Sudden weather changes have long been thought to be associated with negative impacts on human health, but relatively few studies have attempted to quantify these relationships. We use large 6-h changes in atmospheric pressure as a proxy for sudden weather changes and evaluate their association with hospital admissions for cardiovascular diseases (CVD). Winter and summer seasons and positive and negative pressure changes are analysed separately, using data for the city of Prague (population 1.2 million) over a 16-year period (1994-2009). We found that sudden pressure drops in winter are associated with significant rise in hospital admissions. Increased CVD morbidity was observed neither for pressure drops in summer nor pressure increases in any season. Analysis of synoptic weather maps shows that large pressure drops in winter are associated with strong zonal flow and rapidly moving low-pressure systems with centres over northern Europe and atmospheric fronts affecting western and central Europe. Analysis of links between passages of strong atmospheric fronts and hospital admissions, however, shows that the links disappear if weather changes are characterised by frontal passages. Sudden pressure drops in winter are associated also with significant excess CVD mortality. As climate models project strengthening of zonal circulation in winter and increased frequency of windstorms, the negative effects of such weather phenomena and their possible changes in a warmer climate of the twenty-first century need to be better understood, particularly as their importance in inducing excess morbidity and mortality in winter may increase compared to cold spells.

• MeSH
• Child MeSH
• Adult MeSH
• Hospitalization statistics & numerical data   MeSH
• Cardiovascular Diseases epidemiology   MeSH
• Infant MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Infant, Newborn MeSH
• Weather MeSH
• Child, Preschool MeSH
• Seasons MeSH
• Aged MeSH
• Air Pressure * MeSH
• Cities epidemiology   MeSH
• Check Tag
• Child MeSH
• Adult MeSH
• Infant MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Infant, Newborn MeSH
• Child, Preschool MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Czech Republic epidemiology   MeSH
• Cities epidemiology   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.

• MeSH
• Time Factors MeSH
• Humans MeSH
• Monte Carlo Method MeSH
• Survival Rate MeSH
• Mortality * MeSH
• Altitude MeSH
• Cold Temperature MeSH
• Weather * MeSH
• Seasons MeSH
• Air Pressure MeSH
• Geography MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic epidemiology   MeSH