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Research Article
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Investigating Geographical Distribution of Crimean-Congo Hemorrhagic Fever in Tokat County of Turkey
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Hakan Mete Dogan,
Ilhan Cetin
and
Mucahit Egri
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ABSTRACT
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In this study, we investigated the spatial distribution
of CCHF incidences of Tokat Province within the frame of Geographic Information
Systems (GIS). For this purpose, we evaluated the public health data collected
between 2003 and 2006. Frequency data that belongs to 133 settlements
was joined to the referenced point database of Turkey in GIS and interpolated
to raster maps by employing Kriking method with Spherical variogram model.
Produced raster maps of each year and combination of all years were interpreted
visually. Relationships between incidence and other available variables
(population, elevation, cattle number, sheep and goat number) were investigated
by employing bi-variety correlation analysis (Pearson coefficients). According
to the results, CCHF events in Tokat increased from 50 to 100 within the
4 years and showed the tendency to spread in certain geographic locations
of the province. CCHF incidence and elevation showed significant (positive)
correlation (0.687) at 0.05 level under an altitude threshold (1340 m).
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INTRODUCTION
Crimean-Congo Hemorrhagic Fever (CCHF) is an acute viral disease in humans.
It is caused by CCHF virus that is transmitted to humans either by Hyalomma
ticks or by direct contact with the blood of infected humans or domestic
animals. Like other tick-borne zoonotic agents, CCHF virus mostly spreads in
nature unnoticed in an enzootic tick-vertebrate-tick cycle (Whitehouse,
2004). CCHF virus has been isolated from wild vertebrates and numerous domestic
animals such as; cattle and goats (Causey et al.,
1970), hedgehogs (Causey et al., 1970), sheep
(Yen et al., 1985), domestic dogs (Shepherd
et al., 1987a, b), horses, donkeys and pigs
(Watts et al., 1989). Hyalomma ticks are
widespread in North Africa, Southern Europe, Middle East, Central Asia and China
(Burt et al., 1998). The known distribution of
CCHF virus covers the greatest geographic range of any tick-borne virus and
there are reports of viral isolation and/or disease from more than 30 countries
in Africa, Asia, Southeast Europe and the Middle East (Hoogstraal,
1979). Evidences of CCHF in France, Portugal, Egypt and India are based
on limited serologic observations. However, outbreaks are sometimes possible
after several decades of serologic evidences such as the recent outbreak in
the middle Black Sea region of Turkey (Karti et al.,
2004; Bakir et al., 2005). There are two
different genetic lineages of CCHF virus are circulating in Turkey. These closely
resemble virus lineages found in Kosovo and Southwestern Russia and are clearly
distinct from those associated with a recent CCHF outbreak in neighboring Iran
in 2002 (Mardani et al., 2003).
Over the past decade, rapid changes in technology and information management
have started a new era in public health practice. One of the most exciting technological
developments is Geographic Information Systems (GIS) that has the capacity to
bring community health assessment and improvement down to the neighborhood level
(Melnick, 2001; Cromley and McLafferty,
2002). Moreover, the availability of spatial data in digital format has
greatly increased in recent years. Analysis of such data in Geographic Information
Systems (GIS) has led to increasing applications in studies of the population
dynamics of various arthropod vectors in relation to a range of ecological factors
and disease prediction (Hay, 2000). GIS maps displaying
reliable epidemiological data can play an important role in assessing the magnitude
of the problem and defining priority areas for control. Recent applications
of these spatial analytic techniques to vector borne diseases have demonstrated
the emerging utility of this technique (Maupin et al.,
1991; Reisen et al., 1997).
CCHF events among people in Tokat Province of Turkey have increased since
2001. This situation has attracted attention of many public and research
organizations studying in the region. Delineating spatial distribution
of CCHF events is important and necessary to investigate the relationships
between incidences and other related variables. To fulfill these requirements,
present study has aimed to two important objectives that are mapping spatial
distribution of CCHF and researching relationships between CCHF and other
available (population, elevation and animal number) variables in GIS.
Resulting maps have the potential to help the researchers and decision
makers who work in the region and can be helpful for the studies especially
devoted to environmental and ecological modeling of CCHF.
