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Source:
Max Lu, 785-532-3413, maxlu@k-state.edu
News release prepared by: Keener A. Tippin II, 785-532-6415
Tuesday,
August 23, 2005
K-STATE
PROFESSOR USES GEOSPATIAL TECHNIQUES TO STUDY HEART DISEASE MORTALITY
RATES IN SUNFLOWER STATE
MANHATTAN
-- This isn't your father's geography -- and probably not
even your grandfather's.
With
new statistical techniques now available, geographic researchers
can use sophisticated Geographic Information Systems and spatial
techniques to study geographic patterns of disease and other health
problems that may have some connection with the environment. These
techniques can be powerful in terms of isolating areas with particularly
high or low rates of health problems, such as heart disease, cancer,
etc. Based on that knowledge, a hypothesis can be developed to explain
these rates.
Using
these techniques, a Kansas State University professor is analyzing
the geographic patterns of heart disease in white males in the Sunflower
State. A county-level case study conducted by Max Lu, associate
professor of geography, looks at how mortality rates from the early
1980s to 2002 vary by county in the state and if access to health
care, rural area and socio-economic situations affect mortality
risks from heart disease, thereby shaping those geographic differentials.
As
a geographer, Lu is interested in spatial patterns. For this research
he used some recently developed spatial statistical techniques to
identify whether there are hot spots for mortality rates or counties
with particularly high rates of death from heart disease in Kansas.
"By
understanding the geographic pattern of heart disease mortality,
it is possible to guide public health efforts and tailor prevention
and treatments to communities at higher risks of heart disease,"
Lu said.
Lu
has generated maps that show the spatial patterns of heart disease
throughout the state, searching for a correlation between those
rates and aforementioned variables.
"In
the early 1980s, generally speaking, mortality rates in the state
were higher," Lu said. "But in the middle 1990s the situation
changed quite a bit. The mortality rates at the county level have
declined for almost every county. I wanted to look at not only how
things have changed but also how things varied from one part of
the state to another."
Lu's
research has identified five possible "hot spots" in the
state: Bourbon, Chase, Crawford, Kearney and Stafford counties.
"Several
counties in the southeastern part of the state have had a consistently
high mortality rates from heart disease in white males age 35 and
above," he said. "Counties along the Kansas-Nebraska border
seem to have relatively low rates."
Why
those rates vary has not been determined.
"That's
a more difficult question to answer," Lu said. "I compared
how things have changed at the county level from the early 1980s
to middle 1990s, and the northern counties experienced more of a
decline in mortality rates. Some counties declined more than others
but it was not equally shared by them all."
While
a lot of studies examine why mortality rates vary from one part
of the country and from one state to another, Lu wanted to look
at variables such as population density or how isolated a particular
county is, the percentage of people living in poverty and their
education level, and their access adequate to health care.
"I'm
doing additional research with more sophisticated spatial analysis
techniques," he said. "But at this point, only the poverty
rates seem to be a significant factor in explaining the geographic
differences in heart disease mortality rates."
While
Lu's research concluded that heart disease is a major cause of death
for males across the state, the county level of rates of mortality
from heart disease have generally been decreasing. According to
Lu, the declining trend is not shared equally by all counties and
has not been sufficient to eliminate the excessive rates in some
counties.
"Heart
disease death rates show considerable geographic differentials,
which are over and above racial and gender gaps in heart disease
mortality," Lu said. "Poverty appears to be the only significant
factor among these considered in explaining the spatial differences."
Lu
said much of the spatial differences remain unexplained and that
additional variables must still be examined.
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