Purpose: Impact of policies limiting opioid prescribing for acute and post-surgical pain among racially minoritized populations are not well understood. We evaluated the impact of two North Carolina (NC) policies on outpatient opioid prescribing among injury and surgical patients by race, ethnicity, age, and sex. Methods: We conducted controlled and single series interrupted time series using electronic health data from two integrated healthcare systems in NC, among >11 years-old patients having acute injuries and surgery between April 2014 to December 2019. The policy interventions were safe opioid prescribing investigative initiative (SOPI, May 2016) and NC law limiting opioid days’ supply (STOP Act, January 2018). Outcomes included, proportion of patients receiving index opioid prescription after surgery or injury event, receipt of subsequent opioid prescriptions, days’ supply, and milligrams of morphine equivalents (MME). Results: Of the 621,997 surgical and 864,061 injury patients, 69.4% and 19.7%, respectively, received an index opioid analgesic prescription. There were sustained declines in index opioid prescription among post-surgical patients after SOPI [-2.7% per year (-4.6, -0.9)] and STOP act [-4.1% (-5.9, -2.2)], but no change among injury patients. Policy-related opioid prescribing declines were larger among black, native American, and Hispanic post-surgical patients than whites and Asians. Index and subsequent opioid days’ supply showed sustained declines after SOPI and STOP Act among post-surgical patients. There was no policy impact on MME. Conclusions: Policies were associated with reductions in opioid prescribing, particularly in post-surgical patients, however, racialized disparities likely reflect implicit and explicit racialized biases in pain management practices.

Purpose: With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies. Methods: Authors searched the National Library of Medicine’s PubMed, Scopus, and the American Psychological Association Psyc Info’s databases to identify studies published in the United States that applied CPs to identify TG people within electronic health care data. Results: Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n=5), hierarchy of code mechanisms (n=4), key text-strings (n=2), or self-surveys (n=1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text-strings. However, if key text-strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone. Conclusion: CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text-strings. CPs with high validity are essential to identifying TG patients when self-reported gender identity is not available. Still, self-reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.