A Model for Forecasting Emergency Hospital Admissions: Effect of Environmental Variables.Abstract This study modeled patterns and trends in emergency hospital admissions at a hospital in Madrid, Spain. The purpose was to quantify qualitative associations that have been detected between such admissions and a number of environmental variables. The following data were used: unscheduled unscheduled Adjective not planned or intended Adj. 1. unscheduled - not scheduled or not on a regular schedule; "an unscheduled meeting"; "the plane made an unscheduled stop at Gander for refueling" daily emergency hospital admissions, Madrid air pollution data, and meteorological data Meteorological facts pertaining to the atmosphere, such as wind, temperature, air density, and other phenomena that affect military operations. . Timeseries analysis was performed, with Box-Jenkins modeling. A multivariate The use of multiple variables in a forecasting model. model was constructed, incorporating the different causes of admissions and the respective environmental variables, Statistically significant associations were found between hospital admissions and other variables, indicating relationships with temperature, relative humidity relative humidity n. The ratio of the amount of water vapor in the air at a specific temperature to the maximum amount that the air could hold at that temperature, expressed as a percentage. , and mean daily tropospheric ozone concentrations. Whereas the effect of heat on admissions was short term, that of cold was in evidence from the second week. The association with ozone showed a seven-day lag and basically manifested itself as an influence on admissions for circulatory circulatory /cir·cu·la·to·ry/ (ser´ku-lah-tor?e) 1. pertaining to circulation, particularly that of the blood. 2. containing blood. cir·cu·la·to·ry n. 1. disease. Introduction Numerous studies have pointed to a link between air pollution and mortality, not only for cases of episodic episodic sporadic; occurring in episodes. e. falling a paroxymal disorder described in Cavalier King Charles spaniels in which affected dogs, starting at an early age, experience episodes of extensor rigidity, possibly brought on by stress. e. pollution (Firket, 1936; Ministry of Health [United Kingdom], 1954; Shrenk, Heimann, & Clayton, 1949), but also for levels below those specified by the air quality guidelines of the World Health Organization (WHO) (Momas et al., 1993; Seaton, MacNee, Donaldson, & Godden, 1995; WHO, 1987). Some authors have suggested that air pollution plays a role akin to that of a trigger mechanism, accelerating the death of already gravely ill individuals, and that pollution must thus be regarded as being of relatively minor importance (Spix et al., 1993). In recent years, however, a series of epidemiological studies An Epidemiological study is a statistical study on human populations, which attempts to link human health effects to a specified cause. have set out to establish the relationship between air pollution and emergency admissions (Schouten, Vonk, & Graaf, 1996; Schwartz, 1995; Vigotti, Rossi, Bisanti, Zanobetti, & Schwartz, 1996). Within the confines con·fine v. con·fined, con·fin·ing, con·fines v.tr. 1. To keep within bounds; restrict: Please confine your remarks to the issues at hand. See Synonyms at limit. of the limitations to which such studies are said to be subject, the results show air pollut ion taking on a different dimension and an altogether greater transcendence. Once the qualitative aspect of an association has been detected, the logical next step is to quantify the association. This step entails obtaining models that can diagnose and forecast all-cause emergency admissions, taking into account the possible effects of diverse atmospheric variables. The initial premise would be that, while not all admissions can be assumed to be due to such causes, there inevitably will be a proportion of causes that can be explained by reference to environmental factors. With respect to air pollution, Madrid, Spain, is not very different from comparable European cities, Nevertheless, its extreme climate and the characteristics of its population (13.6 percent are over 65 years of age) lend special interest to the study of the relationship between mortality and environmental variables (Alberdi, Diaz, Montero