Walton David M, Beattie Tyler, Putos Joseph, MacDermid Joy C
School of Physical Therapy, Western University, Rm. EC1443, 1201 Western Rd, London, Ontario, Canada N6G 1H1.
School of Physical Therapy, Western University, Rm. EC1443, 1201 Western Rd, London, Ontario, Canada N6G 1H1.
J Clin Epidemiol. 2016 Jun;74:218-26. doi: 10.1016/j.jclinepi.2015.10.022. Epub 2016 Jan 6.
The Brief Pain Inventory is composed of two quantifiable scales: pain severity and pain interference. The reported factor structure of the interference subscale is not consistent in the extant literature, with no clear choice between a single- or two-factor structure. Here, we report on the results of Rasch-based analysis of the interference subscale using a large population-based ambulatory patient database (the Quebec Pain Registry).
Observational cohort.
A total of 1,000 responses were randomly drawn from a total database of 5,654 for this analysis. Both the original 7-item and an expanded 10-item version (Tyler 2002) of the interference subscale were evaluated. Rasch analysis revealed significant misfit of both versions of the scale, with the original 7-item version outperforming the expanded 10-item version. Analysis of dimensionality revealed that both versions showed improved model fit when considered two subscales (affective and physical interference) with the item on sleep interference removed or considered separately. Additionally, significant uniform differential item functioning was identified for 6 of the 7 original items when the sample was stratified by age above or below 55 years. The interference subscale achieved adequate model fit when considered as two separate subscales with age as a mediator of response, while interpreting the sleep interference item separately. A transformation matrix revealed that in all cases, ordinal-level change at the extreme ends of the scale appears to be more meaningful than does a similar change at the midpoints.
The Interference subscale of the BPI should be interpreted as two separate subscales (Affective Interference, Physical Interference) with the sleep item removed or interpreted separately for optimal fit to the Rasch model. Implications for research and clinical use are discussed.
简明疼痛问卷由两个可量化量表组成：疼痛严重程度和疼痛干扰。在现有文献中，干扰分量表报告的因子结构不一致，在单因素或双因素结构之间没有明确选择。在此，我们报告了使用基于人群的大型门诊患者数据库（魁北克疼痛登记处）对干扰分量表进行基于拉施分析的结果。
观察性队列研究。
本次分析从总共5654个数据库中随机抽取了1000份回复。对干扰分量表的原始7项版本和扩展的10项版本（泰勒，2002年）进行了评估。拉施分析显示两个版本的量表均存在显著的不匹配，原始7项版本的表现优于扩展的10项版本。维度分析表明，当将两个子量表（情感干扰和身体干扰）考虑在内，且去除或单独考虑睡眠干扰项时，两个版本的模型拟合度均有所提高。此外，当样本按55岁以上或以下分层时，7个原始项目中的6个被确定存在显著的均匀差异项目功能。当将干扰分量表视为两个单独的子量表，以年龄作为反应的调节因素，同时单独解释睡眠干扰项时，该分量表实现了充分的模型拟合。一个转换矩阵显示，在所有情况下，量表两端的顺序水平变化似乎比中点处的类似变化更有意义。
BPI的干扰分量表应解释为两个单独的子量表（情感干扰、身体干扰），去除睡眠项目或单独解释，以实现与拉施模型的最佳拟合。讨论了对研究和临床应用的影响。
