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. 2007 Dec 26;1(3):e103.
doi: 10.1371/journal.pntd.0000103.

Targeted screening strategies to detect Trypanosoma cruzi infection in children

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Targeted screening strategies to detect Trypanosoma cruzi infection in children

Michael Z Levy et al. PLoS Negl Trop Dis. .

Abstract

Background: Millions of people are infected with Trypanosoma cruzi, the causative agent of Chagas disease in Latin America. Anti-trypanosomal drug therapy can cure infected individuals, but treatment efficacy is highest early in infection. Vector control campaigns disrupt transmission of T. cruzi, but without timely diagnosis, children infected prior to vector control often miss the window of opportunity for effective chemotherapy.

Methods and findings: We performed a serological survey in children 2-18 years old living in a peri-urban community of Arequipa, Peru, and linked the results to entomologic, spatial and census data gathered during a vector control campaign. 23 of 433 (5.3% [95% CI 3.4-7.9]) children were confirmed seropositive for T. cruzi infection by two methods. Spatial analysis revealed that households with infected children were very tightly clustered within looser clusters of households with parasite-infected vectors. Bayesian hierarchical mixed models, which controlled for clustering of infection, showed that a child's risk of being seropositive increased by 20% per year of age and 4% per vector captured within the child's house. Receiver operator characteristic (ROC) plots of best-fit models suggest that more than 83% of infected children could be identified while testing only 22% of eligible children.

Conclusions: We found evidence of spatially-focal vector-borne T. cruzi transmission in peri-urban Arequipa. Ongoing vector control campaigns, in addition to preventing further parasite transmission, facilitate the collection of data essential to identifying children at high risk of T. cruzi infection. Targeted screening strategies could make integration of diagnosis and treatment of children into Chagas disease control programs feasible in lower-resource settings.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. I) Contour map of smoothed density of households with A. T. infestans, B. T.cruzi-infected T. infestans and C. Seropositive children; II) Log relative risk surfaces of households; III) difference in Ripley's K statistic. Guadalupe, Arequipa, Peru, 2005.
Figure 2
Figure 2. Receiver-operator curves from multivariate models predicting T. cruzi infection in children aged 18 years and younger in the community of Guadalupe, Arequipa, Peru, 2005.
Model A includes information on age only, Model B includes data on age and vector presence, Model C includes age and domestic vector density, and Model D includes information on age, domestic vector density, and the presence of domestic T. cruzi-infected vectors (see Table 2). For each graph non-spatial ROC curves are in black. ROC curves for two-stage testing algorithms are in color (red = 10 meter radius, green = 20 m, blue = 30 m, teal = 40 m, magenta = 50 m, yellow = 60 m, grey = 70 m). Seropostives are positive by two tests (ELISA and IFA), seronegatives are negative by ELISA.

Comment in

References

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