Infestation by T. infestans has been found in many urban areas in Latin America, including Santiago, Chile ; Cochabamba and Sucre, Bolivia ; and Arequipa, Peru . In affected areas—urban as well as rural—prevention and control of Chagas disease relies on vector control . While infestation by and control of T. infestans has been extensively examined in the rural context, infestation in the urban milieu is less well understood. Utilizing spatial and multilevel logistic regression analysis of data collected from multiple sources at multiple spatial scales, we offer insights into the dynamics of T. infestans infestation in an urban landscape.
Prior to implementation of vector control, urban and peri-urban households in Arequipa were extensively infested by T. infestans. The intensity of infestation was spatially heterogeneous, with areas of very low and very high prevalence of infestation. Numerous clusters of infestation, small and large, were found across the six study districts, indicating that urban and peri-urban areas are conducive to the proliferation and dispersion of T. infestans. In rural landscapes, T. infestans have been shown to actively disperse by walking or flying at distances up to approximately 100 or 2,000 meters, respectively [16, 17]. In a separate study in urban Arrequipa, streets were shown to be significant barriers to the dispersion of T. infestans, and to strongly influence the spatial distribution of infestation . In contrast, flight has been observed as a main mechanism of infestation in urban San Juan, Argentina . In the present study, spatial dependence between infested households was observed at distances from 0 to 2,000 meters, suggesting that urban T. infestans may disperse by walking at shorter spans that do not cross city streets, as well as by flying at longer distances across urban blocks.
Identifying extant clusters of infestation prior to vector control may have critical consequences for implementing effective surveillance of vector reemergence subsequent to vector control. In an extensive but sparsely populated rural area in the Gran Chaco of Argentina, reinfestation by T. infestans tended to cluster in areas where infestation was aggregated prior to vector control . Infestation clusters in an extensively and densely populated urban area may be similarly problematic. The existence of numerous infestation clusters in Arequipa, many encompassing large areas and many households, should be priority areas for surveillance and control by the GRSA. Where feasible, utilization of a geographic information system to monitor T. infestans reemergence—as well as other health risks and outcomes—might be a cost effective investment for resource-constrained public health institutions in Arequipa, and elsewhere in the developing world [21, 22].
Spatial heterogeneity in urban infestation by T. infestans is likely influenced by myriad factors operating at multiple spatial scales. We evaluated only a few features, which were chosen based on ecological plausibility and data availability. In ordinary univariate and multivariate logistic regression, housing density, elevation, and land surface temperature were all positively, if not always linearly, associated with household infestation. Housing density may mediate vector dispersal. In higher density urban areas, new habitats and blood sources found in nearby houses are located at short distances from one another, thereby facilitating dispersal of refuge- or blood-seeking vectors. Also, attraction to light influences the dissemination of T. infestans, and the plentiful light sources in higher density urban areas may promote insect dispersal . Land surface temperature may affect vector biology and behavior. Both laboratory and field experiments demonstrate that T. infestans flight initiation increases at higher temperatures [17, 24], thereby promoting vector dispersal in warmer urban areas. Laboratory studies also indicate that higher temperatures increase T. infestans feeding and development rates [25, 26], and blood meal seeking is reportedly the principal cause for dispersion of triatomines . In warmer urban areas, increased feeding and development may result in increased vector dispersal. Elevation may act indirectly through socioeconomic circumstances, rather than directly through biophysical constraints. In Arequipa, lower socioeconomic status populations, often rural-to-urban migrants, typically inhabit the higher elevation hillsides, while higher socioeconomic status populations usually reside in lower elevation valleys . As such, higher infestation at higher elevation in Arequipa may be attributable to two factors: passive introduction of insects resulting from seasonal migration to and from nearby rural areas where T. infestans are prevalent, and substandard living conditions that provide habitats suitable for T. infestans infestation . The slight decrease in infestation at the highest elevations may result from the relatively recent inhabitation of these areas, leaving little time for infestation to have occurred. Elevation is unlikely to be a biophysical constraint for infestation in the currently populated areas of Arequipa, since T. infestans have been found as high as 3,682 meters above sea level in Argentina , well above the elevation of the study area.
Multilevel logistic regression revealed the importance of locality-level contextual effects and substantially diminished spatial autocorrelation present in ordinary logistic regression. The locality-level random effect, which estimates the influence of unobserved contextual effects within each locality, indicates that these unmeasured factors are associated, in median, with substantially higher risk of household infestation. In Arequipa, and elsewhere, urban shantytowns have been identified as areas with higher risk for infestation by Chagas disease vectors, and vector-borne transmission of T. cruzi[11, 13–15]. We offer further evidence that shantytowns are at higher risk for infestation by T. infestans. Controlling for locality-level effects, household-level effects for housing density, elevation, and land surface temperature all remained statistically significant and substantial.
We recognize that our study is limited in many respects. First, while we believe that household location and infestation status data are both precise and accurate, more detailed data regarding the number, life stage, and T. cruzi infection status of insects encountered during the vector control campaign were unavailable. Nor did we have in-depth data regarding households (e.g., construction materials, domestic animals) or their occupants. More detailed data would have likely improved the insights provided by our analyses. Second, we recognize that point-level household covariates are extracted from remote sensing data collected at a 30-meter scale (elevation, land surface temperature) or are spatially smoothed estimates (housing density). We also understand that land surface temperature data do not capture fine-scale temporal variability that occurs across and within days of the year, nor do they describe fine-scale spatial variation in ambient micro-climatic conditions. These issues of scale could conceivably bias the relative magnitude of observed effects. Third, at the time of data collection, portions of the six study districts were still undergoing vector control. Future analyses of areas recently reached by the vector control campaign, including districts beyond the current study area, may provide deeper and broader insights into urban and peri-urban T. infestans infestation.
The geography and ecology of T. infestans—as well as vector species for many other infectious diseases—are changing. Decreasing funding and political will and increasing insecticide resistance are endangering gains made towards interruption of vector-borne transmission of T. cruzi. For many vector-borne diseases in many parts of the world, these are not only public health concerns but also social justice issues, as economically and politically marginalized populations may suffer disproportionately. Many potentially powerful tools (e.g., Google Earth, The R Project for Statistical Computing), data sources (e.g., NASA, NOAA), and spatial and statistical methods are now freely available. Finding novel uses for these resources in conjunction with local knowledge and information—as well as increasing capacity to do so—could inspire new perspectives on and solutions to existing and emerging public health problems and their social and environmental causes and consequences.