Analysis of the epidemiological, clinical, sociodemographic, and geospatial conditions of the female population of Isarzama, based on information obtained from Chagas disease screening
DOI:
https://doi.org/10.63688/5c5yt586Keywords:
Chagas disease, Trypanosoma cruzi, machine learning, georeferencing, epidemiological surveillanceAbstract
Chagas disease continues to be a public health problem in Bolivia, especially in women of reproductive age due to the risk of congenital transmission. The objective of the study was to develop and evaluate a predictive model for the early detection and stratification of the risk of seroreactivity for Trypanosoma cruzi in women from Isarzama, Cochabamba. A quantitative, observational, and cross-sectional analytical study was conducted with 326 participants, integrating epidemiological, clinical, sociodemographic, environmental, serological, and geospatial variables. Seroreactivity reached approximately 11%. The main associated factors were maternal history of Chagas disease, presence of kissing bugs, family history, and adobe housing. The model reported sensitivity of 94.4%, specificity of 91.8%, accuracy of 92.6%, and AUC-ROC of 0.95. The geospatial analysis allowed the identification of sectors with greater environmental risk, while the digital monitoring showed high attendance at controls and continuity of the process. It is concluded that the integration of epidemiological factors, georeferencing and digital monitoring has the potential to strengthen the prioritization of screening and epidemiological surveillance at the first level of care.
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