The burden of dengue in children by calculating spatial temperature: A methodological approach using remote sensing techniques

Oliver Mendoza-Cano*, Pedro Rincón-Avalos, Verity Watson, Abdou Khouakhi, Jesús López de la Cruz, Angelica Patricia Ruiz-Montero, Cynthia Monique Nava-Garibaldi, Mario Lopez-Rojas, Efrén Murillo-Zamora* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Dengue fever is one of the most important arboviral diseases. Surface temperature versus dengue burden in tropical environments can provide valuable information that can be adapted in future measurements to improve health policies. Methods: A methodological approach using Daymet-V3 provided estimates of daily weather parameters. A Python code developed by us extracted the median temperature from the urban regions of Colima State (207.3 km2) in Mexico. JointPoint regression models computed the mean temperature-adjusted average annual percentage of change (AAPC) in disability-adjusted life years (DALY) rates (per 100,000) due to dengue in Colima State among school-aged (5–14 years old) children. Results: Primary outcomes were average temperature in urban areas and cumulative dengue burden in DALYs in the school-aged population. A model from 1990 to 2017 medium surface temperature with DALY rates was performed. The increase in DALYs rate was 64% (95% CI, 44–87%), and it seemed to depend on the 2000–2009 estimates (AAPC = 185%, 95% CI 18–588). Conclusion: From our knowledge, this is the first study to evaluate surface temperature and to model it through an extensive period with health economics calculations in a specific subset of the Latin-American endemic population for dengue epidemics.

Original languageEnglish
Article number4230
Number of pages10
JournalInternational Journal of Environmental Research and Public Health
Volume18
Issue number8
DOIs
Publication statusPublished - 16 Apr 2021

Keywords

  • Arbovirus
  • Children
  • Dengue
  • Health economics
  • Remote sensing
  • Temperature

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