Patterns and Correlates of Prescription Opioid Receipt Among US Veterans: A National, 18-Year Observational Cohort Study

Trajectories of Opioid Use Before and After Cancer Diagnosis: A Population-Based Cohort Study

BACKGROUND

Opioid use prior to cancer diagnosis increases the likelihood of long-term use during survivorship, however, patterns of use before and after diagnosis are not understood.

METHODS

We used population-based dispensing data linked with cancer and death notifications to identify two cohorts of adults residing in New South Wales initiating opioids within 24 months prior to a first cancer diagnosed between 2014 and 2016: 'survivors' (alive 24 months following diagnosis) and 'decedents' (died within 24 months). We used group-based trajectory modelling to identify trajectories of monthly opioid dispensings and dispensed oral morphine equivalent milligrams (OMEmg) during the 24 months before/after cancer diagnosis.

RESULTS

There were 21,843 survivors with four prediagnosis opioid dispensing trajectories: infrequent (58% of the cohort), late increasing (26%), moderate (10%), and sustained dispensing (6%). We observed an overall increase in dispensed OMEmg of 83 OMEmg (95% CI: 76-91) during the month of diagnosis, with strong opioid formulations comprising most treatment postdiagnosis. Within each prediagnosis opioid trajectory group, we observed five to six postdiagnosis trajectory groups, including no opioid dispensing. Moderate and sustained prediagnosis groups had large proportions of people continuing or increasing opioid dispensing after diagnosis, while small proportions discontinued opioid treatment. We observed similar trajectories in the decedent cohort.

CONCLUSIONS

There is considerable heterogeneity in opioid use before and after cancer diagnosis. Our findings suggest noncancer factors drive a significant proportion of postdiagnosis opioid use, but use increased significantly from the month of cancer diagnosis and never returned to prediagnosis levels.

Five-Year Trajectories of Prescription Opioid Use

Importance

There are known risks of using opioids for extended periods. However, less is known about the long-term trajectories of opioid use following initiation.

Objective

To identify 5-year trajectories of prescription opioid use, and to examine the characteristics of each trajectory group.

Design, Setting, and Participants

This population-based cohort study conducted in New South Wales, Australia, linked national pharmaceutical claims data to 10 national and state data sets to determine sociodemographic characteristics, clinical characteristics, drug use, and health services use. The cohort included adult residents (aged ≥18 years) of New South Wales who initiated a prescription opioid between July 1, 2003, and December 31, 2018. Statistical analyses were conducted from February to September 2022.

Exposure

Dispensing of a prescription opioid, with no evidence of opioid dispensing in the preceding 365 days, identified from pharmaceutical claims data.

Main Outcomes and Measures

The main outcome was the trajectories of monthly opioid use over 60 months from opioid initiation. Group-based trajectory modeling was used to classify these trajectories. Linked health care data sets were used to examine characteristics of individuals in different trajectory groups.

Results

Among 3 474 490 individuals who initiated a prescription opioid (1 831 230 females [52.7%]; mean [SD] age, 49.7 [19.3] years), 5 trajectories of long-term opioid use were identified: very low use (75.4%), low use (16.6%), moderate decreasing to low use (2.6%), low increasing to moderate use (2.6%), and sustained use (2.8%). Compared with individuals in the very low use trajectory group, those in the sustained use trajectory group were older (age ≥65 years: 22.0% vs 58.4%); had more comorbidities, including cancer (4.1% vs 22.2%); had increased health services contact, including hospital admissions (36.9% vs 51.6%); had higher use of psychotropic (16.4% vs 42.4%) and other analgesic drugs (22.9% vs 47.3%) prior to opioid initiation, and were initiated on stronger opioids (20.0% vs 50.2%).

