King's College London

Research portal

Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities

Research output: Contribution to journalArticle

Standard

Searching for the best modeling specification for assessing the effects of temperature and humidity on health : a time series analysis in three European cities. / Rodopoulou, Sophia; Samoli, Evangelia; Analitis, Antonis; Atkinson, Richard W; de'Donato, Francesca K; Katsouyanni, Klea.

In: International Journal of Biometeorology, 2015.

Research output: Contribution to journalArticle

Harvard

Rodopoulou, S, Samoli, E, Analitis, A, Atkinson, RW, de'Donato, FK & Katsouyanni, K 2015, 'Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities', International Journal of Biometeorology. https://doi.org/10.1007/s00484-015-0965-2

APA

Rodopoulou, S., Samoli, E., Analitis, A., Atkinson, R. W., de'Donato, F. K., & Katsouyanni, K. (2015). Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities. International Journal of Biometeorology. https://doi.org/10.1007/s00484-015-0965-2

Vancouver

Rodopoulou S, Samoli E, Analitis A, Atkinson RW, de'Donato FK, Katsouyanni K. Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities. International Journal of Biometeorology. 2015. https://doi.org/10.1007/s00484-015-0965-2

Author

Rodopoulou, Sophia ; Samoli, Evangelia ; Analitis, Antonis ; Atkinson, Richard W ; de'Donato, Francesca K ; Katsouyanni, Klea. / Searching for the best modeling specification for assessing the effects of temperature and humidity on health : a time series analysis in three European cities. In: International Journal of Biometeorology. 2015.

Bibtex Download

@article{a1074205013845dea573cc7264f41dd6,
title = "Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities",
abstract = "Epidemiological time series studies suggest daily temperature and humidity are associated with adverse health effects including increased mortality and hospital admissions. However, there is no consensus over which metric or lag best describes the relationships. We investigated which temperature and humidity model specification most adequately predicted mortality in three large European cities. Daily counts of all-cause mortality, minimum, maximum and mean temperature and relative humidity and apparent temperature (a composite measure of ambient and dew point temperature) were assembled for Athens, London, and Rome for 6 years between 1999 and 2005. City-specific Poisson regression models were fitted separately for warm (April-September) and cold (October-March) periods adjusting for seasonality, air pollution, and public holidays. We investigated goodness of model fit for each metric for delayed effects up to 13 days using three model fit criteria: sum of the partial autocorrelation function, AIC, and GCV. No uniformly best index for all cities and seasonal periods was observed. The effects of temperature were uniformly shown to be more prolonged during cold periods and the majority of models suggested separate temperature and humidity variables performed better than apparent temperature in predicting mortality. Our study suggests that the nature of the effects of temperature and humidity on mortality vary between cities for unknown reasons which require further investigation but may relate to city-specific population, socioeconomic, and environmental characteristics. This may have consequences on epidemiological studies and local temperature-related warning systems.",
author = "Sophia Rodopoulou and Evangelia Samoli and Antonis Analitis and Atkinson, {Richard W} and de'Donato, {Francesca K} and Klea Katsouyanni",
year = "2015",
doi = "10.1007/s00484-015-0965-2",
language = "English",
journal = "International Journal of Biometeorology",
issn = "0020-7128",
publisher = "Springer New York",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Searching for the best modeling specification for assessing the effects of temperature and humidity on health

T2 - a time series analysis in three European cities

AU - Rodopoulou, Sophia

AU - Samoli, Evangelia

AU - Analitis, Antonis

AU - Atkinson, Richard W

AU - de'Donato, Francesca K

AU - Katsouyanni, Klea

PY - 2015

Y1 - 2015

N2 - Epidemiological time series studies suggest daily temperature and humidity are associated with adverse health effects including increased mortality and hospital admissions. However, there is no consensus over which metric or lag best describes the relationships. We investigated which temperature and humidity model specification most adequately predicted mortality in three large European cities. Daily counts of all-cause mortality, minimum, maximum and mean temperature and relative humidity and apparent temperature (a composite measure of ambient and dew point temperature) were assembled for Athens, London, and Rome for 6 years between 1999 and 2005. City-specific Poisson regression models were fitted separately for warm (April-September) and cold (October-March) periods adjusting for seasonality, air pollution, and public holidays. We investigated goodness of model fit for each metric for delayed effects up to 13 days using three model fit criteria: sum of the partial autocorrelation function, AIC, and GCV. No uniformly best index for all cities and seasonal periods was observed. The effects of temperature were uniformly shown to be more prolonged during cold periods and the majority of models suggested separate temperature and humidity variables performed better than apparent temperature in predicting mortality. Our study suggests that the nature of the effects of temperature and humidity on mortality vary between cities for unknown reasons which require further investigation but may relate to city-specific population, socioeconomic, and environmental characteristics. This may have consequences on epidemiological studies and local temperature-related warning systems.

AB - Epidemiological time series studies suggest daily temperature and humidity are associated with adverse health effects including increased mortality and hospital admissions. However, there is no consensus over which metric or lag best describes the relationships. We investigated which temperature and humidity model specification most adequately predicted mortality in three large European cities. Daily counts of all-cause mortality, minimum, maximum and mean temperature and relative humidity and apparent temperature (a composite measure of ambient and dew point temperature) were assembled for Athens, London, and Rome for 6 years between 1999 and 2005. City-specific Poisson regression models were fitted separately for warm (April-September) and cold (October-March) periods adjusting for seasonality, air pollution, and public holidays. We investigated goodness of model fit for each metric for delayed effects up to 13 days using three model fit criteria: sum of the partial autocorrelation function, AIC, and GCV. No uniformly best index for all cities and seasonal periods was observed. The effects of temperature were uniformly shown to be more prolonged during cold periods and the majority of models suggested separate temperature and humidity variables performed better than apparent temperature in predicting mortality. Our study suggests that the nature of the effects of temperature and humidity on mortality vary between cities for unknown reasons which require further investigation but may relate to city-specific population, socioeconomic, and environmental characteristics. This may have consequences on epidemiological studies and local temperature-related warning systems.

U2 - 10.1007/s00484-015-0965-2

DO - 10.1007/s00484-015-0965-2

M3 - Article

C2 - 25638489

JO - International Journal of Biometeorology

JF - International Journal of Biometeorology

SN - 0020-7128

ER -

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454