In order to improve the quality of care for patients with pain, national organizations, including the Joint Commission on Accreditation of Healthcare Organizations and the Department of Veterans Affairs, have mandated that pain be routinely assessed for all patients. Implementation of this directive has improved the frequency of pain assessment, and pain scores are now documented for a majority of patients. Improved measurement of pain intensity has not, however, translated into improved processes of care or clinical outcomes.
A recent study found that the most common reason for not modifying treatment plans in response to high numeric rating scale (NRS) pain scores was patient refusal to escalate care. While the failure of the pain intensity NRS to affect outcomes is undoubtedly related to numerous factors, including limitations in physician training, patient-physician communication, and lack of effective therapies, this study suggests that NRS scores may not reliably lead to changes in management because they do not adequately reflect the patients' experience of pain.
While pain intensity may indeed contribute to patients' treatment decisions, it represents only one aspect of how patients experience their illness and evaluate their treatment options. Illness perceptions refer to the organized cognitive representations and related beliefs that patients have about their illness. Extant research has demonstrated that illness perceptions affect important outcomes including adherence, coping, self-management and regulation, and treatment response. Because these factors directly measure patients' experience of pain, we hypothesized that they would exhibit a more significant association with treatment preferences, than either pain intensity or physical function.
The objective of this proposal was to improve our understanding of the factors which influence decision making in patients with chronic musculoskeletal pain.
We recruited patients enrolled in a VA Medical Center. Illness perceptions were measured using 18 items. We followed Turk and Salovey's recommendation to factor analyze the illness perception questionnaire and use factor loadings to guide the interpretation. Treatment preference, the dependent variable in this study, was measured using Adaptive Conjoint Analysis (ACA) (Sawtooth Software Inc, SSI Web V 6.0). Factor scores were examined against the dependent variable (preference for a highly effective/high risk treatment). Given the high expected correlations between pain intensity, function and pain impact, separate tests were conducted to examine their association with the dependent variable. These factors were examined further by an adjusted model that included relevant covariates: age, co-morbidities, perceived efficacy of pain medications (summed across current pain treatments), and numeracy. Additional analyses using discrepancy testing examined whether patients' beliefs are aggregated into a strategy that activates decision making. The discrepancy test was developed from image theory studies of the "simple counting rule." The rule proposes that the likelihood of rejecting an alternative increases with the number of incompatible beliefs.
Of invited subjects, 249 (67%) agreed to participate. 75% of participants were male and 71% were Caucasian. Mean age (SD) was 53.5 (19.5) and ranged from 22 to 90. 28% were under age 35, and 32% were age 70 and older. The mean (SD) NRS pain score was 6.18 (2.13); the NRS has a range from 0 to 10. Factor analysis generated five factors with eigenvalues greater than 1 that accounted for 67.1% of the variance. 37.9% of the variance was accounted for by a single factor that we labeled "pain impact." The other four factors were labeled personal control and emotion, treatment control, timeline, and vigilance. Pain impact was the only factor that was significantly associated with the preference for a riskier/more effective treatment: chi-squared = 5.42, df=1, p=0.02, odds ratio (95% CI) = 1.43 (1.06-1.92). Neither NRS nor functioning predicted patient preference: For NRS, chi-squared = 2.31, df=1, p=0.13, odds ratio (95% CI) = 1.11 (0.97-1.27); for physical functioning, chi-squared = 2.81, df=1, p=0.09, odds ratio (95% CI) = 0.98 (0.95-1.0). Pain impact is significantly associated with preference for a riskier/more effective treatment after adjusting for covariates. Despite the high NRS and a mean duration of more than 13 years, only 34% of patients were not actually thinking about making a treatment change. A model containing three beliefs the current consequences of pain, its emotional influence, and its long-term impact on their lives best predict whether or not patients are actually considering a treatment change. Endorsement of one or two of these beliefs is associated with a 35% likelihood of considering a treatment change. The likelihood increases to 70% when all three beliefs are endorsed. The results indicate that patients' beliefs about the prospect of changing their treatment can be assessed efficiently by asking three questions, but one or two questions is not adequate. Consequently, focusing on any single belief, or even two, is unlikely to give a complete and balanced account of when and why patients are actually considering a change in their current treatment.
In this study we found that impact of pain, as measured by a set of items reflecting patients' illness perceptions, is significantly associated with patients' treatment preference, whereas pain intensity numeric rating scales are not. These results help explain why the NRS has not affected delivery of care or outcomes. We also found that, in addition to current impact, emotional impact of pain and patients' worry about the long-term effect of their condition are the factors which most strongly predict whether they are considering a treatment change. Regardless of disease activity and clinical indicators, patients who do not experience an incompatibility between their beliefs and the current situation are likely to decline a recommendation to change their current treatment. The likelihood of considering a treatment change increases as the situation becomes less compatible.
External Links for this Project
- Toupin-April K, Barton J, Fraenkel L, Li L, Grandpierre V, Guillemin F, Rader T, Stacey D, Légaré F, Jull J, Petkovic J, Scholte-Voshaar M, Welch V, Lyddiatt A, Hofstetter C, De Wit M, March L, Meade T, Christensen R, Gaujoux-Viala C, Suarez-Almazor ME, Boonen A, Pohl C, Martin R, Tugwell PS. Development of a Draft Core Set of Domains for Measuring Shared Decision Making in Osteoarthritis: An OMERACT Working Group on Shared Decision Making. The Journal of rheumatology. 2015 Dec 1; 42(12):2442-7. [view]
- Barry CL, Epstein AJ, Fiellin DA, Fraenkel L, Busch SH. Estimating demand for primary care-based treatment for substance and alcohol use disorders. Addiction (Abingdon, England). 2016 Aug 1; 111(8):1376-84. [view]
- Kievit W, van Hulst L, van Riel P, Fraenkel L. Factors that influence rheumatologists' decisions to escalate care in rheumatoid arthritis: results from a choice-based conjoint analysis. Arthritis care & research. 2010 Jun 1; 62(6):842-7. [view]
- Solomon DH, Lee SB, Zak A, Corrigan C, Agosti J, Bitton A, Harrold L, Losina E, Lu B, Pincus T, Radner H, Smolen J, Katz JN, Fraenkel L. Implementation of treat-to-target in rheumatoid arthritis through a Learning Collaborative: Rationale and design of the TRACTION trial. Seminars in Arthritis and Rheumatism. 2016 Aug 1; 46(1):81-7. [view]
- Fraenkel L, Falzer P, Fried T, Kohler M, Peters E, Kerns R, Leventhal H. Measuring pain impact versus pain severity using a numeric rating scale. Journal of general internal medicine. 2012 May 1; 27(5):555-60. [view]
- Sinayev A, Peters E, Tusler M, Fraenkel L. Presenting Numeric Information with Percentages and Descriptive Risk Labels: A Randomized Trial. Medical Decision Making. 2015 Nov 1; 35(8):937-47. [view]
- Falzer PR, Leventhal HL, Peters E, Fried TR, Kerns R, Michalski M, Fraenkel L. The practitioner proposes a treatment change and the patient declines: what to do next? Pain practice : the official journal of World Institute of Pain. 2013 Mar 1; 13(3):215-26. [view]
Mental, Cognitive and Behavioral Disorders, Aging, Older Veterans' Health and Care, Health Systems
Prognosis, Treatment - Observational
Decision support, Pain, Patient preferences