Office of Enterprise Data and Analytics, Centers for Medicare & Medicaid Services, Baltimore, Maryland.
Department of Sociology, University at Albany, Albany, New York.
J Rural Health. 2021 Jan;37(1):5-15. doi: 10.1111/jrh.12497. Epub 2020 Jul 19.
While research has been done comparing rural/urban differences in opioid prescribing to the disabled Medicare Part D population, research on opioid prescribing among the aged Medicare Part D population is lacking. This study aims to fill this gap by exploring the predictors of opioid prescribing to aged Medicare Part D beneficiaries and investigating whether these predictors vary across rural and urban areas.
This is an analysis of ZIP Codes in the continental United States (18,126 ZIP Codes) utilizing 2017 data from Centers for Medicare & Medicaid Services. The analytic approach includes aspatial descriptive analysis, exploratory spatial analysis with geographically weighted regression, and explanatory analysis with spatial error regime modeling.
Both beneficiary and prescriber characteristics play an important role in determining opioid prescribing rates in urban ZIP Codes, but most of them fail to explain the opioid prescribing rates in rural ZIP Codes.
We identify potential spatial nonstationarity in opioid prescribing rates, indicating the complex nature of opioid-related issues. This means that the same stimulus may not lead to the same change in opioid prescribing rates, because the change may be place specific. By understanding the rural/urban differences in the predictors of opioid prescribing, place-specific policies can be developed that can guide more informed opioid prescribing practices and necessary interventions.
虽然已经有研究比较了农村/城市地区在为残疾医疗保险 D 部分患者开具阿片类药物方面的差异，但针对医疗保险 D 部分老年人群体中阿片类药物处方的研究却很少。本研究旨在通过探讨影响老年医疗保险 D 部分受益人的阿片类药物处方的预测因素，并调查这些预测因素在农村和城市地区是否存在差异，来填补这一空白。
本研究利用医疗保险和医疗补助服务中心 2017 年的数据，对美国大陆的邮政编码（18126 个邮政编码）进行了分析。分析方法包括非空间描述性分析、具有地理加权回归的探索性空间分析以及具有空间误差模型的解释性分析。
受益人和处方医生的特征在确定城市邮政编码的阿片类药物处方率方面起着重要作用，但大多数特征都无法解释农村邮政编码的阿片类药物处方率。
我们发现阿片类药物处方率存在潜在的空间非平稳性，这表明阿片类药物相关问题的复杂性。这意味着相同的刺激可能不会导致阿片类药物处方率的相同变化，因为这种变化可能是特定于地点的。通过了解农村/城市地区阿片类药物处方预测因素的差异，可以制定特定于地点的政策，从而指导更明智的阿片类药物处方实践和必要的干预措施。