mon·te·ro n. pl. mon·te·ros A hunter's cap with side flaps. [Spanish, hunter, from monte, mountain, from Latin m , & Miron 1998; Montero, Miron Diaz, & Alberdi, 1997), particularly photochemical photochemical in laser treatment, the laser light is absorbed and converted into chemical energy. pollutants pollutants see environmental pollution. like ozone, which has registered an upward trend in recent years (Diaz, Alberdi, Montero, & Miron 1998). This study modeled emergency-admission patterns and trends in a Madrid hospital to quantify the effect of these environmental variables. Timely prediction of the precise variables that form a model would enable health authorities to use the model to detect variations in the number of hospital admissions well in advance. Interventions could thus be programmed in such a way as to ensure optimal management and allocation of hospital health care resources. Methods The series of daily emergency hospital admissions examined for this study covered the period of January 1, 1994, to September 30, 1996 (1,004 days). This data series, supplied by the Gregorio Maranon University Teaching Hospital, included all unscheduled admissions excluding traumas and births. Causes of admission were defined according to according to prep. 1. As stated or indicated by; on the authority of: according to historians. 2. In keeping with: according to instructions. 3. the International Classification of Diseases, 9th Revision (ICD-9-MC) (Commission on Professional and Hospital Activities, 1978). Thus, admissions were grouped into the following categories: total organic-disease admissions (ICD-9: 1-799), circulatory-disease admissions (ICD-9: 390-459), and admissions due to diseases of the respiratory system respiratory system: see respiration. respiratory system Organ system involved in respiration. In humans, the diaphragm and, to a lesser extent, the muscles between the ribs generate a pumping action, moving air in and out of the lungs through a (ICD-9: 460-519). Data on environmental variables took the form of daily mean air pollution values, in micrograms per cubic meter Noun 1. cubic meter - a metric unit of volume or capacity equal to 1000 liters cubic metre, kiloliter, kilolitre metric capacity unit - a capacity unit defined in metric terms ([micro]g/[m.sup.3]), for sulfur dioxide sulfur dioxide, chemical compound, SO2, a colorless gas with a pungent, suffocating odor. It is readily soluble in cold water, sparingly soluble in hot water, and soluble in alcohol, acetic acid, and sulfuric acid. ([SO.sub.2]), total suspended particles (TSP TSP - travelling salesman problem ), nitrogen oxides Noun 1. nitrogen oxide - any of several oxides of nitrogen formed by the action of nitric acid on oxidizable materials; present in car exhausts pollutant - waste matter that contaminates the water or air or soil ([NO.sub.x]), nitrogen dioxide nitrogen dioxide n. A poisonous brown gas, NO2, often found in smog and automobile exhaust fumes and synthesized for use as a nitrating agent, a catalyst, and an oxidizing agent. Noun 1. ([NO.sub.2]), and ozone ([O.sub.3]), as furnished by the 24 monitoring stations making up Madrid's Municipal Automatic Air Pollution Monitoring Grid. Daily mean readings were recorded at all 24 of the grid's monitoring stations for all pollutants except ozone, which was monitored at only five stations. For ozone, the 24-hour mean was used, as that value had been shown by other studies to register the greatest association with mortality in Madrid (Diaz, Alberdi, Montero, & Miron, 1998). The following meteorological me·te·or·ol·o·gy n. The science that deals with the phenomena of the atmosphere, especially weather and weather conditions. [French météorologie, from Greek variables also were considered: daily maximum temperature (TMax), daily minimum temperature (TMin), and daily mean temperature (TMean), with TMean obtained on the basis of readings ([degrees]C) taken at 7 a.m., 1 p.m., 6 p.m., and 12 a.m. Relative humidity (RH) at 7 am, also was taken into account. Because of its convenient location in the vicinity of the Gregorio Maranon University Teaching Hospital, the Madrid Retiro Observatory observatory, scientific facility especially equipped to detect and record naturally occurring scientific phenomena. Although geological and meteorological