Heat-Attributable Deaths between 1992 and 2009 in Seoul, South Korea

<div><p>Background</p><p>Climate change may significantly affect human health. The possible effects of high ambient temperature must be better understood, particularly in terms of certain diseases’ sensitivity to heat (as reflected in relative risks [RR]) and the consequent disease burden (number or fraction of cases attributable to high temperatures), in order to manage the threat.</p><p>Purpose</p><p>This study investigated the number of deaths attributable to abnormally high ambient temperatures in Seoul, South Korea, for a wide range of diseases.</p><p>Method</p><p>The relationship between mortality and daily maximum temperature using a generalized linear model was analyzed. The threshold temperature was defined as the 90^th percentile of maximum daily temperatures. Deaths were classified according to ICD-10 codes, and for each disease, the RR and attributable fractions were determined. Using these fractions, the total number of deaths attributable to daily maximum temperatures above the threshold value, from 1992 to 2009, was calculated. Data analyses were conducted in 2012–2013.</p><p>Results</p><p>Heat-attributable deaths accounted for 3,177 of the 271,633 deaths from all causes. Neurological (RR 1.07; 95% CI, 1.04–1.11) and mental and behavioral disorders (RR 1.04; 95% CI, 1.01–1.07) had relatively high increases in the RR of mortality. The most heat-sensitive diseases (those with the highest RRs) were not the diseases that caused the largest number of deaths attributable to high temperatures.</p><p>Conclusion</p><p>This study estimated RRs and deaths attributable to high ambient temperature for a wide variety of diseases. Prevention-related policies must account for both particular vulnerabilities (heat-sensitive diseases with high RRs) and the major causes of the heat mortality burden (common conditions less sensitive to high temperatures).</p></div

Estimated relative risk of mortality for every 1°C increase in temperature above the 29.5°C threshold from May through September, 1992–2009.

<p>Estimated relative risk of mortality for every 1°C increase in temperature above the 29.5°C threshold from May through September, 1992–2009.</p

Estimated number of deaths attributable to heat and estimated relative risk of mortality for a 1°C increase in temperature above the threshold temperature (29.5°C).

<p>^a Attributable death / Number of death from specific causes: Proportion of attributable death to each cause of death.</p><p>^b Attributable death / Total number of death: Proportion of attributable deaths to total number of death.</p><p>Estimated number of deaths attributable to heat and estimated relative risk of mortality for a 1°C increase in temperature above the threshold temperature (29.5°C).</p

Descriptive Statistics of meteorological variables (1992–2009) and air pollutants (2001–2009).

<p>Descriptive Statistics of meteorological variables (1992–2009) and air pollutants (2001–2009).</p

Estimated associations for ∑DEHP and MnBP with IR markers.

<p>β and 95% confidence interval (CI) were obtained in single pollutant models of ∑DEHP and MnBP. Changes in glucose, insulin, and HOMA indices by a log change of ∑DEHP and MnBP were obtained by model; Model 1 adjusted for age, sex, BMI, educational attainment, exercise, and cotinine level; model 2 adjusted for model 1 variables plus PM₁₀ on lag day 4, O₃ on lag day 5, NO₂ on lag day 7, and outdoor temperature and dew point in the day; model 3 adjusted for model 2 variables plus total caloric and fat intake. ∑DEHP, molar sum of mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP); MnBP, mono-n-butyl phthalate; HOMA, homeostatic model assessment.</p

Estimated associations for ∑DEHP, MDA, and IR markers.

<p>β and 95% confidence interval (CI) were obtained after adjusted for age, sex, BMI, educational attainment, exercise, cotinine level, PM₁₀ on lag day 4, O₃ on lag day 5, NO₂ on lag day 7, outdoor temperature and dew point in the day, and total caloric and fat intake. MDA levels were log-transformed for normality. ∑DEHP, molar sum of mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP); MDA, malondialdehyde; IR, insulin resistance; HOMA, homeostatic model assessment; DM, diabetes mellitus.</p

A penalized regression spline of ∑DEHP on IR markers in participants with a history of DM (n = 91) and in females (n = 414).

<p><i>P</i>-Values were obtained using GAMM after adjusted for age, sex, BMI, educational attainment, exercise, cotinine level, PM₁₀ on lag day 4, O₃ on lag day 5, NO₂ on lag day 7, outdoor temperature and dew point in the day, and total caloric and fat intake in participants with a history of DM, and age, BMI, educational attainment, exercise, cotinine level, PM₁₀ on lag day 4, O₃ on lag day 5, NO₂ on lag day 7, outdoor temperature and dew point in the day, total caloric and fat intake, and a history of DM in female participants. ∑DEHP, molar sum of mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP); IR, insulin resistance; DM, diabetes mellitus; GAMM, generalized additive mixed model; BMI, body mass index.</p

Baseline characteristics of participants by sex.

<p>SD, standard deviation; BMI, body mass index; HOMA, homeostatic model assessment; DM, diabetes mellitus.</p

Correlations between single and five-sample average measurements of ∑DEHP and MnBP.

<p>∑DEHP, molar sum of mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and mono-(2-ethyl-5-oxohexyl) phthalate (MEOHP); MnBP, mono-n-butyl phthalate.</p

Distribution of phthalate metabolite and oxidative stress biomarker levels with 5 repeated measures.

<p>SD, standard deviation; LOD, limit of detection; MEHHP, mono-(2-ethyl-5-hydroxyhexyl) phthalate; MEOHP, mono-(2-ethyl-5-oxohexyl) phthalate; MnBP, mono-n-butyl phthalate; MDA, malondialdehyde.</p