25
Views
0
CrossRef citations to date
0
Altmetric
Research Article

The influence of attributes on community preferences regarding antibiotic treatment: evidence from a discrete choice model

, , , , , & show all
Received 04 Oct 2022, Accepted 02 Apr 2024, Published online: 03 May 2024
 

ABSTRACT

Antibiotic resistance (AR) rates in Vietnam are among the highest in Asia, and recent infections due to multi-drug resistance in the country have caused thousands of deaths each year. This study investigated a Vietnamese community’s preferences for antibiotic treatment and its knowledge and attitudes regarding antibiotics. A discrete choice experiment-based survey was developed and administered to the population of interest. The respondents were given sociodemographic-, knowledge- and attitude-related items and 17 pairs of choice tasks. Two hypothetical options were included in each choice task. Latent class analysis was conducted to determine the differences among the respondents’ preferences. Among 1,014 respondents, 805 (79.4%) gave valid questionnaires. A three-latent-class model with four covariates (age, healthcare-related education or career, occupation, and attitude classifications) was used in the analysis. All five attributes significantly influenced the respondents’ decisions. The majority, including young employed respondents with non-healthcare-related work or education, found treatment failure more important. Older respondents who had healthcare-related education/careers and/or appropriate antibiotic use- and antibiotics resistance-related attitudes, regarded contribution to antibiotic resistance as an important attribute in selecting antibiotic treatments. Unemployed individuals with correct knowledge identified the cost of antibiotic treatment as the most essential decision-making factor. Findings suggest minimal antibiotic impact on resistance; only 7.83% view it as amajor concern. The respondents exhibited substantial preference heterogeneity, and the general Vietnamese public had poor knowledge of and attitudes toward antibiotic use and antibiotic resistance. This study emphasizes the need for individual responsibility for antibiotic resistance and appropriate antibiotic use.

Abbreviations

AR: antibiotic resistance; DCE: discrete choice experiments: latent class analysis; LCM: latent Class model, USD: United States dollar; VND: Vietnamese Dong.

Acknowledgments

The authors are grateful to all respondents who participated in this study, and to the data collectors for their work on this study. The participants were kind enough to answers all question even when they might be busy with important office works or other errands.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

TQV, QVT, BTN and APNT designed the study. TQV, QVT, APNT and VNTP created the experimental design collected the data. VNTP, BTN, THNA, and TNKH supported the planning phase of the interviews. TQV, QVT and APNT analyzed the data and contributed to the interpretation of the data. TQV, QVT, APNT, BNT, THAN, VNTP, and TNKH wrote the draft manuscript. All authors read and approved the final manuscript.

Transparency declarations

The authors state that they have no known conflicting financial interests, personal or professional affiliations, or other relationships that may appear to have influenced the work presented in this study.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 65.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 402.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.