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Meta-Analysis
. 2022 Aug 3;15(1):277.
doi: 10.1186/s13071-022-05377-6.

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

Prasad Liyanage  1   2 , Yesim Tozan  3 , Hasitha Aravinda Tissera  4 , Hans J Overgaard  5   6 , Joacim Rocklöv  7   8
Affiliations
Meta-Analysis

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

Prasad Liyanage et al. Parasit Vectors. .

Abstract

Background: Dengue 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.

Methods: Monthly 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.

Results: We 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.

Conclusions: This 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.

Keywords: Aedes larval indices; Dengue risk; Kalutara; Lags; Sri Lanka; Thresholds.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Dengue incidence and location of meteorological monitoring stations and entomological sentinel sites in Kalutara district, Sri Lanka. Black solid lines in the map represent the boundaries of Medical Officer of Health (MOH) divisions. Blue circles and red triangles show the location of rainfall and temperature monitoring stations, respectively. Green-shaded boxes indicate the location of long-term entomological surveillance sites. Annual dengue incidence was calculated per 100,000 population from 2010 to 2019. Source of the base file: https://data.humdata.org/dataset/sri-lanka-administrative-levels-0-4-boundaries
Fig. 2
Fig. 2
Time series plot shows the monthly aggregated dengue cases and average Premise Index (PI), Breteau Index (BI) and Container Index (CI) from 2010 to 2019 in all MOH areas in Kalutara district, Sri Lanka. The red line indicates the number of dengue cases; the green, blue and orange dotted lines indicate BI, PI, and CI; the horizontal dashed line indicates the outbreak threshold of 175 cases per month
Fig. 3
Fig. 3
The pooled relative risk (RR) of dengue by PI at lag 0–3 months in ten MOH divisions in Kalutara district, Sri Lanka, 2010–2019. The Y-axis represents the RR for dengue incidence, with a RR of 1.0 at a PI of 0. The shaded areas in gray represent the 95% confidence intervals (CI)
Fig. 4
Fig. 4
The pooled RR of dengue by BI at lag 0–3 months in Kalutara district, Sri Lanka, 2010–2019. The Y-axis represents the RR for dengue incidence, with a RR of 1.0 at a BI of 0. The shaded area in gray represents the 95% CI
Fig. 5
Fig. 5
The pooled RR of dengue by CI at lag 0–3 months in Kalutara district, Sri Lanka, 2010–2019. The Y-axis represents the combined RR for dengue incidence, with a RR of 1.0 at a CI of 5. The shaded areas in gray represent the 95% CI
See this image and copyright information in PMC

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