Zida Husnina1,2, Archie C A Clements1,3,4, Kinley Wangdi1. 1. Research School of Population Health, Australian National University, Canberra, ACT, Australia. 2. Department of Environmental Health, Faculty of Public Health, Universitas Airlangga, Jawa Timur, Indonesia. 3. Faculty of Health Sciences, Curtin University, Perth, WA, Australia. 4. Telethon Kids Institute, Nedlands, WA, Australia.
Abstract
OBJECTIVES: To describe and quantify spatiotemporal trends of dengue fever at district level in Sumatra and Kalimantan, Indonesia in relation to forest cover and climatic factors. METHODS: A spatial ecological study design was used to analyse monthly surveillance data of notified dengue fever cases from January 2006 to December 2016 in the 154 districts of Sumatra and 56 districts of Kalimantan. A multivariate, zero-inflated Poisson regression model was developed with a conditional autoregressive prior structure with posterior parameters estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. RESULTS: There were 230 745 cases in Sumatra and 132 186 cases in Kalimantan during the study period. In Sumatra, the risk of dengue fever decreased by 9% (95% credible interval [CrI] 8.5-9.5%) for a 1% increase in forest cover and by 12.2% (95% CrI 11.9-12.6%) for a 1% increase in relative humidity. In Kalimantan, dengue fever risk fell by 17.6% (95% CrI 17.1-18.1%) for a 1% increase in relative humidity and rose by 7.6% (95% CrI 6.9-8.4%) for a 1 °C increase in minimum temperature. There was no significant residual spatial clustering in Sumatra after accounting for climate and demographic variables. In Kalimantan, high residual risk areas were primarily centred in North and East of the island. CONCLUSIONS: Dengue fever in Sumatra and Kalimantan was highly seasonal and associated with climate factors and deforestation. Incorporation of climate indicators into risk-based surveillance might be warranted for dengue fever in Indonesia.
OBJECTIVES: To describe and quantify spatiotemporal trends of dengue fever at district level in Sumatra and Kalimantan, Indonesia in relation to forest cover and climatic factors. METHODS: A spatial ecological study design was used to analyse monthly surveillance data of notified dengue fever cases from January 2006 to December 2016 in the 154 districts of Sumatra and 56 districts of Kalimantan. A multivariate, zero-inflated Poisson regression model was developed with a conditional autoregressive prior structure with posterior parameters estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. RESULTS: There were 230 745 cases in Sumatra and 132 186 cases in Kalimantan during the study period. In Sumatra, the risk of dengue fever decreased by 9% (95% credible interval [CrI] 8.5-9.5%) for a 1% increase in forest cover and by 12.2% (95% CrI 11.9-12.6%) for a 1% increase in relative humidity. In Kalimantan, dengue fever risk fell by 17.6% (95% CrI 17.1-18.1%) for a 1% increase in relative humidity and rose by 7.6% (95% CrI 6.9-8.4%) for a 1 °C increase in minimum temperature. There was no significant residual spatial clustering in Sumatra after accounting for climate and demographic variables. In Kalimantan, high residual risk areas were primarily centred in North and East of the island. CONCLUSIONS: Dengue fever in Sumatra and Kalimantan was highly seasonal and associated with climate factors and deforestation. Incorporation of climate indicators into risk-based surveillance might be warranted for dengue fever in Indonesia.
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