No abstract

Protozoan pathogen Trypanosoma cruzi (Chagas, 1909) is the etiologic agent of Chagas disease, which affects millions of people in Latin America. Recently, the disease has been gaining attention in Texas and the southern United States. Transmission cycle of the parasite involves alternating infection between insect vectors and vertebrate hosts (including humans, wildlife, and domestic animals). To evaluate vector T. cruzi parasite burden and feeding patterns, we tested triatomine vectors from 23 central, southern, and northeastern counties of Texas. Out of the 68 submitted specimens, the majority were genetically identified as Triatoma gerstaeckeri (Stal, 1859), with a few samples of Triatoma sanguisuga (LeConte, 1855), Triatoma lecticularia (Stal, 1859), Triatoma rubida (Uhler, 1894), and Triatoma protracta woodi (Usinger, 1939). We found almost two-thirds of the submitted insects were polymerase chain reaction-positive for T. cruzi Bloodmeal sources were determined for most of the insects, and 16 different species of mammals were identified as hosts. The most prevalent type of bloodmeal was human, with over half of these insects found to be positive for T. cruzi High infection rate of the triatomine vectors combined with high incidence of feeding on humans highlight the importance of Chagas disease surveillance in Texas. With our previous findings of autochthonous transmission of Chagas disease, urgent measures are needed to increase public awareness, vector control in and around homes, and Chagas screening of residents who present with a history of a triatomine exposure.

Central to developing effective control measures for the COVID-19 pandemic is understanding the epidemiology of transmission in the community. Geospatial analysis of neighborhood-level data could provide insight into drivers of infection. In the current analysis of Harris County, Texas, we used custom interpolation tools in GIS to disaggregate COVID-19 incidence estimates from the zip code to census tract estimates—a better representation of neighborhood-level estimates. We assessed the associations between 29 neighborhood-level characteristics and COVID-19 incidence using a series of aspatial and spatial models. The variables that maintained significant and positive associations with COVID-19 incidence in our final aspatial model and later represented in a geographically weighted regression model were the percentage of the Black/African American population, percentage of the foreign-born population, area derivation index (ADI), percentage of households with no vehicle, and percentage of people over 65 years old inside each census tract. Conversely, we observed negative and significant association with the percentage employed in education. Notably, the spatial models indicated that the impact of ADI was homogeneous across the study area, but other risk factors varied by neighborhood. The current findings could enhance decision making by local public health officials in responding to the COVID-19 pandemic. By understanding factors that drive community transmission, we can better target disease control measures.