the environmental impact on human health has been observed since hipocrates , in 400  bc , when , in his book air , water and places , he emphasized the importance of environmental conditions , such as atmospheric variables , on diseases and general human health . nowadays , however , there is another influence which has been caused directly by man , which is air pollution . the earth 's atmosphere has been contaminated by toxic substances emitted by anthropogenic sources such as vehicles , industries , mining and others since the industrial revolution . such contamination is clearly apparent in large urban centres , like mxico city and the city of so paulo , among others . so paulo is one of the most polluted areas in the world .   according to bhm et al . braga , air pollution constitutes one of the major sources of public health issues , being responsible for a considerable amount of hospital admissions of patients with   respiratory , cardiovascular , stillborns , ophthalmic , dermatological , and haematological diseases [ 29 ] . according to these studies , it is important to note the association of respiratory morbidity with environmental conditions , such as meteorological variables and , mainly , air pollutants . this study is aimed at quantifying the impact of environmental variables on respiratory morbidity in the city of so paulo , in order to prevent them . it should also be noted that respiratory diseases are responsible for around 30% of the total morbidity in brazil , mainly in the southern and southeastern areas , bringing out the importance of such research ( http://www.datasus.gov.br ) . many studies have evaluated the impact of air pollutants and atmospheric conditions on human health over the last sixty years , yielding no hospital admission forecasts nevertheless . in this way , the contribution of this research is to predict respiratory hospital admissions , as well as to consider the effects of thermal comfort indices ( tcis ) on them , besides the effects of air pollutants and meteorological variables . this is an ecological study using time series in the mrsp ( metropolitan region of so paulo ) during a four - year period ( 19972000 ) . the respiratory morbidity of children under thirteen years old was the dependent and discrete variable of the model , and the independent variables were environmental as follows : air pollutants , meteorological variables , and thermal comfort indices all variables are continuous . the covariates patterns were not necessary , because the data by age and sex were selected before of the modeling . daily records of hospital admissions during the researched period , for children under thirteen years of age , were obtained from brazil 's ministry of health . these records related to about 80 hospitals , both public and private , spread over the city of so paulo , which receive support from the public health system ( http://www.datasus.gov.br ) . thus , our sample is probably representative of the poorest segment of the population , since most in this category lack private medical care insurance . the classification of the diseases under analysis was based on icd   ( international code diseases , 9th and 10th revisions ) and was categorized as follows ( uri ) : upper respiratory infections ( j00j06 ) , and other diseases of the upper respiratory   tract ( j30j39 ) ; ( lri ) : lower respiratory infections ( j20j22 ) and chronic lower respiratory diseases ( j40j47 ) ; ( ip ) : influenza and pneumonia ( j10j18 ) . daily records of pm10 , co , so2 , and o3 were obtained from cetesb ( company of technology of environmental sanitation ) . daily records of air temperature , wind velocity , relative humidity , atmospheric pressure , and precipitation were obtained from the meteorological station at parque estadual das fontes do ipiranga ( so paulo ) .   in order to have a clearer picture of the effect of meteorological variables on respiratory morbidity , thermal comfort indices ( tcis ) were computed and added to the meteorological data set , as done by gonalves et al . . cold and heat stress associated with low and high humidity can be strong stressors on heart patients . thermal comfort indices deal with two or more meteorological variables ( temperature , humidity , and wind , for instance ) , given that the human body reacts to all environmental variables at the same time . thermal comfort indices become a useful tool to measure thermal stress , mainly in diseased people , hence their use in this research . historically , thermal comfort analysis was based