Ishigami A, Hajat S, Kovats RS, et al. An ecological time-series study of heat-related mortality in three European cities. Environ Health. 7:5

Public & Environmental Health Research Unit, London School of Hygiene & Tropical Medicine, Kappel Street, London, WC1E 7HT, UK.
Environmental Health (Impact Factor: 3.37). 02/2008; 7(1):5. DOI: 10.1186/1476-069X-7-5
Source: PubMed


Europe has experienced warmer summers in the past two decades and there is a need to describe the determinants of heat-related mortality to better inform public health activities during hot weather. We investigated the effect of high temperatures on daily mortality in three cities in Europe (Budapest, London, and Milan), using a standard approach.
An ecological time-series study of daily mortality was conducted in three cities using Poisson generalized linear models allowing for over-dispersion. Secular trends in mortality and seasonal confounding factors were controlled for using cubic smoothing splines of time. Heat exposure was modelled using average values of the temperature measure on the same day as death (lag 0) and the day before (lag 1). The heat effect was quantified assuming a linear increase in risk above a cut-point for each city. Socio-economic status indicators and census data were linked with mortality data for stratified analyses.
The risk of heat-related death increased with age, and females had a greater risk than males in age groups > or =65 years in London and Milan. The relative risks of mortality (per degrees C) above the heat cut-point by gender and age were: (i) Male 1.10 (95%CI: 1.07-1.12) and Female 1.07 (1.05-1.10) for 75-84 years, (ii) M 1.10 (1.06-1.14) and F 1.08 (1.06-1.11) for > or = or =85 years in Budapest (> or =24 degrees C); (i) M 1.03 (1.01-1.04) and F 1.07 (1.05-1.09), (ii) M 1.05 (1.03-1.07) and F 1.08 (1.07-1.10) in London (> or =20 degrees C); and (i) M 1.08 (1.03-1.14) and F 1.20 (1.15-1.26), (ii) M 1.18 (1.11-1.26) and F 1.19 (1.15-1.24) in Milan (> or =26 degrees C). Mortality from external causes increases at higher temperatures as well as that from respiratory and cardiovascular disease. There was no clear evidence of effect modification by socio-economic status in either Budapest or London, but there was a seemingly higher risk for affluent non-elderly adults in Milan.
We found broadly consistent determinants (age, gender, and cause of death) of heat related mortality in three European cities using a standard approach. Our results are consistent with previous evidence for individual determinants, and also confirm the lack of a strong socio-economic gradient in heat health effects currently in Europe.

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Available from: Luigi Bisanti, Oct 12, 2015
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    • "Currently, heat wave is arbitrarily defined as a period of at least 3 days where daily maximum temperature exceeds 35 °C across China and findings here suggest that policymakers in China need to consider regional sensitivity to heat waves when developing regional specific response plans. We further observed a greater effect of heat waves on cardiovascular mortality and respiratory mortality than total mortality, consistent with some previous studies (Ishigami et al., 2008; Le Tertre et al., 2006), indicating increased susceptibility among people suffering from chronic cardiovascular or respiratory diseases (D'Ippoliti et al., 2010). This may relate to the adaptation of a healthy peripheral circulation to environmental temperatures that occurs within a few minutes (Barnetta et al., 2007). "
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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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    • "Since heat-related deaths may be attributed to many different causes, most heat mortality studies have used all-cause mortality as a measure(Yu et al., 2010; Oudin Åström et al., 2011; Hondula et al., 2012; Xu et al., 2013). Several studies reported mortality due to cardiovascular and respiratory diseases to be associated with heat exposure (Ishigami et al., 2008 "
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    ABSTRACT: Heat stress increasingly affects urban populations in higher geographical latitudes. Related adverse health effects are expected to increase due to urbanization, population aging, and global warming. While many studies have examined the relationship between heat and mortality, only a few have examined the intra-urban spatial variability between them. This missing research is particularly evident for northern mid-latitude cities, where populations are not prepared for heat stress. The aim of this study is therefore to investigate heat-related excess mortality in its spatial variability at the neighborhood scale (397 planning areas) for Berlin, the capital of Germany. We analyzed age-standardized mortality rates by calculating the relative heat mortality risk ratio for months with and without severe heat waves. Local indicators of spatial association were used to locate spatial clusters. The results highlighted the intra-urban variability of heat-related excess mortality, and demonstrated clustering for the planning areas of Berlin. Temporal aggregation of mortality data enabled a neighborhood-scale analysis. Resulting heat-related excess mortality maps allow urban decision makers to identify hot spots for emergency and adaptation planning, and serve as a basis for further investigations of heat stress risk on an individual level.
    Urban Climate 11/2014; 10. DOI:10.1016/j.uclim.2014.10.008 · 0.36 Impact Factor
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    • "Most studies on temperature an mortality relationships agree that the elderly are predominantly affected by heat stress, mainly due to their decreased ability to thermoregulate their body temperature and predispositions resulting from diseases like heart, circulation and respiratory diseases, diabetes mellitus, etc. (Oudin Åström, Bertil, & Joacim, 2011; Reid et al., 2009). Regarding young children as another age-specific sensitivity group (Kovats & Hajat, 2008), they are also expected to be more vulnerable due to their limited thermoregulation capacities, which has, however, not been clearly shown in mortality studies (Ishigami et al., 2008). Apart from age-distribution , population density represents another demographic factor that increase the exposure quantitatively and thus the vulnerability to be subjected to heat stress (Johnson et al., 2012; Romero- Lankao, Qin, & Dickinson, 2012; Scherer et al., 2013). "
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