Atlanta Poster Ams 2010
1
About the possible influence of the weather on asthma episodes: St. Thomas University and surrounding communities. D. Quesada(1), I. Perez(1), A. Audate(1), E. Funtora(2), and O. Hernandez(2) (1) School of Science, Technology and Engineering Management, St. Thomas University, 16401 NW 37 Ave, Miami Gardens FL 33054; (2) Medical Group, 12510 North Kendall Drive, Kendall FL 33186 Asthma is estimated to affect about 5 % of adults and about 10 % of children worldwide. Both, asthma prevalence and mortality have increased considerably over the last ten years and it is foreseen to be one of the most important respiratory and occupational lung diseases in the coming decade. Additionally, climatic and environmental changes occurring since the middle of the Twentieth Century as well as the aggravating pollution levels in megacities seems to exacerbate asthma episodes and number of hospitalizations. According to the latest estimates, in the U.S. the prevalence of asthma in children under 18 years ranges from 4.5 to 7 %, while in children 2 – 5 years is around 5.6 %. In Miami Dade County, where St. Thomas University is located, in 1999 the hospitalization rates were double the Healthy People 2010 objectives in every age group. Motivated by this existing situation, it was decided at St. Thomas School of Science to initiate a gathering of weather and health information, and also, look for possible correlations between these data. It is noteworthy that St. Thomas University is surrounded by many communities with a Hispanic and African – American composition predominantly. These minority groups are the one with the highest rates of asthma prevalence and severity. Then, in partnership with AWS Convergence Technologies (WeatherBug) a weather tracking station operating on campus 24/7 year round has recorded weather data for six years. A careful statistical analysis of these data is included in this presentation. Based on health information obtained from regional hospitals located in different areas of Miami Dade, some seasonal patterns of asthma as well as some possible indicators are discussed for further correlation analysis. Our results are compared with others obtained from different States within continental U.S. as well as from oversea. Introduction Asthma Statistics Worldwide: A brief overview # of people diagnosed: more than 150 M Europe: the # of cases has doubled USA: the # of cases has increased more than 60% India: between 15 and 20 M Africa: between 11 and 18% population # of deaths yearly: around 180,000 Miami Dade County – 7.1% Middle and HS children were reported with asthma The # of hospitalizations due to asthma has doubled. The # 1 cause of school absences and 35 % of parents missed work Seasonal variations in asthma reported cases: Preliminary results 0 50 100 150 200 250 300 350 400 450 Cases of Asthma Number of patients treated with asthma, 2008 -100 -50 0 50 100 150 200 Difference D = N - Nave Difference D = N - Nave Surveillance of Asthma in Florida 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 Num. of Hospitalizations Asthma Hospitalizations treated at Florida hospitals, 2004 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Num. of Hospitalizations 0 2000 4000 6000 8000 10000 Num. of Hospitalizations 0 5000 10000 15000 20000 25000 Num. of Hospitalizations Hospitalizations From or With Asthma 2006 Age distribution (only for asthma) 0 - 5 years old 530 6 - 11 years old 390 12 - 17 years old 480 18 - 23 years old 350 24 - 29 years old 280 30 - 35 years old 270 36 - 41 years old 215 42 - 47 years old 210 48 - 53 years old 225 above 54 years old Ethnicity (only for asthma) White 490 White Hispanic 505 Non White Hispanic 820 African American 510 Chinesse Native Indians Others 625 Average, Nave 230.46 Median 190.5 St. Deviation 77.11 Time Series of Weather Parameters for Miami Dade County Metropolitan Area IESARA – Intelligent Expert System for Asthma Risk Analysis An integrated system for risk analysis which includes: 1. Geospatial analysis of weather and health information (GIS). 2. Modeling of the urban weather in surrounding communities. 3. Mathematical modeling of the weather – asthma connection. 3. Application of techniques of fuzzy logics for decision making. 