Novel Evolutionary Algorithm Identifies Interactions Driving Infestation of Triatoma dimidiata, a Chagas Disease Vector.

Chagas disease is a lethal, neglected tropical disease. Unfortunately, aggressive insecticide-spraying campaigns have not been able to eliminate domestic infestation of Triatoma dimidiata, the native vector in Guatemala. To target interventions toward houses most at risk of infestation, comprehensive socioeconomic and entomologic surveys were conducted in two towns in Jutiapa, Guatemala. Given the exhaustively large search space associated with combinations of risk factors, traditional statistics are limited in their ability to discover risk factor interactions. Two recently developed statistical evolutionary algorithms, specifically designed to accommodate risk factor interactions and heterogeneity, were applied to this large combinatorial search space and used in tandem to identify sets of risk factor combinations associated with infestation. The optimal model includes 10 risk factors in what is known as a third-order disjunctive normal form (i.e., infested households have chicken coops AND deteriorated bedroom walls OR an accumulation of objects AND dirt floors AND total number of occupants ≥ 5 AND years of electricity ≥ 5 OR poor hygienic condition ratings AND adobe walls AND deteriorated walls AND dogs). Houses with dirt floors and deteriorated walls have been reported previously as risk factors and align well with factors currently targeted by Ecohealth interventions to minimize infestation. However, the tandem evolutionary algorithms also identified two new socioeconomic risk factors (i.e., households having many occupants and years of electricity ≥ 5). Identifying key risk factors may help with the development of new Ecohealth interventions and/or reduce the survey time needed to identify houses most at risk.

Geospatial and Temporal Analysis of Thyroid Cancer Incidence in a Rural Population.

BACKGROUND: The increasing incidence of thyroid cancer has resulted in the rate tripling over the past 30 years. Reasons for this increase have not been established. Geostatistics and geographic information system (GIS) tools have emerged as powerful geospatial technologies to identify disease clusters, map patterns and trends, and assess the impact of ecological and socioeconomic factors (SES) on the spatial distribution of diseases. In this study, these tools were used to analyze thyroid cancer incidence in a rural population. METHODS: Thyroid cancer incidence and socio-demographic factors in Vermont (VT), United States, between 1994 and 2007 were analyzed by logistic regression and geospatial and temporal analyses. RESULTS: The thyroid cancer age-adjusted incidence in Vermont (8.0 per 100,000) was comparable to the national level (8.4 per 100,000), as were the ratio of the incidence of females to males (3.1:1) and the mortality rate (0.5 per 100,000). However, the estimated annual percentage change was higher (8.3 VT; 5.7 U.S.). Incidence among females peaked at 30-59 years of age, reflecting a significant rise from 1994 to 2007, while incidence trends for males did not vary significantly by age. For both females and males, the distribution of tumors by size did not vary over time; ≤1.0 cm, 1.1-2.0 cm, and >2.0 cm represented 38%, 22%, and 40%, respectively. In females, papillary thyroid cancer (PTC) accounted for 89% of cases, follicular (FTC) 8%, medullary (MTC) 2%, and anaplastic (ATC) 0.6%, while in males PTC accounted for 77% of cases, FTC 15%, MTC 1%, and ATC 3%. Geospatial analysis revealed locations and spatial patterns that, when combined with multivariate incidence analyses, indicated that factors other than increased surveillance and access to healthcare (physician density or insurance) contributed to the increased thyroid cancer incidence. Nine thyroid cancer incidence hot spots, areas with very high normalized incidence, were identified based on zip code data. Those locations did not correlate with urban areas or healthcare centers. CONCLUSIONS: These data provide evidence of increased thyroid cancer incidence in a rural population likely due to environmental drivers and SES. Geospatial modeling can provide an important framework for evaluation of additional associative risk factors.

Sources of blood meals of sylvatic Triatoma guasayana near Zurima, Bolivia, assayed with qPCR and 12S cloning.

BACKGROUND: In this study we compared the utility of two molecular biology techniques, cloning of the mitochondrial 12S ribosomal RNA gene and hydrolysis probe-based qPCR, to identify blood meal sources of sylvatic Chagas disease insect vectors collected with live-bait mouse traps (also known as Noireau traps). Fourteen T. guasayana were collected from six georeferenced trap locations in the Andean highlands of the department of Chuquisaca, Bolivia. METHODOLOGY/PRINCIPAL FINDINGS: We detected four blood meals sources with the cloning assay: seven samples were positive for human (Homo sapiens), five for chicken (Gallus gallus) and unicolored blackbird (Agelasticus cyanopus), and one for opossum (Monodelphis domestica). Using the qPCR assay we detected chicken (13 vectors), and human (14 vectors) blood meals as well as an additional blood meal source, Canis sp. (4 vectors). CONCLUSIONS/SIGNIFICANCE: We show that cloning of 12S PCR products, which avoids bias associated with developing primers based on a priori knowledge, detected blood meal sources not previously considered and that species-specific qPCR is more sensitive. All samples identified as positive for a specific blood meal source by the cloning assay were also positive by qPCR. However, not all samples positive by qPCR were positive by cloning. We show the power of combining the cloning assay with the highly sensitive hydrolysis probe-based qPCR assay provides a more complete picture of blood meal sources for insect disease vectors.

Ecohealth interventions limit triatomine reinfestation following insecticide spraying in La Brea, Guatemala.

In this study, we evaluate the effect of participatory Ecohealth interventions on domestic reinfestation of the Chagas disease vector Triatoma dimidiata after village-wide suppression of the vector population using a residual insecticide. The study was conducted in the rural community of La Brea, Guatemala between 2002 and 2009 where vector infestation was analyzed within a spatial data framework based on entomological and socio-economic surveys of homesteads within the village. Participatory interventions focused on community awareness and low-cost home improvements using local materials to limit areas of refuge and alternative blood meals for the vector within the home, and potential shelter for the vector outside the home. As a result, domestic infestation was maintained at ≤ 3% and peridomestic infestation at ≤ 2% for 5 years beyond the last insecticide spraying, in sharp contrast to the rapid reinfestation experienced in earlier insecticide only interventions.