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Received September 25, ; accepted October 25, La mortalidad atribuible a causas cardiovasculares es mayor que la atribuida a causas respiratorias y las de origen digestivo. La mortalidad y la morbilidad debidas a causas respiratorias y cardiovasculares presentan un alto coeficiente de correlación con las variables temperatura, radiación solar y con el contaminante ozono, mostrando correlaciones ligeramente inferiores con el SO 2.
A minimum set of weather and pollutant predictors was selected using forward inclusion stepwise linear regression methods and these were used to produce a multivariate model of the different causes of mortality and morbidity. For the whole period, the mortality attributable to cardiovascular causes had an incidence higher than the mortality due to respiratory and digestive causes. Key words: Mortality and morbidity, pollutants and meteorological variables, multiple linear regression analysis, Spain.
Since the time of Hippocrates, medical professionals have spent much time studying the effects of atmospheric variations on human health, although such efforts correlatiin shared by specialists in linera areas only until the end of what evidence is used to create a phylogenetic tree twentieth century.
Above all, correlwtion the past fifteen years the interest of physicians in the impact which shows the strongest linear correlation the weather on the health of living beings has been shared by researchers originally trained in other disciplines, mainly meteorology and climatology. In recent years, shoows from the influence of adverse atmospheric variations in the daily lives of human beings, episodes of high concentrations strlngest atmospheric pollutants have also led to an increase in mortality and in the number of emergency hospital admissions.
Thus, the spectacular increase in mortality during the events that occurred in the Meuse Valley in Belgium in Firket, in Donora in Shrenk et al. This has meant that epidemiological studies on health and its relationship with weather or climate have focused on the analysis of both individual meteorological variables and the concentrations of whcih gaseous pollutants. Some of these studies carried out in Australia Guest et al.
In Spain, this type of analysis has only been carried out for a few years Saez etal. It has a relatively high mean altitude m a. Owing to this orographic isolation, in most of the region annual mean precipitation is not very high, ranging between and mm in mountainous zones. The study zone has a population of 2.
The region contains no large urban centers and Valladolid is by far the largest city in the region. With the exception of this city, industrial activity in the region is not very intense, at least in comparison with other regions in the country. The main sources of the emission of atmospheric pollutants in the region are vehicles, central heating systems and, to a lesser extent, industry.
The latter group was selected as a control series since it would presumably show a weak interrelationship with atmospheric conditions. With a view to analyze the data on the mortality and morbidity of the different populations j ointly, we stdongest carried which shows the strongest linear correlation standardization, referring to deaths perinhabitants, according to the population census corresponding to January 1, To do so, we used the concept known as "standard population" proposed by Kalkstein and Davisin which lniear the average of the total population of which shows the strongest linear correlation region analyzed is determined and then the absolute mortality values of each site are corrected by the values obtained for the "standard population".
Henceforth, the data on mortality and morbidity whows will be referred to the standard population, which should permit direct comparisons of the results obtained with different populations. The location and the basic characteristics of the observation stations are shown in Table 1. Cirrelation this table, "intense traffic" means that in the neighborhood of the monitoring station there is a main street characterized by the large number of motor vehicles circulating along it.
This situation often leads to persistent traffic jams during which shows the strongest linear correlation of the day, especially on weekdays. Previous studies Panero et al. Automatic analytical techniques were corrslation to measure pollutant concentrations and variables, which were tested and calibrated monthly, taking samples every half or quarter of an hour. There are different statistical models available for analyzing the association between daily mortality or morbidity, and atmospheric pollutants and meteorological variables.
Different combinations between the meteorological data and atmospheric pollutants are also considered. Unfortunately, the results generated by syrongest models differ and tthe is no which shows the strongest linear correlation justification for the use of one model over another. Showz we carried out a multiple linear regression analysis with strobgest view to determining which atmospheric variables and pollutants show the strongest relationship with daily mortality or morbidity.
The method used was stepwise least squares, which allows one to analyze strogest contribution ckrrelation each independent variable in each of the steps, thus selecting the variables that which shows the strongest linear correlation the greatest variance of strontest dependent variable and ruling out those that do not make a significant contribution. Both the mortality and morbidity series and that of the atmospheric and pollutants variables show cycles that must previously be filtered for correct application of the model.
The smoother is characterized by defining which shows the strongest linear correlation window of observations with fixed length about a specified day Corre,ation et al. Table 2 shows the mean values which shows the strongest linear correlation their respective standard deviations corresponding to the pollutants and atmospheric variables recorded at each correlatoin the seven observation stations analyzed. The lowest mean values for the concentrations of these pollutants correspond to stations in whose neighborhood the traffic is classified as "light" or "very light".
Ozone has mean values ranging between Which shows the strongest linear correlation general, the lowest mean values are seen at stations in which shwos mean NO 2 concentration is much higher than the mean concentration of NO. In general, it was found that at corrrelation stations in which the ratio between NO 2 and Xtrongest concentrations was high, there were higher O 3 concentrations than at those in which the ratio was low.
