Multi-criteria decision analysis and spatial statistic: an approach to determining human vulnerability to vector transmission of Trypanosoma cruzi

• Autor(a) principal: Montenegro,Diego
• Data de Publicação: 2017
• Outros Autores: Cunha,Ana Paula da, Ladeia-Andrade,Simone, Vera,Mauricio, Pedroso,Marcel, Junqueira,Angela
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Memórias do Instituto Oswaldo Cruz
• Texto Completo: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762017001000709
• topic: vulnerability; Chagas disease; multi-criteria decision analysis; spatial statistic; PROMETHEE method; GAIA method

BACKGROUND Chagas disease (CD), caused by the protozoan Trypanosoma cruzi, is a neglected human disease. It is endemic to the Americas and is estimated to have an economic impact, including lost productivity and disability, of 7 billion dollars per year on average. OBJECTIVES To assess vulnerability to vector-borne transmission of T. cruzi in domiciliary environments within an area undergoing domiciliary vector interruption of T. cruzi in Colombia. METHODS Multi-criteria decision analysis [preference ranking method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive assistance (GAIA) methods] and spatial statistics were performed on data from a socio-environmental questionnaire and an entomological survey. In the construction of multi-criteria descriptors, decision-making processes and indicators of five determinants of the CD vector pathway were summarily defined, including: (1) house indicator (HI); (2) triatominae indicator (TI); (3) host/reservoir indicator (Ho/RoI); (4) ecotope indicator (EI); and (5) socio-cultural indicator (S-CI). FINDINGS Determination of vulnerability to CD is mostly influenced by TI, with 44.96% of the total weight in the model, while the lowest contribution was from S-CI, with 7.15%. The five indicators comprise 17 indices, and include 78 of the original 104 priority criteria and variables. The PROMETHEE and GAIA methods proved very efficient for prioritisation and quantitative categorisation of socio-environmental determinants and for better determining which criteria should be considered for interrupting the man-T. cruzi-vector relationship in endemic areas of the Americas. Through the analysis of spatial autocorrelation it is clear that there is a spatial dependence in establishing categories of vulnerability, therefore, the effect of neighbors’ setting (border areas) on local values should be incorporated into disease management for establishing programs of surveillance and control of CD via vector. CONCLUSIONS The study model proposed here is flexible and can be adapted to various eco-epidemiological profiles and is suitable for focusing anti-T. cruzi serological surveillance programs in vulnerable human populations.