Ciesielski Thomas, Iyengar Reethi, Bothra Amit, Tomala Dave, Cislo Geoffrey, Gage Brian F
Division of Medical Education, Department of Internal Medicine, Washington University School of Medicine, St Louis, Mo.
Express Scripts, St Louis, Mo.
Am J Med. 2016 Jul;129(7):699-705.e4. doi: 10.1016/j.amjmed.2016.02.014. Epub 2016 Mar 9.
Determining risk factors for opioid abuse or dependence will help clinicians practice informed prescribing and may help mitigate opioid abuse or dependence. The purpose of this study is to identify variables predicting opioid abuse or dependence.
A retrospective cohort study using de-identified integrated pharmacy and medical claims was performed between October 2009 and September 2013. Patients with at least 1 opioid prescription claim during the index period (index claim) were identified. We ascertained risk factors using data from 12 months before the index claim (pre-period) and captured abuse or dependency diagnosis using data from 12 months after the index claim (postperiod). We included continuously eligible (pre- and postperiod) commercially insured patients aged 18 years or older. We excluded patients with cancer, residence in a long-term care facility, or a previous diagnosis of opioid abuse or dependence (identified by International Classification of Diseases 9th revision code or buprenorphine/naloxone claim in the pre-period). The outcome was a diagnosis of opioid abuse (International Classification of Diseases 9th revision code 304.0x) or dependence (305.5).
The final sample consisted of 694,851 patients. Opioid abuse or dependence was observed in 2067 patients (0.3%). Several factors predicted opioid abuse or dependence: younger age (per decade [older] odds ratio [OR], 0.68); being a chronic opioid user (OR, 4.39); history of mental illness (OR, 3.45); nonopioid substance abuse (OR, 2.82); alcohol abuse (OR, 2.37); high morphine equivalent dose per day user (OR, 1.98); tobacco use (OR, 1.80); obtaining opioids from multiple prescribers (OR, 1.71); residing in the South (OR, 1.65), West (OR, 1.49), or Midwest (OR, 1.24); using multiple pharmacies (OR, 1.59); male gender (OR, 1.43); and increased 30-day adjusted opioid prescriptions (OR, 1.05).
Readily available demographic, clinical, behavioral, pharmacy, and geographic information can be used to predict the likelihood of opioid abuse or dependence.
确定阿片类药物滥用或依赖的风险因素将有助于临床医生进行明智的处方，并可能有助于减轻阿片类药物滥用或依赖。本研究的目的是识别预测阿片类药物滥用或依赖的变量。
在2009年10月至2013年9月期间进行了一项回顾性队列研究，使用去识别化的综合药房和医疗理赔数据。确定在索引期（索引理赔）至少有1次阿片类药物处方理赔的患者。我们使用索引理赔前12个月（前期）的数据确定风险因素，并使用索引理赔后12个月（后期）的数据获取滥用或依赖诊断。我们纳入了18岁及以上持续符合条件（前期和后期）的商业保险患者。我们排除了患有癌症、居住在长期护理机构或之前有阿片类药物滥用或依赖诊断的患者（通过国际疾病分类第9版代码或前期的丁丙诺啡/纳洛酮理赔识别）。结局是阿片类药物滥用（国际疾病分类第9版代码304.0x）或依赖（305.5）的诊断。
最终样本包括694,851名患者。2067名患者（0.3%）出现阿片类药物滥用或依赖。几个因素预测了阿片类药物滥用或依赖：年龄较小（每十年[较大]比值比[OR]，0.68）；是慢性阿片类药物使用者（OR，4.39）；有精神疾病史（OR，3.45）；非阿片类物质滥用（OR，2.82）；酒精滥用（OR，2.37）；每天使用高吗啡当量剂量的使用者（OR，1.98）；吸烟（OR，1.80）；从多个开处方者处获得阿片类药物（OR，1.71）；居住在南部（OR，1.65）、西部（OR，1.49）或中西部（OR，1.24）；使用多个药房（OR，1.59）；男性（OR，1.43）；以及30天调整后的阿片类药物处方增加（OR，1.05）。
现有的人口统计学、临床、行为、药房和地理信息可用于预测阿片类药物滥用或依赖的可能性。
