IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i21p11715-d674406.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/21/11715/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/21/11715/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kayode Ajewole & Elliott Dennis & Ted C. Schroeder & Jason Bergtold, 2021. "Relative valuation of food and non‐food risks with a comparison to actuarial values: A best–worst approach," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 927-943, November.
    2. Elizabeth Kinter & Thomas Prior & Christopher Carswell & John Bridges, 2012. "A Comparison of Two Experimental Design Approaches in Applying Conjoint Analysis in Patient-Centered Outcomes Research," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(4), pages 279-294, December.
    3. Katrina J Davis & Marit E Kragt & Stefan Gelcich & Michael Burton & Steven Schilizzi & David J Pannell, 2017. "Why are Fishers not Enforcing Their Marine User Rights?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(4), pages 661-681, August.
    4. Shittu, A. & Kehinde, M., 2018. "Willingness to Accept Incentives for a Shift to Climate – Smart Agriculture among Smallholder Farmers in Southwest and Northcentral Nigeria," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275983, International Association of Agricultural Economists.
    5. Hall, Natasha Yvonne & Le, Long & Abimanyi-Ochom, Julie & Mihalopoulos, Cathy, 2023. "Measuring the importance of different barriers to opioid agonist treatment using best-worst scaling in an Australian setting," Health Policy, Elsevier, vol. 138(C).
    6. Seda Erdem & Danny Campbell, 2017. "Preferences for public involvement in health service decisions: a comparison between best-worst scaling and trio-wise stated preference elicitation techniques," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(9), pages 1107-1123, December.
    7. Qingmeng Tong & Lu Zhang & Junbiao Zhang, 2017. "Evaluation of GHG Mitigation Measures in Rice Cropping and Effects of Farmer’s Characteristics: Evidence from Hubei, China," Sustainability, MDPI, vol. 9(6), pages 1-14, June.
    8. Esther W. Bekker-Grob & Bas Donkers & Jorien Veldwijk & Marcel F. Jonker & Sylvia Buis & Jan Huisman & Patrick Bindels, 2021. "What Factors Influence Non-Participation Most in Colorectal Cancer Screening? A Discrete Choice Experiment," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 14(2), pages 269-281, March.
    9. Hasan-Basri, Bakti & Yahya, Nurul & Musa, Rusmani, 2013. "Status Quo Effect and Preferences Uncertainty: A Heteroscedastic Extreme Value (HEV) Model," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 47(1), pages 163-172.
    10. Gökçe Esenduran & James A. Hill & In Joon Noh, 2020. "Understanding the Choice of Online Resale Channel for Used Electronics," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1188-1211, May.
    11. Bliemer, Michiel C.J. & Collins, Andrew T., 2016. "On determining priors for the generation of efficient stated choice experimental designs," Journal of choice modelling, Elsevier, vol. 21(C), pages 10-14.
    12. Araña, Jorge E. & León, Carmelo J., 2013. "Dynamic hypothetical bias in discrete choice experiments: Evidence from measuring the impact of corporate social responsibility on consumers demand," Ecological Economics, Elsevier, vol. 87(C), pages 53-61.
    13. Axel C. Mühlbacher & Anika Kaczynski & Peter Zweifel & F. Reed Johnson, 2016. "Experimental measurement of preferences in health and healthcare using best-worst scaling: an overview," Health Economics Review, Springer, vol. 6(1), pages 1-14, December.
    14. Vardges Hovhannisyan & Hayk Khachatryan, 2017. "Ornamental Plants in the United States: An Econometric Analysis of a Household‐Level Demand System," Agribusiness, John Wiley & Sons, Ltd., vol. 33(2), pages 226-241, April.
    15. Michael P. Keane & Nada Wasi, 2013. "The Structure of Consumer Taste Heterogeneity in Revealed vs. Stated Preference Data," Economics Papers 2013-W10, Economics Group, Nuffield College, University of Oxford.
    16. Yangui, Ahmed & Akaichi, Faical & Costa-Font, Montserrat & Gil, Jose Maria, 2019. "Comparing results of ranking conjoint analyses, best–worst scaling and discrete choice experiments in a nonhypothetical context," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(2), April.
    17. Kaenzig, Josef & Heinzle, Stefanie Lena & Wüstenhagen, Rolf, 2013. "Whatever the customer wants, the customer gets? Exploring the gap between consumer preferences and default electricity products in Germany," Energy Policy, Elsevier, vol. 53(C), pages 311-322.
    18. McKendree, Melissa G.S. & Olynk Widmar, Nicole & Ortega, David L. & Foster, Kenneth A., 2013. "Consumer Preferences for Verified Pork-Rearing Practices in the Production of Ham Products," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(3), pages 1-21.
    19. Áron Török & Ching-Hua Yeh & Davide Menozzi & Péter Balogh & Péter Czine, 2023. "Consumers' preferences for processed meat: a best–worst scaling approach in three European countries," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-24, December.
    20. Olynk Widmar, Nicole J. & Ortega, David L., 2014. "Comparing Consumer Preferences for Livestock Production Process Attributes Across Products, Species, and Modeling Methods," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 46(3), pages 1-17, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11715-:d:674406. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.