ArticleBMC public health2025
Temporal variations and spatial clusters of diarrheal diseases before and during the COVID-19 pandemic in Jakarta province, Indonesia.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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1 citing paper in PubMed.
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- Erratum issued
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDiarrheal diseases continue to be an important public health problem in developing countries including Indonesia, possibly made worse by COVID-19 pandemic. Therefore, this study aimed to outline the epidemiology of diarrheal diseases in Jakarta Province in Indonesia.
methodsThe diarrhoeal data from 2018 to 2021 were obtained from the Epidemiological Surveillance Unit, Jakarta Provincial Health Office. Seasonal and trend decomposition using Loess was used to analyse the temporal analysis. The Local Indicators for Spatial Association was used to detect spatial clustering of diarrheal cases at the sub-district level. Multivariable Poisson regression was used to study the association between diarrhoea and climatic factors.
resultsDiarrheal diseases were highly seasonal with cases peaking in the beginning of each year. The diarrheal diseases incidence showed an increase from 2018, peaking in 2020, and then gradually decreasing until middle 2021, and it slightly increased towards the end of 2021. High-risk sub-districts of diarrhoea were observed in Kalideres and Cengkareng throughout the study period. Additionally, there was a spatial variation of cases across the province with hotspots located in sub-districts in the western part of the province in the peri-urban region. Diarrhoea decreased by 17.0% (RR = 0.83,95% CI = 0.83-0.84) for 1 °C increase in average temperature. Similarly, diarrhoea decreased by 3.0% (RR = 0.97,95%CI = 0.97-0.97) and 27.0% (RR = 0.73, 95%CI = 0.72-0.73) for 1% in average humidity and 1 m/s wind speed. Meanwhile, diarrhoea increased by 3% (RR = 1.03, 95%CI = 1.02-1.03) and 1% (RR = 1.01, 95%CI = 1.01-1.01) in 1 h duration of sunshine per week and 1 mm increase average rainfall respectively.
conclusionSeveral sub-districts demonstrated disease clusters which indicate the area has a significantly higher risk. In addition, climatic factors were associated with diarrheal incidence. Interventions such as environmental and climatic adaptation and climatic data monitoring need to be carried out in areas that have a high risk as an early warning system.
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