Mapping the Spatial Distribution of a Disease-Transmitting Insect in the Presence of Surveillance Error and Missing Data

Maps of the distribution of epidemiological data often ignore surveillance error or possible correlations between missing information and outcomes. We analyse presence–absence data at the household level (12050 points) of a disease‐carrying insect in Mariano Melgar, Peru, collected as part of the Arequipan Ministry of Health\u27s efforts to control Chagas disease. We construct a Bayesian hierarchical model to locate regions that are vulnerable to under‐reporting due to surveillance error, accounting for variability in participation due to infestation status. The spatial correlation in the data allows us to identify relative inspector sensitivity and to elucidate the relationship between participation and infestation. We show that naive estimates of prevalence would be biased by surveillance error and missingness at random assumptions. We validate our results through simulations and observe how randomized inspector assignments may improve prevalence estimates. Our results suggests that bias due to imperfect observations and missingness at random can be assessed and corrected in prevalence estimates of spatially auto-correlated binary variables

Disease Diagnosis From Immunoassays With Plate to Plate Variability: A Hierarchical Bayesian Approach

The standard methods of diagnosing disease based on antibody microtiter plates are quite crude. Few methods create a rigorous underlying model for the antibody levels of populations consisting of a mixture of positive and negative subjects, and fewer make full use of the entirety of the available data for diagnoses. In this paper, we propose a Bayesian hierarchical model that provides a systematic way of pooling data across different plates, and accounts for the subtle sources of variations that occur in the optical densities of typical microtiter data. In addition to our Bayesian method having good frequentist properties, we find that our method outperforms one of the standard crude approaches (the “3SD Rule”) under reasonable assumptions, and provides more accurate disease diagnoses in terms of both sensitivity and specificity

A Multi-disciplinary Overview of Chagas in Periurban Peru

There are between 8 and 11 million cases of America Human Trypanosomiasis, commonly known as Chagas disease, in Latin America. Chagas is endemic in southern Peru, especially the Arequipa region, where it has expanded from poor, rural areas to periurban communities. This paper summarizes the findings of four studies in periurban Arequipa: on determinants of disease-vector infestation; on prevalence, spatial patterns, and risk factors of Chagas; on links between migration, settlement patterns, and disease-vector infestation; and on the relationship between discordant test results and spatially clustered transmission hotspots. These studies identified two risk factors associated with the disease: population dynamics and the urbanization of poverty. Understanding the disease within this new urban context will allow for improved public health prevention efforts and policy initiatives. Discovered in 1909 by Brazilian physician Carlos Chagas, American Human Trypanosomiasis is a chronic and potentially life-threatening illness found throughout Latin America (Moncayo, 2003). Indeed, it is estimated that there are between 8 and 11 million cases in Mexico and Central and South America (Centers for Disease Control [CDC], 2009). Chagas disease, as it is most commonly known, is endemic in southern Peru, especially in the region of Arequipa. Once thought to be limited to poor, rural areas, the disease is now appearing in the periurban communities that surround Arequipa City, the capital of the region (Cornejo del Carpio, 2003). Understanding the urbanization of Chagas disease will allow public health and medical professionals to better combat the further transmission of the disease. After providing an overview of Chagas and introducing the scope of the disease in Latin America, this paper will summarize the findings of four recent studies conducted in periurban districts in Arequipa. Ultimately, this paper seeks to identify the risk factors associated with Chagas infection in Arequipa’s periurban communities

A country bug in the city: urban infestation by the Chagas disease vector Triatoma infestans in Arequipa, Peru

BACKGROUND:Interruption of vector-borne transmission of Trypanosoma cruzi remains an unrealized objective in many Latin American countries. The task of vector control is complicated by the emergence of vector insects in urban areas.METHODS:Utilizing data from a large-scale vector control program in Arequipa, Peru, we explored the spatial patterns of infestation by Triatoma infestans in an urban and peri-urban landscape. Multilevel logistic regression was utilized to assess the associations between household infestation and household- and locality-level socio-environmental measures.RESULTS:Of 37,229 households inspected for infestation, 6,982 (18.8%95% CI: 18.4 - 19.2%) were infested by T. infestans. Eighty clusters of infestation were identified, ranging in area from 0.1 to 68.7 hectares and containing as few as one and as many as 1,139 infested households. Spatial dependence between infested households was significant at distances up to 2,000 meters. Household T. infestans infestation was associated with household- and locality-level factors, including housing density, elevation, land surface temperature, and locality type.CONCLUSIONS:High levels of T. infestans infestation, characterized by spatial heterogeneity, were found across extensive urban and peri-urban areas prior to vector control. Several environmental and social factors, which may directly or indirectly influence the biology and behavior of T. infestans, were associated with infestation. Spatial clustering of infestation in the urban context may both challenge and inform surveillance and control of vector reemergence after insecticide intervention.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

