Effects of high summer temperatures on mortality in 50 Spanish cities

Tobías, Aurelio; Armstrong, Ben; Gasparrini, Antonio; Díaz, Julio César

doi:10.1186/1476-069x-13-48

Cited by 31 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this connection, some authors have proposed certain indices that would allow for this circumstance, such as the so-called heat-wave intensity index, which takes into account both the maximum temperature value reached above a given threshold and the number of days on which this limit temperature is exceeded (Diaz et al 2006b). For this purpose, it will serve to indicate that a recent paper (Tobias et al 2014) compares the risk of dying by taking a temperature in the 90th percentile of the summer months and comparing this to the 99th percentile in the period 1990-2004. The results indicate that for CastileLa Mancha, these values lie within the interval of 9.2-14.5 % and, translated as percentage increases per degree, are very similar to those reported by us here.…”

Section: Causesmentioning

confidence: 98%

Changes in cause-specific mortality during heat waves in central Spain, 1975–2008

et al. 2014

View full text Add to dashboard Cite

The relationship between heat waves and mortality has been widely described, but there are few studies using long daily data on specific-cause mortality. This study is undertaken in central Spain and analysing natural causes, circulatory and respiratory causes of mortality from 1975 to 2008. Time-series analysis was performed using ARIMA models, including data on specific-cause mortality and maximum and mean daily temperature and mean daily air pressure. The length of heat waves and their chronological number were analysed. Data were stratified in three decadal stages: 1975-1985, 1986-1996 and 1997-2008. Heat-related mortality was triggered by a threshold temperature of 37 °C. For each degree that the daily maximum temperature exceeded 37 °C, the percentage increase in mortality due to circulatory causes was 19.3 % (17.3-21.3) in 1975-1985, 30.3 % (28.3-32.3) in 1986-1996 and 7.3 % (6.2-8.4) in 1997-2008. The increase in respiratory cause ranged from 12.4 % (7.8-17.0) in the first period, to 16.3 % (14.1-18.4) in the second and 13.7 % (11.5-15.9) in the last. Each day of heat-wave duration explained 5.3 % (2.6-8.0) increase in respiratory mortality in the first period and 2.3 % (1.6-3.0) in the last. Decadal scale differences exist for specific-causes mortality induced by extreme heat. The impact on heat-related mortality by natural and circulatory causes increases between the first and the second period and falls significantly in the last. For respiratory causes, the increase is no reduced in the last period. These results are of particular importance for the estimation of future impacts of climate change on health.

show abstract

Section: Causesmentioning

confidence: 98%

Changes in cause-specific mortality during heat waves in central Spain, 1975–2008

et al. 2014

View full text Add to dashboard Cite

show abstract

“…But considerably more studies are related to mortality (e.g. Tobías et al 2014;Gómez-Acebo et al 2012;Maté et al 2010;Raga et al 2010;Taracido Trunk et al 2009;Borrell et al 2006;Díaz et al 2006). The seasonal behaviour of morbi-mortality, especially pronounced in respiratory pathologies, shows clear increases during the winter and minimum values in summertime (Montero et al 2010;Raga et al 2010;Analitis et al 2008;Díaz et al 2005).…”

Section: Introductionmentioning

confidence: 94%

“…All human beings are obliged to react to the elements in their immediate surroundings so they can find a balance and ensure correct and optimum organic function. Of all the epidemiological studies focusing on the relation between weatherclimate and human health, leading examples are those that analyse the impact caused by thermal stress from heat or cold (Tobías et al 2014;Ye et al 2012;Michelozzi et al 2009;Basu 2009;Baccini et al 2008;Hassi 2005), air pollution (Pope and Dockery 2014;Cesaroni et al 2014;Carracedo-Martínez et al 2010;Maté et al 2010;Makria and Stilianakisa 2008), and airborne allergens (Khwarahm et al 2014;Newnham et al 2013;Carracedo-Martinez et al 2008;Shea et al 2008;Erbas et al 2007;Breton et al 2006;Tobías et al 2003). Each impact by itself has negative effects in the human health.…”

Section: Introductionmentioning

confidence: 98%

Winter circulation weather types and hospital admissions for respiratory diseases in Galicia, Spain

et al. 2015

View full text Add to dashboard Cite

The link between various pathologies and atmospheric conditions has been a constant topic of study over recent decades in many places across the world; knowing more about it enables us to pre-empt the worsening of certain diseases, thereby optimizing medical resources. This study looked specifically at the connections in winter between respiratory diseases and types of atmospheric weather conditions (Circulation Weather Types, CWT) in Galicia, a region in the north-western corner of the Iberian Peninsula. To do this, the study used hospital admission data associated with these pathologies as well as an automatic classification of weather types. The main result obtained was that weather types giving rise to an increase in admissions due to these diseases are those associated with cold, dry weather, such as those in the east and south-east, or anticyclonic types. A second peak was associated with humid, hotter weather, generally linked to south-west weather types. In the future, this result may help to forecast the increase in respiratory pathologies in the region some days in advance.

show abstract

“…We tested the accuracy of the approximations (1)- (8) for SEðβÞ with the actual values estimated from daily temperature and mortality data from 51 provincial capitals of Spain 1990-2010, previously used in several published studies [19,20] and summarised in Additional file 4. Specifically we considered a simple model with all natural deaths as the outcome and as explanatory term of interest (x) a linear model for "heat" defined as (lag 0) temperature above the 75th centile of daily mean temperature in that city.…”

Section: Comparison Of the Above Approximations With Conventional (Qumentioning

confidence: 99%

Sample size issues in time series regressions of counts on environmental exposures

Armstrong

Gasparrini

Tobías

et al. 2020

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

Background: Regression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude. Existing generic approaches for this have been found somewhat complex to apply and do not easily extend to multiple series studies analysed in two stages. We have sought a simpler approximate approach which can easily extend to multiple series and give insight into factors determining precision. Methods: We derive approximate expressions for precision and hence power in single and multiple time series studies of counts from basic statistical theory, compare the precision predicted by these with that estimated by analysis in real data from 51 cities of varying size, and illustrate the use of these estimators in a realistic planning scenario. Results: In single series studies with Poisson outcome distribution, precision and power depend only on the usable variation of exposure (i.e. that conditional on covariates) and the total number of disease events, regardless of how many days those are spread over. In multiple time series (eg multi-city) studies focusing on the meta-analytic mean coefficient, the usable exposure variation and the total number of events (in all series) are again the sole determinants if there is no between-series heterogeneity or within-series overdispersion. With heterogeneity, its extent and the number of series becomes important. For all but the crudest approximation the estimates of standard errors were on average within + 20% of those estimated in full analysis of actual data. Conclusions: Predicting precision in coefficients from a planned time series study is possible simply and given limited information. The total number of disease events and usable exposure variation are the dominant factors when overdispersion and between-series heterogeneity are low.

show abstract

Effects of high summer temperatures on mortality in 50 Spanish cities

Cited by 31 publications

References 27 publications

Changes in cause-specific mortality during heat waves in central Spain, 1975–2008

Changes in cause-specific mortality during heat waves in central Spain, 1975–2008

Winter circulation weather types and hospital admissions for respiratory diseases in Galicia, Spain

Sample size issues in time series regressions of counts on environmental exposures

Contact Info

Product

Resources

About