Changes in postoperative opioid prescribing across three diverse healthcare systems, 2010-2020 - Jean Coquet; Alban Zammit; Oualid El Hajouji; Keith Humphreys; Keith Humphreys; Steven M. Asch; Steven M. Asch; Thomas F. Osborne; Thomas F. Osborne; Catherine M. Curtin; Catherine M. Curtin; Tina Hernandez-Boussard; et al. (Frontiers Media S.A., 2022)
ObjectiveThe opioid crisis brought scrutiny to opioid prescribing. Understanding how opioid prescribing patterns and corresponding patient outcomes changed during the epidemic is essential for future targeted policies. Many studies attempt to model trends in opioid prescriptions therefore understanding the temporal shift in opioid prescribing patterns across populations is necessary. This study characterized postoperative opioid prescribing patterns across different populations, 2010-2020.Data SourceAdministrative data from Veteran Health Administration (VHA), six Medicaid state programs and an Academic Medical Center (AMC).Data extractionSurgeries were identified using the Clinical Classifications Software.Study DesignTrends in average daily discharge Morphine Milligram Equivalent (MME), postoperative pain and subsequent opioid prescription were compared using regression and likelihood ratio test statistics.Principal FindingsThe cohorts included 595,106 patients, with populations that varied considerably in demographics. Over the study period, MME decreased significantly at VHA (37.5-30.1; p = 0.002) and Medicaid (41.6-31.3; p = 0.019), and increased at AMC (36.9-41.7; p < 0.001). Persistent opioid users decreased after 2015 in VHA (p < 0.001) and Medicaid (p = 0.002) and increase at the AMC (p = 0.003), although a low rate was maintained. Average postoperative pain scores remained constant over the study period.ConclusionsVHA and Medicaid programs decreased opioid prescribing over the past decade, with differing response times and rates. In 2020, these systems achieved comparable opioid prescribing patterns and outcomes despite having very different populations. Acknowledging and incorporating these temporal distribution shifts into data learning models is essential for robust and generalizable models.