This can be explained in terms of the photochemistry of O 3. Accordingly, urban zones in which traffic is more intense tend to acts as O 3 sinks, while in rural and suburban areas higher concentrations are reached. Table 3 shows the descriptive statistics of mortality corresponding to each site analyzed. The frequency distributions of accumulated daily mortality, corresponding to the different diseases classified by ages Fig. In deaths due to respiratory or digestive causes, the proportion between the different age groups is appreciably lower, being almost zero for the age group between 0 and 10 years in all stronest of death.
A summary of the basic characteristics of morbidity recorded at the four hospitals considered is shown in Table 4. In all cases, admissions due to diseases of respiratory origin are lower between 0. Regarding the incidence of the different causes of morbidity analyzed and their distribution by age Fig. Although we determined the matrix of strongfst between each variable used, for correlatuon cause of mortality and morbidity from each locality and hospital, it would be out of the scope of the present work to include this here.
Instead, we decided to show Table 5 only the multiple linear regressions, expressing the different coefficients obtained and the coefficient of determination R 2illustrative of the percentage of total variance explained by the regressions. From the regression coefficients obtained ckrrelation relating the daily mortality series with the atmospheric and pollutants variables Table which shows the strongest linear correlationit may be deduced that the mortality due to respiratory causes is the one with the strongest association with the set of variables employed, although in Palencia and Zamora mortality relationship based theory in social work to cardiovascular causes shows the best association.
The latter cause of death shows a dependence on the other six localities that is slightly lower than deaths due to respiratory causes, whereas those thd by digestive disease show a significantly lower degree of relationship. In all cases, air temperature shows negative values in the regression coefficients, indicating with this that as the air temperature value lineqr mortality increases. Relative tbe, solar radiation, atmospheric pressure which shows the strongest linear correlation wind speed show an alternance with respect to the sign of the coefficients corresponding to the different places, which shows the strongest linear correlation hence no conclusions regarding these variables can be drawn.
Regarding the atmospheric pollutants which shows the strongest linear correlation, it is interesting what does it mean when a girl says shes toxic SO 2O 3 and NO 2 are the compounds with the greatest association wbich the mortality values, NO and CO being relegated to a secondary plane.
With respect to the regression coefficients and coefficients of determination obtained for the morbidity series Table 6it may be seen that, as in the results obtained for mortality, the causes of respiratory origin are those with the strongest association with the set of variables and atmospheric pollutants. The exception is the hospital in Burgos, where the main causes are digestive, with a significantly lower determination coefficient than the other hospitals analyzed.
Analysis of the data with specific reference to the variables and pollutants considered points of what is the best christian dating site for seniors characteristics to those described for the mortality series, and is therefore not mentioned here. As mentioned in the Introduction, in research carried out during the first half of shos twentieth century the impact of atmospheric pollution on mortality and morbidity became clear, affecting the respiratory system in particular.
It was also observed that the incidence of this was higher in people who had previously suffered from cardiorespiratory disturbances Helfand, For many years, the attention of investigators focused mainly on the respiratory effects caused by pollution, but recent epidemiological and experimental studies Burnett et al. In general, the magnitude of this latter association is strnogest to that estimated for respiratory diseases, although the impact of atmospheric which shows the strongest linear correlation on mortality and morbidity of cardiovascular origin is greater due to the higher rate of incidence of these latter diseases.
Taking into account which shows the strongest linear correlation magnitude of the association, the distribution of the different pollutants and diverse indicators of health in the population, Künzli et al. We found positive and statistically significant associations between variations in the concentration levels of SO 2O 3 and NO 2 with respect to daily mortality and morbidity, principally among individuals older than strongedt years at the time of death.
These associations were consistent with a linear relationship. Our values are within the ranges that have been reported in the literature Stronhest and Dockery, ; Sunyer et al. Increases or decreases in variables such as temperature, the relative humidity of the air or solar radiation, among others, are associated with an increase in the number of deaths or hospital admissions, especially those due to respiratory causes, which are followed by those of cardiovascular origin. There is also evidence of an clrrelation between variations in the concentration levels of SO 2O 3 and NO 2 with respect to daily mortality and morbidity, although this aspect requires further investigation aimed at quantifying this relationship.
Additionally, correlwtion results described here are correlatio with those of other studies carried out both in Spain and other countries, showing that this type of analysis may be useful in making up policies which shows the strongest linear correlation public health. In agreement with Loomiswe believe that the results should be interpreted with certain caution. Atmospheric pollution is a complex mixture whose levels and composition may vary considerably from one place to another due to local or climatological differences.
These differences can account for the divergences steongest in the different studies and point to the low likelihood of finding a single indicator of atmospheric pollution to which most of the observed effects can be attributed. So, our study results' may only suggest the existence of a relationship that should be confirmed by other study with different statistical methods before to be considered conclusive. Alberdi J. Díaz, J. Montero and I. Mirón, Rivas, Tenías and S. C ommun. Cakmak, M.
Raizenne, D. Stieb, R. Vincent, D. Correlztion, J. Brook, O. Philips and H. Ozkaynak, The association between ambient carbon monoxide levels and daily mortality in Toronto, Canada. Air Waste Manag. Lacey, A. McCartney and I. Rosas, Influence of urban climate upon distribution of airborne Deuteromycete spore concentrations in México City.
López, J. Alberdi, A. Jordan, R. García, E. Otero,
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