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A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control.
Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia.
Afiliación
  • Phung D; Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia. d.phung@griffith.edu.au.
  • Talukder MR; Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia. radwan.talukder@griffithuni.edu.au.
  • Rutherford S; Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia.
  • Chu C; Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia.
Trop Med Int Health ; 21(10): 1324-1333, 2016 Oct.
Article en En | MEDLINE | ID: mdl-27404323
OBJECTIVE: To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR). METHODS: We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the ß-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis. RESULTS: The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%). CONCLUSION: This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Brotes de Enfermedades / Clima / Dengue Tipo de estudio: Etiology_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Trop Med Int Health Asunto de la revista: MEDICINA TROPICAL / SAUDE PUBLICA Año: 2016 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Brotes de Enfermedades / Clima / Dengue Tipo de estudio: Etiology_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Trop Med Int Health Asunto de la revista: MEDICINA TROPICAL / SAUDE PUBLICA Año: 2016 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido