Analysis of three years correlations between weather variability and            seasonal asthma episodes in Miami Dade, Fl...
Content     • Why Asthma? Motivation of the study.     • Previous results within continental USA and Miami Dade.     • Wea...
Why to study Asthma? How far Bio-Meteorology may help with?    Asthma Statistics WorldwideNumber of people diagnosed: more...
Seasonal Variations in Asthma Hospital Admissions in the United                                      States               ...
Seasonal Variations in Asthma Hospital Admissions in the United                                                           ...
Miami Dade Asthma Snapshot                                                                            180                 ...
Create a database of weather parameters and environmentaltriggers for asthma ( WeatherBug & WeatherBug Achieve)           ...
Zip codes patients came from                            WeatherBug Mesonet stations                            NWS station...
Number of asthma ca                                              ases          100                150                     ...
Seasonal Variations of Asthma diagnosed cases                                                                in standard u...
100                                    90                         90                              Tmax                    ...
30                                                   0.6       ΔT=Tmax-Tmin                                         ΔT/Tme...
30.6                                                 30.6              30.4                  Pmax                         ...
Hmax                            100             100                                                                     90...
Pearson Correlation between the number of cases and the given                                   set of variables (Excel)  ...
Correlations between the number of cases and the given set of variables                                      (IBM-SPSS-19)...
N = Constant + a (Tmax) + b (Tmin) + c (Tmean) + d (ΔT/Tmean) + e (ΔP) + f (ΔH) + g (ΔH/Hmean)         Model Summary      ...
Conclusions    • African Americans and Non White Hispanics are more affected by asthma                                    ...
Asthma Weather Seattle Ams 2011
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Asthma Weather Seattle Ams 2011

  1. 1. Analysis of three years correlations between weather variability and seasonal asthma episodes in Miami Dade, Florida David Quesada School 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 as well as th aggravating pollution l ll the ti ll ti levels i megacities are exacerbating asthma episodes and l in iti b ti th i d d the number of hospitalizations due to this disease. Since 1999, in Miami Dade County the hospitalization rates were doubling the Healthy People 2010 objectives in every age group. A comprehensive weather database including outdoor temperature (T), humidity (H), barometric pressure (P), wind direction (θw) and speed (vw) as well as the values of p ( ) ( p ( maximum and minimum and the range of all these variables has been created. As a result, a seasonal pattern emerged, with a maximum appearing around the middle of December and a minimum 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.
  2. 2. Content • Why Asthma? Motivation of the study. • Previous results within continental USA and Miami Dade. • WeatherBug Mesonet and Asthma – Weather connection. • Mi i l Bio-Physical model. Minimal Bi Ph i l d l • Conclusions.Second Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  3. 3. Why to study Asthma? How far Bio-Meteorology may help with? Asthma Statistics WorldwideNumber 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 180,000Miami Dade County , Florida7.1% 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 workSecond Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  4. 4. Seasonal Variations in Asthma Hospital Admissions in the United States Asthma admission by year 16 14 12 0,000 2000 1999 zed rate per 10 1998 10 1997 1996 1995 8 1994 1993 1992 Annualiz 6 1991 1989 1988 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 Admission month Source:Nationwide Inpatient Sample and US Census Aichatou Hassane UNH; Robert Woodward, Hassane, Woodward • Asthma seasonal variations confirmed PhD, UNH; Ross Gittell, PhD, UNH - May 27, • Larger seasonal variation associated 2004 with a decrease in age.Second Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  5. 5. Seasonal Variations in Asthma Hospital Admissions in the United States 2000 Asthma Admission by US region S 16 14 12 Annualized rate per 10,000 10 Northeast e 8 Midwest South 6 West 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 Admission monthSource: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
