Literature DB >> 23417344

High-mortality days during the winter season: comparing meteorological conditions across 5 US cities.

Michael J Allen1, Scott C Sheridan.   

Abstract

While the relationship between weather and human health has been studied from various perspectives, this study examines an alternative method of analysis by examining weather conditions on specific high-mortality days during the winter season. These high-mortality days, by definition, represent days with dramatic increases in mortality and the days with the highest mortality. By focusing solely on high-mortality days, this research examines the relationship between weather variables and mortality through a synoptic climatology, environment-to circulation approach. The atmospheric conditions during high-mortality days were compared to the days prior and the days not classified as high-mortality days. Similar patterns emerged across all five locations despite the spatial and temporal variability. Southern locations had a stronger relationship with temperature changes while northern locations showed a greater relationship to atmospheric pressure. Overall, all high-mortality days were associated with warmer temperatures, decreased pressure, and a greater likelihood of precipitation when compared to the previous subset of days. While the atmospheric conditions were consistent across all locations, the importance of the lag effect should not be overlooked as a contributing factor to mortality during the winter season. Through a variety of diverse, methodological approaches, future studies may build upon these results and explore in more detail the complex relationship between weather situations and the impact of short-term changes in weather and health outcomes.

Entities:  

Mesh:

Year:  2013        PMID: 23417344     DOI: 10.1007/s00484-013-0640-4

Source DB:  PubMed          Journal:  Int J Biometeorol        ISSN: 0020-7128            Impact factor:   3.787


  28 in total

1.  Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

Authors:  Antonio Gasparrini
Journal:  J Stat Softw       Date:  2011-07       Impact factor: 6.440

2.  Relationships between sudden weather changes in summer and mortality in the Czech Republic, 1986-2005.

Authors:  Eva Plavcová; Jan Kyselý
Journal:  Int J Biometeorol       Date:  2010-02-19       Impact factor: 3.787

3.  Models for the relationship between ambient temperature and daily mortality.

Authors:  Ben Armstrong
Journal:  Epidemiology       Date:  2006-11       Impact factor: 4.822

4.  Who is sensitive to extremes of temperature?: A case-only analysis.

Authors:  Joel Schwartz
Journal:  Epidemiology       Date:  2005-01       Impact factor: 4.822

5.  Temperature, temperature extremes, and mortality: a study of acclimatisation and effect modification in 50 US cities.

Authors:  M Medina-Ramón; J Schwartz
Journal:  Occup Environ Med       Date:  2007-06-28       Impact factor: 4.402

6.  Mortality due to influenza in the United States--an annualized regression approach using multiple-cause mortality data.

Authors:  Jonathan Dushoff; Joshua B Plotkin; Cecile Viboud; David J D Earn; Lone Simonsen
Journal:  Am J Epidemiol       Date:  2005-11-30       Impact factor: 4.897

7.  Distributed lag non-linear models.

Authors:  A Gasparrini; B Armstrong; M G Kenward
Journal:  Stat Med       Date:  2010-09-20       Impact factor: 2.373

8.  Winter mortality and its causes.

Authors:  W R Keatinge
Journal:  Int J Circumpolar Health       Date:  2002-11       Impact factor: 1.228

9.  Modifiers of the temperature and mortality association in seven US cities.

Authors:  Marie S O'Neill; Antonella Zanobetti; Joel Schwartz
Journal:  Am J Epidemiol       Date:  2003-06-15       Impact factor: 4.897

10.  The impact of heat waves and cold spells on mortality rates in the Dutch population.

Authors:  M M Huynen; P Martens; D Schram; M P Weijenberg; A E Kunst
Journal:  Environ Health Perspect       Date:  2001-05       Impact factor: 9.031

View more
  4 in total

1.  Mortality risks during extreme temperature events (ETEs) using a distributed lag non-linear model.

Authors:  Michael J Allen; Scott C Sheridan
Journal:  Int J Biometeorol       Date:  2015-12-08       Impact factor: 3.787

2.  Effects of sudden air pressure changes on hospital admissions for cardiovascular diseases in Prague, 1994-2009.

Authors:  Eva Plavcová; Jan Kyselý
Journal:  Int J Biometeorol       Date:  2013-09-22       Impact factor: 3.787

3.  A systematic evaluation of the lagged effects of spatiotemporally relative surface weather types on wintertime cardiovascular-related mortality across 19 US cities.

Authors:  Cameron C Lee
Journal:  Int J Biometeorol       Date:  2015-02-25       Impact factor: 3.787

4.  New insights into biometeorology. Foreword.

Authors:  Simon N Gosling
Journal:  Int J Biometeorol       Date:  2014-03       Impact factor: 3.787

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.