Transcript 9. Geographic Information Systems and Geospatial Analysis: A 21st Century Tool for Epidemiology and Public Health Management. W.

9. Geographic Information Systems and Geospatial Analysis:
A 21st Century Tool for Epidemiology and Public Health Management.
W. Vreeland
ES473 Environmental Geology
ABSTRACT: Epidemiological studies involve analyzing public health factors in the context of time
and space, with the goal of mitigating disease outbreaks. Emerging advances in computer
processing power and cost are facilitating research into linkages between geospatially-distributed
risk factors for illness and disease. Geographic Information Systems (GIS) provide a software
technology that allows scientists to easily link public health databases to geospatial information
and forms the cornerstone of epidemiology in the 21st century.
Improved resolution of datasets allows visual representation of complex, multilayered, logical,
numerical, and statistical relationships between populations, risk factors, and known or
hypothesized causal factors. Geospatial relationships combined with raw and processed data
enable researchers to identify, mitigate, or prevent both epidemic and endemic disease fostered
by vectors that have a geographic component. This developing technology comes with the cost
of upgrading computer workstations and network bandwidth to accommodate the geometrically
increasing size of the datasets, however the potential benefits to public health management are
significant. This paper provides an overview of GIS applications in the public health sector, and
presents case studies demonstrating the efficacy of the technology.
Figure 1 Dr. John Snow cholera map, 1854.
INTRODUCTION: A geographic Information System (GIS) can be as simple as points plotted on a paper
map with some attribute attached to the points. One classic example of this is the map of cholera cases
prepared by Dr. John Snow during the London epidemic of 1854. This map enabled authorities to close the
well at the center of the cholera cluster and abate the epidemic. GIS today involves sophisticated software
and can be integrated with Remote Sensing (RS) and Global Positioning Systems (GPS) technologies to
provide real time monitoring of geological processes and epidemiological vectors. The key element of GIS is
that attributes are related spatial and some system is utilized to process and analyze these relationships.
The exponential decay in the cost of computer resources is now enabling cost effective studies at spatial
resolutions that could only be dreamed of a few decades ago. Using the cost of computer hard drives as a
proxy for the overall cost of computing resources puts this in perspective. A two terabyte hard drive can be
purchased today for as little as $149. The same amount of disk storage would have cost about $2 billion in
1980.
Figure 2 Graph depicting exponential decline in computer hard
drive storage as proxy for cost of computing.
This poster examines three case studies that rely on these technologies. These studies examine the spatial
and temporal distribution of the Malaria mosquito,
well water consumption and Parkinson's disease in rural California, and the risk of congenital anomalies
around a municipal solid waste incinerator in Italy.
REFERENCES
Ageep et al, 2009, Spatial and temporal distribution of the malaria
mosquito Anopheles arabiensis in northern Sudan: influence of
environmental factors and implications for vector control
Malaria Journal 8:123
doi:10.1186/1475-2875-8-123
DISCUSSION: The Snow cholera map (Fig 1), is the
first and simplest example of GIS presented here. It
shows the spatial relationship between cholera cases
and led to the correct conclusion about the source of the
cholera outbreak. The dramatic decrease in the cost of
computing (Fig 2) enables the study of more complex
processes. The correlation between the presence of
malaria vectors and land use (Fig 3) enables predictive
modeling of malaria risk based on geology, land use,
and seasonal factors such as rainfall and river stage
(Ageep). Public health authorities can now target
malaria vector eradication efforts in the highest risk
areas. This improves the cost effectiveness of these
efforts. This is very important since malaria
disproportionately affects poorer developing nations. An
exposure intensity map of a presumed risk factor from
the Vinceti study of birth abnormalities around a solid
waste incinerator in Italy (Fig 4) demonstrates how data
from a limited number of sample points can be
extrapolated to infer risk or exposure to environmental
hazards. This study found no correlation between
exposure intensity and birth abnormalities. Excluding
exposure to a toxin in the environment as a disease risk
factor is also an important result as demonstrated in the
final case study regarding the correlation between
consumption of well water and Parkinson’s disease in
rural California. This study did find a correlation between
the risk factor (consuming well water) and the disease.
The presumed agents in this study were agricultural
chemicals in the well water. Rather than test specifically
for the presence of the chemicals, this study used
detailed public records of pesticide applications to
create a model of exposure intensity. The researchers
could not rule out other exposure routes in this study nor
could they rule out that exposure to other classes of
chemicals such as industrial waste and toxic metals
may have also played an important role in the observed
increase in disease. Calculating risk based on exposure
is extremely difficult. Not only are there thousands of
chemicals in the environment, the levels of which are
constantly changing over time, but, people do not
normally remain stationary for very long. They therefore
have a constantly changing exposure profile. This study
provided no graphs or maps.
Beale et al, 2008, Methodologic Issues and Approaches to Spatial
Epidemiology:
Environ Health Perspect 116:1105–1110
doi:10.1289/ehp.10816
Bunnell et al., 2005, GIS in Human Health Studies, in Selinus, 2005
doi: unavailable
CONCLUSION: GIS, RS, and GPS are now being
combined to provide a synergistic force multiplier for
efforts to monitor and improve human health. The
world is a very complicated place. The human body is
also very complicated. Individual response to risk
factors varies greatly. GIS technology is now robust
enough to begin sifting through myriad temporal and
spatial relationships and categorize and quantify risk
factors that were not obvious just a few decades ago.
As the technology continues to evolve we can expect
that the cost of protecting human health from
geologic or spatially related risk factors will diminish.
Gatto et al, 2009, Well-Water Consumption and Parkinson’s Disease
in Rural California: Environ Health Perspect 117:1912–1918
doi:10.1289/ehp.0900852
Vinceti et al, 2009, Risk of congenital anomalies around a municipal
solid waste incinerator: a GIS-based case-control study
International Journal of Health Geographics 8:8
doi:10.1186/1476-072X-8-8
Nielsen and Jensen, 2005, Environmental Epidemiology, in Selinus,
2005
doi: unavailable
Saxena et al, 2009,Application of spatial technology in malaria
research & control: some new insights
Indian J Med Res 130, August 2009, pp 125-132
doi: unavailable
Weinstein and Cook, 2007, Epidemiological Transitions and the
Changing Face of Medical Geology, Ambio, v. 36, no. 1
doi: unavailable
Figure 4 Exposure intensity map near a solid waste
incinerator, Italy
Figure 3 Map of mosquito larvae and water sources along Nile River.