ArticleJournal of primary care & community health
Developing Risk Assessment Criteria and Predicting High- and Low-Dengue Risk Villages for Strengthening Dengue Prevention Activities: Community Participatory Action Research, Thailand.
Article in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Facilitators and barriers to engaging communities in health service research on dengue control in Indo-Pacific region: a systematic review.BMC public health · 2023Pooled it
- Structured patient and public involvement programme development in low- and middle-income country health research: a scoping review of evidence gaps and a framework for future practice.Research involvement and engagement · 2026Review
- Beyond top-down: community co-creation approaches for sustainable dengue vector control.Global health action · 2024Review
- Article
- Understanding dengue solution and larval indices surveillance system among village health volunteers in high- and low-risk dengue villages in southern Thailand.One health (Amsterdam, Netherlands) · 2022Article
- Effects of the Developing and Using a Model to Predict Dengue Risk Villages Based on Subdistrict Administrative Organization in Southern Thailand.International journal of environmental research and public health · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRisk assessment criteria for predicting dengue outbreak must be appropriated at village levels. We aimed to develop risk dengue village prediction criteria, predict village dengue risk, and strengthen dengue prevention based on community participation.
methodsThis participatory research conducted in Southern Thailand included the following 5 phases: (i) preparing communities in 3 districts; (ii) developing risk dengue village prediction criteria; (iii) applying computer program; (iv) predicting village dengue risk with 75 public health providers in 39 PCUs; and (v) utilizing findings to strengthen dengue prevention activities in 220 villages. Data collecting for prediction used secondary data from primary care units in the past 5 year and current year. Descriptive statistics used calculating criteria and comparing with standard level to adjust score of risk.
resultsRisk dengue village assessment criteria had 2 aspects: dengue severity (3 factors) and dengue outbreak opportunity (3 factors). Total scores were 33 points and cut-off of 17 points for high and low dengue risks villages. All criteria were applied using computer program (http://surat.denguelim.com). Risk prediction involved stakeholder participation in 220 villages, and used for strengthening dengue prevention activities. The concept of integrated vector management included larval indices surveillance system, garbage management, larval indices level lower than the standard, community capacity activities for dengue prevention, and school-based dengue prevention. The risk prediction criteria and process mobilized villages for dengue prevention activities to decrease morbidity rate.
conclusionDengue risk assessment criteria were appropriated within the village, with its smallest unit, the household, included. The data can be utilized at village levels for evaluating dengue outbreak risks.
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