Asthma Weather Seattle Ams 2011

Analysis of three years correlations between weather variability and seasonal asthma episodes in Miami Dade, Florida

David QuesadaSchool of Science, Technology and Engineering Management,

St. Thomas University, Miami Gardens FL 33054

Climatic and environmental changes occurring since the middle of the Twentieth Century asll th ti ll ti l l i iti b ti th i d dwell as the aggravating pollution levels in megacities are exacerbating asthma episodes and

the number of hospitalizations due to this disease. Since 1999, in Miami Dade County thehospitalization rates were doubling the Healthy People 2010 objectives in every age group. Acomprehensive weather database including outdoor temperature (T), humidity (H),barometric pressure (P), wind direction (θw) and speed (vw) as well as the values ofp ( ) ( w) p ( w)maximum and minimum and the range of all these variables has been created. As a result, aseasonal pattern emerged, with a maximum appearing around the middle of December and aminimum around the middle of March every year for the three years of analysis.

Second Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.

Content • Why Asthma? Motivation of the study.• Previous results within continental USA and Miami Dade.• WeatherBug Mesonet and Asthma – Weather connection.

Mi i l Bi Ph i l d l• Minimal Bio-Physical model.• Conclusions.


Why to study Asthma? How far Bio-Meteorology may help with?

Asthma Statistics Worldwide

Number of people diagnosed: more than 150 MEurope: the # of cases has doubledUSA: the number of cases has increased morethan 60%India: between 15 and 20 MAfrica: between 11 and 18% populationNumber of deaths yearly: around 180,000

Miami Dade County , Florida

7.1% Middle and HS children were reported withasthmaThe number of hospitalizations due to asthmahas doubled.The number 1 cause of school absences and 35 %of parents missed work


Seasonal Variations in Asthma Hospital Admissions in the United Seasonal Variations in Asthma Hospital Admissions in the United StatesStates

Asthma admission by year16

12

14

0,00

0 20001999

6

8

10

zed

rate

per

10 1998

199719961995199419931992

2

4

6

Ann

ualiz 1991

19891988

01 2 3 4 5 6 7 8 9 10 11 12

Admission monthSource:Nationwide Inpatient Sample and US Census

• Asthma seasonal variations confirmedAichatou Hassane UNH; Robert Woodward • Asthma seasonal variations confirmed• Larger seasonal variation associated with a decrease in age.

Aichatou Hassane, UNH; Robert Woodward, PhD, UNH; Ross Gittell, PhD, UNH - May 27, 2004


2000 S

Seasonal Variations in Asthma Hospital Admissions in the United Seasonal Variations in Asthma Hospital Admissions in the United StatesStates

2000 Asthma Admission by US region

14

16

10

12

e pe

r 10,

000

Northeast

4

6

8

Ann

ualiz

ed ra

te NortheastMidwestSouth West

0

2

1 2 3 4 5 6 7 8 9 10 11 12

Admission month

Source:Nationw ide Inpatient Sample and US Census

Regional seasonal variation exists: • Midwest has the largest rate of Asthma - East North Central division: Illinois and Wisconsin• West region has the lowest rate of Asthma - Mountain division: Arizona and Colorado

180

Miami Dade Asthma Snapshot

160

165

170

175

180

pers

ons

145

150

155

160

ate

per 1

00,0

00

130

135

140

2001 2002 2003 2004 2005 2006 2007 2008

Ra

Areas of major incidenceAreas of major incidence


CreateCreate aa databasedatabase ofof weatherweather parametersparameters andand environmentalenvironmentaltriggerstriggers forfor asthmaasthma (( WeatherBugWeatherBug && WeatherBugWeatherBug Achieve)Achieve)

Feature Range (E li h)

Accuracy (E li h)

Range(M t i )

Accuracy(M t i )(English) (English) (Metric) (Metric)

Temperature -55F – 150F +/- 1F -45C – 60C +/- 0.5C

Relative Humidity 0 – 100% +/- 2% 0 – 100% +/- 2%

Wind Speed 0 – 125 mph +/- 2 mph 0 – 275 kph +/- 4 kph

Wind Direction 0 – 360 deg +/- 3 deg 0 – 360 deg +/- 3 deg

Barometric Pressure 28 – 32” Hg +/- 0.05”Hg 900 – 1100 mbar +/- 5 mbar

Rainfall Unlimited +/- 2% Unlimited +/- 2%

Light Intensity 0 – 100% N/A 0 – 100% N/A

Zip codes patients came fromWeatherBug Mesonet stations

NWS stations, MIA & Tamiami

Year White White Hispanic

Non White Hispanic

African American

2008 490 505 820 510

Y T t l T t l T t l % f

2009 350 256 650 525

2010 528 495 605 657

Year Total Patients

Total Respiratory

Total Asthma

% of asthma

2008 5172 2950 2222 43

2009 6981 4301 2680 382009 6981 4301 2680 38

2010 7813 4960 2853 37

Seasonal Variations of Asthma diagnosed cases by the Kendall Medical Group in Miami Dade, FL

450

500

350

400

ases

300

350

r of a

sthm

a ca

200

250

Num

be

100

150

15-J

an15

-Feb

15-M

ar15

-Apr

15-M

ay15

-Jun

15-J

ul15

-Aug

15-S

ep15

-Oct

15-N

ov15

-Dec

15-J

an15

-Feb

15-M

ar15

-Apr

15-M

a y15

-Jun

15-J

ul15

-Aug

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ep15

-Oct

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ov15

-Dec

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an15

-Feb

15-M

ar15

-Apr

15-M

a y15

-Jun

15-J

ul15

-Aug

15-S

ep15

-Oct

15-N

ov15

-Dec

Seasonal Variations of Asthma diagnosed cases in standard units Z = (N – Nave)/S

by the Kendall Medical Group in Miami Dade, FL

1

1.5

0.5

1

ve/S

t.Dev

)

