Neglected tropical diseases risk correlates with poverty and early ecosystem destruction

Abstract Background Neglected tropical diseases affect the most vulnerable populations and cause chronic and debilitating disorders. Socioeconomic vulnerability is a well-known and important determinant of neglected tropical diseases. For example, poverty and sanitation could influence parasite tran...

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Main Authors: Arthur Ramalho Magalhães (Author), Cláudia Torres Codeço (Author), Jens-Christian Svenning (Author), Luis E. Escobar (Author), Paige Van de Vuurst (Author), Thiago Gonçalves-Souza (Author)
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Published: BMC, 2023-04-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Arthur Ramalho Magalhães  |e author 
700 1 0 |a Cláudia Torres Codeço  |e author 
700 1 0 |a Jens-Christian Svenning  |e author 
700 1 0 |a Luis E. Escobar  |e author 
700 1 0 |a Paige Van de Vuurst  |e author 
700 1 0 |a Thiago Gonçalves-Souza  |e author 
245 0 0 |a Neglected tropical diseases risk correlates with poverty and early ecosystem destruction 
260 |b BMC,   |c 2023-04-01T00:00:00Z. 
500 |a 10.1186/s40249-023-01084-1 
500 |a 2049-9957 
520 |a Abstract Background Neglected tropical diseases affect the most vulnerable populations and cause chronic and debilitating disorders. Socioeconomic vulnerability is a well-known and important determinant of neglected tropical diseases. For example, poverty and sanitation could influence parasite transmission. Nevertheless, the quantitative impact of socioeconomic conditions on disease transmission risk remains poorly explored. Methods This study investigated the role of socioeconomic variables in the predictive capacity of risk models of neglected tropical zoonoses using a decade of epidemiological data (2007-2018) from Brazil. Vector-borne diseases investigated in this study included dengue, malaria, Chagas disease, leishmaniasis, and Brazilian spotted fever, while directly-transmitted zoonotic diseases included schistosomiasis, leptospirosis, and hantaviruses. Environmental and socioeconomic predictors were combined with infectious disease data to build environmental and socioenvironmental sets of ecological niche models and their performances were compared. Results Socioeconomic variables were found to be as important as environmental variables in influencing the estimated likelihood of disease transmission across large spatial scales. The combination of socioeconomic and environmental variables improved overall model accuracy (or predictive power) by 10% on average (P < 0.01), reaching a maximum of 18% in the case of dengue fever. Gross domestic product was the most important socioeconomic variable (37% relative variable importance, all individual models exhibited P < 0.00), showing a decreasing relationship with disease indicating poverty as a major factor for disease transmission. Loss of natural vegetation cover between 2008 and 2018 was the most important environmental variable (42% relative variable importance, P < 0.05) among environmental models, exhibiting a decreasing relationship with disease probability, showing that these diseases are especially prevalent in areas where natural ecosystem destruction is on its initial stages and lower when ecosystem destruction is on more advanced stages. Conclusions Destruction of natural ecosystems coupled with low income explain macro-scale neglected tropical and zoonotic disease probability in Brazil. Addition of socioeconomic variables improves transmission risk forecasts on tandem with environmental variables. Our results highlight that to efficiently address neglected tropical diseases, public health strategies must target both reduction of poverty and cessation of destruction of natural forests and savannas. 
546 |a EN 
690 |a Disease ecology 
690 |a Ecological niche model 
690 |a Socioecological system 
690 |a Vector-borne diseases 
690 |a Zoonosis 
690 |a Brazil 
690 |a Infectious and parasitic diseases 
690 |a RC109-216 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Infectious Diseases of Poverty, Vol 12, Iss 1, Pp 1-15 (2023) 
787 0 |n https://doi.org/10.1186/s40249-023-01084-1 
787 0 |n https://doaj.org/toc/2049-9957 
856 4 1 |u https://doaj.org/article/bc4d1c82ccaa4e50b2e66d7120c2f13d  |z Connect to this object online.