  6. 6. Miami Dade Asthma Snapshot 180 175 170 Ra per 100,000 persons 165 160 155 150 145 ate 140 135 130 2001 2002 2003 2004 2005 2006 2007 2008 Areas of major incidenceSecond Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  7. 7. Create a database of weather parameters and environmentaltriggers for asthma ( WeatherBug & WeatherBug Achieve) Feature Range Accuracy Range Accuracy (English) (E li h) (English) (E li h) (Metric) (M t i ) (Metric) (M t i ) 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
  8. 8. Zip codes patients came from WeatherBug Mesonet stations NWS stations, MIA & Tamiami Year White White Non White African Hispanic Hispanic American 2008 490 505 820 510 2009 350 256 650 525 2010 528 495 605 657YearY Total T t l Total T t l Total T t l % of f Patients Respiratory Asthma asthma2008 5172 2950 2222 432009 6981 4301 2680 382010 7813 4960 2853 37
  9. 9. Number of asthma ca ases 100 150 200 250 300 350 400 45015-Jan 50015-Feb15-Mar15-Apr15-May15-Jun 15-Jul15-Aug15-Sep15-Oct15-Nov15-Dec15-Jan15-Feb15-Mar15-Apr15-May15-Jun 15-Jul15-Aug15-Sep15-Oct15-Nov15-Dec15-Jan15-Feb15-Mar15-Apr15-May15-Jun Kendall Medical Group in Miami Dade, FL 15-Jul15-Aug15-Sep15-Oct Seasonal Variations of Asthma diagnosed cases by the15-Nov15-Dec
  10. 10. Seasonal Variations of Asthma diagnosed cases in standard units Z = (N – Nave)/S by the Kendall Medical Group in Miami Dade, FL 1.5 1 ve/St.Dev) 0.5Number of cases in z - units (N - Nav 0 -0.5 -1 -1.5 -2
  11. 11. 100 90 90 Tmax 80 80 70 70 60 60 50 50 40 Tmin 40 30 Tmean=(Tmax+Tmin)/2 ( ) 30 20 1/1/2008 1/1/2009 1/1/2010 500 15 1/1/2008 1/1/2009 1/1/2010 450 dTmean/dt = T[i+1] - T[i] 10Number of asthma cases 400 5 350 300 0 a 250 -5 200 -10 150 100 -15 15… 28… 15… 31… 15… 31… 15… … 15… 28… 15… 31… 15… 31… 15… … 15… 28… 15… 31… 15… 31… 15… 30…
  12. 12. 30 0.6 ΔT=Tmax-Tmin ΔT/Tmean25 0.520 0.415 0.310 0.2 5 0.1 0 01/1/2008 1/1/2009 1/1/2010 1/1/2008 1/1/2009 1/1/2010 Second Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  13. 13. 30.6 30.6 30.4 Pmax Pmean 30.4 30.2 30 2 30.2 30 29.8 30 29.6 29.8 29 8 29.4 Pmin 29.6 29.2 29.4 29 1/1/2008 1/1/2009 1/1/2010 1/1/2008 1/1/2009 1/1/2010 500 0.5 450 0.4 dPmean/dtNumber of asthma cases 400 0.3 0.2 350 0.1 300 a 0 250 -0.1 200 -0.2 150 -0.3 100 -0.4 15… 28… 15… 31… 15… 31… 15… … 15… 28… 15… 31… 15… 31… 15… … 15… 28… 15… 31… 15… 31… 15… 30…
  14. 14. Hmax 100 100 90 80 80 70 60 60 40 50 Hmin Hmean 20 40 30 0 1/1/2008 1/1/2009 1/1/2010 1/1/2008 1/1/2009 1/1/2010 500 40 450 dHmean/dt = H[i+1] - H[i] 30Number of asthma cases 400 20 350 10 300 a 0 250 -10 200 150 20 -20 100 -30 15… 28… 15… 31… 15… 31… 15… … 15… 28… 15… 31… 15… 31… 15… … 15… 28… 15… 31… 15… 31… 15… 30…
  15. 15. Pearson Correlation between the number of cases and the given set of variables (Excel) 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 ΔP Pmean dP/dt ΔP/Pmean # of cases - 0 11 0.11 0.28 0 28 - 0 002 0.002 0.1 01 ΔH Hmean dH/dt ΔH/Hmean # of cases 0.08 - 0.25 - 0.1 - 0.76Second Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  16. 16. Correlations between the number of cases and the given set of variables (IBM-SPSS-19) Tmax 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 - τ 0.269 0 269 0.008 0 008 0.045 0 045 0.291 0 291 0.282 0 282 - 0 052 0.052 0.006 0 006 0.264 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 0.001Second Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
  17. 17. N = Constant + a (Tmax) + b (Tmin) + c (Tmean) + d (ΔT/Tmean) + e (ΔP) + f (ΔH) + g (ΔH/Hmean) Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .695a .483 .427 62.65654 ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression234902.995 7 33557.571 8.548 .000a Residual 251253.880 64 3925.842 Total 486156.875 71CoefficientsaModel Unstandardized Coefficients Standardized Coefficients B Std. Std Error Beta t Sig. Sig1 (Constant) 236.329 292.762 .807 .423 VAR00003 -69.515 20.571 -5.727 -3.379 .001 VAR00004 53.801 19.021 5.375 2.829 .006 VAR00006 15.977 15 977 16.645 16 645 1.436 1 436 .960 960 .341 341 VAR00008 3026.508 1076.097 1.902 2.812 .007 VAR00009 -431.218 480.090 -.114 -.898 .372 VAR00013 14.140 3.409 1.016 4.148 .000 VAR00016 -326 596 -326.596 130.111 130 111 -.571 - 571 -2.510 -2 510 .015 015a. Dependent Variable: VAR00001
  18. 18. Conclusions • African Americans and Non White Hispanics are more affected by asthma asthma. • Zip codes from Miami Dade with the major incidence seem to be related with socio-economic background rather than particular microclimatic conditions. • Among weather variables, Tmean, ΔT/Tmean, Tmin, and ΔH/Hmean appear to correlate better with the number of asthma cases. • The observed patterns seem to be originated in the thermoregulation response to cold weather, rather than in allergic pathways. • More statistical work is needed in order to establish an Asthma Index for Bio-Meteorological applications. applications Acknowledgments • Oscar Hernandez M.D. and Elizabeth Fontora, Medical Group, Miami Dade, FL • School of Science, St. Thomas UniversitySecond Symposium on Environment and Health, AMS 91st Annual Meeting, 23 – 27 January 2011 in Seattle, WA.
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