on two specific biometeorological indices , according to fanger , and the impact on human physiology was evaluated , as described by kalkstein and valimont . in a thermally comfortable environment , no thermal stress should be experienced . this situation is called thermal neutrality , and as such , no action needs to be taken to maintain the proper heat balance of the body , according to fanger , who provides a sufficiently broad discussion of this topic in his book and defines thermal comfort as  that condition of mind which expresses satisfaction with the thermal environment . in the present study , the main meteorological variables studied were air temperature , relative humidity , and wind velocity .   in order to determine the thermal classification , different indices were used , based on the work of several authors , as follows . ono and kawamura   developed the effective temperature concept , based on individual sensitivity / sensitization , combining air temperature and relative humidity . the following equation was used to determine the effective temperature 
										 ( a)et = t0.4[(1rh100)(t10 ) ] , 
									 where t is ( mean , maximum , or minimum ) air temperature in c , rh is ( mean , maximum , or minimum ) relative humidity in % , and et is the effective temperature in c . , is defined as follows : 
										 ( b)etw=37(37t)[0.680.0014rh+(1/1.76 + 1.4v0.75 ) ]  0.29t(1rh100 ) , 
									 where  v is the mean wind velocity in m / s , t is ( mean , maximum , or minimum ) air temperature in c , rh is ( mean , maximum , or minimum ) relative humidity in % , and etw is the effective temperature with wind in c . these indices were calculated from data collected at the meteorological station at parque estadual das fontes do ipiranga . it was decided that the following combinations of temperature ( mean , maximum , and minimum ) , air relative humidity ( mean , maximum , and minimum ) , and mean wind data would be employed , in order to verify whether there was a correlation between an increase on hospital admissions and thermal stress : hot and dry ( et1 ) , hot and wet ( et2 ) , mean ( et3 ) , cold and wet ( et4 ) , and finally cold and dry ( et5 ) . such combination was performed for both indices et and etw .   in the absence of empirical outdoor thermal comfort studies , it has been widely assumed that the indoor thermal comfort theory applies to outdoor settings without modification . besides , due to the nonexistence of a brazilian thermal comfort index , the indexes above , which are considered adequate for brazil , according to maia and gonalves , were adopted in this study . this research took advantage of poisson regression multiple model ( prmm ) for daily admission modeling , as it is the statistical approach recommended for this kind of study [ 2 , 3 , 9 ] . firstly , a lag structure was applied on all the variables ( thermal comfort indices , pollutants , and meteorological variables ) , ranging from one to seven days , at synoptical scale , to verify which lag had more statistically significant correlation with hospital admissions . the lag structure was used because , for example , air temperature can directly affect hospital respiratory admissions on the same day when it is measured and also on several subsequent days . the model was estimated and adjusted by its seasonality ( nonparametric function ) , as well as by season of the year , holidays , days of the week , months , and morbidity of nonrespiratory diseases . the poisson distribution is assumed as 
								 ( 1)log[e(yp , tci , met)]=+1xp+2xtci+3xmet+p , tci , met , 
							 where p , tci , and met are the air pollutants , thermal comfort , indices and meteorological variables , respectively . xp is the pollutant variable , xtci is the thermal comfort index variable , and xmet is the meteorological variable . relative risk ( rr ) and admission increase ( ai ) with confidence interval ( ci ) were also estimated as follows : 
								 ( 2)rr = exp(x ) , 
							 where x is the threshold to estimate the independent variable and  is the poisson regression parameter : 
								 ( 3)ai  ( % ) = [ ( exp(x))1]100,(4)ci95%=exp  [ 1.96se( ) ] , 
							 where se is the standard error for . to check the fit of the model deviance statistic and the pearson statistic were used , respectively 
								 ( 5)dev=2i=1k{yiln  ( yiy^i)+(niyi)ln  ( niyiniy^i)},rp=(yii)i(1i ) 
							 where yi value of the discrete variable and i is estimative of the model . the mean standard error ( mse ) was used after equations were linearized to check predicting hospital admissions : 
