Modifiers of diurnal temperature range and mortality association in six Korean cities
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Rapid temperature changes within a single day may be critical for populations vulnerable to thermal stress who have difficulty adjusting themselves behaviorally and physiologically. We hypothesized that diurnal temperature range (DTR) is associated with mortality, and that this association is modified by season and socioeconomic status (SES). We evaluated meteorological and mortality data from six metropolitan areas in Korea from 1992 to 2007. We applied generalized linear models (GLM) for quantifying the estimated effects of DTR on mortality after adjusting for mean temperature, dew point temperature, day of the week, and seasonal and long-term trends. Most areas showed a linear DTR–mortality relationship, with evidence of increasing mortality with increasing DTR. Deaths among the elderly (75 years or older), females, the less educated, and the non-hospital population were associated more strongly with DTR than with the corresponding categories. DTR was the greatest threat to vulnerable study populations, with greater influence in the fall season. DTR was found to be a predictor of mortality, and this relationship was modified by season and SES.
KeywordsClimate change Diurnal temperature range Mortality Socioeconomic status Seasonal variation Temperature
The authors thank the Korea National Statistics Office (KNSO), the Korean Meteorological Administration (KMA), and the Research Institute of Public Health and Environment for the use of their data. The work was supported by Basic Science Research Program (#2010-0009581) and Global Research Lab (#K21004000001-10A0500-00710) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology.
- CDCP (2010) Flu activity and surveillance. Centers for Disease Control and Prevention. http://www.cdc.gov/flu/weekly/pdf/overview.pdf. Accessed 9 September 2010
- Gu C (2002) Smoothing spline ANOVA models. Springer, New YorkGoogle Scholar
- KCDCP (2008) 2007 Communicable Diseases Surveillance Yearbook. Korea Centers for Disease Control and Prevention, SeoulGoogle Scholar
- Kilbourne EM (1997) Heat waves and hot environments. In: Noji EK (ed) The public health consequences of disasters. Oxford University Press, OxfordGoogle Scholar
- Liberatos P, Link BG, Kelsey JL (1988) The measurement of social class in epidemiology. Epidemiol Rev 10:87–121Google Scholar
- Medina-Ramon M, Schwartz J (2007) Temperature, temperature extremes, and mortality: a study of acclimatization and effect modification in 50 United States Cities. Occup Environ Med. doi: 10.1136/oem.2007.033175
- Meehl GA, Karl T, Easterling DR, Changnon S Jr, RP CD, Evans J, Groisman PY, Knutson TR, Kunkel KE, Mearns LO, Parmesan C, Pulwarty R, Root T, Sylves RT, Whetton P, Zwiers F (2000) An introduction to trends in extreme weather and climate events: observations, socioeconomic impacts, terrestrial ecological impacts, and model projections. Bull Am Meteorol Soc 81:413–416CrossRefGoogle Scholar
- Peng RD, Dominici F (2008) Statistical methods for environemenal epidemiology with R: a case study in air pollution and health. Springer, New YorkGoogle Scholar
- Song G, Chen G, Jiang L, Zhang Y, Zhao N, Chen B, Kan H (2008) Diurnal temperature range as a novel risk factor for COPD death. Respirology 13:1066–1069Google Scholar
- Viechtbauer W (2010) Conducting meta-analyses in R with the metafor Package. J Stat Softw 36:1–48Google Scholar