Trajectories of dispensed prescription opioids among beneficiaries enrolled in a Medicaid controlled substance “lock‐in” program

Naumann, Rebecca B.; Gottfredson, Nisha C.; Lund, Jennifer L.; Ringwalt, Christopher L.; Skinner, Asheley Cockrell

doi:10.1002/pds.4445

Cited by 8 publications

(9 citation statements)

References 22 publications

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“…These findings indicate significant heterogeneity in opioid prescriptions over time among those impacted by interpersonal violence. This heterogeneity echoes findings from prior research that have modelled opioid use trajectories among other high-risk populations and provides further evidence of the presence of clinically significant subgroups of people who use prescription opioids [9][10][11][12].…”

Section: Discussionsupporting

confidence: 75%

“…Consistent with other substance use disorder research, evidence shows that rather than a single linear trajectory, opioid use follows multiple trajectories characterized by significant heterogeneity in behaviors [6][7][8][9]. Prior research has examined opioid use trajectories among diverse groups of patients including veterans, patients receiving care for HIV, patients enrolled in a Medicaid lock-in program, and individuals receiving treatment for OUD [9][10][11][12]. Opioid use trajectories have been shown to vary by amount of use (e.g., low, medium, high) and use patterns over time (e.g., increasing use, decreasing use).…”

Section: Introductionmentioning

confidence: 90%

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

2022

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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.

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Section: Discussionsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 90%

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

2022

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“…Second, our findings emphasize the value of including opioid prescriptions in assessing risk for OUD (Rentsch et al, 2019;Naumann et al, 2019). For example, with the exception of age at first opioid prescription, patients in the Chronic Exposure group most closely resembled the OUD group across most of the characteristics evaluated and may therefore represent the group with highest risk of having or developing OUD (Klimas et al, 2019;Volkow et al, 2019).…”

Section: Discussionmentioning

confidence: 58%

Identifying high-risk comorbidities of short and long-term opioid prescription use

Jennings

Lee

Rocha³

et al. 2021

Preprint

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BackgroundElectronic health records (EHR) are useful tools for understanding complex medical phenotypes, but they have been underutilized for opioid use disorders (OUD). Patterns of prescription opioid use might provide an objective measure of OUD risk.MethodsWe extracted data 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 Prescription, Minimal Exposure (2 prescriptions within 90 days at least once, but never 3 prescriptions <90 days apart), and Chronic Exposure (≥10 opioid prescriptions in a year), and compared them to the full registries and to patients with OUD diagnostic codes. We extracted demographic and clinical characteristics known to co-occur with OUD, including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C.ResultsThe prevalence of substance (alcohol, tobacco, cannabis) use disorders was higher in patients with OUD and Chronic Exposure than those with No Prescription or Minimal Exposure. Patients in the OUD and Chronic Exposure groups had more psychiatric (anxiety, depression, schizophrenia, bipolar disorder) and medical comorbidities (pain, hepatitis C, HIV) than those in the Minimal Exposure group. Notably, patients in the Minimal Exposure group had different comorbidity profiles (higher rates of substance use and psychiatric disorders, more pain conditions) than those in the Unscreened or No Prescription groups, highlighting the value of including opioid exposure in studies of OUD.ConclusionsLong-term opioid prescription use may serve as an additional tool to characterize OUD risk.

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“…Second, our findings emphasize the value of including opioid prescriptions in assessing risk for OUD [41, 42]. For example, with the exception of age at first opioid prescription, patients in the Chronic Exposure group most closely resembled the OUD group across most of the characteristics evaluated and may therefore represent the group with the highest risk of having or developing OUD [33, 43].…”

Section: Discussionmentioning

confidence: 76%

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

Jennings

Lee

Rocha³

et al. 2022

Complex Psychiatry

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Introduction. Opioid use disorders (OUD) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHR) 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.

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Trajectories of dispensed prescription opioids among beneficiaries enrolled in a Medicaid controlled substance “lock‐in” program

Cited by 8 publications

References 22 publications

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

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

Identifying high-risk comorbidities of short and long-term opioid prescription use

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

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