Revista Española de Salud Pública
Print version ISSN 1135-5727
MIRON PEREZ, Isidro J. et al. Treatment and Temperature Series Study for Use in Public Health: The Case of Castilla-La Mancha, Spain. Rev. Esp. Salud Publica [online]. 2006, vol.80, n.2, pp.113-124. ISSN 1135-5727. http://dx.doi.org/10.1590/S1135-57272006000200002.
Background: Numerous articles relate atmospheric variables to health indicators. In large regions, such as Castilla-La Mancha, it may be necessary to divide the region into areas in terms of the atmospheric variables available by selecting a representative weather station for each zone. This article focuses on analyzing the daily temperature data from numerous Castilla La Mancha observatories and reducing the number thereof to a few representative stations for being used in studies relating atmospheric variables to health indicators in this region. Methods: Castilla-La Mancha weather stations were selected in terms of the number of years available and missing data. After filling in the gaps in the selected series, to detect any possible discontinuities and to homogenize the series, the daily temperature data is used in hierarchical cluster and factorial analyses by principal components. Results: Factorial analyses extract one single factor by using the maximum, mean or minimum temperature series. For the maximum temperatures, this factor explains 93.45% of the variance, with an eigenvalue of 39.249. The «Compuesta» station in Toledo shows correlation coefficients in the principal components matrix of 0.987, 0.991 and 0.981 respectively for the maximum, mean and minimum temperature series. Conclusions: Castilla-La Mancha is an isoclimatic region in terms of the temperature, the «Compuesta» station in Toledo being selected as the representative station for the region for public health studies. The results afford the possibility of conducting studies broken down into small units such as the provinces, with the stations in the government capitals as a reference.
Keywords : Temperature; Mortality; Clusters analisys; Principal components analisys.