Gibbons Chris J, Kenning Cassandra, Coventry Peter A, Bee Penny, Bundy Christine, Fisher Louise, Bower Peter
NIHR Collaboration for Leadership in Applied Health Research and Care for Greater Manchester, University of Manchester, Manchester, United Kingdom ; NIHR School for Primary Care Research, NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, United Kingdom.
NIHR School for Primary Care Research, NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Manchester Academic Health Science Centre (MAHSC), University of Manchester, Manchester, United Kingdom.
PLoS One. 2013 Dec 20;8(12):e81852. doi: 10.1371/journal.pone.0081852. eCollection 2013.
Illness perceptions are beliefs about the cause, nature and management of illness, which enable patients to make sense of their conditions. These perceptions can predict adjustment and quality of life in patients with single conditions. However, multimorbidity (i.e. patients with multiple long-term conditions) is increasingly prevalent and a key challenge for future health care delivery. The objective of this research was to develop a valid and reliable measure of illness perceptions for multimorbid patients.
Candidate items were derived from previous qualitative research with multimorbid patients. Questionnaires were posted to 1500 patients with two or more exemplar long-term conditions (depression, diabetes, osteoarthritis, coronary heart disease and chronic obstructive pulmonary disease). Data were analysed using factor analysis and Rasch analysis. Rasch analysis is a modern psychometric technique for deriving unidimensional and intervally-scaled questionnaires.
Questionnaires from 490 eligible patients (32.6% response) were returned. Exploratory factor analysis revealed five potential subscales 'Emotional representations', 'Treatment burden', 'Prioritising conditions', 'Causal links' and 'Activity limitations'. Rasch analysis led to further item reduction and the generation of a summary scale comprising of items from all scales. All scales were unidimensional and free from differential item functioning or local independence of items. All scales were reliable, but for each subscale there were a number of patients who scored at the floor of the scale.
The MULTIPleS measure consists of five individual subscales and a 22-item summary scale that measures the perceived impact of multimorbidity. All scales showed good fit to the Rasch model and preliminary evidence of reliability and validity. A number of patients scored at floor of each subscale, which may reflect variation in the perception of multimorbidity. The MULTIPleS measure will facilitate research into the impact of illness perceptions on adjustment, clinical outcomes, quality of life, and costs in patients with multimorbidity.
疾病认知是关于疾病病因、本质和管理的信念，它能使患者理解自身病情。这些认知能够预测单一疾病患者的适应情况和生活质量。然而，多病共存（即患有多种长期疾病的患者）日益普遍，是未来医疗保健服务面临的一项关键挑战。本研究的目的是开发一种针对多病共存患者的有效且可靠的疾病认知测量方法。
候选条目源自先前对多病共存患者的定性研究。问卷被寄给1500名患有两种或更多典型长期疾病（抑郁症、糖尿病、骨关节炎、冠心病和慢性阻塞性肺疾病）的患者。使用因子分析和拉施分析对数据进行分析。拉施分析是一种用于得出单维且具有区间尺度问卷的现代心理测量技术。
共收回490名符合条件患者的问卷（回复率为32.6%）。探索性因子分析揭示了五个潜在子量表：“情感表征”“治疗负担”“病情优先级”“因果关系”和“活动受限”。拉施分析导致进一步减少条目，并生成了一个由所有量表中的条目组成的汇总量表。所有量表均为单维，不存在条目功能差异或条目局部独立性问题。所有量表都具有可靠性，但每个子量表都有一些患者得分处于量表的最低水平。
MULTIPleS测量方法包括五个单独的子量表和一个由22个条目组成的汇总量表，用于测量多病共存的感知影响。所有量表都很好地拟合了拉施模型，并初步证明了其可靠性和有效性。每个子量表都有一些患者得分处于最低水平，这可能反映了对多病共存认知的差异。MULTIPleS测量方法将有助于研究疾病认知对多病共存患者的适应情况、临床结局、生活质量和成本的影响。
