Asset Details
Do you wish to reserve the book?
Seasonal and meteorological determinants of district level acute watery diarrhea incidence in Fiji, 2016–2023: a time-series analysis
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Seasonal and meteorological determinants of district level acute watery diarrhea incidence in Fiji, 2016–2023: a time-series analysis
Do you wish to request the book?
Seasonal and meteorological determinants of district level acute watery diarrhea incidence in Fiji, 2016–2023: a time-series analysis
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Seasonal and meteorological determinants of district level acute watery diarrhea incidence in Fiji, 2016–2023: a time-series analysis
2026
Overview
Background
Pacific island countries (PICs), including Fiji, are among the most vulnerable to the health impacts of climate change. In Fiji, acute watery diarrhea (AWD) remains a common communicable disease of public health concern, yet evidence regarding direct meteorological drivers remains equivocal, especially in Pacific settings. This study examined the associations between rainfall, temperature, and humidity and AWD incidence across districts, while also assessing seasonal and autoregressive influences.
Methods
An ecological time-series analysis was conducted using eight years (2016–2023) of surveillance data from the Early Warning Alert and Response System (EWARS). Meteorological predictors included monthly averages of rainfall, temperature, and humidity. Negative binomial mixed-effects regression models were used to examine associations between AWD incidence and meteorological variables, adjusting for seasonal indicators, autoregressive terms, and district population. The analyses were stratified by age group (under five years [U5] and individual five years and older [O5]) and by division.
Results
Strong seasonal trends were observed: AWD incidence peaked in Quarter 1 (January–March) for both the U5 (RR 1.20, 95% CI 1.06–1.37) and O5 (RR 1.25, 95% CI 1.12–1.39) populations, compared to Quarter 4 (October–December). Quarter 3 (July–September) showed reduced risk (U5: RR 0.89, 95% CI 0.80–0.99; O5: RR 0.91, 95% CI 0.83–0.99). Prior-month AWD incidence consistently predicted current-month incidence (U5: RR 1.006, 95% CI 1.004–1.007; O5: RR 1.002, 95% CI 1.002–1.003). No significant associations between AWD incidence and average monthly rainfall, temperature, or humidity. Regional heterogeneity emerged: in the Central Division, elevated temperatures increased AWD risk for both age groups (U5: RR 1.03, 95% CI 1.00–1.07; O5: RR 1.05, 95% CI 1.02–1.07) and high humidity increased risk in U5 (RR 1.02, 95% CI 1.01–1.03); In the Western Division, elevated temperature was protective for U5 (RR 0.98, 95% CI 0.96–1).
Conclusion
AWD incidence in Fiji's Central and Western Divisions is driven primarily by seasonal and autoregressive patterns rather than average monthly meteorological factors. Strong Quarter 1 peaks (20–25% elevated risk) provide a predictable signal for proactive intervention. Context-dependent climate-disease relationships emerged, with Central Division showing temperature and humidity associations absent in Western region. These findings support calendar-based preparedness, with district-specific vulnerability assessments guiding pre-positioned WASH and medical resources in high-risk areas before the annual Quarter 1 surge.
