=Paper=
{{Paper
|id=Vol-2646/45-paper
|storemode=property
|title=On the Use of GIS for Health and Epidemiology Control
|pdfUrl=https://ceur-ws.org/Vol-2646/45-paper.pdf
|volume=Vol-2646
|authors=Giuseppe Tradigo,Patrizia Vizza,Pietro Hiram Guzzi,Pierangelo Veltri
|dblpUrl=https://dblp.org/rec/conf/sebd/TradigoVGV20
}}
==On the Use of GIS for Health and Epidemiology Control==
https://ceur-ws.org/Vol-2646/45-paper.pdf
On the Use of GIS for Health and Epidemiology
Control?
Giuseppe Tradigo, Patrizia Vizza, Pietro H. Guzzi, and Pierangelo Veltri
Universit degli Studi Magna Graecia di Catanzaro
Viale Europa, 88100 Catanzaro, Italy
{gtradigo,vizzap,hguzzi,veltri}@unicz.it
Abstract. Monitoring the evolution and diffusion of diseases is an im-
portant task for health monitoring. Recent phenomena, such as the
spread of Coronavirus (Covid-19), highlighted the relevance of adopt-
ing Geographical Information Systems (GIS) in epidemiology modeling.
GIS may offer general views and indications which domain experts and
governments could use to give immediate directions to social actors and
operators.
We report on the possibility of using geographic database model in-
struments in order to acquire, store and manage health-related data.
The reported case study is about an application able to correlate TSH
(Thyroid-Stimulating Hormone) with environmental data. The reported
example aims to show how to acquire, analyze and integrate clinical and
geographical data to evaluate possible correlations for the prevention
of chronic diseases, especially neoplasms, by means of mapping disease
features with environmental factors.
Keywords: Geographic Information Systems · epidemiology · Virus ·
TSH.
1 Introduction
In the last years, GIS tools have become more useful in the public health sector,
especially in the epidemiology field [1–3]. Epidemiology models represent an
important tool to manage and assess environmental risk factors, such as potential
cause of illness, by quantifying their impact on human health in population at
risk [4]. Therefore, they allow to carry out a number of prevention activities, and
also monitoring and evaluating the effectiveness of these activities.
GIS can also be useful in associating epidemiological and health data by
defining how environmental factors affect the health status of group of individu-
als or entire populations living in a territory, in the onset of specific diseases [5–8,
0
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). This volume is published
and copyrighted by its editors. SEBD 2020, June 21-24, 2020, Villasimius, Italy.
?
Discussion Paper, a preliminary version of this paper has been published at IEEE
BIBM 2019 supported by POR PIH-GIS Regional Funds.
22]. GIS systems and analysis methodologies support domain experts in studying
the overlap of clinical and environmental data and can help with the identifica-
tion of possible relations between clinical features and diseases. These relations
can be rendered with rich epidemiological maps which represent the present and
future status (prediction) of the on-set of a disease and its human-to-human
transmission. Correlations between environmental risk factors and clinical data
or events can then be used to evaluate or foresee the impact on human health
and are usually be analyzed by using spatial analysis techniques [9–11]. These
techniques allow to study disease pathogenesis and etiology, identifying any links
between them and the places where they develop [12].
In literature, many contributions exist which focus on spatial epidemiol-
ogy [13–15]. In [16] authors report on the use of GIS in epidemiology, focusing
on methodologies involving geocoding, distance estimation, residential mobility,
record linkage and data integration, spatial clustering, small area estimation,
and disease mapping. Authors in [17] study the correlation between climate and
geographic distribution of tuberculosis by using GIS, aiming to identify high-risk
population groups and their geographic areas. Contribution in [19] investigates
environmental and geographical influences on epidemiology of acromegaly in
Brazil. The authors validate a method to link an acromegaly registry with a GIS
mapping module aiming to represent the spatial distribution of patients and to
identify disease clusters.
We present an example of GIS application to study and evaluate the corre-
lation between geographical and clinical geographical by using TSH (Thyroid-
Stimulating Hormone) data. Shapefiles of environmental layers have been used
in GIS to identify specific territorial aspects and their influence on clinical out-
comes. A dataset has been used containing data of patients enrolled at Magna
Graecia University of Catanzaro. A preliminary version of the paper has been
previously published in [18].