observatories exist, the term is generally applied to astronomical observatories. was chosen as the meteorological observatory of reference. Following analysis of the components in the data series--that is, hospital-admission variables considered as dependent variables and environmental variables considered as independent variables--functional relationships were established between them via scatter-plot diagrams, with the respective correlation coefficients Correlation Coefficient A measure that determines the degree to which two variable's movements are associated. The correlation coefficient is calculated as: being analyzed to determine which afforded the best fit. Using this diagram, any emergency-hospital-admission/environmental-factor pattern could be analyzed. In the case of temperature, for example, a V-shaped distribution was observed, indicating the existence of admission peaks related to low and high temperatures, a pattern that rendered it necessary to divide the data series into two segments with reference to the comfort temperature, or the temperature associated with minimum admissions (Montero, Miron Diaz & Alberdi, 1997). Cold-temperature (TCold) and hot-temperature (THot) series were therefore created, defined as follows: TCold = TComfort - TA if TA [less than] TComfort, and where TA = ambient temperature Outside temperature at any given altitude, preferably expressed in degrees centigrade. . THot = TA - Tcomfort if TA [greater than] Tcomfort, A similar pattern was in evidence for ozone. Consequently, high-ozone ([O.sub.3]-high) and low-ozone ([O.sub.3]-low) series were defined with reference to the ozone concentration associated with minimum admissions (Diaz, Alberdi, Montero, & Mirn 1998). The derivation derivation, in grammar: see inflection. of these series is further discussed in the Results section of this paper. Series-specific analyses were performed for the whole year and for both winter (December to March) and summer (June to September). For analysis of the deterministic 1. (probability) deterministic - Describes a system whose time evolution can be predicted exactly. Contrast probabilistic. 2. (algorithm) deterministic - Describes an algorithm in which the correct next step depends only on the current state. components in the data series, the pertinent basic descriptive statistics descriptive statistics see statistics. were ascertained. Trend and periodicities were analyzed with the relevant frequency spectra yielded by the Fast Fourier Transform See FFT. (algorithm) Fast Fourier Transform - (FFT) An algorithm for computing the Fourier transform of a set of discrete data values. Given a finite set of data points, for example a periodic sampling taken from a real-world signal, the FFT expresses the data in terms of method (Anderson, 1971). Univariate AutoRegressive Integrated Moving Average In statistics, an autoregressive integrated moving average (ARIMA) model is a generalisation of an autoregressive moving average or (ARMA) model. These models are fitted to time series data either to better understand the data or to predict future points in the series. (ARIMA) modeling (Box & Jenkins, 1976) was used to ascertain the nondeterministic components of the series-- that is, the autoregressive (AR) part and the moving average (MA). Using the Box-Ljung Portmanteau test In statistics, a portmanteau test tests whether any of a group of autocorrelations of a time series are different from zero. The term portmanteau test refers both to the Ljung-Box test and to the (now obsolete) Box-Pierce test. , the authors selected these models where their partial autocorrelation Autocorrelation The correlation of a variable with itself over successive time intervals. Sometimes called serial correlation. functions (PACFs) and simple autocorrelation functions (ACFs) indicated white-noise structure. Box-Jenkins prewhitening was performed to eliminate analogous periodicities and autocorrelations, as between the mortality and temperature series (Box & Jenkins, 1976). Next, the cross-correlation functions (CCFs) between their sets of residuals were calculated. This step established the lags at which significant relationships between the variables occurred. Lagged variables were created for all of the environmental variables listed above. Once the hospital-admission/environmental-variable