Leishmaniasis, a serious worldwide public health problem caused by intracellular protozoan parasites, was prevalent in 92 countries in 2018 with an estimated 700,000 to 1 million new annual infections (1, 2). It is a vector-borne disease with a wide range of symptoms, which is transmitted to humans by phlebotomine sand flies (3). There are different variants of this disease, the most common of which is the cutaneous form. Over 85% of new identified cutaneous leishmaniasis (CL) cases originated in Afghanistan, Algeria, Brazil, Colombia, Iran (Islamic Republic of), Pakistan, Peru, and the Syrian Arab Republic in 2018 (2, 4). Recently, the number of CL cases has significantly increased in the endemic countries (5). This increase is attributed to changes in both natural and human-made environments such as fast and unplanned urbanization, increase in agricultural development, large migrations, and deforestation (6, 7). CL causes psychological, social, and economic problems (8), and is a major public health challenge in the affected areas.
Iran is among the top six countries in the world with the highest annual rate of CL incidence (2, 4), with 50–250 cases per 100,000 individuals (9). CL, the second most prevalent arthropod-transmitted disease (10), is a complex public health problem and its distribution follows a spatial pattern in Iran (2, 4, 11). The Khorasan-Razavi province, located in northeastern Iran, has the highest prevalence of CL in the country (12, 13). Mashhad, the capital of Khorasan-Razavi province, is the second most populous city in Iran and is bordered by two neighboring CL-prevalant countries, Turkmenistan and Afghanistan. Moreover, Mashhad, one of the main CL endemic cities (14, 15), has different CL incidence rates across different geographical areas (16).
Identification of geographical distribution and spatio-temporal patterns of disease can help to implement effective preventive strategies. Geographic information system (GIS) has been previously applied to visualize the geographical distributions of disease and the spatial modelling of risk factors, and in particular environmental factors (17, 18). GIS is a powerful tool to gather and analyze spatial and non-spatial data regarding the epidemiology of disease and understanding its underlying ecology. Spatial and spatial-time analyses can provide insight into disease occurrence and guide potential tailored disease control interventions. As such, identifying high-risk areas can be useful to implement surveillance programs and come up with effective disease control strategies (19, 20). Because environmental factors can potentially impact the geographical distribution of leishmaniasis, using spatial analysis can foster valuable insight for health policymakers (21).
According to previous studies, GIS-based techniques led to the detection of countries with the highest rate of CL cases between 2001 and 2011 in Latin America (22). In a study conducted in Brazil, the areas with high CL incidence rate were identified using spatial analysis (23). A number of studies used spatial analysis to assess spatial distribution and pattern of CL in some areas of Iran at provincial level (24–28). Furthermore, other studies have attempted to determine relations between epidemiological and environmental factors and CL incidence in endemic areas (24, 27, 29). A few studies have been carried out in Khorasan provinces (North, Razavi and South) to assess the spatial distribution of CL at provincial and national levels. In these studies, southern areas of North Khorasan province and northern areas of Razavi Khorasan province were identified as hot areas (28, 30). Another study revealed the high prevalence of CL in southwestern areas of Mashhad using molecular methods (14). An epidemiological study assessed CL prevalence in Mashhad over a period of 20 years (1992–2014) and identified climate-related factors with remarkable influence on the prevalence of CL (31). The previous studies confirmed the high prevalence of CL in Mashhad, but to the best of our knowledge, no study has investigated the spatial-time pattern of CL incidence at district and neighborhood levels in Mashhad. This underscores the need to explore the geographical distribution and high-risk disease areas. Exploring high-risk areas is an important step in developing strategies to effectively allocate healthcare resources, so as to overcome the disease and its associated complications (32). The objective of the present study is to apply a spatial analysis of CL cases in the city of Mashhad between 2016 and 2019, and to identify the spatial-time pattern of CL cases and identify the critically affected areas.