Document Type

Journal Article

Publication Title

The Journal of Climate Change and Health

Publisher

Elsevier

School

School of Science / Centre for People, Place and Planet

RAS ID

45433

Funders

Stronger Systems for Health Security grant scheme by the Indo-Pacific Centre for Health Security, Department of Foreign Affairs and Trade Australia (Grant No: SSHS 74427) / Bloomberg Philanthropies Vibrant Oceans Initiative (Grant No: 53006)

Comments

Nelson, S., Jenkins, A., Jupiter, S. D., Horwitz, P., Mangubhai, S., Abimbola, S., ... & Negin, J. (2022). Predicting climate-sensitive water-related disease trends based on health, seasonality and weather data in Fiji. The Journal of Climate Change and Health, 6, Article 100112. https://doi.org/10.1016/j.joclim.2022.100112

Abstract

Leptospirosis, typhoid and dengue are three water-related diseases influenced by environmental factors. We examined whether seasonality and rainfall predict reported syndromes associated with leptospirosis, typhoid and dengue in Fiji. Poisson generalised linear models were fitted with s6 early warning, alert and response system (EWARS) syndromic conditions from March 2016 until December 2020, incorporating seasonality, temperature and rainfall. Watery diarrhoea, prolonged fever and suspected dengue displayed seasonal trends with peaks corresponding with the rainy season, while bloody diarrhoea, acute fever with rash and acute jaundice syndrome did not. Seasonality was the most common predictor for watery and bloody diarrhoea, prolonged fever, suspected dengue, and acute fever plus rash in those aged 5 and over, explaining between 0.4 % – 37.8 % of the variation across all conditions. Higher rainfall was the most common predictor for acute fever plus rash and acute jaundice syndrome in children under 5, explaining between 1.0 % – 7.6 % variation across all conditions. Each EWARS syndromic condition case peak was associated with a different rainfall lag, varying between 0 and 11 weeks. The relationships between EWARS, rainfall and seasonality show that it is possible to predict when outbreaks will occur by following seasonality and rainfall. Pre-positioning of diagnostic and treatment resources could then be aligned with seasonality and rainfall peaks to plan and address water-related disease outbreaks.

DOI

10.1016/j.joclim.2022.100112

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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