association had been established, ARIMA models of the hospital admissions variables were constructed, with the environmental variables included as exogenous Exogenous Describes facts outside the control of the firm. Converse of endogenous. inputs, in order to eliminate the effects of possible colinearities existing between environmental variables (Box & Jenkins, 1976). The authors evaluated goodness of fit Goodness of fit means how well a statistical model fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e. by running the Box-Lujng Portmanteau test on the PACE and ACF (Advanced Communications Function) An earlier official product line name for IBM SNA programs, such as VTAM (ACF/VTAM) and NCP (ACF/NCP). ACF - Advanced Communications Function residuals. In addition to the environmental variables considered, other variables, such as day of the week and annual and six-month circular functions (Math.) See under Function. See See also: Circular Function , were introduced to act as controls for possible confounding confounding when the effects of two, or more, processes on results cannot be separated, the results are said to be confounded, a cause of bias in disease studies. confounding factor factors. Sensitivity analyses were performed on all models to test stability Statistical analysis of data was performed with the SPSS A statistical package from SPSS, Inc., Chicago (www.spss.com) that runs on PCs, most mainframes and minis and is used extensively in marketing research. It provides over 50 statistical processes, including regression analysis, correlation and analysis of variance. computer software package for Windows (Release 6.1). Results Figure 1 depicts the time trend in emergency hospital admissions for the period 1994-1996. A seasonal pattern is in evidence, marked by a maximum peak in winter and peaks of lower intensity in summer. As appears in the graphs in Figure 2 through Figure 5, air pollutants also showed a clear seasonal pattern, registering maximum values in winter. The single exception was ozone; because this pollutant pol·lut·ant n. Something that pollutes, especially a waste material that contaminates air, soil, or water. is secondary in nature and sunlight is an essential prerequisite for its formation, ozone reached its peak during periods of maximum sunshine (Figure 6). Table 1 provides descriptive statistics for the variables, including causes of emergency hospital admissions (total organic disease admissions as well as cause-specific admissions) and the various pollution and meteorological variables employed. Also shown are the periodicities and trends detected by spectral analysis Spectral analysis may refer to:
By way of example, Figure 7 shows the spectrum for organic-disease hospital admissions with the corresponding 99 percent significance level. Periodicities that centered on the low frequencies were in evidence, pointing to an annual cycle. This pattern also was found for circulatory- and respiratory-disease admissions. At high frequencies, periodicities were likewise observed at seven and 3.5 days for organic-and circulatory-disease admissions. For air pollutants, the frequency spectrum showed an annual seasonality for all pollutants except [NO.sub.2], which registered periodicities only at high frequencies (seven and 3.5 days). With respect to TSP, mention should be made of a slight downward trend that was detected over the spectrum. The meteorological variables--TMax, Tmin, and relative humidity--registered an annual cycle. Relative humidity also showed a three-day periodicity periodicity /pe·ri·o·dic·i·ty/ (per?e-ah-dis´i-te) recurrence at regular intervals of time. pe·ri·o·dic·i·ty n. 1. . Scatter-plot diagrams for air pollutants revealed that nitrogen oxides and particulates had a linear relationship with hospital admissions; Figure 8 gives the diagram for nitrogen dioxide as an example. In contrast, the relationship of [SO.sub.2] to hospital admissions was logarithmic logarithmic pertaining to logarithm. logarithmic relationship when the logs of two variables plotted against each other create a straight line. . The relationships existed across the board for any concentration of these pollutants--that is, there were no threshold values below which the associations were not in evidence. Ozone had a quadratic quadratic, mathematical