Conclusions and relevance

Results of this cohort study suggest that most individuals commencing treatment with prescription opioids had relatively low and time-limited exposure to opioids over a 5-year period. The small proportion of individuals with sustained or increasing use was older with more comorbidities and use of psychotropic and other analgesic drugs, likely reflecting a higher prevalence of pain and treatment needs in these individuals.

Long-term opioid therapy trajectories and overdose in patients with and without cancer

Objective

Pain is experienced by most patients with cancer and opioids are a cornerstone of management. Our objectives were (1) to identify patterns or trajectories of long-term opioid therapy (LTOT) and their correlates among patients with and without cancer and (2) to assess the association between trajectories and risk for opioid overdose, considering the potential moderating role of cancer.

Methods and Analysis

We conducted a retrospective cohort study among individuals in the US Veterans Health Administration (VHA) database with incident LTOT with and without cancer (N=44,351; N=285,772, respectively) between 2010-2017. We investigated the relationship between LTOT trajectory and all International Classification of Diseases-9 and 10-defined accidental and intentional opioid-related overdoses.

Results

Trajectories of opioid receipt observed in patients without cancer and replicated in patients with cancer were: low-dose/stable trend, low-dose/de-escalating trend, moderate-dose/stable trend, moderate-dose/escalating with quadratic downturn trend, and high-dose/escalating with quadratic downturn trend. Time to first overdose was significantly predicted by higher-dose and escalating trajectories; the two low-dose trajectories conferred similar, lower risk. Conditional hazard ratios (99% CI) for the moderate-dose, moderate-dose/escalating with quadratic downturn and high-dose/escalating with quadratic downturn trends were 1·84 (1·18, 2·85), 2·56 (1·54, 4·25), and 2·41 (1·37, 4·26), respectively. Effects of trajectories on time to overdose did not differ by presence of cancer; inferences were replicated when restricting to patients with stage 3/4 cancer.

Conclusion

Patients with cancer face opioid overdose risks like patients without cancer. Future studies should seek to expand and address our knowledge about opioid risk in cancer patients.

Trial registration

None.

Trajectories and correlates of opioid prescription receipt among patients experiencing interpersonal violence

Interpersonal violence increases vulnerability to the deleterious effects of opioid use. Increased opioid prescription receipt is a major contributor to the opioid crisis; however, our understanding of prescription patterns and risk factors among those with a history of interpersonal violence remains elusive. This study sought to identify 5-year longitudinal patterns of opioid prescription receipt among patients experiencing interpersonal violence within a large healthcare system and sociodemographic and clinical characteristics associated with prescription patterns. This secondary analysis examined electronic health record data from January 2004–August 2019 for a cohort of patients (N = 1,587) referred for interpersonal violence services. Latent class growth analysis was used to estimate trajectories of opioid prescription receipt over a 5-year period. Standardized differences were calculated to assess variation in sociodemographic and clinical characteristics between classes. Our cohort had a high prevalence of prescription opioid receipt (73.3%) and underlying co-morbidities, including chronic pain (54.6%), substance use disorders (39.0%), and mental health diagnoses (76.9%). Six prescription opioid receipt classes emerged, characterized by probability of any prescription opioid receipt at the start and end of the study period (high, medium, low, never) and change in probability over time (increasing, decreasing, stable). Classes with the highest probability of prescription opioids also had the highest proportions of males, chronic pain diagnoses, substance use disorders, and mental health diagnoses. Black, non-Hispanic and Hispanic patients were more likely to be in low or no prescription opioid receipt classes. These findings highlight the importance of monitoring for synergistic co-morbidities when providing pain management and offering treatment that is trauma-informed, destigmatizing, and integrated into routine care.

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Introduction

Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum.

Methods

Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes.

Results

Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences.

Conclusion

This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.

