Assessing the associations between Aedes larval indices and dengue risk in Kalutara district, Sri Lanka: a hierarchical time series analysis from 2010 to 2019

Parasites & Vectors

Research

Prasad Liyanage¹  Joacim Rocklöv²  Hans J. Overgaard³  Hasitha Aravinda Tissera⁴  Yesim Tozan⁵ 

[1] Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden;Ministry of Health, Colombo, Sri Lanka;Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, SE-901 87, Umeå, Sweden;Heidelberg Institute of Global Health & the Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany;Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway;Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand;Ministry of Health, Colombo, Sri Lanka;School of Global Public Health, New York University, 10003, New York, NY, USA;

关键词: Dengue risk;    Aedes;    Lags;    Thresholds;    Kalutara;    Sri Lanka;   

DOI  :  10.1186/s13071-022-05377-6

 received in 2022-03-18, accepted in 2022-06-26,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundDengue is a major public health problem in Sri Lanka. Aedes vector surveillance and monitoring of larval indices are routine, long-established public health practices in the country. However, the association between Aedes larval indices and dengue incidence is poorly understood.It is crucial to evaluate lagged effects and threshold values of Aedes larval indices to set pragmatic targets for sustainable vector control interventions.MethodsMonthly Aedes larval indices and dengue cases in all 10 Medical Officer of Health (MOH) divisions in Kalutara district were obtained from 2010 to 2019. Using a novel statistical approach, a distributed lag non-linear model and a two-staged hierarchical meta-analysis, we estimated the overall non-linear and delayed effects of the Premise Index (PI), Breteau Index (BI) and Container Index (CI) on dengue incidence in Kalutara district. A set of MOH division-specific variables were evaluated within the same meta-analytical framework to determine their moderator effects on dengue risk. Using generalized additive models, we assessed the utility of Aedes larval indices in predicting dengue incidence.ResultsWe found that all three larval indices were associated with dengue risk at a lag of 1 to 2 months. The relationship between PI and dengue was homogeneous across MOH divisions, whereas that with BI and CI was heterogeneous. The threshold values of BI, PI and CI associated with dengue risk were 2, 15 and 45, respectively. All three indices showed a low to moderate accuracy in predicting dengue risk in Kalutara district.ConclusionsThis study showed the potential of vector surveillance information in Kalutara district in developing a threshold-based, location-specific early warning system with a lead time of 2 months. The estimated thresholds are nonetheless time-bound and may not be universally applicable. Whenever longitudinal vector surveillance data areavailable, the methodological framework we propose here can be used to estimate location-specific Aedes larval index thresholds in any other dengue-endemic setting.Graphical Abstract

【 授权许可】

CC BY   
© The Author(s) 2022

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