MATERIALS AND METHODS
Tokat Province positioned in the transition zone between Inner Anatolia and
Middle Black Sea regions of Turkey (Fig. 1a). The province
covers 9912 km2 area and has 12 administrative districts namely;
Almus, Artova, Başçiftlik, Centre, Erbaa, Niksar, Pazar, Reşadiye,
Sulusaray, Turhal, Yeşilyurt and Zile (Fig. 1b). Semi-arid
Upper Mediterranean Bioclimatic characteristics with cold winters rule in the
region (Akman, 2007). Physical geography of the province
shows mountainous characters with the changing elevation between 85 and 2416
m (Fig. 1b). Yeşilİrmak River and Kelkit Stream
are the main drainages in the region. Agriculture and stockbreeding are basic
economical activities in the lower parts of the province. According to General
Directorate of Rural Services (KHGM) soil database; soils characteristics
of the province can be summarized as; Brown Forest (60.63%), Noncalcerous Brown
Forest (13.62%), Chestnut (7.42%), Alluvial (5.76%), Reddish Chestnut (4.38%),
unclassified (settlement-rock, 3.42%), Colluvial (2.92%), Brown (0.94%), Grey
Brown Podsolic (0.78%), Reddish Brown (0.12%) and Hydromorphic (0.02%) (KHGM,
2002). In geological point of view, Tokat Complex can be summarized as relatively
rare serpentinite and radiolarian chert in blocks of variable size and strongly
deformed tectono-sedimentary mixture of low-grade metamorphic rocks with abundant
re-crystallized limestone (Bozkurt et al., 1997).
In this study, we utilized the CCHF data provided from Communicable Diseases
Division of Health Directorate in Tokat Province. Basically, this data
covers the four years records between 2003 and 2006 and contains the information
about names, ages, addresses of the patients. We firstly processed this
dataset and calculated the frequency of CCHF events of the settlements
in each year. Processing the data, we separately prepared five database
(dbf) files for each year and combination of all years in Microsoft Excel
software (professional edition).
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Fig. 1: |
(a) Geographic location and (b) topographic layouts and administrative
districts of the Tokat Province (Administrative districts names were written
in white) |
Geographic references of the settlements in dbf files were determined
by utilizing the settlement (point) database of Turkey. All geo-referenced
dbf files were converted to shape files separately in ARC/GIS software
(version 9.1). Then, we interpolated the produced shp files to raster
maps (30x30 m resolution) by applying Kriking method with Spherical variogram
model in ARC/GIS. Produced raster maps were interpreted considering spatio-temporal
change and geographical locations.
We statistically investigated relationships between CCHF and available
variables. Instead of combined CCHF frequency data, we utilized incidence
values that were calculated by using the following formula:
Logarithms of incidence (INCLOG), elevation (ELEVLOG), sheep and goat
number (SGLOG), cattle number (CATLOG) and population (POPLOG) values
were calculated to get normal distributions. All these logarithmic variables
were arranged at administrative district level and bivariate correlation
analysis (Pearson coefficients) was applied in SPSS software (version
11). Finally, we produced an INCLOG map in ARCGIS by joining INCLOG values
to administrative district map database.
RESULTS
CCHF events were determined in 133 settlements within the total 718 settlement
units of Tokat Province (Fig. 2a). During this four-year
period, 14 deaths, caused by CCHF, were recorded in 12 settlements (Fig.
2b). The total frequency of the events between the years 2003 and
2006 was observed as 336 (Table 1).
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Fig. 2: |
(a) Settlements with CCHF incidences and no incidences and
(b) settlements with CCHF deaths |
The lowest (50) and the highest (100) frequencies were detected in the
years 2003 and 2006, respectively. Frequency values increased continuously
from 2003 to 2006 and reached to highest level (100) in 2006. Considering
total frequencies, Centre (108), Almus (64), Reşadiye (35), Artova
(32), Zile (29) and Turhal (23) administrative districts have the most
striking results comparing to others. CCHF frequency values changed between
4 and 17 in the other administrative districts, while no incidence was
determined in Başçiftlik. The produced raster maps played
important role to understand the geographical distribution of the CCHF
events in Tokat Province (Fig. 3). First of all, these
maps visualized both spatio-temporal change year to year (Fig.
3a-e) and combination of the four year results
(Fig. 3e).
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Fig. 3: |
Developed raster maps of CCHF events in Tokat Province: (a)
settlement frequencies in 2003, (b) settlement frequencies in 2004, (c)
settlement frequencies in 2005, (d) settlement frequencies in 2006 and (e)
total settlement frequencies between 2003 and 2006 |
Evaluating the years separately, geographical distribution of CCHF incidences
showed similar inclination. On the other hand, the raster map of 2004
showed some difference comparing to others (Fig. 3b),
because incidence frequency of some settlement units increased to the
highest level (27) of four year period. Higher frequency values were found
in Centre, Artova and Almus District in 2003 (Fig. 3a),
respectively. Moreover, Centre, Reşadiye and Almus were determined
as the leading districts where the most events were encountered in 2004
(Fig. 3b). The most events occurred in Centre, Almus,
Turhal and Pazar District in 2005 (Fig. 3c). Finally,
Centre, Artova, Pazar, Sulusaray and Turhal District attracted attention
with higher CCHF events in 2006 (Fig. 3d). The combination
map, produced by aggregating the four year frequency results, made us
possible to evaluate all years results as a whole. According to
the combination map (Fig. 3e); the most CCHF events
occurred in Centre and this was followed by Almus, Reşadiye, Artova,
Zile, Sulusaray, Turhal and Pazar, respectively. The Northern districts
(Erbaa, Niksar and Başçiftlik) were found as the places where
CCHF events rarely occurred.
