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The Impact of Gastrointestinal Symptoms on Patients’ Well-Being: Best–Worst Scaling (BWS) to Prioritize Symptoms of the Gastrointestinal Symptom Score (GIS)

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  • Axel Christian Mühlbacher

    (Institute for Health Economics and Health Care Management, Hochschule Neubrandenburg, 17033 Neubrandenburg, Germany)

  • Anika Kaczynski

    (Institute for Health Economics and Health Care Management, Hochschule Neubrandenburg, 17033 Neubrandenburg, Germany)

Abstract

Background: The gastrointestinal symptom score (GIS) is used in a standardized form to ascertain dyspeptic symptoms in patients with functional dyspepsia in clinical practice. As a criterion for evaluating the effectiveness of a treatment, the change in the summed total point value is used. The total score ranges from 0 to 40 points, in which a higher score represents a more serious manifestation of the disease. Each symptom is included with equal importance in the overall evaluation. The objective of this study was to test this assumption from a patients’ perspective. Our aim was to measure the priorities of patients for the ten gastrointestinal symptoms by using best–worst scaling. Method: A best–worst scaling (BWS) object scaling (Case 1) was applied. Therefore, the symptoms of the GIS were included in a questionnaire using a fractional factorial design (BIBD—balanced incomplete block design). In each choice set, the patients selected the component that had the most and the least impact on their well-being. The BIB design generated a total of 15 choice sets, which each included four attributes. Results: In this study, 1096 affected patients were asked for their priorities regarding a treatment of functional dyspepsia and motility disorder. Based on the data analysis, the symptoms abdominal cramps (SQRT (B/W): −1.27), vomiting (SQRT (B/W): −1.07) and epigastric pain (SQRT (B/W): −0.76) were most important and thus have the greatest influence on the well-being of patients with functional dyspepsia and motility disorders. In the middle range are the symptoms nausea (SQRT (B/W): −0.69), acid reflux/indigestion (SQRT (B/W): −0.29), sickness (SQRT (B/W): −0.26) and retrosternal discomfort (SQRT (B/W): 0.26), whereas the symptoms causing the least impact are the feeling of fullness (SQRT (B/W): 0.80), early satiety (SQRT (B/W): 1.54) and loss of appetite (SQRT(B/W): 1.95). Discussion: Unlike the underlying assumption of the GIS, the BWS indicated that patients did not weight the 10 symptoms equally. The results of the survey show that the three symptoms of vomiting, abdominal cramps and epigastric pain are weighted considerably higher than symptoms such as early satiety, loss of appetite and the feeling of fullness. The evaluation of the BWS data has illustrated, however, that the restrictive assumption of GIS does not reflect the reality of dyspeptic patients. Conclusions: In conclusion, a preference-based GIS is necessary to make valid information about the real burden of illness and to improve the burden of symptoms in the indication of gastrointestinal conditions. The findings of the BWS demonstrate that the common GIS is not applicable to represent the real burden of disease. The results suggest the potential modification of the established GIS by future research using a stated preference study.

Suggested Citation

  • Axel Christian Mühlbacher & Anika Kaczynski, 2021. "The Impact of Gastrointestinal Symptoms on Patients’ Well-Being: Best–Worst Scaling (BWS) to Prioritize Symptoms of the Gastrointestinal Symptom Score (GIS)," IJERPH, MDPI, vol. 18(21), pages 1-13, November.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11715-:d:674406
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    References listed on IDEAS

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    1. Jordan Louviere & Terry Flynn, 2010. "Using Best-Worst Scaling Choice Experiments to Measure Public Perceptions and Preferences for Healthcare Reform in Australia," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 275-283, December.
    2. Marti, Joachim, 2012. "A best–worst scaling survey of adolescents' level of concern for health and non-health consequences of smoking," Social Science & Medicine, Elsevier, vol. 75(1), pages 87-97.
    3. Narelle F. Smith & Deborah J. Street, 2003. "The Use of Balanced Incomplete Block Designs in Designing Randomized Response Surveys," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 45(2), pages 181-194, June.
    4. Jordan J. Louviere & Towhidul Islam & Nada Wasi & Deborah Street & Leonie Burgess, 2008. "Designing Discrete Choice Experiments: Do Optimal Designs Come at a Price?," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(2), pages 360-375, March.
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