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기후변화와 건강 - 저온과 고온이 사망에 미치는 영향에 관한 체계적 고찰
임연희,김호,Lim, Youn-Hee,Kim, Ho 한국환경보건학회 2011 한국환경보건학회지 Vol.37 No.6
Objectives: The impact of climate change on the health has been of increasing concern due to a recent temperature increase and weather abnormality, and the research results of the impact varied depending on regions. We synthesized risk estimates of the overall health effects of low and high temperature taking account of the heterogeneity. Methods: A comprehensive literature search was conducted using PUBMED to identify journal articles of low and/or high temperature effects on mortality. The search was limited to the English language and epidemiological studies using time-series analysis and/or case-crossover design. Random-effect models in meta analysis were used to estimate the percent increase in mortality with $1^{\circ}C$ temperature decrease or increase with 95% confidence intervals (CI) in cold or hot days. Results: Twenty three studies were presented in two tables: 1) low temperature effects; 2) high temperature effects on mortality. The combined effects of low and high temperatures on total mortality were 2% (95% CI, 1-4%) per $1^{\circ}C$ decrease and 4% (95% CI, 2-5%) per $1^{\circ}C$ increase of temperature, respectively. Conclusions: This meta analysis found that both low and high temperatures affected mortality, and the magnitude of high temperature appeared to be stronger than that of low temperature.
Youn-Hee Lim(임연희) 환경독성보건학회 2021 한국독성학회 심포지움 및 학술발표회 Vol.2021 No.5
Long-term exposure to particulate matter less than 2.5 μm (PM2.5) is considered a risk factor for premature death. However, only a few studies have been conducted in areas with moderate PM2.5 concentrations. Moreover, an aging society may be more susceptible to environmental exposure and future burden of mortality and incidence due to PM2.5. This study estimates hazard ratios (HRs) for all-cause and cause-specific mortality and incidence from long-term exposure to moderate PM2.5 concentrations in the elderly populations of seven cities in South Korea. We also projected nationwide elderly mortality caused by long-term exposure to PM2.5, accounting for population aging until 2045. Mortality and incidence in 1,720,230 elderly adults aged 65 years and older in 2008 was monitored across 2009–2016 and linked to modeled PM2.5 concentrations. A total of 421,100 deaths occurred in 2009–2016, and the mean of annual PM2.5 concentration ranged between 21.1 μg/m3 and 31.9 μg/m3 in most regions. The overall HR for a 10 μg/m3 increase in a 36-month PM2.5 moving average was 1.024 [95% confidence intervals (CI): 1.009, 1.039]. We estimated that 11,833 all-cause nationwide elderly deaths were attributable to PM2.5 exposure. Annual death tolls may increase to 17,948 by 2045. However, if PM2.5 is reduced to 5 μg/m3 by 2045, the tolls may show a lower increase to 3,646. Among cause-specific incidence, 54,522 and 259,700 developed asthma and chronic obstructive pulmonary disease (COPD), respectively. A 10 μg/m3 increase in the 36- and 60-month mean PM2.5 concentration was significantly associated with a 9% increase in incident asthma (HR=1.09, 95% CI: 1.04–1.14) and COPD (HR=1.09, 95% CI: 1.07, 1.11). The long-term exposure to moderately high levels of PM2.5 was associated with increased mortality and incidence risk among the elderly. Thus, PM2.5 reduction in response to the projected aging-associated mortality and incidence in South Korea is critical.
이영은(YoungEun Lee),임연희(Youn-Hee Lim),김호(Ho Kim) 서울대학교 보건환경연구소 2012 보건학논집 Vol.49 No.2
Many studies defined daily meteorological conditions such as temperature or humidity and mortality had some association and emphasized other meteorological variable like relative temperature or absolute humidity in this association study. We will estimate the best model for the association study by comparing 4 combination models including daily mean temperature or normalized temperature and relative humidity or absolute humidity. We analyzed 1992-2009 years, 6 major cities in Korea (Seoul, Busan, Deagu, Incheon, Gwangju and Daejeon)data. First, we set the basic model using generalized additive model (GAM)with temperature variable, humidity variable, time trends and day of week variable. From the basic model, we got 4 combination models, daily mean temperature and relative humidity model(M1), daily mean temperature and absolute humidity model(M2), normalized temperature and relative humidity model(M3) and normalized temperature and absolute humidity model(M4). We compared the AIC between the 4 models to find out the best combination model in the study. In comparisons same temperature variable but different humidity variable, absolute humidity models have the smaller AIC. In comparisons same humidity variable but different temperature variable, daily mean temperature models have the smaller AIC. In comparisons 4 models, daily mean temperature and absolute humidity model(M2)have the minimum AIC value among 4 models in every cities except Gwangju in Korea.