College of Nursing.
College of Pharmacy.
Pain Med. 2019 Jan 1;20(1):58-67. doi: 10.1093/pm/pnx340.
Analyzing medication data for research purposes is complex, and methods are rarely described in the literature. Our objective was to describe methods of quantifying opioid and nonopioid analgesics and to compare the utility of five different analgesic coding methods when analyzing relationships between pain, analgesic use, and clinical outcomes. In this study, we used physical function as the outcome variable for its clinical relevance and its relationship to pain in older adults.
Secondary analyses of baseline cross-sectional data from the Advanced Cognitive Training Interventions for Vital Elders (ACTIVE) study.
Community settings in six regions of the United States.
A total of 2,802 community-residing adults older than age 65 years.
A medication audit was conducted. Analgesics were coded as any pain medication, counts (total analgesics, number of opioids and nonopioids), equianalgesics (oral morphine equivalents, oral acetaminophen equivalents), and dose categories. Adjuvant medications used to treat pain (e.g., tricyclic antidepressants and anticonvulsants) and low-dose aspirin typically used for cardiovascular conditions were excluded from these analyses. To examine the utility of these various approaches, a series of hierarchical regression models were conducted with pain and analgesics as predictors and physical functioning as the dependent variable.
Eighty-one point nine percent of participants reported experiencing recent pain, but 26% reported analgesic use. Nonopioids were the most common drug class used. Models revealed that pain was significantly associated with worse physical function (β = -0.45, P = 0.001), after controlling for demographic and analgesic variables. Two basic drug coding methods (e.g., any pain medication, number of pain medications) were equivalent in their explanatory power (β = -0.12, P = 0.001) and were slightly stronger predictors of function than the more complex coding procedures.
Analgesic medications are important variables to consider in community-based studies of older adults. We illustrate several methods of quantifying analgesic medications for research purposes. In this community-based sample, we found no advantage of complex equianalgesic coding methods over simple counts in predicting physical functioning. The results may differ depending on the research question or clinical outcome studied. Thus, methods of analyzing analgesic drug data warrant further research.
分析医学文献中的药物数据非常复杂，而相关方法在文献中却很少被描述。我们的目的是描述量化阿片类药物和非阿片类药物的方法，并比较五种不同的镇痛药物编码方法在分析疼痛、镇痛药物使用与临床结果之间关系时的有效性。在本研究中，我们选择身体功能作为结果变量，因为它与老年人的疼痛具有临床相关性，并与疼痛存在关联。
对高级认知训练干预对健康老年人（ACTIVE）研究的基线横断面数据进行二次分析。
美国六个地区的社区环境。
共有 2802 名居住在社区的 65 岁以上老年人。
进行药物审查。将镇痛药编码为任何疼痛药物、数量（总镇痛药、阿片类药物和非阿片类药物的数量）、等效药物（口服吗啡当量、口服对乙酰氨基酚当量）和剂量类别。这些分析排除了用于治疗疼痛的辅助药物（例如三环类抗抑郁药和抗惊厥药）和用于心血管疾病的低剂量阿司匹林。为了研究这些不同方法的有效性，我们进行了一系列分层回归模型，将疼痛和镇痛药作为预测因子，身体功能作为因变量。
81.9%的参与者报告近期有疼痛，但 26%的参与者报告使用了镇痛药。非阿片类药物是最常用的药物类别。模型显示，疼痛与身体功能恶化显著相关（β=-0.45，P=0.001），控制了人口统计学和镇痛药变量后依然如此。两种基本的药物编码方法（例如，任何疼痛药物、疼痛药物的数量）在解释能力上相当（β=-0.12，P=0.001），并且比更复杂的编码程序略能更好地预测功能。
在针对老年人的社区基础研究中，镇痛药是一个重要的考虑因素。我们举例说明了几种用于研究目的的量化镇痛药的方法。在这个基于社区的样本中，我们发现复杂的等效药物编码方法在预测身体功能方面并没有优于简单的计数方法。研究结果可能因研究问题或临床结果而异。因此，分析镇痛药药物数据的方法值得进一步研究。
