Control of Cutaneous Leishmaniasis Using Geographic Information Systems from 2010 to 2014 in Khuzestan Province, Iran

Distribution and Risk of Cutaneous Leishmaniasis in Khyber Pakhtunkhwa, Pakistan

Cutaneous leishmaniasis (CL) is a zoonotic infection caused by obligate intracellular protozoa of the genus Leishmania. This study aimed to investigate CL in Khyber Pakhtunkhwa, Pakistan and to estimate the risk of epidemics. Clinico-epidemiological data of 3188 CL patients were collected from health facilities in 2021. Risk factors were analyzed using the chi-square test. ArcGIS V.10.7.1 was applied for spatial analysis. The association between CL occurrence and climatic variables was examined by Bayesian geostatistical analysis. The clinical data revealed males or individuals younger than 20 years old were more affected. Most patients presented with a single lesion, and the face was the most attacked body part. CL was prevalent in the southern region in winter. A proportional symbol map, a choropleth map, and a digital elevation model map were built to show the distribution of CL. Focal transmission was predicted by inverse distance weighting interpolation. Cluster and outlier analysis identified clusters in Bannu, Dir Lower, and Mardan, and hotspot analysis suggested Bannu as a high-risk foci. Bayesian geostatistical analysis indicated that increasing precipitation and temperature as well as low altitudes were associated with CL infection. The study has provided important information for public health sectors to develop intervention strategies for future CL epidemics.

Cutaneous leishmaniasis in Iran: A review of epidemiological aspects, with emphasis on molecular findings

Leishmania parasites can cause zoonotic cutaneous leishmaniasis (CL) by circulating between humans, rodents, and sandflies in Iran. In this study, published data were collected from scientific sources such as Web of Science, Scopus, PubMed, Springer, ResearchGate, Wiley Online, Ovid, Ebsco, Cochrane Library, Google scholar, and SID. Keywords searched in the articles, theses, and abstracts from 1983 to 2021 were cutaneous leishmaniasis, epidemiology, reservoir, vector, climatic factors, identification, and Iran. This review revealed that CL was prevalent in the west of Iran, while the center and south of Iran were also involved in recent years. The lack of facilities in suburban regions was an aggravating factor in the human community. Some parts of southern Iran were prominent foci of CL due the presence of potential rodent hosts in these regions. Rhombomys opimus, Meriones lybicus, and Tatera indica were well-documented species for hosting the Leishmania species in Iran. Moreover, R. opimus has been found with a coinfection of Leishmania major and L. turanica from the northeast and center of Iran. Mashhad, Kerman, Yazd, and sometimes Shiraz and Tehran foci were distinct areas for L. tropica. Molecular identifications using genomic diagnosis of kDNA and ITS1 fragments of the parasite indicated that there is heterogeneity in leishmaniasis in different parts of the country. Although cutaneous leishmaniasis has been a predicament for the health system, it is relatively under control in Iran.

Study of fauna, activity patterns and Leishmania infection rate of phlebotomine sand flies in Western Iran

Cutaneous leishmaniasis is a crucial vector-borne disease caused by various species of Leishmania and is transmitted by several species of sandflies. The present study was conducted to describe sand fly fauna on vectors of leishmaniasis and performing molecular identification of Leishmania isolates from them on the Iran-Iraq border. Entomological surveys were done from May to October 2016-2018 in 2 counties (Mehran and Dehloran) of Ilam province, west of Iran. Sandflies were collected by 40 Sticky Traps at each station. Samples were mounted for species identification using morphological characters of the head and abdominal terminalia. DNA was extracted from Phlebotomus papatasi females, and Leishmania isolates were identified through PCR on minicircle kDNA, followed by sequencing. A total of 5592 sandflies including 2 genera of Phlebotomus and Sergentomyia comprising 8 species of sand flies were detected. Leishmania major infection was detected in 3.33% of 300 tested female sandflies. Phlebotomus papatasi was predominant in outdoor and indoor resting places. Phlebotomus papatasi was determined as dominant vector of Leishmania major infection in Mehran and Dehloran counties, West of Iran. It seems the composition of sandfly species in the study area is almost similar to the other parts of Iran. A detailed description of the epidemiology and ecology of Phlebotomine sand flies needs to be established to accomplish effective vector control programs.

