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• W4214892014 abstract "Leptospirosis is a globally disseminated zoonotic disease with no national surveillance systems. On the other hand, surveillance is crucial for improving population health, and surveillance systems produce data that motivates action. Unfortunately, like many other countries, Ecuador put in place a monitoring system that has never been tested. The goal of this study was to use scenario tree modeling to assess the sensitivity of Ecuador's current national surveillance system to human leptospirosis as the basis for an economic assessment of the system. We created a decision-tree model to analyze the current system's sensitivity. The inputs were described as probabilities distributions, and the model assessed the program's sensitivity as an output. The model also considers the geographical and weather variations across Ecuador's three continental regions: Andean, Amazonia, and the Coast. Several data sources were used to create the model, including leptospirosis records from Ecuador's Ministry of Public Health, national and international literature, and expert elicitation, all of which were incorporated in a Bayesian framework. We were able to determine the most critical parameters influencing each scenario's output (CSU) sensitivity through sensitivity analysis. The Coast region had the best sensitivity scenario, with a median of 0.85% (IC 95% 0.41-0.99), followed by the Amazonia with a median of 0.54% (CI 95% 0.18-0.99) and the Andes with a median of 0.29% (CI 95% 0.02-0.89). As per the sensitivity study, the most influential criteria on the system's sensitivity were Attendance or probability of going to a health center and probability of having symptoms, notably for the Coast and Amazonia Regions." @default.
• W4214892014 created "2022-03-05" @default.
• W4214892014 creator A5007824444 @default.
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• W4214892014 date "2022-03-03" @default.
• W4214892014 modified "2023-09-26" @default.
• W4214892014 title "Assessment of the Current Surveillance System for Human Leptospirosis in Ecuador by Decision Analytic Modeling" @default.
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• W4214892014 doi "https://doi.org/10.3389/fpubh.2022.711938" @default.
• W4214892014 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35309218" @default.
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