2 GIS in health related applications
Environmental monitoring and the analysis of clinical data aim to prevent chronic
diseases, especially the neoplasms and their correlation with environmental fac-
tors. In literature there exist many definitions of both correlation (e.g. Pearson
correlation) and spatial correlation (scorr spatial correlation function between
two variables over an X-Y domain). For the purposes of the present work, we
adopt a loose definition of spatial correlation as follows. Let’s consider a clinical
event E in the input clinical dataset and a geographical entity G; we say that
E and G are spatially correlated if there exists a spatial query Q for which the
location of E is contained in the area subtended by G. In order to investigate on
spatial correlations in our dataset, different environmental layers have been used.
These are available on the Geoportal of the Calabria Region in shapefile format
[20]. A shapefile is a standard format for vectorial spatial data. It has been de-
veloped by the Environmental Systems Research Institute (ESRI) to extend the
interoperability between ESRI systems and the other GIS [21]. The shapefile is a
non topological format and it is used in Geographic Information System to store
positions, shapes and attributes of geographical features. It describes primitive
geometric data such as points, lines, polygons and texts, called features, and
their associated information, called attributes (e.g., a data describes a river and
the associated attributes could be the name or temperature of the river). In such
a way, more representations of geographic data can be created, which allow to
influence the power and accuracy of geospatial analyses.
The online Geoportal of Calabria Region shares a cartographic data collection
containing a number of environmental layers with IODL 2.0 license. Among the
available layers, we selected the ones listed below as the most useful for our
investigation:
– urban area;
– extra-urban area;
– commercial and industrial settlements;
– public and private service companies;
– rural settlements;
– mining area;
– yards;
– landfill;
– scarp yards;
– local railways;
– airport areas and heliports;
– regional, provincial and district administrative range.
These layers have been used to verify and analyze possible correlations be-
tween clinical and territorial aspects. The application of shapefiles and the use of
geographic layers are important to assess whether and how some pathologies or
in any case clinical alterations of individuals can be directly or indirectly related
to strictly territorial aspects.
Table 1. Clinical database extraction. TSH values are expressed in µU/ml.
Patient Latitude Longitude Altitude TSH
1 38.632. . . 16.072. . . 465.80 2.40
2 39.527. . . 15.924. . . 5.85 1.86
3 39.523. . . 15.962. . . 277.56 2.07
4 39.533. . . 15.989. . . 346.60 2.02
5 39.516. . . 15.943. . . 134.49 0.05
6 39.527. . . 15.935. . . 188.68 1.26
7 39.511. . . 15.948. . . 182.01 1.27
8 38.246. . . 16.139. . . 323.80 2.84
9 38.245. . . 16.138. . . 301.11 4.23
10 38.246. . . 16.142. . . 341.25 1.25
...
2269 38.698. . . 15.990. . . 302.01 2.32
Fig. 1. Topological overlay between clinical data and extracted areas
As case study, we report the study of the correlation between the patterns of
topological data with an example of thyroid’s data regarding a south Italian re-
gion patients. A Health-GIS system has been implemented aiming to cross-check
the TSH value with further environmental data through a topological overlay
operation. The goal is to identify the geographical areas with the greatest pres-
ence of cases with specific pathology. Moreover, it is also interesting to calculate
and trace the average distance covered by a patient for a specific health service.
3 Experiments
The health dataset regards single file with .xls extension in which each record
represents a patient and contains:
– geo-referenced data (district, home address, latitude, longitude and altitude)
– clinical data (TSH level expressed in µU/ml)
The clinical dataset contains 2269 records and Table 1 reports an extract of
the database we used for the investigation. The QGIS platform has been used to
cross-check these clinical data with data relating to environmental factors. QGIS
is a free and open source geographic information system to visualize and overlap
vector and raster data in different formats and projections without any conver-
sion into a common internal format. The clinical dataset has been converted in
.cvs file and loaded into the QGIS system to represent spatial and clinical infor-
mation together in a geographical map. Patients locations and other logistical
data are geo-referenced and related with clinical data. For instance, red dots in
Figure 1 refer to patients’ location data extracted from the considered clinical
dataset and projected on extractive areas polygons.
Starting from this representation, environmental layers listed above have been
correlated with clinical dataset and some of these correlations are reported below.
