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Measuring Pain Impact Versus Pain Severity Using a Numeric Rating Scale

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Abstract

Background

Routine assessments of pain using an intensity numeric rating scale (NRS) have improved documentation, but have not improved clinical outcomes. This may be, in part, due to the failure of the NRS to adequately predict patients’ preferences for additional treatment.

Objective

To examine whether patients’ illness perceptions have a stronger association with patient treatment preferences than the pain intensity NRS.

Design

Single face-to-face interview.

Participants

Outpatients with chronic, noncancer, musculoskeletal pain.

Main Measures

Experience of pain was measured using 18 illness perception items. Factor analysis of these items found that five factors accounted for 67.1% of the variance; 38% of the variance was accounted for by a single factor labeled “pain impact.” Generalized linear models were used to examine how NRS scores and physical function compare with pain impact in predicting preferences for highly effective/high-risk treatment.

Key Results

Two hundred forty-nine subjects agreed to participate. Neither NRS nor functioning predicted patient preference (NRS: χ2 = 1.92, df = 1, p = 0.16, physical functioning: χ2 = 2.48, df = 1, p = 0.11). In contrast, pain impact was significantly associated with the preference for a riskier/more effective treatment after adjusting for age, comorbidity, efficacy of current medications and numeracy (χ2 = 4.40, df = 1, p = 0.04).

Conclusions

Tools that measure the impact of pain may be a more valuable screening instrument than the NRS. Further research is now needed to determine if measuring the impact of pain in clinical practice is more effective at triggering appropriate management than more restricted measures of pain such as the NRS.

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REFERENCES

  1. Pain assessment: The fifth vital sign. In: http://www.rn.ca.gov/pdfs/regulations/npr-b-27.pdf (Accessed October 17, 2011).

  2. Krebs E, Bair M, Carey T, Weinberger M. Documentation of pain care processes does not accurately reflect pain management delivered in primary care. J Gen Intern Med. 2010;25:194–9.

    Article  PubMed  Google Scholar 

  3. Mularski RA, White-Chu F, Overbay D, Miller L, Asch SM, Ganzini L. Measuring pain as the 5th vital sign does not improve quality of pain management. J Gen Intern Med. 2006;21:607–12.

    Article  PubMed  Google Scholar 

  4. Narasimhaswamy S, Vedi C, Xavier Y, C-h T, Shine D. Effect of implementing pain management standards. J Gen Intern Med. 2006;21:689–93.

    Article  PubMed  Google Scholar 

  5. Zubkoff L, Lorenz K, Lanto A, et al. Does Screening for pain correspond to high quality care for veterans? J Gen Intern Med. 2010;25:900–5.

    Article  PubMed  Google Scholar 

  6. de Conno F, Caraceni A, Gamba A, et al. Pain measurement in cancer patients: A comparison of six methods. Pain. 1994;57:161–6.

    Article  PubMed  Google Scholar 

  7. Jensen M, Chen C, Brugger A. Interpretation of visual analog scale ratings and change scores: A reanalysis of two clinical trials of postoperative pain. J Pain. 2003;4:407–14.

    Article  PubMed  Google Scholar 

  8. Jensen MP, Karoly P, Braver S. The measurement of clinical pain intensity: A comparison of six methods. Pain. 1986;27:117–26.

    Article  PubMed  CAS  Google Scholar 

  9. Lund I, Lundeberg T, Sandberg L, Budh C, Kowalski J, Svensson E. Lack of interchangeability between visual analogue and verbal rating pain scales: a cross sectional description of pain etiology groups. BMC Med Res Methodol. 2005;5:31.

    Article  PubMed  Google Scholar 

  10. Gerbershagen HJ, Rothaug J, Kalkman CJ, Meissner W. Determination of moderate-to-severe postoperative pain on the numeric rating scale: A cut-off point analysis applying four different methods. Br J Anaesth. 2011;107:619–26.

    Article  PubMed  CAS  Google Scholar 

  11. O'Brien BJ, Elswood J, Calin A. Willingness to accept risk in the treatment of rheumatic disease. J Epidemiol Comm Health. 1990;44:249–52.

    Article  Google Scholar 

  12. Fraenkel L, Bogardus ST Jr, Concato J, Wittink DR. Treatment options in knee osteoarthritis: The patient's perspective. Arch Intern Med. 2004;164:1299–304.

    Article  PubMed  Google Scholar 

  13. Petrie KJ, Weinamn JA. Perceptions of health and illness: current research and applications. Amsterdam: Harwood Academic Publishers; 1997.

    Google Scholar 

  14. Leventhal H, Cameron L. Behavioral theories and the problem of compliance. Patient Educ Couns. 1987;10:117–38.

    Article  Google Scholar 

  15. Scharloo M, Kaptein AA, Weinman J, et al. Illness perceptions, coping and functioning in patients with rheumatoid arthritis, chronic obstructive pulmonary disease and psoriasis. J Psychosom Res. 1998;44:573–85.

    Article  PubMed  CAS  Google Scholar 

  16. Halm EA, Mora P, Leventhal H. No symptoms, no asthma: The acute episodic disease belief is associated with poor self-management among inner-city adults with persistent asthma. Chest. 2006;129:573–80.

    Article  PubMed  Google Scholar 

  17. Rabin C, Leventhal H, Goodin S. Conceptualization of disease timeline predicts posttreatment distress in breast cancer patients. Health Psychol. 2004;23:407–12.