expression of the second degree in one or more unknowns (see polynomial). The general quadratic in one unknown has the form ax2+bx+c, where a, b, and c are constants and x is the variable. relationship with hospital admissions; as shown in Figure 9, minimum admissions occurred when ozone was at a concentration of 45 [micro]g/[m.sup.3], and that value then served as the basis for defining high and low ozone. Among the temperature variables that were analyzed, Tmax was the most highly correlated with hospital admissions; minimum admissions were registered at 33[degrees]C. When the authors analyzed cross-correlation functions between total and cause-specific admissions on the one hand and the different pollutants on the other--and controlled for the effect of temperature--the most significant result was that in the winter, [SO.sub.2] and TSP were associated with total hospital admissions at a lag of zero days. In the summer, however, the association held for particulates alone and at a lag of five days. When admissions were stratified stratified /strat·i·fied/ (strat´i-fid) formed or arranged in layers. strat·i·fied adj. Arranged in the form of layers or strata. by cause, TSP registered a yearlong year·long adj. Lasting one year. Adj. 1. yearlong - lasting through a year; "attending yearlong courses" long - primarily temporal sense; being or indicating a relatively great or greater than average duration or association, at lags of zero and seven days, for circulatory-disease admissions; no significant association was observed for respiratory-disease admissions, With respect to daily mean ozone, a statistically significant association was found for all admission causes analyzed, with an immediate effect being registered for organic-disease admissions at a lag of zero. The cross-correlation functions for [NO.sub.x] registered a pattern similar to that recorded for [SO.sub.2] (in essence, a lag of zero). ARIMA models, supplied with the necessary variables to control for possible confounding factors such as seasonality and day of the week, were likewise constructed to ascertain the influence of environmental variables. Table 2 shows the significance of the associations registered, the lags at which these occurred, and the values of the estimators for the different ARIMA models having exogenous variables Exogenous variable A variable whose value is determined outside the model in which it is used. Related: Endogenous variable . These findings showed year-round organic-cause admissions to be linked to [SO.sub.2] and THot at a lag of zero days; high ozone at a lag of seven days; and TCold at a lag of 10 days. For the winter period, the association held solely for [SO.sub.2] and TCold; the summertime pattern proved identical to the year-round pattern. Separate analysis of individual pollutants in relation to cause-specific admissions showed circulatory-disease admissions to have a short-term relationship with [SO.sub.2], high ozone, and relative humidity For respiratory-disease admissions, a relationship with Tcold registered in the long term, and a statistically significant association with ozone registered for values above 45 [micro]g/[m.sup.3] at a lag of zero in the summer. By taking estimator values for the individual pollutants into account, the authors then could determine the respective weights of the pollutants in any increase in emergency hospital admissions. [SO.sub.2] accounted for 2.3 percent of the increase when atmospheric concentrations rose 25 [micro]g/[m.sup.3] above the mean, the proportion climbing to 3.2 percent in the wintertime. The increase attributable to ozone was 18 percent when concentrations rose by 25 [micro]g/[m.sup.3] above 45 [micro]g/[m.sup.3]. With respect to temperature, there was an increase of 1.7 percent in daily all -cause admissions for every degree over 33[degrees]C; this effect was immediate. Discussion and Conclusion Numerous studies have reported the existence of statistically significant associations between air pollution and morbidity and mortality Morbidity and Mortality can refer to:
respiratory disorder, respiratory illness adult respiratory distress syndrome, ARDS, wet lung, white lung - acute lung injury characterized by coughing and rales; inflammation of the (Bates Bates , Katherine Lee 1859-1929. American educator and writer best known for her poem "America the Beautiful," written in 1893 and revised in 1904 and 1911. & Sizto, 1983; Dab et al., 1996). Many such studies calculate the relative risk for admissions in exposed versus unexposed individuals. In many cases, total emergency admissions are analyzed, as in the study undertaken by Bates and Sizto (1983), which described an association between admissions and [SO.sub.2] levels, or in a Los Angeles-based study that linked incidence of overall emergency admissions to wintertime particulate par·tic·u·late adj. Of or occurring in the form of fine particles. n. A particulate substance. particulate composed of separate particles. levels (Goldsmith, Grif fith, Detels, Beeser, & Neumann, 1983). The most important studies have, however, centered on respiratory disease, implicating im·pli·cate tr.v. im·pli·cat·ed, im·pli·cat·ing, im·pli·cates 1. To involve or connect intimately or incriminatingly: evidence that implicates others in the plot. 2. [O.sub.3], [SO.sub.2], and TSP as the causes underlying increases in the number of emergency admissions due to asthma and chronic obstructive pulmonary disease chronic obstructive pulmonary disease n. Abbr. COPD A chronic lung disease, such as asthma or emphysema, in which breathing becomes slowed or forced. (Bates, Baker-Anderson, & Sitzo, 1990; Dab et al., 1996; Ponce de Leon Ponce de Le·ón , Juan 1460-1521. Spanish explorer who sailed with Columbus on his second voyage (1493-1494) and discovered Florida (1513) while looking for the legendary Fountain of Youth. Noun 1. , Anderson, Bland, Strachan, & Bower, 1996; Schwartz, 1994; Sunyer et al., 1993; Vigotti, Rossi, Bisanti, Zanobetti, & Schwartz, 1996). In some cases, an association has also been established between pollutant levels and cardiovascular admissions; an example is the link with [S0.sub.2] found by Sweeney (1982). The results yielded by this study proved very similar to those of studies that have linked air pollution and mortality in Madrid, even though the study periods and populations were different. The functional relationships detected, the response times, and even the estimator values were very similar, thus underscoring the fact that air pollution, rather than acting as a factor that merely triggers deaths destined des·tine tr.v. des·tined, des·tin·ing, des·tines 1. To determine beforehand; preordain: a foolish scheme destined to fail; a film destined to become a classic. 2. to occur within a matter of days, might indeed be an initial link in such a chain of events. If these results are deemed of interest, it is perhaps because the principal contribution that this study sets out to make is not merely to establish a pollutant-hospital admission relationship. The main thrust is the attempt to model the time trend in these admissions, based not only on the history of the data series (the ARIMA part of the models) but also on various statistically significant environmental variables. The latter basis endows univariate models with the possibility of forecasting episodic situations that are linked to environmental variables and that no series-related history could otherwise reproduce unless a comparable event had previously taken place. Hence, the importance of this study does not lie simply in the establishment of a relationship between pollution and hospital admissions. Rather, it lies in the quantification of this relationship; by establishing models capable of diagnosing and forecasting the trend over time with a mean error of 15 percent, this methodology offers a useful tool for hospital management purposes (Figure 10). Moreover, a hospital-admission-forecasting model of this nature opens the way to epidemiological surveillance Epidemiological surveillance is the discipline of continuously gathering, analysing, and interpreting data about diseases, and disseminating conclusions of the analyses to relevant organisations. As such, it is a key element in epidemiology. of pollution, enabling preventive measures to be taken and specific purpose-designed actions to be implemented in the case of health care risks associated with pollution and episodes of extreme temperatures. Currently, the model is being implemented at the Hospital Gregorio Maranon de Madrid. Acknowledgements: This study was funded by Health Sciences Research Project Grant No. 08.7/0007/1999 2 from