4. Determination of risk indexes. Mathematical Modeling of the Asthma – Weather Connection Macroscopic Mechanical Description of Breathing Mathematical expressions appealing to basic laws of aerodynamics and that describe the basics of breathing and disorders within the lung functioning. Mesoscopic description of the immune response during an asthma episode A system of differential equations describes the population dynamics of each one of the cells involved in an asthma episode. In asthmatic individuals, antigen presentation is thought to results in the polarization of T-cells towards a T h2 patterns whereas T cells from non atopic, non-asthmatic individuals show the opposing T h1 (interferon-γ and I L2 ) pattern of cytokine secretion. A very complicated Network of cells (IL4, IL3, IL5, IL13- Cytokines, IgE – Immunoglobuline) Interacting and Competing. Microscopic genetic of asthma - Bio-informatics of Asthma The multigenic nature of asthma has greatly hampered efforts to identify the specific genes involved. Genetic heterogeneity across populations, variability in disease expression, phenocopies and uncontrolled environmental influences confound the analysis of asthma and other complex genetic disorders. 0 50 100 150 200 250 300 350 400 15-Jan 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep 15-Oct Cases of Asthma Number of patients treated with asthma, 2009 -80 -60 -40 -20 0 20 40 60 80 100 120 Difference D = N - Nave Difference D = N - Nave Age distribution (only for asthma) 0 - 5 years old 365 6 - 11 years old 290 12 - 17 years old 190 18 - 23 years old 205 24 - 29 years old 180 30 - 35 years old 150 36 - 41 years old 160 42 - 47 years old 190 48 - 53 years old 215 above 54 years old Ethnicity (only for asthma) White 350 White Hispanic 256 Non White Hispanic 650 African American 525 Chinesse Native Indians Others 169 Average 271.95 Median 260 St. Deviation 41.25846006 30 40 50 60 70 80 90 100 Temperature ( F) Tmax and Tmin versus Time, 2009 30 40 50 60 70 80 90 100 Temperature ( F ) Tmax and Tmin versus Time, 2008 Average 82.26721 69.25525 Median 82.575 71.17 St. Deviation 6.176073 7.718884 Average 82.85869 69.29644 Median 83.75 71.65 St. Deviation 7.0404 8.199648 Conclusions 1. There is a clear seasonal pattern in asthma reported cases. 2. The major incidence seems to occur in highly populated cities in Florida. 3. There are direct and indirect forms on how the weather seems to affect people with asthma. 4. A mathematical model of the weather – breathing mechanism is needed to fully understand the obtained results. Acknowledgments Kendall Medical Group for sharing medical data about Asthma. Science Fellow Program at St. Thomas University .
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Transcript of Atlanta Poster Ams 2010
About the possible influence of the weather on asthma episodes:
St. Thomas University and surrounding communities.D. Quesada(1), I. Perez(1), A. Audate(1), E. Funtora(2), and O. Hernandez(2)
(1) School of Science, Technology and Engineering Management, St. Thomas University, 16401 NW
37 Ave, Miami Gardens FL 33054; (2) Medical Group, 12510 North Kendall Drive, Kendall FL 33186
Asthma is estimated to affect about 5 % of adults and
about 10 % of children worldwide. Both, asthma
prevalence and mortality have increased considerably
over the last ten years and it is foreseen to be one of
the most important respiratory and occupational lung
diseases in the coming decade. Additionally, climatic
and environmental changes occurring since the middle
of the Twentieth Century as well as the aggravating
pollution levels in megacities seems to exacerbate
asthma episodes and number of hospitalizations.
According to the latest estimates, in the U.S. the
prevalence of asthma in children under 18 years
ranges from 4.5 to 7 %, while in children 2 – 5 years is
around 5.6 %. In Miami Dade County, where St. Thomas
University is located, in 1999 the hospitalization rates
were double the Healthy People 2010 objectives in
every age group. Motivated by this existing situation, it
was decided at St. Thomas School of Science to
initiate a gathering of weather and health information,
and also, look for possible correlations between these
data. It is noteworthy that St. Thomas University is
surrounded by many communities with a Hispanic and
African – American composition predominantly. These
minority groups are the one with the highest rates of
asthma prevalence and severity. Then, in partnership
with AWS Convergence Technologies (WeatherBug) a
weather tracking station operating on campus 24/7
year round has recorded weather data for six years. A
careful statistical analysis of these data is included in
this presentation. Based on health information
obtained from regional hospitals located in different
areas of Miami Dade, some seasonal patterns of
asthma as well as some possible indicators are
discussed for further correlation analysis. Our results
are compared with others obtained from different
States within continental U.S. as well as from oversea.
Introduction
Asthma Statistics Worldwide:
A brief overview
# of people diagnosed: more than 150 M
Europe: the # of cases has doubled
USA: the # of cases has increased more than 60%
India: between 15 and 20 M
Africa: between 11 and 18% population
# of deaths yearly: around 180,000
Miami Dade County – 7.1% Middle and HS children
were reported with asthma
The # of hospitalizations due to asthma has doubled.
