01/2007 and 01/2018. Leveraging unique linkages in addition to data that are typically unavailable in the publicly licensed data, we ascertained date of death from the Social Security Death Index, inpatient discharge status, and death indicators in the administrative data. Models including candidate predictors for age, sex, Census region, month of disenrollment, year of disenrollment, chronic condition indicators (components of the Elixhauser score), and prior healthcare utilization were estimated using used elastic net regression tuned by 5-fold cross-validation and final models evaluated in an independent testing set.
Weighted analysis adjusts for rare outcome (i.e., class imbalance). Sensitivity, specificity, and ROC associated with various thresholds of predicted probability to classify death at disenrollment were calculated.
Results: Overall, we identified 13,360,460 beneficiaries who disenrolled during the study period, with 5% of patients who died within the year of disenrollment. The strongest predictors of death were age at disenrollment, diagnosis of metastatic cancer in the year prior to death, and type of care received (e.g., inpatient stay, hospice care). Using a prediction threshold of 30%, the algorithm classified death at disenrollment with a sensitivity of 0.684 and specificity of 0.985 (ROC=0.97. At the same prediction threshold, the weighted algorithm classified death with a sensitivity of .947 and a specificity of 0.898 (ROC=.973).
Conclusions: Our algorithm uses publicly defined chronic conditions and utilization patterns that are easy to implement in claims data and predicts death at disenrollment with high specificity and varying sensitivity depending on the chosen prediction threshold. Users can easily implement the algorithm and can choose the prediction threshold (balancing sensitivity and specificity) to meet the needs of the specific study at hand.
Affiliation Label Tesim:
Injury Prevention Research Center and University of North Carolina at Chapel Hill
Analytical Enhancements in Kentucky’s Drug Overdose Mortality Surveillance: Rapid Monitoring of Trends and Decedents’ Recent Controlled Substance Prescription History
Background: Timely drug overdose mortality surveillance is key to informing public health action to reduce overdose-related fatalities. States are increasingly using linked data sources to enhance surveillance activities, yet approaches to their effective utilization and analyses are needed.
Objectives: The objective of this study is to describe the development and utilization of analytical tools for rapid, ongoing monitoring of drug overdose trends in Kentucky (KY) and decedents’ exposure to prescribed controlled substances (CS).
Methods: KY established a monthly process of linking all-cause death certificate (DC) with prescription drug monitoring program (DC-PDMP) data to enhance mortality surveillance. Using provisional 2018-2020 DC-PDMP data we developed scheduled quarterly analytical reports. Drug overdose deaths are identified based on underlying cause of death (ICD-10 X40-X44, X60-X64, X85, or Y10-Y14); involved drugs/drug classes are identified from multiple cause of death codes (T36
– T50). Common contributing substances are identified from DC cause of death section text fields. Drugs listed on DCs are compared with decedents’ past 90 days CS prescriptions.
Results: KY resident drug overdose deaths accounted for 2.8% of all-cause mortality, but among age group 26-40 years, 28.6% of all-cause deaths were due to drug overdose. Drug overdose decedents were disproportionally male (65.4% vs. 51.8% among all-cause deaths). From 2018 to 2020, the number of drug overdose deaths increased 42%. Deaths involving synthetic opioids and psychostimulants increased (56.2%vs 71.7% and 27.3% vs 35.1%, respectively) and deaths involving heroin (10.4% vs 6.0%), benzodiazepines (24.1% vs 15.3%), cocaine (9.6% vs 8.4%) and natural/semi-synthetic opioids (22.7% vs 21.3%) declined. The five substances most frequently listed in the DC in 2020 were fentanyl, methamphetamine, 4-ANPP, gabapentin, and acetyl fentanyl. Sixty-three percent of deaths involving natural/semi- synthetic opioids and 76% of cases involving benzodiazepines had no dispensed prescriptions for those drug classes in the previous 90 days, suggesting possible diversion. A historically high level of drug overdose deaths was observed in the first months of COVID-19 pandemic, with April-June 2020 overdose deaths (n=557), 80%higher than the same period in 2019.
Conclusions: The analytical enhancement of KY’s drug overdose surveillance supports rapid assessment to inform public health action and provides a rich dataset for pharmacoepidemiologic studies.
Affiliation Label Tesim:
Injury Prevention Research Center
Type:
http://purl.org/dc/dcmitype/Text
DOI:
https://doi.org/10.17615/d4mt-5a51
Language Label:
English
ORCID:
Other Affiliation:
U.S. Food and Drug Administration, University of Kentucky, and
Background: Abuse-deterrent formulations (ADFs) of extended-release (ER) opioids are manufactured to address opioid abuse. However, little is known about characteristics of patients who initiate ADF opioids, which is important to identify appropriate comparators to address confounding by indication.
Objectives: To describe demographics and medical characteristics of patients prescribed ADF and non-ADF ER opioids in two sources of commercial claims.
Methods: Using IBM Marketscan commercial claims (Data A) and a large private insurance provider in North Carolina [USA] (Data B) (both 2009-2018), we conducted a retrospective cohort study to examine patterns of ADF opioid use compared to non-ADF ER opioid use. Patients who initiated ADF and non-ADF ER opioids (18-64 years-old) were selected using both a traditional new user design (no opioid claims during the washout period, defined as six-months prior to ER opioid initiation) and a prevalent new user design (allowed non-ER opioid claims during the washout period and excluded the patients with no six-months eligibility prior to the first immediate-release (IR) opioid claim). Patient characteristics including demographics, medications (gabapentin, benzodiazepine, antidepressants, IR opioids), pain-related symptoms, and cancer were measured during the washout period for patients with ADF and non-ADF ER opioids.
Results: Among eligible ER opioid initiators in Data A (N=330,728) and B (N=20,992), 31% and 34% initiated with ADF opioids, respectively. Among these patients, demographics were as follows (Data A and B): age [mean (SD)] = 49.4 (11.8) and
48.4 (11.8); male sex = 51.2% and 55.4%. Among patients with non-ADF ER opioids, demographics were as follows (Data A and Data B): age [mean (SD)] = 49.2 (11.4) and 47.8 (11.3); male sex = 45.8% and 50.4%. About 50% and 62% of patients with ADF opioids initiated with IR opioids, whereas 29%and 34% of patients with non-ADF ER opioids initiated with IR opioids in Data A and B, respectively. In both data sources, the prevalence of several types of pain was higher among patients with ADF opioids than in non-ADF ER group, including acute pain (Data A: 54.5% vs. 40.3%; Data B: 56.7% vs. 41.5%), arthritis pain (35.7% vs. 20.1%; 36.4% vs. 22.7%), and chronic pain (84.8% vs. 76.3%; 89.5% vs. 85.3%). The prevalence of use of medications and cancer was higher in patients with non-ADF ER opioids than in patients with ADF opioids in both data sources.
Conclusions: Both data sources revealed differences in characteristics between patients with ADF and non-ADF ER opioids. The implications for research design include identifying appropriate comparator groups when examining ADF opioid use related outcomes.
Background: In drug studies, research designs requiring no prior exposure to certain drug classes may restrict research on important populations. For example, currently marketed abuse-deterrent formulation (ADF) opioids are routinely used in patients with prior prescription opioid exposure. The traditional new user design excludes patients with prior exposure to prescription opioids, hence incident ADF users are not representative of the overall ADF user population. A prevalent new user design, wherein patients are prescribed similar treatments (or potential comparators) before starting the new treatment, likely better represents the intended ADF patient population.
Objectives: To evaluate the appropriateness of new user versus prevalent new user design for estimating post-market effectiveness of ADFs and examine patterns of ADF initiation.
Methods: We used pharmaceutical claims data from a large private insurer in North Carolina [USA] from 2009-2018. Included patients were new ADF users age 18-64 with 6 months of continuous enrollment prior to their first ADF claim. Incident users were identified as those with no prescription opioid claims in a 6-month washout period prior to ADF initiation. Prevalent new users were identified as those with non-ADF opioid claims during the 6 months before ADF initiation, so long as they also had a 6-month washout period of no opioid claims prior to first non-ADF opioid claim. We compared sample sizes by study design and described ADF utilization patterns.
Results: We identified 8,841 eligible patients who initiated an ADF. Of these, 2,332 (26%) were classified as incident users, whereas 6,509 (74%) were prevalent new users and would be excluded in a traditional new user design. Most incident ADF users started with both an ADF and an immediate-release (IR) opioid concurrently (85%). Among prevalent new users, common ADF initiation patterns were: adding an ADF to an IR opioid regimen (43%), an immediate switch from IR opioids to an ADF (15%), and a delayed switch from IR opioids to an ADF (14%).
Conclusions: Three-quarters of patients initiating ADFs had prior prescription opioid use and would be excluded in a traditional new user study design. A prevalent new user design would increase sample size and better capture clinically meaningful patients. These findings may apply to studies of other medications where prior exposure is a labeled prerequisite, such as higher dose ER opioids and second-line therapies. Future work will explore prevalent new user designs and consider nuances in ADF initiation such as immediate versus delayed switching by incorporating time-matching to address opioid tolerance.
Affiliation Label Tesim:
Gillings School of Global Public Health and Injury Prevention Research Center