Knowledge, attitude, and practice (KAP), and acceptance and willingness to pay (WTP) for mosquito-borne diseases control through sterile mosquito release in Bangkok, Thailand
Bibliographic record
Abstract
BACKGROUND: Arboviral diseases such as dengue, chikungunya and Zika are public health concerns worldwide. Prevention and control of these diseases still depend on controlling Aedes aegypti mosquito vectors. Sterile insect technique (SIT) and incompatible insect technique (IIT) are environmental friendly approaches that show promising impacts. In order to plan an implementation of SIT/IIT technology, background knowledge, attitudes and practices (KAP) related to mosquito-borne diseases, mosquito vectors and their prevention and control, as well as acceptance and willingness to pay (WTP) for the technology, in the targeted communities are needed. METHODOLOGY/PRINCIPAL FINDINGS: In this paper, we conducted questionnaire surveys on KAP and WTP in 400 sampled households in seven communities located in two districts in Bangkok, Thailand. Multivariate logistic regressions analysis was used to determine the association among knowledge, attitudes and practices regarding dengue, chikungunya, and Zika. Our findings indicated that participants had high knowledge on dengue (85.25%), and they were more concerned with the severity of dengue than chikungunya and Zika. Participants with ages lower than 35 years old (p = 0.047) and incomes higher than 5,000 THB (p = 0.016) had more knowledge of mosquito vectors. Moreover, 47% of respondents had positive attitude toward sterile mosquitoes and their application in vector control even though 45.5% of them had never heard about the technology. However, the majority of them were not willing to pay (52%); and if they had to pay, the maximum would be 1-2 THB (US$ 1 = ~34 THB) per sterile mosquito, since most of them expected to receive the service as public support from the government. CONCLUSIONS/SIGNIFICANCE: Our study was the first to study KAP and WTP related to SIT/IIT technology. It provided unique insights into how communities view the technology. It also suggested potential for successful implementation with proper education as well as highlighted the need for cost-sharing strategies with government subsidization for SIT/IIT deployment. Municipal officials and community health volunteers were key communication channels and targeted public education was needed, especially on under-recognized diseases like chikungunya and Zika. These findings should be useful for health authorities in planning to integrate SIT/IIT technology with traditional approaches for disease vector control and prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".