								 ( 6)mse=1ni=1n(pioi)2,where   pi is the admissions estimate , oi   is the admissions that actually occurred and n is the the sample . table 1 presents the pearson correlation matrix for uri , lri , and ip and meteorological , air pollutant , and thermal comfort indices variables . the proportions of such hospital admissions compared to total hospital admissions been 7.6% ( 1.1% to lri , 10% to uri , and 5.9% to ip , resp . ) . the variables chosen , in bold , were those with higher statistical significance and correlation coefficients . based on those results , prmm was deployed , as shown in table 2 , as follows in section 3.2 .   to build the models uri , lri , and ip ( section 3.2 ) , air temperature and relative humidity should not be used separately , as their pearson correlation coefficient ( etw ) is higher than the correlation coefficients obtained by these meteorological variables separately . table 2 shows the poisson coefficients for the three groups : lri , uri , and ip . in this model , and also according to mazumdar et al . , the control variables were months , days of the week , seasons of the year , and holidays , as usual . in this table , the chosen variables , which presented the highest correlation and statistical significance , were based on a correlation matrix ( see table 1 ) . therefore , due to the colinearity between both variables , the significance of one decreases the significance of the other , when analysed at the same time . thus , the selection criterion was to choose those variables with the highest correlation coefficient . the pressure and precipitation variables do not present a statistically significant correlation with uri , lri , and ip . from ( 5 ) ( section 2.2 ) , was obtained for all the three disease categories , in order to evaluate the model 's fit through of the pearson statistic ( figure 1 ) . from ( 6 ) ( section 2.2 ) , the mean standard error ( mse ) during the year of 2001 was obtained for all the three disease categories , in order to evaluate the model 's skill ( figure 2 ) .   for uri morbidity , the most significant variables were so2 and co with no time lag and etw4 with a four days time lag ( lag4 ) . the seasonal variability of uri is the least among all the disease groups , presenting similar numbers throughout the years , with a slight increase in fall / autumn and winter . during this analysis , so2 , even presenting values far below the cetesb safe standard ( 80  g / m ) , is related to an increase in uri morbidity ( see table 1 ) . co also presents a significant correlation coefficient to uri ( 0.274 at table 1 ) , despite cetesb 's control and the constant decrease in concentration over the last years . with respect to tci , etw4 presents the highest negative correlation ( smaller tci values , higher respiratory morbidity and vice versa , as expected ) with uri compared to the other indices ( 0.136 ) of the same disease group , presenting a lag of   four days ( lag4 ) . this result means that an arriving cold mass affects children after four days later , which is in accordance with the hospital observations . prmm for uri presents robust results with mean error around 15% . for lri morbidity , the most significant variables were pm10 with no time lag and   o3 and etw4 both with a lag of three days ( lag3 ) . this disease group presents a clear seasonality , with higher numbers in winter and fall / autumn . pm10 and so2 were the most significant air pollutants ( 0.175 and 0.154 , resp . ) . at prmm , both pollutants could not be together , because so2 lost its significance while pm10 kept it , due to their colinearity . besides , pm10 and o3 also presents a significant correlation coefficient ( 0.093 ) and might be a lri predictor . similar results were found in the mrsp by such other authors as braga et al . ozone in the mrsp is generated by vehicular emissions and the presence of vocs ( volatile organic compounds ) ( andrade . , ozone presents a different behavior , that is , higher in springtime , compared to the other pollutants , which are higher in fall / autumn and winter . frequently , ozone overtakes the cetesb safe standard values ( 160  g / m ) , reaching concentrations as high as 200  g / m , which characterizes the air as being of very bad quality . ozone is the only air pollutant which cetesb can not control properly . with respect to tci , again , etw4 presents the highest negative correlation with lri compared to the other indices ( 0.187 ) with a time lag of three days ( lag3 ) . prmm for lri presents excellent results , as well , with a mean error of less than 30% .   