Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran.

INTRODUCTION: Cutaneous Leishmaniasis (CL) is a significant public health concern worldwide. Iran is among the most CL-affected countries, being one of the six most endemic countries in the world. This study aimed to provide a spatio-temporal visualisation of CL cases in an endemic urban area in north-eastern Iran identifying high-risk and low-risk areas during the period 2016-2019. METHODS: This ecological study was conducted in the city of Mashhad, north-eastern Iran. All cases (n=2425) were diagnosed based on clinical findings and parasitological tests. The patient data were aggregated at the census tract level (the highest resolution available). CL incidence rates were subjected to Empirical Bayesian smoothing across the census tracts followed by spatial autocorrelation analysis to identify clusters and outliers. Spatial scan statistic was used to explore the purely temporal, purely spatial and spatio-temporal trend of the disease. In all instances, the null hypothesis of no clusters was rejected at p ≤0.05. RESULTS: The overall crude incidence rate decreased from 34.6 per 100,000 individuals in 2016 to 19.9 per 100,000 in 2019. Cluster analysis identified high-risk areas in south-western Mashhad and low-risk areas in the north-eastern areas. Purely time scan statistics identified March to July as the time period with highest risk for CL occurrence. One most likely purely high-risk spatial cluster and six secondary purely high-risk spatial clusters were identified. Further, two spatio-temporal high-risk clusters, one in the north of the city from April to August and a second in the south-western part from March to September were observed. CONCLUSIONS: Significant spatial, temporal and spatio-temporal patterns of CL distribution were observed in the study area, which should be considered when designing tailored interventions, such as effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generated new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in high-risk areas.

A geodatabase of blood pressure level and the associated factors including lifestyle, nutritional, air pollution, and urban greenspace.

OBJECTIVES: Hypertension is a prevalent chronic disease globally. A multifaceted combination of risk factors is associated with hypertension. Scientific literature has shown the association among individual and environmental factors with hypertension, however, a comprehensive database including demographic, environmental, individual attributes and nutritional status has been rarely studied. Moreover, an integrated spatial-epidemiological approach has been scarcely researched. Therefore, this study aims to provide and describe a geodatabase including individual-based and socio-environmental data related to people living in the city of Mashhad, Iran in 2018. DATA DESCRIPTION: The database has been extracted from the PERSIAN Organizational Cohort study in Mashhad University of Medical Sciences. The data note includes three shapefiles and a help file. The shapefile format is a digital vector storage format for storing geometric location and associated attribute information. The first shapefile includes the data of population, air pollutants and amount of available green space for each census block of the city. The second shapefile consists of aggregated blood pressure data to the census blocks of the city. The third shapefile comprises the individual characteristics data (i.e., demographic, clinical, and lifestyle). Finally, the fourth file is a guide to the previous data files for users.