Spatial Quantification of the Population Exposed to Cryptococcus neoformans and Cryptococcus gattii Species Complexes in Europe: Estimating the Immunocompetent and HIV/AIDS Patients Under Risk

Alberto J. Alaniz, Jorge G. Carvajal, Mario A. Carvajal, Massimo Cogliati and Pablo M. Vergara

Risk Analysis, 2020, vol. 40, issue 3, 524-533

Abstract: Cryptococcus is an important fungal pathogen worldwide, causing serious clinical manifestations that can affect immunocompetent patients and can be particularly severe for immunocompromised patients. The Cryptococcus gattii s.s. (AFLP4/VGI), Cryptococcus tetragattii (AFLP/VGIV), Cryptococcus neoformans, and Cryptococcus deneoformans have been isolated from both clinical and environmental sources in Europe. We aim to quantify the people in Europe and the entire Mediterranean area who are under risk associated with each of the three fungal pathogens in a spatially explicit way, generating a series of maps and population statistics per country. Niche modeling was applied to estimate the potential distribution of each fungal pathogen, then these models were overlapped with a map of population density to estimate risk levels. The potential number of people per risk level and per country was quantified using a map of population count per pixel. Prevalence of HIV per country was also included in the analysis to quantify the HIV‐infected population under potential risk. People under risk associated with exposure to C. gattii species (C. gattii s.s. and C. tetragattii) reached 137.65 million, whereas those exposed to C. neoformans and C. deneoformans were 268.58 and 360.78 million people, respectively. More than a half million HIV‐infected patients are exposed to each of the two species of the C. neoformans species complex, and more than 200,000 to the C. gattii species complex. The present results can be useful for public health planning by European governments, focusing on the provision of inputs for a “screen‐and‐treat” approach, availability of medical resources, and continuous monitoring programs in risk zones.

Date: 2020
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https://doi.org/10.1111/risa.13410

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Persistent link: https://EconPapers.repec.org/RePEc:wly:riskan:v:40:y:2020:i:3:p:524-533

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