Opioid-Prescribing Metrics in Washington State: Trends and Challenges

Abstract

Context: Analyses of prescribing trends using prescription drug monitoring programs (PDMP) are impacted by changes in reporting requirements and in the scheduling of medications by the Drug Enforcement Administration. In 2014, the Drug Enforcement Administration changed the status of tramadol from an unscheduled to a scheduled medication. The addition of tramadol to the PDMP may affect the prevalence of opioid-prescribing metrics and the interpretation of prescribing trends. Objective: The objectives were to (1) examine trends in opioid prescribing in Washington State between 2012 and 2017, (2) assess the potential impact of adding tramadol to PDMP on these trends, and (3) describe challenges in defining and implementing opioid-prescribing metrics. Design: Analysis of quarterly summary statistics of opioid prescribing. Setting: Washington State. Participants: Washington State residents. Main Outcome Measures: The metrics include measures of opioid prescribing overall and by age group, chronic opioid prescribing, high-dose prescribing among those on chronic opioid therapy, prescribing of concurrent opioids and sedatives, days' supply of new opioid prescriptions, and transition from short-term to long-term use of opioids. Results: In Washington, the prevalence of any opioid prescribing, chronic opioid prescribing, high-dose opioid prescribing, and prescribing of concurrent opioids and sedatives declined between 2012 and 2017. The prevalence of opioid prescribing was higher in older than in younger age groups. The addition of tramadol to the Washington PDMP in 2014 affected the observed prevalence of all opioid metrics and of all opioid-prescribing trends. Conclusions about trends in opioid prescribing differ substantially depending on whether tramadol is included or not, particularly in 2014 and 2015. Conclusions: The development of opioid-prescribing metrics is relatively new. There is likely much benefit of standard definitions of opioid metrics at the state and national levels to track important trends and compare progress from state to state.

Changes in early high‐risk opioid prescribing practices after policy interventions in Washington State

“…The three population‐based time series had level and/or trend decreases associated with all three policy interventions; trends in both low‐risk and high‐risk WC‐related opioid prescribing were entangled with the overall decrease in any WC‐related opioid prescribing over this timeframe (Figure 1 ). 33 Fitted trend lines between policy intervention timepoints for each of the five high‐risk prescribing indicators are depicted in (Figure 2 ; Figure S2 and Table S3 for corresponding comparisons of the two versions of the >7 days‘ supply indicator; although the alternate consecutive indicator was less prevalent than the cumulative indicator, post‐policy trend changes were substantially similar, and the cumulative indicator provided more conservative confidence intervals.) At baseline, among those prescribed any opioids, the concurrent and chronic high‐risk prescribing indicators were the least prevalent—at 4 percent and 2 percent, respectively—compared with 32 percent for the >7 days‘ supply indicator, and 21 percent for the high‐dose indicator (Table 3 ).…”

“… 22 , 28 , 29 , 30 The aim of this study was to assess associations between implementation of a series of opioid prescribing policies in Washington State and changes in opioid prescribing practices during the acute (0‐6 weeks) and subacute (6 weeks to 3 months) phases of pain after injury. We used WC data to investigate this aim because injured workers—like the general population—have been exposed to dramatic temporal changes in opioid prescribing practices, 31 , 32 , 33 and because Washington State WC data are population‐based and thus suitable for state‐level policy evaluations. 34 Also, this is a population in which the prevalence of chronic opioid use is low (1.9 percent of injured workers during the 3 months before injury, based on 2012‐2015 Washington PMP data 35 ); hence, the 3 months after injury primarily reflects new‐onset opioid prescribing and can be used to assess acute/subacute WC‐related prescribing practices.…”

“…However, among injured workers with any WC-related opioids prescribed during the 3 months after injury, the share exposed to low-risk (versus high-risk) opioid prescribing gradually increased from 50 percent in January 2008 to 61 percent in June 2015 (Figure S1).Interrupted time series analysis regression estimates for the eight time series are presented inTable 3. The three population-based time series had level and/or trend decreases associated with all three policy interventions; trends in both low-risk and high-risk WC-related opioid prescribing were entangled with the overall decrease in any WC-related opioid prescribing over this timeframe(Figure 1) 33. Fitted trend lines between policy intervention timepoints for each of the five high-risk prescribing Change over time in the population-based proportion of injured workers exposed to high-risk, low-risk, and any workers' compensation-related opioid prescribing within 3 mo after injury(1/1/2008-6/30/2015).…”

Using Electronic Health Record Data to Explore the Challenges of Opioid Prescribing: A Short Communication

“…We, thus, strongly advocate for data driven solutions to provide guidance at institutional, local, and state-wide levels. So, in line with researchers and providers at Washington State University, we argue that before attempting to institute changes toward a reduction in opioid-related mortality, we must first track and understand opioid prescribing metrics in our health systems 8 .…”