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

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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.

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Abbreviations

CI:

Confidence interval

Df:

Degrees of freedom

ICD:

International classification of diseases

NO2 :

Nitrogen dioxide

O3 :

Ozone

DLNM:

Distributed lag nonlinear model

GAM:

Generalized additive model

AIC:

Akaike information criterion

GCV:

Generalized cross-validation

PACF:

Partial autocorrelation function

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No competing financial interests.

Ethical standards

Our work does not involved human subjects or any experiment.

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Authors and Affiliations

  1. Department of Hygiene, Epidemiology and Medical Statistics, Medical School, University of Athens, 75 Mikras Asias Str, 115 27, Athens, Greece

    Sophia Rodopoulou, Evangelia Samoli, Antonis Analitis & Klea Katsouyanni

  2. Population Health Research Institute and MRC-PHE Centre for Environment and Health, St George’s, University of London, London, UK

    Richard W. Atkinson

  3. Department of Epidemiology, Lazio Regional Health Authority, Rome, Italy

    Francesca K. de’Donato

  4. Environmental Research Group and Department of Primary Care & Public Health Sciences, King’s College London, London, UK

    Klea Katsouyanni

Authors
  1. Sophia Rodopoulou
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  2. Evangelia Samoli
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  3. Antonis Analitis
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  4. Richard W. Atkinson
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  5. Francesca K. de’Donato
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  6. Klea Katsouyanni
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Correspondence to Klea Katsouyanni.

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Rodopoulou, S., Samoli, E., Analitis, A. et al. Searching for the best modeling specification for assessing the effects of temperature and humidity on health: a time series analysis in three European cities. Int J Biometeorol 59, 1585–1596 (2015). https://doi.org/10.1007/s00484-015-0965-2

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  • DOI: https://doi.org/10.1007/s00484-015-0965-2

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