INCLOG values of administrative districts also verified this general
distribution (Fig. 4, Table 2). INCLOG
values changed between 1.29 and -0.26 and Artova (1.29), Sulusaray (1.18)
and Almus (1.17) districts have higher INCLOG and Elevation (ELEV) values
comparing to others (Table 2). Correlation analysis
results clarified relationships between INCLOG and the focused variables
(Table 3) and INCLOG and ELEVLOG (0.687) showed significant
(positive) correlation at 0.05 level.
Table 1: |
Number of infected and non infected settlements within each
administrative districts of Tokat Province |
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Table 2: |
Values of population (POP), CCHF incidence (INC), logarithm
of incidence (INCLOG), elevation (ELEV), sheep/goat (SG), cattle (CAT) and
total animal (TA) number values of administrative districts |
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Başçiftlik District excluded from correlation
analyze because it has no CCHF incidence |
Table 3: |
Correlation analysis results (Pearson coefficients) |
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*Correlation is significant at the 0.05 level (2-tailed),
**Correlation is significant at the 0.01 level (2-tailed), INCLOG:
Logarithm of incidence, ELEVLOG: Elevation, SGLOG: Sheep-goat number,
CATLOG: Cattle number, TALOG: Total animal number, POPLOG: Population |
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Fig. 4: |
Logarithms of incidence values (INCLOG) in administrative
districts |
DISCUSSION
Basically, low CCHF events occurred in Niksar and Erbaa plains, while
higher rates were determined in Kazova plain. Although all these plains
have similar geographic characteristics, the difference could be related
to different agricultural practices in those places. It is thought that
the low CCHF events in Niksar and Erbaa plains might be related to excessive
pesticide usage because of incentive agricultural applications. Comparing
to Niksar and Erbaa plains, pesticide usage is low in Kazova plain. This
case can be investigated in a quantitative way if a detailed database
related to pesticide usage will be implemented. Database establishment
about this subject are still continuing.
We observed that the positive correlation (0.687) between INCLOG and
ELEVLOG variables changed sharply after a certain altitude. Başçiftlik,
for instance, is the district which has the highest elevation value (1340
m) but no CCHF events. Moreover, there is almost no pesticide usage in
this area. This situation suggested that there might be an altitude threshold
(1340 m) where Hyalomma ticks can not find an opportunity to establish
in it. Under this threshold, CCHF events rise with the increasing elevation
values, because moderate agricultural activities with less pesticide applications
substitute the incentive agricultural activities in higher elevations.
When the habitat characteristics of Hyalomma ticks will be researched
and modeled, all these anthropogenic factors should be taken into consideration
in a detailed manner.
Lowest incidence frequency in the northern districts (Erbaa, Niksar and
Başçiftlik) is another important point that took our attention.
One of the reasons of this situation could be related to the Köse
Mountains that established a natural barrier between these districts and
highly infected districts (Fig. 1). On the contrary,
higher incidence frequency in the centralized districts may be related
to higher animal movements because of vigorous economical activities.
After deep examination of all these factors, the relationship among all
these factors could be modeled. Present studies about modeling have been
continuing.
Finally, the frequency values of 2003 change between 0 and 17 in Table
1, while they alter between 0 and 6 in Fig. 3a.
At a first glance, it may be thought that there is discordance between
Table 1 and Fig. 3. This is fairly
normal because the values in Table 1 and Fig.
3 represent disparate facts. The values in Table 1
corresponded to the total frequency values of administrative districts.
On the other hand, the values in Fig. 3 represented
the frequency values of different settlement units in a single administrative
district. For instance, the highest frequency value (17) of Almus in 2003
(Table 3) belongs to different settlement units of Almus
District and it represented the total incidence number in the whole district
area. This situation emphasized the importance of raster maps that made
us possible to produce robust interpretations.
CONCLUSION
Using geography, GIS linked and integrated simple CCHF frequency data
to settlement database of Tokat Province rapidly and accurately. The process
of linking these data sets encouraged data-sharing partnership between
local health departments other relevant establishments such as governorship,
research institutions and university. Producing raster maps, GIS gave
us easily understandable visual pictures of CCHF events in the study area.
GIS presented these pictures in attention capturing way that program managers,
policy makers, community partners and others can easily evaluate. Moreover,
GIS allowed us to analyze and display CCHF data at the province level.
We found this level is particularly useful to understand the nature of
geographic distributions of CCHF events within the borders of Tokat Province.
In this way, an initiative raster database was created so that researchers
in the region can access and use. This kind of database is extremely important
for the successful public health management. This study put forwarded
that how effective maps related to public health could be produced by
using the interpolation techniques which is one of the simplest functions
of GIS. Determination of geographic distribution of frequency values of
CCHF events is extremely important to develop models and to determine
habitat characteristics of acarids that are effective on CCHF spreading.
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