Artículo

Spatial patterns of intestinal parasite infections among children and adolescents in some indigenous communities in Argentina

Scavuzzo, Carlos Matias Icon; Campero, Micaela Natalia Icon; Maidana, Rosana Elizabeth Icon; Oberto, María Georgina ; Periago, Maria Victoria Icon; Porcasi Gomez, Ximena Icon

Otros responsables

Fecha

05/2024

Editorial

Univ Naples Federico Ii

Revista

Geospatial Health

Título del libro

Nombre del evento

Fecha del evento

Institución organizadora

ISSN

1827-1987

eISSN

ISBN

Idioma

Inglés

Tipo del evento

Clasificación temática

Resumen

Argentina has a heterogeneous prevalence of infections by intestinal parasites (IPs), with the north in the endemic area, especially for soil-transmitted helminths (STHs). We analyzed the spatial patterns of these infections in the city of Tartagal, Salta province, by an observational, correlational, and cross-sectional study in children and adolescents aged 1 to 15 years from native communities. One fecal sample per individual was collected to detect IPs using various diagnostic techniques: Telemann sedimentation, Baermann culture, and Kato-Katz. Moran’s global and local indices were applied together with SaTScan to assess the spatial distribution, with a focus on cluster detection. The extreme gradient boosting (XGBoost) machine-learning model was used to predict the presence of IPs and their transmission pathways. Based on the analysis of 572 fecal samples, a prevalence of 78.3% was found. The most frequent parasite was Giardia lamblia (30.9%). High- and low-risk clusters were observed for most species, distributed in an east-west direction and polarized in two large foci, one near the city of Tartagal and the other in the km 6 community. Spatial XGBoost models were obtained based on distances with a minimum median accuracy of 0.69. Different spatial patterns reflecting the mechanisms of transmission were noted. The distribution of the majority of the parasites studied was aligned in a westerly direction close to the city, but the STH presence was higher in the km 6 community, toward the east. The purely spatial analysis provides a different and complementary overview for the detection of vulnerable hotspots and strategic intervention. Machine-learning models based on spatial variables explain a large percentage of the variability of the IPs.

Aprobación

Revisión

Complementado por

Referenciado por

Licencia Creative Commons

Excepto donde se indique lo contrario, la licencia de este ítem se describe como by-nc