2.5
Analysis of three years correlations between weather variability and seasonal asthma episodes in Miami Dade, Florida

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Monday, 24 January 2011: 5:00 PM
Analysis of three years correlations between weather variability and seasonal asthma episodes in Miami Dade, Florida
4C-2 (Washington State Convention Center)
David Quesada, St. Thomas University, Miami Gardens, FL

Climatic and environmental changes occurring since the middle of the Twentieth Century as well 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 the hospitalization rates were doubling the Healthy People 2010 objectives in every age group. Motivated by this situation, the Weather Laboratory at School of Science, Technology and Engineering Management in St. Thomas University started gathering weather and health information. In partnership with AWS Convergence Technologies (WeatherBug) a weather tracking station has been operating in campus 24/7 year round for six years. As a result, a comprehensive weather database including outdoor temperature (T), humidity (H), barometric pressure (P), wind direction (w) and speed (vw) as well as the values of maximum and minimum and the range of all these variables has been created. Despite of the lack of detailed health (asthma) information time-series from large hospitals a sample for the present study was obtained from one of the medical groups operating in Miami Dade. The statistical validation of the recorded health data (total number of cases of asthma visits every fifteen days) was verified and plotted in standard deviations (z-variable) units. 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. Despite of the differences in temporal resolution between the weather and health time series, correlations and anti-correlations appear clearly. Weather variables were averaged over periods of 15 days in order to keep the consistency with the health (asthma) time series. Even though, the temperature range T = Tmax Tmin as well as Tmax appeared as the best predictors in a preliminary analysis, the rate of change (f(t+1) f(t)) over a day of both correlates even better, showing areas of strong variations during the months where the increase in the number of cases were observed. It is worth to notice that seasonal patterns of asthma were observed over many places in United States, where the position of the peak depends on the geographical area of analysis (climatic zone). Even though pollens are well recognized as environmental triggers of asthma, they rather seem to affect the mean values of the number of cases than the seasonal pattern of the disease. Furthermore, a minimal biophysical model that includes the gas exchange in lungs and the dynamics of respiration is discussed in order to understand the above results.