Fig. 2. Topological overlay between clinical data and landfills and scrap yards.
Since several diseases have a close link with overexposure to toxic substances,
it is relevant to measure the occurrence of patients locations with potential toxic
areas. Air pollution is one of the major environmental risks and in 2015 it has
been recognized by the World Health Assembly (WHA) as one of the world’s
major public health problems. Pollutants are elements and chemical compounds
deriving mostly from human activities such as, for example, industrial and com-
mercial production. For this reason, the onset of several cancer cases in a specific
geographic area has led to the urge to investigate and monitor industrial activi-
ties insisting on the same area.
Figure 1 shows the topological overlay between clinical data (points in red)
and extractive areas (dark green areas) also reporting urban areas (areas in yel-
low). The extraction areas generate a series of pollutants whose exposure has
been associated with a series of harmful effects on human health, especially
for the respiratory and cardiovascular system and for neoplasms. Pollutants are
mainly responsible for a substantial impoverishment of the soil (e.g., caused
by deforestation and intensive agriculture), a continuous deterioration of water
quality (e.g., caused especially by industrial discharges), and a growing contam-
ination of the air (e.g., caused from the presence of fine dust and particulate in
smog). Many substances such as, for example, nitrogen oxide, sulfur oxide, ozone
and dust act as irritants; pollen and other agents present in the air are respon-
sible for allergies such as asthma and seasonal colds; some toxic agents such as
benzene and polycyclic aromatic hydrocarbons are carcinogenic; carbon monox-
ide compromises the transport of oxygen by the blood with serious effects on the
brain. Other metals come into contact with various organs and tissues causing
biological alterations and causing damage to the heart and the apparatus.
Figure 2 shows the topological overlay of clinical data with the level of land-
fills and scrap deposits (areas in brown), always bringing the urban area (areas
in yellow) as reference. The economic growth of the most industrialized coun-
tries has generated a substantial increase in consumption and urbanization with
a consequent increasing in waste production and in its difficult regular disposal.
Waste has negative consequences on the environment and on human health. As-
bestos is harmful to human health; breathing dust containing asbestos fibers can
cause serious diseases, pleural tumors (pleural mesothelioma) and lung cancer.
4 Future Works
Spatial information in clinical data is crucial for prevention and monitoring
evolution of global epidemiology. In this paper we analyze environmental data
related with health related ones. We are working to complete a model and general
purpose system able to relate clinical, environmental and epidemiology related
information for real time data analysis and simulation. The aim is to support
prevention in a large scale context. Our framework is general enough to support
the loading and preprocessing of generic datasets in standard format (e.g. csv).
We are planning to integrate user-friendly functionalities to import different
datasets. We are also working on consolidating the representation of time as an
analysis dimension and refining its use in the data analysis pipeline by adopting
time series analysis techniques.
Acknowledgment
This research has been supported by POR CALABRIA FESR-FSE 2014-2020
PIH-GIS project CUP J88C17000320006. The authors thank Giuseppe Brescia
for his contribution to a preliminary version of the paper.
References
1. M. Craglia and R. Maheswaran, GIS in public health practice. CRC press, 2016.
2. G. Tradigo, P. Vizza, P. Veltri, P. H. Guzzi, An Information System to Track data
and processes for food quality and bacterial pathologies prevention. Italian Sympo-
sium on Advanced Database Systems (SEBD), 2019.
3. G. Tradigo, C. Pagliaro, G. Canino, P. H. Guzzi and P. Veltri, GIS for the analysis
and monitoring of environmental issues related to life quality. Italian Symposium
on Advanced Database Systems (SEBD). 320-326, 2015.
4. A. B. Lawson, S. Banerjee, R. P. Haining and M. D. Ugarte, Handbook of spatial
epidemiology. CRC Press, 2016.
5. S. M. Fletcher-Lartey and G. Caprarelli, Application of GIS technology in public
health: successes and challenges. Parasitology, 143(4): 401-415, 2016.
6. G. Tradigo, P. Veltri, O. Marasco, G. Scozzafava, G. Parlato and S. Greco, Studying
neonatal TSH distribution by using GIS. In Proceedings of the First ACM SIGSPA-
TIAL International Workshop on Use of GIS in Public Health, 36-39, 2012.
