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Integrating evidence, models and maps to enhance Chagas disease vector surveillance

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dc.contributor.author Gutfraind, Alexander
dc.contributor.author Peterson, Jennifer K.
dc.contributor.author Billig Rose, Erica
dc.contributor.author Arevalo Nieto, Claudia Rebeca
dc.contributor.author Sheen, Justin
dc.contributor.author Condori-Luna, Gian Franco
dc.contributor.author Tankasala, Narender
dc.contributor.author Castillo Neyra, Ricardo
dc.contributor.author Condori-Pino, Carlos
dc.contributor.author Anand, Priyanka
dc.contributor.author Naquira Velarde, Cesar Gabriel
dc.contributor.author Levy, Michael Z.
dc.date.accessioned 2019-03-05T15:23:30Z
dc.date.available 2019-03-05T15:23:30Z
dc.date.issued 2018
dc.identifier.uri https://hdl.handle.net/20.500.12866/5895
dc.description.abstract Background: Until recently, the Chagas disease vector, Triatoma infestans, was widespread in Arequipa, Perú, but as a result of a decades-long campaign in which over 70,000 houses were treated with insecticides, infestation prevalence is now greatly reduced. To monitor for T. infestans resurgence, the city is currently in a surveillance phase in which a sample of houses is selected for inspection each year. Despite extensive data from the control campaign that could be used to inform surveillance, the selection of houses to inspect is often carried out haphazardly or by convenience. Therefore, we asked, how can we enhance efforts toward preventing T. infestans resurgence by creating the opportunity for vector surveillance to be informed by data? Methodology/principal findings: To this end, we developed a mobile app that provides vector infestation risk maps generated with data from the control campaign run in a predictive model. The app is intended to enhance vector surveillance activities by giving inspectors the opportunity to incorporate the infestation risk information into their surveillance activities, but it does not dictate which houses to surveil. Therefore, a critical question becomes, will inspectors use the risk information? To answer this question, we ran a pilot study in which we compared surveillance using the app to the current practice (paper maps). We hypothesized that inspectors would use the risk information provided by the app, as measured by the frequency of higher risk houses visited, and qualitative analyses of inspector movement patterns in the field. We also compared the efficiency of both mediums to identify factors that might discourage risk information use. Over the course of ten days (five with each medium), 1,081 houses were visited using the paper maps, of which 366 (34%) were inspected, while 1,038 houses were visited using the app, with 401 (39%) inspected. Five out of eight inspectors (62.5%) visited more higher risk houses when using the app (Fisher’s exact test, p < 0.001). Among all inspectors, there was an upward shift in proportional visits to higher risk houses when using the app (Mantel-Haenszel test, common odds ratio (OR) = 2.42, 95% CI 2.00–2.92), and in a second analysis using generalized linear mixed models, app use increased the odds of visiting a higher risk house 2.73-fold (95% CI 2.24–3.32), suggesting that the risk information provided by the app was used by most inspectors. Qualitative analyses of inspector movement revealed indications of risk information use in seven out of eight (87.5%) inspectors. There was no difference between the app and paper maps in the number of houses visited (paired t-test, p = 0.67) or inspected (p = 0.17), suggesting that app use did not reduce surveillance efficiency. Conclusions/significance: Without staying vigilant to remaining and re-emerging vector foci following a vector control campaign, disease transmission eventually returns and progress achieved is reversed. Our results suggest that, when provided the opportunity, most inspectors will use risk information to direct their surveillance activities, at least over the short term. The study is an initial, but key, step toward evidence-based vector surveillance. en_US
dc.language.iso eng
dc.publisher Public Library of Science
dc.relation.ispartofseries PLoS Neglected Tropical Diseases
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Peru en_US
dc.subject Humans en_US
dc.subject Animals en_US
dc.subject Insect Vectors en_US
dc.subject Chagas disease en_US
dc.subject Chagas Disease en_US
dc.subject Insecticides en_US
dc.subject vector control en_US
dc.subject Triatoma infestans en_US
dc.subject human en_US
dc.subject Article en_US
dc.subject Pilot Projects en_US
dc.subject decision making en_US
dc.subject prevalence en_US
dc.subject transmission en_US
dc.subject drug effect en_US
dc.subject nonhuman en_US
dc.subject animal en_US
dc.subject Animal Distribution en_US
dc.subject Epidemiological Monitoring en_US
dc.subject procedures en_US
dc.subject infection risk en_US
dc.subject disease carrier en_US
dc.subject physiology en_US
dc.subject animal dispersal en_US
dc.subject epidemiological data en_US
dc.subject mobile application en_US
dc.subject insecticide en_US
dc.subject disease surveillance en_US
dc.subject pilot study en_US
dc.subject insect vector en_US
dc.subject Triatoma en_US
dc.subject epidemiological monitoring en_US
dc.subject insect control en_US
dc.subject Insect Control en_US
dc.subject infestation en_US
dc.title Integrating evidence, models and maps to enhance Chagas disease vector surveillance en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1371/journal.pntd.0006883
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.03.06
dc.relation.issn 1935-2735

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