    Article  PubMed  Google Scholar 

  18. Moss-Morris R, Weinman J, Petrie KJ, Horne R, Cameron LD, Buick D. The revised illness perception questionnaire (IPQ-R). Psychol Health. 2002;17:1–16.

    Article  Google Scholar 

  19. Turk DC, Rudy TE, Salovey P. Implicit models of illness. J Behav Med. 1986;9:453–74.

    Article  PubMed  CAS  Google Scholar 

  20. Fabrigar L, Wegener D, MacCallum R, Strahan E. Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods. 1999;4:272–99.

    Article  Google Scholar 

  21. Fraenkel L, Bogardus ST, Wittink DR. Understanding patient preferences for the treatment of lupus nephritis with adaptive conjoint analysis. Med Care. 2001;39:1203–16.

    Article  PubMed  CAS  Google Scholar 

  22. Bridges JFP, Onukwugha E, Johnson FR, Hauber AB. Patient preference methods - A patient-centered evaluation paradigm. ISPOR Connections. 2007;13:4–7.

    Google Scholar 

  23. Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ. 2000;320:1530–3.

    Article  PubMed  CAS  Google Scholar 

  24. Green PE, Srinivasan V. Conjoint analysis in marketing: new developments with implications for research and practice. J Marketing. 1990;54:3–17.

    Article  Google Scholar 

  25. http://www.sawtoothsoftware.com/download/techpap/acatech.pdf. (Accessed October 17, 2011).

  26. Stewart AL, Sherbourne C, Hays RD, et al. Summary and discussion of MOS measures. In: Stewart AL, E. Ware JE, eds. Measuring functioning and well-being: The Medical Outcomes Study approach. 1992nd ed. Durham, NC: Duke University Press; 1992:345–71.

    Google Scholar 

  27. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.

    Article  PubMed  CAS  Google Scholar 

  28. Fagerlin A, Zikmund-Fisher BJ, Ubel PA, Jankovic A, Derry HA, Smith DM. Measuring numeracy without a math test: Development of the Subjective Numeracy Scale (SNS). Med Decis Making. 2007;27:672–80.

    Article  PubMed  Google Scholar 

  29. Zikmund-Fisher BJ, Smith DM, Ubel PA, Fagerlin A. Validation of the subjective numeracy scale (SNS): Effects of low numeracy on comprehension of risk communications and utility elicitations. Med Decis Making. 2007;27:663–71.

    Article  PubMed  Google Scholar 

  30. Orme B. Market simulators for conjoint analysis. In: Getting started with conjoint analysis: strategies for product design and pricing research. Secondth ed. Madison: Research Publishers LLC; 2010.

    Google Scholar 

  31. SPSS advanced 18 for Windows. Chicago: SPSS Corporation; 2010.

  32. Lindsey JK, Jones B. Choosing among generalized liner models applied to medical data. Stat Med. 1998;17:59–68.

    Article  PubMed  CAS  Google Scholar 

  33. Krebs E, Carey T, Weinberger M. Accuracy of the pain numeric rating scale as a screening test in primary care. J Gen Intern Med. 2007;22:1453–8.

    Article  PubMed  Google Scholar 

  34. Fraenkel L, Bogardus S, Concato J, Felson D. Unwillingness of rheumatoid arthritis patients to risk adverse effects. Rheumatology. 2002;41:253–61.

    Article  PubMed  CAS  Google Scholar 

  35. Ho M, Lavery B, Pullar T. The risk of treatment: a study of rheumatoid arthritis patients' attitudes. Rheumatology. 1998;37:459–60.

    Article  CAS  Google Scholar 

  36. Suarez-Almazor ME, Conner-Spady B, Kendall CJ, Russell AS, Skeith K. Lack of congruence in the ratings of patients' health status by patients and their physicians. Med Decis Making. 2001;21:113–21.

    PubMed  CAS  Google Scholar 

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Acknowledgement

We would like to thank Marion Michalski for her dedication to the subjects of this study. To the best of our knowledge, no conflict of interest, financial or other, exists. This work was supported by VA Health Services and Research Department grant no. IIR 07-090-3.

Conflicts of Interests

None disclosed.

Author information

Authors and Affiliations

  1. VA CT Healthcare System, Yale University School of Medicine, New Haven, CT, USA

    Liana Fraenkel MD, MPH, Terri Fried MD, Minna Kohler MD & Robert Kerns PhD

  2. VA CT Healthcare System, New Haven, CT, USA

    Paul Falzer PhD

  3. Ohio State University, Columbus, OH, USA

    Ellen Peters PhD

  4. Rutgers, The State University of New Jersey, Newark, NJ, USA

    Howard Leventhal PhD

  5. Section of Rheumatology, Yale University School of Medicine, 300 Cedar ST, TAC Bldg, RM no. 525, P.O. Box 208031, New Haven, CT, 06520-8031, USA

    Liana Fraenkel MD, MPH

Authors
  1. Liana Fraenkel MD, MPH
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  2. Paul Falzer PhD
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  3. Terri Fried MD
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  4. Minna Kohler MD
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  5. Ellen Peters PhD
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  6. Robert Kerns PhD
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Corresponding author

Correspondence to Liana Fraenkel MD, MPH.

Additional information

This grant was supported by VA Health Services and Research Department grant no. IIR07-090-3.

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Fraenkel, L., Falzer, P., Fried, T. et al. Measuring Pain Impact Versus Pain Severity Using a Numeric Rating Scale. J GEN INTERN MED 27, 555–560 (2012). https://doi.org/10.1007/s11606-011-1926-z

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  • DOI: https://doi.org/10.1007/s11606-011-1926-z

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