Clara B Ocampo1, Lina Guzmán-Rodríguez2, Mabel Moreno2, María Del Mar Castro3, Carlos Valderrama-Ardila4, Neal Alexander5. 1. Centro Internacional de Entrenamiento e Investigaciones Médicas, CIDEIM. Cali, Colombia; Universidad Icesi., Cali, Colombia; Ministerio de Ciencia Tecnología e Innovación (Minciencias), Bogotá, Colombia. 2. Centro Internacional de Entrenamiento e Investigaciones Médicas, CIDEIM. Cali, Colombia. 3. Centro Internacional de Entrenamiento e Investigaciones Médicas, CIDEIM. Cali, Colombia; Universidad Icesi., Cali, Colombia. 4. Universidad del Rosario. Bogotá, Colombia. 5. Centro Internacional de Entrenamiento e Investigaciones Médicas, CIDEIM. Cali, Colombia; Universidad Icesi., Cali, Colombia. Electronic address: nalexander@cideim.org.co.
Abstract
INTRODUCTION: Passive surveillance systems are thought to under-estimate the true incidence of American cutaneous leishmaniasis (ACL) by two- to five-fold. Ecological niche models based on remotely sensed data can identify environmental factors which favor phlebotomine vectors. Here we report an integrated approach to identifying areas at risk of cutaneous leishmaniasis by applying spatial analysis methods to niche model results, and local surveillance data, in two locations in Colombia with differing vector ecology. The objective was to identify townships in which later phases of the project could implement community-based surveillance to obtain direct estimates of under-reporting. MATERIALS AND METHODS: The study was carried out in one municipality in each of two departments of the Andean region of Colombia: Pueblo Rico in Risaralda, and Rovira in Tolima. Niche mapping by maximum entropy, based on published and unpublished existing locations of Pintomyia (Pifanomyia) longiflocosa and Psychodopygus panamensis, and using variables on land cover, climate and elevation. Field catches were done in each municipality to test predictions of high relative probability of presence. The niche model results were included as a predictor in a conditional autoregressive spatial model, in which the outcome variable was the number of cases by township, as detected by passive surveillance. RESULTS: Having rarefied 173 geolocated records, 46 of Pi. longiflocosa and 57 of Ps. panamensis were used for the niche modelling. At the national level, both species had high relative probability of presence on parts of the slopes of the three Andean cordilleras. Pi. longiflocosa also has a high relative probability of presence in the higher parts of the Magdalena valley, as does Ps. panamensis in some areas close to the Caribbean coast. At the local level, field catches confirmed that Pi. longiflocosa was the most abundant species in Rovira, and likewise Ps. panamensis in Pueblo Rico. The spatial regression showed that the incidence of ACL, according to surveillance, was positively, but not statistically significantly, associated with the relative probability of presence from the risk model. CONCLUSIONS: These niche maps bring together published and unpublished results on phlebotomine species which are important vectors in Colombia. Maps of the fitted values of incidence were used to guide the selection of townships in which further phases of the study will attempt to quantify the extent of under-estimation of ACL incidence.
INTRODUCTION: Passive surveillance systems are thought to under-estimate the true incidence of American cutaneous leishmaniasis (ACL) by two- to five-fold. Ecological niche models based on remotely sensed data can identify environmental factors which favor phlebotomine vectors. Here we report an integrated approach to identifying areas at risk of cutaneous leishmaniasis by applying spatial analysis methods to niche model results, and local surveillance data, in two locations in Colombia with differing vector ecology. The objective was to identify townships in which later phases of the project could implement community-based surveillance to obtain direct estimates of under-reporting. MATERIALS AND METHODS: The study was carried out in one municipality in each of two departments of the Andean region of Colombia: Pueblo Rico in Risaralda, and Rovira in Tolima. Niche mapping by maximum entropy, based on published and unpublished existing locations of Pintomyia (Pifanomyia) longiflocosa and Psychodopygus panamensis, and using variables on land cover, climate and elevation. Field catches were done in each municipality to test predictions of high relative probability of presence. The niche model results were included as a predictor in a conditional autoregressive spatial model, in which the outcome variable was the number of cases by township, as detected by passive surveillance. RESULTS: Having rarefied 173 geolocated records, 46 of Pi. longiflocosa and 57 of Ps. panamensis were used for the niche modelling. At the national level, both species had high relative probability of presence on parts of the slopes of the three Andean cordilleras. Pi. longiflocosa also has a high relative probability of presence in the higher parts of the Magdalena valley, as does Ps. panamensis in some areas close to the Caribbean coast. At the local level, field catches confirmed that Pi. longiflocosa was the most abundant species in Rovira, and likewise Ps. panamensis in Pueblo Rico. The spatial regression showed that the incidence of ACL, according to surveillance, was positively, but not statistically significantly, associated with the relative probability of presence from the risk model. CONCLUSIONS: These niche maps bring together published and unpublished results on phlebotomine species which are important vectors in Colombia. Maps of the fitted values of incidence were used to guide the selection of townships in which further phases of the study will attempt to quantify the extent of under-estimation of ACL incidence.