TY - JOUR
T1 - Correlation of Opioid Mortality with Prescriptions and Social Determinants
T2 - A Cross-sectional Study of Medicare Enrollees
AU - Grigoras, Christos A.
AU - Karanika, Styliani
AU - Velmahos, Elpida
AU - Alevizakos, Michail
AU - Flokas, Myrto Eleni
AU - Kaspiris-Rousellis, Christos
AU - Evaggelidis, Ioannis Nektarios
AU - Artelaris, Panagiotis
AU - Siettos, Constantinos I.
AU - Mylonakis, Eleftherios
N1 - Publisher Copyright:
© 2017, Springer International Publishing AG, part of Springer Nature.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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 (pwhite < 0.001, Coeff = 0.22), black (pblack < 0.001, Coeff = − 0.19) and male population (pmale < 0.001, Coeff = 0.13) were associated with opioid prescription rates, while poverty (p < 0.001, Coeff = 0.41) and proportion of white population (pwhite < 0.001, Coeff = 0.27) were risk factors for opioid-related mortality (pmodel < 0.001, R2 = 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) (pmodel = 0.03, R2 = 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.
AB - 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 (pwhite < 0.001, Coeff = 0.22), black (pblack < 0.001, Coeff = − 0.19) and male population (pmale < 0.001, Coeff = 0.13) were associated with opioid prescription rates, while poverty (p < 0.001, Coeff = 0.41) and proportion of white population (pwhite < 0.001, Coeff = 0.27) were risk factors for opioid-related mortality (pmodel < 0.001, R2 = 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) (pmodel = 0.03, R2 = 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.
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U2 - 10.1007/s40265-017-0846-6
DO - 10.1007/s40265-017-0846-6
M3 - Article
C2 - 29159797
AN - SCOPUS:85034614363
SN - 0012-6667
VL - 78
SP - 111
EP - 121
JO - Drugs
JF - Drugs
IS - 1
ER -