Predictive Capacity of Indicators for Early Dengue Warning in Cienfuegos, Cuba (2010-2020)
Keywords:
epidemiological surveillance; dengue; predictive capacity of indicators; early warning system.Abstract
Introduction: Dengue remains the region's primary health problem regarding its impact on incidence, mortality, and the strain placed on health services. An early warning system based on indicators and events would improve the timeliness of outbreak detection and response preparedness.
Objective: To explore the predictive capacity of selected indicators for the early warning of dengue outbreaks in Cienfuegos.
Methods: A retrospective validation study of selected dengue outbreak warning indicators was conducted in the municipality of Cienfuegos during the 2010–2020 period. Epidemiological, diagnostic test, entomological, and climatological indicators were selected based on the best available international evidence and local expert opinion using a consensus technique. Calibration was performed using the operational guide based on the online dashboard proposed by the World Health Organization and R software (version 3.4.3). The outbreak warning threshold, prediction lead time, sensitivity, and positive predictive value (PPV) were determined for each indicator.
Results: The indicators showing the best predictive capacity were: hospitalizations for febrile syndrome (sensitivity [95% confidence interval, CI]: 95.7% [88.7%; 100.0%]; PPV: 75.9% [64.0%; 87.7%]), number of IgM+ cases (sensitivity: 84.8% [73.3%; 96.3%], PPV: 86.7% [75.6%; 97.7%]) with a four-week lead time, precipitation (sensitivity: 100.0% [98.9%; 100.0%], PPV: 51.7% [40.7%; 62.6%]) with a six-week lead time, and maximum temperature (sensitivity: 80.4% [67.9%; 93.0%], PPV: 52.1% [39.8%; 64.4%]) with a 12-week lead time. Entomological indicators showed acceptable sensitivity values, but positive predictive values below 50%.
Conclusions: Hospitalizations for nonspecific febrile syndrome, as well as IgM positivity (both epidemiological and based on diagnostic test results), proved useful for predicting the occurrence of dengue outbreaks; thus, they could serve as indicators for implementing an early warning system in the Cuban context.
Keywords: epidemiological surveillance; dengue; predictive capacity of indicators; early warning system.
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1. Guzmán M, Vázquez S, Álvarez M, Pelegrino J, Ruiz D, Martínez P, et al. Vigilancia de laboratorio de dengue y otros arbovirus en Cuba, 1970-2017. Rev Cubana Med Trop. 2019 [acceso 21/02/2024];71(1):e338. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S037507602019000100008&lng=es
2. Arredondo Bruce A E, Guerrero Jiménez G, de Quezada López F, Santana Gutiérrez O. Presencia y diseminación del Dengue, Chikungunya y otras arbovirosis en las Américas. Rev. Med. Electrón. 2019 [acceso 03/02/2024];41(2):423-34. Disponible en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1684-18242019000200423&lng=es
3. Guzmán MG (ed.). Dengue. Cap. 7. Dengue en Cuba. La Habana: Editorial de Ciencias Médicas; 2016 [acceso 03/02/2024];61-82. Disponible en: https://www.researchgate.net/publication/314419809_Dengue_Coleccion_de_Autores_Cubanos_ECIMED
4. World Health Organization. Technical handbook for dengue surveillance, outbreak prediction/detection and outbreak response. World Health Organization; 2016 [acceso 12/01/2025]. Disponible en: https://apps.who.int/iris/handle/10665/250240
5. Baharom M, Ahmad N, Hod R, Abdul Manaf M R. Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review. Risk Management and Healthcare Policy. 2020 [acceso 12/01/2025];15:871-86. Disponible en: https://www.tandfonline.com/doi/epdf/10.2147/RMHP.S361106?needAccess=true
