Claims-based measures of prescription opioid utilization: A practical guide for researchers

Comparison of methods to identify individuals prescribed opioid analgesics for pain

INTRODUCTION

While identifying opioid prescriptions in claims data has been instrumental in informing best practises, studies have not evaluated whether certain methods of identifying opioid prescriptions yield better results. We compared three common approaches to identify opioid prescriptions in large, nationally representative databases.

METHODS

We performed a retrospective cohort study, analyzing MarketScan, Optum, and Medicare claims to compare three methods of opioid classification: claims database-specific classifications, National Drug Codes (NDC) from the Centers for Disease Control and Prevention (CDC), or NDC from Overdose Prevention Engagement Network (OPEN). The primary outcome was discrimination by area under the curve (AUC), with secondary outcomes including the number of opioid prescriptions identified by experts but not identified by each method.

RESULTS

All methods had high discrimination (AUC>0.99). For MarketScan (n=70,162,157), prescriptions that were not identified totalled 42,068 (0.06%) for the CDC list, 2,067,613 (2.9%) for database-specific categories, and 0 (0%) for the OPEN list. For Optum (n=61,554,852), opioid prescriptions not identified totalled 9,774 (0.02%) for the CDC list, 83,700 (0.14%) for database-specific categories, and 0 (0%) for the OPEN list. In Medicare claims (n=92,781,299), the number of opioid prescriptions not identified totalled 8,694 (0.01%) for the CDC file and 0 (0%) for the OPEN list.

DISCUSSION

This analysis found that identifying opioid prescriptions using methods from CDC and OPEN were similar and superior to prespecified database-specific categories. Overall, this study shows the importance of carefully selecting the approach to identify opioid prescriptions when investigating claims data.

Predictors of Long-Term Opioid Use After Hospitalization for Traumatic Injury in a Racially and Ethnically Diverse Population: A 12-Month Prospective Observational Study

BACKGROUND

Long-term prescription opioid use is a significant risk factor for opioid morbidity and mortality, and severe traumatic injury is an important initiation point for prescription opioid use. This study examines predictors of long-term prescription opioid use among a racially and ethnically diverse population of patients hospitalized for traumatic injury.

METHODS

Study participants (N= 650) from two urban Level I trauma centers were enrolled. Baseline information on demographics, injury characteristics, self-reported pre-injury substance use and mental health, and personality characteristics and attitudes was collected through interviews during the initial hospitalization. Patients were interviewed again at 3 months and 12 months and asked about prescription opioid use in the prior 7 days. Multivariable logistic regressions assessed participants' baseline characteristics associated with opioid use at one or more follow-up interviews.

RESULTS

Pre-injury use of prescription painkillers had the strongest association with prescription opioid use at follow-up (adjusted odds ratio: 3.10; 95% confidence interval: 1.86-5.17). Older age, health insurance coverage at baseline, length of hospitalization, higher current pain level, pre-injury post-traumatic stress disorder symptoms, and discharge to a location other than home were also associated with significantly higher odds of prescription opioid use at follow-up.

CONCLUSIONS

Providers could consider screening for past use of prescription pain relievers and post-traumatic stress disorder before hospital discharge to identify patients who might benefit from additional resources and support. However, providers should ensure that these patients' pain management needs are still being met and avoid abrupt discontinuation of prescription opioid use among those with a history of long-term use.