Christos A Grigoras1,2, Styliani Karanika1,3, Elpida Velmahos1, Michail Alevizakos1, Myrto-Eleni Flokas1, Christos Kaspiris-Rousellis2, Ioannis-Nektarios Evaggelidis2, Panagiotis Artelaris4, Constantinos I Siettos2, Eleftherios Mylonakis5. 1. Medical Science, Program on Outcomes Research, Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, Rhode Island, 02903, USA. 2. School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece. 3. General Internal Medicine Section, Boston Medical Center, Boston University School of Medicine, Boston, Massachussets, USA. 4. Department of Geography, Harokopio University, Athens, Greece. 5. Medical Science, Program on Outcomes Research, Infectious Diseases Division, Rhode Island Hospital, Warren Alpert Medical School of Brown University, 593 Eddy Street, POB, 3rd Floor, Suite 328/330, Providence, Rhode Island, 02903, USA. emylonakis@lifespan.org.
Abstract
BACKGROUND: The opioid epidemic is an escalating health crisis. We evaluated the impact of opioid prescription rates and socioeconomic determinants on opioid mortality rates, and identified potential differences in prescription patterns by categories of practitioners. METHODS: We combined the 2013 and 2014 Medicare Part D data and quantified the opioid prescription rate in a county level cross-sectional study with data from 2710 counties, 468,614 unique prescribers and 46,665,037 beneficiaries. We used the CDC WONDER database to obtain opioid-related mortality data. Socioeconomic characteristics for each county were acquired from the US Census Bureau. RESULTS: The average national opioid prescription rate was 3.86 claims per beneficiary that received a prescription for opioids (95% CI 3.86-3.86). At a county level, overall opioid prescription rates (p < 0.001, Coeff = 0.27) and especially those provided by emergency medicine (p < 0.001, Coeff = 0.21), family medicine physicians (p = 0.11, Coeff = 0.008), internal medicine (p = 0.018, Coeff = 0.1) and physician assistants (p = 0.021, Coeff = 0.08) were associated with opioid-related mortality. Demographic factors, such as proportion of white (p white < 0.001, Coeff = 0.22), black (p black < 0.001, Coeff = - 0.19) and male population (p male < 0.001, Coeff = 0.13) were associated with opioid prescription rates, while poverty (p < 0.001, Coeff = 0.41) and proportion of white population (p white < 0.001, Coeff = 0.27) were risk factors for opioid-related mortality (p model < 0.001, R 2 = 0.35). Notably, the impact of prescribers in the upper quartile was associated with opioid mortality (p < 0.001, Coeff = 0.14) and was twice that of the remaining 75% of prescribers together (p < 0.001, Coeff = 0.07) (p model = 0.03, R 2 = 0.03). CONCLUSIONS: The prescription opioid rate, and especially that by certain categories of prescribers, correlated with opioid-related mortality. Interventions should prioritize providers that have a disproportionate impact and those that care for populations with socioeconomic factors that place them at higher risk.
BACKGROUND: The opioid epidemic is an escalating health crisis. We evaluated the impact of opioid prescription rates and socioeconomic determinants on opioid mortality rates, and identified potential differences in prescription patterns by categories of practitioners. METHODS: We combined the 2013 and 2014 Medicare Part D data and quantified the opioid prescription rate in a county level cross-sectional study with data from 2710 counties, 468,614 unique prescribers and 46,665,037 beneficiaries. We used the CDC WONDER database to obtain opioid-related mortality data. Socioeconomic characteristics for each county were acquired from the US Census Bureau. RESULTS: The average national opioid prescription rate was 3.86 claims per beneficiary that received a prescription for opioids (95% CI 3.86-3.86). At a county level, overall opioid prescription rates (p < 0.001, Coeff = 0.27) and especially those provided by emergency medicine (p < 0.001, Coeff = 0.21), family medicine physicians (p = 0.11, Coeff = 0.008), internal medicine (p = 0.018, Coeff = 0.1) and physician assistants (p = 0.021, Coeff = 0.08) were associated with opioid-related mortality. Demographic factors, such as proportion of white (p white < 0.001, Coeff = 0.22), black (p black < 0.001, Coeff = - 0.19) and male population (p male < 0.001, Coeff = 0.13) were associated with opioid prescription rates, while poverty (p < 0.001, Coeff = 0.41) and proportion of white population (p white < 0.001, Coeff = 0.27) were risk factors for opioid-related mortality (p model < 0.001, R 2 = 0.35). Notably, the impact of prescribers in the upper quartile was associated with opioid mortality (p < 0.001, Coeff = 0.14) and was twice that of the remaining 75% of prescribers together (p < 0.001, Coeff = 0.07) (p model = 0.03, R 2 = 0.03). CONCLUSIONS: The prescription opioid rate, and especially that by certain categories of prescribers, correlated with opioid-related mortality. Interventions should prioritize providers that have a disproportionate impact and those that care for populations with socioeconomic factors that place them at higher risk.
Authors: William C Becker; Joanna L Starrels; Moonseong Heo; Xuan Li; Mark G Weiner; Barbara J Turner Journal: Ann Fam Med Date: 2011 May-Jun Impact factor: 5.166
Authors: Donald M Goldenbaum; Myra Christopher; Rollin M Gallagher; Scott Fishman; Richard Payne; David Joranson; Drew Edmondson; Judith McKee; Arthur Thexton Journal: Pain Med Date: 2008-07-24 Impact factor: 3.750
Authors: Natalie DuPre; Lyndsey Blair; Sarah Moyer; E Francis Cook; Bert Little; Jeffrey Howard Journal: Am J Public Health Date: 2020-07-16 Impact factor: 11.561
Authors: Lucas K Vitzthum; Vinit Nalawade; Paul Riviere; Whitney Sumner; Tyler Nelson; Loren K Mell; Timothy Furnish; Brent Rose; María Elena Martínez; James D Murphy Journal: JCO Oncol Pract Date: 2021-02-03
Authors: Nicoleta Stoicea; Andrew Costa; Luis Periel; Alberto Uribe; Tristan Weaver; Sergio D Bergese Journal: Medicine (Baltimore) Date: 2019-05 Impact factor: 1.817