The # 1 cause of school absences and 35 % of
parents missed work
Seasonal variations in asthma
reported cases: Preliminary results
0
50
100
150
200
250
300
350
400
450
Ca
se
s o
f A
sth
ma
Number of patients treated with asthma, 2008
-100
-50
0
50
100
150
200
Dif
fere
nc
e D
= N
-N
ave
Difference D = N - Nave
Surveillance of Asthma in Florida
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Nu
m. o
f H
os
pit
aliza
tio
ns
Asthma Hospitalizations treated at Florida hospitals, 2004
0100020003000400050006000700080009000
10000
Nu
m. o
f H
os
pit
ali
za
tio
ns
0
2000
4000
6000
8000
10000
Nu
m. o
f H
os
pit
ali
za
tio
ns
0
5000
10000
15000
20000
25000
Nu
m. o
f H
osp
itali
zati
on
s
Hospitalizations From or With Asthma 2006
Age distribution (only for asthma)
0 - 5 years old 530
6 - 11 years old 390
12 - 17 years old 480
18 - 23 years old 350
24 - 29 years old 280
30 - 35 years old 270
36 - 41 years old 215
42 - 47 years old 210
48 - 53 years old 225
above 54 years old
Ethnicity (only for asthma)
White 490
White Hispanic 505
Non White Hispanic 820
African American 510
Chinesse
Native Indians
Others 625
Average, Nave 230.46
Median 190.5
St. Deviation 77.11
Time Series of Weather Parameters for
Miami Dade County Metropolitan AreaIESARA – Intelligent Expert
System for Asthma Risk Analysis
An integrated system for risk analysis which includes:
1. Geospatial analysis of weather and health
information (GIS).
2. Modeling of the urban weather in surrounding
communities.
3. Mathematical modeling of the weather – asthma
connection.
3. Application of techniques of fuzzy logics for decision
making.
4. Determination of risk indexes.
Mathematical Modeling of the Asthma –
Weather Connection
Macroscopic Mechanical
Description of Breathing
Mathematical expressions
appealing to basic laws of
aerodynamics and that
describe the basics of
breathing and disorders
within the lung functioning.
Mesoscopic description of
the immune response
during an asthma episode
A system of differential
equations describes the
population dynamics of each
one of the cells involved in an
asthma episode.
In asthmatic individuals, antigen
presentation is thought to results in
the polarization of T-cells towards a
Th2 patterns whereas T cells from
non atopic, non-asthmatic
individuals show the opposing Th1
(interferon-γ and IL2) pattern of
cytokine secretion.
A very complicated Network of cells
(IL4, IL3, IL5, IL13- Cytokines, IgE –
Immunoglobuline) Interacting and
Competing.
Microscopic genetic of
asthma - Bio-informatics
of Asthma
The multigenic nature of asthma has greatly
hampered efforts to identify the specific
genes involved. Genetic heterogeneity
across populations, variability in disease
expression, phenocopies and uncontrolled
environmental influences confound the
analysis of asthma and other complex
genetic disorders.
0
50
100
150
200
250
300
350
400
15-Jan 15-Feb 15-Mar 15-Apr 15-May 15-Jun 15-Jul 15-Aug 15-Sep 15-Oct
Ca
se
s o
f A
sth
ma
Number of patients treated with asthma, 2009
-80
-60
-40
-20
0
20
40
60
80
100
120
Dif
fere
nc
e D
= N
-N
ave
Difference D = N - Nave
Age distribution (only for asthma)
0 - 5 years old 365
6 - 11 years old 290
12 - 17 years old 190
18 - 23 years old 205
24 - 29 years old 180
30 - 35 years old 150
36 - 41 years old 160
42 - 47 years old 190
48 - 53 years old 215
above 54 years old
Ethnicity (only for asthma)
White 350
White Hispanic 256
Non White Hispanic 650
African American 525
Chinesse
Native Indians
Others 169
Average 271.95
Median 260
St. Deviation 41.25846006
30
40
50
60
70
80
90
100
Te
mp
era
ture
( F
)
Tmax and Tmin versus Time, 2009
30
40
50
60
70
80
90
100
Te
mp
era
ture
( F
)
Tmax and Tmin versus Time, 2008
Average 82.26721 69.25525
Median 82.575 71.17
St. Deviation 6.176073 7.718884
Average 82.85869 69.29644
Median 83.75 71.65
St. Deviation 7.0404 8.199648
Conclusions
1. There is a clear seasonal pattern in
asthma reported cases.
2. The major incidence seems to occur in
highly populated cities in Florida.
3. There are direct and indirect forms on
how the weather seems to affect people
with asthma.
4. A mathematical model of the weather –
breathing mechanism is needed to fully
understand the obtained results.
Acknowledgments
Kendall Medical Group for sharing medical
data about Asthma.
Science Fellow Program at St. Thomas
University .
