Relationships between sudden weather changes in summer and mortality in the Czech Republic, 1986-2005
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
- MeSH
- časové faktory MeSH
- lidé MeSH
- metoda Monte Carlo MeSH
- míra přežití MeSH
- mortalita * MeSH
- nadmořská výška MeSH
- nízká teplota MeSH
- počasí * MeSH
- roční období MeSH
- tlak vzduchu MeSH
- zeměpis MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Česká republika epidemiologie MeSH
The study examines the relationship between sudden changes in weather conditions in summer, represented by (1) sudden air temperature changes, (2) sudden atmospheric pressure changes, and (3) passages of strong atmospheric fronts; and variations in daily mortality in the population of the Czech Republic. The events are selected from data covering 1986-2005 and compared with the database of daily excess all-cause mortality for the whole population and persons aged 70 years and above. Relative deviations of mortality, i.e., ratios of the excess mortality to the expected number of deaths, were averaged over the selected events for days D-2 (2 days before a change) up to D+7 (7 days after), and their statistical significance was tested by means of the Monte Carlo method. We find that the periods around weather changes are associated with pronounced patterns in mortality: a significant increase in mortality is found after large temperature increases and on days of large pressure drops; a decrease in mortality (partly due to a harvesting effect) occurs after large temperature drops, pressure increases, and passages of strong cold fronts. The relationship to variations in excess mortality is better expressed for sudden air temperature/pressure changes than for passages of atmospheric fronts. The mortality effects are usually more pronounced in the age group 70 years and above. The impacts associated with large negative changes of pressure are statistically independent of the effects of temperature; the corresponding dummy variable is found to be a significant predictor in the ARIMA model for relative deviations of mortality. This suggests that sudden weather changes should be tested also in time series models for predicting excess mortality as they may enhance their performance.
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