0

-uni

ts (N

-N

av

-0.5

of c

ases

in z

-

-1.5

-1

Num

ber

-2

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Tmax 80

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Tmean=(Tmax+Tmin)/220

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1/1/2008 1/1/2009 1/1/2010

450

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301/1/2008 1/1/2009 1/1/2010

mean ( max min)

10

15dTmean/dt = T[i+1] - T[i]

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asth

ma

case

s

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ber o

f a

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ΔT/Tmean

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01/1/2008 1/1/2009 1/1/2010

01/1/2008 1/1/2009 1/1/2010


30 2

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30.6PmeanPmax

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1/1/2008 1/1/2009 1/1/2010

Pmin

1/1/2008 1/1/2009 1/1/2010 1/1/2008 1/1/2009 1/1/2010

450

500

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dP /dt

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asth

ma

case

s

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f a

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30… -0.4

-0.3

80

100Hmax

80

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20Hmin Hmean

30

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01/1/2008 1/1/2009 1/1/2010 1/1/2008 1/1/2009 1/1/2010

450

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dHmean/dt = H[i+1] - H[i]

300

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asth

ma

case

s

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ber o

f a

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30… -30

-20

Pearson Correlation between the number of cases and the given t f i bl (E l)

Tmax Tmin ΔT Tmean dT/dt ΔT/Tmean

# cases - 0.52 - 0.59 - 0.55 0.99 - 0.16 - 0.86

set of variables (Excel)

ΔP Pmean dP/dt ΔP/Pmean

# of cases 0 11 0 28 0 002 0 1# of cases - 0.11 0.28 - 0.002 0.1

ΔH Hmean dH/dt ΔH/Hmean

# of cases 0.08 - 0.25 - 0.1 - 0.76


Correlations between the number of cases and the given set of variables (IBM-SPSS-19)

Tmax Tmin ΔT Tmean dT/dt ΔT/TmeanTmax Tmin ΔT Tmean dT/dt ΔT/Tmean

Pearson (r) - 0.524 - 0.529 0.357 - 0.531 - 0.122 0.487

P - value 0.000 0.000 0.002 0.000 0.306 0.000

Kendall - τ - 0.325 - 0.301 0.159 - 0.311 - 0.122 0.264

P - value 0.000 0.000 0.048 0.000 0.132 0.002

Spearman - ρ - 0.485 - 0.463 0.224 - 0.475 - 0.148 0.375

P - value 0.000 0.000 0.059 0.000 0.215 0.001

ΔP Pmean dP/dt ΔP/Pmean ΔH Hmean dH/dt ΔH/Hmean

Pearson (r) 0.367 - 0.021 0.082 0.42 0.452 - 0.213 - 0.015 0.445

P - value 0.002 0.862 0.491 0.000 0.000 0.073 0.899 0.000

Kendall 0 269 0 008 0 045 0 291 0 282 0 052 0 006 0 264Kendall - τ 0.269 0.008 0.045 0.291 0.282 - 0.052 0.006 0.264

P - value 0.001 0.922 0.579 0.000 0.000 0.521 0.938 0.001

Spearman - ρ 0.388 0.001 0.063 0.415 0.402 -0.091 0.003 0.373


P - value 0.001 0.996 0.600 0.000 0.000 0.445 0.979 0.001

N = Constant + a (Tmax) + b (Tmin) + c (Tmean) + d (ΔT/Tmean) + e (ΔP) + f (ΔH) + g (ΔH/Hmean)

Model SummaryModel R R Square Adjusted R Square Std. Error of the Estimate1 .695a .483 .427 62.65654

ANOVAbANOVAModel Sum of Squares df Mean Square F Sig.

1 Regression234902.995 7 33557.571 8.548 .000a

Residual 251253.880 64 3925.842Residual 251253.880 64 3925.842Total 486156.875 71

Coefficientsa

Model Unstandardized Coefficients Standardized CoefficientsB Std Error Beta t SigB Std. Error Beta t Sig.

1 (Constant) 236.329 292.762 .807 .423VAR00003 -69.515 20.571 -5.727 -3.379 .001VAR00004 53.801 19.021 5.375 2.829 .006VAR00006 15 977 16 645 1 436 960 341VAR00006 15.977 16.645 1.436 .960 .341VAR00008 3026.508 1076.097 1.902 2.812 .007VAR00009 -431.218 480.090 -.114 -.898 .372VAR00013 14.140 3.409 1.016 4.148 .000VAR00016 -326 596 130 111 - 571 -2 510 015VAR00016 -326.596 130.111 -.571 -2.510 .015

a. Dependent Variable: VAR00001

Conclusions• African Americans and Non White Hispanics are more affected by asthmaAfrican Americans and Non White Hispanics are more affected by asthma.

• Zip codes from Miami Dade with the major incidence seem to be related withsocio-economic background rather than particular microclimatic conditions.

• Among weather variables, Tmean, ΔT/Tmean, Tmin, and ΔH/Hmean appear tocorrelate better with the number of asthma cases.

• The observed patterns seem to be originated in the thermoregulation response• The observed patterns seem to be originated in the thermoregulation responseto cold weather, rather than in allergic pathways.

• More statistical work is needed in order to establish an Asthma Index for Bio-Meteorological applicationsBio-Meteorological applications.

Acknowledgments• Oscar Hernandez M.D. and Elizabeth Fontora, Medical Group, Miami Dade, FLOscar Hernandez M.D. and Elizabeth Fontora, Medical Group, Miami Dade, FL • School of Science, St. Thomas University


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Asthma Weather Seattle Ams 2011