6. Hussain Alkhateeb L, Rivera Ramírez T, Kroeger A, Gozzer E, Runge-Ranzinger S. Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review. PLOS Neglected Tropical Diseases. 2021 [acceso 12/04/2024];15(9):e0009686. Disponible en: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0009686
7. Meckawy R, Stuckler D, Mehta A, Al-Ahdal T, Doebbeling B N. Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review. BMC Public Health. 2022 [acceso 12/01/2025];22(1):2216. Disponible en: https://link.springer.com/article/10.1186/s12889-022-14625-4
8. Sylvestre E, Joachim C, Ce´cilia-Joseph E, Bouzille´ G, Campillo-Gimenez B, Cuggia M, Cabie A. Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review. PLoS Neglected Tropical Diseases. 2022 [acceso 12/12/2024];16(1):e0010056. Disponible en: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010056
9. Leung XY, Islam RM, Adhami M, Ilic D, McDonald L, Palawaththa S. A systematic review of dengue outbreak prediction models: Current scenario and future directions. PLOS Neglected Tropical Diseases. 2023 [acceso 12/12/2024];17(2):e0010631. Disponible en: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010631
10. Sistema de alerta y respuesta temprana ante brotes de dengue: guía operativa basada en el tablero de mandos en línea. Washington, D.C.: Organización Panamericana de la Salud. 2021. Licencia: CC BY-NC-SA 3.0 IGO. Disponible en: http://iris.paho.org
11. Sanchez Tejeda G, Benitez Valladares D, Correa Morales F, Toledo Cisneros J, Espinoza Tamarindo B. E., Hussain Alkhateeb L. Early warning and response system for dengue outbreaks: Moving from research to operational implementation in Mexico. PLOS Glob Public Health.2023 [acceso 12/04/2024];3(9):e0001691. Disponible en: https://www.scienceopen.com/document_file/ea647828-89a1-4a2b-a1b7-27789253f4f7/PubMedCentral/ea647828-89a1-4a2b-a1b7-27789253f4f7.pdf
12. Yip S, Him N C, Jamil N I, He D, Sahu S K Spatio temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale. Climate Risk Management. 2022 [acceso 12/12/2024];36:100429. Disponible en: https://www.medrxiv.org/content/10.1101/2021.09.22.21263997v2.full.pdf
13. Cardenas R, Hussain Alkhatee L, Benitez Valladares D, Sánchez-Tejeda G., Kroeger A. The Early Warning and Response System (EWARS-TDR) for dengue outbreaks: can it also be applied to chikungunya and Zika outbreak warning? BMC Infectious Diseases. 2022 [acceso 12/04/2024];22(1):235. Disponible en: https://link.springer.com/article/10.1186/s12879-022-07197-6
14. Udayanga L, Aryaprema S, Gunathilaka N, Iqbal M C M, Fernando T, Abeyewickreme W. Larval indices of vector mosquitoes as predictors of dengue epidemics: an approach to manage dengue outbreaks based on entomological parameters in the districts of Colombo and Kandy, Sri Lanka. BioMed Research International. 2020 [acceso 12/04/2024];2020(1):6386952. Disponible en: https://onlinelibrary.wiley.com/doi/epdf/10.1155/2020/6386952
15. Sánchez L, Vanlerberghe V, Alfonso L, Marquetti MC, Guzmán MG, Bisset J, Van der Stuyft P. Aedes aegypti larval indices identify neighborhood high risk for dengue epidemics. Emerg Inf Disease. 2006;12(5):800-6. DOI: https://doi.org/10.3201/eid1205.050866
16. Bisset J, Marquetti MC, García A, Vanderlerberghe V, Leyva M, Van der Stuyft P, et al. Vigilancia pupal de Aedes aegypti como una herramienta en el control de este vector en un municipio con baja densidad poblacional en la Ciudad de La Habana, Cuba. Rev Biomédica. 2008 [acceso 12/04/2024];19:92-103. Disponible en: https://www.bing.com/ck/a?!&&p=1d5b0989fdfd34d2ea2cddfa29ab44eb3530b9fe3733e6a009473122ac563706JmltdHM9MTc4NjkyNDgwMA&ptn=3&ver=2&hsh=4&fclid=149625df-f3f6-61c7-24c7-32f7f2876035&u=a1aHR0cHM6Ly9kaWFsbmV0LnVuaXJpb2phLmVzL2Rlc2NhcmdhL2FydGljdWxvLzYwNTkzNjUucGRm&ntb=1
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