Genetics and prescription opioid use (GaPO): study design for consenting a cohort from an existing biobank to identify clinical and genetic factors influencing prescription opioid use and abuse

BACKGROUND

Prescription opioids (POs) are commonly used to treat moderate to severe chronic pain in the health system setting. Although they improve quality of life for many patients, more work is needed to identify both the clinical and genetic factors that put certain individuals at high risk for developing opioid use disorder (OUD) following use of POs for pain relief. With a greater understanding of important risk factors, physicians will be better able to identify patients at highest risk for developing OUD for whom non-opioid alternative therapies and treatments should be considered.

METHODS

We are conducting a prospective observational study that aims to identify the clinical and genetic factors most stongly associated with OUD. The study design leverages an existing biobank that includes whole exome sequencing and array genotyping. The biobank is maintained within an integrated health system, allowing for the large-scale capture and integration of genetic and non-genetic data. Participants are enrolled into the health system biobank via informed consent and then into a second study that focuses on opioid medication use. Data capture includes validated self-report surveys measuring addiction severity, depression, anxiety, and nicotine use, as well as additional clinical, prescription, and brain imaging data extracted from electronic health records.

DISCUSSION

We will harness this multimodal data capture to establish meaningful patient phenotypes in order to understand the genetic and non-genetic contributions to OUD.

HIV Infection and the Risk of World Health Organization-Defined Sudden Cardiac Death

Background People living with HIV have higher sudden cardiac death (SCD) rates compared with the general population. Whether HIV infection is an independent SCD risk factor is unclear. Methods and Results This study evaluated participants from the Veterans Aging Cohort Study, an observational, longitudinal cohort of veterans with and without HIV infection matched 1:2 on age, sex, race/ethnicity, and clinical site. Baseline for this study was a participant's first clinical visit on or after April 1, 2003. Participants were followed through December 31, 2014. Using Cox proportional hazards regression, we assessed whether HIV infection, CD4 cell counts, and/or HIV viral load were associated with World Health Organization (WHO)-defined SCD risk. Among 144 336 participants (30% people living with HIV), the mean (SD) baseline age was 50.0 years (10.6 years), 97% were men, and 47% were of Black race. During follow-up (median, 9.0 years), 3035 SCDs occurred. HIV infection was associated with increased SCD risk (hazard ratio [HR], 1.14; 95% CI, 1.04-1.25), adjusting for possible confounders. In analyses with time-varying CD4 and HIV viral load, people living with HIV with CD4 counts <200 cells/mm3 (HR, 1.57; 95% CI, 1.28-1.92) or viral load >500 copies/mL (HR, 1.70; 95% CI, 1.46-1.98) had increased SCD risk versus veterans without HIV. In contrast, people living with HIV who had CD4 cell counts >500 cells/mm3 (HR, 1.03; 95% CI, 0.90-1.18) or HIV viral load <500 copies/mL (HR, 0.97; 95% CI, 0.87-1.09) were not at increased SCD risk. Conclusions HIV infection is associated with increased risk of WHO-defined SCD among those with elevated HIV viral load or low CD4 cell counts.

Endogenous opiates and behavior: 2019

This paper is the forty-second consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2019 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

Association of OPRM1 Functional Coding Variant With Opioid Use Disorder: A Genome-Wide Association Study

IMPORTANCE

With the current opioid crisis, it is important to improve understanding of the biological mechanisms of opioid use disorder (OUD).

OBJECTIVES

To detect genetic risk variants for OUD and determine genetic correlations and causal association with OUD and other traits.