Use of Individual-Level Covariates to Improve Latent Class Analysis of Trypanosoma Cruzi Diagnostic Tests

Statistical methods such as latent class analysis can estimate the sensitivity and specificity of diagnostic tests when no perfect reference test exists. Traditional latent class methods assume a constant disease prevalence in one or more tested populations. When the risk of disease varies in a known way, these models fail to take advantage of additional information that can be obtained by measuring risk factors at the level of the individual. We show that by incorporating complex field-based epidemiologic data, in which the disease prevalence varies as a continuous function of individual-level covariates, our model produces more accurate sensitivity and specificity estimates than previous methods. We apply this technique to several simulated populations and to actual Chagas disease test data from a community near Arequipa, Peru. Results from our model estimate that the first-line enzyme-linked immunosorbent assay has a sensitivity of 78% (95% CI: 62-100%) and a specificity of 100% (95% CI: 99-100%). The confirmatory immunofluorescence assay is estimated to be 73% sensitive (95% CI: 65-81%) and 99% specific (95% CI: 96-100%)

Retracing Micro-Epidemics of Chagas Disease Using Epicenter Regression

Vector-borne transmission of Chagas disease has become an urban problem in the city of Arequipa, Peru, yet the debilitating symptoms that can occur in the chronic stage of the disease are rarely seen in hospitals in the city. The lack of obvious clinical disease in Arequipa has led to speculation that the local strain of the etiologic agent, Trypanosoma cruzi, has low chronic pathogenicity. The long asymptomatic period of Chagas disease leads us to an alternative hypothesis for the absence of clinical cases in Arequipa: transmission in the city may be so recent that most infected individuals have yet to progress to late stage disease. Here we describe a new method, epicenter regression, that allows us to infer the spatial and temporal history of disease transmission from a snapshot of a population\u27s infection status. We show that in a community of Arequipa, transmission of T. cruzi by the insect vector Triatoma infestans occurred as a series of focal micro-epidemics, the oldest of which began only around 20 years ago. These micro-epidemics infected nearly 5% of the community before transmission of the parasite was disrupted through insecticide application in 2004. Most extant human infections in our study community arose over a brief period of time immediately prior to vector control. According to our findings, the symptoms of chronic Chagas disease are expected to be absent, even if the strain is pathogenic in the chronic phase of disease, given the long asymptomatic period of the disease and short history of intense transmission

Retracing Micro-Epidemics of Chagas Disease Using Epicenter Regression

Vector-borne transmission of Chagas disease has become an urban problem in the city of Arequipa, Peru, yet the debilitating symptoms that can occur in the chronic stage of the disease are rarely seen in hospitals in the city. The lack of obvious clinical disease in Arequipa has led to speculation that the local strain of the etiologic agent, Trypanosoma cruzi, has low chronic pathogenicity. The long asymptomatic period of Chagas disease leads us to an alternative hypothesis for the absence of clinical cases in Arequipa: transmission in the city may be so recent that most infected individuals have yet to progress to late stage disease. Here we describe a new method, epicenter regression, that allows us to infer the spatial and temporal history of disease transmission from a snapshot of a population's infection status. We show that in a community of Arequipa, transmission of T. cruzi by the insect vector Triatoma infestans occurred as a series of focal micro-epidemics, the oldest of which began only around 20 years ago. These micro-epidemics infected nearly 5% of the community before transmission of the parasite was disrupted through insecticide application in 2004. Most extant human infections in our study community arose over a brief period of time immediately prior to vector control. According to our findings, the symptoms of chronic Chagas disease are expected to be absent, even if the strain is pathogenic in the chronic phase of disease, given the long asymptomatic period of the disease and short history of intense transmission. Traducción al español disponible en Alternative Language Text S1/A Spanish translation of this article is available in Alternative Language Text S

Is Participation Contagious? Evidence From a Household Vector Control Campaign in Urban Peru

Objective: High rates of household participation are critical to the success of door-to-door vector control campaigns. We used the Health Belief Model to assess determinants of participation, including neighbour participation as a cue to action, in a Chagas disease vector control campaign in Peru. Methods: We evaluated clustering of participation among neighbours; estimated participation as a function of household infestation status, neighbourhood type and number of participating neighbours; and described the reported reasons for refusal to participate in a district of 2911 households. Results: We observed significant clustering of participation along city blocks (p\u3c0.0001). Participation was significantly higher for households in new versus established neighbourhoods, for infested households, and for households with more participating neighbours. The effect of neighbour participation was greater in new neighbourhoods. Conclusions: Results support a ‘contagion’ model of participation, highlighting the possibility that one or two participating households can tip a block towards full participation. Future campaigns can leverage these findings by making participation more visible, by addressing stigma associated with spraying, and by employing group incentives to spray

The Effects of City Streets on an Urban Disease Vector.

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran’s spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran’s decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p,0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies