Using Controlled Substance Receipt Patterns to Predict Prescription Overdose Death

Providers' Perceptions of the Effectiveness of Electronic Health Records in Identifying Opioid Misuse

GOAL

This study aimed to understand prescribing providers' perceptions of electronic health record (EHR) effectiveness in enabling them to identify and prevent opioid misuse and addiction.

METHODS

We used a cross-sectional survey designed and administered by KLAS Research to examine healthcare providers' perceptions of their experiences with EHR systems. Univariate analysis and mixed-effects logistic regression analysis with organization-level random effects were performed.

PRINCIPAL FINDINGS

A total of 17,790 prescribing providers responded to the survey question related to this article's primary outcome about opioid misuse prevention. Overall, 34% of respondents believed EHRs helped prevent opioid misuse and addiction. Advanced practice providers were more likely than attending physicians and trainees to believe EHRs were effective in reducing opioid misuse, as were providers with fewer than 5 years of experience.

PRACTICAL APPLICATIONS

Understanding providers' perceptions of EHR effectiveness is critical as the health outcome of reducing opioid misuse depends upon their willingness to adopt and apply new technology to their standardized routines. Healthcare managers can enhance providers' use of EHRs to facilitate the prevention of opioid misuse with ongoing training related to advanced EHR system features.

Comparing person-level matching algorithms to identify risk across disparate datasets among patients with a controlled substance prescription: retrospective analysis

Background

The opioid epidemic in the United States has precipitated a need for public health agencies to better understand risk factors associated with fatal overdoses. Matching person-level information stored in public health, medical, and human services datasets can enhance the understanding of opioid overdose risk factors and interventions.

Objective

This study compares approximate match versus exact match algorithms to link disparate datasets together for identifying persons at risk from an applied perspective.

Methods

This study used statewide prescription drug monitoring program (PDMP), arrest, and mortality data matched at the person-level using an approximate match and 2 exact match algorithms. Impact of matching was assessed by analyzing 3 independent concepts: (1) the prevalence of key risk indicators used by PDMP programs in practice, (2) the prevalence of arrests and fatal opioid overdose, and (3) the performance of a multivariate logistic regression for fatal opioid overdose. The PDMP key risk indicators included (1) multiple provider episodes (MPE), or patients with prescriptions from multiple prescribers and dispensers, (2) high morphine milligram equivalents (MMEs), which represents an opioid’s potency relative to morphine, and (3) overlapping opioid and benzodiazepine prescriptions.

Results

Prevalence of PDMP-based risk indicators were higher in the approximate match population for MPEs (n = 4893/1 859 445 [0.26%]) and overlapping opioid/benzodiazepines (n = 57 888/1 859 445 [4.71%]), but the exact-basic match population had the highest prevalence of individuals with high MMEs (n = 664/1 910 741 [3.11%]). Prevalence of arrests and deaths were highest for the approximate match population compared with the exact match populations. Model performance was comparable across the 3 matching algorithms (exact-basic validation area under the receiver operating characteristic curve [AUC]: 0.854; approximate validation AUC: 0.847; exact + zip validation AUC: 0.826) but resulted in different cutoff points balancing sensitivity and specificity.

Conclusions

Our study illustrates the specific tradeoffs of different matching methods. Further research should be performed to compare matching algorithms and its impact on the prevalence of key risk indicators in an applied setting that can improve understanding of risk within a population.

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee

Objective

To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.

Materials and Methods

Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric “weak learner” models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.

Results

Within 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79–0.83; Spiegelhalter P value: 0–.12). Risk concentration captured 47–52% of cases in the top quantiles of predicted probabilities.

Discussion

Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.

Conclusion

Predicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.

Patterns of opioid prescriptions received prior to unintentional prescription opioid overdose death among Veterans

BACKGROUND

Few studies have assessed prescription opioid supply preceding death in individuals dying from unintentional prescription opioid overdoses, or described the characteristics of these individuals, particularly among Veterans.

OBJECTIVES

To describe the history of prescription opioid supply preceding prescription opioid overdose death among Veterans.

METHODS

In a national cohort of Veterans who filled ≥1 opioid prescriptions from the Veterans Health Administration (VA) or Medicare Part D during 2008-2013, we identified deaths from unintentional or undetermined-intent prescription opioid overdoses in 2012-2013. We captured opioid prescriptions using both linked VA and Part D data, and VA data only.

RESULTS

Among 1181 decedents, 643 (54.4%) had prescription opioid supply on the day of death, and 735 (62.2%) within 30 days based on linked data, compared to 40.1% and 46.7%, respectively, using VA data alone. Decedents with prescription opioid supply were significantly older and less likely to have alcohol or illicit drugs as co-occurring substances involved in the overdose. Using linked data, 241 (20.4%) decedents lacked prescription opioid supply within a year of death.

CONCLUSIONS

Many VA patients who die from prescription opioid overdose receive opioid prescriptions outside VA or not at all. It is important to supplement VA with non-VA data to more accurately measure prescription opioid exposure and improve opioid medication safety.