Causal inference for evaluating prescription opioid abuse using trend-in-trend design.

PURPOSE: One response to the opioid crisis in the United States has been the development of opioid analgesics with properties intended to reduce non-oral use. Previous evaluations of abuse in the community have relied on population averaged interrupted time series Poisson models with utilization offsets. However, competing interventions and secular trends complicate interpretation of time-series analyses. An alternative research design, trend-in-trend, accounts for heterogeneity in per capita opioid dispensing and unmeasured time-varying confounding, which provides a causal evaluation, provided that underlying assumptions are met.

METHODS: Trend-in-trend can be modeled using a logistic regression framework. In logistic regression, exposure was any product-specific outpatient dispensing by three-digit ZIP code and calendar quarter, for 22 opioids. The outcome was any product-specific abuse case ascertained from poison centers and drug treatment programs, covering 94% of the US population, between July 2009 and December 2016. Product-specific odds ratios compared places without dispensing with places with any dispensing; the causal contrast represents the odds of product-specific abuse in the community given exposure.

RESULTS: Dispensing of new and low-volume opioids varied considerably across the country, with no region showing high of all products. Of 22 opioids analyzed, the three with approved labeling as intended to deter abuse ranked near the lowest in both absolute (population-adjusted rates: 1.7, 0.9, and 8.2 per million people per quarter, respectively) and relative measures (trend-in-trend ORs: 1.96, 1.79, 1.69, respectively).

CONCLUSIONS: Postmarketing studies of prescription opioid abuse may benefit by evolving from unadjusted surveillance rates to a causal inference approach.