DESIGN, SETTING, AND PARTICIPANTS

A genome-wide association study of electronic health record-defined OUD in the Million Veteran Program sample was conducted, comprising 8529 affected European American individuals and 71 200 opioid-exposed European American controls (defined by electronic health record trajectory analysis) and 4032 affected African American individuals and 26 029 opioid-exposed African American controls. Participants were enrolled from January 10, 2011, to May 21, 2018, with electronic health record data for OUD diagnosis from October 1, 1999, to February 7, 2018. Million Veteran Program results and additional OUD case-control genome-wide association study results from the Yale-Penn and Study of Addiction: Genetics and Environment samples were meta-analyzed (total numbers: European American individuals, 10 544 OUD cases and 72 163 opioid-exposed controls; African American individuals, 5212 cases and 26 876 controls). Data on Yale-Penn participants were collected from February 14, 1999, to April 1, 2017, and data on Study of Addiction: Genetics and Environment participants were collected from 1990 to 2007. The key result was replicated in 2 independent cohorts: proxy-phenotype buprenorphine treatment in the UK Biobank and newly genotyped Yale-Penn participants. Genetic correlations between OUD and other traits were tested, and mendelian randomization analysis was conducted to identify potential causal associations.

MAIN OUTCOMES AND MEASURES

Main outcomes were International Classification of Diseases, Ninth Revision-diagnosed OUD or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision-diagnosed OUD (Million Veteran Program), and DSM-IV-defined opioid dependence (Yale-Penn and Study of Addiction: Genetics and Environment).

RESULTS

A total of 114 759 individuals (101 016 men [88%]; mean [SD] age, 60.1 [12.8] years) were included. In 82 707 European American individuals, a functional coding variant (rs1799971, encoding Asn40Asp) in OPRM1 (μ-opioid receptor gene, the main biological target for opioid drugs; OMIM 600018) reached genome-wide significance (G allele: β = -0.066 [SE = 0.012]; P = 1.51 × 10-8). The finding was replicated in 2 independent samples. Single-nucleotide polymorphism-based heritability of OUD was 11.3% (SE = 1.8%). Opioid use disorder was genetically correlated with 83 traits, including multiple substance use traits, psychiatric illnesses, cognitive performance, and others. Mendelian randomization analysis revealed the following associations with OUD: risk of tobacco smoking, depression, neuroticism, worry neuroticism subcluster, and cognitive performance. No genome-wide significant association was detected for African American individuals or in transpopulation meta-analysis.

CONCLUSIONS AND RELEVANCE

This genome-wide meta-analysis identified a significant association of OUD with an OPRM1 variant, which was replicated in 2 independent samples. Post-genome-wide association study analysis revealed associated pleiotropic characteristics. Recruitment of additional individuals with OUD for future studies-especially those of non-European ancestry-is a crucial next step in identifying additional significant risk loci.

Trajectories of Self-Reported Opioid Use Among Patients With HIV Engaged in Care: Results From a National Cohort Study

BACKGROUND

No prior studies have characterized long-term patterns of opioid use regardless of source or reason for use among patients with HIV (PWH). We sought to identify trajectories of self-reported opioid use and their correlates among a national sample of PWH engaged in care.

SETTING

Veterans Aging Cohort Study, a prospective cohort including PWH receiving care at 8 US Veterans Health Administration (VA) sites.

METHODS

Between 2002 and 2018, we assessed past year opioid use frequency based on self-reported "prescription painkillers" and/or heroin use at baseline and follow-up. We used group-based trajectory models to identify opioid use trajectories and multinomial logistic regression to determine baseline factors independently associated with escalating opioid use compared to stable, infrequent use.

RESULTS

Among 3702 PWH, we identified 4 opioid use trajectories: (1) no lifetime use (25%); (2) stable, infrequent use (58%); (3) escalating use (7%); and (4) de-escalating use (11%). In bivariate analysis, anxiety; pain interference; prescribed opioids, benzodiazepines and gabapentinoids; and marijuana use were associated with escalating opioid group membership compared to stable, infrequent use. In multivariable analysis, illness severity, pain interference, receipt of prescribed benzodiazepine medications, and marijuana use were associated with escalating opioid group membership compared to stable, infrequent use.

CONCLUSION

Among PWH engaged in VA care, 1 in 15 reported escalating opioid use. Future research is needed to understand the impact of psychoactive medications and marijuana use on opioid use and whether enhanced uptake of evidence-based treatment of pain and psychiatric symptoms can prevent escalating use among PWH.