Opioid-Prescribing Continuity and Risky Opioid Prescriptions

Hallvik, Sara E.; Geissert, Peter; Wakeland, Wayne W.; Hildebran, Christi; Carson, Jody; O’Kane, Nicole; Deyo, Richard A.

doi:10.1370/afm.2285

Cited by 22 publications

(11 citation statements)

References 10 publications

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“…This concept of routing during the early stages of opioid use is also related to studies on continuity of care. Such work typically focuses on the long-term management of patients who are already suffering from specific chronic conditions, e.g., heart failure (Senot 2019), diabetes (Ahuja et al 2020), or opioid dependence (Hallvik et al 2018). While most studies in these chronic settings find that patients benefit from repeated appointments with the same practitioner, we focus on patients prior to chronicity.…”

Section: Related Literaturementioning

confidence: 99%

“…1 While a different clinician can expose the patient to a "fresh perspective" and prevent anchoring to the original opioid treatment course, it may also lead to more fragmented care and reduce physician "ownership" of the long-term health of the patient (Ahuja et al 2020, Ariely et al 2003, Senot 2019. Although prescriber continuity is typically recommended for patients already dependent on opioids (Hallvik et al 2018, Jena et al 2014, the overall impact of exposing a patient to variation in providers in the initial stages of opioid use is not immediately clear.…”

Section: Introductionmentioning

confidence: 99%

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Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation

et al. 2022

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Although medical research has addressed the clinical management of chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient’s likelihood of long-term opioid use. Using a nationwide U.S. database of medical and pharmaceutical claims, we investigate the care delivery process at the most common entry point to opioid use: the primary care setting. For patients who return to primary care for a follow-up appointment within 30 days of opioid initiation, we ask who should revisit and potentially revise the opioid-based treatment plan: the initial prescriber (provider concordance) or an alternate clinician (provider discordance)? First, using a fully controlled logistic model, we find that provider discordance reduces the likelihood of long-term opioid use 12 months after opioid initiation by 31% (95% Confidence Interval: [18%, 43%]). Both the instrumental variable analysis technique and propensity-score matching (utilizing the minimum-bias estimator approach) account for omitted variable bias and indicate that this is a conservative estimate of the true causal effect. Second, looking at patient activities immediately after the follow-up appointment, we find that this long-term reduction is at least partially explained by an immediate reduction in opioids prescribed after the follow-up appointment. Third, the data suggest that the benefit associated with provider discordance remains significant regardless of whether the patient’s initial prescriber was their regular primary care provider or another clinician. Overall, our analysis indicates that systematic, operational changes in the early stages of managing new opioid patients may offer a promising, and hitherto overlooked, opportunity to curb the opioid epidemic. This paper was accepted by David Simchi-Levi, healthcare management.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation

et al. 2022

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“…Evidence-based guidelines for postcesarean opioid prescribing should also include recommendations for optimizing nonopioid analgesics after discharge to minimize potentially high risk opioid prescribing. 23…”

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confidence: 99%

Predicting Opioid Use Following Discharge After Cesarean Delivery

et al. 2020

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PURPOSE Although cesarean delivery is the most common surgical procedure in the United States, postoperative opioid prescribing varies greatly. We hypothesized that patient characteristics, procedural characteristics, or both would be associated with high vs low opioid use after discharge. This information could help individualize prescriptions. METHODS In this prospective cohort study, we quantified opioid use for 4 weeks following hospital discharge after cesarean delivery. Predischarge characteristics were obtained from health records, and patients self-reported total opioid use postdischarge on weekly questionnaires. Opioid use was quantified in milligram morphine equivalents (MMEs). Binomial and Poisson regression analyses were performed to assess predictors of opioid use after discharge. RESULTS Of the 233 patients starting the study, 203 (87.1%) completed at least 1 questionnaire and were included in analyses (86.3% completed all 4 questionnaires). A total of 113 patients were high users (>75 MMEs) and 90 patients were low users (≤75 MMEs) of opioids postdischarge. The group reporting low opioid use received on average 44% fewer opioids in the 24 hours before discharge compared with the group reporting high opioid use (mean = 33.0 vs 59.3 MMEs, P <.001). Only a minority of patients (11.4% to 15.8%) stored leftover opioids in a locked location, and just 31 patients disposed of leftover opioids. CONCLUSIONS Knowledge of predischarge opioid use can be useful as a tool to inform individualized opioid prescriptions, help optimize nonopioid analgesia, and reduce opioid use. Additional studies are needed to evaluate the impact of implementing such measures on prescribing practices, pain, and functional outcomes.

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“…[4][5][6][7] Use of PDMP data for public health surveillance and epidemiologic studies has increased in recent years with the implementation of PDMPs through the United States, including cohort studies of linked PDMP and health outcome data. [8][9][10][11][12][13][14] Methods for data/record linkage (including de-duplication [matching individuals within the same data source] and linkage of individuals between data sources) [see Dusetzina et al 15 and Sayers et al 16 for a review of terms and concepts in linkage methodology]) can influence complete patient identification and medical history. [17][18][19][20] Accuracy of data/record linkage (hereafter referred to as record linkage) is particularly important for cohort studies and can influence estimation of incidence and mortality rates, and effect estimates due to misclassification of covariates, exposures, and outcomes, as well as generalizability of results if certain groups are excluded (e.g., individuals of lower socioeconomic status).…”

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confidence: 99%

Record Linkage Approaches Using Prescription Drug Monitoring Program and Mortality Data for Public Health Analyses and Epidemiologic Studies

et al. 2020

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Background: The use of Prescription Drug Monitoring Program (PDMP) data has greatly increased in recent years as these data have accumulated as part of the response to the opioid epidemic in the United States. We evaluated the accuracy of record linkage approaches using the Controlled Substance Monitoring Database (Tennessee's [TN] PDMP, 2012-2016) and mortality data on all drug overdose decedents in Tennessee (2013-2016). Methods: We compared total, missed, and false positive (FP) matches (with manual verification of all FPs) across approaches that included a variety of data cleaning and matching methods (probabilistic/fuzzy vs. deterministic) for patient and death linkages, and prescription history. We evaluated the influence of linkage approaches on key prescription measures used in public health analyses. We evaluated characteristics (e.g., age, education, sex) of missed matches and incorrect matches to consider potential bias. Results: The most accurate probabilistic/fuzzy matching approach identified 4,714 overdose deaths (vs. the deterministic approach, n = 4,572), with a low FP linkage error (<1%) and high correct match proportion (95% vs. 92% and ~90% for probabilistic approaches not using comprehensive data cleaning). Estimation of all prescription measures improved (vs. deterministic approach). For example, frequency (%) of decedents filling an oxycodone prescription in the last 60 days (n = 1,371 [32%] vs. n = 1,443 [33%]). Missed overdose decedents were more likely to be younger, male, nonwhite, and of higher education. Conclusion: Implications of study findings include underreporting, prescribing and outcome misclassification, and reduced generalizability to population risk groups, information of importance to epidemiologists and researchers using PDMP data.

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Opioid-Prescribing Continuity and Risky Opioid Prescriptions

Cited by 22 publications

References 10 publications

Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation

Curbing the Opioid Epidemic at Its Root: The Effect of Provider Discordance After Opioid Initiation

Predicting Opioid Use Following Discharge After Cesarean Delivery

Record Linkage Approaches Using Prescription Drug Monitoring Program and Mortality Data for Public Health Analyses and Epidemiologic Studies

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