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. 2020 Nov 2;19(1):44.
doi: 10.1186/s12942-020-00241-1.

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico

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Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico

Matthew J Watts et al. Int J Health Geogr. .

Abstract

Background: This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue.

Methods: A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation.

Results: The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income.

Conclusions: Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.

Keywords: Climate-change; Dengue; GAM; GDP; Global-warming; Mosquito-borne; Socio-economic; Vector-borne-diseases.

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Conflict of interest statement

No competing interests.

Figures

Fig. 1
Fig. 1
Aedes sample locations and SDM results 1 Top left: Aedes point locations. 2 Top right: Results of Aedes aegypti SDM 3 Bottom left: Results of Aedes albopictus SDM 4 Bottom right: Receptive regions/data extraction locations
Fig. 2
Fig. 2
Koppen-Geiger climate classification in study regions (Source: koeppen-geiger.vu-wien.ac.at)
Fig. 3
Fig. 3
Crude incidence rates of dengue per 100,0000 people
Fig. 4
Fig. 4
Partial effects of explanatory variables: GAM Mex/US model
Fig. 5
Fig. 5
Partial effects of explanatory variables: GAM Mexico model

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References

    1. Murray NEA, Quam MB, Wilder-Smith A. Epidemiology of dengue: past, present and future prospects. Clin Epidemiol. 2013;5:299. https://www.dovepress.com/getfile.php?fileID=17199. - PMC - PubMed
    1. Anosike JC, Nwoke BE, Okere AN, Oku EE, Asor JE, Emmy-Egbe IO, et al. Epidemiology of tree-hole breeding mosquitoes in the tropical rainforest of Imo state, south-east Nigeria. Ann Agric Environ Med. 2007;14:31–8. - PubMed
    1. Gomez-Dantes H, Ramsey Willoquet J. Dengue in the Americas: challenges for prevention and control. Cadernos De Saude Publica. 2009;25:S19–31. doi: 10.1590/s0102-311x2009001300003. - DOI - PubMed
    1. WHO. Dengue and severe dengue. 2020. https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue.
    1. WHO. Immunization, vaccines and biologicals. 2017; 2018. http://www.who.int/immunization/research/development/dengue_vaccines/en/.

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