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      Effects of cold weather on mortality: results from 15 European cities within the PHEWE project.

      American Journal of Epidemiology
      Adolescent, Adult, Aged, Cardiovascular Diseases, mortality, Cause of Death, trends, Child, Child, Preschool, Cold Temperature, adverse effects, Europe, epidemiology, Humans, Infant, Infant, Newborn, Middle Aged, Respiratory Tract Diseases, Risk Factors, Urban Health, Urban Population, statistics & numerical data, Young Adult

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          Abstract

          Weather-related health effects have attracted renewed interest because of the observed and predicted climate change. The authors studied the short-term effects of cold weather on mortality in 15 European cities. The effects of minimum apparent temperature on cause- and age-specific daily mortality were assessed for the cold season (October-March) by using data from 1990-2000. For city-specific analysis, the authors used Poisson regression and distributed lag models, controlling for potential confounders. Meta-regression models summarized the results and explored heterogeneity. A 1 degrees C decrease in temperature was associated with a 1.35% (95% confidence interval (CI): 1.16, 1.53) increase in the daily number of total natural deaths and a 1.72% (95% CI: 1.44, 2.01), 3.30% (95% CI: 2.61, 3.99), and 1.25% (95% CI: 0.77, 1.73) increase in cardiovascular, respiratory, and cerebrovascular deaths, respectively. The increase was greater for the older age groups. The cold effect was found to be greater in warmer (southern) cities and persisted up to 23 days, with no evidence of mortality displacement. Cold-related mortality is an important public health problem across Europe. It should not be underestimated by public health authorities because of the recent focus on heat-wave episodes.

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          Most cited references35

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          Longitudinal data analysis using generalized linear models

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            A random-effects regression model for meta-analysis.

            Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.
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              Confounding and effect modification in the short-term effects of ambient particles on total mortality: results from 29 European cities within the APHEA2 project.

              We present the results of the Air Pollution and Health: A European Approach 2 (APHEA2) project on short-term effects of ambient particles on mortality with emphasis on effect modification. We used daily measurements for particulate matter less than 10 microm in aerodynamic diameter (PM10) and/or black smoke from 29 European cities. We considered confounding from other pollutants as well as meteorologic and chronologic variables. We investigated several variables describing the cities' pollution, climate, population, and geography as potential effect modifiers. For the individual city analysis, generalized additive models extending Poisson regression, using a smoother to control for seasonal patterns, were applied. To provide quantitative summaries of the results and explain remaining heterogeneity, we applied second-stage regression models. The estimated increase in the daily number of deaths for all ages for a 10 microg/m3 increase in daily PM10 or black smoke concentrations was 0.6% [95% confidence interval (CI) = 0.4-0.8%], whereas for the elderly it was slightly higher. We found important effect modification for several of the variables studied. Thus, in a city with low average NO2, the estimated increase in daily mortality for an increase of 10 microg/m3 in PM10 was 0.19 (95% CI = 0.00-0.41), whereas in a city with high average NO2 it was 0.80% (95% CI = 0.67-0.93%); in a relatively cold climate the corresponding effect was 0.29% (95% CI = 0.16-0.42), whereas in a warm climate it was 0.82% (95% CI = 0.69-0.96); in a city with low standardized mortality rate it was 0.80% (95% CI = 0.65-0.95%), and in one with a high rate it was 0.43% (95% CI = 0.24-0.62). Our results confirm those previously reported on the effects of ambient particles on mortality. Furthermore, they show that the heterogeneity found in the effect parameters among cities reflects real effect modification, which is explained by specific city characteristics.
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