The Impact of Heat Waves on Mortality

Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Epidemiology (Cambridge, Mass.) (Impact Factor: 6.2). 01/2011; 22(1):68-73. DOI: 10.1097/EDE.0b013e3181fdcd99
Source: PubMed

ABSTRACT Heat waves have been linked with an increase in mortality, but the associated risk has been only partly characterized.
We examined this association by decomposing the risk for temperature into a "main effect" due to independent effects of daily high temperatures, and an "added" effect due to sustained duration of heat during waves, using data from 108 communities in the United States during 1987-2000. We adopted different definitions of heat-wave days on the basis of combinations of temperature thresholds and days of duration. The main effect was estimated through distributed lag nonlinear functions of temperature, which account for nonlinear delayed effects and short-time harvesting. We defined the main effect as the relative risk between the median city-specific temperature during heat-wave days and the 75th percentile of the year-round distribution. The added effect was defined first using a simple indicator, and then a function of consecutive heat-wave days. City-specific main and added effects were pooled through univariate and multivariate meta-analytic techniques.
The added wave effect was small (0.2%-2.8% excess relative risk, depending on wave definition) compared with the main effect (4.9%-8.0%), and was apparent only after 4 consecutive heat-wave days.
Most of the excess risk with heat waves in the United States can be simply summarized as the independent effects of individual days' temperatures. A smaller added effect arises in heat waves lasting more than 4 days.