Epidemic analysis of COVID-19 in Italy based on spatiotemporal geographic information and Google Trends

Since the first two novel coronavirus cases appeared in January of 2020, the outbreak of the COVID-19 epidemic seriously threatens the public health of Italy. In this article, the distribution characteristics and spreading of COVID-19 in various regions of Italy were analysed by heat maps. Meanwhile, spatial autocorrelation, spatiotemporal clustering analysis and kernel density method were also applied to analyse the spatial clustering of COVID-19. The results showed that the Italian epidemic has a temporal trend and spatial aggregation. The epidemic was concentrated in northern Italy and gradually spread to other regions. Finally, the Google Trends index of the COVID-19 epidemic was further employed to build a prediction model combined with machine learning algorithms. By using Adaboost algorithm for single-factor modelling,the results show that the AUC of these six features (mask, pneumonia, thermometer, ISS, disinfection and disposable gloves) are all >0.9, indicating that these features have a large contribution to the prediction model. It is also implied that the public's attention to the epidemic is increasing as well as the awareness of the need for protective measures. This increased awareness of the epidemic will prompt the public to pay more attention to protective measures, thereby reducing the risk of coronavirus infection.

Strategic Radiology Outreach Planning for Underserved Populations Using Geographic Information Systems

PURPOSE

Geographic information systems (GIS) are widely used in public health research but rarely used in radiology research. GIS can be an impactful tool in radiology global health to locate medically underserved populations and poor transportation infrastructure, characterize medical needs, and design outreach programs. Using the example of aircraft-based outreach in Alaska, we demonstrate the utility of GIS in radiological program planning for global health.

METHODS

Multicriteria GIS evaluations were performed to create a health severity index, using life expectancy and percentage uninsured data, and an accessibility severity index, using distance from roads and health centers or hospitals. These indices were combined with population density to create a final health access severity index (HASI). A map presenting suitable hybrid airship operating areas was produced using land cover data. Alaskan health care facilities were georeferenced to create a coordinate data set. Infrastructure was obtained from OpenStreetMap. Health data were accessed from the 2017 American Community Survey and CDC US Small-area Life Expectancy Estimates Project.

RESULTS

GIS analyzed 738,050 Alaskans. The health severity index identified decreased health outcomes (high or very high severity) in 285,446 (39%) Alaskans, and the accessibility severity index determined decreased access to care in 218,201 (30%). Combined, the HASI established 165,108 (22%) Alaskans as underserved with high or very high overall severity. Thirty-nine percent of Alaska land area is suitable for hybrid airship operations, including 27% of HASI high and very high severity areas.

CONCLUSIONS

GIS identified underserved populations for mobile radiology outreach in Alaska and may be useful for global health outreach planning and resource allocation.

One Health Approach to Leishmaniases: Understanding the Disease Dynamics through Diagnostic Tools

Leishmaniases are zoonotic vector-borne diseases caused by protozoan parasites of the genus Leishmania that affect millions of people around the globe. There are various clinical manifestations, ranging from self-healing cutaneous lesions to potentially fatal visceral leishmaniasis, all of which are associated with different Leishmania species. Transmission of these parasites is complex due to the varying ecological relationships between human and/or animal reservoir hosts, parasites, and sand fly vectors. Moreover, vector-borne diseases like leishmaniases are intricately linked to environmental changes and socioeconomic risk factors, advocating the importance of the One Health approach to control these diseases. The development of an accurate, fast, and cost-effective diagnostic tool for leishmaniases is a priority, and the implementation of various control measures such as animal sentinel surveillance systems is needed to better detect, prevent, and respond to the (re-)emergence of leishmaniases.

Spatial modeling of cutaneous leishmaniasis in Iranian army units during 2014-2017 using a hierarchical Bayesian method and the spatial scan statistic

OBJECTIVES

This study aimed to map the incidence of cutaneous leishmaniasis (CL) in Iranian army units (IAUs) and to identify possible spatial clusters.

METHODS

This ecological study investigated incident cases of CL between 2014 and 2017. CL data were extracted from the CL registry maintained by the deputy of health of AJA University of Medical Sciences. The standardized incidence ratio (SIR) of CL was computed with a Besag, York, and Mollié model. The purely spatial scan statistic was employed to detect the most likely highand low-rate clusters and to obtain the observed-to-expected (O/E) ratio for each detected cluster. The statistical significance of the clusters was assessed using the log likelihood ratio (LLR) test and Monte Carlo hypothesis testing.

RESULTS

A total of 1,144 new CL cases occurred in IAUs from 2014 to 2017, with an incidence rate of 260 per 100,000. Isfahan and Khuzestan Provinces were found to have more CL cases than expected in all studied years (SIR>1), while Kermanshah, Kerman, and Fars Provinces were observed to have been high-risk areas in only some years of the study period. The most significant CL cluster was in Kermanshah Province (O/E, 67.88; LLR, 1,200.62; p<0.001), followed by clusters in Isfahan Province (O/E, 6.02; LLR, 513.24; p<0.001) and Khuzestan Province (O/E, 2.35; LLR, 73.71; p<0.001), while low-rate clusters were located in the northeast areas, including Razavi Khorasan, North Khorasan, Semnan, and Golestan Provinces (O/E, 0.03; LLR, 95.11; p<0.001).

CONCLUSIONS

This study identified high-risk areas for CL. These findings have public health implications and should be considered when planning control interventions among IAUs.