therefore , the lri model may forecast morbidity for the mrsp in the same way as explained for uri . with respect to ip , the most significant air pollutants were pm10 and so2 ( 0.321 and 0.354 , resp . ) . however , so2 lost significance , and pm10 kept it . with respect to tci , again , etw4 presents a quite strong and the highest negative correlation with ip , compared to the other indices ( 0.496 ) , with a time lag of three days ( lag3 ) . the prmm deployment for ip presents a mean error of 40% , the worst value among the disease groups , which suggests a new modeling approach . the model behaves satisfactorily , based on the uri morbidity estimation from the prmm equation . from these results , it is possible to verify that the increase in respiratory morbidity is due to an increase in so2 or co or a decrease in etw4 ( see table 1 ) . from the coefficients found in the prmm model , it was possible to calculate the increase in uri morbidity , so that an increase of 10  g / m in the concentration of so2 is related to a nonlinear behavior in the uri morbidity increase , ranging from 13.9% at low values of so2   to 182.9% at high values of so2.co also does not show linear behavior , considering that an increase of 2  ppm is related to an increase of 9% at low values of co to 99% at high values of co. on the other hand , etw4 presents a linear behaviour , as for every increase of 2c in tci , uri morbidity decreases approximately 2.0% ( see table 3 ) . therefore , colder temperatures imply an increase in uri morbidity .   the relative risk rises from 1 to 2.8 with ci95% ranging from 1 to + 1 when so2 varies from 0 to 80  g / m ( see figure 3(a ) ) . regarding co , the relative risk rises from 1 to 1.9 with ci95% ranging from 0.6 to + 1.0 when co varies from 0 to 16  ppm ( see figure 3(b ) ) . when it comes to etw4 , the relative risk decreases from 1 to 0.8 with ci95% raging from 1.0 to + 0.8 when etw4 varies from 0 to 16c ( see figure 3(c ) ) . the model presents robust results which forecast morbidity with an mse below 25% . in this case , the variables used in this analysis were pm10 , o3 , and etw4 , as shown in table 4 . with respect to pm10 , an increase of 20  g / m indicates a quasilinear increment of lri , with an average increase of approximately 2.2% in the morbidity of this disease group . , present similar results . regarding ozone , which is the air pollutant that has more frequently overpassed safe thresholds in the mrsp , an increase of 40  g / m did not represent a linear increase in lri morbidity . with respect to etw4 , an increase of 2c is related to a linear decrease of approximately 1.5% in the lri morbidity , bringing out the protection effect of a more comfortable weather ( warmer and drier ) . the relative risk rises from 1 to 1.2 with ci95% ranging from 1 to + 1 when pm10 varies from 0 to 160  g / m ( see figure 4(a ) ) . regarding o3 , the relative risk rises from 1 to 2 with ci95% ranging from 1 to + 1.0 when o3 varies from 0 to 320  g / m ( see figure 4(b ) ) . when it comes to etw4 , the relative risk decreases from 1 to 0.8 with ci95% ranging from 1.0 to 0.8 when etw4 varies from 0 to 16c ( see figure 4(c ) ) . with regard to ip , the results are similar to the other disease groups so that there was a decrease of ip morbidity in response to an increase in etw4 , as expected . a decrease of 2c indicates an average increase of 3.3% in ip morbidity , which is considerably higher than the decrease observed in uri and lri ( see table 4 ) . on the other hand , an increase in pm10 is related to an increase in ip morbidity ( see table 5 ) . a 20  g / m increment is related to an average increase of approximately 5% in ip morbidity , which is over twice as high as that in lri ( see table 4 ) . the relative risk rises from 1 to 1.5 with ci95% ranging from 1 to + 1 when pm10 varies from 0 to 160  g / m ( see figure 5(a ) ) . regarding etw4 , the relative risk decreases from 1 to 0.6 with ci95% ranging from 1.0 to + 0.8 when etw4 varies from 0 to 16c ( see figure 5(b ) ) . in summary , with respect to air pollutants , uri shows the impact of so2 and co. with respect to lri , it is affected by pm10 and ozone .   with regard to ip with respect to tci ( an index obtained from meteorological variables ) , etw4 shows an impact on the three disease groups , more considerably on ip .   