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Available from: Antonio Gasparrini, May 25, 2015
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    • "Specifically, we used a generalized additive model (GAM) to analyze the relationships between daily mortality, PM 10 , and temperature data with the assumption that the daily number of counts had an overdispersed Poisson distribution (Dominici et al., 2004). GAM allows nonparametric smoothing functions to account for smooth fluctuations of confounding factors such as seasonal variation and weather conditions on the daily number of deaths (Bell et al., 2004; Gasparrini and Armstrong, 2011; Hastie and Tibshirani, 1990; Schwartz and Zanobetti, 2000). We performed a stage-by-stage analysis. "
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    ABSTRACT: Substantial epidemiologic literature has demonstrated the effects of air pollution and temperature on mortality. However, there is inconsistent evidence regarding the temperature modification effect on acute mortality due to air pollution. Herein, we investigated the effects of temperature on the relationship between air pollution and mortality due to non-accidental, cardiovascular, and respiratory death in seven cities in South Korea. We applied stratified time-series models to the data sets in order to examine whether the effects of particulate matter <10μm (PM10) on mortality were modified by temperature. The effect of PM10 on daily mortality was first quantified within different ranges of temperatures at each location using a time-series model, and then the estimates were pooled through a random-effects meta-analysis using the maximum likelihood method. From all the data sets, 828,787 non-accidental deaths were registered from 2000-2009. The highest overall risk between PM10 and non-accidental or cardiovascular mortality was observed on extremely hot days (daily mean temperature: >99th percentile) in individuals aged <65years. In those aged ≥65years, the highest overall risk between PM10 and non-accidental or cardiovascular mortality was observed on very hot days and not on extremely hot days (daily mean temperature: 95-99th percentile). There were strong harmful effects from PM10 on non-accidental mortality with the highest temperature range (>99th percentile) in men, with a very high temperature range (95-99th percentile) in women. Our findings showed that temperature can affect the relationship between the PM10 levels and cause-specific mortality. Moreover, the differences were apparent after considering the age and sex groups. Copyright © 2015 Elsevier B.V. All rights reserved.
    Science of The Total Environment 04/2015; 524-525C:376-383. DOI:10.1016/j.scitotenv.2015.03.137 · 4.10 Impact Factor
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    • "The statistical analysis followed an approach already proposed for several multicity studies (Lin et al., 2011; Wu et al., 2013). We first applied a Distributed Lag Non-linear Model (DLNM) to each community and then combined the estimates using a meta-analysis (Gasparrini et al., 2010; Lin et al., 2011). We also explored whether effect estimates differ by region to understand spatial distribution of heat wave effects on mortality. "
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    ABSTRACT: Many studies have reported increased mortality risk associated with heat waves. However, few have assessed the health impacts at a nation scale in a developing country. This study examines the mortality effects of heat waves in China and explores whether the effects are modified by individual-level and community-level characteristics. Daily mortality and meteorological variables from 66 Chinese communities were collected for the period 2006-2011. Heat waves were defined as ≥2 consecutive days with mean temperature ≥95th percentile of the year-round community-specific distribution. The community-specific mortality effects of heat waves were first estimated using a Distributed Lag Non-linear Model (DLNM), adjusting for potential confounders. To investigate effect modification by individual characteristics (age, gender, cause of death, education level or place of death), separate DLNM models were further fitted. Potential effect modification by community characteristics was examined using a meta-regression analysis. A total of 5.0% (95% confidence intervals (CI): 2.9%-7.2%) excess deaths were associated with heat waves in 66 Chinese communities, with the highest excess deaths in north China (6.0%, 95% CI: 1%-11.3%), followed by east China (5.2%, 95% CI: 0.4%-10.2%) and south China (4.5%, 95% CI: 1.4%-7.6%). Our results indicate that individual characteristics significantly modified heat waves effects in China, with greater effects on cardiovascular mortality, cerebrovascular mortality, respiratory mortality, the elderly, females, the population dying outside of a hospital and those with a higher education attainment. Heat wave mortality effects were also more pronounced for those living in urban cities or densely populated communities. Heat waves significantly increased mortality risk in China with apparent spatial heterogeneity, which was modified by some individual-level and community-level factors. Our findings suggest adaptation plans that target vulnerable populations in susceptible communities during heat wave events should be developed to reduce health risks. Copyright © 2014 Elsevier Ltd. All rights reserved.
    Environment International 11/2014; 75C:103-109. DOI:10.1016/j.envint.2014.11.004 · 5.56 Impact Factor
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    • "Thus, in the present study, we made use of the DLNM to simultaneously investigate non-linear and delayed dependencies in the association between daily mean temperature and mortality for each city. This methodology is based on a " cross-basis " function that describes a bi-dimensional association along the dimensions of temperature and lag days, which not only allows for examination of the relationships between temperature and mortality at each lag period, but also allows for the estimation of non-linear effects across lags (Gasparrini et al., 2010; Goldberg et al., 2011). Moreover, to capture the non-linear and delayed temperature–mortality association, the lag-stratified natural cubic spline (NS) was adopted, and in order to control for potential confounders, the RH, BP, long-term and seasonal trends, day of the week (DOW), public holidays and atmospheric pollutants (PM 10 ) were introduced into the model simultaneously. "
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    ABSTRACT: Few multi-city studies have been conducted to explore the regional level definition of heat wave and examine the association between extreme high temperature and mortality in developing countries. The purpose of the present study was to investigate the impact of extreme high temperature on mortality and to explore the local definition of heat wave in five Chinese cities. We first used a distributed lag non-linear model to characterize the effects of daily mean temperature on non-accidental mortality. We then employed a generalized additive model to explore the city-specific definition of heat wave. Finally, we performed a comparative analysis to evaluate the effectiveness of the definition. For each city, we found a positive non-linear association between extreme high temperature and mortality, with the highest effects appearing within 3days of extreme heat event onset. Specifically, we defined individual heat waves of Beijing and Tianjin as being two or more consecutive days with daily mean temperatures exceeding 30.2°C and 29.5°C, respectively, and Nanjing, Shanghai and Changsha heat waves as ≥3 consecutive days with daily mean temperatures higher than 32.9°C, 32.3°C and 34.5°C, respectively. Comparative analysis generally supported the definition. We found extreme high temperatures were associated with increased mortality, after a short lag period, when temperatures exceeded obvious threshold levels. The city-specific definition of heat wave developed in our study may provide guidance for the establishment and implementation of early heat-health response systems for local government to deal with the projected negative health outcomes due to heat waves. Copyright © 2014 Elsevier B.V. All rights reserved.
    Science of The Total Environment 10/2014; 505C:535-544. DOI:10.1016/j.scitotenv.2014.10.028 · 4.10 Impact Factor
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