the Madrid Regional Education and Culture Authority. Corresponding Author: Dr. Julio Diaz Jimenez, Centro Universitario de Salud Publica, C/ General Oraa 39, E-28006 Madrid, Spain. E-mail: [less than]julio.diaz@uam.es[greater than]. REFERENCES Alberdi, J.C., Diaz, J., Montero, J.C., & Miron, I.J. (1998). Daily mortality in Madrid community 1986-1992: Relationship with meteorological variables. European journal European Journal is a weekly Deutsche Welle (DW) news program produced in English. It is broadcast from Brussels, Belgium and primarily covers political and economic developments across the European Union and the rest of Europe, as well as issues of particular concern to of Epidemiology, 14,571-578. Anderson, T.W (1971). The statistical analysis of time series. New York New York, state, United States New York, Middle Atlantic state of the United States. 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TABLE 1
Statistics for Hospital-Admission and Environmental Variables
Variable Mean Standard Deviation
Organic disease (daily admissions) 59.9 12.7
Circulatory disease (daily admissions) 9.8 3.5
Respiratory disease (daily admissions) 7.6 3.7
TMax ([degrees]C) 20.9 8.6
Thin ([degrees]C) 10.7 6.5
[SO.sub.2] ([micro]g/[m.sup.3]) 26.1 18.5
[O.sub.3] ([micro]g/[m.sup.3]) 26.2 14.3
TSP ([micro]g/[m.sup.3]) 39.1 13.8
[NO.sub.2] ([micro]g/[m.sup.3]) 65.0 17.4
[NO.sub.x] ([micro]g/[m.sup.3]) 160 80.8
Relative humidity (%) 64.6 17.8
Variable Maximum Minimum
Organic disease (daily admissions) 108 23
Circulatory disease (daily admissions) 25 2
Respiratory disease (daily admissions) 29 0
TMax ([degrees]C) 40.9 0.4
Thin ([degrees]C) 25.8 -7.6
[SO.sub.2] ([micro]g/[m.sup.3]) 128 6
[O.sub.3] ([micro]g/[m.sup.3]) 71 2
TSP ([micro]g/[m.sup.3]) 147 18
[NO.sub.2] ([micro]g/[m.sup.3]) 143 26
[NO.sub.x] ([micro]g/[m.sup.3]) 596 46
Relative humidity (%) 99 28
Variable Trend
Organic disease (daily admissions) No
Circulatory disease (daily admissions) No
Respiratory disease (daily admissions) No
TMax ([degrees]C) No
Thin ([degrees]C) No
[SO.sub.2] ([micro]g/[m.sup.3]) No
[O.sub.3] ([micro]g/[m.sup.3]) Yes (upward)
TSP ([micro]g/[m.sup.3]) Yes (downward)
[NO.sub.2] ([micro]g/[m.sup.3]) No
[NO.sub.x] ([micro]g/[m.sup.3]) No
Relative humidity (%) No
Variable Periodicity
Organic disease (daily admissions) Annual, 7 days, 3.5 days
Circulatory disease (daily admissions) Annual, 7 days, 3.5 days
Respiratory disease (daily admissions) Annual, 3.5 days
TMax ([degrees]C) Annual
Thin ([degrees]C) Annual
[SO.sub.2] ([micro]g/[m.sup.3]) Annual, 7 days, 3.5 days
[O.sub.3] ([micro]g/[m.sup.3]) Annual, 7 days (95%), 3.5 days
TSP ([micro]g/[m.sup.3]) Annual, 7 days, 3.5 days
[NO.sub.2] ([micro]g/[m.sup.3]) 7 days, 3.5 days
[NO.sub.x] ([micro]g/[m.sup.3]) Annual, 7 days, 3.5 days
Relative humidity (%) Annual, 3 days
TABLE 2
Associations Between Hospital Admissions and Exogenous
Variables--Significant Coefficients and Lags (in Parentheses) for Nine
ARIMA Models
Organic Disease
Variable Year Winter Summer
Log [SO.sub.2] 2.005 [*] (0) 2.72 (0) --
TSP -- -- --
[O.sub.3] (high) 0.43 [***] (7) -- 0.30 [*] (7)
[0.sub.3] (low) -- -- --
[NO.sub.2] -- -- --
[NO.sub.X] -- -- --
THot 0.98 [**] (0) -- 1.17 [**] (0)
TCold 0.32 [***](10) 0.50 [**] (10) 0.42 [**] (3)
Relative humidity -- -- --
Circulatory Disease
Variable Year Winter Summer
Log [SO.sub.2] 0.63 [*] (1) -- --
TSP -- -- --
[O.sub.3] (high) 0.09 [*] (6) -- 0.10 [*] (6)
[0.sub.3] (low) -- -- --
[NO.sub.2] -- -- --
[NO.sub.X] -- -- --
THot -- -- --
TCold -- 0.15 [***] (7) 0.13 [**] (3)
Relative humidity 0.03 [***] (7) -- 0.03 [*] (9)
Respiratory Disease
Variable Year Winter
Log [SO.sub.2] -- --
TSP -- --
[O.sub.3] (high) -- --
[0.sub.3] (low) -- --
[NO.sub.2] -- --
[NO.sub.X] -- --
THot -- --
TCold 0.21 [***] (8,12) 0.32 [*] (10,12)
Relative humidity -- 0.03 [*] (5)
Variable Summer
Log [SO.sub.2] --
TSP --
[O.sub.3] (high) 0.07 [*] (0)
[0.sub.3] (low) --
[NO.sub.2] --
[NO.sub.X] --
THot --
TCold 0.19 [*] (11,3)
Relative humidity --
[O.sub.3]-high = [O.sub.3] values higher than 45 [micro]g/[m.sup.3]
[0.sub.3]-low = [0.sub.3] values lower than 45 [micro]g/[m.sup.3]
THot = TMax values higher than 33[degree]C.
TCold = TMax values lower than 33[degree]C.
(*)p [less than] .05.
(**)p [less than] .01.
(***)p [less than] .001.
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