Presentation on theme: "Dr. Christos Giannakopoulos Dr. Basil Psiloglou Meteorological and pollution factors affecting hospital admissions in Athens, Greece ENSEMBLES WP6.2 Meeting."— Presentation transcript:
Dr. Christos Giannakopoulos Dr. Basil Psiloglou Meteorological and pollution factors affecting hospital admissions in Athens, Greece ENSEMBLES WP6.2 Meeting Helsinki, 26-27 April, 2007
Available Data Daily Men and Women Hospital Admissions at LAIKO Hospital, for Acute Coronary Syndromes (max # of available beds = 20), for the Period: 1/1/2000 – 31/12/2004 Source: LAIKO Hospital, Athens, Greece Hourly Values of Meteorological Parameters (Air Temperature ( o C), Relative Humidity (%) and Pressure (hPa), Wind Speed (m/s) and Direction (deg.), Global (W/m 2 ) and Diffuse (W/m 2 ) radiaytion on Horizontal surface, for the Period: 1/1/2000 – 31/12/2004 Source: National Observatory of Athens Hourly Values of Air Pollution Parameters (O 3, NO, NO 2 (μg/m 3 ) for ATHINAS, PATISION, MAROUSI, LIKOVRISI Stations) for the Period: 1/1/2000 – 31/12/2004 Source: Hellenic Ministry for the Environment, Physical Planning and Public Works
Aims Analyse the effects of weather and climate on daily hospital admissions Explore the synergy between weather and air pollution in health
Definition of terms ALL PERIOD: Includes all months from JANUARY to DECEMBER COLD PERIOD: Includes the following months: JANUARY, FEBRUARY, MARCH, NOVEMBER, DECEMBER WARM PERIOD: Includes the following months: MAY, JUNE, JULY, AUGUST, SEPTEMBER Months APRIL and OCTOBER have been excluded.
Definition of terms ORIGINAL DATA: All the available hourly/daily data collected/calculated SMOOTHED DATA: NEW Daily Data created from the original Daily ones using the following Moving Average (1,2,1) method: ORIGINAL DATA: a 1, a 2, a 3, a 4, …………………… a k-1, a k, a k+1, …… a last ADMISSIONS: c 1, c 2, c 3, c 4, …………………… c k-1, c k, c k+1, ……… c last b 1 =(a 1 +2 x a 2 +a 3 )/4 and generaly b k =(a k-1 +2 x a k +a k+1 )/4 SMOOTHED: b 1, b 2, b 3, b 4, ……………… b k-1, b k, b k+1, …… b last ADMISSIONS: c 1, c 2, c 3, c 4, ……………… c k-1, c k, c k+1, …………… c last xx
Conclusions(1) Correlation between meteorological parameters is generally low (25%) but statistically significant at the 99% and the 99.9% confidence level especially for the total and the cold period of the year. SMOOTHED DATA: show a similar, slightly better behaviour.
Conclusions (2) When meteorological indexes are used, correlation between them and hospital admissions is low but significant at the 99% level for practically all of them. The cold period of the year seems to be the dominant factor in the association.
Thermal comfort models Calculate the thermal sensation of a large population exposed to a certain environment and are based on the heat balance equation for the human body Tmrt: Mean Radiant Temperature PMV: Predicted Mean Vote (P.O. Fanger, 1970) – PET: Physiological Effective Temperature (Hoeppe, 1999) SET: Standard Effective Temperature The RAYMAN code, (ver. 1.2, by F. Rutz, A. Mazarakis, and H. Mayer) have been used for the estimation of the above indexes
Rayman needs daily values of : Air Temp, Rel. Humidity, Wind Speed & Total Solar radiation on Horizontal Plane Fo the human beings the following values were used: MALE : Age=70, Weight=85Kg, Height=1.75(default) FEMALE : Age=70, Weight=75Kg, Height=1.75(default) A cloth index is used in the model which varies according to season:ANALOGA ME THN EPOXH (MHNA TOY ETOYS) H PARAMETROS clo PHRE TIS TIMES: clo=0.9 COLD PERIOD (months 1,2, 11, 12), clo=0.5 WARM PERIOD (months 5 to 9), clo=0.7 MIDDLE PERIOD (months 4 and 10), TOTALindex=[(#MALE x Index) + (#FEMALE x Index)] / (#MALE + #FEMALE) Rayman model runs
Conclusions (3) When pollutant concentrations are used to explain admissions, correlation is low (both for the same and previous day) at all measuring stations around Athens. Values are higher for the suburban station of Maroussi which has been often blamed for secondary pollutant formation, especially ozone.
Conclusions (4) Scatter plots indicate once more the low association pollutant levels with admissions but show a clear relation with met data: Ozone shows a positive correlation with temperature and with global horizontal radiation Ozone shows a negative correlation with relative humidity NO and NO2 are more short-lived and they dont show such an association.
Planned work Get admissions data from another major hospital to have a more complete dataset Complete statistical model construction and validation (half of the period providing the climate/hospital admissions relationship for the other period to predict) Use also daily mortality data for longer period and construct an equivalent model. Model construction will be based on calculation of excess deaths, ie deaths above those expected for that period. It is expected that the mortality/climate relationship will be higher than the one with the admissions Use ENSEMBLES multiple regional climate model output to estimate future no of admissions/deaths (with and without an acclimatization factor- eg. 3 decades needed for population to completely acclimatize to 1 o C change )