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conferenceseries
.com
Volume 7, Issue 5 (Suppl)
Epidemiology (Sunnyvale), an open access journal
ISSN: 2161-1165
Epidemiology 2017
October 23-25, 2017
EPIDEMIOLOGY & PUBLIC HEALTH
October 23-25, 2017 | Paris, France
6
th
International Conference on
SPATIOTEMPORALCLUSTERING OFCUTANEOUS LEISHMANIASIS IN FARS PROVINCE,
IRAN
Marjan Zare
a
, Abbas Rezaianzadeh
a
, Hamidreza Tabatabaee
a
, Mohsen Aliakbarpour
a
, Hossein Framarzi
a
and
Mostafa Ebrahimi
a
a
Shiraz University of Medical Sciences, Iran
Background
: Cutaneous Leishmaniasis (CL) is an infectious endemic disease in most provinces of Iran, which is among public
health problems. CL is prevalent in 88 countries in the world, infecting nearly 12 million individuals. Almost two million new
leishmaniasis cases occur yearly, with CL accounting for 1500000 cases. Fars province is known to be an endemic area for CL.
Study objective
: The objective of this study is to assess the spatiotemporal trait of CL in Fars province, Iran.
Materials & Methods
: Spatiotemporal cluster analysis was done retrospectively to find spatiotemporal clusters of CL cases.
Time-series data were recorded from 29201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the
cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters
within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using
SaTScan software.
Results
: This study resulted in statistically significant spatiotemporal clusters of CL (p<0.05). The most likely cluster contained
350 cases from 1 July 2010 to 30 November 2010. Besides, 5 secondary clusters were detected in different periods of time.
Finally, statistically significant sub-clusters were found within the three large clusters (p<0.05).
Conclusion
: Transmission of CL followed spatiotemporal pattern in Fars province, Iran. This can have an important effect on
future studies on prediction and prevention of CL.
Biography
Marjan Zare pursued MS in Biostatistics and is a PhD student in Epidemiology. She has expertise in statistical and epidemiological modelling, and has passion in
improving health care and public health policies. She has been working for Shiraz Medical School research center for three years and she knows how to work with
R, ITSM, CMA, Lisrel, Arc GIS, SaTScan, SPSS, Python language softwares to do ordinary statistical analysis. Also, she is interested in doing micro array analysis
using Mega Data in field of genetics; hereby she knows how to work with the related softwares like Plink and Hoploview. The basis of this research is to predict the
potential outbreaks in the future using Time-Series data.
mj_zare@sums.ac.irMarjan Zare et al., Epidemiology (Sunnyvale) 2017, 7:5(Suppl)
DOI: 10.4172/2161-1165-C1-017