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Record W4401238507 · doi:10.1093/ofid/ofae445

Understanding Acceptability and Willingness-to-pay for a C-reactive Protein Point-of-care Testing Service to Improve Antibiotic Dispensing for Respiratory Infections in Vietnamese Pharmacies: A Mixed-methods Study

2024· article· en· W4401238507 on OpenAlexfundno aff
Nam Vinh Nguyen, Nga Thi Thuy, Vu Thi Lan Huong, Phuong Bich Bui, Phạm Quang Thái, Vinh Thanh Khuong, Anh Tuan Lai, H. Rogier van Doorn, Sonia Lewycka

Bibliographic record

VenueOpen Forum Infectious Diseases · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council Canada
KeywordsMedicinePharmacyPoint-of-care testingFamily medicineAntibioticsPsychological interventionVietnameseOdds ratioFocus groupConfidence intervalEmergency medicineEnvironmental healthNursingInternal medicineMarketingBusiness

Abstract

fetched live from OpenAlex

Background: Pharmacies are popular first points of contact for mild infections in the community. Pharmacy services in many countries have expanded to include vaccines and point-of-care tests. In low- and middle-income countries such as Vietnam, poor enforcement of regulations results in substantial volumes of over-the-counter antibiotic sales. Point-of-care tests could provide an economically viable way to reduce antibiotic sales, while still satisfying customer demand for convenient healthcare. C-reactive protein point-of-care testing (CRP-POCT) can reduce antibiotic prescribing for respiratory illness in primary care. Here, we explore the acceptability and feasibility of implementing CRP-POCT in pharmacies in Vietnam. Methods: We conducted a mixed-methods study between April and June 2021. A customer exit survey with 520 participants seeking acute respiratory infection treatment at 25 pharmacies evaluated acceptability and willingness-to-pay (WTP) for CRP-POCT and post-service satisfaction. Factors driving customers" acceptance and WTP were explored through mixed-effects multivariable regression. Three focus group discussions with customers (20 participants) and 12 in-depth interviews with pharmacists and other stakeholders were conducted and analyzed thematically. Results: Antibiotics were sold to 81.4% of patients with CRP levels <10 mg/L (antibiotics not recommended). A total of 96.5% of customers who experienced CRP-POCT supported its future introduction at pharmacies. Patients with antibiotic transactions (adjusted odds ratio [aOR], 2.25; 95% confidence interval [CI], 1.13-4.48) and those suffering acute respiratory infection symptoms for more than 3 days (aOR, 2.10; 95% CI, 1.08-4.08) were more likely to accept CRP-POCT, whereas customers visiting for children (aOR, 0.20; 95% CI, .10-.54) and those with preference for antibiotic treatment (aOR, 0.45; 95% CI, 0.23-0.89) were less likely to accept CRP-POCT. A total of 78.3% (95% CI, 74.8-81.7) of customers were willing to pay for CRP-POCT, with a mean cost of US$2.4 (±1.1). Customer's income and cost of total drug treatment were associated with increased WTP. Enablers for implementing CRP-POCT included customers' and pharmacists' perceived benefits of CRP-POCT, and the impact of COVID-19 on perceptions of POCT. Perceived challenges for implementation included the additional burden of service provision, lack of an enabling policy environment, and potential risks for customers. Conclusions: Implementing CRP-POCT at pharmacies is a feasible and well-accepted strategy to tackle the overuse of antibiotics in the community, with appeal for both supply and demand sides. Creating an enabling policy environment for its implementation, and transparent discussion of values and risks would be key for its successful implementation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.378
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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