Environmental and socio-economic risk modelling for Chagas disease in Bolivia

• Main Authors: Paula Mischler, Michael Kearney, Jennifer C. McCarroll, Ronaldo G.C. Scholte, Penelope Vounatsou, John B. Malone
• Format: Article
• Language: English
• Published: PAGEPress Publications 2012-09-01
• Series: Geospatial Health
• Subjects: Trypanosoma cruzi, Chagas disease, ecological niche model, risk maps, maximum entropy, geographical information system, remote sensing, Bolivia.
• Online Access: http://www.geospatialhealth.net/index.php/gh/article/view/123

Accurately defining disease distributions and calculating disease risk is an important step in the control and prevention of diseases. Geographical information systems (GIS) and remote sensing technologies, with maximum entropy (Maxent) ecological niche modelling computer software, were used to create predictive risk maps for Chagas disease in Bolivia. Prevalence rates were calculated from 2007 to 2009 household infection survey data for Bolivia, while environmental data were compiled from the Worldclim database and MODIS satellite imagery. Socio-economic data were obtained from the Bolivian National Institute of Statistics. Disease models identified altitudes at 500-3,500 m above the mean sea level (MSL), low annual precipitation (45-250 mm), and higher diurnal range of temperature (10-19 °C; peak 16 °C) as compatible with the biological requirements of the insect vectors. Socio-economic analyses demonstrated the importance of improved housing materials and water source. Home adobe wall materials and having to fetch drinking water from rivers or wells without pump were found to be highly related to distribution of the disease by the receiver operator characteristic (ROC) area under the curve (AUC) (0.69 AUC, 0.67 AUC and 0.62 AUC, respectively), while areas with hardwood floors demonstrated a direct negative relationship (-0.71 AUC). This study demonstrates that Maxent modelling can be used in disease prevalence and incidence studies to provide governmental agencies with an easily learned, understandable method to define areas as either high, moderate or low risk for the disease. This information may be used in resource planning, targeting and implementation. However, access to high-resolution, sub-municipality socio-economic data (e.g. census tracts) would facilitate elucidation of the relative influence of poverty-related factors on regional disease dynamics.