7. G. Ambrogio, R. Conte, L. De Napoli, G. Fragomeni, F. Gagliardi, Forming ap-
proaches comparison for high customised skull manufacturing. Key Engineering
Materials, 651, 925-931, 2015.
8. M. V. Caruso, V. Gramigna, G. F. Serraino, A. Renzulli, G. Fragomeni, Influence
of aortic outflow cannula orientation on epiaortic flow pattern during pulsed car-
diopulmonary bypass. Journal of Medical and Biological Engineering, 35(4):455-463,
2015.
9. T. Olsson, L. F. Barcellos and L. Alfredsson, Interactions between genetic, lifestyle
and environmental risk factors for multiple sclerosis. Nature Reviews Neurology,
13(1): 25, 2017.
10. M. Chin-Chan, J. Navarro-Yepes and B. Quintanilla-Vega, Environmental pol-
lutants as risk factors for neurodegenerative disorders: Alzheimer and Parkinson
diseases. Frontiers in cellular neuroscience, 9: 124, 2015.
11. G. Fragomeni, R. Iannelli, G. Falvo D’Urso Labate, M. Schwentenwein, G. Cata-
pano, Validation of a novel 3D flow model for the optimization of construct perfusion
in radial-flow packed-bed bioreactors (rPBBs) for long-bone tissue engineering. New
Biotechnology, 52:110-120, 2019.
12. E. L. MacQuillan, A. B. Curtis, K. M. Baker, R. Paul and Y. O. Back, Using GIS
mapping to target public health interventions: examining birth outcomes across GIS
techniques. Journal of community health, 42(4): 633-638, 2017.
13. L. N. Carroll, A. P. Au, L. T. Detwiler, T. Fu, I. S. Painter and N. F. Abernethy,
Visualization and analytics tools for infectious disease epidemiology: A systematic
review. Journal of Biomedical Informatics, 51: 287-298, 2014.
14. G. Canino, P. H. Guzzi, G. Tradigo, A. Zhang and P. Veltri, A system for Geoanal-
ysis of Clinical and Geographical Data. In Proceedings of the Third ACM SIGSPA-
TIAL International Workshop on the Use of GIS in Public Health, 57-62, 2014.
15. G. Tradigo, C. Pagliaro, G. Canino, F. Casalinuovo, C. Graziani and P. Veltri, A
model for the Geographical Analysis and monitoring of agricultural areas example
and tests in south Italian regions. ACM SIGSPATIAL HealthGIS, 2014.
16. R. S. Kirby, E. Delmelle and Jan M. Eberth, Advances in spatial epidemiology and
geographic information systems. Annals of Epidemiology, 27(1): 1-9, 2017.
17. R. Beiranvand, A. Karimi, A. Delpishes, K. Sayehmiri, S. Soleimani and S. Ghala-
vandi, Correlation Assessment of Climate and Geographic Distribution of Tuber-
culosis Using Geographical Information System (GIS). Iranian Journal of Public
Health, 45(1): 86-93, 2016.
18. G. Tradigo, P. Vizza, G. Brescia, P. H. Guzzi, P. Veltri, A geographical patients
based health information system. IEEE International Conference on Bioinformatics
and Biomedicine (BIBM), 2019.
19. L. A. Naves, L. B. Porto, J. W. Corra Rosa, L. A. Casulari, J. W. Corra Rosa,
Geographical information system (GIS) as a new tool to evaluate epidemiology based
on spatial analysis and clinical outcomes in acromegaly. Pituitary, 18(1): 8-15, 2015.
20. Geoportale Nazionale, available online at http://www.pcn.minambiente.it/mattm/.
21. ESRI Shapefile Technical Description. An ESRI White PaperJuly, available online
at https://www.esri.com/library/whitepapers/pdfs/shapefile.pdf, 1998.
22. Y. Aliakbarpoor, S. Comai, G. Pozzi, Designing a HL7 compatible personal health
record for mobile devices, IEEE 3rd International Forum on Research and Tech-
nologies for Society and Industry, (RTSI), 1-6, 2017.
23. Caruso, M.V., Gramigna, V., Serraino, G.F., Renzulli, A., Fragomeni, G., Influ-
ence of aortic outflow cannula orientation on epiaortic flow pattern during pulsed
cardiopulmonary bypass, Journal of Medical and Biological Engineering, 35(4): 455-
463, 2015.