also not surprisingly , there is more discomfort from cold and wet weather , which generates higher rates of morbidity . from the coefficients estimated in the uri , lri , and ip models , it became possible to predict hospital admission variations as well as their relative risks , according to the individual variation of each associated variable . the increase in so2 around 80  m / m , which is a feasible variation in so paulo , rises in uri hospital admissions and may reach 182.9% . this result is worrying as , according to cetesb , 365  g / m is the maximum acceptable level of such air pollutant , which is considerably high . with regard to co , in case there is a 16  ppm increase , hospital admissions are expected to rise by 99.0% . with regard to lri , it was noticed that with an increase in pm10 of up to 160  g / m , which is considered acceptable , admissions will rise by 17.4% , and the relative risk will rise from 1 to 1.2 . about o3 , it was noted that if it increases up to 160  g / m , which is considered acceptable according to cetesb . however the admissions will rise by 37.7% , and the relative risk will rise from 1.0 to 2.0 . this pollutant has frequently overtaken safe levels of air quality , reaching peaks of 283.4  m / m , resulting in a hospital admission rise of 75.0% . regarding ip , it was observed that if the tci variation is positive , it behaves as a protector factor , as the higher the index the more comfortable ( less cold ) is the sensation . nevertheless , it should be considered that such index tends to be uncomfortable , naturally , as it uses minimum temperatures in its calculation . if etw4 varies from 0c to 20.0c , admissions due to this variation will decrease by 16.1% , 14.8% , and 33% for uri , lri , and ip , respectively . after deploying the prmm , a hybrid model was built , named brazilian climate and health model ( bchm ) , capable of predicting hospital admissions from environmental variables , whose values may either be measured or obtained from a meteorological forecasting mathematical model . such approach of allowing the use of variables came from a meteorological forecasting mathematical model is similar to the approach adopted by the model output statistics ( karl , 1979 , ) . according to the analysis in this article , it is possible to state that air pollutants , thermal comfort indexes , and meteorological variables show a statistically significant correlation with lri , uri , and ip hospital admissions . the projected model for lri and uri shows robust overall results with an mse below 25% , while the ip model accuracy was not as good as the other two , with a mse of approximately 40% .   with an error smaller than 30% , it is possible to forecast uri and lri morbidity based on the thermal comfort indexes and air pollutants measures , as well as based on meteorological variables forecasting . relative risk results show uri associated with co and so2 , lri with pm10 and ozone , and ip with pm10 , as well . the models were satisfactory because the objective of this paper was to show that environmental variables could be used to estimate hospital admissions . however , health depends not only on environmental factors , but also on several other factors : hereditary , nutritional , and economic , for instance . nevertheless , the bchm , besides estimating hospital admission from real data , can also be used to predict scenarios resulting from the climate change and extreme weather events ( cold and hot air masses ) .
this study is aimed at creating a stochastic model , named brazilian climate and health model ( bchm ) , through poisson regression , in order to predict the occurrence of hospital respiratory admissions ( for children under thirteen years of age ) as a function of air pollutants , meteorological variables , and thermal comfort indices ( effective temperatures , et ) . 
 the data used in this study were obtained from the city of so paulo , brazil , between 1997 and 2000 . 
 the respiratory tract diseases were divided into three categories : uri ( upper respiratory tract diseases ) , lri ( lower respiratory tract diseases ) , and ip ( influenza and pneumonia ) . 
 the overall results of uri , lri , and ip show clear correlation with so2 and co , pm10 and o3 , and   pm10 , respectively , and the etw4 ( effective temperature ) for all the three disease groups . 
 it is extremely important to warn the government of the most populated city in brazil about the outcome of this study , providing it with valuable information in order to help it better manage its resources on behalf of the whole population of the city of sao paulo , especially those with low incomes .