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.
School of Applied Mathematics and Physical Sciences, National Technical University of Athens, Athens, Greece.
Drugs. 2018 Jan;78(1):111-121. doi: 10.1007/s40265-017-0846-6.
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.
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.
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 < 0.001, Coeff = 0.22), black (p < 0.001, Coeff = - 0.19) and male population (p < 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 < 0.001, Coeff = 0.27) were risk factors for opioid-related mortality (p < 0.001, R = 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 = 0.03, R = 0.03).
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.
阿片类药物泛滥是一场不断升级的健康危机。我们评估了阿片类药物处方率和社会经济决定因素对阿片类药物死亡率的影响，并确定了不同类别的医生的处方模式可能存在差异。
我们结合了 2013 年和 2014 年的医疗保险处方药物数据，并在一项县一级的横剖研究中量化了阿片类药物处方率，该研究的数据来自 2710 个县、468614 名独特的开处方者和 46665037 名受益人。我们使用疾病预防控制中心 WONDER 数据库获取与阿片类药物相关的死亡率数据。每个县的社会经济特征均从美国人口普查局获得。
全国平均阿片类药物处方率为每位接受阿片类药物处方的受益人的 3.86 份索赔（95%CI 3.86-3.86）。在县一级，总体阿片类药物处方率（p<0.001，Coeff=0.27），特别是急诊医学（p<0.001，Coeff=0.21）、家庭医生（p=0.11，Coeff=0.008）、内科医生（p=0.018，Coeff=0.1）和医师助理（p=0.021，Coeff=0.08）提供的阿片类药物处方率与阿片类药物相关的死亡率相关。人口统计学因素，如白人比例（p<0.001，Coeff=0.22）、黑人比例（p<0.001，Coeff=-0.19）和男性人口比例（p<0.001，Coeff=0.13）与阿片类药物处方率相关，而贫困（p<0.001，Coeff=0.41）和白人人口比例（p<0.001，Coeff=0.27）是阿片类药物相关死亡率的危险因素（p<0.001，R=0.35）。值得注意的是，处方医生中处于较高四分位数的医生与阿片类药物死亡率相关（p<0.001，Coeff=0.14），是其余 75%的医生的两倍（p<0.001，Coeff=0.07）（p<0.001，Coeff=0.07）（p=0.03，R=0.03）。
阿片类药物处方率，特别是某些类别的医生开出的阿片类药物处方率，与阿片类药物相关的死亡率相关。干预措施应优先考虑具有不成比例影响的提供者和那些照顾社会经济因素使他们处于更高风险的人群。
