Ecological study and risk mapping of visceral leishmaniasis in an endemic area of Iran based on a geographical information systems approach

Abstract

Abstract. Between 1998 and 2001, a total of 1,062 human cases of visceral leishmaniasis were reported from the rural district of Meshkin-Shahr in the mountainous, north-western Iranian province of Ardabil. In the summer of 2008, a cross-sectional study of dogs was conducted in this endemic area by randomly selecting 384 animals from 21 villages and testing them serologically for leishmaniasis. Villages, in which more than 10% of investigated dogs showed antiLeishmania titres ≥1/320, were considered to be high-risk environments. Regression analysis showed no statistically significant correlation between topographic conditions and the prevalence of positive cases. However, when the results were compared with past meteorological records, a statistically significant positive correlation (P = 0.007) was found between the number of infected dogs with anti-Leishmania titres ≥1/640 and the number of days in a year with temperatures below 0 °C. While humidity showed an inverse correlation (P = 0.009) with the anti-Leishmania titres, a positive correlation (P <0.001) was found in relation to the amount of rainfall. Mapping of the areas at risk for kala-azar in the Meshkin-Shahr district supports the impression that the low temperatures prevalent in the Ardebil province constitute an important factor influencing the distribution of leishmaniasis there.

Environmental modelling of visceral leishmaniasis by susceptibility-mapping using neural networks: a case study in north-western Iran

“…nomadic people migrating twice annually. Among other factors, Fu et al (2013) point out the importance of access to healthcare services and lifestyle, while the correlation of VL with rainfall, humidity, elevation, and temperature found here has been confirmed by Salahi-Moghaddam et al (2010).…”

“…A number of studies around the world have demonstrated that environmental, demographic and statistical data about the ecology of VL can provide the basis for the development of spatial, predictive risk models (Peterson and Shaw, 2003;Castillo-Riquelme et al, 2008;Salahi-Moghaddam et al, 2010). Geographical information systems (GIS) can be applied for risk mapping and identifying endemic areas for diseases (Elnaiem et al, 2003;Rinaldi et al, 2006;Pezeshki et al, 2012).…”

“…sheepdogs, guard dogs and stray dogs) (Edrissian et al, 1988;Mirsamadi et al, 2003;Moshfe et al, 2008;Salahi-Moghaddam et al, 2010;Rajabi et al, 2012). The Meshkin-Shahr district in the Ardabil province is one of the most important endemic zones for VL in north-western Iran (SalahiMoghaddam et al, 2010) and the Iranian Ministry of Health (MoH) records indicate that the number of VL infections has noticeably increased in this and neighbouring districts during the last decade (Soleimanzadeh et al, 1993;Tamook et al, 2006).…”

“…To specify the input data for the prediction model, the association between VL endemicity and various factors in the literature was explored. In this regard, seven items were deduced from literature to be the critical recognition criteria (CRC) for predictive mapping of VL risk-prone areas: temperature, precipitation, proximity to rivers, altitude, presence of health-centres, land cover and presence of nomads (Sudhakar et al, 2006;Bhunia et al, 2010;Salahi-Moghaddam et al, 2010;Rajabi et al, 2012;Tsegaw et al, 2013).…”

Susceptibility mapping of visceral leishmaniasis based on fuzzy modelling and group decision-making methods

“…No such studies have been conducted in Iran or the neighbouring trans-Caucasus countries, but there is good evidence that the distribution of VL and its vectors in this region depend on environmental variables (Castillo Riquelme et al, 2006;Salahi-Moghaddam et al, 2010). Although multy criteria decision making (MCDM) methods have only been sparsely used for Health-GIS applications based on geographical information systems (GIS), Clements et al (2006) used this approach to obtain a greater understanding of uncertainty related to the geographical distribution of Rift Valley fever (RVF).…”