Your browser doesn't support javascript.
loading
Naloxone prescribing practices in the Military Health System before and after policy implementation.
Pakieser, Jennifer; Peters, Sidney; Tilley, Laura C; Costantino, Ryan C; Scott-Richardson, Maya; Highland, Krista B.
Affiliation
  • Pakieser J; School of Medicine, Uniformed Services University, Bethesda, MD, USA.
  • Peters S; School of Medicine, Uniformed Services University, Bethesda, MD, USA.
  • Tilley LC; Department of Military and Emergency Medicine, Uniformed Services University, Bethesda, MD, USA.
  • Costantino RC; Department of Military and Emergency Medicine, Uniformed Services University, Bethesda, MD, USA.
  • Scott-Richardson M; Enterprise Intelligence and Data Solutions Program Management Office (EIDS), Defense Healthcare Management Systems (PEO DHMS), San Antonio, TX, USA.
  • Highland KB; Defense and Veterans Center for Integrative Pain Management, Department of Anesthesiology, Uniformed Services University, Bethesda, MD, USA.
Pain Rep ; 7(2): e993, 2022.
Article in En | MEDLINE | ID: mdl-35311027
Introduction: Despite public health campaigns, policies, and educational programs, naloxone prescription rates among people receiving opioids remains low. In June 2018, the U.S. Military Health System (MHS) released 2 policies to improve naloxone prescribing. Objectives: The objective of this study was to examine whether the policies resulted in increased naloxone coprescription rates for patients who met the criteria for 1 or more risk indicators (eg, long-term opioid therapy, benzodiazepine coprescription, morphine equivalent daily dose ≥50 mg, and elevated overdose risk score) at the time of opioid dispense. Methods: Prescription and risk indicator data from January 2017 to February 2021 were extracted from the MHS Data Repository. Naloxone coprescription rates from January 2017 to September 2018 were used to forecast prescribing rates from October 2018 to February 2021 overall and across risk indicators. Forecasted rates were compared with actual rates using Bayesian time series analyses. Results: The probability of receiving a naloxone coprescription was higher for patients whose opioid prescriber and pharmacy were both within military treatment facilities vs both within the purchased-care network. Bayesian time series results indicated that the number of patients who met the criteria for any risk indicator decreased throughout the study period. Naloxone prescribing rates increased across the study period from <1% to 20% and did not significantly differ from the forecasted rates across any and each risk indicator (adjusted P values all >0.05). Conclusion: Future analyses are needed to better understand naloxone prescribing practices and the impact of improvements to electronic health records, decision support tools, and policies.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Aspects: Implementation_research Language: En Journal: Pain Rep Year: 2022 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Aspects: Implementation_research Language: En Journal: Pain Rep Year: 2022 Document type: Article Affiliation country: United States Country of publication: United States