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Statistical analysis of temperature impact on daily hospital admissions: analysis of data from Udine, Italy
Author(s) -
Pauli Francesco,
Rizzi Laura
Publication year - 2006
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.749
Subject(s) - quantile , quantile regression , poisson regression , generalized additive model , wind speed , statistics , poisson distribution , regression analysis , linear regression , covariate , population , environmental science , daytime , air temperature , meteorology , atmospheric sciences , geography , mathematics , medicine , environmental health , geology
This article is devoted to the analysis of the relationship between the health status of an urban population and meteorological variables. The analysis considers daily number of hospital admissions, not due to surgery, regarding the population resident in the Municipality of Udine, aged 75 and over. Hourly records on temperature, humidity, rain, atmospheric pressure, solar radiation, wind velocity and direction recorded at an observation site located near the center of Udine are considered. The study also considers hourly measures of pollutant concentrations collected by six monitoring stations. All data are relative to the summer periods of years 1995–2003. Generalized additive models (GAM) are used in which the response variable is the number of hospital admissions and is assumed to be distributed as a Poisson whose rate varies as a possibly non‐linear function of the meteorological variables and variables allowing for calendar effects and pollutant concentrations. The subsequent part of the analysis explores the distribution of temperature conditional on the number of daily admissions through quantile regression. A non‐linear (N‐shaped) relationship between hospital admissions and temperature is estimated; temperature at 07:00 is selected as a covariate, revealing that nighttime temperature is more relevant than daytime. The quantile regression analysis points out, as expected, that the distribution of temperature on days with more admissions has higher q ‐quantiles with q near unity, while a clear‐cut conclusion is not reached for q quantiles with q near 0. Copyright © 2005 John Wiley & Sons, Ltd.